### Monitoring Data for the Cincinnati-H amilton, 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 PM 2.5 Standard

### Monitoring Data for the Cincinnati-H amilton, 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 PM 2.5 standard.



### Monitoring Data for the Cincinnati-H amilton, 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 PM 2.5 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 Fi eld Airport monitor in Ohio was discontinued on December 31, 2007.Design Values for the Cinc innati Area for Fine Part icles, 2003 through 2009

Cincinnati Area Annual Fiine Pa rticles Trends, 2005 through 2009

Note: The Wilwood and Winneste Ave. monitors in Ohio were disc ontinued 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 20.0AlexandriaPk.JohnHillRd.Univ.CollegeBonita&St.JohnNillesRd.WilwoodHookFieldAirportClermontDr.GroomsRd.Seymour&Vine...HowardTaftWinnesteAve.W.8thStKemperRd.ShermanAve.MurrayRd.SoutheastSt.

03

‐ 05 04

‐ 06 05

‐ 07 06

‐ 08 07

‐ 09μg/m3

# APPENDIX B

Nitrogen Oxides (NO x 

# ), Sulfur Dioxides (SO 2 

# ) and Direct Fine Particulate Matter (PM 2.5 

# ) 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 NO x SO 2 Direct PM 2.5 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- NO x NON-EGUNO x EGU-SO 2 NON-EGUSO 2 EGUDirect PM 2.5 NON-EGUDirect PM 2.5 Dearborn, IN 7,961.30 2,024.68 46,533.70 1,331.15 673.94 67.38
NO x SO 2 Direct PM 2.5 Grand Total 9,985.98 47,864.85 741.32

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

County EGU- NO x NON-EGUNO x EGU-SO 2 NON-EGUSO 2 EGUDirect PM 2.5 NON-EGUDirect PM 2.5 Dearborn, IN 7,429.20 1,979.83 25,729.10 1,334.33 804.18 62.02
NO x SO 2 Direct PM 2.5 Grand Total 9,409.03 27,063.43 866.20



### Dearborn County, IN NO x 

### 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 2008TonsperYear

Year NOX

### B-2 Dearborn County, IN SO 2 

### Point Source Emission Trends, 2005 and 2008

Dearborn County, IN Direct PM 2.5 

### 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 2008TonsperYear

Year SO2

660 680 700 720 740 760 780 800 820 840 860 880

2005 2008TonsperYear

Year Direct PM 2.5

### B-3 Entire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area Point Source Totals (Tons per Year)
Year NO x SO 2 Direct PM 2.5 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 Ar ea Point Source Emissions (Tons per Year)
County EGU NO x NON-EGUNO x EGU SO 2 NON-EGUSO 2 EGUDirect PM 2.5 NON-EGUDirect PM 2.5 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 NO x SO 2 Direct PM 2.5 Grand Total 66,302.14 233,927.65 3,415.69

2008-Entire Cincinnati-Hamilton , OH-KY-IN Nonattainment Ar ea Point Source Emissions (Tons per Year)
County EGU NO x NON-EGUNO x EGU SO 2 NON-EGUSO 2 EGUDirect PM 2.5 NON-EGUDirect PM 2.5 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 NO x SO 2 Direct PM 2.5 Grand Total 56,644.39 111,818.09 3,091.67

### B-4 Entire Cincinnati-Hamilton, OH-K Y-IN Nonattainment Area NO x 

### Point Source Emission Trends, 2005 and 2008

Entire Cincinnati-Hamilton, OH-K Y-IN Nonattainment Area SO 2 

### 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 2008TonsperYear

Year NOX

50,000 70,000 90,000 110,000 130,000 150,000 170,000 190,000 210,000 230,000 250,000

2005 2008TonsperYear

Year SO2

### B-5 Entire Cincinnati-Hamilton, OH-KY- IN Nonattainment Area Direct PM 2.5 

### Point Source Emission Trends, 2005 and 2008

2,900 3,000 3,100 3,200 3,300 3,400 3,500

2005 2008TonsperYear

Year Direct PM 2.5

# APPENDIX C

Nitrogen Oxides (NO x 

# ), Sulfur Dioxides (SO 2 

# ) and Direct Fine Particulate Matter (PM 2.5 

# ) (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 NO X Direct PM 2.5 SO 2 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
NO X 865.46 382.53 141.37 9,985.98 11,375.34 Direct PM 2.5 33.98 23.96 4.29 741.32 803.55 SO 2 2.45 40.16 78.72 47,864.85 47,986.18

2008-Dearborn County, IN COUNTY, STATE Sector NO X Direct PM 2.5 SO 2 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
NO X 748.81 318.09 145.42 9,409.03 10,621.35 Direct PM 2.5 29.89 19.91 4.29 866.20 920.29 SO 2 2.69 17.38 81.02 27,063.43 27,164.52

### C-2 NO x 

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

SO 2 

### 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 2008TonsperYear

Year NOX

‐ 10,000 20,000 30,000 40,000 50,000 60,000

2005 2008TonsperYear

Year SO2

### C-3 Direct PM 2.5 

### 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 2008TonsperYear

Year

Direct PM 2.5

### C-4 2005-Entire Cincinnati-Hamilton OH-KY-IN Nonattainment Area COUNTY, STATE Sector NO X Direct PM 2.5 SO 2 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
NO X 71,919.89 21,770.25 7,810.74 66,302.14 167,803.02 Direct PM 2.5 2,810.30 1,400.55 1,828.85 3,415.69 9,455.39 SO 2 206.81 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 NO X Direct PM 2.5 SO 2 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
NO X 64,471.22 19,614.87 7,975.67 56,644.39 148,706.15 Direct PM 2.5 2,462.43 1,268.32 1,864.80 3,091.67 8,687.22 SO 2 227.87 1,399.69 3,520.77 111,818.09 116,966.42

### C-6 NO x 

### Emission Trends, All Sources in En tire Cincinnati-Hamilton, OH-KY-IN Nonattainment Area, 2005 and 2008-With CAIR

SO 2 

### Emission Trends, All Sources in En tire 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 2008TonsperYear

Year NOX

‐ 50,000 100,000 150,000 200,000 250,000 300,000

2005 2008TonsperYear

Year SO2

### C-7 Direct PM 2.5 

### 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 2008TonsperYear

Year Direct PM 2.5

# APPENDIX D

Nitrogen Oxides (NO x 

# ) and Sulfur Dioxide (SO 2 

# ) 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 NO x 

### Emissions from EGUs

Lawrenceburg Township, Dearborn County, Indiana NO x 

### Emissions from EGUs

Year Total NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs

Year Total NO x 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-K Y-IN Nonattainment Area NO x 

### 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 1999 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2000 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2001 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2002 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2003 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2004 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2005 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2006 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2007 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2008 State Facility NO x 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-K Y-IN Nonattainment Area NO x 

### Emissions from EGUs, 2009 State Facility NO x 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 SO 2 

### Emissions from EGUs

Year Total SO 2 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 SO 2 

### 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs

Year Total SO 2 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-K Y-IN Nonattainment Area SO 2 

### 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 1999
State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2000 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2001 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2002 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2003 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2004 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2005 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2006 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2007 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2008 State Facility SO 2 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-K Y-IN Nonattainment Area SO 2 

### Emissions from EGUs, 2009 State Facility SO 2 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 (NO x 

# ), Sulfur Dioxides (SO 2 

# ) and Direct Fine Particulate Matter (PM 2.5 

# ) 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)

PM 2.5 ONROAD NONROAD AREA EGU POINT TOTAL DEARBORN COUNTY, IN 29.89 19.91 4.29 804.18 62.02 920.29

SO 2 ONROAD NONROAD AREA EGU POINT TOTAL DEARBORN COUNTY, IN 2.69 17.38 81.02 25,729.10 1,334.33 27,164.52

NO x 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)

PM 2.5 ONROAD NONROAD AREA EGU POINT TOTAL DEARBORN COUNTY, IN 25.14 13.34 4.11 847.16 60.00 949.75

SO 2 ONROAD NONROAD AREA EGU POINT TOTAL DEARBORN COUNTY, IN 2.87 4.73 77.64 39,295.70 1,335.94 40,716.88

NO x 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)

