Monitoring Data for the Cincinnati-Hamilton, OH-KY-IN Nonattainment Area 

SITE ID STATE COUNTY SITE NAME YEAR 

Annual Average 

μg/m3 

2007-2009 

Average 

μg/m3 

21-037-3002 Kentucky Campbell John Hill Rd. 2007 14.36 

12.51 

21-037-3002 Kentucky Campbell John Hill Rd. 2008 11.83 

21-037-3002 Kentucky Campbell John Hill Rd. 2009 11.34 

21-117-0007 Kentucky Kenton Univ. College 2007 14.20 

12.41 

21-117-0007 Kentucky Kenton Univ. College 2008 11.99 

21-117-0007 Kentucky Kenton Univ. College 2009 11.04 

39-017-0003 Ohio Butler Bonita & St. John 2007 15.41 

13.93 

39-017-0003 Ohio Butler Bonita & St. John 2008 13.69 

39-017-0003 Ohio Butler Bonita & St. John 2009 12.68 

39-017-0016 Ohio Butler Niles Rd. 2007 14.94 

13.92 

39-017-0016 Ohio Butler Niles Rd. 2008 13.75 

39-017-0016 Ohio Butler Niles Rd. 2009 13.08 

39-017-1004 Ohio Butler Hook Fld. Airport 2007 14.63 

N/A 

39-017-1004 Ohio Butler Hook Fld. Airport 2008 

39-017-1004 Ohio Butler Hook Fld. Airport 2009 

39-025-0022 Ohio Clermont Clermont Dr. 2007 14.01 

12.26 

39-025-0022 Ohio Clermont Clermont Dr. 2008 11.75 

39-025-0022 Ohio Clermont Clermont Dr. 2009 11.01 

39-061-0006 Ohio Hamilton Grooms Rd. 2007 14.63 

13.07 

39-061-0006 Ohio Hamilton Grooms Rd. 2008 12.48 

39-061-0006 Ohio Hamilton Grooms Rd. 2009 12.11 

39-061-0014 Ohio Hamilton Seymour & Vine St. 2007 16.59 

15.04 

39-061-0014 Ohio Hamilton Seymour & Vine St. 2008 15.12 

39-061-0014 Ohio Hamilton Seymour & Vine St. 2009 13.40 

39-061-0040 Ohio Hamilton Howard Taft 2007 15.09 

13.48 

39-061-0040 Ohio Hamilton Howard Taft 2008 12.62 

39-061-0040 Ohio Hamilton Howard Taft 2009 12.73 

39-061-0042 Ohio Hamilton W. 8th St. 2007 15.90 

14.67 

39-061-0042 Ohio Hamilton W. 8th St. 2008 14.40 

39-061-0042 Ohio Hamilton W. 8th St. 2009 13.71 

39-061-0043 Ohio Hamilton Kemper Rd. 2007 14.85 

N/A 

39-061-0043 Ohio Hamilton Kemper Rd. 2008 13.32 

39-061-0043 Ohio Hamilton Kemper Rd. 2009 

39-061-7001 Ohio Hamilton Sherman Ave. 2007 15.09 

13.93 

39-061-7001 Ohio Hamilton Sherman Ave. 2008 13.74 

39-061-7001 Ohio Hamilton Sherman Ave. 2009 12.97 

39-061-8001 Ohio Hamilton Murray Rd. 2007 16.07 

14.64 

39-061-8001 Ohio Hamilton Murray Rd. 2008 14.40 

39-061-8001 Ohio Hamilton Murray Rd. 2009 13.44 

39-165-0007 Ohio Warren Southeast St. 2007 13.98 

12.53 

39-165-0007 Ohio Warren Southeast St. 2008 11.92 

39-165-0007 Ohio Warren Southeast St. 2009 11.70 

Valued Above the Annual PM2.5 Standard 
Monitoring Data for the Cincinnati-Hamilton, OH-KY-IN Nonattainment Area

Site ID County Site Name 

Yearly Annual Means 

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 

21-037-0003 Campbell Alexandria Park 15.09 13.44 14.81 13.42 12.77 14.84 11.54 

21-037-3002 Campbell John Hill Rd. 14.36 11.83 11.34 

21-117-0007 Kenton Univ. College 16.26 15.25 15.06 14.30 13.42 15.86 13.29 14.20 11.99 11.04 

39-017-0003 Butler Bonita & St. John 16.96 16.43 16.83 15.05 14.06 19.04 14.05 15.41 13.69 12.68 

39-017-0016 Butler Nilles Rd. 18.85 15.87 15.34 15.83 14.65 17.88 13.99 14.94 13.75 13.08 

39-017-0017 Butler Wilwood 17.93 15.79 15.51 14.66 14.20 17.23 

39-017-1004 Butler Hook Field Airport 11.62 13.85 14.99 13.57 16.87 13.38 14.63 

39-025-0022 Clermont Clermont Dr. 15.73 12.72 14.01 11.75 11.01 

39-061-0006 Hamilton Grooms Rd. 16.61 13.29 14.63 12.48 12.11 

39-061-0014 Hamilton Seymour & Vine S 19.25 18.16 17.89 16.95 15.91 19.75 15.51 16.59 15.12 13.40 

39-0610040 Hamilton Howard Taft 16.72 15.93 15.29 15.50 14.63 17.53 13.57 15.09 12.62 12.73 

39-061-0041 Hamilton Winneste Ave. 15.88 16.11 15.10 15.30 14.63 15.77 

39-061-0042 Hamilton W. 8th St 20.61 17.63 16.83 16.69 15.99 19.09 14.94 15.90 14.40 13.71 

39-061-0043 Hamilton Kemper Rd. 19.10 16.07 15.42 15.67 14.92 16.89 14.47 14.85 13.32 

39-061-7001 Hamilton Sherman Ave. 17.24 16.76 16.08 16.01 15.33 18.37 14.37 15.09 13.74 12.97 

39-061-8001 Hamilton Murray Rd. 19.27 17.02 16.98 17.31 16.39 20.00 15.90 16.07 14.40 13.44 

39-165-0007 Warren Southeast St. 13.98 11.92 11.70 

Value above the annual PM2.5 standard. 

Monitoring Data for the Cincinnati-Hamilton, OH-KY-IN Nonattainment Area

Site ID County Site Name 

Three Year Design Values 

00-02 01-03 02-04 03-05 04-06 05-07 06-08 07-09 

21-037-0003 Campbell Alexandria Park 14.4 13.9 13.7 13.7 13.1 

21-037-3002 Campbell John Hill Rd. 12.5 

21-117-0007 Kenton Univ. College 15.5 14.9 14.3 14.5 14.2 14.5 13.2 12.4 

39-017-0003 Butler Bonita & St. John 16.7 16.1 15.3 16.1 15.7 16.2 14.4 13.9 

39-017-0016 Butler Nilles Rd. 16.7 15.7 15.3 16.1 15.5 15.6 14.2 13.9 

39-017-0017 Butler Wilwood 16.4 15.3 14.8 15.4 

39-017-1004 Butler Hook Field Airport 13.5 14.1 15.1 14.6 15.0 

39-025-0022 Clermont Clermont Dr. 14.2 12.8 12.3 

39-061-0006 Hamilton Grooms Rd. 14.8 13.5 13.1 

39-061-0014 Hamilton Seymour & Vine St. 18.4 17.7 16.9 17.5 17.1 17.3 15.7 15.0 

39-0610040 Hamilton Howard Taft 16.0 15.6 15.1 15.9 15.2 15.4 13.8 13.5 

39-061-0041 Hamilton Winneste Ave. 15.7 15.5 15.0 15.2 

39-061-0042 Hamilton W. 8th St 18.4 17.1 16.5 17.3 16.7 16.6 15.1 14.7 

39-061-0043 Hamilton Kemper Rd. 16.9 15.7 15.3 15.8 15.4 15.4 14.2 

39-061-7001 Hamilton Sherman Ave. 16.7 16.3 15.8 16.6 16.0 15.9 14.4 13.9 

39-061-8001 Hamilton Murray Rd. 17.8 17.1 16.9 17.9 17.4 17.3 15.5 14.6 

39-165-0007 Warren Southeast St. 12.5 

Value above the annual PM2.5 standard. 

Note: The Wilwood and Winneste Ave. monitors in Ohio were discontinued on December 31, 2005. The Alexandria Park monitor 

in Kentucky was discontinued on December 31, 2006. The Hook Field Airport monitor in Ohio was discontinued on December 31, 

2007. 
Design Values for the Cincinnati Area for Fine Particles, 2003 through 2009 

Cincinnati Area Annual Fiine Particles Trends, 2005 through 2009 

Note: The Wilwood and Winneste Ave. monitors in Ohio were discontinued on December 31, 2005. The Alexandria Park monitor 

in Kentucky was discontinued on December 31, 2006. The Hook Field Airport monitor in Ohio was discontinued on December 31, 

2007. 

0

5

10

15

20

25

2005 2006 2007 2008 2009

Alexandria Pk. John Hill Rd. Univ. College Bonita & St. John

Nilles Rd. Wilwood Hook Field Airport Clermont Dr.

Grooms Rd. Seymour & Vine St. Howard Taft Winneste Ave.

W. 8th St Kemper Rd. Sherman Ave. Murray Rd.

Southeast St.

μg/m3 0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

Alexandria Pk.

John Hill Rd.

Univ. College

Bonita

& St. John

Nilles Rd.

Wilwood

Hook Field Airport

Clermont Dr.

Grooms Rd.

Seymour

& Vine

...

Howard Taft

Winneste Ave.

W. 8th St

Kemper Rd.

Sherman Ave.

Murray Rd.

Southeast St.

03‐05 04‐06 05‐07 06‐08 07‐09

μg/m3
APPENDIX B 

Nitrogen Oxides (NOx), Sulfur Dioxides (SO2) and 

Direct Fine Particulate Matter (PM2.5) Point 

Source Emissions (2005 and 2008) for the 

Cincinnati-Hamilton, OH-KY-IN Nonattainment 

Area 
This page left intentionally blank.
B-1 

Dearborn County, IN Point Source Totals (Tons per Year)

Year NOx SO2 Direct PM2.5

2005 9,985.98 47,864.85 741.32 

2008 9,409.03 27,063.43 866.20 

2005-Dearborn County, IN Point Source Emissions (Tons per Year)

County EGU- NOx

NON-EGUNOx

EGU-SO2

NON-EGUSO2

EGUDirect 

PM2.5

NON-EGUDirect PM2.5

Dearborn, IN 7,961.30 2,024.68 46,533.70 1,331.15 673.94 67.38 

 NOx SO2 Direct PM2.5

Grand Total 9,985.98 47,864.85 741.32 

2008-Dearborn County, IN Point Source Emissions (Tons per Year)

County EGU- NOx

NON-EGUNOx

EGU-SO2

NON-EGUSO2

EGUDirect 

PM2.5

NON-EGUDirect PM2.5

Dearborn, IN 7,429.20 1,979.83 25,729.10 1,334.33 804.18 62.02 

 NOx SO2 Direct PM2.5

Grand Total 9,409.03 27,063.43 866.20 

Dearborn County, IN NOx Point Source Emission Trends, 2005 and 2008 

9,100

9,200

9,300

9,400

9,500

9,600

9,700

9,800

9,900

10,000

10,100

2005 2008

Tons per Year

NOX Year
B-2

Dearborn County, IN SO2 Point Source Emission Trends, 2005 and 2008 

Dearborn County, IN Direct PM2.5 Point Source Emission Trends, 2005 and 2008 

‐

2,500

5,000

7,500

10,000

12,500

15,000

17,500

20,000

22,500

25,000

27,500

30,000

32,500

35,000

37,500

40,000

42,500

45,000

47,500

50,000

52,500

2005 2008

Tons per Year

SO2 Year

660

680

700

720

740

760

780

800

820

840

860

880

2005 2008

Tons per Year

Direct PM 2.5 Year
B-3

Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Point 

Source Totals (Tons per Year)

Year NOx SO2 Direct PM2.5

2005 66,302.14 233,927.65 3,415.69 

2008 56,644.39 111,818.09 3,091.67 

2005-Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Point Source Emissions 

(Tons per Year) 

County EGU 

NOx

NON-EGUNOxEGU 

SO2

NON-EGUSO2EGUDirect PM2.5NON-EGUDirect PM2.5

Dearborn County, IN 7,961.30 2,024.68 46,533.70 1,331.15 673.94 67.38 

Boone County, KY 3,926.27 58.03 3,644.98 16.82 76.85 58.77 

Campbell County, KY 0.00 53.68 0.00 0.97 0.00 84.25 

Kenton County, KY 0.00 19.50 0.00 12.91 0.00 9.53 

Butler County, OH 743.27 4,367.15 1,959.10 6,185.26 15.27 944.29 

Clermont County, OH 28,063.56 67.50 88,876.65 162.19 648.21 7.93 

Hamilton County, OH 15,236.04 2,756.21 77,381.13 7,819.40 648.64 161.88 

Warren County, OH 0.00 1,024.95 0.00 3.39 0.00 18.75 

NOx SO2 Direct PM2.5

Grand Total 66,302.14 233,927.65 3,415.69 

2008-Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Point Source Emissions 

(Tons per Year) 

County EGU 

NOx

NON-EGUNOxEGU 

SO2

NON-EGUSO2EGUDirect PM2.5NON-EGUDirect PM2.5

Dearborn County, IN 7,429.20 1,979.83 25,729.10 1,334.33 804.18 62.02 

Boone County, KY 1,962.59 61.66 2,812.16 17.97 76.70 68.81 

Campbell County, KY 0.00 49.52 0.00 0.96 0.00 89.52 

Kenton County, KY 0.00 20.44 0.00 13.89 0.00 11.11 

Butler County, OH 856.92 3,940.28 2,181.63 5,442.54 16.78 1,045.15 

Clermont County, OH 24,233.18 42.71 42,918.28 118.05 532.61 3.86 

Hamilton County, OH 12,372.00 2,652.79 24,693.00 6,552.65 202.88 158.14 

Warren County, OH 0.00 1,043.27 0.00 3.53 0.00 19.91 

NOx SO2 Direct PM2.5

Grand Total 56,644.39 111,818.09 3,091.67 
B-4

Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area NOx Point Source Emission Trends, 

2005 and 2008 

Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area SO2 Point Source Emission Trends, 

2005 and 2008 

50,000

52,000

54,000

56,000

58,000

60,000

62,000

64,000

66,000

68,000

2005 2008

Tons per Year

NOX Year

50,000

70,000

90,000

110,000

130,000

150,000

170,000

190,000

210,000

230,000

250,000

2005 2008

Tons per Year

SO2 Year
B-5

Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Direct PM2.5 Point Source 

Emission Trends, 2005 and 2008 

2,900

3,000

3,100

3,200

3,300

3,400

3,500

2005 2008

Tons per Year

Direct PM 2.5 Year
APPENDIX C 

Nitrogen Oxides (NOx), Sulfur Dioxides (SO2) and 

Direct Fine Particulate Matter (PM2.5) (2005 and 

2008) Emission Trends, All Sources, Entire 

Cincinnati-Hamilton, OH-KY-IN Nonattainment 

Area
This page left intentionally blank. 
C-1 

2005-Dearborn County, IN 

COUNTY, STATE Sector NOX Direct PM2.5 SO2

DEARBORN COUNTY, IN ONROAD 865.46 33.98 2.45 

DEARBORN COUNTY, IN NONROAD 382.53 23.96 40.16 

DEARBORN COUNTY, IN AREA 141.37 4.29 78.72 

DEARBORN COUNTY, IN POINT 9,985.98 741.32 47,864.85 

 2005 Dearborn County, IN Totals 

 ONROAD NONROAD AREA POINT GRAND TOTAL 

NOX 865.46 382.53 141.37 9,985.98 11,375.34 

Direct PM2.5 33.98 23.96 4.29 741.32 803.55 

SO2 2.45 40.16 78.72 47,864.85 47,986.18 

2008-Dearborn County, IN 

COUNTY, STATE Sector NOX Direct PM2.5 SO2

DEARBORN COUNTY, IN ONROAD 748.81 29.89 2.69 

DEARBORN COUNTY, IN NONROAD 318.09 19.91 17.38 

DEARBORN COUNTY, IN AREA 145.42 4.29 81.02 

DEARBORN COUNTY, IN POINT 9,409.03 866.20 27,063.43 

 2008 Dearborn County, IN Totals 

 ONROAD NONROAD AREA POINT GRAND TOTAL 

NOX 748.81 318.09 145.42 9,409.03 10,621.35 

Direct PM2.5 29.89 19.91 4.29 866.20 920.29 

SO2 2.69 17.38 81.02 27,063.43 27,164.52 
C-2 

NOx Emission Trends, All Sources in Dearborn County, IN, 2005 and 2008-With 

CAIR 

SO2 Emission Trends, All Sources in Dearborn County, IN, 2005 and 2008-With 

CAIR 

10,200

10,400

10,600

10,800

11,000

11,200

11,400

11,600

2005 2008

Tons per Year

NOX Year

‐

10,000

20,000

30,000

40,000

50,000

60,000

2005 2008

Tons per Year

Year SO2
C-3 

Direct PM2.5 Emission Trends, All Sources in Dearborn County, IN, 2005 and 2008-

With CAIR 

740

760

780

800

820

840

860

880

900

920

940

2005 2008

Tons per Year

Year Direct PM 2.5
C-4 

2005-Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area 

COUNTY, STATE Sector NOX Direct PM2.5 SO2

DEARBORN COUNTY, IN ONROAD 865.46 33.98 2.45 

DEARBORN COUNTY, IN NONROAD 382.53 23.96 40.16 

DEARBORN COUNTY, IN AREA 141.37 4.29 78.72 

DEARBORN COUNTY, IN POINT 9,985.98 741.32 47,864.85 

BOONE COUNTY, KY ONROAD 5,126.88 205.21 15.91 

BOONE COUNTY, KY NONROAD 3,858.96 304.76 494.27 

BOONE COUNTY, KY AREA 1,844.50 351.27 1,054.33 

BOONE COUNTY, KY POINT 3,984.30 135.62 3,661.80 

CAMPBELL COUNTY, KY ONROAD 3,041.21 120.30 9.30 

CAMPBELL COUNTY, KY NONROAD 1,902.55 80.95 239.99 

CAMPBELL COUNTY, KY AREA 523.45 200.08 471.77 

CAMPBELL COUNTY, KY POINT 53.68 84.25 0.97 

KENTON COUNTY, KY ONROAD 5,328.44 212.29 16.24 

KENTON COUNTY, KY NONROAD 2,684.68 119.08 248.34 

KENTON COUNTY, KY AREA 1,542.27 365.74 1,196.61 

KENTON COUNTY, KY POINT 19.50 9.53 12.91 

BUTLER COUNTY, OH ONROAD 10,910.37 413.97 30.01 

BUTLER COUNTY, OH NONROAD 3,268.33 216.47 341.20 

BUTLER COUNTY, OH AREA 796.34 173.24 224.54 

BUTLER COUNTY, OH POINT 5,110.42 959.56 8,144.36 

CLERMONT COUNTY, OH ONROAD 7,295.87 281.79 20.51 

CLERMONT COUNTY, OH NONROAD 1,477.30 110.65 161.66 

CLERMONT COUNTY, OH AREA 612.97 193.70 164.72 

CLERMONT COUNTY, OH POINT 28,131.06 656.14 89,038.84 

HAMILTON COUNTY, OH ONROAD 31,127.09 1,222.02 88.85 

HAMILTON COUNTY, OH NONROAD 6,309.86 398.01 592.45 

HAMILTON COUNTY, OH AREA 1,923.27 303.61 163.45 

HAMILTON COUNTY, OH POINT 17,992.25 810.52 85,200.53 

WARREN COUNTY, OH ONROAD 8,224.57 320.74 23.54 

WARREN COUNTY, OH NONROAD 1,886.04 146.67 31.67 

WARREN COUNTY, OH AREA 426.57 236.92 140.25 

WARREN COUNTY, OH POINT 1,024.95 18.75 3.39 

 2005 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Totals 

 ONROAD NONROAD AREA POINT GRAND TOTAL 

NOX 71,919.89 21,770.25 7,810.74 66,302.14 167,803.02 

Direct PM2.5 2,810.30 1,400.55 1,828.85 3,415.69 9,455.39 

SO2 206.81 2,149.74 3,494.39 233,927.65 239,778.59 
C-5 

2008-Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area 

COUNTY, STATE Sector NOX Direct PM2.5 SO2

DEARBORN COUNTY, IN ONROAD 748.81 29.89 2.69 

DEARBORN COUNTY, IN NONROAD 318.09 19.91 17.38 

DEARBORN COUNTY, IN AREA 145.42 4.29 81.02 

DEARBORN COUNTY, IN POINT 9,409.03 866.20 27,063.43 

BOONE COUNTY, KY ONROAD 5,067.94 251.85 16.71 

BOONE COUNTY, KY NONROAD 3,772.42 310.52 435.93 

BOONE COUNTY, KY AREA 1,897.28 353.71 1,066.79 

BOONE COUNTY, KY POINT 2,024.25 145.51 2,830.13 

CAMPBELL COUNTY, KY ONROAD 2,988.33 146.46 9.69 

CAMPBELL COUNTY, KY NONROAD 1,833.46 76.09 206.21 

CAMPBELL COUNTY, KY AREA 536.71 201.26 479.14 

CAMPBELL COUNTY, KY POINT 49.52 89.52 0.96 

KENTON COUNTY, KY ONROAD 5,057.93 29.89 16.34 

KENTON COUNTY, KY NONROAD 2,562.60 110.61 190.40 

KENTON COUNTY, KY AREA 1,581.60 366.69 1,210.42 

KENTON COUNTY, KY POINT 20.44 11.11 13.89 

BUTLER COUNTY, OH ONROAD 9,803.70 377.64 34.25 

BUTLER COUNTY, OH NONROAD 2,833.89 185.81 174.34 

BUTLER COUNTY, OH AREA 807.64 180.43 221.09 

BUTLER COUNTY, OH POINT 4,797.20 1,061.93 7,624.17 

CLERMONT COUNTY, OH ONROAD 6,516.40 256.60 23.32 

CLERMONT COUNTY, OH NONROAD 1,284.92 95.48 66.25 

CLERMONT COUNTY, OH AREA 619.27 196.15 162.20 

CLERMONT COUNTY, OH POINT 24,275.89 536.47 43,036.33 

HAMILTON COUNTY, OH ONROAD 27,020.93 1,080.54 98.30 

HAMILTON COUNTY, OH NONROAD 5,402.04 345.12 274.62 

HAMILTON COUNTY, OH AREA 1,955.47 323.94 161.80 

HAMILTON COUNTY, OH POINT 15,024.79 361.02 31,245.65 

WARREN COUNTY, OH ONROAD 7,267.18 289.56 26.57 

WARREN COUNTY, OH NONROAD 1,607.45 124.78 34.56 

WARREN COUNTY, OH AREA 432.28 238.33 138.31 

WARREN COUNTY, OH POINT 1,043.27 19.91 3.53 

 2008 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Totals 

 ONROAD NONROAD AREA POINT GRAND TOTAL 

NOX 64,471.22 19,614.87 7,975.67 56,644.39 148,706.15 

Direct PM2.5 2,462.43 1,268.32 1,864.80 3,091.67 8,687.22 

SO2 227.87 1,399.69 3,520.77 111,818.09 116,966.42 
C-6 

NOx Emission Trends, All Sources in Entire Cincinnati-Hamilton, OH-KY-IN 

Nonattainment Area, 2005 and 2008-With CAIR 

SO2 Emission Trends, All Sources in Entire Cincinnati-Hamilton, OH-KY-IN 

Nonattainment Area, 2005 and 2008-With CAIR 

135,000

140,000

145,000

150,000

155,000

160,000

165,000

170,000

2005 2008

Tons per Year

NOX Year

‐

50,000

100,000

150,000

200,000

250,000

300,000

2005 2008

Tons per Year

Year SO2
C-7 

Direct PM2.5 Emission Trends, All Sources in Entire Cincinnati-Hamilton, OH-KYIN Nonattainment Area, 2005 and 2008-IN/KY-With CAIR, OH-Without CAIR 

8,200

8,400

8,600

8,800

9,000

9,200

9,400

9,600

2005 2008

Tons per Year

Year Direct PM 2.5
APPENDIX D 

Nitrogen Oxides (NOx) and Sulfur Dioxide (SO2) 

Emissions from Electric Generating Units, 

Lawrenceburg Township, Dearborn County, 

Indiana and Entire Cincinnati-Hamilton, OH-KYIN Nonattainment Area 
This page left intentionally blank.
D-1

Lawrenceburg Township, Dearborn County, Indiana NOx Emissions from EGUs 

Lawrenceburg Township, Dearborn County, Indiana NOx Emissions from EGUs 

Year Total NOx Emissions, tons/year 

1999 33,807.1 

2000 32,657.1 

2001 25,774.7 

2002 17,533.8 

2003 13,416.7 

2004 12,552.8 

2005 7,961.3 

2006 8,041.6 

2007 8,739.2 

2008 7,429.2 

2009 3,529.3 

0.0

5,000.0

10,000.0

15,000.0

20,000.0

25,000.0

30,000.0

35,000.0

40,000.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Total NOx Emissions, tons / year
D-2

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx Emissions from EGUs 

Year Total NOx Emissions, tons/year 

1999 115,477.8 

2000 107,227.9 

2001 90,347.2 

2002 80,808.6 

2003 73,084.4 

2004 65,491.6 

2005 55,492.4 

2006 52,004.5 

2007 50,979.6 

2008 48,464.0 

2009 24,997.8 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx Emissions from EGUs 

0.0

20,000.0

40,000.0

60,000.0

80,000.0

100,000.0

120,000.0

140,000.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Total NOx Emissions, tons / year
D-3

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 1999 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 33,807.1 

Kentucky East Bend 10,113.8 

Ohio Miami Fort Generating Station 26,429.1 

Ohio 

William H Zimmer Generating 

Station 22,792.3 

Ohio 

Walter C Beckjord Generating 

Station 22,091.4 

Ohio Woodsdale 244.1 

Total 115,477.8 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2000 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 32,657.1 

Kentucky East Bend 8,671.0 

Ohio Madison Generating Station 15.1 

Ohio Miami Fort Generating Station 25,518.8 

Ohio 

William H Zimmer Generating 

Station 18,682.3 

Ohio 

Walter C Beckjord Generating 

Station 21,408.7 

Ohio Woodsdale 274.9 

Total 107,227.9 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2001 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 25,774.7 

Kentucky East Bend 8,161.5 

Ohio Madison Generating Station 32.0 

Ohio Miami Fort Generating Station 18,598.8 

Ohio 

William H Zimmer Generating 

Station 20,886.3 

Ohio 

Walter C Beckjord Generating 

Station 16,743.0 

Ohio Woodsdale 150.9 

Total 90,347.2 
D-4

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2002 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 17,533.8 

Kentucky East Bend 5,454.9 

Ohio Madison Generating Station 48.7 

Ohio Miami Fort Generating Station 17,941.5 

Ohio 

William H Zimmer Generating 

Station 20,965.6 

Ohio 

Walter C Beckjord Generating 

Station 18,736.8 

Ohio Woodsdale 127.3 

Total 80,808.6 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2003 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 13,416.7 

Kentucky East Bend 7,056.0 

Ohio Madison Generating Station 51.7 

Ohio Miami Fort Generating Station 15,593.7 

Ohio 

William H Zimmer Generating 

Station 20,174.0 

Ohio 

Walter C Beckjord Generating 

Station 16,727.9 

Ohio Woodsdale 64.4 

Total 73,084.4 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2004 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 12,552.8 

Kentucky East Bend 6,187.2 

Ohio Madison Generating Station 14.0 

Ohio Miami Fort Generating Station 17,102.2 

Ohio 

William H Zimmer Generating 

Station 14,692.7 

Ohio 

Walter C Beckjord Generating 

Station 14.914.2 

Ohio Woodsdale 28.5 

Total 65,491.6 
D-5

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2005 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 7,961.3 

Kentucky East Bend 3,952.2 

Ohio Madison Generating Station 91.5 

Ohio Miami Fort Generating Station 15,264.6 

Ohio 

William H Zimmer Generating 

Station 15,153.0 

Ohio 

Walter C Beckjord Generating 

Station 13,012.8 

Ohio Woodsdale 57.0 

Total 55,492.4 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2006 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 8,041.6 

Kentucky East Bend 5,399.7 

Ohio Madison Generating Station 38.4 

Ohio Miami Fort Generating Station 12,797.9 

Ohio 

William H Zimmer Generating 

Station 13,851.3 

Ohio 

Walter C Beckjord Generating 

Station 11,830.2 

Ohio Woodsdale 45.4 

Total 52,004.5 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2007 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 8,739.2 

Kentucky East Bend 5,563.0 

Ohio Madison Generating Station 44.3 

Ohio Miami Fort Generating Station 9,754.6 

Ohio 

William H Zimmer Generating 

Station 13,736.6 

Ohio 

Walter C Beckjord Generating 

Station 13,031.8 

Ohio Woodsdale 110.1 

Total 50,979.6 
D-6

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2008 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 7,429.2 

Kentucky East Bend 4,492.4 

Ohio Madison Generating Station 16.1 

Ohio Miami Fort Generating Station 12,371.7 

Ohio 

William H Zimmer Generating 

Station 16,531.1 

Ohio 

Walter C Beckjord Generating 

Station 7,549.0 

Ohio Woodsdale 74.5 

Total 48,464.0 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area NOx 

Emissions from EGUs, 2009 

State Facility NOx Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 3,529.3 

Kentucky East Bend 2,436.2 

Ohio Madison Generating Station 25.6 

Ohio Miami Fort Generating Station 4,337.8 

Ohio 

William H Zimmer Generating 

Station 3,646.4 

Ohio 

Walter C Beckjord Generating 

Station 10,948.2 

Ohio Woodsdale 74.3 

Total 24,997.8 
D-7

Lawrenceburg Township, Dearborn County, Indiana SO2 Emissions from EGUs 

Year Total SO2 Emissions, tons/year 

1999 50,715.7 

2000 67,446.1 

2001 55,430.6 

2002 62,531.7 

2003 53,175.0 

2004 64,387.3 

2005 46,533.7 

2006 35,494.2 

2007 33,828.9 

2008 25,729.1 

2009 16,442.3 

Lawrenceburg Township, Dearborn County, Indiana SO2 Emissions from EGUs 

0.0

10,000.0

20,000.0

30,000.0

40,000.0

50,000.0

60,000.0

70,000.0

80,000.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Total SO2 Emissions, tons / year
D-8

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 Emissions from EGUs 

Year Total SO2 Emissions, tons/year 

1999 240,983.6 

2000 254,655.4 

2001 225,526.3 

2002 252,572.9 

2003 237,439.2 

2004 272,465.6 

2005 217,111.1 

2006 186,150.3 

2007 154,905.1 

2008 95,498.4 

2009 99,757.0 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 Emissions from EGUs 

0.0

50,000.0

100,000.0

150,000.0

200,000.0

250,000.0

300,000.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Total SO2 Emissions, tons / year
D-9

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 1999 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 50,715.7 

Kentucky East Bend 18,095.8 

Ohio Miami Fort Generating Station 78,086.2 

Ohio 

William H Zimmer Generating 

Station 25,482.4 

Ohio 

Walter C Beckjord Generating 

Station 68,601.7 

Ohio Woodsdale 1.8 

Total 240,983.6 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2000 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 67,446.1 

Kentucky East Bend 14,850.4 

Ohio Madison Generating Station 0.2 

Ohio Miami Fort Generating Station 81,512.4 

Ohio 

William H Zimmer Generating 

Station 19,410.6 

Ohio 

Walter C Beckjord Generating 

Station 71,433.5 

Ohio Woodsdale 2.2 

Total 254,655.4 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2001 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 55,430.6 

Kentucky East Bend 13,106.5 

Ohio Madison Generating Station 0.6 

Ohio Miami Fort Generating Station 73,538.9 

Ohio 

William H Zimmer Generating 

Station 21,651.5 

Ohio 

Walter C Beckjord Generating 

Station 61,797.4 

Ohio Woodsdale 0.8 

Total 225,526.3 
D-10

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2002 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 62,531.7 

Kentucky East Bend 12,918.1 

Ohio Madison Generating Station 0.7 

Ohio Miami Fort Generating Station 85,699.4 

Ohio 

William H Zimmer Generating 

Station 21,491.8 

Ohio 

Walter C Beckjord Generating 

Station 69,930.6 

Ohio Woodsdale 0.6 

Total 252,572.9 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2003 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 53,175.0 

Kentucky East Bend 14,959.8 

Ohio Madison Generating Station 0.2 

Ohio Miami Fort Generating Station 81,514.6 

Ohio 

William H Zimmer Generating 

Station 22,917.9 

Ohio 

Walter C Beckjord Generating 

Station 64,871.3 

Ohio Woodsdale 0.4 

Total 237,439.2 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2004 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 64,387.3 

Kentucky East Bend 11,545.5 

Ohio Madison Generating Station 0.2 

Ohio Miami Fort Generating Station 100,576.7 

Ohio 

William H Zimmer Generating 

Station 21,638.3 

Ohio 

Walter C Beckjord Generating 

Station 74,317.5 

Ohio Woodsdale 0.1 

Total 272,465.6 
D-11

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2005 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 46,533.7 

Kentucky East Bend 3,666.7 

Ohio Madison Generating Station 1.6 

Ohio Miami Fort Generating Station 77,583.2 

Ohio 

William H Zimmer Generating 

Station 22,379.5 

Ohio 

Walter C Beckjord Generating 

Station 66,946.1 

Ohio Woodsdale 0.3 

Total 217,111.1 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2006 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 35,494.2 

Kentucky East Bend 3,946.5 

Ohio Madison Generating Station 0.7 

Ohio Miami Fort Generating Station 62,028.0 

Ohio 

William H Zimmer Generating 

Station 22,054.1 

Ohio 

Walter C Beckjord Generating 

Station 62,626.6 

Ohio Woodsdale 0.2 

Total 186,150.3 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2007 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 33,828.9 

Kentucky East Bend 2,451.8 

Ohio Madison Generating Station 0.8 

Ohio Miami Fort Generating Station 46,938.9 

Ohio 

William H Zimmer Generating 

Station 16,776.4 

Ohio 

Walter C Beckjord Generating 

Station 54,907.7 

Ohio Woodsdale 0.6 

Total 154,905.1 
D-12

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2008 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 25,729.1 

Kentucky East Bend 2,713.4 

Ohio Madison Generating Station 0.2 

Ohio Miami Fort Generating Station 24,693.2 

Ohio 

William H Zimmer Generating 

Station 15,961.6 

Ohio 

Walter C Beckjord Generating 

Station 26,400.5 

Ohio Woodsdale 0.4 

Total 95,498.4 

Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area SO2 

Emissions from EGUs, 2009 

State Facility SO2 Emissions, tons/year 

Indiana 

American Electric Power (AEP)-

Tanners Creek Generating Station 16,442.3 

Kentucky East Bend 1,724.6 

Ohio Madison Generating Station 0.4 

Ohio Miami Fort Generating Station 25,339.9 

Ohio 

William H Zimmer Generating 

Station 14,284.9 

Ohio 

Walter C Beckjord Generating 

Station 41,964.5 

Ohio Woodsdale 0.4 

Total 99,757.0 
APPENDIX E 

2008 Base Year Emissions Inventory and 2015 and 

2021 Projected Emissions Inventory for Nitrogen 

Oxides (NOx), Sulfur Dioxides (SO2) and Direct 

Fine Particulate Matter (PM2.5) in Entire 

Cincinnati-Hamilton, OH-KY-IN Nonattainment 

Area 
This page left intentionally blank.
E-1 

2008 Lawrenceburg Township, Dearborn County, IN (Tons Per Year)

PM2.5

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 29.89 19.91 4.29 804.18 62.02 920.29 

SO2

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 2.69 17.38 81.02 25,729.10 1,334.33 27,164.52 

NOx

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 748.81 318.09 145.42 7,429.20 1,979.83 10,621.35 

2015 Lawrenceburg Township, Dearborn County, IN (Tons Per Year)

PM2.5

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 25.14 13.34 4.11 847.16 60.00 949.75 

SO2

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 2.87 4.73 77.64 39,295.70 1,335.94 40,716.88 

NOx

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 482.33 219.83 143.39 9,862.76 1,965.19 12,673.50 

2021 Lawrenceburg Township, Dearborn County, IN (Tons Per Year)

PM2.5

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 18.11 9.07 3.98 922.81 57.32 1,011.29 

SO2

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 3.19 1.14 75.69 36,843.66 1,337.95 38,261.63 

NOx

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 297.95 154.18 142.90 11,229.31 1,943.22 13,767.56 

Lawrenceburg Township, Dearborn County, IN Percent Change (Tons Per Year) 

2008 2021 Change % Change 

NOx 10,621.35 13,767.56 3,146.21 29.6% 

SO2 27,164.52 38,261.63 11,097.11 40.8% 

Direct PM2.5 920.29 1,011.29 91.00 9.8% 
E-2

Comparison of 2008 and 2015 and 2021 Projected NOx Emissions, Lawrenceburg 

Township, Dearborn County, Indiana-With CAIR 

Comparison of 2008 and 2015 and 2021 Projected SO2 Emissions, Lawrenceburg 

Township, Dearborn County, Indiana-With CAIR 

‐

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

2008 2015 2021

Tons per Year

SO2 Year

‐

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2008 2015 2021

Tons per Year

NOX Year
E-3

Comparison of 2008 and 2015 and 2021 Projected Direct PM2.5 Emissions, 

Lawrenceburg Township, Dearborn County, Indiana-With CAIR 

Comparison of 2008 and 2015 and 2021 Projected SO2, NOx and Direct PM2.5

Emissions, Lawrenceburg Township, Dearborn County, Indiana-With CAIR 

860

880

900

920

940

960

980

1,000

1,020

2008 2015 2021

Tons per Year

Direct PM 2.5 Year

‐

5,000.00

10,000.00

15,000.00

20,000.00

25,000.00

30,000.00

35,000.00

40,000.00

45,000.00

2008 2015 2021

Tons per Year

NOX SO2 Direct PM2.5
E-4
E-5

2008 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

PM2.5

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 29.89 19.91 4.29 804.18 62.02 920.29 

BOONE COUNTY, KY 251.85 310.52 353.71 76.70 68.81 1,061.59 

CAMPBELL COUNTY, KY 146.46 76.09 201.26 0.00 89.52 513.33 

KENTON COUNTY, KY 29.89 110.61 366.69 0.00 11.11 518.30 

BUTLER COUNTY, OH 377.64 185.81 180.43 16.78 1,045.15 1,805.81 

CLERMONT COUNTY, OH 256.60 95.48 196.15 532.61 3.86 1,084.70 

HAMILTON COUNTY, OH 1,080.54 345.12 323.94 202.88 158.14 2,110.62 

WARREN COUNTY, OH 289.56 124.78 238.33 0.00 19.91 672.58 

2,462.43 1,268.32 1,864.80 1,633.15 1,458.52 

GRAND TOTAL 8,687.22 

2008 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

SO2

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 2.69 17.38 81.02 25,729.10 1,334.33 27,164.52 

BOONE COUNTY, KY 16.71 435.93 1,066.79 2,812.16 17.97 4,349.56 

CAMPBELL COUNTY, KY 9.69 206.21 479.14 0.00 0.96 696.00 

KENTON COUNTY, KY 16.34 190.40 1,210.42 0.00 13.89 1,431.05 

BUTLER COUNTY, OH 34.25 174.34 221.09 2,181.63 5,442.54 8,053.85 

CLERMONT COUNTY, OH 23.32 66.25 162.20 42,918.28 118.05 43,288.10 

HAMILTON COUNTY, OH 98.30 274.62 161.80 24,693.00 6,552.65 31,780.37 

WARREN COUNTY, OH 26.57 34.56 138.31 0.00 3.53 202.97 

227.87 1,399.69 3,520.77 98,334.17 13,483.92 

GRAND TOTAL 116,966.42 

2008 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

NOx

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 748.81 318.09 145.42 7,429.20 1,979.83 10,621.35 