PM 2.5 ONROAD NONROAD AREA EGU POINT TOTAL DEARBORN COUNTY, IN 18.11 9.07 3.98 922.81 57.32 1,011.29

SO 2 ONROAD NONROAD AREA EGU POINT TOTAL DEARBORN COUNTY, IN 3.19 1.14 75.69 36,843.66 1,337.95 38,261.63

NO x 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 Count y, IN Percent Change (Tons Per Year) 2008 2021 Change % Change NO x 10,621.35 13,767.56 3,146.21 29.6% SO 2 27,164.52 38,261.63 11,097.11 40.8% Direct PM 2.5 920.29 1,011.29 91.00 9.8%

### E-2 Comparison of 2008 and 2015 and 2021 Projected NO x 

### Emissions, Lawrenceburg Township, Dearborn Co unty, Indiana-With CAIR

Comparison of 2008 and 2015 and 2021 Projected SO 2 

### Emissions, Lawrenceburg Township, Dearborn Co unty, Indiana-With CAIR

‐ 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000

2008 2015 2021TonsperYear

Year SO2 ‐ 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000

2008 2015 2021TonsperYear

Year NOX

### E-3 Comparison of 2008 and 2015 and 2021 Projected Direct PM 2.5 

### Emissions, Lawrenceburg Township, Dearbo rn County, Indiana-With CAIR

Comparison of 2008 and 2015 and 2021 Projected SO 2 

### , NO x 

### and Direct PM 2.5 

### Emissions, Lawrenceburg Township, Dearborn County, Indiana-With CAIR 860 880 900 920 940 960 980 1,000 1,020

2008 2015 2021TonsperYear

Year Direct PM 2.5

‐ 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 2021TonsperYear

NOX SO2 Direct PM2.5

### E-4

### E-5 2008 Entire Cincinnati-Hamilton, OH-KY-I N Nonattainment Area (Tons Per Year)

PM 2.5 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-I N Nonattainment Area (Tons Per Year)

SO 2 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-I N Nonattainment Area (Tons Per Year)

NO x 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-I N Nonattainment Area (Tons Per Year)

PM 2.5 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-I N Nonattainment Area (Tons Per Year)

SO 2 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-I N Nonattainment Area (Tons Per Year)

NO x 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-I N Nonattainment Area (Tons Per Year)

PM 2.5 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-I N Nonattainment Area (Tons Per Year)

SO 2 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-I N Nonattainment Area (Tons Per Year)

NO x 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 Nonattai nment Area Percent Change (Tons Per Year) 2008 2021 Change % Change NO x 148,706.15 78,819.18 -69,886.97 -46.9% SO 2 116,966.42 88,540.12 -28,426.30 -24.3% Direct PM 2.5 8,687.22 8,202.63 -484.59 -5.5%

### E-8 Comparison of 2008 and 2015 and 2021 Projected NO x 

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

Comparison of 2008 and 2015 and 2021 Projected SO 2 

### 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 2021TonsperYear

Year SO2 ‐ 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

2008 2015 2021TonsperYear

Year NOX

### E-9 Comparison of 2008 and 2015 and 2021 Projected Direct PM 2.5 

### 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 SO 2 

### , NO x 

### and Direct PM 2.5 

### Emissions for the Entire Cincinnati-H amilton, OH-KY-IN Nonattainment Area- (NO x 

### and SO 2 

### With CAIR, IN/KY PM 2.5 

### With CAIR, OH PM 2.5 

### -Without CAIR) 7,900 8,000 8,100 8,200 8,300 8,400 8,500 8,600 8,700 8,800

2008 2015 2021TonsperYear

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 2021TonsperYear

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, Cle rmont, 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 Implementat ion Plan

August 2010

P repared for the Indiana Department of Environmental Management, the Kentucky Division

for Air Quality and the Ohio Environmental Protecti on Agency by

OKI Regional Council of Governments

## Acknowledgments

Title Mobile Source Emissions Inventory for Cincinnati P M2.5 Nonattainment
Area

Abstract This report was prepared for the Indiana Department of Environmental Management, the Kentucky Division for Air Quality a nd the Ohio Environmental Protection Agency. The Cincinnati PM 2.5 nonattainment area includes a portion of Dearborn County Indiana, the counties of Boone, Campbell, Kenton in Kentucky, and the counties of B utler, 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 genera te the emission

rates.

Date August 2010

Agency Ohio-Kentucky-Indiana Regional Council of Governme nts 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 coope ratively by the Federal Highway Administration, the Federal Transit Administration, the Commonwealt h of Kentucky Transportation Cabinet, the Ohio Department of Transportation, and the units of loca l and county government in the OKI region. The opinions, findings, and conclusions expressed in th is document are those of the OKI Regional Council o f Governments and are not necessarily those of the U. S. Department of Transportation. This report does not constitute a standard, specification, or regula tion.

# 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 C ounty Indiana, the counties of Boone, Campbell, Kenton in Kentucky, and the counties of Butler, Cle rmont, Hamilton, and Warren in Ohio. This report includes emission estimates for the years 2005, 200 8, 2011, 2015, 2018 and 2021 was generated to support the attainment SIPs for the annual PM2.5 st andard. 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 f actor 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 wer e generated for direct PM2.5, PM2.5 tirewear, PM2.5 brakewear, NO x 

### and SO 2



### .
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 Implementa tion Plans (SIPs) for Indiana, Kentucky and Ohio. The S IPs include an inventory of projected emissions fro m vehicles. One or more of the analysis years in the projected inventory may be designated as the motor vehicle emissions budget (MVEB). This budget establ ishes a maximum allowable limit on future emissions from vehicles (mobile sources). OKI’s tr ansportation plans and programs must be shown to be in conformity with all SIP provisions. The conf ormity process is a quantitative analysis, using U.S.EPA’s vehicle emissions software (currently MOV ES), demonstrating that forecasted regional vehicle emissions do not exceed the established budget.

Table 1 shows daily and annual mobile source emissi ons for the combined Indiana and Ohio portions of the nonattainment area, as well as the Kentucky por tion of the nonattainment area. Separate MVEB’s are typically designated for these two areas. Alth ough 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 ai r 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 util ized 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** Annual VMT: 3,289,109,202 Daily VMT: 9,621,110 Vehicle Population: 364,081
**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** Annual VMT: 3,425,339,505 Daily VMT: 9,991,179 Vehicle Population: 375,873
**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** Annual VMT: 3,587,796,186 Daily VMT: 10,490,143 Vehicle Population: 381,911
**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** Annual VMT: 3,931,385,741 Daily VMT: 11,495,496 Vehicle Population: 394,278
**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** Annual VMT: 4,163,203,435 Daily VMT: 12,173,549 Vehicle Population: 403,817
**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** Annual VMT: 4,286,834,360 Daily VMT: 12,534,236 Vehicle Population: 413,587
**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 Page 1 of 2 MOVES 2010 and OKI Travel Demand Model v7.6Year Pollutant Name DailyEmissions AnnualEmissions 

### Ohio/Indiana Portion of NA Area

**2005** Annual VMT: 13,541,324,003 Daily VMT: 39,564,030 Vehicle Population: 1,754,582
**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** Annual VMT: 14,015,754,874 Daily VMT: 40,858,751 Vehicle Population: 1,811,406
**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** Annual VMT: 14,383,526,419 Daily VMT: 42,044,841 Vehicle Population: 1,840,505
**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** Annual VMT: 14,830,453,053 Daily VMT: 43,316,281 Vehicle Population: 1,900,111
**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** Annual VMT: 15,513,701,656 Daily VMT: 45,314,292 Vehicle Population: 1,946,080
**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** Annual VMT: 15,521,916,278 Daily VMT: 46,689,707 Vehicle Population: 1,993,161
**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 Page 2 of 2 MOVES 2010 and OKI Travel Demand Model v7.6

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

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

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

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

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

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

O xides 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 Page 1 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions 