BOONE COUNTY, KY 5,067.94 3,772.42 1,897.28 1,962.59 61.66 12,761.89 

CAMPBELL COUNTY, KY 2,988.33 1,833.46 536.71 0.00 49.52 5,408.02 

KENTON COUNTY, KY 5,057.93 2,562.60 1,581.60 0.00 20.44 9,222.57 

BUTLER COUNTY, OH 9,803.70 2,833.89 807.64 856.92 3,940.28 18,242.43 

CLERMONT COUNTY, OH 6,516.40 1,284.92 619.27 24,233.18 42.71 32,696.48 

HAMILTON COUNTY, OH 27,020.93 5,402.04 1,955.47 12,372.00 2,652.79 49,403.23 

WARREN COUNTY, OH 7,267.18 1,607.45 432.28 0.00 1,043.27 10,350.18 

64,471.22 19,614.87 7,975.67 46,853.89 9,790.50 

GRAND TOTAL 148,706.15 
E-6

2015 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

PM2.5

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 25.14 13.34 4.11 847.16 60.00 949.75 

BOONE COUNTY, KY 151.35 268.43 359.57 80.70 84.35 944.40 

CAMPBELL COUNTY, KY 82.36 57.43 200.05 0.00 101.84 441.68 

KENTON COUNTY, KY 137.40 83.03 363.77 0.00 13.50 597.70 

BUTLER COUNTY, OH 301.16 125.76 180.86 15.86 1,254.70 1,878.34 

CLERMONT COUNTY, 

OH 204.32 66.05 193.49 651.88 6.42 1,122.16 

HAMILTON COUNTY, OH 826.00 242.40 330.03 554.65 171.28 2,124.36 

WARREN COUNTY, OH 242.05 81.22 233.88 0.00 19.01 576.16 

1,969.78 937.66 1,865.76 2,150.25 1,711.10 

GRAND TOTAL 8,634.55 

2015 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

SO2

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 2.87 4.73 77.64 39,295.70 1,335.94 40,716.88 

BOONE COUNTY, KY 20.67 328.37 1,093.47 2,617.84 19.50 4,079.85 

CAMPBELL COUNTY, KY 11.21 149.28 491.66 0.00 1.04 653.19 

KENTON COUNTY, KY 18.62 127.09 1,238.92 0.00 15.16 1,399.79 

BUTLER COUNTY, OH 34.28 77.70 209.01 654.49 6,847.48 7,822.96 

CLERMONT COUNTY, 

OH 23.34 13.31 151.29 32,590.92 148.28 32,927.14 

HAMILTON COUNTY, OH 94.43 93.43 151.81 16,390.65 7,739.34 24,469.66 

WARREN COUNTY, OH 27.77 34.11 131.36 0.00 3.45 196.69 

233.19 828.02 3,545.16 91,549.60 16,110.19 

GRAND TOTAL 112,266.16 

2015 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

NOx

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 482.33 219.83 143.39 9,862.76 1,965.19 12,673.50 

BOONE COUNTY, KY 2,788.45 2,892.72 1,985.25 1,504.39 66.48 9,237.29 

CAMPBELL COUNTY, KY 1,570.14 1,345.37 563.83 0.00 53.81 3,533.15 

KENTON COUNTY, KY 2,637.63 1,848.86 1,654.75 0.00 21.79 6,163.03 

BUTLER COUNTY, OH 6,064.61 1,774.59 811.94 343.95 4,626.45 13,621.54 

CLERMONT COUNTY, 

OH 3,993.63 814.05 620.94 16,491.26 60.83 21,980.71 

HAMILTON COUNTY, OH 15,925.19 3,374.79 1,974.77 7,236.90 2,943.73 31,455.38 

WARREN COUNTY, OH 4,598.44 979.43 434.26 0.00 1,035.29 7,047.42 

38,060.42 13,249.64 8,189.13 35,439.26 10,773.57 

GRAND TOTAL 105,712.02 
E-7

2021 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

PM2.5

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 18.11 9.07 3.98 922.81 57.32 1,011.29 

BOONE COUNTY, KY 114.05 236.53 364.58 83.42 98.94 897.52 

CAMPBELL COUNTY, KY 60.09 41.99 199.32 0.00 112.39 413.79 

KENTON COUNTY, KY 101.24 59.98 361.65 0.00 15.76 538.63 

BUTLER COUNTY, OH 215.76 73.41 182.45 15.59 1,337.03 1,824.24 

CLERMONT COUNTY, OH 145.39 40.37 191.83 711.22 7.33 1,096.14 

HAMILTON COUNTY, OH 571.48 152.80 338.37 708.74 179.45 1,950.84 

WARREN COUNTY, OH 177.61 43.32 230.65 0.00 18.60 470.18 

1,403.73 657.47 1,872.83 2,441.78 1,826.82 

GRAND TOTAL 8,202.63 

2021 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

SO2

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 3.19 1.14 75.69 36,843.66 1,337.95 38,261.63 

BOONE COUNTY, KY 24.37 250.36 1,116.53 2,534.56 21.01 3,946.83 

CAMPBELL COUNTY, KY 12.77 103.78 502.75 0.00 1.09 620.39 

KENTON COUNTY, KY 21.48 78.99 1,263.63 0.00 16.41 1,380.51 

BUTLER COUNTY, OH 37.90 50.24 198.96 0.00 6,828.13 7,115.23 

CLERMONT COUNTY, OH 25.66 1.21 142.32 20,589.16 160.98 20,919.33 

HAMILTON COUNTY, OH 100.82 36.13 143.71 7,508.46 8,309.88 16,099.00 

WARREN COUNTY, OH 31.58 36.61 125.59 0.00 3.42 197.20 

257.77 558.46 3,569.18 67,475.84 16,678.87 

GRAND TOTAL 88,540.12 

2021 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area (Tons Per Year)

NOx

 ONROAD NONROAD AREA EGU POINT TOTAL 

DEARBORN COUNTY, IN 297.95 154.18 142.90 11,229.31 1,943.22 13,767.56 

BOONE COUNTY, KY 1,772.72 2,189.66 2,063.30 1,308.08 71.21 7,404.97 

CAMPBELL COUNTY, KY 985.28 951.58 587.37 0.00 55.21 2,579.44 

KENTON COUNTY, KY 1,677.96 1,269.32 1,718.86 0.00 23.09 4,689.23 

BUTLER COUNTY, OH 3,757.91 870.06 817.28 124.10 4,686.11 10,255.46 

CLERMONT COUNTY, OH 2,449.31 412.09 623.36 10,451.28 68.68 14,004.72 

HAMILTON COUNTY, OH 9,530.16 1,630.33 1,995.51 5,036.15 3,139.37 21,331.52 

WARREN COUNTY, OH 2,875.72 439.48 436.82 0.00 1,034.26 4,786.28 

23,347.01 7,916.70 8,385.40 28,148.92 11,021.15 

GRAND TOTAL 78,819.18 

Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Percent Change (Tons Per Year) 

2008 2021 Change % Change 

NOx 148,706.15 78,819.18 -69,886.97 -46.9% 

SO2 116,966.42 88,540.12 -28,426.30 -24.3% 

Direct PM2.5 8,687.22 8,202.63 -484.59 -5.5% 
E-8

Comparison of 2008 and 2015 and 2021 Projected NOx Emissions for the Entire 

Cincinnati-Hamilton, OH-KY-IN Nonattainment Area-With CAIR 

 Comparison of 2008 and 2015 and 2021 Projected SO2 Emissions for the Entire 

Cincinnati-Hamilton, OH-KY-IN Nonattainment Area-With CAIR

‐

20,000

40,000

60,000

80,000

100,000

120,000

140,000

2008 2015 2021

Tons per Year

Year SO2

‐

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

2008 2015 2021

Tons per Year

Year NOX
E-9

Comparison of 2008 and 2015 and 2021 Projected Direct PM2.5 Emissions for the 

Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area-IN/KY-With CAIR, 

OH-Without CAIR

Comparison of 2008 and 2015 and 2021 Projected SO2, NOx and Direct PM2.5

Emissions for the Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area-

(NOx and SO2 With CAIR, IN/KY PM2.5 With CAIR, OH PM2.5-Without CAIR)

7,900

8,000

8,100

8,200

8,300

8,400

8,500

8,600

8,700

8,800

2008 2015 2021

Tons per Year

Year Direct PM 2.5

‐

20,000.00

40,000.00

60,000.00

80,000.00

100,000.00

120,000.00

140,000.00

160,000.00

2008 2015 2021

Tons per Year

NOX SO2 Direct PM2.5
APPENDIX F 

Mobile Source Input/Output Calculation Files 
F - 1 

This page left intentionally blank. 
Mobile Source Emissions Inventory 

for Cincinnati PM2.5 Nonattainment 

Area 

Includes a portion of Dearborn County, Indiana, the counties of Boone, Campbell, 

Kenton in Kentucky, and the counties of Butler, Clermont, Hamilton, and Warren in 

Ohio. Emission estimates for the Year 2005, 2008, 2011, 2015, 2018, and 2021 

developed in support of the PM2.5 State Implementation Plan 

August 2010 

Prepared for the Indiana Department of Environmental Management, the Kentucky Division 

for Air Quality and the Ohio Environmental Protection Agency by 

OKI Regional Council of Governments

Acknowledgments 

Title Mobile Source Emissions Inventory for Cincinnati PM2.5 Nonattainment 

Area 

Abstract This report was prepared for the Indiana Department of Environmental 

Management, the Kentucky Division for Air Quality and the Ohio 

Environmental Protection Agency. The Cincinnati PM2.5 nonattainment 

area includes a portion of Dearborn County Indiana, the counties of Boone, 

Campbell, Kenton in Kentucky, and the counties of Butler, Clermont, 

Hamilton, and Warren in Ohio. This report includes emission estimates for 

the years 2005, 2008, 2011, 2015, 2018 and 2021 was generated to support 

the attainment SIPs for the annual PM2.5 standard. EPA’s Motor Vehicle 

Emission Simulation (MOVES) 2010 was used to generate the emission 

rates. 

Date August 2010 

Agency Ohio-Kentucky-Indiana Regional Council of Governments 

 Mark Policinski, Executive Director 

 Robert Koehler, P.E., Deputy Director 

 

Project Manager Andrew J. Reser, AICP 

Project Staff Harikishan Perugu, PTP 

 Larry Buckler 

 

 

The preparation of this document was financed cooperatively by the Federal Highway Administration, 

the Federal Transit Administration, the Commonwealth of Kentucky Transportation Cabinet, the Ohio 

Department of Transportation, and the units of local and county government in the OKI region. The 

opinions, findings, and conclusions expressed in this document are those of the OKI Regional Council of 

Governments and are not necessarily those of the U.S. Department of Transportation. This report does 

not constitute a standard, specification, or regulation. 

MOBILE Source Emissions Inventory for the 

Cincinnati PM2.5 nonattainment area 

This report was prepared for the Indiana Department of Environmental Management, the Kentucky 

Division for Air Quality and the Ohio Environmental Protection Agency. The Cincinnati PM2.5 

nonattainment area includes a portion of Dearborn County Indiana, the counties of Boone, Campbell, 

Kenton in Kentucky, and the counties of Butler, Clermont, Hamilton, and Warren in Ohio. This report 

includes emission estimates for the years 2005, 2008, 2011, 2015, 2018 and 2021 was generated to 

support the attainment SIPs for the annual PM2.5 standard. EPA’s Motor Vehicle Emissions Simulator 

(MOVES) 2010 model was used to generate the vehicle emission rates. In December 2009, MOVES 

replaced MOBILE6.2 as the EPA’s official emission factor model. Technical details on OKI’s use of 

MOVES can be found in the Appendix. The OKI travel demand model version 7.6 was used to generate 

VMT and speed estimates. MOVES emission rates were generated for direct PM2.5, PM2.5 tirewear, 

PM2.5 brakewear, NOx

 and SO2. 

OKI, as the MPO, is responsible for transportation planning and air quality/transportation conformity.

Transportation conformity is a mechanism to ensure that federal funding and approval are given to 

those transportation activities that are consistent with the air quality goals of the State Implementation 

Plans (SIPs) for Indiana, Kentucky and Ohio. The SIPs include an inventory of projected emissions from 

vehicles. One or more of the analysis years in the projected inventory may be designated as the motor

vehicle emissions budget (MVEB). This budget establishes a maximum allowable limit on future 

emissions from vehicles (mobile sources). OKI’s transportation plans and programs must be shown to 

be in conformity with all SIP provisions. The conformity process is a quantitative analysis, using 

U.S.EPA’s vehicle emissions software (currently MOVES), demonstrating that forecasted regional vehicle

emissions do not exceed the established budget. 

Table 1 shows daily and annual mobile source emissions for the combined Indiana and Ohio portions of 

the nonattainment area, as well as the Kentucky portion of the nonattainment area. Separate MVEB’s 

are typically designated for these two areas. Although official federal guidance on the use of MOVES for 

PM2.5 SIP development was not available at the time of this analysis, the Federal Highway 

Administration (FHWA) along with state and local air quality staff were consulted periodically 

throughout the development of these emissions. An additional safety margin should be added to the 

MVEB’s due uncertainty with growth assumptions utilized in the OKI travel demand model and 

uncertainty regarding the use of MOVES. Daily and annual mobile source emissions for each county in 

the nonattainment area are shown in Table 2. 
Table 1. Mobile Source Emissions for the Cincinnati PM2.5 Nonattainment Area (tons)

Year Pollutant Name DailyEmissions AnnualEmissions

Kentucky Portion of NA Area

2005 Vehicle Population: 364,081 Daily VMT: 9,621,110 Annual VMT: 3,289,109,202

Oxides of Nitrogen 39.10 13,496.54

Primary Exhaust PM2.5 - Total 1.36 466.23

Primary PM2.5 - Brakewear Particulate 0.16 54.04

Primary PM2.5 - Tirewear Particulate 0.05 17.52

Sulfur Dioxide (SO2) 0.12 41.46

2008 Vehicle Population: 375,873 Daily VMT: 9,991,179 Annual VMT: 3,425,339,505

Oxides of Nitrogen 37.91 13,114.20

Primary Exhaust PM2.5 - Total 1.64 562.84

Primary PM2.5 - Brakewear Particulate 0.18 62.10

Primary PM2.5 - Tirewear Particulate 0.06 20.70

Sulfur Dioxide (SO2) 0.12 42.74

2011 Vehicle Population: 381,911 Daily VMT: 10,490,143 Annual VMT: 3,587,796,186

Oxides of Nitrogen 29.33 10,141.52

Primary Exhaust PM2.5 - Total 1.19 407.74

Primary PM2.5 - Brakewear Particulate 0.20 68.38

Primary PM2.5 - Tirewear Particulate 0.07 22.68

Sulfur Dioxide (SO2) 0.13 45.36

2015 Vehicle Population: 394,278 Daily VMT: 11,495,496 Annual VMT: 3,931,385,741

Oxides of Nitrogen 20.18 6,996.21

Primary Exhaust PM2.5 - Total 0.78 267.30

Primary PM2.5 - Brakewear Particulate 0.23 77.94

Primary PM2.5 - Tirewear Particulate 0.08 25.88

Sulfur Dioxide (SO2) 0.15 50.50

2018 Vehicle Population: 403,817 Daily VMT: 12,173,549 Annual VMT: 4,163,203,435

Oxides of Nitrogen 15.78 5,480.81

Primary Exhaust PM2.5 - Total 0.59 202.15

Primary PM2.5 - Brakewear Particulate 0.27 91.15

Primary PM2.5 - Tirewear Particulate 0.09 30.09

Sulfur Dioxide (SO2) 0.16 56.28

2021 Vehicle Population: 413,587 Daily VMT: 12,534,236 Annual VMT: 4,286,834,360

Oxides of Nitrogen 12.75 4,435.96

Primary Exhaust PM2.5 - Total 0.43 146.79

Primary PM2.5 - Brakewear Particulate 0.28 96.84

Primary PM2.5 - Tirewear Particulate 0.09 31.74

Sulfur Dioxide (SO2) 0.17 58.63

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 1 of 2
Year Pollutant Name DailyEmissions AnnualEmissions

Ohio/Indiana Portion of NA Area

2005 Vehicle Population: 1,754,582 Daily VMT: 39,564,030 Annual VMT: 13,541,324,003

Oxides of Nitrogen 168.89 58,423.36

Primary Exhaust PM2.5 - Total 5.74 1,979.63

Primary PM2.5 - Brakewear Particulate 0.65 223.20

Primary PM2.5 - Tirewear Particulate 0.20 69.67

Sulfur Dioxide (SO2) 0.48 165.35

2008 Vehicle Population: 1,811,406 Daily VMT: 40,858,751 Annual VMT: 14,015,754,874

Oxides of Nitrogen 148.02 51,357.02

Primary Exhaust PM2.5 - Total 4.85 1,675.04

Primary PM2.5 - Brakewear Particulate 0.80 273.84

Primary PM2.5 - Tirewear Particulate 0.25 85.37

Sulfur Dioxide (SO2) 0.54 185.13

2011 Vehicle Population: 1,840,505 Daily VMT: 42,044,841 Annual VMT: 14,383,526,419

Oxides of Nitrogen 135.95 47,061.53

Primary Exhaust PM2.5 - Total 5.54 1,904.61

Primary PM2.5 - Brakewear Particulate 0.85 290.00

Primary PM2.5 - Tirewear Particulate 0.27 91.52

Sulfur Dioxide (SO2) 0.53 182.01

2015 Vehicle Population: 1,900,111 Daily VMT: 43,316,281 Annual VMT: 14,830,453,053

Oxides of Nitrogen 89.45 31,064.21

Primary Exhaust PM2.5 - Total 3.57 1,227.86

Primary PM2.5 - Brakewear Particulate 0.82 280.25

Primary PM2.5 - Tirewear Particulate 0.26 90.54

Sulfur Dioxide (SO2) 0.53 182.69

2018 Vehicle Population: 1,946,080 Daily VMT: 45,314,292 Annual VMT: 15,513,701,656

Oxides of Nitrogen 70.34 24,451.43

Primary Exhaust PM2.5 - Total 2.78 958.57

Primary PM2.5 - Brakewear Particulate 0.90 307.39

Primary PM2.5 - Tirewear Particulate 0.29 99.03

Sulfur Dioxide (SO2) 0.57 195.09

2021 Vehicle Population: 1,993,161 Daily VMT: 46,689,707 Annual VMT: 15,521,916,278

Oxides of Nitrogen 55.50 18,911.05

Primary Exhaust PM2.5 - Total 2.10 705.30

Primary PM2.5 - Brakewear Particulate 0.96 320.17

Primary PM2.5 - Tirewear Particulate 0.31 102.89

Sulfur Dioxide (SO2) 0.60 199.14

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 2 of 2
Table 2. Mobile Source Emissions by County for the Cincinnati PM2.5 Nonattainment Area (tons)

County Year Pollutant Name DailyEmissions AnnualEmissions

Indiana

Dearborn NA

2005 Vehicle Population: 24,915 Daily VMT: 578,642 Annual VMT: 196,738,031

Oxides of Nitrogen 2.40 865.46

Primary Exhaust PM2.5 - Total 0.08 29.68

Primary PM2.5 - Brakewear Particulate 0.01 3.28

Primary PM2.5 - Tirewear Particulate 0.00 1.02

Sulfur Dioxide (SO2) 0.01 2.45

2008 Vehicle Population: 25,722 Daily VMT: 587,583 Annual VMT: 199,778,078

Oxides of Nitrogen 2.09 748.81

Primary Exhaust PM2.5 - Total 0.07 24.72

Primary PM2.5 - Brakewear Particulate 0.01 3.94

Primary PM2.5 - Tirewear Particulate 0.00 1.23

Sulfur Dioxide (SO2) 0.01 2.69

2011 Vehicle Population: 26,135 Daily VMT: 605,621 Annual VMT: 205,911,005

Oxides of Nitrogen 1.92 685.40

Primary Exhaust PM2.5 - Total 0.08 27.88

Primary PM2.5 - Brakewear Particulate 0.01 4.19

Primary PM2.5 - Tirewear Particulate 0.00 1.32

Sulfur Dioxide (SO2) 0.01 2.65

2015 Vehicle Population: 26,982 Daily VMT: 657,779 Annual VMT: 223,644,622

Oxides of Nitrogen 1.31 482.33

Primary Exhaust PM2.5 - Total 0.05 19.43

Primary PM2.5 - Brakewear Particulate 0.01 4.32

Primary PM2.5 - Tirewear Particulate 0.00 1.39

Sulfur Dioxide (SO2) 0.01 2.87

2018 Vehicle Population: 27,635 Daily VMT: 684,362 Annual VMT: 232,682,971

Oxides of Nitrogen 1.03 376.85

Primary Exhaust PM2.5 - Total 0.04 15.09

Primary PM2.5 - Brakewear Particulate 0.01 4.70

Primary PM2.5 - Tirewear Particulate 0.00 1.51

Sulfur Dioxide (SO2) 0.01 3.04

2021 Vehicle Population: 28,303 Daily VMT: 706,829 Annual VMT: 240,321,759

Oxides of Nitrogen 0.81 297.95

Primary Exhaust PM2.5 - Total 0.03 11.44

Primary PM2.5 - Brakewear Particulate 0.01 5.05

Primary PM2.5 - Tirewear Particulate 0.00 1.62

Sulfur Dioxide (SO2) 0.01 3.19

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 1 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Kentucky

Boone

2005 Vehicle Population: 129,823 Daily VMT: 3,924,117 Annual VMT: 1,273,226,967

Oxides of Nitrogen 14.94 5,126.88

Primary Exhaust PM2.5 - Total 0.52 177.58

Primary PM2.5 - Brakewear Particulate 0.06 20.86

Primary PM2.5 - Tirewear Particulate 0.02 6.77

Sulfur Dioxide (SO2) 0.05 15.91

2008 Vehicle Population: 134,028 Daily VMT: 4,076,584 Annual VMT: 1,350,001,539

Oxides of Nitrogen 14.73 5,067.94

Primary Exhaust PM2.5 - Total 0.64 219.29

Primary PM2.5 - Brakewear Particulate 0.07 24.42

Primary PM2.5 - Tirewear Particulate 0.02 8.14

Sulfur Dioxide (SO2) 0.05 16.71

2011 Vehicle Population: 136,181 Daily VMT: 4,383,716 Annual VMT: 1,448,879,491

Oxides of Nitrogen 11.61 3,990.01

Primary Exhaust PM2.5 - Total 0.48 162.47

Primary PM2.5 - Brakewear Particulate 0.08 27.55

Primary PM2.5 - Tirewear Particulate 0.03 9.14

Sulfur Dioxide (SO2) 0.05 18.16

2015 Vehicle Population: 140,590 Daily VMT: 4,950,741 Annual VMT: 1,628,041,282

Oxides of Nitrogen 8.11 2,788.45

Primary Exhaust PM2.5 - Total 0.32 108.49

Primary PM2.5 - Brakewear Particulate 0.09 32.17

Primary PM2.5 - Tirewear Particulate 0.03 10.69

Sulfur Dioxide (SO2) 0.06 20.67

2018 Vehicle Population: 143,991 Daily VMT: 5,260,102 Annual VMT: 1,729,595,156

Oxides of Nitrogen 6.34 2,182.28

Primary Exhaust PM2.5 - Total 0.24 82.19

Primary PM2.5 - Brakewear Particulate 0.11 37.76

Primary PM2.5 - Tirewear Particulate 0.04 12.47

Sulfur Dioxide (SO2) 0.07 23.14

2021 Vehicle Population: 147,476 Daily VMT: 5,478,224 Annual VMT: 1,800,571,684

Oxides of Nitrogen 5.14 1,772.72

Primary Exhaust PM2.5 - Total 0.18 60.19

Primary PM2.5 - Brakewear Particulate 0.12 40.56

Primary PM2.5 - Tirewear Particulate 0.04 13.30

Sulfur Dioxide (SO2) 0.07 24.37

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 2 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Campbell

2005 Vehicle Population: 86,065 Daily VMT: 2,286,217 Annual VMT: 741,790,595

Oxides of Nitrogen 8.87 3,041.21

Primary Exhaust PM2.5 - Total 0.31 104.22

Primary PM2.5 - Brakewear Particulate 0.04 12.14

Primary PM2.5 - Tirewear Particulate 0.01 3.94

Sulfur Dioxide (SO2) 0.03 9.30

2008 Vehicle Population: 88,853 Daily VMT: 2,339,542 Annual VMT: 774,762,718

Oxides of Nitrogen 8.63 2,988.33

Primary Exhaust PM2.5 - Total 0.37 127.73

Primary PM2.5 - Brakewear Particulate 0.04 14.05

Primary PM2.5 - Tirewear Particulate 0.01 4.68

Sulfur Dioxide (SO2) 0.03 9.69

2011 Vehicle Population: 90,279 Daily VMT: 2,421,600 Annual VMT: 800,372,692

Oxides of Nitrogen 6.61 2,287.81

Primary Exhaust PM2.5 - Total 0.27 91.36

Primary PM2.5 - Brakewear Particulate 0.04 15.26

Primary PM2.5 - Tirewear Particulate 0.01 5.06

Sulfur Dioxide (SO2) 0.03 10.15

2015 Vehicle Population: 93,204 Daily VMT: 2,663,159 Annual VMT: 875,774,487

Oxides of Nitrogen 4.55 1,570.14

Primary Exhaust PM2.5 - Total 0.17 59.30

Primary PM2.5 - Brakewear Particulate 0.05 17.31

Primary PM2.5 - Tirewear Particulate 0.02 5.75

Sulfur Dioxide (SO2) 0.03 11.21

2018 Vehicle Population: 95,458 Daily VMT: 2,771,476 Annual VMT: 911,300,097

Oxides of Nitrogen 3.52 1,216.21

Primary Exhaust PM2.5 - Total 0.13 44.14

Primary PM2.5 - Brakewear Particulate 0.06 19.90

Primary PM2.5 - Tirewear Particulate 0.02 6.57

Sulfur Dioxide (SO2) 0.04 12.28

2021 Vehicle Population: 97,768 Daily VMT: 2,849,127 Annual VMT: 936,445,352

Oxides of Nitrogen 2.84 985.28

Primary Exhaust PM2.5 - Total 0.09 32.07

Primary PM2.5 - Brakewear Particulate 0.06 21.10

Primary PM2.5 - Tirewear Particulate 0.02 6.92

Sulfur Dioxide (SO2) 0.04 12.77

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 3 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Kenton

2005 Vehicle Population: 148,193 Daily VMT: 3,927,743 Annual VMT: 1,274,091,641

Oxides of Nitrogen 15.29 5,328.44

Primary Exhaust PM2.5 - Total 0.53 184.43

Primary PM2.5 - Brakewear Particulate 0.06 21.04

Primary PM2.5 - Tirewear Particulate 0.02 6.82

Sulfur Dioxide (SO2) 0.05 16.24

2008 Vehicle Population: 152,992 Daily VMT: 3,927,332 Annual VMT: 1,300,575,248

Oxides of Nitrogen 14.55 5,057.93

Primary Exhaust PM2.5 - Total 0.62 215.81

Primary PM2.5 - Brakewear Particulate 0.07 23.63

Primary PM2.5 - Tirewear Particulate 0.02 7.87

Sulfur Dioxide (SO2) 0.05 16.34

2011 Vehicle Population: 155,451 Daily VMT: 4,049,886 Annual VMT: 1,338,544,003

Oxides of Nitrogen 11.11 3,863.70

Primary Exhaust PM2.5 - Total 0.45 153.90

Primary PM2.5 - Brakewear Particulate 0.07 25.57

Primary PM2.5 - Tirewear Particulate 0.02 8.48

Sulfur Dioxide (SO2) 0.05 17.05

2015 Vehicle Population: 160,484 Daily VMT: 4,341,124 Annual VMT: 1,427,569,972

Oxides of Nitrogen 7.51 2,637.63

Primary Exhaust PM2.5 - Total 0.29 99.51

Primary PM2.5 - Brakewear Particulate 0.08 28.45

Primary PM2.5 - Tirewear Particulate 0.03 9.44

Sulfur Dioxide (SO2) 0.05 18.62

2018 Vehicle Population: 164,368 Daily VMT: 4,629,694 Annual VMT: 1,522,308,182

Oxides of Nitrogen 5.93 2,082.32

Primary Exhaust PM2.5 - Total 0.22 75.82

Primary PM2.5 - Brakewear Particulate 0.10 33.49

Primary PM2.5 - Tirewear Particulate 0.03 11.04

Sulfur Dioxide (SO2) 0.06 20.86

2021 Vehicle Population: 168,343 Daily VMT: 4,715,306 Annual VMT: 1,549,817,325

Oxides of Nitrogen 4.76 1,677.96

Primary Exhaust PM2.5 - Total 0.16 54.53

Primary PM2.5 - Brakewear Particulate 0.10 35.19

Primary PM2.5 - Tirewear Particulate 0.03 11.52

Sulfur Dioxide (SO2) 0.06 21.48

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 4 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Ohio

Butler

2005 Vehicle Population: 401,759 Daily VMT: 7,452,293 Annual VMT: 2,469,168,490

Oxides of Nitrogen 32.00 10,910.37

Primary Exhaust PM2.5 - Total 1.06 361.06

Primary PM2.5 - Brakewear Particulate 0.12 40.31

Primary PM2.5 - Tirewear Particulate 0.04 12.60

Sulfur Dioxide (SO2) 0.09 30.01

2008 Vehicle Population: 414,771 Daily VMT: 7,745,693 Annual VMT: 2,598,061,793

Oxides of Nitrogen 28.56 9,803.70

Primary Exhaust PM2.5 - Total 0.91 311.45

Primary PM2.5 - Brakewear Particulate 0.15 50.45

Primary PM2.5 - Tirewear Particulate 0.05 15.74

Sulfur Dioxide (SO2) 0.10 34.25

2011 Vehicle Population: 421,434 Daily VMT: 8,050,709 Annual VMT: 2,693,718,927

Oxides of Nitrogen 26.50 9,074.89

Primary Exhaust PM2.5 - Total 1.05 356.91

Primary PM2.5 - Brakewear Particulate 0.16 53.99

Primary PM2.5 - Tirewear Particulate 0.05 17.06

Sulfur Dioxide (SO2) 0.10 34.00

2015 Vehicle Population: 435,082 Daily VMT: 8,361,495 Annual VMT: 2,792,190,918

Oxides of Nitrogen 17.64 6,064.61

Primary Exhaust PM2.5 - Total 0.68 231.78

Primary PM2.5 - Brakewear Particulate 0.16 52.42

Primary PM2.5 - Tirewear Particulate 0.05 16.96

Sulfur Dioxide (SO2) 0.10 34.28

2018 Vehicle Population: 445,608 Daily VMT: 8,806,051 Annual VMT: 2,940,852,857

Oxides of Nitrogen 13.98 4,813.27

Primary Exhaust PM2.5 - Total 0.54 182.29

Primary PM2.5 - Brakewear Particulate 0.17 57.91

Primary PM2.5 - Tirewear Particulate 0.06 18.68

Sulfur Dioxide (SO2) 0.11 36.85

2021 Vehicle Population: 456,389 Daily VMT: 9,150,040 Annual VMT: 2,966,040,396

Oxides of Nitrogen 11.13 3,757.91

Primary Exhaust PM2.5 - Total 0.41 135.39

Primary PM2.5 - Brakewear Particulate 0.19 60.81

Primary PM2.5 - Tirewear Particulate 0.06 19.56

Sulfur Dioxide (SO2) 0.12 37.90

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 5 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Clermont

2005 Vehicle Population: 232,380 Daily VMT: 5,083,336 Annual VMT: 1,684,261,582

Oxides of Nitrogen 21.21 7,295.87

Primary Exhaust PM2.5 - Total 0.72 245.48

Primary PM2.5 - Brakewear Particulate 0.08 27.67

Primary PM2.5 - Tirewear Particulate 0.03 8.64

Sulfur Dioxide (SO2) 0.06 20.51

2008 Vehicle Population: 239,906 Daily VMT: 5,262,494 Annual VMT: 1,765,146,867

Oxides of Nitrogen 18.81 6,516.40

Primary Exhaust PM2.5 - Total 0.61 211.40

Primary PM2.5 - Brakewear Particulate 0.10 34.46

Primary PM2.5 - Tirewear Particulate 0.03 10.74

Sulfur Dioxide (SO2) 0.07 23.32

2011 Vehicle Population: 243,760 Daily VMT: 5,489,550 Annual VMT: 1,836,770,645

Oxides of Nitrogen 17.48 6,039.51

Primary Exhaust PM2.5 - Total 0.71 243.25

Primary PM2.5 - Brakewear Particulate 0.11 37.00

Primary PM2.5 - Tirewear Particulate 0.03 11.68

Sulfur Dioxide (SO2) 0.07 23.23

2015 Vehicle Population: 251,654 Daily VMT: 5,687,704 Annual VMT: 1,899,319,930

Oxides of Nitrogen 11.54 3,993.63

Primary Exhaust PM2.5 - Total 0.46 156.92

Primary PM2.5 - Brakewear Particulate 0.11 35.82

Primary PM2.5 - Tirewear Particulate 0.03 11.58

Sulfur Dioxide (SO2) 0.07 23.34

2018 Vehicle Population: 257,742 Daily VMT: 5,952,609 Annual VMT: 1,987,922,558

Oxides of Nitrogen 9.09 3,146.47

Primary Exhaust PM2.5 - Total 0.36 122.57

Primary PM2.5 - Brakewear Particulate 0.12 39.31

Primary PM2.5 - Tirewear Particulate 0.04 12.67

Sulfur Dioxide (SO2) 0.07 24.94

2021 Vehicle Population: 263,978 Daily VMT: 6,186,447 Annual VMT: 2,005,373,961

Oxides of Nitrogen 7.22 2,449.31

Primary Exhaust PM2.5 - Total 0.27 90.84

Primary PM2.5 - Brakewear Particulate 0.12 41.28

Primary PM2.5 - Tirewear Particulate 0.04 13.27

Sulfur Dioxide (SO2) 0.08 25.66

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 6 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Hamilton

2005 Vehicle Population: 862,422 Daily VMT: 21,859,473 Annual VMT: 7,241,536,812

Oxides of Nitrogen 89.30 31,127.09

Primary Exhaust PM2.5 - Total 3.06 1,064.67

Primary PM2.5 - Brakewear Particulate 0.35 119.94

Primary PM2.5 - Tirewear Particulate 0.11 37.41

Sulfur Dioxide (SO2) 0.26 88.85

2008 Vehicle Population: 890,352 Daily VMT: 22,124,524 Annual VMT: 7,421,012,594

Oxides of Nitrogen 77.45 27,020.93

Primary Exhaust PM2.5 - Total 2.56 889.81

Primary PM2.5 - Brakewear Particulate 0.42 145.42

Primary PM2.5 - Tirewear Particulate 0.13 45.31

Sulfur Dioxide (SO2) 0.28 98.30

2011 Vehicle Population: 904,655 Daily VMT: 22,426,043 Annual VMT: 7,503,619,525

Oxides of Nitrogen 70.18 24,435.59

Primary Exhaust PM2.5 - Total 2.88 997.06

Primary PM2.5 - Brakewear Particulate 0.44 151.73

Primary PM2.5 - Tirewear Particulate 0.14 47.86

Sulfur Dioxide (SO2) 0.28 95.30

2015 Vehicle Population: 933,953 Daily VMT: 22,849,516 Annual VMT: 7,630,239,650

Oxides of Nitrogen 45.58 15,925.19

Primary Exhaust PM2.5 - Total 1.83 634.62

Primary PM2.5 - Brakewear Particulate 0.42 144.67

Primary PM2.5 - Tirewear Particulate 0.14 46.71

Sulfur Dioxide (SO2) 0.27 94.43

2018 Vehicle Population: 956,548 Daily VMT: 23,630,577 Annual VMT: 7,891,625,119

Oxides of Nitrogen 35.51 12,422.37

Primary Exhaust PM2.5 - Total 1.41 490.62

Primary PM2.5 - Brakewear Particulate 0.45 156.90

Primary PM2.5 - Tirewear Particulate 0.15 50.52

Sulfur Dioxide (SO2) 0.29 99.78

2021 Vehicle Population: 979,689 Daily VMT: 24,098,721 Annual VMT: 7,811,745,310

Oxides of Nitrogen 27.80 9,530.16

Primary Exhaust PM2.5 - Total 1.06 357.87

Primary PM2.5 - Brakewear Particulate 0.48 161.69

Primary PM2.5 - Tirewear Particulate 0.15 51.92

Sulfur Dioxide (SO2) 0.30 100.82

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 7 of 8
County Year Pollutant Name DailyEmissions AnnualEmissions

Warren

2005 Vehicle Population: 233,106 Daily VMT: 5,884,222 Annual VMT: 1,949,619,088

Oxides of Nitrogen 23.98 8,224.57

Primary Exhaust PM2.5 - Total 0.82 278.74

Primary PM2.5 - Brakewear Particulate 0.09 32.00

Primary PM2.5 - Tirewear Particulate 0.03 10.00

Sulfur Dioxide (SO2) 0.07 23.54

2008 Vehicle Population: 240,655 Daily VMT: 6,057,344 Annual VMT: 2,031,755,542

Oxides of Nitrogen 21.11 7,267.18

Primary Exhaust PM2.5 - Total 0.69 237.65

Primary PM2.5 - Brakewear Particulate 0.12 39.57

Primary PM2.5 - Tirewear Particulate 0.04 12.34

Sulfur Dioxide (SO2) 0.08 26.57

2011 Vehicle Population: 244,521 Daily VMT: 6,406,290 Annual VMT: 2,143,506,318

Oxides of Nitrogen 19.88 6,826.15

Primary Exhaust PM2.5 - Total 0.82 279.53

Primary PM2.5 - Brakewear Particulate 0.13 43.09

Primary PM2.5 - Tirewear Particulate 0.04 13.60

Sulfur Dioxide (SO2) 0.08 26.83

2015 Vehicle Population: 252,440 Daily VMT: 6,842,835 Annual VMT: 2,285,057,933

Oxides of Nitrogen 13.37 4,598.44

Primary Exhaust PM2.5 - Total 0.54 185.12

Primary PM2.5 - Brakewear Particulate 0.13 43.02

Primary PM2.5 - Tirewear Particulate 0.04 13.91

Sulfur Dioxide (SO2) 0.08 27.77

2018 Vehicle Population: 258,547 Daily VMT: 7,368,042 Annual VMT: 2,460,618,151

Oxides of Nitrogen 10.73 3,692.47

Primary Exhaust PM2.5 - Total 0.43 148.00

Primary PM2.5 - Brakewear Particulate 0.14 48.57

Primary PM2.5 - Tirewear Particulate 0.05 15.66

Sulfur Dioxide (SO2) 0.09 30.49

2021 Vehicle Population: 264,802 Daily VMT: 7,707,508 Annual VMT: 2,498,434,852

Oxides of Nitrogen 8.54 2,875.72

Primary Exhaust PM2.5 - Total 0.33 109.76

Primary PM2.5 - Brakewear Particulate 0.16 51.34

Primary PM2.5 - Tirewear Particulate 0.05 16.51

Sulfur Dioxide (SO2) 0.10 31.58

17-Aug-10 MOVES 2010 and OKI Travel Demand Model v7.6 Page 8 of 8
Mobile Source Emission Forecast Process

Emission Factor Model

OKI’s conformity assessment utilized U.S.EPA’s emissions model MOVES 2010 to develop emission 

factors for SO2, NOX and PM2.5. Table 3 summarizes the settings used in the MOVES run specification 

file. Table 4 lists the data used in the MOVES County-Data Manager. Further details on the use of 

MOVES are found in the Appendix. 