## Kentucky

Boone

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

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

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

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

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

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

O xides 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 Page 2 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions
Campbell

2 005 Annual VMT: 741,790,595 Daily VMT: 2,286,217 Vehicle Population: 86,065

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

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

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

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

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

O xides 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 Page 3 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions

Kenton

2 005 Annual VMT: 1,274,091,641 Daily VMT: 3,927,743 Vehicle Population: 148,193

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

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

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

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

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

O xides 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 Page 4 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions 

## Ohio

Butler

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

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

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

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

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

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

O xides 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 Page 5 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions

Clermont

2 005 Annual VMT: 1,684,261,582 Daily VMT: 5,083,336 Vehicle Population: 232,380

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

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

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

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

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

O xides 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 Page 6 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions

Hamilton

2 005 Annual VMT: 7,241,536,812 Daily VMT: 21,859,473 Vehicle Population: 862,422

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

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

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

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

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

O xides 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 Page 7 of 8 MOVES 2010 and OKI Travel Demand Model v7.6County Year Pollutant Name DailyEmissions AnnualEmissions

Warren

2 005 Annual VMT: 1,949,619,088 Daily VMT: 5,884,222 Vehicle Population: 233,106

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

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

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

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

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

O xides 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 Page 8 of 8 MOVES 2010 and OKI Travel Demand Model v7.6

### Mobile Source Emission Forecast Process

Emission Factor Model

OKI’s conformity assessment utilized U.S.EPA’s emis sions model MOVES 2010 to develop emission factors for SO2, NO X 

### and PM2.5. Table 3 summarizes the settings used i n the MOVES run specification
file. Table 4 lists the data used in the MOVES Cou nty-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 NO x 

### , All PM2.5 categories, SO 2 

### , 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 an d ODOT from motor vehicle registration data. Default data used for source types 41, 61 and 62. In addition , default data for sour ce types 31, 32 and 54 used for KY. Vehicle Type VMT Local and defa ult. HPMSVTypeYear VMT=daily VMT from OKI travel demand model with EPA’s daily to annual VMT convert er applied. monthVMTFraction = default. dayVMTFraction=default, hourVMTFraction=local. I/M Programs Default modified to reflect discontinued I/M progra m 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 an d ODOT from motor vehicle registration data. Default data used for source types 41, 61 and 62. In addition , default data for sour ce types 31, 32 and 54 used for KY. Average Speed Distribution Loca l. OKI travel demand model.

OKI Travel Demand Model

Transportation system performance was estimated usi ng 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 generati on, distribution, modal choice and assignment. The model uses demographic and land use data and capaci ty 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 spee d/capacity relationships.

Travel analysis zones are the basic geographic unit for estimating travel in the OKI model. The OKI re gion is subdivided into 1608 traffic analysis zones to p ermit detail as well as manageability. A variety o f socioeconomic data items are used in the OKI transp ortation planning process. These data are used primarily to forecast future travel patterns by ser ving as independent variables in OKI trip generatio n equations. The following categories of planning dat a 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 d emand forecasting model are population and employment. From these variables, other characteris tics including households, labor force, and persona l 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 Analysi s Zone (TAZ) level, and originate in the 2000 Census of Population and Housing. Base year 2005 and future y ear 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 fol lows. All of the variables represent the latest OK I planning assumptions.

### Population

Base and Future Year Data: Population data for bas e year 2005 and future years 2010, 2020 and 2030 originate with the 2000 Census of Population and Ho using. Utilizing ArcView GIS, population data at th e zonal level for 2000 was derived from the area prop ortion allocation of block level population.

As a tri-state regional planning agency, OKI uses c ounty level projections as prepared by the respecti ve state data centers (Ohio Department of Development Office of Strategic Research, Kentucky State Data Center and Indiana Business Research Center) as con trol 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 s ize 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 syste m ArcMap, household data at the zonal level for 200 0 was derived from the area proportion allocation of block level households. Year 2000 household data was updated to 2005 with residential building permi ts issued between January 2000 and December 2004. The residential building locations were geoco ded in ArcMap, then aggregated to the TAZs. The housing unit totals for each TAZ were converted to h ouseholds by applying a vacancy rate, an adjustment for permitted but unbuilt units, and sub tracting 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 proj ections was accomplished by calculating the number of households for a projected county populat ion 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, existin g 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 ho usehold vehicle data were obtained from the 2000 Census of Population and Housing. The 2000 Census i s the only source of household vehicle data available at the block group level. Average vehicle s per household were calculated for block groups th en 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 pe r household.



### Labor Force Base and Future Year Data: The OKI labor force is a function of the population as determined by a lab or force participation ratio (the number of employed p ersons in the labor force per persons 16 and over). Household data for base year 2005 originates with t he 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 base d on the most recent projections of national labor force participation rates by age and sex cohorts fr om the U.S. Department of Labor, Bureau of Labor Statistics for each of those years. These rates wer e then applied to the projected county age/sex coho rts and adjusted to eliminate the unemployed to arrive at a county employed labor force control total. Employed labor force at the zonal level is calculat ed by multiplying the labor force participation rat e 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 tot al.



### Employment Base Year Data: Quarterly Census of Employment and Wages (QCEW or ES202) data for 2005 was utilized as the primary tool to calculate employmen t at the zonal level. Individual business records containing physical location, number of employees a nd SIC code were geocoded through ArcMap and aggregated to the TAZ level. This data set was suppl emented by other sources of data to complete the commuting employment picture in the OKI region. Eac h zone’s employment was divided according to the SIC code into three classes (retail, office, in dustrial) based upon the potential for generating t rips.

Future Year Data: For future year employment proje ction, calculation was first made of the employment at the regional level. At the regional l evel, 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 coun ty 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 informatio n, 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 a nd employment densities.

Model Calibration

OKI’s Travel Demand Model has been validated to obs erved 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 in cludes 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 di screpancies 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 informat ion 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 Departmen t of Transportation (ODOT), the Kentucky

### Transportation Cabinet, many county and local gover nments, transportation engineering consultants,

and OKI. These traffic counts cover nearly 50% per cent of the links in the OKI portion of the modelin g 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, a s well as other travel-related parameters. Free flow speeds used on the highway and transit networks ar e based on travel time studies performed locally. Th e 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 tr affic distribution and directional split factors fo r different roadway types as developed by OKI. The ma in 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 ty pe: urban and rural, and functional classification: freeway, arterial and collector. Speeds representin g various “loaded” conditions (with traffic volumes ) are estimated using techniques from the 1997 Highwa y Capacity Manual. This permits the estimation of speeds as conditions vary from hour to hour on the different facility types throughout the region. Th e IMPACT program performs the appropriate summation b y 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 da ta collected at permanent traffic counting stations during 1994-1997 utilizing the average daily traffi c monthly conversion factors for June, July and Aug ust. Further information on OKI’s IMPACT program is docu mented 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 invento ry, 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 calle d Runspec [sic]. Using graphical user interface user can modify all these attributes of Runspec [si c]. 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 Ru nspec [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 hav e details such as analysis years, area and pollutants analyzed.



## 1.2 Scale

In this option, we need to specify about the Domai n/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 d ata like time aggregation level, year of analysis, month of analysis, whether analysis day i s Weekday or Weekend, and hours of analysis. In all of our runs, time aggregation leve l is considered as hour, which is the most disaggregated level possible in MOVES and it is als o 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-hou r set of average annual temperatures. The annual average minimum temperature, maximum tempera ture and humidity values for each hour were calculated and assigned the April month I D.


## 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 Count y. The state ID is fixed as 99 and we have assigned an arbitrary CountyID 390 for Ohio to dist inguish 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 barom etric 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 fra ctions are assigned as 0.00. In this input panel we also need to specify the Domain Input Data bases. 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 diff erent type of vehicles considered for analysis in the region. MOVES [sic] provide us with 13 diffe rent types of vehicles or equipment and four different fuel types and we need to select appropri ate 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 r egion. There are five types of road types available in MOVES, since OKI travel demand model c ould 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 rura l. All expressways and freeways are considered as restricted roadways and all other roa d types are considered as unrestricted roadways.



## 1.7 Pollutants and Processes

There are different pollutants and corresponding pr ocesses are available in MOVES. A separate panel is available for selecting different pollutan ts 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 co nsumption. In addition to PM2.5, Oxides of Nitrogen are also selected.



## 1.8 Miscellaneous

Further, if we have information about future or pre sent 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 Alte rnative 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 the re would not be any change in transit bus fuels. MOVES [sic] also provide us the options whe ther we would like to save the MOVESactivityoutput [sic] and MOVESOutput [sic] dat abases or not.

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



### Table 1 : Alternative Vehicle and Fueling Technolog y 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 an d need to create new database for each new Runspec [sic]. We also have options like specif ying the units for emission rates and energy consumption. These options are available in the Gen eral 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 impor ted a MOVES input format. This dataset has different data items like month ID, Zone ID, hour I D, Temperature and Relative Humidity. For OKI region and Ohio portion runs we have used tempe rature 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 te mperatures 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 regi stered vehicle population in each county in the region for 13 MOVES vehicle types. KYTC has pro vided 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 popul ation 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 S ource Type Populations has been estimated for past years. The MOVES default source type popu lation for intercity bus, refuse trucks, motor homes and combination trucks was used. In ad dition, MOVES default source type

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



### population for passenger trucks and light commercia l trucks was used for Kentucky. The MOVES default source type population was acquired f rom the MOVES activity output tables from county-level inventory runs.

Table3 : Source Type Population for Ohio Custom Dom ain (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 vehi cle 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 s ource type into MOVES format. It has input option as HPMS Base Year VMT, for which we can eith er use HPMS data or the Travel Demand Model output. We have used annual VMT calculated fr om the OKI Regional Travel Demand Model. There are options like the Month VMT fractio n, Day VMT fraction and Hour VMT fraction, which are useful for calculating emission s for different time periods. We have used default Monthly VMT distribution factors provided i n the VMT Converter provided by EPA. Hourly distribution factors are developed from traf fic count data collected in the region and the same set of Hourly Distribution Factors are used fo r all vehicle types and road types. OKI model could only predict VMT of two different vehicle typ es’ 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 OK I 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 fr om 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 fro m OKI travel demand model for 2011

Table 12 :Annual VMT for Ohio Custom Domain from OK I 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 fro m 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 O KI 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 fr om 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 20 08)

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 f or 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 spee d data specific to vehicle type, road type, and time of day/ type of day. The MOVES model defin es 16 “speed bins” which describe the average driving speed on each road type. Unlike MOB ILE 6.2 model, which uses VMT-based speed distribution, MOVES use fraction of driving t ime in each speed bin for each vehicle type, for each road type, and for each hour. Thus, for ea ch 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, t his 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 t ype is used as an input in this importer. OKI travel demand model can calculate the VMT distribut ion 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 i s no capability to model Emission Rates for Ramps. To circumvent this problem, FHWA has suggest ed a temporary solution. This solution discussed in the Section 3.


## 2.8 Fuel Formulation Importer and Fuel Supply Import er

Fuel formulation importer allows the user to select an existing fuel in the MOVES database and change its properties, or create a new fuel formula tion with different fuel properties. But we have used only default fuels available in MOVES def ault database. Fuel supply importer allows the user to assign existing fuels to counties, mont hs, and years, and the associated market share for each fuel. We have used default fuel supp ly 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 Doma in (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 informat ion 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 o f 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 SHO allocfactor for each road type which means that all of the VMT input by the users is ass igned to custom domain.

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



### Table 21 : Kentucky Custom Domain Zone road activit y 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 da ta (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 suggeste d 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 to tal VMT of Urban Restricted and Rural Restricted road types using MOVESactivityoutput opt ion (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 I nventory.

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-process ing. An ODBC connection with the MOVES output directory was established. Information on h ow 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 selecte d (i.e. VOC). Tables were renamed with state and analysis year in the format OH_20xxratepe rdistance. All rateperdistance tables were merged with a Union query. The SQL commands are sh own 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 (MOVES RunID) from OH_2008rateperdistance) AND pollutantID=110 Or MOVE SRunID = (select max (MOVESRunID) from OH_2008rateperdistance) AND pollu tantID=116 Or MOVESRunID = (select max (MOVESRunID) from OH_2008rateperdistanc e) AND pollutantID=117 Or MOVESRunID = (select max (MOVESRunID) from OH_2008r ateperdistance) 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 fo r each process were summed by pollutant and a stateID field is created. The SQL commands f or creating the “Rateperdistance_state” table are shown in Table 25. Unique index fields w ere 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_rat eperdistance_state.yearID, Union_rateperdistance_state.monthID, Union_rateperd istance_state.linkID, Union_rateperdistance_state.hourID, Union_rateperdi stance_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_rateperdista nce_state.yearID, Union_rateperdistance_state.monthID, Union_rateperd istance_state.linkID, Union_rateperdistance_state.hourID, Union_rateperdi stance_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_rateperdi stance_state.pollutantID;


## 4.1 Creating a VMT Table by County

The VMT table includes Daily VMT by county by analy sis year from the OKI Travel Demand Model (TDM). Summer factors and applied by functio nal 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 Fa ctors as Used in Air Quality Impact Calculations”, September 2001. Annual VMT is calcu lated by using EPA’s VMT converter to grow daily VMT to annual VMT. In order to accommod ate an error in MOVES 2010, all VMT values are exclusive of ramp VMT. Ramp VMT and emi ssion are added in later in the process. In order to apply the emission rates, it is necessa ry 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 distr ibution
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 ra te 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 wa s 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). VM T 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 bi ns are provided by the OKI TDM. The average speed fractions vary by state, year, road t ype and hour.

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

roadT ypeID 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 factor s 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, avgSpeedDistribut ion.avgSpeedBinID, sourecetypepopulation.sourceTypeFraction, hourvmtfr action.hourVMTFraction, roadtypedistribution1.roadTypeVMTFraction, avgSpeedDistribution.avgSpeedFraction, First(VMT.[A nnual VMT]) AS [FirstOfAnnual VMT], First(VMT.[Summer VMT]) AS [Fi rstOfSummer VMT], [FirstOfSummer VMT]*[hourVMTFraction]*[sourceTypeFraction]*[roadTy peVMTFraction]*[avgSpeedFr action] AS DailyVMT, [FirstOfAnnual VMT]*[sourceTypeFraction]*[hourVMTFraction]*[roadTy peVMTFraction]*[avgSpeedFr action] AS AnnualizedVMT INTO CountyVMT_Table FROM (((avgSpeedDistribution INNER JOIN hourvmtfrac tion ON (avgSpeedDistribution.hourDayID = hourvmtfraction.h ourID) AND (avgSpeedDistribution.roadTypeID = hourvmtfraction. roadTypeID) AND (avgSpeedDistribution.sourceTypeID = hourvmtfractio n.sourceTypeID)) INNER JOIN roadtypedistribution1 ON (avgSpeedDistribution .stateID = roadtypedistribution1.stateID) AND (avgSpeedDistrib ution.roadTypeID = roadtypedistribution1.roadTypeID) AND (avgSpeedDist ribution.sourceTypeID = roadtypedistribution1.sourceTypeID)) INNER JOIN sou recetypepopulation ON (avgSpeedDistribution.sourceTypeID = sourecetypepop ulation.sourceTypeID) AND (avgSpeedDistribution.stateID = sourecetypepopulati on.stateID)) INNER JOIN VMT ON (avgSpeedDistribution.YearID = VMT.yearID) A ND (avgSpeedDistribution.stateID = VMT.stateID) GROUP BY VMT.yearID, VMT.State, roadtypedistributio n1.stateID, VMT.County, roadtypedistribution1.sourceTypeID, hourvmtfraction .hourID, roadtypedistribution1.roadTypeID, avgSpeedDistribut ion.avgSpeedBinID, sourecetypepopulation.sourceTypeFraction, hourvmtfr action.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 To tal Emissions