Table 3. 

MOVES Runspec [sic] Parameter Settings

MOVES Version 2009/12/21, MOVES default 

database 2010615111524

Scale County, Emission Rates

Time Span Time aggregation = Hour

1 month representing average annual 

temperatures 

All hours of day selected 

Weekdays only 

Geographic Bounds 2 Custom Domains – 4 Ohio counties, 3 Kentucky 

counties 

Vehicles/Equipment All source types, gasoline and diesel

Road Type All road types including off-network

Pollutants and Processes NOx, All PM2.5 categories, SO2, Total Energy 

Consumption 

Strategies none

General Output Units= grams, joules and miles

Output Emissions Time = hour, Location =county, on-road emission 

rates by road type and source use type. 

Advanced Performance none

Table 4 

County Data Manager Data Source

Source Type Population Local and default. Local data (2010) from KYTC and ODOT from 

motor vehicle registration data. Default data used for source types 

41, 61 and 62. In addition , default data for source types 31, 32 and 

54 used for KY. 

Vehicle Type VMT Local and default. HPMSVTypeYear VMT=daily VMT from OKI travel 

demand model with EPA’s daily to annual VMT converter applied. 

monthVMTFraction = default. dayVMTFraction=default,

hourVMTFraction=local. 

I/M Programs Default modified to reflect discontinued I/M program

Fuel Formulation Default
Fuel Supply Default

Meteorology Data Local. Kentucky Division for Air Quality.

Ramp Fraction Local. Ramp emissions calculated outside of MOVES

Road Type Distribution Local. OKI travel demand model.

Age Distribution Local and default. Local data (2010) from KYTC and ODOT from 

motor vehicle registration data. Default data used for source types 

41, 61 and 62. In addition , default data for source types 31, 32 and 

54 used for KY. 

Average Speed Distribution Local. OKI travel demand model.

OKI Travel Demand Model 

Transportation system performance was estimated using the OKI Travel Demand Model Version 7.6. 

The OKI Travel Demand Model is composed of TRANPLAN programs, CUBE Voyager programs and a 

series of FORTRAN programs written by OKI. It is a state of the practice model that uses the standard 4 

phase sequential modeling approach of trip generation, distribution, modal choice and assignment. The 

model uses demographic and land use data and capacity and free-flow speed characteristics for each 

roadway segment in the network to produce a “loaded” highway network with forecasted traffic 

volumes with revised speeds based on specified speed/capacity relationships. 

Travel analysis zones are the basic geographic unit for estimating travel in the OKI model. The OKI region 

is subdivided into 1608 traffic analysis zones to permit detail as well as manageability. A variety of 

socioeconomic data items are used in the OKI transportation planning process. These data are used 

primarily to forecast future travel patterns by serving as independent variables in OKI trip generation 

equations. The following categories of planning data are utilized: 

• Population (household and group quarter) 

• Households 

• Household vehicles 

• Employment (by employment category and zone of work) 

• Labor force participation (by zone of residence) 

• Area type 

The principal data requirements of the OKI travel demand forecasting model are population and 

employment. From these variables, other characteristics including households, labor force, and personal 

vehicles may be derived. Chapter 5 of OKI 2030 Regional Transportation Plan 2008 Update provides a 

complete demographic overview of the region. 

OKI utilizes both base year (2005) and future year data (2010, 2020 and 2030) in the planning process.

Planning data are maintained at the Traffic Analysis Zone (TAZ) level, and originate in the 2000 Census of 

Population and Housing. Base year 2005 and future year data for each variable are developed through 

various methods. More detailed explanation of base year and future year data generation for each of 

the above-mentioned categories of planning data follows. All of the variables represent the latest OKI 

planning assumptions. 
Population 

Base and Future Year Data: Population data for base year 2005 and future years 2010, 2020 and 2030 

originate with the 2000 Census of Population and Housing. Utilizing ArcView GIS, population data at the 

zonal level for 2000 was derived from the area proportion allocation of block level population. 

As a tri-state regional planning agency, OKI uses county level projections as prepared by the respective 

state data centers (Ohio Department of Development Office of Strategic Research, Kentucky State Data 

Center and Indiana Business Research Center) as control totals. The most current projections (years 

2005 to 2030) were released by the Ohio and Indiana state data centers in 2003 and the Kentucky State 

Data Center in 2004. Population projections at the zonal level are calculated by multiplying household

size by the projected zonal households. Household size is factored so that, in each county, the sum of

the zonal populations equals the control total. 

Households 

Base Year Data: Household data for base year 2005 originates with the 2000 Census of Population and 

Housing. Utilizing the geographic information system ArcMap, household data at the zonal level for 2000 

was derived from the area proportion allocation of block level households. Year 2000 household data 

was updated to 2005 with residential building permits issued between January 2000 and December 

2004. The residential building locations were geocoded in ArcMap, then aggregated to the TAZs. The 

housing unit totals for each TAZ were converted to households by applying a vacancy rate, an 

adjustment for permitted but unbuilt units, and subtracting demolitions (where data was available). 

These households were then added to the year Census 2000 zonal household total to arrive at 2005 

households for each TAZ. 

Future Year Data: The preparation of household projections was accomplished by calculating the 

number of households for a projected county population using ratios of householders to total 

population by age specific cohorts derived from the 2000 Census for each analysis year. Disaggregation

to TAZs was determined by historical trends, existing and future land use, topography, flood plain 

information, availability of land, local knowledge and other factors. 

Household Vehicles 

Base and Future Year Data: Base and future year household vehicle data were obtained from the 2000 

Census of Population and Housing. The 2000 Census is the only source of household vehicle data 

available at the block group level. Average vehicles per household were calculated for block groups then 

applied to the TAZs associated with each block group. The 2005, 2010, 2020 and 2030 vehicles per 

household level was held at the 2000 level based on the fact that, since 2002, the number of vehicles 

per household has exceeded the number of drivers per household. 

Labor Force 

Base and Future Year Data: The OKI labor force is a function of the population as determined by a labor 

force participation ratio (the number of employed persons in the labor force per persons 16 and over).

Household data for base year 2005 originates with the 2000 Census of Population and Housing. Utilizing
the geographic information system ArcMap, household data at the zonal level for 2000 was derived 

from the area proportion allocation of block group level employed labor force. The labor force 

projections for 2005, 2010, 2020 and 2030 were based on the most recent projections of national labor 

force participation rates by age and sex cohorts from the U.S. Department of Labor, Bureau of Labor 

Statistics for each of those years. These rates were then applied to the projected county age/sex cohorts 

and adjusted to eliminate the unemployed to arrive at a county employed labor force control total. 

Employed labor force at the zonal level is calculated by multiplying the labor force participation rate by 

the zonal population. The labor force participation rate is adjusted so that, in each county, the sum of 

the zonal labor force counts equals the control total. 

Employment 

Base Year Data: Quarterly Census of Employment and Wages (QCEW or ES202) data for 2005 was 

utilized as the primary tool to calculate employment at the zonal level. Individual business records 

containing physical location, number of employees and SIC code were geocoded through ArcMap and 

aggregated to the TAZ level. This data set was supplemented by other sources of data to complete the 

commuting employment picture in the OKI region. Each zone’s employment was divided according to 

the SIC code into three classes (retail, office, industrial) based upon the potential for generating trips. 

Future Year Data: For future year employment projection, calculation was first made of the 

employment at the regional level. At the regional level, employment is a calculation of the region’s 

employed labor force minus workers who live in the region but commute out to work, plus workers who 

live outside the region but commute in to work. The regional total was disaggregated first to the county 

level based on historic trends and expected changes in the county’s share of the region’s employment 

and then to the TAZ level. Disaggregation to TAZs was determined by historical trends, existing and 

future land use, topography, flood plain information, availability of land, local knowledge and other 

factors. 

Area Type 

Base and Future Year Data: For each analysis year, each TAZ is assigned an area type designation as 

CBD, Urban, Suburban or Rural based on population and employment densities. 

Model Calibration

OKI’s Travel Demand Model has been validated to observed traffic volumes for the model base year 

2005. The modeling network encompasses the entire ozone nonattainment area with the exception of 

Clinton County, Ohio. The modeling network also includes Greene, Miami and Montgomery counties in 

Ohio and the remainder of Dearborn County Indiana. The difference between estimated vehicle miles 

traveled (VMT) and 2005 observed VMT is less than 1%. A highway screenline analysis compares the 

screenline observed and simulated traffic volume discrepancies with the ODOT standard of maximum 

desirable deviation. The comparison shows that the model performs at a satisfactory level and all the

errors were under the ODOT curve. Further information can be found in OKI’s 2007 report, “OKI/MVRPC 

Travel Demand Model Methodology/ Validation Report”. For the calibration, OKI used over 3000 traffic

counts collected through 2006 by the Ohio Department of Transportation (ODOT), the Kentucky 
Transportation Cabinet, many county and local governments, transportation engineering consultants, 

and OKI. These traffic counts cover nearly 50% percent of the links in the OKI portion of the modeling 

network. The methodology provides consistency with past emission inventory and conformity analysis 

work performed by OKI. 

Local Inputs and Post-Model Processing

OKI incorporates a variety of sources of local data to both improve and confirm the accuracy of VMT, as 

well as other travel-related parameters. Free flow speeds used on the highway and transit networks are 

based on travel time studies performed locally. The OKI post-processing program, IMPACT, uses the 

loaded highway network to generate VMT by hour, VMT by speed distribution and VMT by facility type. 

These tables are then included as input into MOVES. Two separate sets of VMT tables are generated: 

one for the four Ohio counties plus Dearborn County Indiana, and a second for the three Kentucky 

counties. The VMT by hour tables utilize hourly traffic distribution and directional split factors for 

different roadway types as developed by OKI. The main source of the data was the permanent traffic 

counting stations located throughout the OKI region for the years of 1998-2002. This data was 

supplemented with data collected at coverage count stations (locations with counts taken on only onetwo days). The stations were classified by area type: urban and rural, and functional classification:

freeway, arterial and collector. Speeds representing various “loaded” conditions (with traffic volumes) 

are estimated using techniques from the 1997 Highway Capacity Manual. This permits the estimation of 

speeds as conditions vary from hour to hour on the different facility types throughout the region. The 

IMPACT program performs the appropriate summation by area and roadway type as well as regional 

totals. OKI has also developed seasonal conversion factors to adjust traffic volumes to summer 

conditions. The factors were derived from local data collected at permanent traffic counting stations

during 1994-1997 utilizing the average daily traffic monthly conversion factors for June, July and August. 

Further information on OKI’s IMPACT program is documented in the report, “Travel Demand Model 

Summary Reporting and Impact Summary Reporting: OKI/MVRPC Travel Demand Model User’s Guide”, 

OKI 2003.

 
11 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

APPENDIX 

OKI Technical Documentation for Using 

EPA MOVES to Develop MOBILE Source 

Emissions 

August 2010 

 
12 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

1. Using MOVES 

To determine specific emission profiles and inventory, user has to define the input data like 

area, time span, type of vehicles, road types, fuel types, emission producing processes etc. 

These data are stored in an XML file which is called Runspec [sic]. Using graphical user interface 

user can modify all these attributes of Runspec [sic]. In the following sections, how input data is 

entered and modified is explained. All these input options are found in the navigation panel of 

Graphical User Interface of MOVES software. 

1.1 Description 

This input tells about the specifications of the Runspec [sic] and it is useful to distinguish 

between the Runspecs [sic]. We can also explain the brief overview of the particular Runspec 

[sic]. In all of our current Runspecs [sic], we have details such as analysis years, area and 

pollutants analyzed. 

1.2 Scale 

 In this option, we need to specify about the Domain/Scale and Calculation type. The Domain 

specifies the level of default data we need to use for analysis and also the scale of the analysis. 

We have considered the County scale for Ohio Custom Domain and the calculation type we 

have used is “Emission Rates”. 

1.3 Time Spans 

This input panel has different time-related input data like time aggregation level, year of 

analysis, month of analysis, whether analysis day is Weekday or Weekend, and hours of 

analysis. In all of our runs, time aggregation level is considered as hour, which is the most 

disaggregated level possible in MOVES and it is also specified in the technical guidance† for all 

SIP runs. We have used different years of analysis (i.e. 2005, 2008, 2011, 2015, 2018, and 2021). 

We have used two different months, July and April. Ozone season daily analysis is done using 

July temperatures. Annual analysis uses one 24-hour set of average annual temperatures. The 

annual average minimum temperature, maximum temperature and humidity values for each 

hour were calculated and assigned the April month ID. 

1.4 Geographic Bounds 

In this input type, we need to specify about region of analysis (eg. Nation, State, Custom 

Domain). We have created a separate input database through combining four Ohio counties 

namely, Hamilton, Butler, Clermont and Warren. Upon selecting the custom domain, MOVES 

will consider this region as separate Generic County. The state ID is fixed as 99 and we have 

assigned an arbitrary CountyID 390 for Ohio to distinguish between default county codes. User 

also need to provide a fraction geographic phase in area, in this case we do not have any phase 
13 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

in area fraction and we also provided average barometric pressure to identify whether it is low 

altitude area or high altitude area (the barometric pressures are averages of all constituent 

counties). Since we do not have I/M program in the region the refueling program adjustment 

fraction and refueling spill program adjustment fractions are assigned as 0.00. In this input 

panel we also need to specify the Domain Input Databases. For all of our runs we have defined 

different input databases for each year. 

1.5 Vehicles/Equipment 

In MOVES [sic], user also needs to provide the different type of vehicles considered for analysis 

in the region. MOVES [sic] provide us with 13 different types of vehicles or equipment and four 

different fuel types and we need to select appropriate fuel and vehicle combinations. In MOVES 

[sic] vehicle types are called SourceUseTypes [sic]. We have considered all possible types of 

fuel/vehicle type combinations. 

1.6 Road Type 

Next input panel is about type of roadways in the region. There are five types of road types 

available in MOVES, since OKI travel demand model could not predict the VMT in parking lots 

(off network) only four road types are considered. These road types are relatively simple and 

are based on area type, whether it is urban or rural. All expressways and freeways are 

considered as restricted roadways and all other road types are considered as unrestricted 

roadways. 

1.7 Pollutants and Processes 

There are different pollutants and corresponding processes are available in MOVES. A separate 

panel is available for selecting different pollutants and processes. In these particular set of runs, 

total PM2.5 emissions are selected with an addition of sulfur dioxide. To perform calculation of 

PM2.5 it is also required to select Total energy consumption. In addition to PM2.5, Oxides of 

Nitrogen are also selected. 

1.8 Miscellaneous 

Further, if we have information about future or present Alternative Vehicle Fuels & 

Technologies, on-road retrofit and rate of progress information that can be given as input to 

the Runspec [sic]. If we do not specify future Alternative Vehicle Fuel & Technologies, MOVES 

[sic] is going to assume default alternative fuels. So, we have modified default AVFT through 

importing new AVFT strategy file which includes there would not be any change in transit bus 

fuels. MOVES [sic] also provide us the options whether we would like to save the 

MOVESactivityoutput [sic] and MOVESOutput [sic] databases or not. 
14 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 1 : Alternative Vehicle and Fueling Technology used in all Runspecs [sic] 

sourceTypeID modelYearID fuelTypeID engTechID fuelEngFraction

42 1960 2 1 1 

42 1961 2 1 1 

42 1962 2 1 1 

42 1963 2 1 1 

42 1964 2 1 1 

42 1965 2 1 1 

42 1966 2 1 1 

42 1967 2 1 1 

42 1968 2 1 1 

42 1969 2 1 1 

42 1970 2 1 1 

42 1971 2 1 1 

42 1972 2 1 1 

42 1973 2 1 1 

42 1974 2 1 1 

42 1975 2 1 1 

42 1976 2 1 1 

42 1977 2 1 1 

42 1978 2 1 1 

42 1979 2 1 1 

42 1980 2 1 1 

42 1981 2 1 1 

42 1982 2 1 1 

42 1983 2 1 1 

42 1984 2 1 1 

42 1985 2 1 1 

42 1986 2 1 1 

42 1987 2 1 1 

42 1988 2 1 1 

42 1989 2 1 1 

42 1990 2 1 1 

42 1991 2 1 1 

42 1992 2 1 1 

42 1993 2 1 1 

42 1994 2 1 1 

42 1995 2 1 1 

42 1996 2 1 1 

42 1997 2 1 1 

42 1998 2 1 1 

42 1999 2 1 1 

42 2000 2 1 1 

42 2001 2 1 1 

42 2002 2 1 1 

42 2003 2 1 1 

42 2004 2 1 1 

42 2005 2 1 1 
15 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

42 2006 2 1 1 

42 2007 2 1 1 

42 2008 2 1 1 

42 2009 2 1 1 

42 2010 2 1 1 

42 2011 2 1 1 

42 2012 2 1 1 

42 2013 2 1 1 

42 2014 2 1 1 

42 2015 2 1 1 

42 2016 2 1 1 

42 2017 2 1 1 

42 2018 2 1 1 

42 2019 2 1 1 

42 2020 2 1 1 

42 2021 2 1 1 

42 2022 2 1 1 

42 2023 2 1 1 

42 2024 2 1 1 

42 2025 2 1 1 

42 2026 2 1 1 

42 2027 2 1 1 

42 2028 2 1 1 

42 2029 2 1 1 

42 2030 2 1 1 

42 2031 2 1 1 

42 2032 2 1 1 

42 2033 2 1 1 

42 2034 2 1 1 

42 2035 2 1 1 

42 2036 2 1 1 

42 2037 2 1 1 

42 2038 2 1 1 

42 2039 2 1 1 

42 2040 2 1 1 

42 2041 2 1 1 

42 2042 2 1 1 

42 2043 2 1 1 

42 2044 2 1 1 

42 2045 2 1 1 

42 2046 2 1 1 

42 2047 2 1 1 

42 2048 2 1 1 

42 2049 2 1 1 

42 2050 2 1 1 
16 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

1.9 Output 

 In MOVES we need to specify the output database and need to create new database for each 

new Runspec [sic]. We also have options like specifying the units for emission rates and energy 

consumption. These options are available in the General Output panel. There is one more 

option available within the output which is called output emissions detail, which provides user 

different options for data aggregation. 

2. Data Importers 

In order to enter local data into Runspec [sic], we need to use pre processing option in the 

MOVES. We can select either Data Importer or County Importer for Custom Domain option. 

These Importers convert the data in excel format to MySQL tables. This is the preferred input 

format of MOVES software. 

2.1 Meteorology Data Importer 

In this type of Importer, meteorology data is imported a MOVES input format. This dataset has 

different data items like month ID, Zone ID, hour ID, Temperature and Relative Humidity. For 

OKI region and Ohio portion runs we have used temperature data obtained from the Kentucky 

Division for Air Quality (KDAQ). Even though ODOT has provided the temperature data 

(collected from local airports), KDAQ data appeared to be more applicable. In the data set, April 

Meteorology data is replaced with annual average temperatures and relative humidity. 

Table 2 : Meteorology data obtained from KDAQ 

monthID zoneID hourID temperature relHumidity 

4 993900 1 47.5 72.9 

4 993900 2 46.4 75.8 

4 993900 3 45.5 77.9 

4 993900 4 44.8 79.4 

4 993900 5 44.3 80.7 

4 993900 6 43.7 82.1 

4 993900 7 43.2 83.3 

4 993900 8 43.6 82.3 

4 993900 9 46.1 76.4 

4 993900 10 50.1 67.0 

4 993900 11 54.2 57.8 

4 993900 12 57.7 50.9 

4 993900 13 60.8 45.7 

4 993900 14 62.5 43.1 
17 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

4 993900 15 63.1 42.2 

4 993900 16 63.2 42.0 

4 993900 17 62.8 42.6 

4 993900 18 61.7 44.3 

4 993900 19 59.7 47.6 

4 993900 20 57.1 52.2 

4 993900 21 54.5 57.2 

4 993900 22 52.2 62.1 

4 993900 23 50.6 65.9 

4 993900 24 49.1 69.4 

7 993900 1 69.3 69.5 

7 993900 2 68.1 72.4 

7 993900 3 67.1 74.8 

7 993900 4 66.4 76.6 

7 993900 5 65.9 78 

7 993900 6 65.3 79.7 

7 993900 7 64.8 81.1 

7 993900 8 65.2 79.9 

7 993900 9 67.8 73.1 

7 993900 10 72 63.4 

7 993900 11 76.2 55 

7 993900 12 79.8 48.8 

7 993900 13 83 44 

7 993900 14 84.7 41.6 

7 993900 15 85.3 40.8 

7 993900 16 85.5 40.6 

7 993900 17 85.1 41.2 

7 993900 18 83.9 42.8 

7 993900 19 81.8 45.8 

7 993900 20 79.1 49.9 

7 993900 21 76.4 54.6 

7 993900 22 74.1 59 

7 993900 23 72.5 62.3 

7 993900 24 70.8 65.9 

2.2 Source Type Population Importer 

This importer imports vehicle type, and registered vehicle population in the region into MOVES 

input databases. ODOT has provided us with the registered vehicle population in each county in 

the region for 13 MOVES vehicle types. KYTC has provided registered vehicle population by 

county for 6 HPMS vehicle types. The KYTC data was converted to the 13 MOVES vehicle types 

based on the Ohio distribution. Same vehicle population was used for all analysis years. As per 

suggestions made by FHWA and KYTC, the Source Type Population has been forecasted for 

future years with +0.8 % per year. Similarly, the Source Type Populations has been estimated 

for past years. The MOVES default source type population for intercity bus, refuse trucks, 

motor homes and combination trucks was used. In addition, MOVES default source type 
18 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

population for passenger trucks and light commercial trucks was used for Kentucky. The 

MOVES default source type population was acquired from the MOVES activity output tables 

from county-level inventory runs. 

Table3 : Source Type Population for Ohio Custom Domain (2008) 

yearID sourceTypeID sourceTypePopulation

2008 11 68559 

2008 21 1191067 

2008 31 482420 

2008 32 15817 

2008 41 454 

2008 42 81 

2008 43 3651 

2008 51 409 

2008 52 366 

2008 53 361 

2008 54 4888 

2008 61 4839 

2008 62 5548 

Table 4: Kentucky Source Type population (acquired from KYTC) 

yearID sourceTypeID sourceTypePopulation

2008 11 7975 

2008 21 197009 

2008 31 120518 

2008 32 40263 

2008 41 127 

2008 42 21 

2008 43 977 

2008 51 115 

2008 52 761 

2008 53 751 

2008 54 1379 

2008 61 1580 

2008 62 1811 

2.3 Age Distribution Importer 

For emission calculation the MOVES need vehicle Age Distribution by Source Type. Vehicle Age 

Distribution is divided into 30 years based on vehicle model years. For each vehicle type, the 

distribution sum adds up to one. ODOT has obtained vehicle registration data from the Bureau 

of Motor Vehicles for all the counties in Ohio and processed them to convert into MOVES Age 

Distribution for 13 vehicle types. We have used the same Age Distribution for all year runs. All 
19 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

the vehicles older than 30 years are considered as 30-years old. Same age distribution is used 

for all analysis years. KYTC also provided similar information, but for the 6 HPMS types only. 

For Kentucky, identical age distributions are used within each HPMS vehicle type. 