5.1 Summarizing Distance-based Emissions by Source T ype 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 dista nce 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.Sta te, CountyVMT_Table.County, CountyVMT_Table.yearID, rateperdistance_state.monthID, CountyVMT_Table.hour ID, rateperdistance_state.sourceTypeID, CountyVMT_Table .roadTypeID, CountyVMT_Table.avgSpeedBinID, rateperdistance_stat e.pollutantID, CountyVMT_Table.DailyVMT, CountyVMT_Table.Annualize dVMT, rateperdistance_state.SumOfratePerDistance, [DailyV MT]*[SumOfratePerDistance] AS EmissionsDist, [AnnualizedVMT]*[SumOfratePerDist ance] AS AnnualEmissionsDist FROM rateperdistance_state INNER JOIN CountyVMT_Tab le ON (rateperdistance_state.avgSpeedBinID = CountyVMT_Ta ble.avgSpeedBinID) AND (rateperdistance_state.roadTypeID = CountyVMT_Table .roadTypeID) AND (rateperdistance_state.sourceTypeID = CountyVMT_Tab le.sourceTypeID) AND (rateperdistance_state.hourID = CountyVMT_Table.hou rID) AND (rateperdistance_state.StateID = CountyVMT_Table.st ateID) AND (rateperdistance_state.yearID = CountyVMT_Table.yea rID) GROUP BY CountyVMT_Table.stateID, CountyVMT_Table.S tate, CountyVMT_Table.County, CountyVMT_Table.yearID, rateperdistance_state.monthID, CountyVMT_Table.hour ID, rateperdistance_state.sourceTypeID, CountyVMT_Table .roadTypeID, CountyVMT_Table.avgSpeedBinID, rateperdistance_stat e.pollutantID, CountyVMT_Table.DailyVMT, CountyVMT_Table.Annualize dVMT, 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 indep endent of road type and speed.



## 5.2 Summarizing Vehicle-based Emissions by Source ty pe 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_stat e.yearID, ratepervehicle_state.monthID, ratepervehicle_state. hourID, ratepervehicle_state.sourceTypeID, sourecetypepopul ation.sourceTypeFraction, VMT.SourceTypePopulation, ratepervehicle_state.poll utantID, ratepervehicle_state.SumOfratePerVehicle, First(VMT .BudgetAreaPop) AS FirstOfBudgetAreaPop, ((Nz([VMT]![sourceTypePopulation]*[sourceTypeFracti on],0)/24)) AS STPop, Nz([VMT]![sourceTypePopulation]*[sourceTypeFraction ]*[SumOfratePerVehicle],0) AS emissionsVehicle, Nz(([VMT]![sourceTypePopulation]*[sourceTypeFractio n]*[SumOfratePerVehicle])* 365,0) AS AnnualemissionsVehicle FROM sourecetypepopulation INNER JOIN (ratepervehic le_state INNER JOIN VMT ON (ratepervehicle_state.yearID = VMT.yearID) AND (rat epervehicle_state.StateID = VMT.stateID)) ON (sourecetypepopulation.sourceTyp eID = ratepervehicle_state.sourceTypeID) AND (sourecetype population.stateID = ratepervehicle_state.StateID) GROUP BY VMT.stateID, VMT.County, ratepervehicle_st ate.yearID, ratepervehicle_state.monthID, ratepervehicle_state. hourID, ratepervehicle_state.sourceTypeID, sourecetypepopul ation.sourceTypeFraction, VMT.SourceTypePopulation, ratepervehicle_state.poll utantID, 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 Sec tion 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, hourvmt fraction.hourID, hourvmtfraction.sourceTypeID, hourvmtfraction.hourVMTFraction, ramp_rate.pollutan tID, VMT.[Ramp VMT], ([Ramp VMT]*[hourVMTFraction])/13 AS HourlyRampVMT, ramp_r ate.ramprate, [HourlyRampVMT]*[ramprate] AS RampEmissions, ([HourlyRampVMT]*[ramprate])*340 AS RampEmissionsAnnual FROM hourvmtfraction INNER JOIN (VMT INNER JOIN ram p_rate ON (VMT.stateID = ramp_rate.StateID) AND (VMT.yearID = ramp_rate.yearID)) ON hourvmtfrac tion.hourID = ramp_rate.hourID WHERE (((hourvmtfraction.roadTypeID)=4)) ORDER BY VMT.stateID, VMT.County, VMT.yearID, hourv mtfraction.hourID, hourvmtfraction.sourceTypeID, ramp_rate.pollutantID ;



## 5.4 Summarizing Results Distance-based emissions by source type, vehicle-ba sed emissions by source type, and ramp emissions by source type are summed by county, year and pollu tant. 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 verifica tion that all steps were run properly. The appropr iate monthID criteria should be set here. The annual av erage 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, Emissi onsDistance_bySourceType.County, EmissionsDistance_bySourceType.yearID, EmissionsDis tance_bySourceType.pollutantName, Sum(EmissionsDistance_bySourceType.SumOfDailyVMT) A S SumOfSumOfDailyVMT, Sum(RampEmissions_Query.HourlyRampVMT) AS SumOfHour lyRampVMT, Sum(EmissionsDistance_bySourceType.SumOfAnnualizedV MT) AS SumOfSumOfAnnualizedVMT, First(EmissionsVehicle_Query.SourceTypePopulation) AS FirstOfSourceTypePopulation, Sum(EmissionsDistance_bySourceType.SumOfEmissionsDi st) AS SumOfSumOfEmissionsDist, Sum(Nz([emissionsVehicle],0)) AS EmissionsVeh, Sum( RampEmissions_Query.RampEmissions) AS SumOfRampEmissions, Sum(EmissionsDistance_bySourceT ype.SumOfAnnualEmissionsDist) AS SumOfSumOfAnnualEmissionsDist, Sum(Nz([Annualemissi onsVehicle],0)) AS AnnualEmissionsVeh, Sum(RampEmissions_Query.RampEmissionsAnnual) AS Sum OfRampEmissionsAnnual, (([SumOfSumOfEmissionsDist]+[EmissionsVeh]+[SumOfHo urlyRampVMT])/1000)*0.001102 AS DailyEmissionsTONS, (([SumOfSumOfAnnualEmissionsDist]+[AnnualEmissionsV eh]+[SumOfRampEmissionsAnnual])/1000)*0.0 01102 AS AnnualEmissionsTONS, Sum([RampEmissions_Query]![HourlyRampVMT]+[Emission sDistance_bySourceType]![SumOfDailyVMT] ) AS AllVMT

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



### FROM (EmissionsDistance_bySourceType LEFT JOIN Emis sionsVehicle_Query ON (EmissionsDistance_bySourceType.pollutantID = Emiss ionsVehicle_Query.pollutantID) AND (EmissionsDistance_bySourceType.sourceTypeID = Emis sionsVehicle_Query.sourceTypeID) AND (EmissionsDistance_bySourceType.hourID = EmissionsV ehicle_Query.hourID) AND (EmissionsDistance_bySourceType.monthID = Emissions Vehicle_Query.monthID) AND (EmissionsDistance_bySourceType.yearID = EmissionsV ehicle_Query.yearID) AND (EmissionsDistance_bySourceType.County = EmissionsV ehicle_Query.County)) INNER JOIN RampEmissions_Query ON (EmissionsDistance_bySourceT ype.County = RampEmissions_Query.County) AND (EmissionsDistance_bySourceType.yearID = RampEm issions_Query.yearID) AND (EmissionsDistance_bySourceType.hourID = RampEmissi ons_Query.hourID) AND (EmissionsDistance_bySourceType.sourceTypeID = Ramp Emissions_Query.sourceTypeID) AND (EmissionsDistance_bySourceType.pollutantID = RampE missions_Query.pollutantID) GROUP BY EmissionsDistance_bySourceType.State, Emis sionsDistance_bySourceType.County, EmissionsDistance_bySourceType.yearID, EmissionsDis tance_bySourceType.pollutantName, EmissionsDistance_bySourceType.monthID, EmissionsDi stance_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 ev ery 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

2Area Source Inventory S-006-OAQ-R-MO-08-S-R1



### 1. Overview work flow chart The process described is not part of a larger syst em 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 ha ve 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 pl an (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, calculati ng, 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 O ffice 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.

3Area 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 us ed 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 source s 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 categor y 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

4Area 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 Indu strial” for the following f uels: coal, distillate oil, natural gas, and liquefied petroleum gas (LPG). Us e 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 followin g 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 comb ustions, subtract reported source totals from the total statewide fuel consumption by querying the to tal process rates for the various SCC codes using the ACCESS data tables at K:\OAQ_INV\Steptool\Stp tl_02.mdb. The remaining number is the area source fuel consumpt ion for the state.

3. To distribute the remaining fuel to the county leve l, calculate the ratio of county to state employment for the manufacturing sector by dividing the numbe r 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 followin g 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 manuf acturing under the paid employees’ column

4. Multiply the ratio calculated above in step 3 by t he area source fuel consumpt ion to distribute the fuel to the county level. The remaining number is the pr ocess rate for each county. Multiply the process rate by the appropriate EPA emission factors for t he various fuels for industrial manufacturing found in AP-42, Fifth Edition, Volume 1, Chap ter 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 fr om 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.

5Area Source Inventory S-006-OAQ-R-MO-08-S-R1
2. To avoid double calculating the various fuel comb ustions, subtract reported source totals from the total statewide fuel consumption by querying the to tal process rates for the various fuels using the SIC codes greater than 49 99 using the ACCESS data tables at K :\OAQ_INV\Steptool\Stptl_02.mdb. These are the SIC codes that identify all t he commercial/institutional area sources.

3. To distribute the remaining fuel to the county leve l, calculate the ratio of county to state employment for the commercial/institutional sector by dividi ng the number of commercial/ institutional employees for each county by the number of commercial/instit utional 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 navi gate through the U.S. Census Bureau’s website.

4. Multiply the ratio calculated above in step 3 by t he area source fuel consumpt ion 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 t he 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 consumpti on for “Residential” for the following fuels: coal, distillate oil, natural gas, and liquid petroleum gas. Use the Ener gy 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 house hold 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 Characterist ics” and select “show more” e. On the left hand side, select “chan ge 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 abov e in step 3 by the area residential f uel 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 approp riate EPA emission factors fo r 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/

.

6Area Source Inventory 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 emission s 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 followin g 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) NATION AL 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 (fireplac e without inserts, fireplaces with inserts and woodstoves).

3. To distribute to the county level for the three ca tegories above, calculate a ratio of county to state using the statewide total of households and the count y 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 pr ocess rate by the appropriate EPA em ission factors fo r each of the categories using the EIIP, Volume 3, Ch apter 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)

7Area Source Inventory S-006-OAQ-R-MO-08-S-R1

2. Multiply the per capita consumption factor ca lculated 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 foll owing 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 queryi ng the total process rates for the SCC 30203202 (straight-dough) and SCC 30203201 (sponge-do ugh) using the ACC ESS data tables at K:\OAQ_INV\Steptool\Stptl_02.mdb.

4. Multiply the remaining process rate by the strai ght-dough emission factor of .5 lbs VOC/1,000 pounds baked found in the EIIP, Volume 3, Area Source Me thod 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 I ndiana 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 fo llowing 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 paint s 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

8Area Source Inventory S-006-OAQ-R-MO-08-S-R1
Note: As of the date of this SOP, the followin g 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 coatin g. 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 abov e 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 empl oyees from the reported sources using the SIC 7532- Body Repair and Paint Shops using the ACCESS data t ables at K:\OAQ_INV\Steptool\Stptl_02.mdb. Subtract this number from the coun ty 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 foll owing 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/vol ume03/archsfc.pdf and the county employment found above in step 1 to calcul ate 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 t he 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 paint s from the EIIP, Volume 3, Chapter 14, Traffic Markings at http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii14.pdf

.

9Area Source Inventory S-006-OAQ-R-MO-08-S-R1
Note: As of the date of this SOP, the foll owing 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 “Tra ffic 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/po licy/ohim/hs04/htm/sf2.htm

. 3. Calculate the emission factor for Indiana by divi ding 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 t he 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

10Area Source Inventory S-006-OAQ-R-MO-08-S-R1

* Use the National default emission fact or because the reporting sources are low. ** Emissions reported in point source

2. Divide the reported VOC emission s for each of the SIC’s by the re ported employment for each SIC. Use this number for the emission factor.

3. Subtract the number of report ed 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 f ound 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 i ndustrial surface coating using the default emission factor:

1. Calculate industrial surface coat ing emissions using the default emission fa ctor 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 Manuf acturing 0.600 lbs VOC/person

24-01-100-000 Industrial Maintena nce Coatings 0.800 lbs VOC/person

24-01-200-000 Other Special Pur pose 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

11Area Source Inventory S-006-OAQ-R-MO-08-S-R1

39 Miscellaneous manufac turing 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 manufac turing 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:\O AQ_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 mult iplying 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 numbe r 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 coun ties, 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\St eptool\Stptl_02.mdb and subtract this number from the county total.

4. Multiply the process rate for each of the counties above by the em ission factor to calculate for VOC emissions.

12Area Source Inventory S-006-OAQ-R-MO-08-S-R1
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 th rough 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 pop ulation 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 th rough 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 emissi ons and number of reported employees for all SIC’s beginning with 30 using the ACCESS data tabl es 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 Ai r 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 Employm ent from the U.S. Census Bureau’s website http://www.census.gov

. Note: As of the date of this SOP, the following st eps 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

13Area Source Inventory 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 re porting 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 st ate and county from the Indiana Department of Transportation’s, Division of Roadway Management Section.

3. Divide the county roadway miles by the state roadw ay 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 E IIP, Volume 3, Chapter 17, Asphalt Paving http://www.epa.gov/ttn/chief/eiip/te chreport/volume03/ iii17_apr2001.pdf

.

14Area Source Inventory S-006-OAQ-R-MO-08-S-R1
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 spec ific emission factor. Develop the factor using a methodology that includes the retrieval of informat ion of pesticides used, an emission factor for each pesticide used, a calculation about the inert ingredien ts 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 emis sions from crop oil concentrates obtained in the pesticide Excel table, pesticides, and solvent carrie rs 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 t he Federal Highway Administration, U.S. Department of Transportation http://www.fhwa.dot.gov/po licy/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. Cens us 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 t he 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 mult iplying by the number of gallons sold statewide found above in step 1.

5. EPA guidance suggests that only 25% of all gas oline 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:

15Area 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 submer ged 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 emi ssions 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 docu ment Public Meeting to Consider Approval of California’s Portable Gasoline-Cont ainer Emissions Inventory. Use the excel spreadsheet found at K:\OAQ_INV\Inv\Area Source\Gasoline.zip to calcul ate the emissions for permeation, diurnal, and transport

. Both the Spillage and Vapor losses are esti mated in the nonroad emissions inventory by EPA models.

2. Using the survey results below in Table 1, esti mate 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\GasCan s.xls, insert the number of oc cupied 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%

16Area Source Inventory 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, esti mate 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 bot h residential and commercial by using the emission rates given in the California document. Use 1.57g/gal /day for plastic contai ners and 0.6g/gal/day for metal containers. Insert the numbers for both resi dential and commercial into the excel spreadsheet at K:\OAQ_INV\Inv\Area Source\ Gaso line.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/po licy/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. Cens us Bureau’s, Economic Census at http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447

.

17Area Source Inventory S-006-OAQ-R-MO-08-S-R1

3. Run a query to find the amount of point source reported gasoline using t he 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 tank s 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. Filt er 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 t anks 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 t anks 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 co unties 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 factor s 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 t he Federal Highway Administration, U.S. Department of Transportation http://www.fhwa.dot.gov/po licy/ohim/hs04/htm/mf21.htm

. 2. Find the amount of gasoline sold statewi de and by county using the NAICS code 447-Gasoline Service Station from the U.S. Cens us 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 file s supplied from the mobile model. Table 1 and Table 2 show examples of how the emission factors fo r 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 Countie s, Clark/Floyd, and Lake/Porter.