Table 5 : Ohio Custom Domain Age distribution 

Source

TypeID

yearID ageID ageFraction

11 2008 0 0.0019 

11 2008 1 0.0191 

11 2008 2 0.0531 

11 2008 3 0.0688 

11 2008 4 0.0773 

11 2008 5 0.0737 

11 2008 6 0.0611 

11 2008 7 0.0780 

11 2008 8 0.0636 

11 2008 9 0.0537 

11 2008 10 0.0435 

11 2008 11 0.0359 

11 2008 12 0.0282 

11 2008 13 0.0230 

11 2008 14 0.0220 

11 2008 15 0.0183 

11 2008 16 0.0160 

11 2008 17 0.0146 

11 2008 18 0.0097 

11 2008 19 0.0080 

11 2008 20 0.0072 

11 2008 21 0.0086 

11 2008 22 0.0084 

11 2008 23 0.0121 

11 2008 24 0.0171 

11 2008 25 0.0179 

11 2008 26 0.0137 

11 2008 27 0.0171 

11 2008 28 0.0249 

11 2008 29 0.0172 

11 2008 30 0.0862 

21 2008 0 0.0121 

21 2008 1 0.0331 

21 2008 2 0.0440 

21 2008 3 0.0528 

21 2008 4 0.0534 

21 2008 5 0.0566 

21 2008 6 0.0570 

21 2008 7 0.0592 

21 2008 8 0.0591 

21 2008 9 0.0542 

21 2008 10 0.0590 

21 2008 11 0.0568 

21 2008 12 0.0507 

21 2008 13 0.0499 

21 2008 14 0.0438 

21 2008 15 0.0453 

21 2008 16 0.0368 

21 2008 17 0.0308 

21 2008 18 0.0261 

21 2008 19 0.0207 

21 2008 20 0.0165 

21 2008 21 0.0132 

21 2008 22 0.0095 

21 2008 23 0.0073 

21 2008 24 0.0059 

21 2008 25 0.0043 

21 2008 26 0.0033 

21 2008 27 0.0017 

21 2008 28 0.0011 

21 2008 29 0.0010 

21 2008 30 0.0346 

31 2008 0 0.0103 

31 2008 1 0.0279 

31 2008 2 0.0502 

31 2008 3 0.0570 

31 2008 4 0.0659 

31 2008 5 0.0806 

31 2008 6 0.0796 

31 2008 7 0.0733 

31 2008 8 0.0727 

31 2008 9 0.0599 

31 2008 10 0.0625 

31 2008 11 0.0603 

31 2008 12 0.0516 

31 2008 13 0.0432 

31 2008 14 0.0380 

31 2008 15 0.0386 

31 2008 16 0.0302 

31 2008 17 0.0260 

31 2008 18 0.0165 

31 2008 19 0.0125 

31 2008 20 0.0093 
20 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

31 2008 21 0.0084 

31 2008 22 0.0067 

31 2008 23 0.0051 

31 2008 24 0.0037 

31 2008 25 0.0025 

31 2008 26 0.0017 

31 2008 27 0.0009 

31 2008 28 0.0004 

31 2008 29 0.0002 

31 2008 30 0.0041 

32 2008 0 0.0178 

32 2008 1 0.0459 

32 2008 2 0.0871 

32 2008 3 0.0699 

32 2008 4 0.0707 

32 2008 5 0.0357 

32 2008 6 0.0355 

32 2008 7 0.0369 

32 2008 8 0.0366 

32 2008 9 0.0407 

32 2008 10 0.0491 

32 2008 11 0.0547 

32 2008 12 0.0427 

32 2008 13 0.0413 

32 2008 14 0.0383 

32 2008 15 0.0602 

32 2008 16 0.0476 

32 2008 17 0.0381 

32 2008 18 0.0304 

32 2008 19 0.0181 

32 2008 20 0.0212 

32 2008 21 0.0184 

32 2008 22 0.0135 

32 2008 23 0.0134 

32 2008 24 0.0095 

32 2008 25 0.0070 

32 2008 26 0.0054 

32 2008 27 0.0021 

32 2008 28 0.0014 

32 2008 29 0.0008 

32 2008 30 0.0100 

41 2008 0 0.0000 

41 2008 1 0.0309 

41 2008 2 0.0884 

41 2008 3 0.0890 

41 2008 4 0.0768 

41 2008 5 0.0746 

41 2008 6 0.0967 

41 2008 7 0.0635 

41 2008 8 0.0486 

41 2008 9 0.0801 

41 2008 10 0.0751 

41 2008 11 0.0624 

41 2008 12 0.0254 

41 2008 13 0.0271 

41 2008 14 0.0188 

41 2008 15 0.0193 

41 2008 16 0.0133 

41 2008 17 0.0177 

41 2008 18 0.0094 

41 2008 19 0.0177 

41 2008 20 0.0171 

41 2008 21 0.0099 

41 2008 22 0.0039 

41 2008 23 0.0055 

41 2008 24 0.0061 

41 2008 25 0.0011 

41 2008 26 0.0033 

41 2008 27 0.0033 

41 2008 28 0.0028 

41 2008 29 0.0017 

41 2008 30 0.0105 

42 2008 0 0.0000 

42 2008 1 0.0366 

42 2008 2 0.1098 

42 2008 3 0.0366 

42 2008 4 0.1585 

42 2008 5 0.0366 

42 2008 6 0.0610 

42 2008 7 0.0610 

42 2008 8 0.0244 

42 2008 9 0.1098 

42 2008 10 0.0366 

42 2008 11 0.0976 

42 2008 12 0.0366 

42 2008 13 0.0244 

42 2008 14 0.0244 

42 2008 15 0.0122 

42 2008 16 0.0244 

42 2008 17 0.0244 

42 2008 18 0.0366 

42 2008 19 0.0000 

42 2008 20 0.0000 

42 2008 21 0.0122 

42 2008 22 0.0000 

42 2008 23 0.0000 

42 2008 24 0.0000 

42 2008 25 0.0000 
21 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

42 2008 26 0.0122 

42 2008 27 0.0000 

42 2008 28 0.0000 

42 2008 29 0.0122 

42 2008 30 0.0122 

43 2008 0 0.0905 

43 2008 1 0.0302 

43 2008 2 0.0549 

43 2008 3 0.0467 

43 2008 4 0.0592 

43 2008 5 0.0723 

43 2008 6 0.0481 

43 2008 7 0.0334 

43 2008 8 0.0668 

43 2008 9 0.0647 

43 2008 10 0.0842 

43 2008 11 0.0864 

43 2008 12 0.0473 

43 2008 13 0.0500 

43 2008 14 0.0242 

43 2008 15 0.0185 

43 2008 16 0.0106 

43 2008 17 0.0228 

43 2008 18 0.0109 

43 2008 19 0.0130 

43 2008 20 0.0125 

43 2008 21 0.0092 

43 2008 22 0.0062 

43 2008 23 0.0079 

43 2008 24 0.0090 

43 2008 25 0.0035 

43 2008 26 0.0030 

43 2008 27 0.0011 

43 2008 28 0.0027 

43 2008 29 0.0016 

43 2008 30 0.0087 

51 2008 0 0.0054 

51 2008 1 0.0488 

51 2008 2 0.0623 

51 2008 3 0.0705 

51 2008 4 0.0867 

51 2008 5 0.0434 

51 2008 6 0.0434 

51 2008 7 0.0542 

51 2008 8 0.0542 

51 2008 9 0.0759 

51 2008 10 0.0217 

51 2008 11 0.0407 

51 2008 12 0.0786 

51 2008 13 0.0542 

51 2008 14 0.0515 

51 2008 15 0.0678 

51 2008 16 0.0325 

51 2008 17 0.0081 

51 2008 18 0.0163 

51 2008 19 0.0027 

51 2008 20 0.0081 

51 2008 21 0.0000 

51 2008 22 0.0027 

51 2008 23 0.0027 

51 2008 24 0.0136 

51 2008 25 0.0000 

51 2008 26 0.0000 

51 2008 27 0.0000 

51 2008 28 0.0027 

51 2008 29 0.0000 

51 2008 30 0.0515 

52 2008 0 0.0054 

52 2008 1 0.0488 

52 2008 2 0.0623 

52 2008 3 0.0705 

52 2008 4 0.0867 

52 2008 5 0.0434 

52 2008 6 0.0434 

52 2008 7 0.0542 

52 2008 8 0.0542 

52 2008 9 0.0759 

52 2008 10 0.0217 

52 2008 11 0.0407 

52 2008 12 0.0786 

52 2008 13 0.0542 

52 2008 14 0.0515 

52 2008 15 0.0678 

52 2008 16 0.0325 

52 2008 17 0.0081 

52 2008 18 0.0163 

52 2008 19 0.0027 

52 2008 20 0.0081 

52 2008 21 0.0000 

52 2008 22 0.0027 

52 2008 23 0.0027 

52 2008 24 0.0136 

52 2008 25 0.0000 

52 2008 26 0.0000 

52 2008 27 0.0000 

52 2008 28 0.0027 

52 2008 29 0.0000 

52 2008 30 0.0515 
22 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

53 2008 0 0.0000 

53 2008 1 0.0062 

53 2008 2 0.0373 

53 2008 3 0.0093 

53 2008 4 0.0280 

53 2008 5 0.0342 

53 2008 6 0.0186 

53 2008 7 0.0186 

53 2008 8 0.0124 

53 2008 9 0.0155 

53 2008 10 0.0217 

53 2008 11 0.0373 

53 2008 12 0.0093 

53 2008 13 0.0311 

53 2008 14 0.0217 

53 2008 15 0.0373 

53 2008 16 0.0217 

53 2008 17 0.0342 

53 2008 18 0.0124 

53 2008 19 0.0186 

53 2008 20 0.0248 

53 2008 21 0.0373 

53 2008 22 0.0186 

53 2008 23 0.0248 

53 2008 24 0.0062 

53 2008 25 0.0373 

53 2008 26 0.0155 

53 2008 27 0.0186 

53 2008 28 0.0217 

53 2008 29 0.0186 

53 2008 30 0.3509 

54 2008 0 0.0077 

54 2008 1 0.0170 

54 2008 2 0.0377 

54 2008 3 0.0424 

54 2008 4 0.0471 

54 2008 5 0.0579 

54 2008 6 0.0552 

54 2008 7 0.0485 

54 2008 8 0.0406 

54 2008 9 0.0439 

54 2008 10 0.0505 

54 2008 11 0.0539 

54 2008 12 0.0435 

54 2008 13 0.0360 

54 2008 14 0.0348 

54 2008 15 0.0375 

54 2008 16 0.0303 

54 2008 17 0.0231 

54 2008 18 0.0196 

54 2008 19 0.0150 

54 2008 20 0.0183 

54 2008 21 0.0208 

54 2008 22 0.0218 

54 2008 23 0.0217 

54 2008 24 0.0186 

54 2008 25 0.0173 

54 2008 26 0.0163 

54 2008 27 0.0118 

54 2008 28 0.0084 

54 2008 29 0.0059 

54 2008 30 0.0968 

61 2008 0 0.0030 

61 2008 1 0.0167 

61 2008 2 0.0334 

61 2008 3 0.0393 

61 2008 4 0.0506 

61 2008 5 0.0530 

61 2008 6 0.0620 

61 2008 7 0.0625 

61 2008 8 0.0562 

61 2008 9 0.0551 

61 2008 10 0.0595 

61 2008 11 0.0569 

61 2008 12 0.0458 

61 2008 13 0.0493 

61 2008 14 0.0380 

61 2008 15 0.0435 

61 2008 16 0.0425 

61 2008 17 0.0312 

61 2008 18 0.0262 

61 2008 19 0.0235 

61 2008 20 0.0201 

61 2008 21 0.0225 

61 2008 22 0.0212 

61 2008 23 0.0141 

61 2008 24 0.0137 

61 2008 25 0.0096 

61 2008 26 0.0069 

61 2008 27 0.0039 

61 2008 28 0.0030 

61 2008 29 0.0027 

61 2008 30 0.0343 

62 2008 0 0.0078 

62 2008 1 0.0232 

62 2008 2 0.0307 

62 2008 3 0.0907 

62 2008 4 0.0721 
23 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

62 2008 5 0.0808 

62 2008 6 0.0564 

62 2008 7 0.0520 

62 2008 8 0.0360 

62 2008 9 0.0552 

62 2008 10 0.1019 

62 2008 11 0.0813 

62 2008 12 0.0603 

62 2008 13 0.0425 

62 2008 14 0.0439 

62 2008 15 0.0442 

62 2008 16 0.0273 

62 2008 17 0.0202 

62 2008 18 0.0122 

62 2008 19 0.0101 

62 2008 20 0.0103 

62 2008 21 0.0080 

62 2008 22 0.0079 

62 2008 23 0.0058 

62 2008 24 0.0050 

62 2008 25 0.0036 

62 2008 26 0.0038 

62 2008 27 0.0001 

62 2008 28 0.0012 

62 2008 29 0.0010 

62 2008 30 0.0046 

Table 6 : Kentucky Custom Domain Age 

distribution 

Source

TypeID

yearID ageID ageFraction

11 2008 0 0.0020 

11 2008 1 0.0323 

11 2008 2 0.0606 

11 2008 3 0.0826 

11 2008 4 0.0831 

11 2008 5 0.0774 

11 2008 6 0.0667 

11 2008 7 0.0830 

11 2008 8 0.0650 

11 2008 9 0.0495 

11 2008 10 0.0424 

11 2008 11 0.0345 

11 2008 12 0.0287 

11 2008 13 0.0214 

11 2008 14 0.0240 

11 2008 15 0.0208 

11 2008 16 0.0138 

11 2008 17 0.0129 

11 2008 18 0.0092 

11 2008 19 0.0051 

11 2008 20 0.0052 

11 2008 21 0.0058 

11 2008 22 0.0078 

11 2008 23 0.0108 

11 2008 24 0.0153 

11 2008 25 0.0168 

11 2008 26 0.0124 

11 2008 27 0.0160 

11 2008 28 0.0228 

11 2008 29 0.0152 

11 2008 30 0.0568 

21 2008 0 0.0118 

21 2008 1 0.0665 

21 2008 2 0.0596 

21 2008 3 0.0642 

21 2008 4 0.0611 

21 2008 5 0.0705 

21 2008 6 0.0694 

21 2008 7 0.0699 

21 2008 8 0.0719 

21 2008 9 0.0619 

21 2008 10 0.0633 

21 2008 11 0.0591 

21 2008 12 0.0490 

21 2008 13 0.0442 

21 2008 14 0.0348 

21 2008 15 0.0318 

21 2008 16 0.0241 

21 2008 17 0.0191 

21 2008 18 0.0142 

21 2008 19 0.0111 

21 2008 20 0.0088 

21 2008 21 0.0066 

21 2008 22 0.0049 

21 2008 23 0.0039 

21 2008 24 0.0028 

21 2008 25 0.0024 
24 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

21 2008 26 0.0018 

21 2008 27 0.0009 

21 2008 28 0.0005 

21 2008 29 0.0005 

21 2008 30 0.0094 

31 2008 0 0.0000 

31 2008 1 0.0000 

31 2008 2 0.0000 

31 2008 3 0.0000 

31 2008 4 0.0238 

31 2008 5 0.0119 

31 2008 6 0.0119 

31 2008 7 0.0119 

31 2008 8 0.0119 

31 2008 9 0.0000 

31 2008 10 0.0238 

31 2008 11 0.0357 

31 2008 12 0.0119 

31 2008 13 0.0952 

31 2008 14 0.0833 

31 2008 15 0.0595 

31 2008 16 0.1071 

31 2008 17 0.0357 

31 2008 18 0.0357 

31 2008 19 0.0357 

31 2008 20 0.0119 

31 2008 21 0.0476 

31 2008 22 0.0238 

31 2008 23 0.0119 

31 2008 24 0.0119 

31 2008 25 0.0595 

31 2008 26 0.0357 

31 2008 27 0.0000 

31 2008 28 0.0238 

31 2008 29 0.0238 

31 2008 30 0.1548 

32 2008 0 0.0000 

32 2008 1 0.0000 

32 2008 2 0.0000 

32 2008 3 0.0000 

32 2008 4 0.0238 

32 2008 5 0.0119 

32 2008 6 0.0119 

32 2008 7 0.0119 

32 2008 8 0.0119 

32 2008 9 0.0000 

32 2008 10 0.0238 

32 2008 11 0.0357 

32 2008 12 0.0119 

32 2008 13 0.0952 

32 2008 14 0.0833 

32 2008 15 0.0595 

32 2008 16 0.1071 

32 2008 17 0.0357 

32 2008 18 0.0357 

32 2008 19 0.0357 

32 2008 20 0.0119 

32 2008 21 0.0476 

32 2008 22 0.0238 

32 2008 23 0.0119 

32 2008 24 0.0119 

32 2008 25 0.0595 

32 2008 26 0.0357 

32 2008 27 0.0000 

32 2008 28 0.0238 

32 2008 29 0.0238 

32 2008 30 0.1548 

41 2008 0 0.0455 

41 2008 1 0.1136 

41 2008 2 0.0000 

41 2008 3 0.0114 

41 2008 4 0.0227 

41 2008 5 0.0000 

41 2008 6 0.0000 

41 2008 7 0.0114 

41 2008 8 0.0114 

41 2008 9 0.0227 

41 2008 10 0.0114 

41 2008 11 0.0568 

41 2008 12 0.1250 

41 2008 13 0.0227 

41 2008 14 0.0000 

41 2008 15 0.0341 

41 2008 16 0.0341 

41 2008 17 0.0682 

41 2008 18 0.0455 

41 2008 19 0.0909 

41 2008 20 0.0568 

41 2008 21 0.0455 

41 2008 22 0.0341 

41 2008 23 0.0455 

41 2008 24 0.0227 

41 2008 25 0.0227 

41 2008 26 0.0114 

41 2008 27 0.0000 

41 2008 28 0.0227 

41 2008 29 0.0000 

41 2008 30 0.0114 
25 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

42 2008 0 0.0455 

42 2008 1 0.1136 

42 2008 2 0.0000 

42 2008 3 0.0114 

42 2008 4 0.0227 

42 2008 5 0.0000 

42 2008 6 0.0000 

42 2008 7 0.0114 

42 2008 8 0.0114 

42 2008 9 0.0227 

42 2008 10 0.0114 

42 2008 11 0.0568 

42 2008 12 0.1250 

42 2008 13 0.0227 

42 2008 14 0.0000 

42 2008 15 0.0341 

42 2008 16 0.0341 

42 2008 17 0.0682 

42 2008 18 0.0455 

42 2008 19 0.0909 

42 2008 20 0.0568 

42 2008 21 0.0455 

42 2008 22 0.0341 

42 2008 23 0.0455 

42 2008 24 0.0227 

42 2008 25 0.0227 

42 2008 26 0.0114 

42 2008 27 0.0000 

42 2008 28 0.0227 

42 2008 29 0.0000 

42 2008 30 0.0114 

43 2008 0 0.0455 

43 2008 1 0.1136 

43 2008 2 0.0000 

43 2008 3 0.0114 

43 2008 4 0.0227 

43 2008 5 0.0000 

43 2008 6 0.0000 

43 2008 7 0.0114 

43 2008 8 0.0114 

43 2008 9 0.0227 

43 2008 10 0.0114 

43 2008 11 0.0568 

43 2008 12 0.1250 

43 2008 13 0.0227 

43 2008 14 0.0000 

43 2008 15 0.0341 

43 2008 16 0.0341 

43 2008 17 0.0682 

43 2008 18 0.0455 

43 2008 19 0.0909 

43 2008 20 0.0568 

43 2008 21 0.0455 

43 2008 22 0.0341 

43 2008 23 0.0455 

43 2008 24 0.0227 

43 2008 25 0.0227 

43 2008 26 0.0114 

43 2008 27 0.0000 

43 2008 28 0.0227 

43 2008 29 0.0000 

43 2008 30 0.0114 

51 2008 0 0.0025 

51 2008 1 0.0200 

51 2008 2 0.0386 

51 2008 3 0.0436 

51 2008 4 0.0495 

51 2008 5 0.0579 

51 2008 6 0.0667 

51 2008 7 0.0698 

51 2008 8 0.0620 

51 2008 9 0.0611 

51 2008 10 0.0675 

51 2008 11 0.0619 

51 2008 12 0.0508 

51 2008 13 0.0529 

51 2008 14 0.0397 

51 2008 15 0.0397 

51 2008 16 0.0375 

51 2008 17 0.0276 

51 2008 18 0.0204 

51 2008 19 0.0184 

51 2008 20 0.0158 

51 2008 21 0.0174 

51 2008 22 0.0152 

51 2008 23 0.0108 

51 2008 24 0.0108 

51 2008 25 0.0071 

51 2008 26 0.0052 

51 2008 27 0.0031 

51 2008 28 0.0021 

51 2008 29 0.0021 

51 2008 30 0.0220 

52 2008 0 0.0025 

52 2008 1 0.0200 

52 2008 2 0.0386 

52 2008 3 0.0436 

52 2008 4 0.0495 
26 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

52 2008 5 0.0579 

52 2008 6 0.0667 

52 2008 7 0.0698 

52 2008 8 0.0620 

52 2008 9 0.0611 

52 2008 10 0.0675 

52 2008 11 0.0619 

52 2008 12 0.0508 

52 2008 13 0.0529 

52 2008 14 0.0397 

52 2008 15 0.0397 

52 2008 16 0.0375 

52 2008 17 0.0276 

52 2008 18 0.0204 

52 2008 19 0.0184 

52 2008 20 0.0158 

52 2008 21 0.0174 

52 2008 22 0.0152 

52 2008 23 0.0108 

52 2008 24 0.0108 

52 2008 25 0.0071 

52 2008 26 0.0052 

52 2008 27 0.0031 

52 2008 28 0.0021 

52 2008 29 0.0021 

52 2008 30 0.0220 

53 2008 0 0.0025 

53 2008 1 0.0200 

53 2008 2 0.0386 

53 2008 3 0.0436 

53 2008 4 0.0495 

53 2008 5 0.0579 

53 2008 6 0.0667 

53 2008 7 0.0698 

53 2008 8 0.0620 

53 2008 9 0.0611 

53 2008 10 0.0675 

53 2008 11 0.0619 

53 2008 12 0.0508 

53 2008 13 0.0529 

53 2008 14 0.0397 

53 2008 15 0.0397 

53 2008 16 0.0375 

53 2008 17 0.0276 

53 2008 18 0.0204 

53 2008 19 0.0184 

53 2008 20 0.0158 

53 2008 21 0.0174 

53 2008 22 0.0152 

53 2008 23 0.0108 

53 2008 24 0.0108 

53 2008 25 0.0071 

53 2008 26 0.0052 

53 2008 27 0.0031 

53 2008 28 0.0021 

53 2008 29 0.0021 

53 2008 30 0.0220 

54 2008 0 0.0025 

54 2008 1 0.0200 

54 2008 2 0.0386 

54 2008 3 0.0436 

54 2008 4 0.0495 

54 2008 5 0.0579 

54 2008 6 0.0667 

54 2008 7 0.0698 

54 2008 8 0.0620 

54 2008 9 0.0611 

54 2008 10 0.0675 

54 2008 11 0.0619 

54 2008 12 0.0508 

54 2008 13 0.0529 

54 2008 14 0.0397 

54 2008 15 0.0397 

54 2008 16 0.0375 

54 2008 17 0.0276 

54 2008 18 0.0204 

54 2008 19 0.0184 

54 2008 20 0.0158 

54 2008 21 0.0174 

54 2008 22 0.0152 

54 2008 23 0.0108 

54 2008 24 0.0108 

54 2008 25 0.0071 

54 2008 26 0.0052 

54 2008 27 0.0031 

54 2008 28 0.0021 

54 2008 29 0.0021 

54 2008 30 0.0220 

61 2008 0 0.0000 

61 2008 1 0.0064 

61 2008 2 0.0295 

61 2008 3 0.0205 

61 2008 4 0.0321 

61 2008 5 0.0346 

61 2008 6 0.0423 

61 2008 7 0.0308 

61 2008 8 0.0269 

61 2008 9 0.0179 
27 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

61 2008 10 0.0462 

61 2008 11 0.0410 

61 2008 12 0.0359 

61 2008 13 0.0513 

61 2008 14 0.0333 

61 2008 15 0.0359 

61 2008 16 0.0423 

61 2008 17 0.0269 

61 2008 18 0.0295 

61 2008 19 0.0231 

61 2008 20 0.0385 

61 2008 21 0.0397 

61 2008 22 0.0333 

61 2008 23 0.0346 

61 2008 24 0.0295 

61 2008 25 0.0192 

61 2008 26 0.0346 

61 2008 27 0.0128 

61 2008 28 0.0141 

61 2008 29 0.0128 

61 2008 30 0.1244 

62 2008 0 0.0000 

62 2008 1 0.0064 

62 2008 2 0.0295 

62 2008 3 0.0205 

62 2008 4 0.0321 

62 2008 5 0.0346 

62 2008 6 0.0423 

62 2008 7 0.0308 

62 2008 8 0.0269 

62 2008 9 0.0179 

62 2008 10 0.0462 

62 2008 11 0.0410 

62 2008 12 0.0359 

62 2008 13 0.0513 

62 2008 14 0.0333 

62 2008 15 0.0359 

62 2008 16 0.0423 

62 2008 17 0.0269 

62 2008 18 0.0295 

62 2008 19 0.0231 

62 2008 20 0.0385 

62 2008 21 0.0397 

62 2008 22 0.0333 

62 2008 23 0.0346 

62 2008 24 0.0295 

62 2008 25 0.0192 

62 2008 26 0.0346 

62 2008 27 0.0128 

62 2008 28 0.0141 

62 2008 29 0.0128 

62 2008 30 0.1244 
28 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2.4 Vehicle Type VMT and VMT Fractions 

This option is useful to import the annual VMT by source type into MOVES format. It has input 

option as HPMS Base Year VMT, for which we can either use HPMS data or the Travel Demand 

Model output. We have used annual VMT calculated from the OKI Regional Travel Demand 

Model. There are options like the Month VMT fraction, Day VMT fraction and Hour VMT 

fraction, which are useful for calculating emissions for different time periods. We have used 

default Monthly VMT distribution factors provided in the VMT Converter provided by EPA. 

Hourly distribution factors are developed from traffic count data collected in the region and the 

same set of Hourly Distribution Factors are used for all vehicle types and road types. OKI model 

could only predict VMT of two different vehicle types’ autos and trucks. So, we have distributed 

total Annual VMT based on vehicle population in the region. 

Table 7 : Annual VMT for Ohio Custom Domain from OKI travel demand model for 2005 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2005 67065022 0 

20 2005 7405961237 0 

30 2005 4943917030 0 

40 2005 24512225 0 

50 2005 334351024 0 

60 2005 567810955 0 

Table 8 :Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2005 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2005 16658465 0 

20 2005 1815341688 0 

30 2005 1209494070 0 

40 2005 5968488 0 

50 2005 82065068 0 

60 2005 160708291 0 

Table 9: Annual VMT for Ohio Custom Domain from OKI travel demand model for 2008 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 69438850 0 

20 2008 7668102136 0 

30 2008 5118911580 0 

40 2008 25379858 0 

50 2008 346185690 0 

60 2008 587909153 0 
29 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 10 : Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2008 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 17342291 0 

20 2008 1889861055 0 

30 2008 1259143528 0 

40 2008 6213493 0 

50 2008 85433821 0 

60 2008 167305330 0 

Table 9: Annual VMT for Ohio Custom Domain from OKI travel demand model for 2011 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 69438850 0 

20 2008 7668102136 0 

30 2008 5118911580 0 

40 2008 25379858 0 

50 2008 346185689 0 

60 2008 587909153 0 

Table 11: Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2011 

Table 12 :Annual VMT for Ohio Custom Domain from OKI travel demand model for 2015 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2015 73413634 0 

20 2015 8107035747 0 

30 2015 5411925719 0 

40 2015 26832639 0 

50 2015 366001875 0 

60 2015 621561950 0 

Table 13: Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2015 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2015 19903870 0 

20 2015 2169006811 0 

30 2015 1445127874 0 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2011 18163152 0 

20 2011 1979313603 0 

30 2011 1318742406 0 

40 2011 6507596 0 

50 2011 89477649 0 

60 2011 175224371 0 
30 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

40 2015 7131270 0 

50 2015 98052996 0 

60 2015 192017502 0 

Table 14:Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2018 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2018 76802311 0 

20 2018 8481245879 0 

30 2018 5661733109 0 

40 2018 28071199 0 

50 2018 382896041 0 

60 2018 650252434 0 

Table 13:Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2018 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2018 21077884 0 

20 2018 2296944011 0 

30 2018 1530367631 0 

40 2018 7551902 0 

50 2018 103836577 0 

60 2018 203343507 0 

Table 14 : Annual VMT for Ohio Custom Domain from OKI travel demand model for 2021 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2021 79128218 0 

20 2021 8738094842 0 

30 2021 5833194979 0 

40 2021 28921317 0 

50 2021 394491797 0 

60 2021 669944902 0 

Table 15 : Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2021 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2021 21702396 0

20 2021 2364999583 0

30 2021 1575710506 0

40 2021 7775656 0

50 2021 106913124 0

60 2021 209368320 0
31 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 16 : Default Monthly VMT Distribution(year 2008) 

sourceTypeID isLeapYear monthID monthVMTFraction

11 Y 1 0.072904 

11 Y 2 0.072023 

11 Y 3 0.081529 

11 Y 4 0.082098 

11 Y 5 0.087286 

11 Y 6 0.088052 

11 Y 7 0.092096 

11 Y 8 0.093198 

11 Y 9 0.08447 

11 Y 10 0.086301 

11 Y 11 0.080029 

11 Y 12 0.080015 

21 Y 1 0.072904 

21 Y 2 0.072023 

21 Y 3 0.081529 

21 Y 4 0.082098 

21 Y 5 0.087286 

21 Y 6 0.088052 

21 Y 7 0.092096 

21 Y 8 0.093198 

21 Y 9 0.08447 

21 Y 10 0.086301 

21 Y 11 0.080029 

21 Y 12 0.080015 

31 Y 1 0.072904 

31 Y 2 0.072023 

Table 17 : Default Daily VMT distribution ( same for all Source Types and all years) 

sourceTypeID monthID roadTypeID dayID dayVMTFraction

11 1 1 2 0.237635

11 1 1 5 0.762365 

11 1 2 2 0.237635 

11 1 2 5 0.762365 

11 1 3 2 0.237635 

11 1 3 5 0.762365 

11 1 4 2 0.237635 

11 1 4 5 0.762365 

11 1 5 2 0.237635 

11 1 5 5 0.762365 
32 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 18 : Hourly VMT Distribution from local data 

sourceTypeID roadTypeID dayID hourID hourVMTFraction

11 1 2 1 0.021474 

11 1 2 2 0.014443 

11 1 2 3 0.010968 

11 1 2 4 0.007495 

11 1 2 5 0.006839 

11 1 2 6 0.010359 

11 1 2 7 0.01843 

11 1 2 8 0.026812 

11 1 2 9 0.036385 

11 1 2 10 0.047541 

11 1 2 11 0.057466 

11 1 2 12 0.065079 

11 1 2 13 0.071323 

11 1 2 14 0.071492 

11 1 2 15 0.071723 

11 1 2 16 0.072006 

11 1 2 17 0.071149 

11 1 2 18 0.067887 

11 1 2 19 0.061772 

11 1 2 20 0.051688 

11 1 2 21 0.042866 

11 1 2 22 0.03803 

11 1 2 23 0.032207 

11 1 2 24 0.024568 

2.5 Average Speed Distribution Importer 

This importer allows the user to input average speed data specific to vehicle type, road type, 

and time of day/ type of day. The MOVES model defines 16 “speed bins” which describe the 

average driving speed on each road type. Unlike MOBILE 6.2 model, which uses VMT-based 

speed distribution, MOVES use fraction of driving time in each speed bin for each vehicle type, 

for each road type, and for each hour. Thus, for each combination of vehicle type, road type, 

and hour/day type, the fractions will add to one. We have used OKI travel model to calculate 

average speed distribution based on VHT. However, this input is ignored by MOVES when we 

are running emission rate runs† (See Table 27). 

2.6 Road Type Distribution Importer 

User supplied vehicle-miles-traveled data by road type is used as an input in this importer. OKI 

travel demand model can calculate the VMT distribution by functional class, which is further 
33 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

processed to obtain road type VMT distribution. But, our model could not predict off network 

VMT, which is assumed as zero. However, this input is also ignored by MOVES when we are 

running Emission Rate runs † (see Table 26). 

2.7 Ramp Fraction Importer 

This option allows the user to modify the fraction of ramp driving time on selected road types. 

But, in the current version of MOVES model, there is no capability to model Emission Rates for 

Ramps. To circumvent this problem, FHWA has suggested a temporary solution. This solution 

discussed in the Section 3. 

2.8 Fuel Formulation Importer and Fuel Supply Importer 

Fuel formulation importer allows the user to select an existing fuel in the MOVES database and 

change its properties, or create a new fuel formulation with different fuel properties. But we 

have used only default fuels available in MOVES default database. Fuel supply importer allows 

the user to assign existing fuels to counties, months, and years, and the associated market 

share for each fuel. We have used default fuel supply from MOVES default database. And same 

type of fuel is used for Whole Custom Domain. 

Table 19 : Fuel supply data for Ohio Custom Domain ( same for all years) 

countyID fuelYearID monthGroupID fuelFormulationID marketShare marketShareCV

99390 2008 1 3982 1 

99390 2008 1 20011 1 

99390 2008 2 3982 1 

99390 2008 2 20011 1 

99390 2008 3 3982 1 

99390 2008 3 20011 1 

99390 2008 4 3982 1 

99390 2008 4 20011 1 

99390 2008 5 3982 1 

99390 2008 5 20011 1 

99390 2008 6 3982 1 

99390 2008 6 20011 1 

99390 2008 7 3982 1 

99390 2008 7 20011 1 

99390 2008 8 3982 1 

99390 2008 8 20011 1 

99390 2008 9 3982 1 

99390 2008 9 20011 1 

99390 2008 10 3982 1 

99390 2008 10 20011 1 

99390 2008 11 3982 1 

99390 2008 11 20011 1 
34 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

99390 2008 12 3982 1 

99390 2008 12 20011 1 

Table 20: Fuel supply data for Kentucky Custom Domain (same for all years) 

countyID fuelYearID monthGroupID fuelFormulationID marketShare marketShareCV

99210 2012 1 3982 1 

99210 2012 1 20011 1 

99210 2012 2 3982 1 

99210 2012 2 20011 1 

99210 2012 3 3982 1 

99210 2012 3 20011 1 

99210 2012 4 3982 1 

99210 2012 4 20011 1 

99210 2012 5 3982 1 

99210 2012 5 20011 1 

99210 2012 6 3982 1 

99210 2012 6 20011 1 

99210 2012 7 3982 1 

99210 2012 7 20011 1 

99210 2012 8 3982 1 

99210 2012 8 20011 1 

99210 2012 9 3982 1 

99210 2012 9 20011 1 

99210 2012 10 3982 1 

99210 2012 10 20011 1 

99210 2012 11 3982 1 

99210 2012 11 20011 1 

99210 2012 12 3982 1 

99210 2012 12 20011 1 

2.9 I/M Importer 

The I/M Importer allows the user to import information describing the inspection and 

maintenance programs. In the default database there is an option, whether to use default I/M 

program or not. We choose no I/M program for all of the Runspecs[sic] in the whole region. 

2.10 Zone Road Activity Importer 

The Zone Road Activity Importer is used only if the Custom Domain option is chosen in the 

County Domain Manager. We have used value 1 for SHOallocfactor for each road type which 

means that all of the VMT input by the users is assigned to custom domain. 
35 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 21 : Kentucky Custom Domain Zone road activity data (same for all years) 

zoneID roadTypeID SHOAllocFactor 

992100 1 1 

992100 2 1 

992100 3 1 

992100 4 1 

992100 5 1 

Table 22 : Ohio Custom Domain Zone road activity data (same for all years) 

zoneID roadTypeID SHOAllocFactor 

993900 1 1 

993900 2 1 

993900 3 1 

993900 4 1 

993900 5 1 

3. Ramp Inventory Runs 

As discussed earlier, current version of the MOVES model cannot calculate Emission Rates for 

Ramps. To deal with this problem, FHWA has suggested an approach. The steps involved in this 

method are: (a) Calculating Emission Inventory for Urban Restricted and Rural Restricted road 

types keeping Ramp fraction as 1 (b) Finding out total VMT of Urban Restricted and Rural 

Restricted road types using MOVESactivityoutput option (c) Calculation Emission Rates for 

Ramps through dividing Emission Inventory with VMT ( d) Finally, using the Emission Rates in 

post processing for calculating regional Emission Inventory. 

Table 23 :Ramp fraction Input 

roadTypeID rampFraction

2 1

4 1
36 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

4. Post-Processing of MOVES Output 

4.1 Linking SQL tables to Microsoft Access 

Microsoft Access 2007 was used for the post-processing. An ODBC connection with the MOVES 

output directory was established. Information on how to link or import SQL tables to Access 

can be found in the MOVES Users Guide. 

4.2 Creating Emission Rate Lookup Tables 

The ratepervehicle and rateperdistance SQL tables, one set for each state (Kentucky and Ohio) 

and analysis year, were imported into Access. Ohio emission rates are used for the 

nonattainment portion of Dearborn County Indiana. Rateperprofile output was not generated 

by MOVES because evaporative output was not selected (i.e. VOC). Tables were renamed with 

state and analysis year in the format OH_20xxrateperdistance. All rateperdistance tables were 

merged with a Union query. The SQL commands are shown in Figure 3.1. ratepervehicle tables 

were merged in the same manner. 

 

Table 24 :Rateperdistance Union Query 

SELECT * 

FROM OH_2008rateperdistance 

WHERE MOVESRunID = (select max (MOVESRunID) from OH_2008rateperdistance) AND 

pollutantID = 3 Or MOVESRunID = (select max (MOVESRunID) from 

OH_2008rateperdistance) AND pollutantID=110 Or MOVESRunID = (select max 

(MOVESRunID) from OH_2008rateperdistance) AND pollutantID=116 Or MOVESRunID = 

(select max (MOVESRunID) from OH_2008rateperdistance) AND pollutantID=117 Or 

MOVESRunID = (select max (MOVESRunID) from OH_2008rateperdistance) AND 

pollutantID = 31 

UNION ALL select * 

FROM OH_2011rateperdistance 

WHERE ..... (repeated for each file) 

“Rateperdistance_state” and “Ratepervehicle_state” tables were created from the union query 

output using a Make Table query. Emission rates for each process were summed by pollutant 

and a stateID field is created. The SQL commands for creating the “Rateperdistance_state” 

table are shown in Table 25. Unique index fields were identified for each of the two tables. 

Indexes facilitate more efficient data processing. 

Table 25: Rateperdistance_State Query 

SELECT Val(Mid([LinkID],3,2)) AS StateID, Union_rateperdistance_state.yearID, 

Union_rateperdistance_state.monthID, Union_rateperdistance_state.linkID, 

Union_rateperdistance_state.hourID, Union_rateperdistance_state.sourceTypeID, 

Union_rateperdistance_state.roadTypeID, 

Union_rateperdistance_state.avgSpeedBinID, 

Union_rateperdistance_state.pollutantID, 

Sum(Union_rateperdistance_state.ratePerDistance) AS SumOfratePerDistance INTO 

rateperdistance_state 
37 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

FROM Union_rateperdistance_state 

GROUP BY Val(Mid([LinkID],3,2)), Union_rateperdistance_state.yearID, 

Union_rateperdistance_state.monthID, Union_rateperdistance_state.linkID, 

Union_rateperdistance_state.hourID, Union_rateperdistance_state.sourceTypeID, 

Union_rateperdistance_state.roadTypeID, 

Union_rateperdistance_state.avgSpeedBinID, 

Union_rateperdistance_state.pollutantID 

ORDER BY Union_rateperdistance_state.linkID, 

Union_rateperdistance_state.hourID, Union_rateperdistance_state.pollutantID; 

4.1 Creating a VMT Table by County 

The VMT table includes Daily VMT by county by analysis year from the OKI Travel Demand 

Model (TDM). Summer factors and applied by functional class to create Summer VMT. 

Seasonal factors by functional class are contained in the report, “OKI Travel Demand 

Forecasting Model, Update of Hourly and Seasonal Factors as Used in Air Quality Impact 

Calculations”, September 2001. Annual VMT is calculated by using EPA’s VMT converter to 

grow daily VMT to annual VMT. In order to accommodate an error in MOVES 2010, all VMT 

values are exclusive of ramp VMT. Ramp VMT and emission are added in later in the process. 

In order to apply the emission rates, it is necessary to factor the county VMT by source type, 

hour, road type and speed bin. 

Table 26 : VMT and Source Type Population by County and Year 

County Daily VMT Summer 

VMT 

Annual VMT yearID SourceType

Population 

stateID

Boone 3924117 4186006 1273226984 2005 129823 21

Boone 4076584 4355527 1350001557 2008 134028 21

Boone 4383716 4681593 1448879510 2011 136181 21

Boone 4950741 5276742 1628041303 2015 140590 21

Boone 5260102 5597287 1729595179 2018 143991 21

Boone 5478224 5826768 1800571708 2021 147476 21

Campbell 2286217 2437698 741790605 2005 86065 21

Campbell 2339542 2495174 774762729 2008 88853 21

Campbell 2421600 2582758 800372702 2011 90279 21

Campbell 2663159 2844504 875774499 2015 93204 21

Campbell 2771476 2958827 911300109 2018 95458 21

Campbell 2849127 3041704 936445364 2021 97768 21

Kenton 3927743 4182042 1274091658 2005 148193 21

Kenton 3927332 4185652 1300575265 2008 152992 21

Kenton 4049886 4327836 1338544021 2011 155451 21

Kenton 4341124 4614242 1427569992 2015 160484 21

Kenton 4629694 4880614 1522308203 2018 164368 21

Kenton 4715306 5006383 1549817345 2021 168343 21

Butler 578641 7804476 196737836 2005 24915 39
38 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Butler 587582 8133554 199777880 2008 25722 39

Butler 605620 8454053 205910800 2011 26135 39

Butler 657778 8768598 223644400 2015 26982 39

Butler 684361 9232457 232682740 2018 27635 39

Butler 706828 9592567 240321520 2021 28303 39

Clermont 7452286 5391578 2469166037 2005 401759 39

Clermont 7745685 5599530 2598059212 2008 414771 39

Clermont 8050701 5841102 2693716250 2011 421434 39

Clermont 8361487 6035155 2792188144 2015 435082 39

Clermont 8806042 6314640 2940849935 2018 445608 39

Clermont 9150031 6562428 2966037449 2021 456389 39

Dearborn NA 5083331 599761 1684259908 2005 232380 39

Dearborn NA 5262489 613027 1765145113 2008 239906 39

Dearborn NA 5489545 631914 1836768820 2011 243760 39

Dearborn NA 5687698 685272 1899318043 2015 251654 39

Dearborn NA 5952603 712461 1987920583 2018 257742 39

Dearborn NA 6186441 735862 2005371969 2021 263978 39

Hamilton 21859452 23170766 7241529618 2005 862422 39

Hamilton 22124503 23447460 7421005221 2008 890352 39

Hamilton 22426021 23803187 7503612070 2011 904655 39

Hamilton 22849494 24259554 7630232069 2015 933953 39

Hamilton 23630554 25096560 7891617279 2018 956548 39

Hamilton 24098698 25596996 7811737549 2021 979689 39

Warren 5884216 6263010 1949617151 2005 233106 39

Warren 6057338 6464217 2031753523 2008 240655 39

Warren 6406284 6835660 2143504189 2011 244521 39

Warren 6842828 7279441 2285055662 2015 252440 39

Warren 7368035 7836746 2460615706 2018 258547 39

Warren 7707500 8194596 2498432370 2021 264802 39

4.2 Source type population and source type VMT distribution 

A combination of local and MOVES default data were used for the source type populations. The 

source type VMT fractions are based on the ratio of MOVES default source type population and 

MOVES default source type VMT. It is assumed that the growth rate of source type populations 

is equal to the regional annual household growth rate of 0.8%. Source type VMT fractions are 

the same for all analysis years. 
39 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 27: Base Year Source Type Population and VMT Fraction 

stateID sourceTypeID sourceType

Population 

sourceTypeFraction sourceTypeVMTFraction

39 11 69121 0.038559 0.005026

39 21 1200827 0.669872 0.555019

39 31 486373 0.271319 0.277725

39 32 15947 0.008896 0.092783

39 41 458 0.000255 0.000754

39 42 82 0.000046 0.000225

39 43 3681 0.002053 0.000858

39 51 0 0.000000 0.000644

39 52 369 0.000206 0.020527

39 53 364 0.000203 0.002663

39 54 4928 0.002749 0.001224

39 61 4879 0.002722 0.017977

39 62 5593 0.003120 0.024576

21 11 8040 0.021370 0.005063

21 21 198623 0.527931 0.551736

21 31 121506 0.322958 0.275546

21 32 40593 0.107894 0.092055

21 41 128 0.000340 0.000745

21 42 21 0.000056 0.000222

21 43 985 0.002618 0.000847

21 51 0 0.000000 0.000641

21 52 767 0.002039 0.020433

21 53 757 0.002012 0.002650

21 54 1390 0.003695 0.001218

21 61 1593 0.004234 0.020634

21 62 1826 0.004853 0.028210

4.3 Hourly distribution 

MOVES default hourly distribution by source type was used during the post-processing. 

4.4 Road type distribution 

Road type VMT fractions by source type are default values, except for passenger cars (source 

type 21) and passenger trucks (source type 31). VMT fractions from the OKI TDM are used for 

passenger cars and passenger trucks. 
40 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 28: Base Year Source Type Population and VMT Fraction

sourceTypeID roadTypeID roadTypeVMTFraction stateID

21 1 0 21

21 2 0.0952 21

21 3 0.0818 21

21 4 0.4741 21

21 5 0.3489 21

31 1 0 21

31 2 0.0952 21

31 3 0.0818 21

31 4 0.4741 21

31 5 0.3489 21

21 1 0 39

21 2 0.0436 39

21 3 0.1256 39

21 4 0.4143 39

21 5 0.4165 39

31 1 0 39

31 2 0.0436 39

31 3 0.1256 39

31 4 0.4143 39

31 5 0.4165 39

4.5 Average speed distribution 

Average speed fractions for each of the 16 speed bins are provided by the OKI TDM. The 

average speed fractions vary by state, year, road type and hour. 

Table 29: Average Speed Distribution (Example: only road type 2, year 2011, Ohio values shown) 

roadTypeID hourID avgSpeedBinID avgSpeedFraction YearID stateID

2 1 1 0.00000000 2011 39

2 1 2 0.00000000 2011 39

2 1 3 0.00000000 2011 39

2 1 4 0.00000000 2011 39

2 1 5 0.00000000 2011 39

2 1 6 0.00000000 2011 39

2 1 7 0.00000000 2011 39

2 1 8 0.00000000 2011 39
41 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 1 9 0.00000000 2011 39

2 1 10 0.11922233 2011 39

2 1 11 0.12547629 2011 39

2 1 12 0.19752816 2011 39

2 1 13 0.00589550 2011 39

2 1 14 0.00000000 2011 39

2 1 15 0.55187773 2011 39

2 1 16 0.00000000 2011 39

2 2 1 0.00000000 2011 39

2 2 2 0.00000000 2011 39

2 2 3 0.00000000 2011 39

2 2 4 0.00000000 2011 39

2 2 5 0.00000000 2011 39

2 2 6 0.00000000 2011 39

2 2 7 0.00000000 2011 39

2 2 8 0.00000000 2011 39

2 2 9 0.00000000 2011 39

2 2 10 0.11922233 2011 39

2 2 11 0.12547629 2011 39

2 2 12 0.19752816 2011 39

2 2 13 0.00589550 2011 39

2 2 14 0.00000000 2011 39

2 2 15 0.55187773 2011 39

2 2 16 0.00000000 2011 39

2 3 1 0.00000000 2011 39

2 3 2 0.00000000 2011 39

2 3 3 0.00000000 2011 39

2 3 4 0.00000000 2011 39

2 3 5 0.00000000 2011 39

2 3 6 0.00000000 2011 39

2 3 7 0.00000000 2011 39

2 3 8 0.00000000 2011 39

2 3 9 0.00000000 2011 39

2 3 10 0.11922233 2011 39

2 3 11 0.12547629 2011 39

2 3 12 0.19752816 2011 39

2 3 13 0.00589550 2011 39

2 3 14 0.06436270 2011 39
42 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 3 15 0.48751503 2011 39

2 3 16 0.00000000 2011 39

2 4 1 0.00000000 2011 39

2 4 2 0.00000000 2011 39

2 4 3 0.00000000 2011 39

2 4 4 0.00000000 2011 39

2 4 5 0.00000000 2011 39

2 4 6 0.00000000 2011 39

2 4 7 0.00000000 2011 39

2 4 8 0.00000000 2011 39

2 4 9 0.00000000 2011 39

2 4 10 0.11922233 2011 39

2 4 11 0.12547629 2011 39

2 4 12 0.19752816 2011 39

2 4 13 0.00589550 2011 39

2 4 14 0.12369152 2011 39

2 4 15 0.42818621 2011 39

2 4 16 0.00000000 2011 39

2 5 1 0.00000000 2011 39

2 5 2 0.00000000 2011 39

2 5 3 0.00000000 2011 39

2 5 4 0.00000000 2011 39

2 5 5 0.00000000 2011 39

2 5 6 0.00000000 2011 39

2 5 7 0.00000000 2011 39

2 5 8 0.00000000 2011 39

2 5 9 0.00000000 2011 39

2 5 10 0.11922233 2011 39

2 5 11 0.12547629 2011 39

2 5 12 0.26189086 2011 39

2 5 13 0.09782827 2011 39

2 5 14 0.06250896 2011 39

2 5 15 0.33307330 2011 39

2 5 16 0.00000000 2011 39

2 6 1 0.00000000 2011 39

2 6 2 0.12369152 2011 39

2 6 3 0.03260396 2011 39

2 6 4 0.03085494 2011 39
43 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 6 5 0.01601927 2011 39

2 6 6 0.01563475 2011 39

2 6 7 0.00000000 2011 39

2 6 8 0.00000000 2011 39

2 6 9 0.00000000 2011 39

2 6 10 0.11922233 2011 39

2 6 11 0.12547629 2011 39

2 6 12 0.19752816 2011 39

2 6 13 0.00589550 2011 39

2 6 14 0.00278921 2011 39

2 6 15 0.33028409 2011 39

2 6 16 0.00000000 2011 39

2 7 1 0.21880443 2011 39

2 7 2 0.00000000 2011 39

2 7 3 0.00278921 2011 39

2 7 4 0.00000000 2011 39

2 7 5 0.01131028 2011 39

2 7 6 0.03548550 2011 39

2 7 7 0.03519436 2011 39

2 7 8 0.03514937 2011 39

2 7 9 0.00617093 2011 39

2 7 10 0.08564905 2011 39

2 7 11 0.14691446 2011 39

2 7 12 0.13064235 2011 39

2 7 13 0.00589550 2011 39

2 7 14 0.14352342 2011 39

2 7 15 0.14247115 2011 39

2 7 16 0.00000000 2011 39

2 8 1 0.21880443 2011 39

2 8 2 0.00278921 2011 39

2 8 3 0.01131028 2011 39

2 8 4 0.01356074 2011 39

2 8 5 0.05711912 2011 39

2 8 6 0.03514937 2011 39

2 8 7 0.02902228 2011 39

2 8 8 0.01193860 2011 39

2 8 9 0.07229727 2011 39

2 8 10 0.05473480 2011 39
44 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 8 11 0.08493157 2011 39

2 8 12 0.11645227 2011 39

2 8 13 0.04360641 2011 39

2 8 14 0.20648097 2011 39

2 8 15 0.04180267 2011 39

2 8 16 0.00000000 2011 39

2 9 1 0.21880443 2011 39

2 9 2 0.00000000 2011 39

2 9 3 0.00000000 2011 39

2 9 4 0.00000000 2011 39

2 9 5 0.00000000 2011 39

2 9 6 0.00278921 2011 39

2 9 7 0.00000000 2011 39

2 9 8 0.04679577 2011 39

2 9 9 0.03519436 2011 39

2 9 10 0.08369185 2011 39

2 9 11 0.14590244 2011 39

2 9 12 0.15349370 2011 39

2 9 13 0.02733367 2011 39

2 9 14 0.03771092 2011 39

2 9 15 0.24828365 2011 39

2 9 16 0.00000000 2011 39

2 10 1 0.15629548 2011 39

2 10 2 0.04687421 2011 39

2 10 3 0.01563475 2011 39

2 10 4 0.00000000 2011 39

2 10 5 0.00000000 2011 39

2 10 6 0.00000000 2011 39

2 10 7 0.00000000 2011 39

2 10 8 0.00000000 2011 39

2 10 9 0.00000000 2011 39

2 10 10 0.11922233 2011 39

2 10 11 0.13957578 2011 39

2 10 12 0.18621788 2011 39

2 10 13 0.00589550 2011 39

2 10 14 0.04428952 2011 39

2 10 15 0.28599457 2011 39

2 10 16 0.00000000 2011 39
45 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 11 1 0.06436270 2011 39

2 11 2 0.12278771 2011 39

2 11 3 0.01601927 2011 39

2 11 4 0.01563475 2011 39

2 11 5 0.00000000 2011 39

2 11 6 0.00000000 2011 39

2 11 7 0.00000000 2011 39

2 11 8 0.00000000 2011 39

2 11 9 0.00000000 2011 39

2 11 10 0.11922233 2011 39

2 11 11 0.13678656 2011 39

2 11 12 0.18900709 2011 39

2 11 13 0.00589550 2011 39

2 11 14 0.02285135 2011 39

2 11 15 0.30743274 2011 39

2 11 16 0.00000000 2011 39

2 12 1 0.06436270 2011 39

2 12 2 0.09193278 2011 39

2 12 3 0.04687421 2011 39

2 12 4 0.01563475 2011 39

2 12 5 0.00000000 2011 39

2 12 6 0.00000000 2011 39

2 12 7 0.00000000 2011 39

2 12 8 0.00000000 2011 39

2 12 9 0.00000000 2011 39

2 12 10 0.11922233 2011 39

2 12 11 0.12547629 2011 39

2 12 12 0.20031737 2011 39

2 12 13 0.00589550 2011 39

2 12 14 0.02285135 2011 39

2 12 15 0.30743274 2011 39

2 12 16 0.00000000 2011 39

2 13 1 0.06436270 2011 39

2 13 2 0.09193278 2011 39

2 13 3 0.04687421 2011 39

2 13 4 0.01563475 2011 39

2 13 5 0.00000000 2011 39

2 13 6 0.00000000 2011 39
46 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 13 7 0.00000000 2011 39

2 13 8 0.00000000 2011 39

2 13 9 0.00000000 2011 39

2 13 10 0.11922233 2011 39

2 13 11 0.12547629 2011 39

2 13 12 0.19752816 2011 39

2 13 13 0.00868471 2011 39

2 13 14 0.00000000 2011 39

2 13 15 0.33028409 2011 39

2 13 16 0.00000000 2011 39

2 14 1 0.06436270 2011 39

2 14 2 0.09193278 2011 39

2 14 3 0.03085494 2011 39

2 14 4 0.03165402 2011 39

2 14 5 0.00000000 2011 39

2 14 6 0.00000000 2011 39

2 14 7 0.00000000 2011 39

2 14 8 0.00000000 2011 39

2 14 9 0.00000000 2011 39

2 14 10 0.11922233 2011 39

2 14 11 0.12547629 2011 39

2 14 12 0.19752816 2011 39

2 14 13 0.00868471 2011 39

2 14 14 0.00000000 2011 39

2 14 15 0.33028409 2011 39

2 14 16 0.00000000 2011 39

2 15 1 0.06436270 2011 39

2 15 2 0.09193278 2011 39

2 15 3 0.04687421 2011 39

2 15 4 0.01563475 2011 39

2 15 5 0.00000000 2011 39

2 15 6 0.00000000 2011 39

2 15 7 0.00000000 2011 39

2 15 8 0.00000000 2011 39

2 15 9 0.00000000 2011 39

2 15 10 0.11922233 2011 39

2 15 11 0.12547629 2011 39

2 15 12 0.19752816 2011 39
47 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 15 13 0.00868471 2011 39