18Area Source Inventory S-006-OAQ-R-MO-08-S-R1

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

19Area Source Inventory S-006-OAQ-R-MO-08-S-R1

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 Fue ling (Stage II) – Spillage

SCC: 2501060103

Follow these steps when calculating emi ssions from vehicle fueling – Spillage:

1. Find the amount of gasoline sold in Indiana at t he Federal Highway Administration, U.S. Department of Transportation http://www.fhwa.dot.gov/po licy/ohim/hs04/htm/mf21.htm

. 2. Find the amount of gasoline sold statewi de and by county using the NAICS code 447-Gasoline Service Station from the U.S. Cens us 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 emi ssions from underground tank breathing:

1. Find the amount of gasoline sold in Indiana at t he Federal Highway Administration, U.S. Department of Transportation http://www.fhwa.dot.gov/po licy/ohim/hs04/htm/mf21.htm

. 2. Find the amount of gasoline sold statewi de and by county using the NAICS code 447-Gasoline Service Station from the U.S. Cens us Bureau’s, Economic Census at http://www.census.gov/econ/census02/data/in/IN000_44.HTM#N447

.

20Area Source Inventory S-006-OAQ-R-MO-08-S-R1
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 t he Federal Highway Administration, U.S. Department of Transportation http://www.fhwa.dot.gov/po licy/ohim/hs04/htm/mf21.htm

. 2. Find the amount of gasoline sold statewi de and by county using the NAICS code 447-Gasoline Service Station from the U.S. Cens us 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, Chapt er 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/c gi-bin/cbpnaic/cbpsel.pl

. Note: Use the steps in 3.2-1 to navi gate 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, Fi fth Edition, Volume 1, Chapter 2-1.12, Solid Waste Disposal at http://www.epa.gov/ttn/chief/ap42/ch02/index.html

.

21Area Source Inventory S-006-OAQ-R-MO-08-S-R1

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 thr ough 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 co mmercial 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, Fi fth 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 residentia l 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, Fi fth 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 l eaf burning and a per capita for brus h 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 c apita factor for leaves at 25% and for brush at 25%. Of the total waste generated only 28% burns.

22Area Source Inventory 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 follow ing 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 wast e generated to account for the percentage of forest in each county. The percentages come from a doc ument 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 re sidential waste incinerati on 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 ge nerated by rural popul ation 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 residentia l 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:

23Area Source Inventory 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 em ission factors in FIRE 6.25 using the SCC code 2630020000.

Sub-Category 5.4: Treatment, St orage, and Disposal Facilities

SCC: 2640000004

Follow these steps when calculating emissions fr om 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 th e 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 chec klist 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 el ectronically at K:\OAQ_INV\Inv\Area Source.

The Branch Contact for the Air Programs Branch a nd 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.

24Area Source Inventory S-006-OAQ-R-MO-08-S-R1 

### 8. Quality Assuran ce / 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 CF R 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, PM 2. 5 

# , and Regional Haze:

F inal Technical Support Document

April 25 , 2008

States of Illinois, Indiana, Michigan, Ohio, and Wisconsinii 

# Table of Contents

Section

- Title Pa ge
Executive Summary i i i

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 PM 2. 5 

### 2 1 2

. 3 Regional Haze 35

3

. 0 Air Quality Model ing 4 6 3

. 1 Selection of Base Year 46 3

. 2 Future Years of Interest 46 3

. 3 Modeling System 4 7 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 De monstration for Ozone and PM 2.5 

### 7 1 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 PM 2. 5 

### 90

5

. 0 Reasonable Progress Assessment for Regional Haze 9 3 5

. 1 Class I Areas Impacted 93 5

. 2 Future Year Modeling Results 96 5

. 3 Weight of Evidence Determination for Regional Haze 1 04

6

. 0 Summary 1 11

7

. 0 References 1 1 4

Appendix I Ozone and PM 2.5 

### Modeling Results Appendix II Ozone Source Apportio nment Modeling Results Appendix III PM 2.5 

### Source Apportionment Modeling Results Appendix IV Haze Source Apportionment Modeling Resultsiii 

### EXECUTIVE SUMMARY

States in the upper Midwest face a number of air quality challenges. More than 5 0 counties are
cu rrently classified as nonattainment for the 8

- hour ozone standard and 60 for the f ine particle ( PM 2.5 

### ) standard (1997 version s ). 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 PM 2. 5 (right )



### T o support the development of State Implementation Plans (SIPs) for ozone, PM 2.5 

### , 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.

M onitoring 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 t o federal and state emission control programs.

• Ozone concentrations are strongly influenced by meteorological con ditions, with more high ozone days and higher ozone levels during summers with above normal temperatures.iv



### • Inter

- and intra

- regional t ransport of ozone and ozone precursors aff ects many portions of the five states , and is the principal cause of nonattainme nt in some areas far from population or industrial centers

.

PM 2.5



### • Current monitoring data (200 5

- 200 7 ) show 3 0 sites in violation of the annual PM 2.5 

### standard of 15 ug/m 3



### . N onattainment sites are characterized by an elevated regional background ( about 12

– 14 ug/m 3 

### ) and a significant local (urban) increment ( about 2

– 3 ug/m 3 

### ). Historical PM 2.5 

### data show a slight downward trend since deployment of the PM 2.5 

### monitoring network in 1999.

• PM 2.5 

### concentrations are also influenced by meteorology , but the re lationship is more complex and less well understood compared to ozone.

• O n an annual average basis, PM 2.5 

### 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 2 2

– 2 4 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 im pairment is dominated by sulfate and nitrate.

Air quality models were applied to s upport the regional planning efforts

. Two base years were used in the modeling analyses: 2002 and 2005. B asecase modeling was conducted to evaluate model performance (i.e., assess the model's ability to reproduce observed concentrations ) . This exercise wa s intended to build confidence in the model prior to its use in examining co ntrol strategies. M odel performance for ozone and PM 2.5 

### 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 PM 2.5 

### and if not, then what additional emission reductions would be necessary f or attainment. Based on th e modeling and other supplemental analyses , the following general conclusions can be made:

• E xisting controls are expected to produce significant improvement in ozone and 

### PM 2.5 

### 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 decisio n (i.e., how much safeguard to incorporate).

• M odeling suggests that mo st 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 a nd northeast ern Ohio.v



### • Modeling suggests that m ost sites a re expected to meet the current PM 2.5 

### standard by the applicable attainment date, except for sites in Detroit, Cleveland, and Granite City

.

T he regiona l modeling for PM 2.5 

### does not include air quality benefit s expected from local controls. States are conducting local

- scale analyses and will us e these results, in conjunction with the regional

- scale modeling, to support their attainment demonstrations for PM 2.5



### .

• These findings of residual nonattainment for ozon e and PM 2.5 

### 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 PM 2.5 

### d esign values on the order of 16

- 17 ug/m 3 

### ). 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 a re 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). I f either of these conditions is n ot met, then attainment may be less likely.