2 15 14 0.02285135 2011 39

2 15 15 0.30743274 2011 39

2 15 16 0.00000000 2011 39

2 16 1 0.12369152 2011 39

2 16 2 0.07947816 2011 39

2 16 3 0.01563475 2011 39

2 16 4 0.00000000 2011 39

2 16 5 0.00000000 2011 39

2 16 6 0.00000000 2011 39

2 16 7 0.00000000 2011 39

2 16 8 0.00000000 2011 39

2 16 9 0.00000000 2011 39

2 16 10 0.11922233 2011 39

2 16 11 0.13957578 2011 39

2 16 12 0.18621788 2011 39

2 16 13 0.00589550 2011 39

2 16 14 0.04428952 2011 39

2 16 15 0.28599457 2011 39

2 16 16 0.00000000 2011 39

2 17 1 0.20316968 2011 39

2 17 2 0.01563475 2011 39

2 17 3 0.00000000 2011 39

2 17 4 0.00000000 2011 39

2 17 5 0.00000000 2011 39

2 17 6 0.00000000 2011 39

2 17 7 0.00278921 2011 39

2 17 8 0.02487101 2011 39

2 17 9 0.05711912 2011 39

2 17 10 0.05722137 2011 39

2 17 11 0.15811770 2011 39

2 17 12 0.14489757 2011 39

2 17 13 0.05018502 2011 39

2 17 14 0.00000000 2011 39

2 17 15 0.28599457 2011 39

2 17 16 0.00000000 2011 39

2 18 1 0.15629548 2011 39

2 18 2 0.04687421 2011 39
48 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 18 3 0.01563475 2011 39

2 18 4 0.00000000 2011 39

2 18 5 0.00000000 2011 39

2 18 6 0.00000000 2011 39

2 18 7 0.00000000 2011 39

2 18 8 0.00000000 2011 39

2 18 9 0.00000000 2011 39

2 18 10 0.11922233 2011 39

2 18 11 0.13957578 2011 39

2 18 12 0.18621788 2011 39

2 18 13 0.00589550 2011 39

2 18 14 0.04428952 2011 39

2 18 15 0.28599457 2011 39

2 18 16 0.00000000 2011 39

2 19 1 0.00000000 2011 39

2 19 2 0.00000000 2011 39

2 19 3 0.00000000 2011 39

2 19 4 0.00000000 2011 39

2 19 5 0.06436270 2011 39

2 19 6 0.00000000 2011 39

2 19 7 0.05932882 2011 39

2 19 8 0.03260396 2011 39

2 19 9 0.00000000 2011 39

2 19 10 0.15007727 2011 39

2 19 11 0.14149556 2011 39

2 19 12 0.21316291 2011 39

2 19 13 0.00589550 2011 39

2 19 14 0.00000000 2011 39

2 19 15 0.33307330 2011 39

2 19 16 0.00000000 2011 39

2 20 1 0.00000000 2011 39

2 20 2 0.00000000 2011 39

2 20 3 0.00000000 2011 39

2 20 4 0.00000000 2011 39

2 20 5 0.00000000 2011 39

2 20 6 0.00000000 2011 39

2 20 7 0.00000000 2011 39

2 20 8 0.00000000 2011 39
49 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 20 9 0.00000000 2011 39

2 20 10 0.11922233 2011 39

2 20 11 0.12547629 2011 39

2 20 12 0.19752816 2011 39

2 20 13 0.00589550 2011 39

2 20 14 0.15629548 2011 39

2 20 15 0.39558226 2011 39

2 20 16 0.00000000 2011 39

2 21 1 0.00000000 2011 39

2 21 2 0.00000000 2011 39

2 21 3 0.00000000 2011 39

2 21 4 0.00000000 2011 39

2 21 5 0.00000000 2011 39

2 21 6 0.00000000 2011 39

2 21 7 0.00000000 2011 39

2 21 8 0.00000000 2011 39

2 21 9 0.00000000 2011 39

2 21 10 0.11922233 2011 39

2 21 11 0.12547629 2011 39

2 21 12 0.19752816 2011 39

2 21 13 0.00589550 2011 39

2 21 14 0.00000000 2011 39

2 21 15 0.55187773 2011 39

2 21 16 0.00000000 2011 39

2 22 1 0.00000000 2011 39

2 22 2 0.00000000 2011 39

2 22 3 0.00000000 2011 39

2 22 4 0.00000000 2011 39

2 22 5 0.00000000 2011 39

2 22 6 0.00000000 2011 39

2 22 7 0.00000000 2011 39

2 22 8 0.00000000 2011 39

2 22 9 0.00000000 2011 39

2 22 10 0.11922233 2011 39

2 22 11 0.12547629 2011 39

2 22 12 0.19752816 2011 39

2 22 13 0.00589550 2011 39

2 22 14 0.00000000 2011 39
50 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 22 15 0.55187773 2011 39

2 22 16 0.00000000 2011 39

2 23 1 0.00000000 2011 39

2 23 2 0.00000000 2011 39

2 23 3 0.00000000 2011 39

2 23 4 0.00000000 2011 39

2 23 5 0.00000000 2011 39

2 23 6 0.00000000 2011 39

2 23 7 0.00000000 2011 39

2 23 8 0.00000000 2011 39

2 23 9 0.00000000 2011 39

2 23 10 0.11922233 2011 39

2 23 11 0.12547629 2011 39

2 23 12 0.19752816 2011 39

2 23 13 0.00589550 2011 39

2 23 14 0.00000000 2011 39

2 23 15 0.55187773 2011 39

2 23 16 0.00000000 2011 39

2 24 1 0.00000000 2011 39

2 24 2 0.00000000 2011 39

2 24 3 0.00000000 2011 39

2 24 4 0.00000000 2011 39

2 24 5 0.00000000 2011 39

2 24 6 0.00000000 2011 39

2 24 7 0.00000000 2011 39

2 24 8 0.00000000 2011 39

2 24 9 0.00000000 2011 39

2 24 10 0.11922233 2011 39

2 24 11 0.12547629 2011 39

2 24 12 0.19752816 2011 39

2 24 13 0.00589550 2011 39

2 24 14 0.00000000 2011 39

2 24 15 0.55187773 2011 39

2 24 16 0.00000000 2011 39
51 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

4.6 Creating a VMT Table by County, Year, Source Type, Hour, Road Type, and 

Average Speed Bin 

The ‘CountyVMT’ query creates a County VMT Table by source type, hour, road type and 

average speed utilizing the VMT distribution factors described in 4.2, 4.3, 4.4 and 4.5. The SQL 

commands for this query are shown in Table 30. 

Table 30 :County VMT Table Query 

SELECT VMT.yearID, VMT.State, roadtypedistribution1.stateID, VMT.County, 

roadtypedistribution1.sourceTypeID, hourvmtfraction.hourID, 

roadtypedistribution1.roadTypeID, avgSpeedDistribution.avgSpeedBinID, 

sourecetypepopulation.sourceTypeFraction, hourvmtfraction.hourVMTFraction, 

roadtypedistribution1.roadTypeVMTFraction, 

avgSpeedDistribution.avgSpeedFraction, First(VMT.[Annual VMT]) AS 

[FirstOfAnnual VMT], First(VMT.[Summer VMT]) AS [FirstOfSummer VMT], 

[FirstOfSummer 

VMT]*[hourVMTFraction]*[sourceTypeFraction]*[roadTypeVMTFraction]*[avgSpeedFr

action] AS DailyVMT, [FirstOfAnnual 

VMT]*[sourceTypeFraction]*[hourVMTFraction]*[roadTypeVMTFraction]*[avgSpeedFr

action] AS AnnualizedVMT INTO CountyVMT_Table 

FROM (((avgSpeedDistribution INNER JOIN hourvmtfraction ON 

(avgSpeedDistribution.hourDayID = hourvmtfraction.hourID) AND 

(avgSpeedDistribution.roadTypeID = hourvmtfraction.roadTypeID) AND 

(avgSpeedDistribution.sourceTypeID = hourvmtfraction.sourceTypeID)) INNER 

JOIN roadtypedistribution1 ON (avgSpeedDistribution.stateID = 

roadtypedistribution1.stateID) AND (avgSpeedDistribution.roadTypeID = 

roadtypedistribution1.roadTypeID) AND (avgSpeedDistribution.sourceTypeID = 

roadtypedistribution1.sourceTypeID)) INNER JOIN sourecetypepopulation ON 

(avgSpeedDistribution.sourceTypeID = sourecetypepopulation.sourceTypeID) AND 

(avgSpeedDistribution.stateID = sourecetypepopulation.stateID)) INNER JOIN 

VMT ON (avgSpeedDistribution.YearID = VMT.yearID) AND 

(avgSpeedDistribution.stateID = VMT.stateID) 

GROUP BY VMT.yearID, VMT.State, roadtypedistribution1.stateID, VMT.County, 

roadtypedistribution1.sourceTypeID, hourvmtfraction.hourID, 

roadtypedistribution1.roadTypeID, avgSpeedDistribution.avgSpeedBinID, 

sourecetypepopulation.sourceTypeFraction, hourvmtfraction.hourVMTFraction, 

roadtypedistribution1.roadTypeVMTFraction, 

avgSpeedDistribution.avgSpeedFraction 

HAVING (((avgSpeedDistribution.avgSpeedFraction)>0)) 

ORDER BY VMT.yearID, roadtypedistribution1.stateID, VMT.County, 

hourvmtfraction.hourID; 

5. Combining VMT and Emission Rates; Calculating Total Emissions 

5.1 Summarizing Distance-based Emissions by Source Type 

The daily VMT and annual VMT in each county, year, hour, source type, road type, and speed 

bin is multiplied by the appropriate rate per distance for each pollutant. This query is shown in 

Table 31. 
52 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 31 :Emissions distance Query 

SELECT CountyVMT_Table.stateID, CountyVMT_Table.State, 

CountyVMT_Table.County, CountyVMT_Table.yearID, 

rateperdistance_state.monthID, CountyVMT_Table.hourID, 

rateperdistance_state.sourceTypeID, CountyVMT_Table.roadTypeID, 

CountyVMT_Table.avgSpeedBinID, rateperdistance_state.pollutantID, 

CountyVMT_Table.DailyVMT, CountyVMT_Table.AnnualizedVMT, 

rateperdistance_state.SumOfratePerDistance, [DailyVMT]*[SumOfratePerDistance] 

AS EmissionsDist, [AnnualizedVMT]*[SumOfratePerDistance] AS 

AnnualEmissionsDist 

FROM rateperdistance_state INNER JOIN CountyVMT_Table ON 

(rateperdistance_state.avgSpeedBinID = CountyVMT_Table.avgSpeedBinID) AND 

(rateperdistance_state.roadTypeID = CountyVMT_Table.roadTypeID) AND 

(rateperdistance_state.sourceTypeID = CountyVMT_Table.sourceTypeID) AND 

(rateperdistance_state.hourID = CountyVMT_Table.hourID) AND 

(rateperdistance_state.StateID = CountyVMT_Table.stateID) AND 

(rateperdistance_state.yearID = CountyVMT_Table.yearID) 

GROUP BY CountyVMT_Table.stateID, CountyVMT_Table.State, 

CountyVMT_Table.County, CountyVMT_Table.yearID, 

rateperdistance_state.monthID, CountyVMT_Table.hourID, 

rateperdistance_state.sourceTypeID, CountyVMT_Table.roadTypeID, 

CountyVMT_Table.avgSpeedBinID, rateperdistance_state.pollutantID, 

CountyVMT_Table.DailyVMT, CountyVMT_Table.AnnualizedVMT, 

rateperdistance_state.SumOfratePerDistance; 

A second query further summarizes the emissions by source type. This is necessary in order to 

combine with vehicle-based emissions that are independent of road type and speed. 

5.2 Summarizing Vehicle-based Emissions by Source type 

The source population for each county, year, hour, and source type is multiplied by the rate per 

vehicle for each pollutant. This query is shown in Table 32. 

 

Table 32: Emissions Vehicle Query 

SELECT VMT.stateID, VMT.County, ratepervehicle_state.yearID, 

ratepervehicle_state.monthID, ratepervehicle_state.hourID, 

ratepervehicle_state.sourceTypeID, sourecetypepopulation.sourceTypeFraction, 

VMT.SourceTypePopulation, ratepervehicle_state.pollutantID, 

ratepervehicle_state.SumOfratePerVehicle, First(VMT.BudgetAreaPop) AS 

FirstOfBudgetAreaPop, 

((Nz([VMT]![sourceTypePopulation]*[sourceTypeFraction],0)/24)) AS STPop, 

Nz([VMT]![sourceTypePopulation]*[sourceTypeFraction]*[SumOfratePerVehicle],0) 

AS emissionsVehicle, 

Nz(([VMT]![sourceTypePopulation]*[sourceTypeFraction]*[SumOfratePerVehicle])*

365,0) AS AnnualemissionsVehicle 

FROM sourecetypepopulation INNER JOIN (ratepervehicle_state INNER JOIN VMT ON 

(ratepervehicle_state.yearID = VMT.yearID) AND (ratepervehicle_state.StateID 

= VMT.stateID)) ON (sourecetypepopulation.sourceTypeID = 

ratepervehicle_state.sourceTypeID) AND (sourecetypepopulation.stateID = 

ratepervehicle_state.StateID) 

GROUP BY VMT.stateID, VMT.County, ratepervehicle_state.yearID, 

ratepervehicle_state.monthID, ratepervehicle_state.hourID, 

ratepervehicle_state.sourceTypeID, sourecetypepopulation.sourceTypeFraction, 

VMT.SourceTypePopulation, ratepervehicle_state.pollutantID, 

ratepervehicle_state.SumOfratePerVehicle; 
53 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

5.3 Ramp Emissions 

Ramp emission rates, calculated as discussed in Section 3, are multiplied by ramp VMT in each 

county, year and source type. This query is shown in Table 33.

Table 33: Ramp Emissions Query

SELECT VMT.stateID, VMT.County, VMT.yearID, hourvmtfraction.hourID, hourvmtfraction.sourceTypeID, 

hourvmtfraction.hourVMTFraction, ramp_rate.pollutantID, VMT.[Ramp VMT], ([Ramp 

VMT]*[hourVMTFraction])/13 AS HourlyRampVMT, ramp_rate.ramprate, [HourlyRampVMT]*[ramprate] 

AS RampEmissions, ([HourlyRampVMT]*[ramprate])*340 AS RampEmissionsAnnual 

FROM hourvmtfraction INNER JOIN (VMT INNER JOIN ramp_rate ON (VMT.stateID = ramp_rate.StateID) 

AND (VMT.yearID = ramp_rate.yearID)) ON hourvmtfraction.hourID = ramp_rate.hourID 

WHERE (((hourvmtfraction.roadTypeID)=4))

ORDER BY VMT.stateID, VMT.County, VMT.yearID, hourvmtfraction.hourID, 

hourvmtfraction.sourceTypeID, ramp_rate.pollutantID; 

5.4 Summarizing Results 

Distance-based emissions by source type, vehicle-based emissions by source type, and ramp emissions 

by source type are summed by county, year and pollutant. This query is shown below. This is also 

where criteria may be set for limiting the results by state, county, year or pollutant. A sum of VMT and 

source type population is also useful as a verification that all steps were run properly. The appropriate 

monthID criteria should be set here. The annual average temperature profile is contained in April 

(monthID=4). July (monthID=7) should be used for summer weekday emissions. 

Table 34:Results Query 

SELECT EmissionsDistance_bySourceType.State, EmissionsDistance_bySourceType.County, 

EmissionsDistance_bySourceType.yearID, EmissionsDistance_bySourceType.pollutantName, 

Sum(EmissionsDistance_bySourceType.SumOfDailyVMT) AS SumOfSumOfDailyVMT, 

Sum(RampEmissions_Query.HourlyRampVMT) AS SumOfHourlyRampVMT, 

Sum(EmissionsDistance_bySourceType.SumOfAnnualizedVMT) AS SumOfSumOfAnnualizedVMT, 

First(EmissionsVehicle_Query.SourceTypePopulation) AS FirstOfSourceTypePopulation, 

Sum(EmissionsDistance_bySourceType.SumOfEmissionsDist) AS SumOfSumOfEmissionsDist, 

Sum(Nz([emissionsVehicle],0)) AS EmissionsVeh, Sum(RampEmissions_Query.RampEmissions) AS 

SumOfRampEmissions, Sum(EmissionsDistance_bySourceType.SumOfAnnualEmissionsDist) AS 

SumOfSumOfAnnualEmissionsDist, Sum(Nz([AnnualemissionsVehicle],0)) AS AnnualEmissionsVeh, 

Sum(RampEmissions_Query.RampEmissionsAnnual) AS SumOfRampEmissionsAnnual, 

(([SumOfSumOfEmissionsDist]+[EmissionsVeh]+[SumOfHourlyRampVMT])/1000)*0.001102 AS 

DailyEmissionsTONS, 

(([SumOfSumOfAnnualEmissionsDist]+[AnnualEmissionsVeh]+[SumOfRampEmissionsAnnual])/1000)*0.0

01102 AS AnnualEmissionsTONS, 

Sum([RampEmissions_Query]![HourlyRampVMT]+[EmissionsDistance_bySourceType]![SumOfDailyVMT]

) AS AllVMT 
54 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

FROM (EmissionsDistance_bySourceType LEFT JOIN EmissionsVehicle_Query ON 

(EmissionsDistance_bySourceType.pollutantID = EmissionsVehicle_Query.pollutantID) AND 

(EmissionsDistance_bySourceType.sourceTypeID = EmissionsVehicle_Query.sourceTypeID) AND 

(EmissionsDistance_bySourceType.hourID = EmissionsVehicle_Query.hourID) AND 

(EmissionsDistance_bySourceType.monthID = EmissionsVehicle_Query.monthID) AND 

(EmissionsDistance_bySourceType.yearID = EmissionsVehicle_Query.yearID) AND 

(EmissionsDistance_bySourceType.County = EmissionsVehicle_Query.County)) INNER JOIN 

RampEmissions_Query ON (EmissionsDistance_bySourceType.County = RampEmissions_Query.County) 

AND (EmissionsDistance_bySourceType.yearID = RampEmissions_Query.yearID) AND 

(EmissionsDistance_bySourceType.hourID = RampEmissions_Query.hourID) AND 

(EmissionsDistance_bySourceType.sourceTypeID = RampEmissions_Query.sourceTypeID) AND 

(EmissionsDistance_bySourceType.pollutantID = RampEmissions_Query.pollutantID) 

GROUP BY EmissionsDistance_bySourceType.State, EmissionsDistance_bySourceType.County, 

EmissionsDistance_bySourceType.yearID, EmissionsDistance_bySourceType.pollutantName, 

EmissionsDistance_bySourceType.monthID, EmissionsDistance_bySourceType.pollutantID 

HAVING (((EmissionsDistance_bySourceType.monthID)=4)); 
APPENDIX G 

Indiana Department of Environmental 

Management (IDEM) – Area Source Inventory 

Standard Operating Procedure 
This page left intentionally blank.
Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

Standard Operating Procedure 

Office: Office of Air Quality

Branch: Air Programs Branch

Section: Technical Support and Modeling Section

Revised: 02/27/2008 Revision Cycle: 2 years

Effective date: 02/15/07

Scope of operations 

This SOP is to identify source categories and develop emissions not calculated in point source 

inventories. This data is compiled every three years as mandated by EPA. 

Scope of applicability 

This SOP is for the Senior Environmental Manager and the Environmental Manager in the Emissions 

Group. 

Authorized Signatures 

I approve and authorize this Standard Operating Procedure: 
Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

Table of Contents

Scope of operations....................................................................................................................................1

Scope of applicability .................................................................................................................................1

Authorized Signatures................................................................................................................................1

1. Overview work flow chart.......................................................................................................................3

2. Definitions................................................................................................................................................3

3. Roles.........................................................................................................................................................4

Responsibilities: .........................................................................................................................................4

4. Description of equipment, forms, and/or software to be used...........................................................4

5. Procedure ................................................................................................................................................4

5.1 Procedural Flowchart ....................................................................................................................4

5.2 Procedure .......................................................................................................................................5

6. Standards and checklists.....................................................................................................................24

7. Records Management...........................................................................................................................24

8. Quality Assurance / Quality Control ...................................................................................................25

9. Continuous Improvement Cycle..........................................................................................................25

10. References...........................................................................................................................................25

11. History of Revisions ...........................................................................................................................25

12. Appendices..........................................................................................................................................25

2 
Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

1. Overview work flow chart 

The process described is not part of a larger system and does not need an Overview work flow chart.

2. Definitions 

AP-42 – Compilation of Air Pollutant Emission Factors AP-42, Fifth Edition, Volume I: Stationary Point 

and Area Sources (January 1995) plus Supplements A – F (Updates 2001 – 2004). AP-42 can be 

obtained at www.epa.gov/ttn/chief/ap42/.

Area Sources - A collection of similar emission units within a geographic area that collectively represent 

individual sources that are small and numerous and have not been inventoried as a specific point, mobile, 

or biogenic source.

Authorized - Established by official authority and usage; as with a policy, standard operating procedure 

(SOP), or quality assurance project plan (QAPP) that is signed and dated. 

EIIP (Emission Inventory Improvement Program) -The EIIP is an EPA program established in 1993 to 

promote the development and use of standard procedures for collecting, calculating, storing, reporting, 

and sharing air emissions date. 

Emission Factors - An emission factor is the estimate of the quantity of pollutant released to the 

atmosphere (because of some operation or activity such as combustion or industrial production) divided 

by the level of that activity. 

Process - The term “process” used when describing area sources is used to name an operation or 

activity that produces emissions.

NEI - National Emission Inventory Air Pollutant Emission Trends, U.S. EPA.

Standard Industrial Classification (SIC) Code - A Standard Industrial Classification code from the 

series of codes devised by the United States Office of Management and Budget (OMB) to classify 

establishments according to the type of economic activity in which they engage. 

Source Classification Code (SCC) - Source Classification Code is a process-level code that describes 

the equipment or operation emitting pollutants.

3 
Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

3. Roles 

Title # of Staff Experience Qualifications Location 

Senior Environmental Manager 1 N/A 

MS ACCESS, Emission 

Inventories and familiarity 

with the EIIP 

Air Programs 

Branch 

Environmental Manager 1 N/A 

MS ACCESS, Emission 

Inventories and familiarity 

with the EIIP 

Air Programs 

Branch 

 Responsibilities: 

Senior Environmental Manager

Oversees work of the Environmental Manager and ensures that all goals are met. The Senior 

Environmental Manager also does the final upload to the NEI. 

Environmental Manager 

The Environmental Manager calculates the Area Source Emissions using the EIIP or other EPA 

guidance as provided. The Environmental Manager is also responsible for updating the SOP for 

the Emissions Group. 

4. Description of equipment, forms, and/or software to be used 

Equipment, Form, &/or Software Who uses it? Location 

AP42 Senior Environmental Manager and 

Environmental Manager 

EPA’s website: 

http://www.epa.gov/ttn/chief/ap42/index.

html 

Emission Inventory Improvement 

Program (EIIP) 

Senior Environmental Manager and 

Environmental Manager 

EPA’s website: 

http://www.epa.gov/ttn/chief/eiip/techrep

ort/ 

National Emission Inventory (NEI) Air 

Pollutant Emission Trends, U.S. EPA 

Senior Environmental Manager and 

Environmental Manager 

EPA’s website 

http://www.epa.gov/ttn/chief/trends/ 

5. Procedure 

5.1 Procedural Flowchart 

The procedural flowchart below titled “Area Source Inventory” is used to calculate non-point source 

inventories. This data is compiled every three years as mandated by EPA. The guidance followed is 

located in the EIIP. Emissions from area sources are calculated at the county level and consist of 

individual sources that are small, numerous and that have not been inventoried as specific point, mobile, 

or biogenic sources according to the EIIP. 

YES

 NO

Identify 

Categories

Is the category

an Area 

Source?

Then calculate emissions using the SOP for 

Receiving Emission Statements or the SOP 

for On-Road Mobile Source Inventory

Calculate Area Source 

categories using the 

EIIP or other EPA 

guidance as provided

Enter data into

NEI 

4 
Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

5.2 Procedure 

Category 1: Stationary Fuel Combustion 

Sub-Category 1.1: Industrial Fuel Combustion 

SCC: 2102002000, 210200400, 2102005000, 2102006000, 2102007000 

Follow these steps when calculating emissions from industrial fuel combustion: 

1. Obtain statewide fuel consumption for “Other Industrial” for the following fuels: coal, distillate oil, 

natural gas, and liquefied petroleum gas (LPG). Use the Energy Information Administration’s website 

at http://www.eia.doe.gov/ to find fuel consumption. 

Note: As of the date of this SOP, the following steps will lead to data for fuel consumption. 

a. Go to http://www.eia.doe.gov/

b. Click on link for the various types of fuel consumption 

c. Click on consumption tab for state totals 

2. To avoid double calculating the various fuel combustions, subtract reported source totals from the 

total statewide fuel consumption by querying the total process rates for the various SCC codes using 

the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. The remaining number is the area 

source fuel consumption for the state. 

3. To distribute the remaining fuel to the county level, calculate the ratio of county to state employment 

for the manufacturing sector by dividing the number of Manufacturing Employees for each county by 

the number of manufacturing employees statewide. Use the County Business Patterns website at 

http://www.census.gov/ to find the number of manufacturing employees for each county. 

Note: As of the date of this SOP, the following steps will lead to data for Economic Census. 

a. Go to http://www.census.gov/

b. Click on Economic Census 

c. Under 2002 Reports by State, use the down arrow key to select Indiana 

d. Now, select each of the counties to find the county manufacturing employees 

e. Use the total of employees for manufacturing under the paid employees’ column 

4. Multiply the ratio calculated above in step 3 by the area source fuel consumption to distribute the fuel 

to the county level. The remaining number is the process rate for each county. Multiply the process 

rate by the appropriate EPA emission factors for the various fuels for industrial manufacturing found 

in AP-42, Fifth Edition, Volume 1, Chapter 1, External Combustion Sources at 

http://www.epa.gov/ttn/chief/ap42/ch01/. 

Sub-Category 1.2: Commercial/Institutional Fuel Combustion 

SCC: 2103004000, 2103005000, 2103006000, 2103007000 

Follow these steps when calculating emissions from commercial/institutional fuel combustion: 

1. Obtain statewide fuel consumption for “Commercial” for the following fuels: distillate fuel oil, liquefied 

petroleum gas (LPG), natural gas, and residual fuel oil. Use the Energy Information Administration’s 

website at http://www.eia.doe.gov/ to find fuel consumption. 

Note: Use the steps in sub-category 1.1-1 to navigate through the Energy Information 

Administration’s website. 

5 
Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

2. To avoid double calculating the various fuel combustions, subtract reported source totals from the 

total statewide fuel consumption by querying the total process rates for the various fuels using the 

SIC codes greater than 4999 using the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. 

These are the SIC codes that identify all the commercial/institutional area sources. 

3. To distribute the remaining fuel to the county level, calculate the ratio of county to state employment 

for the commercial/institutional sector by dividing the number of commercial/institutional employees 

for each county by the number of commercial/institutional employees statewide. Use the County 

Business Patterns website at http://www.census.gov/ to find the number of commercial/institutional 

employees for each county. 

Note: Use the steps in sub-category 1.1-3 to navigate through the U.S. Census Bureau’s website. 

4. Multiply the ratio calculated above in step 3 by the area source fuel consumption to distribute the fuel 

to the county level. The remaining number is the process rate for each county. Multiply the process 

rate by the appropriate EPA emission factors for the various fuels for commercial/institutional found in 

AP-42, Fifth Edition, Volume 1, Chapter 1, External Combustion Sources at 

http://www.epa.gov/ttn/chief/ap42/ch01/. 

Sub-Category 1.3: Residential Fuel Combustion 

SCC: 2104002000, 2104004000, 2104006000, 2104007000 

Follow these steps when calculating emissions from residential fuel combustion: 

1. Obtain statewide fuel consumption for “Residential” for the following fuels: coal, distillate oil, natural 

gas, and liquid petroleum gas. Use the Energy Information Administration’s website at 

http://www.eia.doe.gov/ to find fuel consumption. 

Note: Use the steps in sub-category 1.1-1 to navigate through the Energy Information 

Administration’s website. 

2. To distribute residential fuel to the county level, calculate the ratio of county fuel usage to statewide 

fuel usage using the breakdown of fuels by household per county divided by the breakdown of fuels 

by household per state using the U.S. Census Bureau’s website at http://www.census.gov/ . 

Note: As of the date of this SOP, the following steps will lead to data for breakdown of fuels by 

household. 

a. Go to http://www.census.gov/

b. On the left hand side click on “American Fact Finder” 

c. Using the drop down menu, click on Indiana 

d. Scroll to “Housing Characteristics” and select “show more” 

e. On the left hand side, select “change geography (state, county, place...)” 

f. Using the drop down menu, select county, state, and each county name to obtain housing 

information 

3. Multiply the ratio calculated above in step 3 by the area residential fuel use by state to distribute the 

fuel to the county level. The remaining number is the process rate for each county for the various 

fuels. Multiply the process rate by the appropriate EPA emission factors for the various fuels for 

residential found in AP-42, Fifth Edition, Volume 1, Chapter 1 External Combustion Sources at 

http://www.epa.gov/ttn/chief/ap42/ch01/. 

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S-006-OAQ-R-MO-08-S-R1 

Sub-Category 1.4: Residential Heating Using Wood 

SCC: 2104008001, 2104008002, 2104008003, 2104008004, 2104008010, 2104008030, 2104008050 

Follow these steps when calculating emissions from residential heating using wood: 

1. Obtain statewide wood consumption for “Residential” using the Energy Information Administration’s 

website at http://www.eia.doe.gov/. To convert the statewide wood consumption from cords of wood 

consumed to tons, multiply the total cords consumed by 1.25. 

Note: As of the date of this SOP, the following steps will lead to data for wood consumption. 

a. Go to http://www.eia.doe.gov/

b. Click on Households, Buildings & Industry 

c. Under Consumption Summaries, click on “Annual” 

d. Now, over to the right click on “State Energy” 

e. Using the drop down menu at the bottom, select “Indiana” 

f. Under “Consumption” click on the “Residential” document 

2. Using the ratio estimates provided by EPA found in the “Documentation For The Final 2002 

NONPOINT SECTOR (FEB 06 version) NATIONAL EMISSIONS INVENTORY FOR CRITERIA AND 

HAZARDOUS AIR POLLUTANTS” at 

http://www.epa.gov/ttn/chief/net/2002inventory.html#documentaiton the number calculated above in 

step 1 is broken out into three categories (fireplace without inserts, fireplaces with inserts and 

woodstoves). 

3. To distribute to the county level for the three categories above, calculate a ratio of county to state 

using the statewide total of households and the county total of households that burn wood found at 

the U.S. Census Bureau website http://www.census.gov/. The remaining number is the process rate 

for each county. Multiply the process rate by the appropriate EPA emission factors for each of the 

categories using the EIIP, Volume 3, Chapter 2, Residential Wood Combustion at 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii02_apr2001.pdf. 

Note: Use the steps in sub-category 1.3-2 to navigate through the Energy Information 

Administration’s website. 

Category 2: Industrial Processes 

Sub-Category 2.1: Bakeries 

SCC: 2302050000

Follow these steps when calculating emissions from bakeries: 

1. Calculate a per capita consumption factor using the reported weight of yeast–raised product reported 

under the Bread, Cake, and Frozen Bakery Products from the Economic Census Bureau at 

http://www.census.gov/econ/census02/ and the U.S. population at the U.S. Census Bureau at 

http://census,gov/. 

Note: As of the date of this SOP, the following steps will lead to data for yeast-raised product. 

a. Go to http://www.census.gov

b. Under Business & Industry open “Economic Census” 

c. Now open “Subject Series” 

d. Under Manufacturing, open the table “Product Summary” 

e. Use the yeast – raised product under Commercial Bakeries (NAICS code 311812) and Frozen cakes, 

pies, and other pastries manufacturing (NAICS code 311813) 

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S-006-OAQ-R-MO-08-S-R1 

2. Multiply the per capita consumption factor calculated above in step 1 by the Indiana population found 

at the U.S. Census Bureau at http://www.census.gov. 

 Note: As of the date of this SOP, the following steps will lead to Indiana population data. 

 a. Go to http://www.census.gov

 b. Under Population Finder, use the drop down menu to select Indiana 

3. To avoid double calculating the amount consumed for the state, subtract the reported process rate for 

both the straight-dough and sponge-dough by querying the total process rates for the SCC 30203202 

(straight-dough) and SCC 30203201 (sponge-dough) using the ACCESS data tables at 

K:\OAQ_INV\Steptool\Stptl_02.mdb. 

4. Multiply the remaining process rate by the straight-dough emission factor of .5 lbs VOC/1,000 pounds 

baked found in the EIIP, Volume 3, Area Source Method Abstracts: Baked Goods at Commercial/Retail 

Bakeries at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/index.html. 

5. Calculate a per capita factor by dividing the Indiana population found in step 2 by the remaining process 

rate. Now multiply the per capita factor by each of the county populations to calculate the VOC emissions 

for each county. 

 Note: As of the date of this SOP, the following steps will lead to county population data. 

 a. Go to http://www.census.gov

 b. Under Population Finder, use the drop down menu to select Indiana 

 c. Under “View more results”, select the county table 

Category 3: Solvent Utilization 

Sub-Category 3.1: Architectural Coatings 

SCC: 2401001000 

Follow these steps when calculating emissions from architectural coatings: 

1. Calculate an emission factor for architectural coating area sources first by adding all the solventbased paints together and all the water based paints together using the U.S. Census Bureau’s 

website http://www.census.gov. Use Table 1 to select all solvent-based paints and Table 2 to select 

all water based paints. 

Table 1 

National Solvent Coating Sales 

Solvent Type 1,000 gallons 

Exterior Solvent Type XX 

Interior Solvent Type XX 

Architectural Lacquers XX 

Architectural Coating 

N.S.K. XX 

Total Solvents XX 

Table 2 

National Water Based Coating Sales 

Water Type 1,000 gallons 

Exterior Water Type XX 

Interior Water Type XX 

Total Water Type XX 

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Note: As of the date of this SOP, the following steps will lead to architectural coating data. 

 a. Go to http://www.census.gov

 b. Under Business & Industry, select more 

 c. Now select Current Industrial Reports (CIR) 

 d. Select CIRs by Subject 

 e. Tab down to find the report “Paints and Allied Products” 

2. Now multiply the total national number for solvent-based paints by the average solvent-based coating 

content number (3.87 lbs VOC/gallon) and the total national number for water-based paints by the 

average water-based coating content number (0.74 lbs VOC/gal) found in the EIIP, Volume 3, 

Chapter 3: Architectural Surface Coating at 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/archsfc.pdf. 

3. Add the total solvent-based coatings and the water-based paints together for a total national VOC 

emission factor from architectural surface coating. Then divide this number by the total national 

population using the U.S. Census Bureau’s website http://www.census.gov. 

4. Multiply the number calculated above in step 3 by each of the county populations to calculate the total 

emissions per county. 

Note: Use the steps in sub-category 2.1-5 to navigate through the Census Bureau’s website. 

Sub-Category 3.2: Automobile Refinishing 

SCC: 2401005000 

Follow these steps when calculating emissions from automobile refinishing: 

1. To avoid double calculating, first query the employees from the reported sources using the SIC 7532- 

Body Repair and Paint Shops using the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. 

Subtract this number from the county employment for the same SIC using the U.S. Census Bureau’s 

website http://www.census.gov. 

 Note: As of the date of this SOP, the following steps will lead to county employment data. 

 a. Go to http://www.census.gov

 b. Under Business & Industry, select more 

 c. Now select the County Business Patterns report for county 

 d. Select Indiana 

 e. Select each of the counties to find the number of employees for the corresponding SIC or NAICS 

code 

2. Multiply the emission factor 3,519 lbs VOC/employee found in the EIIP, Volume 3, Chapter 13 Auto 

Body Refinishing at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/archsfc.pdf and the county 

employment found above in step 1 to calculate the VOC emissions for each county. 

Sub-Category 3.3: Traffic Markings 

SCC: 2401008000 

Follow these steps when calculating for traffic markings: 

1. First calculate the national emissions by finding the amount of sales for traffic marking paints from the 

U.S. Census Bureau’s website http://www.census.gov and multiply 3.36 lb VOC/gallon the national 

average VOC content for water and solvent-based paints from the EIIP, Volume 3, Chapter 14, Traffic 

Markings at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii14.pdf. 

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 Note: As of the date of this SOP, the following steps will lead to traffic marking paints. 

 

 a. Go to http://www.census.gov

 b. Under Business & Industry, select more 

 c. Now select Current Industrial Reports (CIR) 

 d. Select CIRs by Subject 

 e. Tab down to find the report “Paints and Allied Products” 

f. Use the quantity amount in 1000/gallons under “Traffic marking paints (all types: shelf goods and 

highway department) 

2. Allocate the national emissions calculated above in step 1 to the state level by dividing the amount of 

money spent in Indiana by the money spent nationally on highway maintenance using the category “Total 

Disbursements” at the Federal Highway Administration’s website 

http://www.fhwa.dot.gov/policy/ohim/hs04/htm/sf2.htm. 

3. Calculate the emission factor for Indiana by dividing the state level emissions by the total number of 

roadway miles in Indiana, given by contacting the Program Development Division, Highway Statistics, 

Indiana Department of Transportation or the Office of Air Quality, Technical Support and Modeling 

Section’s mobile inventory preparer. 

4. Multiply the emission factor by the total number of roadway miles in each county using the information 

supplied from above in step 3. 

Sub-Category 3.4: Industrial Surface Coating (employment based emission factor) 

SCC: 2401015000, 2401020000, 2401030000, 2401040000, 2401045000, 2401055000, 2401060000, 

2401065000, 2401070000, 2401075000, 2401080000

Follow these steps when calculating for industrial surface coating using the employment based 

emission factor: 

1. Calculate an employee based emission factor for the following SIC’s in the table below running a query 

to find the point source employment for each of the SIC’s and the reported VOC emissions for each 

using the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. 

SCC Description SIC’s 

2401015000 Factory Finished Wood 2426-2429,243-245, 2492, 2499 

2401020000 Wood Furniture 25 

2401030000 Paper Coating 26 

2401040000 Metal Cans * 341 

2401045000 Metal Coils * 3479 

2401055000 Machinery and Equipment 35 

2401060000 Appliances * 363 

2401065000 Electronic and Other Electrical 3612, 3357 

2401070000 New Motor Vehicles ** 3711 

2401075000 Other Transportation 37 (not 3711, 373) 

2401080000 Marine Coatings 373 

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* Use the National default emission factor because the reporting sources are low. 

** Emissions reported in point source 

2. Divide the reported VOC emissions for each of the SIC’s by the reported employment for each SIC. 

Use this number for the emission factor. 

3. Subtract the number of reported employees found in step 1 from each of the SIC county totals using 

the U.S. Census Bureau’s website http://www.census.gov. Use the remaining number for the process 

rate for each of the counties. 

 Note: Use the steps in sub-category 3.2-1 to navigate through the County Business Patterns. 

4. Multiply the process rates above found for each of the SIC’s in step 4 by the emission factors found in 

step 3 to allocate the emissions to each of the counties. 

Sub-Category 3.5: Industrial Surface Coating (default emission factor) 

SCC: 2401090000, 2401100000, 2401200000 

Follow these steps when calculating emissions from industrial surface coating using the default emission 

factor: 

1. Calculate industrial surface coating emissions using the default emission factor in the EIIP, Volume 3, Chapter 

8, Industrial Surface Coating at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii08.pdf and multiply by 

the county populations found at the U.S. Census Bureau’s website http://www.census.gov. 

 Note: Use the steps in 2.1-5 to navigate through U.S. Census Bureau’s website. 

SCC’s Description Default Emission Factor 

24-01-090-000 Miscellaneous Manufacturing 0.600 lbs VOC/person 

24-01-100-000 Industrial Maintenance Coatings 0.800 lbs VOC/person 

24-01-200-000 Other Special Purpose Coatings 0.800 lbs VOC/person 

Sub-Category 3.6: Degreasing 

SCC: 2415230000, 2415245000, 2415345000, 2415360000 

Follow these steps when calculating emissions from degreasing activities: 

1. Use the U.S. Census Bureau to find employment numbers for each of the counties for the categories 

in Table 1 below at http://www.census.gov. 

Note: Use the steps in 2.1-5 to navigate through U.S. Census Bureau’s website. 

Source Classification Codes and Industries Associated with Degreasing 

SCC SIC Description 

2415230000 36 Electronic and other electronic equipment 

25 Furniture and fixtures 

33 Primary metal industries 

34 Fabricated metal products 

35 Industrial machinery and equipment 

37 Transportation equipment 

38 Instruments and related products 

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Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

39 Miscellaneous manufacturing industries 

417 Bus Terminal and Service Facilities 

423 Trucking terminal facilities 

551 New and used car dealers 

552 Used car dealers 

554 Gasoline service stations 

555 Boat dealers 

556 Recreational vehicle dealers 

2415245000 

753 Automotive repair shops 

25 Furniture and fixtures 

33 Primary metal industries 

34 Fabricated metal products 

35 Industrial machinery and equipment 

36 Electronic and other electronic equipment 

37 Transportation equipment 

38 Instruments and related products 

2415345000 

2415345000 cont. 

39 Miscellaneous manufacturing industries 

417 Bus Terminal and Service Facilities 

423 Trucking terminal facilities 

551 New and used car dealers 

552 Used car dealers 

554 Gasoline service stations 

555 Boat dealers 

556 Recreational vehicle dealers 

2415360000 

753 Automotive repair shops 

2. Run a query to find reported employment numbers for each of the categories in the table above using 

the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. 

3. Subtract the reported employment from the U.S Census Bureau’s numbers to find the process rates 

for each of the counties. 

4 Calculate the VOC emissions by multiplying the default emission factor in the EIIP, Volume 3, Chapter 6, 

Solvent Cleaning at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii06fin.pdf and the process rate 

for each of the counties found in step 3.

Sub-Category 3.7: Dry Cleaners 

SCC: 2420010370 

Follow these steps when calculating emissions from dry cleaners:

1. Calculate an emission factor by finding the number of employees state wide and county wide for SIC 

7216(Laundry and Garment Services) at the U.S. Census Bureau’s website http://www.census.gov. 

Note: Use the steps in 2.1-5 to navigate through U.S. Census Bureau’s website 

2. Take the sum of the employment from the counties, multiply by 2000, and divide by the statewide 

total found in step 1. Use this number for the emission factor. 

3. Calculate the process rate by running a query to find the number of reported employees for SIC 7216 

using the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb and subtract this number from 

the county total. 

4. Multiply the process rate for each of the counties above by the emission factor to calculate for VOC 

emissions. 

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Sub-Category 3.8: Graphic Arts 

SCC: 2425000000 

Follow these steps when calculating emissions from graphic arts activities: 

1. Multiply the per capita factor found in the EIIP, Volume 3, Chapter 7, Graphic Arts at 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii07.pdf by the state population from the 

Census Bureau http://www.census.gov to find the total emissions for the state. 

 Note: Use the steps in 2.1-2 to navigate through the U.S. Census Bureau’s website. 

2. Develop an emission factor by subtracting point source emissions from the total emissions and 

dividing by the state population found in step 1. 

3. Distribute to the counties by multiplying the emission factor by the population for each county. 

 Note: Use the steps in 2.1-5 to navigate through the U.S. Census Bureau’s website. 

Sub-Category 3.9: Rubber and Plastics 

SCC: 2430000000 

Follow these steps when calculating emissions from rubber and plastics activities: 

1. Run a query to find the total of reported emissions and number of reported employees for all SIC’s 

beginning with 30 using the ACCESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. 

2. Calculate the emission factor by dividing the point source emissions by the reported employees. 

3. Subtract the reported employment for SIC’s beginning with 30 from total employment for each of the 

counties. 

 Note: Use step 3.2-1 to navigate through the County Business Patterns. 

4. Multiply the remaining number from above with the emission factor calculated in step 2. 

Sub-Category 3.10: Miscellaneous Industrial Adhesives 

SCC: 2440020000 

Follow these steps when calculating emissions from industrial adhesives activities: 

1. Using the guidance in the Air Pollutant Emission Trends at http://www.epa.gov/ttn/chief/trends, 

calculate an emission factor by finding the total National Emissions from Industrial Adhesives and 

divide by the National Manufacturing Employment from the U.S. Census Bureau’s website 

http://www.census.gov. 

Note: As of the date of this SOP, the following steps will lead to emission trends data for industrial 

adhesives. 

a. Go to http://www.epa.gov/air/airtrends/aqtrnd03/

b. Select “Appendix A –Data Tables” 

c. Search for industrial adhesives 

Note: As of the date of this SOP, the following steps will lead to National Manufacturing Employment. 

a. Go to http://www.census.gov

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S-006-OAQ-R-MO-08-S-R1 

b. Select Economic Census 

c. Now select “Businesses with paid employees” 

d. Use the manufacturing number under “paid employees” 

2. To avoid double calculating, run a query collecting sources reporting adhesives using the ACCESS 

data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb. Subtract the reported employment from the total 

amount of manufacturing employment. The remaining number is the process rate. 

Sub-Category 3.11: Commercial/Consumer Solvents 

SCC: 2460100000, 2460200000, 2460400000, 2460500000, 2460600000, 2460800000, 2460900000 

Follow these steps when calculating emissions from commercial/consumer solvent usage: 

1. Using the EIIP, Volume 3, Chapter 5, Consumer, and Commercial Solvent Use at 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii05.pdf, multiply the per capita factors for each of 

SCC codes by the population for each county from the U.S. Census Bureau’s website 

http://www.census.gov. 

 Note: Use the steps in 2.1-5 to navigate through the U.S. Census Bureau’s website. 

Emission Factors for Commercial/Consumer Solvents 

Source Classification Codes Product Category Per Capita Emission Factor 

(lb VOC/person) 

2460100000 Personal Care Products 2.32 

2460200000 Household Products 0.79 

2460400000 Automotive Aftermarket Products 1.36 

2460500000 Coatings and Related Products 0.95 

2460600000 Adhesives and Sealants 0.57 

2460800000 FIFRA-Regulated Products 1.78 

2460900000 Miscellaneous Products 0.07 

Sub-Category 3.12: Asphalt Emulsions 

SCC: 2461022000 

Follow these steps when calculating emissions from asphalt emulsions: 

1. To calculate the process rate, find the number of barrels of asphalt used for the state found at the 

State Energy Data website at http://www.eia.doe.gov/emeu/states/_seds_updates.html. 

2. Obtain the amount of roadway miles for the state and county from the Indiana Department of 

Transportation’s, Division of Roadway Management Section. 

3. Divide the county roadway miles by the state roadway miles and multiply by the total asphalt usage for 

the state found above in step 1. 

4. Multiply the process rate by the default emission factor in the EIIP, Volume 3, Chapter 17, Asphalt 

Paving http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii17_apr2001.pdf. 

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Sub-Category 3.13: Pesticide Usage 

SCC: 2461800000 

Follow these steps when calculating emissions from pesticide usage: 

1. Calculate pesticide usage by using a state specific emission factor. Develop the factor using a 

methodology that includes the retrieval of information of pesticides used, an emission factor for each 

pesticide used, a calculation about the inert ingredients in each pesticide, and an estimate of the 

amount of crop oil concentrate (an adjuvant used for the application of herbicides) used in the state of 

Indiana. 

2. Find the amount of active ingredients for herbicides and insecticides applied to Indiana fields at the 

Indiana Agricultural Statistics Service at http://www.usda.gov/nass/pubs/agr02/acro02.htm. 

3. Insert the numbers for both corn and soybeans to the Excel pesticide table found at K:OAQ_INV\Inv\pesticide. 

4. Calculate the emission factor by adding the emissions from crop oil concentrates obtained in the 

pesticide Excel table, pesticides, and solvent carriers and then divide by the total number of acres of 

corn and soybeans in Indiana found at the National Agricultural Statistics Services, United States 

Department of Agriculture http://www.nass.usda.gov/QuickStats/. 

5. Multiply the emission factor by the county-specific acreage for both corn and soybeans found at the 

National Agricultural Statistics Services, United States Department of Agriculture 

http://www.nass.usda.gov/QuickStats/. 

Category 4: Petroleum Marketing 

Follow these steps when calculating emissions for bulk terminals: 

Sub-Category 4.1: Bulk Terminals 

SCC: 2501050120 

1. Find the amount of gasoline sold in Indiana at the Federal Highway Administration, U.S. Department 

of Transportation http://www.fhwa.dot.gov/policy/ohim/hs04/htm/mf21.htm. 

2. Find the amount of gasoline sold statewide and by county using, the NAICS code 447-Gasoline 

Service Station from the U.S. Census Bureau’s, Economic Census at 

http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447. 

3. Run a query to find the amount of point source reported gasoline using the ACCESS data tables at 

K:\OAQ_INV\Steptool\Stptl_02.mdb and subtract from the amount sold statewide. Use this to 

allocate to each county. 

 

4. Allocate the amount gasoline sold to each of the counties by dividing the amount of sales in each 

county by statewide sales and multiplying by the number of gallons sold statewide found above in 

step 1. 

5. EPA guidance suggests that only 25% of all gasoline consumed goes through bulk plants. To 

calculate process rate, multiply each county by 25% to estimate the amount of fuel transferred 

through bulk terminals. 

6. Multiply process rate by the emission factors in the table below: 

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Area Source Inventory 

S-006-OAQ-R-MO-08-S-R1 

Emission Factors 

Source Emission Factor 

(lb VOC/1000) gal 

Storage Tanks Breathing Loss 5.0 

Storage Tank Working Loss - Filling 9.6 

Storage Tank Working Loss - Emptying 3.8 

Gasoline Loading Racks 

(Vapor balance controlled) 

11.9 

(0.3) 

Total 30.3 

7. Bulk terminals also have controls set forth in the Indiana rule (326 IAC 8-4). This rule says that any 

source of this type that is new after January 1, 1980 is required to make sure that any transfer 

between a tank and transport uses a submerged pipe vapor balance system. Using EPA’s default 

rule effectiveness, multiply the number in step 2 by the Control Efficiency (CE) 38%, a Rule 

Effectiveness (RE) of 80%, and a Rule Penetration (RP) of 13%, i.e. process rate X emission factor X 

(1-(CE x RE x RP)) X 1 ton/2000 lb = VOC tons. 

Sub-Category 4.2: Portable Fuel Containers 

SCC: 2501011011, 2501011012, 2501011016, 2501012011, 2501012012, 2501012016 

Follow these steps when calculating emissions for portable fuel containers: 

 

1. Calculate the emissions for Commercial and Residential gas cans by using the method developed by 

the California Environmental Protection Agency’s document Public Meeting to Consider Approval of 

California’s Portable Gasoline-Container Emissions Inventory. Use the excel spreadsheet found at 

K:\OAQ_INV\Inv\Area Source\Gasoline.zip to calculate the emissions for permeation, diurnal, and 

transport. Both the Spillage and Vapor losses are estimated in the nonroad emissions inventory by 

EPA models. 

2. Using the survey results below in Table 1, estimate the number of fuel containers in the state for 

residential categories. The calculations are set up in an excel spreadsheet at K:\OAQ_INV\Inv\Area 

Source\ Gasoline.zip\250101\GasCans.xls, insert the number of occupied housing, from the U.S. 

Census Bureau’s website at http://www.census.gov/, in the space marked “households”. 

Note: As of the data of this SOP, the following steps will lead to number of households in Indiana. 

a. Go to http://www.census.gov/

b. On the left hand side select American Fact finder 

c. Now select housing 

d. Under “Occupancy Status”, select occupies housing units 

e. Now use the drop down menu and select Indiana 

Table 1 

Residential Survey Results 

Percentage of households with at least one gas can 46% 

Number of gas cans per household 1.8 

Percentage of plastic cans/metal cans 76% / 24% 

Weighted average gas can capacity (gal) 2.34 

Percentage of gas cans stored with fuel 70% 

Weighted average stored fuel volume (% of capacity) 49% 

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S-006-OAQ-R-MO-08-S-R1 

Percentage of all gas cans that are plastic and stored open/closed 23% / 53% 

Percentage of all gas cans that are metal and stored open/closed 11% / 13% 

Percent of all cans stored open/closed 34% / 66% 

3. Using the survey results below in Table 2, estimate the number of fuel containers for commercial 

categories for the state. Do this by using the commercial population based on the number of identified 

businesses in Table 3 and insert into the excel spreadsheet at K:\OAQ_INV\Inv\Area Source\ 

Gasoline.zip\250101\GasCans.xls. 

 

Table 2 

Commercial Survey Results 

Percentage of businesses with at least one gas can 80% 

Number of gas cans per business 6.9 

Percentage of plastic cans/metal cans 72% / 28% 

Weighted average gas can capacity (gal) 3.43 

Weighted average stored fuel volume (% of capacity) 49% 

Percentage of all gas cans that are plastic and stored open/closed 39% / 33% 

Percentage of all gas cans that are metal and stored open/closed 10% / 18% 

Percent of all cans stored open/closed 49% / 51% 

Table 3 

Category NAICS 

Agricultural 115 

Automotive Club and Towing Services 48841 

Service Stations 8111 

Lawn and Garden Maintenance Services 81141 

General Contractors 23 

Construction and Rental Yards 5324 

Landscaping Services 561730 

4. Calculate permeable emissions separately for both residential and commercial by using the emission 

rates given in the California document. Use 1.57g/gal/day for plastic containers and 0.6g/gal/day for 

metal containers. Insert the numbers for both residential and commercial into the excel spreadsheet at 

K:\OAQ_INV\Inv\Area Source\ Gasoline.zip\250101\GasCans.xls. 

5. Calculate diurnal emissions by inserting the numbers for both residential and commercial into the 

excel spreadsheet at K:\OAQ_INV\Inv\Area Source\ Gasoline.zip\250101\GasCans.xls. 

6. Calculate transport spillage emissions by inserting the numbers for both residential and commercial into the 

excel spreadsheet at K:\OAQ_INV\Inv\Area Source\ Gasoline.zip\250101\GasCans.xls 

Sub-Category 4.3: Service Station Tank Loading or Tank Truck Unloading (Stage 1) 

SCC: 2501060052 (uncontrolled), 2501060053 (controlled) 

Follow these steps when calculating emissions from tank loading and unloading 

1. Find the amount of gasoline sold in Indiana at the Federal Highway Administration, U.S. Department of 

Transportation http://www.fhwa.dot.gov/policy/ohim/hs04/htm/mf21.htm. 

2. Find the amount of gasoline sold statewide and county wide by using the NAICS code 447-Gasoline 

Service Station from the U.S. Census Bureau’s, Economic Census at 

http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447. 

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3. Run a query to find the amount of point source reported gasoline using the ACCESS data tables at 

K:\OAQ_INV\Steptool\Stptl_02.mdb and subtract from the amount sold statewide. Use this to 

allocate to each county. 

 

4. Allocate the amount sold to each of the counties by dividing the amount of sales in each county by 

statewide sales and multiplying by the number of gallons sold statewide found above in step 1. 

5. Find the amount of gasoline tanks from the Underground Storage Tank data files from the Office of 

Land Quality, Indiana Department of Environmental Management 

http://www.in.gov/idem/programs/land/ust/ust.html. 

6. Now copy the data into an Excel spreadsheet. Filter finding the tanks that have only gasoline. Also 

filter out the tanks that are “permanently out of service”, “suspended per inspection”, and 

“unregulated”. 

7. Using the Petroleum Sources Applicability Rule 326 IAC 8-4-1, filter out the tanks that are located in 

Clark, Boone, Dearborn, Elkhart, Floyd, Hamilton, Hancock, Harrison, Hendricks, Johnson, Lake, 

Marion, Morgan, Porter, Saint Joseph, and Shelby counties. 

8. To find the amount of balanced tanks in Indiana, use the total of gasoline tanks found in step 7 and 

divide by the number of tanks that constructed after 1985 through current year. Use the spreadsheet 

created in step 7 and filter out the tanks that constructed prior to 1985. 

9. Now apply the percentage found in step 8 to the amount of gasoline found in each county. 

10. Apply the controlled emission factor to only those counties identified in 326 IAC 8-4, i.e. Boone, Clark, 

Dearborn, Elkhart, Hamilton, Hancock, Harrison, Hendricks, Johnson, Lake, Marion, Morgan, Porter, 

Saint Joseph, and Shelby. Use the emission factors for stage 1 controlled and uncontrolled in the 

EIIP, Volume 3, Chapter 11, Gasoline Marketing (Stage 1 and Stage 2) 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii11_apr2001.pdf. 

Sub-Category 4.4: Vehicle Fueling (Stage II) – Vapor Displacement 

SCC: 2501060101 (uncontrolled), 2501060102 (controlled) 

Follow these steps when calculating emissions from vehicle fueling – Vapor Displacement: 

1. Find the amount of gasoline sold in Indiana at the Federal Highway Administration, U.S. Department 

of Transportation http://www.fhwa.dot.gov/policy/ohim/hs04/htm/mf21.htm. 

2. Find the amount of gasoline sold statewide and by county using the NAICS code 447-Gasoline 

Service Station from the U.S. Census Bureau’s, Economic Census at 

http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447. 

3. Allocate the amount sold to each of the counties by dividing the amount of sales in each county by 

statewide sales and multiplying by the number of gallons sold statewide found above in step 1. 

4. Calculate an emission factor using the input files supplied from the mobile model. Table 1 and Table 

2 show examples of how the emission factors for January and July for the Southern Counties were 

calculated. By using these two months, the other months are distributed. Use the average of all 

months for the emission factor for the Southern counties. Use the same methodology for the 

Northern counties, Central Counties, Clark/Floyd, and Lake/Porter. 

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Table 1 

January Run for Southern Counties 

VTYPE GM_MILE MILES MPG VMT G/GAL Month Factor 

1 0.0628 29.4642 23.89 0.463793 0.322719 1 1.01 

2 0.1058 35.2923 18.77 0.070491 0.009868 2 1.14 

3 0.1058 35.2923 18.77 0.234672 0.109364 3 1.28 

4 0.1486 34.0851 14.31 0.071379 0.010834 4 1.41 

5 0.1486 34.0851 14.31 0.032825 0.002291 5 1.55 

6 0.2152 35.8919 9.88 0.028896 0.001775 6 1.69 

7 0.2342 32.3617 9.08 0.001027 2.24E-06 7 1.82 

8 0.2465 19.9098 8.63 0.000522 5.8E-07 8 1.69 

9 0.2719 27.6093 7.82 0.001164 2.88E-06 9 1.55 

10 0.2733 27.4686 7.78 0.002489 1.32E-05 10 1.41 

11 0.2972 24.3758 7.15 0.001132 2.72E-06 11 1.28 

12 0.3169 23.6257 6.71 0.000004 3.4E-11 12 1.14 

25 0.3421 27.2301 6.22 0.000496 5.23E-07 Sum 16.97 

0.456873 g/gal Average 1.41 

1.007222 lb/E3gal 

19 
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Table 2 

July Run for Southern Counties 

VTYPE GM_MILE MILES MPG VMT G/GAL 

1 0.1144 29.1752 23.9 0.456768 0.570447 

2 0.1955 34.8826 18.75 0.071404 0.018689 

3 0.1955 34.8826 18.75 0.237712 0.207133 

4 0.2882 33.944 14.3 0.072838 0.021865 

5 0.2882 33.944 14.3 0.033496 0.004624 

6 0.4164 35.8288 9.9 0.029201 0.003515 

7 0.4529 32.4716 9.1 0.001038 4.44E-06 

8 0.4763 19.6757 8.66 0.000509 1.07E-06 

9 0.5264 27.4602 7.83 0.00116 5.55E-06 

10 0.5283 27.3328 7.8 0.002482 2.54E-05 

11 0.5749 24.2458 7.17 0.001122 5.19E-06 

12 0.6128 23.3718 6.73 0.000004 6.6E-11 

25 0.6629 27.2301 6.22 0.000485 9.7E-07 

0.826316 g/gal 

1.821697 lb/E3gal 

 5. Multiply the process rate in step 4 by the emission factor found in the mobile model. 

Sub-Category 4.5: Vehicle Fueling (Stage II) – Spillage 

SCC: 2501060103 

Follow these steps when calculating emissions from vehicle fueling – Spillage: 

1. Find the amount of gasoline sold in Indiana at the Federal Highway Administration, U.S. Department 

of Transportation http://www.fhwa.dot.gov/policy/ohim/hs04/htm/mf21.htm. 

2. Find the amount of gasoline sold statewide and by county using the NAICS code 447-Gasoline 

Service Station from the U.S. Census Bureau’s, Economic Census at 

http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447. 

3. Allocate the amount sold to each of the counties by dividing the amount of sales in each county by 

statewide sales and multiplying by the number of gallons sold statewide found above in step 1. 

4. Apply the emission factor 0.7 lb VOC/1000 gallons in AP-42, Fifth Edition, Volume 1, Chapter 5, 

Petroleum Industry, Transportation, and Marketing of Petroleum Liquids 

http://www.epa.gov/ttn/chief/ap42/ch05/final/c05s02.pdf to the process rate found in step 4. 

Sub-Category 4.6: Underground Tank Breathing 

SCC: 2501060200 

Follow these steps when calculating emissions from underground tank breathing: 

1. Find the amount of gasoline sold in Indiana at the Federal Highway Administration, U.S. Department 

of Transportation http://www.fhwa.dot.gov/policy/ohim/hs04/htm/mf21.htm. 

2. Find the amount of gasoline sold statewide and by county using the NAICS code 447-Gasoline 

Service Station from the U.S. Census Bureau’s, Economic Census at 

http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447. 

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3. Allocate the amount sold to each of the counties by dividing the amount of sales in each county by 

statewide sales and multiplying by the number of gallons sold statewide found above in step 1. 

4. Apply the emission factor 1.0 lb VOC/1000 gallons in AP-42, Fifth Edition, Volume 1, Chapter 5, 

Petroleum Industry, Transportation, and Marketing of Petroleum Liquids 

http://www.epa.gov/ttn/chief/ap42/ch05/final/c05s02.pdf to the process rate found in step 4. 

Sub-Category 4.7: Tank Trucks in Transit 

SCC: 2505030120 

Follow these steps when calculating emissions from tank trucks in transit: 

1. Find the amount of gasoline sold in Indiana at the Federal Highway Administration, U.S. Department 

of Transportation http://www.fhwa.dot.gov/policy/ohim/hs04/htm/mf21.htm. 

2. Find the amount of gasoline sold statewide and by county using the NAICS code 447-Gasoline 

Service Station from the U.S. Census Bureau’s, Economic Census at 

http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447. 

3. Allocate the amount sold to each of the counties by dividing the amount of sales in each county by 

statewide sales and multiplying by the number of gallons sold statewide found above in step 1. 

4. Using the guidance in the EIIP, Volume 3, Chapter 11, Gasoline Marketing (Stage I and State II) at 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii11_apr2001.pdf), multiply the activity rate 1.25 

by the amount sold per county found in step 4. 

5. Now multiply the process rate found in step 5 by the emission factor .06 lb VOC/gallon transported 

using the EIIP guidance above. 

Category 5: Waste Management Practices 

Sub-Category 5.1: Solid Waste Incineration 

5.1.1: Industrial Solid Waste Incineration 

SCC: 2601010000 

Follow these steps when calculating emissions from industrial solid waste incineration: 

1. Find the number of manufacturing employees, NAICS code 31, for each county using the County Business Patterns 

 at the U.S. Census Bureau’s website http://censtats.census.gov/cgi-bin/cbpnaic/cbpsel.pl. 

 Note: Use the steps in 3.2-1 to navigate through the county business patterns. 

2. Multiply the county manufacturing employment by the default fuel-loading factor 420 tons / 1,000 

manufacturing employees. 

3. Multiply the process rate in step 2 by AP-42, Fifth Edition, Volume 1, Chapter 2-1.12, Solid Waste 

Disposal at http://www.epa.gov/ttn/chief/ap42/ch02/index.html. 

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5.1.2: Commercial Solid Waste Incineration 

SCC: 2601020000 

Follow these steps when calculating emissions from commercial solid waste incineration: 

1. Find the population for each county at the U.S. Census Bureau’s website http://www.census.gov/. 

 Note: Use steps 2.1-5 to navigate through the U.S. Census Bureau’s website. 

2. Next find the default factor of .65lb/person/day from U.S. EPA Municipal Solid Waste Report 

http://www.epa.gov/epaoswer/non-hw/muncpl/msw99.htm. 

3. Find the percent of commercial solid waste from the U.S. EPA Municipal Solid Waste Report above. 

4. Now, calculate the process rate for commercial solid waste incineration by multiplying population by 

the default factor of .65lb/person/day by the percent of commercial solid waste and number of days in 

a year. 

5. Multiply the process rate in step 4 by AP-42, Fifth Edition, Volume 1, Chapter 2-1.12, Solid Waste 

Disposal at http://www.epa.gov/ttn/chief/ap42/ch02/index.html. 

5.1.3: Residential Solid Waste Incineration 

SCC: 2601030000 

Follow these steps when calculating emissions from residential solid waste incineration: 

1. Find the population for each county at the U.S. Census Bureau’s website http://www.census.gov/. 

 Note: Use step 2.1-5 to navigate through the U.S. Census Bureau’s website. 

2. Next find the default factor of .65lb/person/day from U.S. EPA Municipal Solid Waste Report 

http://www.epa.gov/epaoswer/non-hw/muncpl/msw99.htm. 

3. Find the percent of residential solid waste from the U.S. EPA Municipal Solid Waste Report above. 

4. Now, calculate the process rate for residential solid waste incineration by multiplying population by 

the default factor of .65lb/person/day by the percent of commercial solid waste and number of days in 

a year. 

5. Multiply the process rate in step 4 by AP-42, Fifth Edition, Volume 1, Chapter 2-1.12, Solid Waste 

Disposal at http://www.epa.gov/ttn/chief/ap42/ch02/index.html. 

Sub-Category 5.2: Residential Open Burning 

5.2.1: Leaf and Brush Burning 

SCC: 2610000100 and 2610000400 

Follow theses step when calculating emissions from leaf and brush burning: 

1. Find a per capita factor for leaf burning and a per capita for brush burning by using the U.S. EPA’s 

Solid Waste Report at http://www.epa.gov/epaoswer/non-hw/muncpl/msw99.htm. 

2. Allocate the amount burned by adjusting the per capita factor for leaves at 25% and for brush at 25%. 

Of the total waste generated only 28% burns. 

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S-006-OAQ-R-MO-08-S-R1 

3. Once all the percentages from above are calculated, multiply the adjusted per capita factor by the rural 

population for each county from the U.S. Census Bureau at http://www.census.gov/

Note: As of the data of this SOP, the following steps will lead to county rural population. 

a. Go to http://www.census.gov/

b. On the left hand side, select American Fact Finder 

c. Select data sets 

d. Detailed tables 

e. County 

f. Indiana 

g. All counties 

4 Use the table below to adjust the amount of waste generated to account for the percentage of forest 

in each county. The percentages come from a document from the United States Department of 

Agriculture at http://ncrs.fs.fed.us/pubs/rb/rb_nc253b.pdf. 

Percent Forested Acres per County Adjusted for Yard Waste Generated 

< 10% 0% generated 

>= 10%, and < 50% 50% generated 

>= 50% 100% generated 

5. Now, multiply the amount of leaves and brush by the emission factors found in AP-42, Fifth Edition, 

Volume 1, Chapter 2, Solid Waste Disposal, Table 2.5-5, and Table 2.5-6 at 

http://www.epa.gov/ttn/chief/ap42/ch02/final/c02s05.pdf. 

5.2.2: Residential Waste Incineration 

SCC: 2610030000 

Follow these steps when calculating emissions from for residential waste incineration: 

1. Find a per capita factor for residential waste incineration by using the U.S. EPA’s Solid Waste Report 

at http://www.epa.gov/epaoswer/non-hw/muncpl/pubs/mswchar05.pdf. 

2. Using the Solid Waste Report above, subtract the percentage of recycled and composted material 

from the per capita factor above. 

3. Now, subtract the percentages of combustibles i.e. glass, metal, yard trimmings, and other waste. 

4. Using a document from EPA, it states that only 28% of waste generated by rural population burns and 

of that percent, 49% is actually combusted. Using this information multiply the per capita factor by 

0.28 and then multiply that number by 0.49 actually burned in rural counties. 

5. Once all the percentages are calculated, multiply the adjusted per capita factor by the rural population 

for each county from the U.S. Census Bureau at http://www.census.gov/. 

 Note: Use steps 5.2.1-3 to find county rural population. 

6. Calculate the amount of residential waste by the emission factors in the EIIP, Volume 3, Chapter 16, 

Open Burning at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/index.html. 

Sub-Category 5.3: Public Owned Treatment Works (POTW’s) 

SCC: 2630020000 

Follow these steps when calculating emissions from POTW’s: 

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S-006-OAQ-R-MO-08-S-R1 

1. To calculate the amount of annual flow for public owned treatment works, obtain the amount of 

monthly flow rate for each county. This is data is supplied by the Office of Water Quality. To 

calculate for annual flow multiply the monthly flow by the default of 0.16 that represents the amount of 

industrial flow. 

2. Calculate the process rate above by the emission factors in FIRE 6.25 using the SCC code 

2630020000. 

Sub-Category 5.4: Treatment, Storage, and Disposal Facilities 

SCC: 2640000004 

Follow these steps when calculating emissions from treatment, storage, and disposal facilities: 

1. Obtain a list of treatment facilities and the amount of ignitable waste from each facility from IDEM’s 

Office of Land Quality. 

2. Using the list of facilities from step 1, run a query using the ACCESS data tables at 

K:\OAQ_INV\Steptool\Stptl_02.mdb to obtain the amount of ignitable waste reported to IDEM’s Office 

of Air Quality. 

3. Compare the two lists obtained in step 1 and step 2, for each facility subtract any quantity reported to 

OAQ from the quantity reported to OLQ. Do this in order to avoid double counting quantities reported 

to both offices. Combine the quantities reported from facilities within the same counties. Use these 

quantities as the process rate for each county. 

4. Multiply the process rate above with the combined emission factor in the table below: 

Emission Source Emission Factor in AP-42 

(lb VOC/Ton) 

Emission Factor Used 

(lb VOC/Ton) 

Storage Tank Vent 0.004-0.09 0.09 

Spillage (filling) 0.20 0.20 

Loading (filling) 0.00024-1.42 1.42 

Spillage (emptying) 0.20 0.20 

Loading (emptying) 0.00024-1.42 1.42 

Combined Emission Factor 3.33 

Category 6: Submit Data to EPA 

Submit data in a format that is acceptable to EPA. At the present time the format is the 

National Emission Inventory (NEI). 

6. Standards and checklists 

The Emission Reporting program does not have any checklist for the Area Source Inventory at this time. 

The Emission Group does this electronically through an excel spreadsheet that is created when needed. 

7. Records Management 

The Area Source Inventory files are kept electronically at K:\OAQ_INV\Inv\Area Source. 

The Branch Contact for the Air Programs Branch and the Section contact for the Technical Support and 

Modeling Section will keep copies of the SOPs for the Technical Support and Modeling Section to be 

referenced as needed. An electronic copy will also be available on K:\OAQ_INV\SOPs. 

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8. Quality Assurance / Quality Control 

Comparisons are made against the emissions estimates made by The U.S. EPA in the NEI. 

9. Continuous Improvement Cycle 

A periodic review will be completed per updates and changes made to the EIIP. 

10. References 

The Area Source Inventory is a requirement of 40 CFR Part 51 Subpart A - Emission Inventory Reporting 

Requirements. 

11. History of Revisions 

 

Date 

Month/day/year Revision Number Description 

02/27/2008 1 Revised using new SOP template. 

12. Appendices 

None 

25 
APPENDIX H 

Lake Michigan Air Directors Consortium Emission 

Estimates Technical Support Document 
This page left intentionally blank. 
Regional Air Quality Analyses for

Ozone, PM2.5, and Regional Haze: 

Final Technical Support Document

April 25, 2008

States of Illinois, Indiana, Michigan, Ohio, and Wisconsin
ii

Table of Contents

Section-Title Page

Executive Summary iii

1.0 Introduction 1

1.1 SIP Requirements 1

1.2 Organization 2

1.3 Technical Work: Overview 3

2.0 Ambient Data Analyses 4

2.1 Ozone 4

2.2 PM2.5 21

2.3 Regional Haze 35

3.0 Air Quality Modeling 46

3.1 Selection of Base Year 46

3.2 Future Years of Interest 46

3.3 Modeling System 47

3.4 Domain/Grid Resolution 47

3.5 Model Inputs: Meteorology 48

3.6 Model Inputs: Emissions 51

3.7 Base Year Modeling Results 60

4.0 Attainment Demonstration for Ozone and PM2.5 71

4.1 Future Year Modeling Results 71

4.2 Supplemental Analyses 82

4.3 Weight of Evidence Determination for Ozone 82

4.4 Weight of Evidence Determination for PM2.5 90

5.0 Reasonable Progress Assessment for Regional Haze 93

5.1 Class I Areas Impacted 93

5.2 Future Year Modeling Results 96

5.3 Weight of Evidence Determination for Regional Haze 104

6.0 Summary 111

7.0 References 114

Appendix I Ozone and PM2.5 Modeling Results

Appendix II Ozone Source Apportionment Modeling Results

Appendix III PM2.5Source Apportionment Modeling Results

Appendix IV Haze Source Apportionment Modeling Results
iii

EXECUTIVE SUMMARY

States in the upper Midwest face a number of air quality challenges. More than 50 counties are 

currently classified as nonattainment for the 8-hour ozone standard and 60 for the fine particle 

(PM2.5) standard (1997 versions). A map of these nonattainment areas is provided in the figure 

below. In addition, visibility impairment due to regional haze is a problem in the larger national 

parks and wilderness areas (i.e., Class I areas). There are 156 Class I areas in the U.S., 

including two in northern Michigan.

Figure i. Current nonattainment counties for ozone (left) and PM2.5 (right)

To support the development of State Implementation Plans (SIPs) for ozone, PM2.5, and 

regional haze in the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin, technical 

analyses were conducted by the Lake Michigan Air Directors Consortium (LADCO), its member 

states, and various contractors. The analyses include preparation of regional emissions 

inventories and meteorological data, evaluation and application of regional chemical transport 

models, and collection and analysis of ambient monitoring data. 

Monitoring data were analyzed to produce a conceptual understanding of the air quality 

problems. Key findings of the analyses include:

Ozone

• Current monitoring data (2005-2007) show about 20 sites in violation of the 8-hour 

ozone standard of 85 parts per billion (ppb). Historical ozone data show a steady 

downward trend over the past 15 years, especially since 2001-2003, due likely to 

federal and state emission control programs.

• Ozone concentrations are strongly influenced by meteorological conditions, with 

more high ozone days and higher ozone levels during summers with above normal 

temperatures.
iv

• Inter- and intra-regional transport of ozone and ozone precursors affects many 

portions of the five states, and is the principal cause of nonattainment in some areas 

far from population or industrial centers. 

PM2.5

• Current monitoring data (2005-2007) show 30 sites in violation of the annual PM2.5

standard of 15 ug/m3. Nonattainment sites are characterized by an elevated 

regional background (about 12 – 14 ug/m3) and a significant local (urban) increment 

(about 2 – 3 ug/m3). Historical PM2.5 data show a slight downward trend since 

deployment of the PM2.5 monitoring network in 1999.

• PM2.5 concentrations are also influenced by meteorology, but the relationship is 

more complex and less well understood compared to ozone.

• On an annual average basis, PM2.5 chemical composition consists mostly of sulfate, 

nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data (2000-2004) show visibility levels in the Class I areas in 

northern Michigan are on the order of 22 – 24 deciviews. The goal of EPA’s visibility 

program is to achieve natural conditions, which is about 12 deciviews for these 

Class I areas, by the year 2064.

• Visibility impairment is dominated by sulfate and nitrate.

Air quality models were applied to support the regional planning efforts. Two base years were 

used in the modeling analyses: 2002 and 2005. Basecase modeling was conducted to evaluate 

model performance (i.e., assess the model's ability to reproduce observed concentrations). This 

exercise was intended to build confidence in the model prior to its use in examining control 

strategies. Model performance for ozone and PM2.5 was found to be generally acceptable.

Future year strategy modeling was conducted to determine whether existing (“on the books”) 

controls would be sufficient to provide for attainment of the standards for ozone and PM2.5 and if 

not, then what additional emission reductions would be necessary for attainment. Based on the 

modeling and other supplemental analyses, the following general conclusions can be made:

• Existing controls are expected to produce significant improvement in ozone and 

PM2.5 concentrations and visibility levels.

• The choice of the base year affects the future year model projections. A key 

difference between the base years of 2002 and 2005 is meteorology. 2002 was 

more ozone conducive than 2005. The choice of which base year to use as the 

basis for the SIP is a policy decision (i.e., how much safeguard to incorporate).

• Modeling suggests that most sites are expected to meet the current 8-hour ozone 

standard by the applicable attainment date, except for sites in western Michigan 

and, possibly, in eastern Wisconsin and northeastern Ohio.
v

• Modeling suggests that most sites are expected to meet the current PM2.5

standard by the applicable attainment date, except for sites in Detroit, Cleveland, 

and Granite City.

The regional modeling for PM2.5 does not include air quality benefits expected 

from local controls. States are conducting local-scale analyses and will use

these results, in conjunction with the regional-scale modeling, to support their 

attainment demonstrations for PM2.5.

• These findings of residual nonattainment for ozone and PM2.5 are supported by 

current (2005 – 2007) monitoring data which show significant nonattainment in 

the region (e.g., peak ozone design values on the order of 90 – 93 ppb, and peak 

PM2.5 design values on the order of 16 - 17 ug/m3). It is unlikely that sufficient 

emission reductions will occur in the next couple of years to provide for 

attainment at all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual 

future year meteorology (e.g., if the weather conditions are consistent with [or 

less severe than] 2005, then attainment is likely) and actual future year 

emissions (e.g., if the emission reductions associated with the existing controls 

are achieved, then attainment is likely). If either of these conditions is not met, 

then attainment may be less likely.

• Modeling suggests that the new PM2.5 24-hour standard and the new lower 

ozone standard will not be met at several sites, even by 2018, with existing 

controls.

• Visibility levels in a few Class I areas in the eastern U.S. are expected to be 

greater than (less improved than) the uniform rate of visibility improvement 

values in 2018 based on existing controls, including those in northern Michigan 

and some in the northeastern U.S. Visibility levels in many other Class I areas in 

the eastern U.S. are expected to be less than (more improved than) the uniform 

rate of visibility improvement values in 2018. These results, along with 

information on the costs of compliance, time necessary for compliance, energy 

and non air quality environmental impacts of compliance, and remaining useful 

life of existing sources, should be considered by the states in setting reasonable 

progress goals for regional haze.
1

Section 1.0 Introduction

This Technical Support Document summarizes the final air quality analyses conducted by the 

Lake Michigan Directors Consortium (LADCO)1and its contractors to support the development 

of State Implementation Plans (SIPs) for ozone, fine particles (PM2.5 ), and regional haze in the 

States of Illinois, Indiana, Michigan, Ohio, and Wisconsin. The analyses include preparation of 

regional emissions inventories and meteorological modeling data for two base years (2002 and 

2005), evaluation and application of regional chemical transport models, and analysis of 

ambient monitoring data. 

Two aspects of the analyses should be emphasized. First, a regional, multi-pollutant approach 

was taken in addressing ozone, PM2.5, and haze for technical reasons (e.g., commonality in

precursors, emission sources, atmospheric processes, transport influences, and geographic 

areas of concern), and practical reasons (e.g., more efficient use of program resources). 

Furthermore, EPA has consistently encouraged multi-pollutant planning in its rule for the haze 

program (64 FR 35719), and its implementation guidance for ozone (70 FR 71663) and PM2.5

(72 FR 20609). Second, a weight-of-evidence approach was taken in considering the results of 

the various analyses (i.e., two sets of modeling results -- one for a 2002 base year and one for a 

2005 base year -- and ambient data analyses) in order to provide a more robust assessment of 

expected future year air quality.

The report is organized in the following sections. This Introduction provides an overview of 

regulatory requirements and background information on regional planning. Section 2 reviews 

the ambient monitoring data and presents a conceptual model of ozone, PM2.5, and haze for the 

region. Section 3 discusses the air quality modeling analyses, including development of the key 

model inputs (emissions inventory and meteorological data), and basecase model performance 

evaluation. A modeled attainment demonstration for ozone and PM2.5 is presented in Section 4, 

along with relevant data analyses considered as part of the weight-of-evidence determination. 

Section 5 documents the reasonable progress assessment for regional haze, along with

relevant data analyses considered as part of the weight-of-evidence determination. Finally, key 

study findings are reviewed and summarized in Section 6.

1.1 SIP Requirements

For ozone, EPA promulgated designations on April 15, 2004 (69 FR 23858, April 30, 2004). In 

the 5-state region, more than 100 counties were designated as nonattainment.

2

 The

designations became effective on June 15, 2004. SIPs for ozone were due no later than three 

years from the effective date of the nonattainment designations (i.e., by June 2007). The

attainment date for ozone varies as a function of nonattainment classification. For the region, 

the attainment dates are either June 2007 (marginal nonattainment areas), June 2009 (basic 

nonattainment areas), or June 2010 (moderate nonattainment areas).

 

1

 A sub-entity of LADCO, known as the Midwest Regional Planning Organization (MRPO), is responsible 

for the regional haze activities of the multi-state organization.

2

 Based on more recent air quality data, many counties in Indiana, Michigan, and Ohio were 

subsequently redesignated as attainment. As of December 31, 2007, there are 53 counties designated 

as nonattainment in the region.
2

For PM2.5, EPA promulgated designations on December 17, 2004 (70 FR 944, January 5, 2005). 

In the 5-state region, 70 counties were designated as nonattainment.3 The designations became 

effective on April 5, 2005. SIPs for PM2.5 are due no later than three years from the effective 

date of the nonattainment designations (per section 172(b) of the Clean Air Act) (i.e., by April 

2008) and for haze no later than three years after the date on which the Administrator 

promulgated the PM2.5 designations (per the Omnibus Appropriations Act of 2004) (i.e., by 

December 2007). The applicable attainment date for PM2.5 nonattainment areas is five years 

from the date of the nonattainment designation (i.e., by April 2010). 

For haze, the Clean Air Act sets “as a national goal the prevention of any future, and the 

remedying of any existing, impairment of visibility in Class I areas which impairment results from 

manmade air pollution.” There are 156 Class I areas, including two in northern Michigan: Isle 

Royale National Park and Seney National Wildlife Refuge4. EPA’s visibility rule (64 FR 35714, 

July 1, 1999) requires reasonable progress in achieving “natural conditions” by the year 2064. 

As noted above, the first regional haze SIP was due in December 2007 and must address the 

initial 10-year implementation period (i.e., reasonable progress by the year 2018). SIP 

requirements (pursuant to 40 CFR 51.308(d)) include setting reasonable progress goals, 

determining baseline conditions, determining natural conditions, providing a long-term control 

strategy, providing a monitoring strategy (air quality and emissions), and establishing BART 

emissions limitations and associated compliance schedule. 

1.2 Organization

LADCO was established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. The 

four states and EPA signed a Memorandum of Agreement (MOA) that initiated the Lake 

Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee the study. 

Additional MOAs were signed by the States in 1991 (to establish the Lake Michigan Ozone 

Control Program), January 2000 (to broaden LADCO’s responsibilities), and June 2004 (to 

update LADCO’s mission and reaffirm the commitment to regional planning). In March 2004, 

Ohio joined LADCO. LADCO consists of a Board of Directors (i.e., the State Air Directors), a 

technical staff, and various workgroups. The main purposes of LADCO are to provide technical 

assessments for and assistance to its member states, and to provide a forum for its member 

states to discuss regional air quality issues. 

MRPO is a similar entity led by the five LADCO States and involves the federally recognized 

tribes in Michigan and Wisconsin, EPA, and Federal Land Managers (i.e., National Park

Service, U.S. Fish & Wildlife Agency, and U.S. Forest Service). In October 2000, the States of 

Illinois, Indiana, Michigan, Ohio, and Wisconsin signed an MOA that established the MRPO. An 

operating principles document for MRPO, which describe the roles and responsibilities of states, 

tribes, federal agencies, and stakeholders, was issued in March 2001. MRPO has a similar 

purpose as LADCO, but is focused on visibility impairment due to regional haze in the Federal 

Class I areas located inside the borders of the five states, and the impact of emissions from the 

five states on visibility impairment due to regional haze in the Federal Class I areas located

outside the borders of the five states. MRPO works cooperatively with the Regional Planning 

Organizations (RPOs) representing other parts of the country. The RPOs sponsored several

 

3

 USEPA subsequently adjusted the final designations, which resulted in 63 counties in the region being 

designated as nonattainment (70 FR 19844, April 15, 2005).

4

 Although Rainbow Lake in northern Wisconsin is also a Class I area, the visibility rule does not apply

because the Federal Land Manager determined that visibility is not an air quality related value there.
3

joint projects and, with assistance by EPA, maintain regular contact on technical and policy 

matters.

1.3 Technical Work: Overview

To ensure the reliability and effectiveness of its planning process, LADCO has made data 

collection and analysis a priority. More than $7M in RPO grant funds were used for special 

purpose monitoring, preparing and improving emissions inventories, and conducting air quality 

analyses5. An overview of the technical work is provided below.

Monitoring: Numerous monitoring projects were conducted to supplement on-going state and 

local air pollution monitoring. These projects include rural monitoring (e.g., comprehensive 

sampling in the Seney National Wildlife Refuge and in Bondville, IL); urban monitoring (e.g., 

continuation of the St. Louis Supersite); aloft (aircraft) measurements; regional ammonia 

monitoring; and organic speciation sampling in Seney, Bondville, and five urban areas.

Emissions: Baseyear emissions inventories were prepared for 2002 and 2005. States provided 

point source and area source emissions data, and MOBILE6 input files and mobile source 

activity data. LADCO and its contractors developed the emissions data for other source 

categories (e.g., select nonroad sources, ammonia, fires, and biogenics) and processed the 

data for input into an air quality model. To support control strategy modeling, future year 

inventories were prepared. The future years of interest include 2008 (planning year to address 

the 2009 attainment year for basic ozone nonattainment ares), 2009 (planning year to address 

the 2010 attainment year for PM2.5 and moderate ozone nonattainment areas), 2012 (planning

to address a 2013 alternative attainment date), and 2018 (first milestone year for regional haze).

Air Quality Analyses: The weight-of-evidence approach relies on data analysis and modeling. 

Air quality data analyses were used to provide both a conceptual model (i.e., a qualitative 

description of the ozone, PM2.5, and regional haze problems) and supplemental information for 

the attainment demonstration. Given uncertainties in emissions inventories and modeling, 

especially for PM2.5, these data analyses are a necessary part of the overall technical support.

Modeling includes baseyear analyses for 2002 and 2005 to evaluate model performance and 

future year strategy analyses to assess candidate control strategies. The analyses were 

conducted in accordance with EPA’s modeling guidelines (EPA, 2007a). The PM/haze 

modeling covers the full calendar year (2002 and 2005) for an eastern U.S. 36 km domain, while 

the ozone modeling focuses on the summer period (2002 and 2005) for a Midwest 12 km 

subdomain. The same model (CAMx) was used for ozone, PM2.5, and regional haze.

 

5

 Since 1999, MRPO has received almost $10M in RPO grant funds from USEPA.
4

Section 2.0 Ambient Data Analyses

An extensive network of air quality monitors in the 5-state region provides data for ozone (and 

its precursors), PM2.5 (both total mass and individual chemical species), and visibility. These 

data are used to determine attainment/nonattainment designations, support SIP development, 

and provide air quality information to public (see, for example, www.airnow.gov).

Analyses of the data were conducted to produce a conceptual model, which is a qualitative 

summary of the physical, chemical, and meteorological processes that control the formation and 

distribution of pollutants in a given region. This section reviews the relevant data analyses and 

describes our understanding of ozone, PM2.5, and regional haze with respect to current 

conditions, data variability (spatial, temporal, and chemical), influence of meteorology (including 

transport patterns), precursor sensitivity, and source culpability.

2.1 Ozone

In 1979, EPA adopted an ozone standard of 0.12 ppm, averaged over a 1-hour period. This 

standard is attained when the number of days per calendar year with maximum hourly average 

concentrations above 0.12 ppm is equal to or less than 1.0, averaged over a 3-year period, 

which generally reflects a design value (i.e., the 4th highest daily 1-hour value over a 3-year 

period) less than 0.12 ppm.

In 1997, EPA tightened the ozone standard to 0.08 ppm, averaged over an 8-hour period6. The 

standard is attained if the 3-year average of the 4th-highest daily maximum 8-hour average 

ozone concentrations (i.e., the design value) measured at each monitor within an area is less 

than 0.08 ppm (or 85 ppb). 

Current Conditions: A map of the 8-hour ozone design values at each monitoring site in the 

region for the 3-year period 2005-2007 is shown in Figure 1. The “hotter” colors represent 

higher concentrations, where yellow and orange dots represent sites with design values above 

the standard. Currently, there are 19 sites in violation of the 8-hour ozone NAAQS in the 5-state 

region, including sites in the Lake Michigan area, Detroit, Cleveland, Cincinnati, and Columbus.

Table 1 provides the 4th-highest daily 8-hour ozone values and the associated design values 

since 2001 for several high monitoring sites throughout the region.

 

6

 On March 12, 2008, USEPA further tightened the 8-hour ozone standard to increase public health 

protection and prevent environmental damage from ground-level ozone. USEPA set the primary (health) 

standard and secondary (welfare) standard at the same level: 0.075 ppm (75 ppb), averaged over an 8-

hour period.
5

Figure 1. 8-hour ozone design values (2005-2007)
Key Sites

'01 '02 '03 '04 '05 '06 '07 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

Lake Michigan Area

Chiwaukee 99 116 88 78 93 79 85 101 94 86 83 85

Racine 92 111 82 69 95 71 77 95 87 82 78 81

Milwaukee-Bayside 93 99 92 73 93 73 83 94 88 86 79 83

Harrington Beach 102 93 99 72 94 72 84 98 88 88 79 83

Manitowoc 97 83 92 74 95 78 85 90 83 87 82 86

Sheboygan 102 105 93 78 97 83 88 100 92 89 86 89

Kewaunee 90 92 97 73 88 76 85 93 87 86 79 83

Door County 95 95 93 78 101 79 92 94 88 90 86 90

Hammond 90 101 81 67 87 75 77 90 83 78 76 79

Whiting 64 88 81 88 77 85

Michigan City 90 107 82 70 84 75 73 93 86 78 76 77

Ogden Dunes 85 101 77 69 90 70 84 87 82 78 76 81

Holland 92 105 96 79 94 91 94 97 93 89 88 93

Jenison 86 93 91 69 86 83 88 90 84 82 79 85

Muskegon 95 96 94 70 90 90 86 95 86 84 83 88

Indianapolis Area

Noblesville 88 101 101 75 87 77 84 96 92 87 79 82

Fortville 89 101 92 72 80 75 81 94 88 81 75 78

Fort B. Harrison 87 100 91 73 80 76 83 92 88 81 76 79

Detroit Area

New Haven 95 95 102 81 88 78 93 97 92 90 82 86

Warren 94 92 101 71 89 78 91 95 88 87 79 86

Port Huron 84 100 87 74 88 78 89 90 87 83 80 85

Cleveland Area

Ashtabula (Conneaut) 97 103 99 81 93 86 92 99 94 91 86 90

Notre Dame (Geauga) 99 115 97 75 88 70 68 103 95 86 77 75

Eastlake (Lake) 89 104 92 79 97 83 74 95 91 89 86 84

Akron (Summit) 98 103 89 77 89 77 91 96 89 85 81 85

Cincinnati Area

Wilmington (Clinton) 93 99 96 78 83 81 82 96 91 85 80 82

Sycamore (Hamilton) 88 100 93 76 89 81 90 93 89 86 82 86

Hamilton (Butler) 83 100 94 75 86 79 91 92 89 85 80 85

Middleton (Butler) 87 98 83 76 88 76 91 89 85 82 80 85

Lebanon (Warren) 85 98 95 81 92 86 88 92 91 89 86 88

Columbus Area

London (Madison) 84 97 90 75 81 76 83 90 87 82 77 80

New Albany (Franklin) 90 103 94 78 92 82 87 95 91 88 84 87

Franklin (Franklin) 83 99 84 73 86 79 79 88 85 81 79 81

Ohio Other Areas

Marietta (Washington) 85 95 80 77 88 81 86 86 84 81 82 85

St. Louis Area

W. Alton (MO) 85 99 91 77 89 91 89 91 89 85 85 89

Orchard (MO) 88 98 90 76 92 92 83 92 88 86 86 89

Sunset Hills (MO) 88 98 88 70 89 80 89 91 85 82 79 86

Arnold (MO) 86 93 82 70 92 79 87 87 81 81 80 86

Margaretta (MO) 80 98 90 72 91 76 91 89 86 84 79 86

Maryland Heights (MO) 88 84 94 88

4th High 8-hour Value Design Values

Table 1. Ozone Data for Select Sites in 5-State Region
7

Meteorology and Transport: Most pollutants exhibit some dependence on meteorological 

factors, especially wind direction, because that governs which sources are upwind and thus 

most influential on a given sample. Ozone is even more dependent, since its production is 

driven by high temperatures and sunlight, as well as precursor concentrations (see, for 

example, Figure 2). 

Figure 2. Number of hot days and 8-hour “exceedance” days in 5-state region

Qualitatively, ozone episodes in the region are associated with hot weather, clear skies 

(sometimes hazy), low wind speeds, high solar radiation, and southerly to southwesterly winds. 

These conditions are often a result of a slow-moving high pressure system to the east of the 

region. The relative importance of various meteorological factors is discussed later in this 

section.

Transport of ozone (and its precursors) is a significant factor and occurs on several spatial 

scales. Regionally, over a multi-day period, somewhat stagnant summertime conditions can 

lead to the build-up in ozone and ozone precursor concentrations over a large spatial area. This

pollutant air mass can be advected long distances, resulting in elevated ozone levels in 

locations far downwind. An example of such an episode is shown in Figure 3. 

Figure 3. Example of elevated regional ozone concentrations (June 23 – 25, 2005)

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8-

hour standard
8

Locally, emissions from urban areas add to the regional background leading to ozone 

concentration hot spots downwind. Depending on the synoptic wind patterns (and local landlake breezes), different downwind areas are affected (see, for example, Figure 4).

Figure 4. Examples of recent high ozone days in the Lake Michigan area

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8-

hour standard

Aloft (aircraft) measurements in the Lake Michigan area also provide evidence of elevated 

regional background concentrations and “plumes” from urban areas. For one example summer 

day (August 20, 2003 – see Figure 5), the incoming background ozone levels were on the order 

of 80 – 100 ppb and the downwind ozone levels over Lake Michigan were on the order of 100 -

150 ppb (STI, 2004).

Figure 5. Aircraft ozone measurements over Lake Michigan (left) and along upwind boundary 

(right) – August 20, 2003 (Note: aircraft measurements reflect instantaneous values)
9

As discussed in Section 4, residual nonattainment is projected in at least one area in the 5-state 

region –i.e., western Michigan. To understand the source regions likely impacting high ozone 

concentrations in western Michigan and estimate the impact of these source regions, two simple 

transport-related analyses were performed.

First, back trajectories were constructed using the HYSPLIT model for high ozone days (8-hour 

peak > 80 ppb) during the period 2002-2006 in western Michigan to characterize general

transport patterns. Composite trajectory plots for all high ozone days based on data from three 

sites (Cass County, Holland, and Muskegon) are provided in Figure 6. The plots point back to 

areas located to the south-southwest (especially, northeastern Illinois and northwestern Indiana) 

as being upwind on these high ozone days.

Figure 6 Back trajectory analysis showing upwind areas associated with high ozone

concentrations

Second, to assess the impact from Chicago/NW Indiana, Blanchard (2005a) compared ozone 

concentrations upwind (Braidwood, IL), within Chicago (ten sites in the City), and downwind 

(Holland and Muskegon) for days in 1999 – 2002 with southwesterly winds - i.e., transport 

towards western Michigan. Figure 7 shows the distribution of daily peak 8-hour ozone 

concentrations by day-of-week, with a line connecting the mean values. The difference 

between day-of-week mean values at downwind and upwind sites indicates that Chicago/NW 

Indiana contributes about 10-15 ppb to downwind ozone levels.
10

Figure 7. Mean day-of-week peak 8-hour ozone concentrations at sites upwind, within, and 

downwind of Chicago, 1999 – 2002 (southwesterly wind days)

Based on this information, the following key findings related to transport can be made:

• Ozone transport is a problem affecting many portions of the eastern U.S. The Lake 

Michigan area (and other areas in the LADCO region) both receive high levels of 

incoming (transported) ozone and ozone precursors from upwind source areas on many 

hot summer days, and contribute to the high levels of ozone and ozone precursors 

affecting downwind receptor areas.

• The presence of a large body of water (i.e., Lake Michigan) influences for the formation 

and transport of ozone in the Lake Michigan area. Depending on large-scale synoptic 

winds and local-scale lake breezes, different parts of the area experience high ozone 

concentrations. For example, under southerly flow, high ozone can occur in eastern 

Wisconsin, and under southwesterly flow, high ozone can occur in western Michigan. 

• Downwind shoreline areas around Lake Michigan are affected by both regional transport 

of ozone and subregional transport from major cities in the Lake Michigan area. 

Counties along the western shore of Michigan (from Benton Harbor to Traverse City, and 

even as far north as the Upper Peninsula) are impacted by high levels of incoming 

(transported) ozone.
11

Data Variability: Since 1980, considerable progress has been made to meet the previous 1-

hour ozone standard. Figure 8 shows the decline in both the 1-hour and 8-hour design values 

for the 5-state LADCO region over the last 25 years. 

Figure 8 Ozone design value trends in 5-State region

The trend is more dramatic for the higher ozone sites in the 5-state region (see Figure 9). This 

plot shows a pronounced downward trend in the design value since the 2001-2003 period, due, 

in part, to the very low 4th high values in 2004.

 

Figure 9. Trend in ozone design values and 4th high values for higher ozone sites in region

The improvement in ozone concentrations is also seen in the decrease in the number of sites

measuring nonattainment over the past 15 years in the Lake Michigan area (see Figure 10).

75

85

95

105

'95-'97 '96-'98 '97-'99 '98-'00 '99-'01 '00-'02 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

65

75

85

95

105

115

'95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07

Design Values

4

th High Values
12

 

Figure 10. Ozone design value maps for 1995-1997, 2000-2002, and 2005-2007
13

Given the effect of meteorology on ambient ozone levels, year-to-year variations in meteorology 

can make it difficult to assess trends in ozone air quality. Two approaches were considered to 

adjust ozone trends for meteorological influences: an air quality-meteorology statistical model 

developed by EPA (i.e., Cox method), and statistical grouping of meteorological variables 

performed by LADCO (i.e., Classification and Regression Trees, or CART).

Cox Method: This method uses a statistical model to ‘remove’ the annual effect of meteorology 

on ozone (Cox and Chu, 1993). A regression model was fit to the 1997-2007 data to relate daily 

peak 8-hour ozone concentrations to six daily meteorological variables plus seasonal and 

annual factors (Kenski, 2008a). Meteorological variables included were daily maximum 

temperature, mid-day average relative humidity, morning and afternoon wind speed and wind 

direction. The model is then used to predict 4th high ozone values. By holding the 

meteorological effects constant, the long term trend can be examined independently of 

meteorology. Presumably, any trend reflects changes in emissions of ozone precursors. 

Figure 11a shows the meteorologically-adjusted 4th high ozone concentrations for several 

monitors near major urban areas in the region. The plots indicate a general downward trend 

since the late 1990s for most cities, indicating that recent emission reductions have had a 

positive effect in improving ozone air quality. 

A similar model was run to examine meteorologically adjusted trends in seasonal average 

ozone. This model incorporates more meteorological variables, including rain and long-distance 

transport (direction and distance). Model development was documented in Camalier et al., 

2007. The seasonal average trends are shown in Figure 11b. Trends determined by seasonal

model for the same set of sites examined above are consistent with those developed by the 4

th

high model.
14

Chiwaukee, WI Sheboygan, WI

Cleveland (Ashtabula), OH Cincinnati (Sycamore), OH

Detroit (New Haven), MI St. Louis, MO

Indianapolis, IN

Figure 11a. Trends in meteorologically 

adjusted 4th high 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)
15

Chiwaukee, WI Sheboygan, WI

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Cleveland (Ashtabula), OH Cincinnati (Sycamore), OH

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Detroit (New Haven), MI St. Louis, MO

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Indianapolis, IN

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Figure 11b. Trends in seasonal 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)
16

CART: Classification and Regression Tree (CART) analysis is another statistical technique 

which partitions data sets into similar groups (Breiman et al., 1984). CART analysis was 

performed using data for the period 1995-2007 for 22 selected ozone monitors with current 8-

hour design values close to or above the standard (Kenski, 2008b). The CART model searches 

through 60 meteorological variables to determine which are most efficient in predicting ozone. 

Although the exact selection of predictive variables changes from site to site, the most common 

predictors were temperature, wind direction, and relative humidity. Only occasionally were

upper air variables, transport time or distance, lake breeze, or other variables significant. (Note, 

the ozone and meteorological data for the CART analysis are the same as used in the EPA/Cox 

analysis.)

For each monitor, regression trees were developed that classify each summer day (MaySeptember) by its meteorological conditions. Similar days are assigned to nodes, which are 

equivalent to branches of the regression tree. Ozone time series for the higher concentration

nodes are plotted for select sites in Figure 12. By grouping days with similar meteorology, the 

influence of meteorological variability on the trend in ozone concentrations is partially removed; 

the remaining trend is presumed to be due to trends in precursor emissions or other nonmeteorological influences. Trends over the 13-year period at most sites were found to be 

declining, with the exception of Detroit which showed fairly flat trends. Comparison of the 

average of the high concentration node values for 2001-2003 v. 2005-2007 showed an 

improvement of about 5 ppb across all sites (even Detroit).

The effect of meteorology was further examined by using an ozone conduciveness index

(Kenski, 2008b). This metric reflects the variability from the 13-year average in the number of 

days in the higher ozone concentration nodes (see Figure 13). Examination of these plots 

indicates:

• 2002 and 2005 were both above normal, with 2002 tending to be more severe; and

• 2001-2003 and 2005-2007 were both above normal, with no clear pattern in which 

period was more severe (i.e., ozone conduciveness values were similar at most sites, 

2001-2003 values were higher at a few sites, and 2005-2007 values were higher at a 

few sites).

Given the similarity in ozone conduciveness between 2001-2003 and 2005-2007, the 

improvement in ozone levels noted above is presumed to be due to non-meteorological factors 

(i.e., emission reductions).

In conclusion, all three statistical approaches (CART and the two nonlinear regression models) 

show a similar result; ozone in the urban areas of the LADCO region has declined during the 

1997-2007 period, even when meteorological variability is accounted for. The decreases are 

present whether seasonal average ozone, peak values (annual 4th highs), or a subset of high 

days with similar meteorology are considered. The consistency in results across models is a 

good indication that these trends reflect impacts of emission control programs.
17

Chiwaukee, WI Sheboygan, WI

Cleveland (Ashtabula), OH Cincinnati (Sycamore), OH

Detroit (New Haven), MI St. Louis, MO

Indianapolis, IN

Figure 12. Trends for higher ozone CART 

groups (average ozone > 65 ppb) for seven 

Midwestern sites (1995 – 2007)

Note: line represents linear best fit
18

Figure 13. Ozone conduciveness index (and 

number of high ozone days) for seven 

Midwestern site (1995 – 2007)
19

Precursor Sensitivity: Ozone is formed from the reactions of hydrocarbons and nitrogen oxides 

under meteorological conditions that are conducive to such reactions (i.e., warm temperatures 

and strong sunlight). In areas with high VOC/NOx ratios, typical of rural environments (with low 

NOx), ozone tends to be more responsive to reductions in NOx. Conversely, in areas with low 

VOC/NOx ratios, typical of urban environments (with high NOx), ozone tends to be more 

responsive to VOC reductions. 

An analysis of VOC and NOx-limitation was conducted with the ozone MAPPER program, which 

is based on the Smog Production (SP) algorithm (Blanchard, et al., 2003). The “Extent of 

Reaction” parameter in the SP algorithm provides an indication of VOC and NOx sensitivity:

Extent Range Precursor Sensitivity

< 0.6 VOC-sensitive

0.6 – 0.8 Transitional

> 0.8 NOx-sensitive

A map of the Extent of Reaction values for high ozone days is provided in Figure 14. As can be 

seen, ozone is usually VOC-limited in cities and NOx-limited in rural areas. (Data from aircraft 

measurements suggest that ozone is usually NOx-limited over Lake Michigan and away from 

urban centers on days when ozone in the urban centers is VOC-limited.) The highest ozone 

days were found to be NOx-limited. This analysis suggests that a NOx reduction strategy would 

be effective in reducing ozone levels. Examination of day-of-week concentrations, however,

raises some question about the effectiveness of NOx reductions.

Figure 14. Mean afternoon extent of reaction (1998 – 2002)
20

Blanchard (2004 and 2005a) examined weekend-weekday differences in ozone and NOx in the 

Midwest. All urban areas in these two studies exhibited substantially lower (40-60%) weekend 

concentrations of NOx compared to weekday concentrations. Despite lower weekend NOx

concentrations, weekend ozone concentrations were not lower; in fact, most urban sites had 

higher concentrations of ozone, although the increase was generally not statistically significant 

(see Figure 15). This small but counterproductive change in local ozone concentrations 

suggests that local urban-scale NOx reductions alone may not be very effective. 

Figure 15. Weekday/weekend differences in 8-hour ozone – number of sites with weekend 

increase (positive values) v. number of sites with weekend decreases (negative values)

Two additional analyses, however, demonstrate the positive effect of NOx emission reductions 

on downwind ozone concentrations. First, Blanchard (2005a) looked at the effect of changes in 

precursor emissions in Chicago on downwind ozone levels in western Michigan. For the 

transport days of interest (i.e., southwesterly flow during the summers of 1999 – 2002), mean 

NOx concentrations in Chicago are about 50% lower and mean ozone concentrations at the 

(downwind) western Michigan sites are about 1.5 – 5.2 ppb (3 – 8 %) lower on Sunday 

compared to Wednesday. This degree of change in downwind ozone levels suggests a 

positive, albeit non-linear response to urban area emission reductions.

Second, Environ (2007a) examined the effect of differences in day-of-week emissions in 

southeastern Michigan on downwind ozone levels. This modeling study found that weekend 

changes in ozone precursor emissions cause both increases and decreases in Southeast 

Michigan ozone, depending upon location and time:
21

• Weekend increases in 8-hour maximum ozone occur in and immediately downwind of 

the Detroit urban area (i.e., in VOC-sensitive areas).

• Weekend decreases in 8-hour maximum ozone occur outside and downwind of the 

Detroit urban area (i.e., in NOx-sensitive areas).

• At the location of the peak 8-hour ozone downwind of Detroit, ozone was lower on 

weekends than weekdays.

• Ozone benefits (reductions) due to weekend emission changes in Southeast Michigan 

can be transported downwind for hundreds of miles.

• Southeast Michigan benefits from lower ozone transported into the region on Saturday

through Monday because of weekend emission changes in upwind areas.

In summary, these analyses suggest that urban VOC reductions and regional (urban and rural) 

NOx reductions will be effective in lowering ozone concentrations. Local NOx reductions can 

lead to local ozone increases (i.e., NOx disbenefits), but this effect does not appear to pose a 

problem with respect to attainment of the standard. It should also be noted that urban VOC and 

regional NOx reductions are likely to have multi-pollutant benefits (e.g., both lower ozone and 

PM2.5 impacts).

2.2 PM2.5

In 1997, EPA adopted the PM2.5 standards of 15 ug/m

3

 (annual average) and 65 ug/m3 (24-hour 

average). The annual standard is attained if the 3-year average of the annual average PM2.5

concentration is less than or equal to the level of the standard. The daily standard is attained if 

the 98th percentile of 24-hour PM2.5 concentrations in a year, averaged over three years, is less 

than or equal to the level of the standard.

In 2006, EPA revised the PM2.5 standards to 15 ug/m3 (annual average) and 35 ug/m3 (24-hour 

average). 

Current Conditions: Maps of annual and 24-hour PM2.5 design values for the 3-year period 

2005-2007 are shown in Figure 16. The “hotter” colors represent higher concentrations, where 

red dots represent sites with design values above the annual standard. Currently, there are 30

sites in violation of the annual PM2.5 standard.

Table 2 provides the annual PM2.5 concentrations and associated design values since 2003 for 

several high monitoring sites throughout the region.
22

Figure 16. PM2.5 design values - annual average (top) and 24-hour average (bottom) (2005-2007)
2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07

Average 

w/ 2007

Average

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6

Indy - Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2

Indy - W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0

Indy - Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.0 15.3 17.0 16.2 16.2 16.5 16.7

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.4 13.6 14.7 15.4 14.9 14.9 15.1 16.0

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 15.9 16.2 16.3

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.4 15.2 15.7

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6

Annual Average Conc. Design Values

Table 2. PM2.5 Data for Select Sites in 5-State Region
24

When EPA initially set the 24-hour standard at 65 μg/m3, it also adopted the following 

concentration ranges for its Air Quality Index (AQI) scale:

Good < 15 ug/m3

Moderate 15-40 μg/m3

Unhealthy for Sensitive Groups (USG) 40-65 μg/m3

Unhealthy 65-150 μg/m3

Figure 17 shows the frequency of these AQI categories for major metropolitan areas in the 

region. Daily average concentrations are often in the moderate range and occasionally in the 

USG range. Moderate and USG levels can occur any time of the year. 

Figure 17. Percent of days in AQI categories for PM2.5 (2002-2004)

Data Variability: PM2.5 concentrations vary spatially, temporally, and chemically in the region. 

This variability is discussed further below.

On an annual basis, PM2.5 exhibits a distinct and consistent spatial pattern. As seen in Figure 

16, across the Midwest, annual concentrations follow a gradient from low values (5-6 μg/m3) in 

northern and western areas (Minnesota and northern Wisconsin) to high values (17-18 μg/m3) in 

Ohio and along the Ohio River. In addition, concentrations in urban areas are higher than in 

upwind rural areas, indicating that local urban sources add a significant increment of 2-3 μg/m3

to the regional background of 12 - 14 μg/m3 (see Figure 18). 

Figure 18. Regional (lighter shading) v. local components (darker shading) of annual average PM2.5 

concentrations
25

Because monitoring for PM2.5 only began in earnest in 1999, after promulgation of the PM2.5

standard, limited data are available to assess trends. Time series based on federal reference 

method (FRM) PM2.5-mass data show a downward trend in each state (see Figure 19)

7

.

Figure 19. PM2.5 trends in annual average (top) and daily concentrations (bottom)

 

7

 Despite the general downward trend since 1999, all states experienced an increase during 2005. 

Further analyses are underway to understand this increase (e.g., examination of meteorological and 

emissions effects).
26

A statistical analysis of PM2.5 trends was performed using the nonparametric Theil test for slope 

(Hollander and Wolfe, 1973). Trends were generally consistent around the region, for both PM 

mass and for the individual components of mass. Figure 20 shows trends for PM2.5 based on 

FRM data at sites with six or more years of data since 1999. The size and direction of each 

arrow shows the size and direction of the trend for each site; solid arrows show statistically 

significant trends and open arrows show trends that are not significant. Region-wide decreases 

are widespread and consistent; all sites had decreasing concentration trends (13 of the 38 were 

statistically significant). The average decrease for this set of sites is -0.24 ug/m3/year. 

Figure 20. Annual trends in PM2.5 mass (1999 – 2006)

Seasonal trends show mostly similar patterns (Figure 21). Trends were downward at most sites 

and seasons, with overall seasonal averages varying between -0.15 to -0.56 ug/m3/year. The 

strongest and most significant decreases took place during the winter quarter (January - March). 

No statistically significant increasing trends were observed.
27

Figure 21. Seasonal trends in PM2.5 mass (1999 – 2006)

PM2.5 shows a slight variation from weekday to weekend, as seen in Figure 22. Although most 

cities have slightly lower concentrations on the weekend, the difference is usually less than 1 

μg/m3. There is a more pronounced weekday/weekend difference at monitoring sites that are 

strongly source-influenced. Rural monitors tend to show less of a weekday/weekend pattern 

than urban monitors.

Figure 22 Day-of-week variability in PM2.5 (2002-2004)
28

In the Midwest, PM2.5 is made up of mostly ammonium sulfate, ammonium nitrate, and organic 

carbon in approximately equal proportions on an annual average basis. Elemental carbon and 

crustal matter (also referred to as soil) contribute less than 5% each. 

Figure 23. Spatial map of PM2.5 chemical composition in the Midwest (2002-2003)

The three major components vary spatially (Figure 23), including notable urban and rural 

differences (Figure 24). The components also vary seasonally (Figure 25). These patterns 

account for much of the annual variability in PM2.5 mass noted above.

Figure 24. Average regional (lighter shading) v. local (darker shading) of PM2.5 chemical species
29

Figure 25 Seasonal and spatial variability in PM2.5 components
30

Ammonium sulfate peaks in the summer and is highest in the southern and eastern parts of the 

Midwest, closest to the Ohio River Valley. Sulfate is primarily a regional pollutant; 

concentrations are similar in rural and urban areas and highly correlated over large distances. It 

is formed when sulfuric acid (an oxidation product of sulfur dioxide) and ammonia react in the 

atmosphere, especially in cloud droplets. Coal combustion is the primary source of sulfur 

dioxide; ammonia is emitted primarily from animal husbandry operations and fertilizer use.

Ammonium nitrate has almost the opposite spatial and seasonal pattern, with the highest 

concentrations occurring in the winter and in the northern parts of the region. Nitrate seems to 

have both regional and local sources, because urban concentrations are higher than rural 

upwind concentrations. Ammonium nitrate forms when nitric acid reacts with ammonia, a 

process that is enhanced when temperatures are low and humidity is high. Nitric acid is a 

product of the oxidation of nitric oxide, a pollutant that is emitted by combustion processes.

Organic carbon is more consistent from season to season and city to city, although 

concentrations are generally slightly higher in the summer. Like nitrate, organic carbon has 

both regional and local components. Particulate organic carbon can be emitted directly from 

cars and other fuel combustion sources or formed in a secondary process as volatile organic 

gases react and condense. In rural areas, summer organic carbon has significant contributions 

from biogenic sources.

Precursor Sensitivity: Data from the Midwest ammonia monitoring network were analyzed with 

thermodynamic equilibrium models to assess the effect of changes in precursor gas 

concentrations on PM2.5 concentrations (Blanchard, 2005b). These analyses indicate that 

particle formation responds in varying degrees to reductions in sulfate, nitric acid, and ammonia. 

Based on Figure 26, which shows PM2.5 concentrations as a function of sulfate, nitric acid 

(HNO3), and ammonia (NH3), several key findings should be noted: 

• PM2.5 mass is sensitive to reductions in sulfate at all times of the year and all parts of the 

region. Even though sulfate reductions cause more ammonia to be available to form 

ammonium nitrate (PM-nitrate increases slightly when sulfate is reduced), this increase 

is generally offset by the sulfate reductions, such that PM2.5 mass decreases.

• PM2.5 mass is also sensitive to reductions in nitric acid and ammonia. The greatest PM2.5

decrease in response to nitric acid reductions occurs during the winter, when nitrate is a

significant fraction of PM2.5.

• Under conditions with lower sulfate levels (i.e., proxy of future year conditions), PM2.5 is 

more sensitive to reductions in nitric acid compared to reductions in ammonia.

• Ammonia becomes more limiting as one moves from west to east across the region.

Examination of weekend/weekday difference in PM-nitrate and NOx concentrations in the 

Midwest demonstrate that reductions in local (urban) NOx lead to reductions, albeit nonproportional reductions, in PM-nitrate (Blanchard, 2004). This result is consistent with analyses 

of continuous PM-nitrate from several US cities, including St. Louis (Millstein, et al, 2007). 
31

Figure 26. Predicted mean PM fine mass concentrations at Bondville, IL (top) and Detroit (Allen Park), MI 

(bottom) as functions of changes in sulfate, nitric acid (HNO3), and ammonia (NH3)

Note: starting at the baseline values (represented by the red star), either moving downward (reductions in nitric 

acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM2.5 values
32

Meteorology: PM2.5 concentrations are not as strongly influenced by meteorology as ozone, but 

the two pollutants share some similar meteorological dependencies. In the summer, conditions 

that are conducive to ozone (hot temperatures, stagnant air masses, and low wind speeds due 

to stationary high pressure systems) also frequently give rise to high PM2.5. In the case of PM,

the reason is two-fold: (1) stagnation and limited mixing under these conditions cause PM2.5 to 

build up, usually over several days, and (2) these conditions generally promote higher 

conversion of important precursors (SO2 to SO4) and higher emissions of some precursors, 

especially biogenic carbon. Wind direction is another strong determinant of PM2.5; air 

transported from polluted source regions has higher concentrations.

Unlike ozone, PM2.5 has occasional winter episodes. Conditions are similar to those for summer 

episodes, in that stationary high pressure and (seasonally) warm temperatures are usually 

factors. Winter episodes are also fueled by high humidity and low mixing heights. 

PM2.5 chemical species show noticeable transport influences. Trajectory analyses have 

demonstrated that high PM-sulfate is associated with air masses that traveled through the 

sulfate-rich Ohio River Valley (Poirot, et al, 2002 and Kenski, 2004). Likewise, high PM-nitrate 

is associated with air masses that traveled through the ammonia-rich Midwest. Figure 27

shows results from an ensemble trajectory analysis of 17 rural eastern IMPROVE sites. 

Figure 27. Sulfate and nitrate source regions based on ensemble trajectory analysis

When these results are considered together with analyses of precursor sensitivity (e.g., Figure 

26), one possible conclusion is that ammonia control in the Midwest could be effective at 

reducing nitrate concentrations. The thermodynamic equilibrium modeling shows that ammonia 

reductions would reduce PM concentrations in the Midwest, but that nitric acid reductions are 

more effective when the probable reductions in future sulfate levels are considered. 
33

Source Culpability: Three source apportionment studies were performed using speciated PM2.5

monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 2008). 

Figure 28 summarizes the source contributions from these studies. The studies show that a

large portion of PM2.5 mass consists of secondary, regional impacts, which cannot be attributed 

to individual facilities or sources (e.g., secondary sulfate, secondary nitrate, and secondary 

organic aerosols). Nevertheless, wind analyses (e.g., Figure 27) provide information on likely 

source regions. Regional- or national-scale control programs may be the most effective way to 

deal with these impacts. EPA's CAIR, for example, will provide for substantial reductions in 

SO2 emissions over the eastern half of the U.S., which will reduce sulfate (and PM2.5) 

concentrations and improve visibility levels.

The studies also show that a smaller, yet significant portion of PM2.5 mass is due to emissions 

from nearby (local) sources. Local (urban) excesses occur in many urban areas for organic and 

elemental carbon, crustal matter, and, in some cases, sulfate. The statistical analysis methods 

help to identify local sources and quantify their impact. This information is valuable to states 

wishing to develop control programs to address local impacts. A combination of 

national/regional-scale and local-scale emission reductions may be necessary to provide for 

attainment.

The carbon sources are not easily identified in complex urban environments. LADCO’s Urban 

Organics Study (STI, 2006) identified four major sources of organic carbon: mobile sources, 

burning, industrial sources, and secondary organic aerosols. Additional sampling and analysis 

is underway in Cleveland and Detroit to provide further information on sources of organic 

carbon.
34

0

1

2

3

4

5

6

7

Coal

Combustion

Secondary

Nitrate

Mobile

Industrial

Soil

Burning

ug/m3

Northbrook (Jan 03-Mar 05)

Cincinnati (Dec 03-Mar 05)

Indianapolis (Jan 02-Mar 05)

Allen Park (2002-2004)

Dearborn (Jun 02-May 05)

Bondville (2002-2004)

St. Louis (2004)

Granite City (2002-2004)

Tikhon (Jan 03-Jun05)

Orange (Apr 01-Mar 05)

Lorain (Feb 02-Dec04)

 

0

2

4

6

8

10

sulfate

nitrate

OM

diesel

soil

steel

ind. zinc

ind. copper

mixed ind.

Allen Park

Dearborn

Law ndale

Springfield

Tikhon

Craig

Taft

Figure 28. Major Source Contributions in the Midwest based on Hopke, 2005 (upper left), STI, 2006 (upper right), and STI, 2008 (lower left)

(Note: the labeling of similar source types varies between studies – e.g., organic carbon/mobile sources are named gasoline and diesel by 

Hopke, mobile by STI 2006, and OM and diesel by STI 2008)
35

2.3 Haze

Section 169A of the Clean Air Act sets as a national goal “the prevention of any future, and the 

remedying of any existing, impairment of visibility in mandatory Class I Federal areas which 

impairment results from manmade air pollution”. To implement this provision, in 1999, EPA 

adopted regulations to address regional haze visibility impairment (USEPA, 1999). EPA’s rule 

requires states to “make reasonable progress toward meeting the national goal”. Specifically, 

states must establish reasonable progress goals, which provide for improved visibility on the 

most impaired (20% worst) days sufficient to achieve natural conditions by the year 2064, and 

for no degradation on the least impaired (20% best) days.

The primary cause of impaired visibility in the Class I areas is pollution by fine particles that 

scatter light. The degree of impairment, which is expressed in terms of visual range, light 

extinction (1/Mm), or deciviews (dv), depends not just on the total PM2.5 mass concentration, but 

also on the chemical composition of the particles and meteorological conditions.

Current Conditions: A map of the average light extinction values for the most impaired (20% 

worst) visibility days for the 5-year baseline period (2000-2004) is shown in Figure 29. 

Figure 29. Baseline Visibility Levels for 20% Worst Days (2000 – 2004), units: Mm-1
36

Initially, the baseline (2000 – 2004) visibility condition values were derived using the average for 

the 20% worst and 20% best days for each year, as reported on the VIEWS website: 

http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx . These values were 

calculated using the original IMPROVE equation for reconstructed light extinction.

Three changes were made to the baseline calculations to produce a new set of values. First, 

the reconstructed light extinction equation was revised by the IMPROVE Steering Committee in 

2005. The new IMPROVE equation was used to calculate updated baseline values. 

Second, due to sampler problems, the 2002-2004 data for Boundary Waters were invalid for 

certain chemical species. (Note, sulfate and nitrate data were valid.) A “substituted” data set 

was developed by using values from Voyageurs for the invalid species.

Third, LADCO identified a number of days during 2000-2004 where data capture at the Class I

monitors was incomplete (Kenski, 2007b). The missing data cause these days to be excluded 

from the baseline calculations. However, the light extinction due to the remaining measured 

species is significant (i.e., above the 80th percentile). It makes sense to include these days in 

the baseline calculations, because they are largely dominated by anthropogenic sources. (Only 

one of these days is driven by high organic carbon, which might indicate non-anthropogenic 

aerosol from wildfires.) As seen in Table 3, inclusion of these days in the baseline calculation

results in a small, but measurable, effect on the baseline values (i.e., values increase from 0.2 

to 0.8 dv).

Table 3. Average of 20% worst days, with and without missing data days

Average Worst Day 

DV, per RHR

Average Worst Day DV,

with Missing Data Days

Difference

BOWA 19.59 19.86 0.27

ISLE 20.74 21.59 0.85

SENE 24.16 24.38 0.22

VOYA 19.27 19.48 0.21

A summary of the initial and updated baseline values for the Class I areas in northern Michigan 

and northern Minnesota are presented in Table 4. The updated baseline values reflect the most 

current, complete understanding of visibility impairing effects and, as such, will be used for SIP 

planning purposes.
37

Table 4. Summary of visibility metrics (deciviews) for northern Class I areas

Old IMPROVE Equation (Cite: VIEWS, November 2005)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural 

Conditions

Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.09

BWCA 19.85 19.99 19.68 19.73 17.65 19.38 17.47 11.21

Isle Royale 20.00 22.00 20.80 19.50 19.10 20.28 18.17 11.22

Seney 22.60 24.90 24.00 23.80 22.60 23.58 20.73 11.37

20% Best Days

2000 2001 2002 2003 2004

Baseline

Value

Natural

Conditions

Voyageurs 6.30 6.20 6.70 7.00 5.40 6.32 3.41

BWCA 5.90 6.52 6.93 6.67 5.61 6.33 3.53

Isle Royale 5.70 6.40 6.40 6.30 5.30 6.02 3.54

Seney 5.80 6.10 7.30 7.50 5.80 6.50 3.69

New IMPROVE Equation (Cite: VIEWS, March 2006)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural

Conditions

Voyageurs 19.55 18.57 20.14 20.25 18.87 19.48 17.74 12.05

BWCA 20.20 20.04 20.76 20.13 18.18 19.86 17.94 11.61

Isle Royale 20.53 23.07 21.97 22.35 20.02 21.59 19.43 12.36

Seney 22.94 25.91 25.38 24.48 23.15 24.37 21.64 12.65

20% Best Days

2000 2001 2002 2003 2004

Baseline

Value

Natural

Conditions

Voyageurs 7.01 7.12 7.53 7.68 6.37 7.14 4.26

BWCA 6.00 6.92 7.00 6.45 5.77 6.43 3.42

Isle Royale 6.49 7.16 7.07 6.99 6.12 6.77 3.72

Seney 6.50 6.78 7.82 8.01 6.58 7.14 3.73

Notes: (1) BWCA values for 2002 - 2004 reflect "substituted" data.

 (2) New IMPROVE equation values include Kenski, 2007 adjustment for missing days

 URI = uniform rate of improvement
38

As noted above, the goal of the visibility program is to achieve natural conditions. Initially, the 

natural conditions values for each Class I area were taken directly from EPA guidance (EPA, 

2003). These values were calculated using the original IMPROVE equation. This equation was 

revised by the IMPROVE Steering Committee in 2005, and the new IMPROVE equation was 

used to calculate updated natural conditions values. The updated values are reported on the 

VIEWS website.

A summary of the initial and updated natural conditions values are presented in Table 4. The 

updated natural conditions values (based on the new IMPROVE equation) will be used for SIP 

planning purposes.

Data Variability: For the four northern Class I areas, the most important PM2.5 chemical species 

are ammonium sulfate, ammonium nitrate, and organic carbon. The contribution of these 

species on the 20% best and 20% worst visibility days (based on 2000 – 2004 data) is provided 

in Figure 30. For the 20% worst visibility days, the contributions are: sulfate = 35-55%, nitrate = 

25-30%, and organic carbon = 12-22%. Although the chemical composition is similar, sulfate 

increases in importance from west to east and concentrations are highest at Seney (the 

easternmost site). It should also be noted that sulfate and nitrate contribute more to light 

extinction than to PM2.5 mass because of their hygroscopic properties.

20% Best Days 20% Worst Days 

0

20

40

60

80

100

120

VOYA BOWA ISRO Seney VOYA BOWA ISRO Seney

sea salt

soil

elem. carbon

coarse mass

organic carbon

nitrate

sulfate

Figure 30. Chemical composition of light extinction for 20% best visibility days (left) and 20% 

worst visibility days (right) in terms of Mm-1

Analysis of PM2.5 mass and chemical species for rural IMPROVE (and IMPROVE-protocol) sites 

in the eastern U.S. showed a high degree of correlation between PM2.5-mass, sulfate, and 

nitrate levels (see Figure 31). The Class I sites in northern Michigan and northern Minnesota, in 

particular, are highly correlated for PM2.5 mass, sulfates, and organic carbon mass (AER, 2004).
39

Figure 31. Correlations among IMPROVE (and IMPROVE-protocol) monitoring sites in Eastern U.S.

Long-term trends at Boundary Waters (the only regional site with a sufficient data record) show 

significant decreases in total PM2.5 (-0.005 ug/year) and SO4 (-0.04 ug/year) and an increase in 

NO3 (+0.01 ug/year). These PM2.5 and SO4 trends are generally consistent with long-term 

trends at other IMPROVE sites in the eastern U.S., which have shown widespread decreases in 

SO4 and PM2.5 (DeBell, et al, 2006). Detecting changes in nitrate has been hampered by 

uncertainties in the IMPROVE data for particular years and, thus, this estimate should be 

considered tentative. 

Haze in the Midwest Class I areas has no strong seasonal pattern. Poor visibility days occur 

throughout the year, as indicated in Figure 32. (Note, in contrast, other parts of the country, 

such as Shenandoah National Park in Virginia, show a strong tendency for the worst air quality 

days to occur in the summer months.) This figure and Figure 33 (which presents the monthly 

average light extinction values based on all sampling days) also show that sulfate and organic 

carbon concentrations are higher in the summer, and nitrate concentrations are higher in the 

winter, suggesting the importance of different sources and meteorological conditions at different 

times of the year.
40

Figure 32. Daily light extinction values for 20% worst days at Boundary Waters (2000 – 2004)
41

Figure 33. Monthly average light extinction values for northern Class I areas
42

Precursor Sensitivity: Results from two analyses using thermodynamic equilibrium models 

provide information on the effect of changes in precursor concentrations on PM2.5

concentrations (and, in turn, visibility levels) in the northern Class I areas. First, a preliminary 

analysis using data collected at Seney indicated that PM2.5 there is most sensitive to reductions 

in sulfate, but is also sensitive to reductions in nitric acid (Blanchard, 2004). 

Second, an analysis was performed using data from the Midwest ammonia monitoring network

for a site in Minnesota -- Great River Bluffs, which is the closest ammonia monitoring site to the 

northern Class I areas (Blanchard, 2005b). Figure 34 shows PM2.5 concentrations as a function 

of sulfate, nitric acid (HNO3), and ammonia (NH3). Reductions in sulfate (i.e., movement to the 

left of baseline value [represented by the red star]), as well as reductions in nitric acid (i.e., 

movement downward) and NH3 (i.e., movement to the left), result in lower PM2.5 concentrations. 

Thus, reductions in sulfate, nitric acid, and ammonia will lower PM2.5 concentrations and 

improve visibility in the northern Class I areas.

Figure 34. Predicted PM2.5 mass concentrations at Great River Bluffs, MN as functions of changes 

in sulfate, nitric acid, and ammonia

Meteorology and Transport: The role of meteorology in haze is complex. Wind speed and wind 

direction govern the movement of air masses from polluted areas to the cleaner wilderness 

areas. As noted above, increasing humidity increases the efficiency with which sulfate and 

nitrate aerosols scatter light. Temperature and humidity together govern whether ammonium 

nitrate can form from its precursor gases, nitric acid and ammonia. Temperature and sunlight 

also play an indirect role in emissions of biogenic organic species that condense to form 

particulate organic matter; emissions increase in the summer daylight hours. 

Trajectory analyses were performed to understand transport patterns for the 20% worst and 

20% best visibility days. The composite results for the four northern Class I areas are provided 

in Figure 35. The orange areas are where the air is most likely to come from, and the green 

areas are where the air is least likely to come from. As can be seen, bad air days are generally 

associated with transport from regions located to the south, and good air days with transport 

from Canada. 
43

Figure 35. Composite back trajectories for light extinction- 20% best visibility days (left) and 

20% worst visibility days (right) (2000 – 2005)

Source Culpability: Air quality data analyses (including the trajectory analyses above) and 

dispersion modeling were used to provide information on source region and source sector 

contributions to regional haze in the northern Class I areas (see MRPO, 2008). Based on this 

information, the most important contributing states are Michigan, Minnesota, and Wisconsin, as 

well as Missouri, North Dakota, Iowa, Indiana and Illinois (see, for example, Figure 35 above). 

The most important contributing pollutants and source sectors are SO2 emissions from 

electrical generating units (EGUs) and certain non-EGUs, which lead to sulfate formation, and 

NOx emissions from a variety of source types (e.g., motor vehicles), which lead to nitrate 

formation. Ammonia emissions from livestock waste and fertilizer applications are also 

important, especially for nitrate formation.

A source apportionment study was performed using monitoring data from Boundary Waters and 

statistical analysis methods (DRI, 2005). The study shows that a large portion of PM2.5 mass 

consists of secondary, regional impacts, which cannot be attributed to individual facilities or 

sources (e.g., secondary sulfate, secondary nitrate, and secondary organic aerosols). Industrial 

sources contribute about 3-4% and mobile sources about 4-7% to PM2.5 mass. 

A special study was performed in Seney to identify sources of organic carbon (Sheesley, et al, 

2004). As seen in Figure 36, the highest PM2.5 concentrations occurred during the summer, 

with organic carbon being the dominant species. The higher summer organic carbon 

concentrations were attributed mostly to secondary organic aerosols of biogenic origin because 

of the lack of primary emission markers, and concentrations of know biogenic-related species 

(e.g., pinonic acid – see Figure 36) were also high during the summer.
44

Figure 36. Monthly concentrations of PM2.5 species (top), and secondary and biogenic-related 

organic carbon species in Seney (bottom)
45

Although the Seney study showed that biomass burning was a relatively small contributor to 

organic carbon on an annual average basis, episodic impacts are apparent (see, for example, 

high organic carbon days in Figure 32). To assess further whether burning is a significant 

contributor to visibility impairment in the northern Class I areas, the PM2.5 chemical speciation 

data were examined for days with high organic carbon and elemental carbon concentrations, 

which are indicative of biomass burning impacts. Only a handful of such days were identified:

Table 5. Days with high OC and EC concentrations in northern Class I areas

Site 2000 2001 2002 2003 2004

Voyageurs --- --- Jun 1 Aug 25 Jul 17

Jun 28

Jul 19

Boundary Waters --- --- Jun 28 Aug 25 Jul 17

Jul 19

Isle Royale --- --- Jun 1 Aug 25 ---

Jun 28

Seney --- --- Jun 28 --- ---

Back trajectories on these days point mostly to wildfires in Canada. Elimination of these high 

organic carbon concentration days has a small effect in lowering the baseline visibility levels in 

the northern Class I areas (i.e., Minnesota Class I areas change by about 0.3 deciviews and 

Michigan Class I areas change by less than 0.2 deciviews). This suggests that fire activity, 

although significant on a few days, is on average a relatively small contributor to visibility 

impairment in the northern Class I areas.

In summary, these analyses show that organic carbon in the northern Class I is largely 

uncontrollable.
46

Section 3.0 Air Quality Modeling

Air quality models are relied on by federal and state regulatory agencies to support their 

planning efforts. Used properly, models can assist policy makers in deciding which control 

programs are most effective in improving air quality, and meeting specific goals and objectives. 

For example, models can be used to conduct “what if” analyses, which provide information for 

policy makers on the effectiveness of candidate control programs.

The modeling analyses were conducted in accordance with EPA’s modeling guidelines (EPA, 

2007a). Further details of the modeling are provided in two protocol documents: LADCO, 2007a 

and LADCO, 2007b. 

This section reviews the development and evaluation of the modeling system used for the multipollutant analyses. Application of the modeling system (i.e., attainment demonstration for ozone 

and PM2.5, and reasonable progress assessment for haze) is covered in the following sections.

3.1 Selection of Base Year

Two base years were used in the modeling analyses: 2002 and 2005. EPA’s modeling 

guidance recommends using 2002 as the baseline inventory year, but also allows for use of an 

alternative baseline inventory year, especially a more recent year. Initially, LADCO conducted 

modeling with a 2002 base year (i.e., Base K/Round 4 modeling, which was completed in 2006). 

A decision was subsequently made to conduct modeling with a 2005 base year (i.e., Base 

M/Round 5, which was completed in 2007). As discussed in the previous section, 2002 and 

2005 both had above normal ozone conducive conditions, although 2002 was more severe 

compared to 2005. Examination of multiple base years provides for a more complete technical 

assessment. Both sets of model runs are discussed in this document. 

3.2 Future Years of Interest

To address the multiple attainment requirements for ozone and PM2.5, and reasonable progress 

goals for regional haze, several future years are of interest:

2008 Planning year for ozone basic nonattainment areas (attainment date 2009)8

2009 Planning year for ozone moderate nonattainment areas and PM2.5 nonattainment 

areas (attainment date 2010)

2012 Planning year for ozone moderate nonattainment areas and PM2.5 nonattainment 

areas, with 3-year extension (attainment date 2013)

2018 First milestone year for regional haze planning

 

8

According to USEPA’s ozone implementation rule (USEPA, 2005), emission reductions needed for 

attainment must be implemented by the beginning of the ozone season immediately preceding the area’s 

attainment date. The PM2.5 implementation rule contains similar provisions – i.e., emission reductions 

should be in place by the beginning of the year preceding the attainment date (USEPA, 2007c). The logic 

for requiring emissions reductions by the year (or season) immediately preceding the attainment year 

follows from language in the Clean Air Act, and the ability for an area to receive up to two 1-year 

extensions. Therefore, emissions in the year preceding the attainment year should be at a level that is 

consistent with attainment. It also follows that the year preceding the attainment year should be modeled 

for attainment planning purposes.
47

Detailed emissions inventories were developed for 2009 and 2018. To support modeling for 

other future years, less rigorous emissions processing was conducted (e.g., 2012 emissions 

were estimated for several source sectors by interpolating between 2009 and 2018 emissions).

3.3 Modeling System

The air quality analyses were conducted with the CAMx model, with emissions and meteorology 

generated using EMS (and CONCEPT) and MM5, respectively. The selection of CAMx as the 

primary model is based on several factors: performance, operator considerations (e.g., ease of 

application and resource requirements), technical support and documentation, model 

extensions (e.g., 2-way nested grids, process analysis, source apportionment, and plume-ingrid), and model science. CAMx model set-up for Base M and Base K is summarized below:

Base M (2005) Base K (2002)

• CAMx v4.50 * CAMx 4.30

• CB05 gas phase chemistry * CB-IV with updated gas-phase chemistry

• SOA chemistry updates * No SOA chemistry updates

• AERMOD dry deposition scheme * Wesley-based dry deposition

• ISORROPIA inorganic chemistry • ISORROPIA inorganic chemistry

• SOAP organic chemistry • SOAP organic chemistry

• RADM aqueous phase chemistry • RADM aqueous phase chemistry

• PPM horizontal transport • PPM horizontal transport

3.4 Domain/Grid Resolution

The National RPO grid projection was used for this modeling. A subset of the RPO domain was 

used for the LADCO modeling. For PM2.5 and haze, the large eastern U.S. grid at 36 km (see 

box on right side of Figure 36) was used. A PM2.5 sensitivity run was also performed for this 

domain at 12 km. For ozone, the smaller grid at 12 km (see shaded portion of the box on the 

right side of Figure 37) was used for most model runs. An ozone sensitivity run was also 

performed with a 4km sub-grid over the Lake Michigan area and Detroit/Cleveland.

The vertical resolution in the air quality model consists of 16 layers extending up to 15 km, with 

higher resolution in the boundary layer. 

Figure 37. Modeling grids – RPO domain (left) and LADCO modeling domain (right)

12 km

36 km
48

3.5 Model Inputs: Meteorology

Meteorological inputs were derived using the Fifth-Generation NCAR/Penn State Meteorological 

Model (MM5) – version 3.6.3 for the years 2001–2003, and version 3.7 for the year 2005. The 

MM5 modeling domains are consistent with the National RPO grid projections (see Figure 38). 

Figure 38. MM5 modeling domain for 2001-2003 (left) and 2005 (right)

The annual 2002 36 km MM5 simulation was completed by Iowa DNR. The 36/12 km 2-way 

nested simulation for the summers of 2001, 2002, and 2003 were conducted jointly by Illinois 

EPA and LADCO. The 36 km non-summer portion of the annual 2003 simulation was conducted 

by Wisconsin DNR. The annual 2005 36/12 km (and summer season 4 km) MM5 modeling was 

completed by Alpine Geophysics. Wisconsin DNR also completed 36/12 km MM5 runs for the 

summer season of 2005.

Model performance was assessed quantitatively with the METSTAT tool from Environ. The 

metrics used to quantify model performance include mean observation, mean prediction, bias, 

gross error, root mean square error, and index of agreement. Model performance metrics were 

calculated for several sub-regions of the modeling domain (Figure 39) and represent hourly 

spatial averages of multiple monitor locations. Additional analysis of rainfall is done on a 

monthly basis.

Figure 39. Sub-domains used for model performance for 2001-2003 (left) and 2005 (right)

A summary of the performance evaluation results for the meteorological modeling is provided 

below. Further details are provided in two summary reports (LADCO, 2005 and LADCO, 2007c).
49

Temperature: The biggest issue with the performance in the upper Midwest is the existence of a 

cool diurnal temperature bias in the winter and warm temperature bias over night during the 

summer (see Figure 40). These features are common to other annual MM5 simulations for the 

central United States and do not appear to adversely affect model performance. 

Figure 40. Daily temperature bias for 2002 (left) and 2005 (right) with hotter colors 

(yellow/orange/red) representing overestimates and cooler colors (blues) representing 

underestimates

Note: months are represented from left to right (January to December) and days are represented 

from top to bottom (1 to 30(31) – i.e., upper left hand corner is January 1 and lower right hand 

corner is December 31

Wind Fields: The wind fields are generally good. Wind speed bias is less than 0.5 m/sec and 

wind speed error is consistently between 1.0 and 1.5 m/sec. Wind direction error is generally 

within 15-30 degrees.

Mixing Ratio: The mixing ratio (a measure of humidity) is over-predicted in the late spring and 

summer months, and mixing ratio error is highest during this period. There is little bias and 

error during the cooler months when there is less moisture in the air.

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

terms of magnitude in the winter, fall, and early spring months. There are, however, large overpredictions of rainfall in the late spring and summer months (see Figure 41). These overpredictions are seen spatially and in magnitude over the entire domain, particularly in the 

Southeast United States, and are likely due to excessive convective rainfall being predicted in 

MM5. This over-prediction of rainfall in MM5 does not necessarily translate into over-prediction 

of wet deposition in the photochemical model. CAMx does not explicitly use the convective and 

non-convective rainfall output by MM5, but estimates wet scavenging by hydrometeors using 

cloud, ice, snow, and rain water mixing ratios output by MM5. Nevertheless, this could have an 

effect on model performance for PM2.5, as discussed in Section 3.7, and may warrant further 

attention.
50

Figure 41. Comparison of observed (left column) and modeled (right column) monthly rainfall for 

July 2002 (top) and July 2005 (bottom)
51

3.6 Model Inputs: Emissions

Emission inventories were prepared for two base years: 2002 (Base K) and 2005 (Base M), and 

several future years: 2008, 2009, 2012, and 2018. Further details of the emission inventories 

are provided in two summary reports (LADCO, 2006a and LADCO, 2008a) and the following 

pages of the LADCO web site:

http://www.ladco.org/tech/emis/basek/BaseK_Reports.htm

http://www.ladco.org/tech/emis/r5/round5_reports.htm

For on-road, nonroad, ammonia, and biogenic sources, emissions were estimated by models. 

For the other sectors (point sources, area sources, and MAR [commercial marine, aircraft, and 

railroads]), emissions were prepared using data supplied by the LADCO States and other 

RPOs.

Base Year Emissions: State and source sector emission summaries for 2002 (Base K) and 

2005 (Base M) are compared in Figure 42. Additional detail is provided in Tables 6a (all sectors 

– tons per day) and 6b (EGUs – tons per year). 

VOC NOx SO2

 

0

2000

4000

6000

8000

10000

12000

2002

(K)

2005

(M)

2002

(K)

2005

(M)

2002

(K)

2005

(M)

WI

OH

MI

IN

IL

0

2000

4000

6000

8000

10000

12000

2002

(K)

2005

(M)

2002

(K)

2005

(M)

2002

(K)

2005

(M)

Area

Nonroad+MAR

Onroad

Non-EGU

EGU

Figure 42. Base K and Base M emissions for 5-state LADCO region by state (top) and source 

sector (bottom), units: tons per summer weekday

A summary of the base year emissions by sector for the LADCO States is provided below.
VOC Base M BaseK Base M BaseK BaseK Base M NOx Base M BaseK Base M BaseK BaseK Base M SOX Base M BaseK Base M BaseK BaseK Base M PM2.5 Base M BaseK Base M BaseK BaseK Base M

July 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018

Nonroad

IL 224 321 164 257 149 130 213 324 333 263 275 224 154 155 31 33 5 5 0.6 0.4 0.4 30 24 14

IN 125 195 94 160 95 95 128 178 191 142 158 141 141 89 17 19 3 3 3 0.3 0.2 17 13 7

MI 348 414 307 350 276 222 271 205 239 159 197 133 93 112 19 22 3 3 0.5 0.3 0.3 22 18 11

OH 222 356 161 294 145 126 238 253 304 195 246 162 109 135 23 29 4 5 0.5 0.3 0.4 27 22 13

WI 214 238 194 203 175 140 157 145 157 114 129 97 69 77 13 15 2 2 0.3 0.2 0.2 14 12 7

5-State Total 1133 1524 920 1264 840 713 1007 1105 1224 873 1005 757 566 568 103 118 17 18 4.9 1.5 1.5 110 89 52

U.S. Total 8463 9815 5442 8448 5244 6581 6041 9060 6057 8120 5832 5100 505 654 117 153 104 13 573 750 475

MAR

IL 10 11 10 10 10 10 6 277 246 201 228 195 186 165 0 22 0 19 0 0 17 7 6 4

IN 5 5 5 5 5 5 3 123 93 89 87 87 84 65 0.2 8 0.2 7 0.2 0.2 6 2 2 2

MI 7 7 7 7 7 8 7 114 87 112 82 111 110 65 0.6 21 0.7 14 0.7 0.8 8 3 3 2

OH 8 7 8 7 8 8 5 177 134 128 126 126 122 94 0.4 14 0.3 12 0.3 0.3 10 4 4 2

WI 4 4 4 4 4 4 3 79 58 59 54 59 57 41 12.7 8 9.5 6 9.5 8.7 5 2 2 1

5-State Total 34 34 34 33 34 35 24 770 618 589 577 578 559 430 13.9 73 10.7 58 10.7 10 46 18 17 11

U.S. Total 307 317 321 157 329 346 334 4968 4515 4002 1813 3964 3919 3812 620 512 509 122 509 503 290 147 57 165

OtherArea

IL 679 675 688 594 700 738 582 62 48 68 48 70 73 49 11 11 12 16 12 13 16 40 64 69

IN 354 391 365 358 373 398 384 62 56 65 58 67 69 59 158 32 150 32 151 153 32 2 2 2

MI 518 652 516 562 520 541 549 49 49 52 50 53 54 51 71 29 68 29 68 68 28 111 114 120

OH 546 604 550 506 558 593 487 50 93 59 108 60 62 108 22 6 34 15 35 35 14 19 35 34

WI 458 315 467 290 474 506 293 32 37 34 37 34 35 37 9 17 9 13 10 10 13 11 12 12

5-State Total 2555 2637 2586 2310 2625 2776 2295 255 283 278 301 284 293 304 271 95 273 105 276 279 103 183 227 237

U.S. Total 17876 21093 18638 18683 20512 24300 3856 4899 4100 4220 4418 5357 2075 2947 2062 2559 2189 2709 2735 2621 2570

On-Road

IL 446 341 314 268 260 197 151 890 748 578 528 474 300 201 9 4 3 13 10 6

IN 405 282 237 235 193 150 138 703 541 425 402 313 187 173 11 3 2 9 7 2

MI 522 351 335 269 303 217 163 926 722 680 501 619 385 204 14 4 3 12 9 3

OH 574 680 365 424 340 238 242 1035 934 609 693 512 270 274 18 4 4 16 12 4

WI 238 175 144 119 117 88 68 481 457 303 322 226 118 138 9 2 2 8 6 2

5-State Total 2185 1829 1395 1315 1213 890 762 4035 3402 2595 2446 2144 1260 990 61 17 14 58 44 17

U.S. Total 14263 7825 23499 13170

EGU

IL 9 7 8 6 8 9 7 712 305 227 275 244 231 224 1310 1158 944 958 789 810 869 13 34 77

IN 6 6 6 6 7 6 6 830 393 406 370 424 283 255 2499 2614 1267 1033 1263 1048 1036 16 73 74

MI 12 6 11 4 11 12 4 448 393 218 242 219 247 243 1103 1251 1022 667 1031 1058 725 15 25 29

OH 5 4 6 5 7 7 6 1139 408 330 280 322 271 285 3131 3405 1463 1326 994 701 983 28 94 80

WI 3 5 3 2 4 4 3 293 213 146 165 139 147 177 602 545 512 460 492 500 435 0 22 25

5-State Total 35 28 34 23 37 38 26 3422 1712 1327 1332 1348 1179 1184 8645 8973 5208 4444 4569 4117 4048 72 248 285

U.S. Total 214 140 195 124 197 215 138 14371 10316 7746 7274 7721 7007 6095 31839 34545 20163 16903 17629 14727 14133 685 1131 1571

Non-EGU

IL 313 221 286 218 305 350 258 356 330 334 218 338 343 235 373 423 251 335 257 249 346 16 17 19

IN 150 130 160 137 170 199 167 238 179 212 175 216 225 178 292 218 270 216 274 290 180 35 36 44

MI 123 116 115 119 122 139 140 216 240 208 242 214 229 271 162 158 166 148 171 185 163 20 21 25

OH 77 84 75 87 79 90 104 177 175 157 166 160 167 178 240 289 231 288 210 216 293 27 28 33

WI 88 84 97 87 104 120 106 98 97 91 93 92 94 81 163 156 154 152 155 156 85 0 0.1 0.1

5-State Total 751 635 733 648 780 898 775 1085 1021 1002 894 1020 1058 943 1230 1244 1072 1139 1067 1096 1067 98 102 121

U.S. Total 4087 3877 4409 4700 5378 6446 6730 6129 6435 6952 5759 5630 6093 6340 6970 1444 1777

IL 1681 1576 1470 1353 1432 1434 1217 2621 2010 1671 1572 1545 1287 1029 1725 1656 1212 1337 1059 1072 1251 119 155 189

IN 1045 1009 867 901 843 853 826 2134 1453 1339 1250 1248 989 819 2966 2902 1690 1294 1691 1492 1256 81 133 131

MI 1530 1546 1291 1311 1239 1139 1134 1958 1730 1429 1314 1349 1118 946 1356 1495 1260 865 1271 1312 927 183 190 190

OH 1432 1735 1165 1323 1137 1062 1082 2831 2048 1478 1619 1342 1001 1074 3416 3761 1732 1650 1240 953 1304 121 195 166

WI 1005 821 909 705 878 862 630 1128 1019 747 800 647 520 551 800 750 687 635 667 675 540 35 54 47

5-State Total 6693 6687 5702 5593 5529 5350 4889 10672 8260 6664 6555 6131 4915 4419 10263 10564 6581 5781 5928 5504 5280 539 727 723
Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35

IPM 2.1.9 241,000 73,000

1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107

IPM3.0 - will do 140,296 0.214 62,990 0.096

IPM3.0 - may do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45

IPM 2.1.9 377,000 95,000

1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120

IPM3.0 - will do 417,000 0.552 94,000 0.125

IPM3.0 - may do 417,000 0.552 94,000 0.125

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35

IPM 2.1.9 399,000 100,000

1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158

IPM3.0 - will do 244,151 0.484 79,962 0.158

IPM3.0 - may do 244,151 0.484 79,962 0.158

OH 1,306,296,589 2001 - 2003 (average) 1,144,484 1.75 353,255 0.54

IPM 2.1.9 216,000 84,000

1,628,081,545 IPM3.0 (base) 316,883 0.389 96,103 0.118

IPM3.0 - will do 348,000 101,000

IPM3.0 - may do 348,000 101,000

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36

IPM 2.1.9 155,000 46,000

675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167

IPM3.0 - will do 150,340 0.445 55,019 0.163

IPM3.0 - may do 62,439 0.185 46,154 0.137

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40

IPM 2.1.9 147,000 51,000

534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224

IPM3.0 - will do 115,938 0.434 59,994 0.224

IPM3.0 - may do 100,762 0.377 58,748 0.220

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42

IPM 2.1.9 86,000 42,000

447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186

IPM3.0 - will do 54,315 0.243 49,488 0.221

IPM3.0 - may do 51,290 0.229 39,085 0.175

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37

IPM 2.1.9 281,000 78,000

893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163

IPM3.0 - will do 237,600 0.532 72,950 0.163

IPM3.0 - may do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45

IPM 2.1.9 109,000 72,000

342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258

IPM3.0 - will do 56,175 0.328 58,850 0.343

IPM3.0 - may do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80

IPM 2.1.9 12,000 15,000

44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114

IPM3.0 - will do 4,464 0.199 2,548 0.114

IPM3.0 - may do 4,464 0.199 2,548 0.114

Table 6b. EGU Emissions for Midwest States (2018)
54

On-road Sources: For 2002, EMS was run by LADCO using VMT and MOBILE6 inputs supplied 

by the LADCO States. EMS was run to generate 36 days (weekday, Saturday, Sunday for each 

month) at 36 km, and 9 days (weekday, Saturday, Sunday for June – August) at 12 km. For 

2005, CONCEPT was run by a contractor (Environ) using transportation data (e.g., VMT and 

vehicle speeds) supplied by the state and local planning agencies in the LADCO States and 

Minnesota for 24 networks. These data were first processed with T3 (Travel Demand Modeling 

[TDM] Transformation Tool) to provide input files for CONCEPT to calculate link-specific, hourly 

emission estimates (Environ, 2008). CONCEPT was run with meteorological data for a July and 

January weekday, Saturday, and Sunday (July 15 – 17 and January 16 – 18). A spatial plot of 

emissions is provided in Figure 43.

VOC Emissions NOx Emissions

Figure 43. Motor vehicle emissions for VOC (left) and NOx (right) for a July weekday (2005)

Off-road Sources: For 2002 and 2005, NMIM and NMIM2005, respectively, were run by 

Wisconsin DNR. Additional off-road sectors (i.e., commercial marine, aircraft, and railroads 

[MAR]) were handled separately. Local data for agricultural equipment, construction equipment, 

commercial marine, recreational marine, and railroads were prepared by contractors (Environ, 

2004, and E.H. Pechan, 2004). For Base M, updated local data for railroads and commercial 

marine were prepared by a contractor (Environ, 2007b, 2007c). Table 7 compares the Base M

2005 and Base K 2002 emissions. Compared to 2002, the new 2005 emissions reflect 

substantially lower commercial marine emissions and lower locomotive NOx emissions.

Table 7. Locomotive and commercial marine emissions for the five LADCO States (2002 v. 2005)

Railroads (TPY) Commercial Marine (TPY)

2002 2005 2002 2005

VOC 7,890 7,625 1,562 828

CO 20,121 20,017 8,823 6,727

NOx 182,226 145,132 64,441 42,336

PM 5,049 4,845 3,113 1,413

SO2 12,274 12,173 25,929 8,637

NH3 86 85 ---- ----
55

Area Sources: For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO 

States to produce weekday, Saturday, and Sunday emissions for each month. For 2005, 

special attention was given to two source categories: industrial adhesive and sealant solvents 

(which were dropped from the inventory to avoid double-counting) and outdoor wood boilers

(which were added to the inventory).

Point Sources: For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO 

States to produce weekday, Saturday, and Sunday emissions for each month. For EGUs, the 

annual and summer season emissions were temporalized for modeling purposes using profiles 

prepared by Scott Edick (Michigan DEQ) based on CEM data. 

 

Biogenics: For Base M, a contractor (Alpine) provided an updated version of the 

CONCEPT/MEGAN biogenics model. Compared to the previous (EMS/BIOME) emissions, 

there is more regional isoprene using MEGAN compared to the BIOME estimates used for Base 

K (see Figure 44). Also, with the secondary organic aerosol updates to the CAMx air quality 

model, Base M includes emissions for monoterpenes and sesquiterpenes, which are precursors of secondary PM2.5 organic carbon mass.

Figure 44. Isoprene emissions for Base M (left) v. Base K (right)

Ammonia: For Base M, the CMU-based 2002 (Base K) ammonia emissions were projected to 

2005 using growth factors from the Round 4 emissions modeling. These emissions were then 

adjusted by applying temporal factors by month based on the process-based ammonia 

emissions model (Zhang, et al, 2005, and Mansell, et al, 2005). A plot of average daily 

emissions by state and month is provided in Figure 45. A spatial plot of emissions is provided in 

Figure 46, which shows high emissions densities in the central U.S.

0

300

600

900

1200

1500

1800

Jan Fed March Apr May Jun Jul Aug Sept Oct Nov Dec

Illinois Indiana Iowa Michigan

Minnesota Ohio Wisconsin

Figure 45. Average daily ammonia emissions for Midwest States by month (2005) - (units: average 

daily emissions – tons per day)
56

Figure 46. Ammonia emissions for a July weekday (2005) – 12 km modeling domain

Canadian Emissions: For Base M, Scott Edick (Michigan DEQ) processed the 2005 Canadian 

National Pollutant Release Inventory, Version 1.0 (NPRI). Specifically, a subset of the NPRI 

data (emissions and stack parameters) relevant to the air quality modeling were reformatted. 

The resulting emissions represent a significant improvement in the base year emissions. 

A spatial plot of point source SO2 and NOx emissions is provided in Figure 47. Additional plots 

and emission reports are available on the LADCO website 

(http://www.ladco.org/tech/emis/basem/canada/index.htm). 

Figure 47. Canadian point source emissions for SO2 (left) and NOx (right)