• M ode ling s uggests that the new PM 2.5 

### 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 eas tern U.S. are expected to be greater than

(less improved than) the uniform rate of visibility improvement value s in 2018 based on existing controls , including those in northern Michigan and s ome in the northeastern U.S. Visibility levels in m any other Cla ss I areas in the eastern U.S. are expected to be less than (more improved than) the uniform rate of visibility improvement value s 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) 1 

### and its contractors to support the development of State Implementation Plans (SIPs) for ozone, fine particles ( PM 2.5 

### ), 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 emphasize d. First, a regional , multi

- pollutant approach was taken in addressing ozone, PM 2.5 

### , and haze f or technical reasons (e.g., common ality in precursors, emission sources, atmosphe ric 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 357 19), and its implementation guidance for ozone (70 FR 71663) and PM 2.5 

### (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 on e 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 orga nized in the following sections. This I ntroduction p rovides an overview of regulatory requirements and background information on regional planning . Section 2 r eviews the ambient monitoring data and presents a conceptual model of ozone, PM 2.5 

### , and haze for the region . Section 3 d iscusses the air quality modeling analyses, including development of the k ey model inputs (emissions inventory and meteorological data), and basecase model performance evaluation . A modeled a ttainment d emonstration for o zone and PM 2.5 

### is presented in Section 4, along with relevant data analyses considered as part of the weight

- of

- evidence de termination

. Section 5 documents the r easonable p rogress a ssessment for r egional h aze , a long with relevant data analyses considered as part of the weight

- of

- evidence determination . Finally, ke y 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, m ore than 100 counties were designated as nonattainment

. 2 

### The designations became effective on June 15, 2004. SIPs for ozone we re 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 a re 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 n onattainment in the region.2



### For PM 2.5 

### , EPA promulgated designations on December 17, 2004 (70 FR 944, January 5, 2005). In the 5

- state region, 70 c ounties were designated as nonattainment. 3 

### The designations bec a me effective on April 5, 2005. SIPs for PM 2.5 

### 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 A pril 2008) and for haze no later than three years after the date on which the Administrator promulgate d the PM 2.5 

### designations (per the Omnibus Appropriations Act of 2004) (i.e., by December 2007). The applicable attainment date for PM 2.5 

### nonattainment ar eas 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 ar eas which impairment results from manmade air pollution.” T here are 156 Class I areas, including two in northern Michigan: Isle Royale National Park and Seney National Wildlife Refuge 4



### . EPA’s visibility rule (64 FR 35714, July 1, 1999) require s reasonabl e progress in achieving “natural conditions” by the year 2064. As noted above, the first regional haze SIP wa s 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 wa s established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. T he four states and EPA signed a Memorandum of Agreement (MOA) that initiated t he Lake Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee th e 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 p urpose s 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 fe derally 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 a n MOA that established the MRPO. An

o perating principles document for M RPO, which describe the roles and responsibilities of states, tribes, federal agencies, and stakeholders, w as issued in March 2001

. M RPO has a similar purpose as LADCO , but is focus ed 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 R egional Planning Organizations (R POs ) 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 designate d 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 collec tion and analysis a priority. M ore than $ 7 M in RPO grant funds were used for special purpose monitor ing, preparing and improving emissions inventories, and conducting air quality analyses 5 

### . An overview of the technical work is provided below.

Monitoring: Numerous monitoring projects were conducted to supplement on

- going state and local air pollution mo nitoring. 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: B aseyear emissions inventor ies were prepared for 2002 and 2005 . States provided point source and area source emissions data , and MOBILE6 input files and mob ile 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 stra tegy 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 P M 2.5 

### and m oderate ozone nonattainment areas ), 2012 ( planning to address a 201 3 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 o zone , PM 2.5 

### , and regional haze problems) and supplemental information for the attainment demonstration . Given uncertainties in emissions inventories and mo deling

, especially for PM 2. 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 stra tegies. The analyses wer e conducted in accordance with EPA’s modeling guidelines ( EPA, 2007a)

. The PM/haze modeling cover s the full calendar year (2002 and 2005 ) for an eastern U.S. 36 km domain, while the ozone modeling focus es on the summer period (200 2 and 2005 ) for a Midwest 12 km subdomain. The same model (CAMx) was used for ozone, PM 2.5 

### , 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 precurs ors)

, PM 2. 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 ).

Analys e s of the data were conducted to produce a conceptual model, which is a qualitative

summar y of the physical, chemical

, and m eteorological processes that control the formation and distribution of pollutants i n a given region. This section reviews the relevant data analyses and describes our understanding of ozone, PM 2.5, 

### and r egional haze with respect to c urrent

co nd i tions

, d ata variability ( spatial, temporal, and chemical)

, i nfluence of meteorology

( includin g t ransport patterns )

, p recursor s ensi tivity, and s ource culpability.

2.1 Ozone In 1979, EPA adopted an ozone standard of 0.12 ppm, averaged over a 1

- hour period. This standard i s attained when the number of days per calendar year with maximum hourly av erage 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 4 th 

### highest daily 1

- hour value over a 3

- year period) less than 0.12 ppm.

In 1997, EPA tightened the ozone s tandard to 0.08 ppm, averaged over an 8

- hour period 6



### . T he standard is attain ed 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 pp m (or 85 ppb)

.
Current C onditions : 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 re present sites w ith design values above the standard

. Currently, there are 1 9 sites in violation of the 8

- hour oz one NAAQS in the 5

- state region, including sites in the Lake Michigan area, Detroit, Cleveland, Cincinnati, and Columbus.

Table 1 provides the 4 th



### - 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 t ighten ed 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 ove r an 8

- hour period.5 Figure 1

. 8

- h our o zone d esign v alues ( 200 5

- 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 Region7



### 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 sunligh t

, as well as precursor concentrations (see, for example, Figure 2 )

.

Figure 2. Number of h ot d ays and 8

- h our “ e xceedance” d ays 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 s low

- moving high pressure system to the east of the region. The relative importance of various meteorological factors is discussed la ter in this section.

Transport of ozone (and its precursors) is a significant factor and occurs on several spatial scales. Regionally, o ver 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

. Th is 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 e levated r egional o zone c oncentrations (June 23

– 25, 2005)

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8

- hour standard8



### Local ly, emissions from urban areas add to the regional background leading to ozone concentration hot spots downwind. Depending on the synoptic wind patterns (and local land

lake breezes), different downwind areas are affected (see, for example, Figure 4).

Figure 4. Examples of r ecent h igh o zone d a ys in the Lake Michigan a rea

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 backgro und 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 o zone m easurements over Lake Michigan (left) and a long u pwind b oundary (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 e stimate the impact of these source regions , two s imple 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. Comp osite 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 I ndiana) as being upwind on these high ozone days.

Figure 6 Back t rajectory a nalysis s howing u pwind a reas a ssociated with h igh o zone
c oncentrations



### Second, to assess the impact from Chicago /NW Indiana , Blanchard (2005 a ) compar ed ozone concentrat ions 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 ozon e 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 contribute s 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 af fecting 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 hi gh 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.

• 

### Downwi nd 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 V ariability : Since 1980, considerable progress has been made to meet the previous 1

- hour ozone standard. Fig ure 8 shows the decline in both the 1

- hour a nd 8

- hour design values for the 5

- state LADCO region over the last 25 years.

Figure 8 Oz one d esign v alue t rends in 5

- State r egion



### The trend i s more dramatic for the higher ozone sites in the 5

- state region (see Figure 9 ) . This plot shows a pronoun ced downward trend in the design value since the 2001

- 2003 period , due, in part, to the very low 4 th 

### high values in 2004.

Figure 9. Trend in o zone d esign v alues and 4 th h igh v alues for higher ozone s ites in r egion



### The imp rovement 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 Values12 Figure 10 . Ozone d esign v alue m aps for 1995

- 1 997, 2000

- 2002, and 2 00 5

- 200 713



### 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 m odel 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 M ethod

: This method uses a statistical m odel 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 (Kensk i, 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 4 th 

### high ozone values. By holding the meteorological e ffects 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 4 th 

### high ozone concentrations for several monitor s 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 e xamine 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 docume nted in Camalier et al., 2007. The seasonal average trend s 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 11 a

. Trends in meteorologically adjusted 4 th high 8

- hour ozone concentrations for seven Mid western sites (199 7

– 200 7 )15 Chiwaukee, WI Sheboygan, WI

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Cleveland (Ashtabula), OH Cincinnati (Sycamore) , OH

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Detroit (New Haven), MI St. Louis, MO

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Indianapolis, IN

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather Figure 11b

. Trends in seasonal 8

- hour ozone concentrations for seven Mid western sites (199 7

– 200 7 )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 w ith current 8

- h ou r design values close to or above the standard (Kenski, 2008 b )

. T he CART model searches through 60 meteorological variables to determine which are most efficient in predicting ozone. Although the exact sel ection of predictive variables changes from site to site, the most common predictors we re temperature, wind direction, and relative humidity. Only occasionally were upper air variables, transport time or distance, lake breeze, or other variables s ignifica nt. (Note, the o zone and met eorological data for the CART analysis a re the same as used in the EPA/Cox analysis.)

For each monitor, regression trees were developed that classify each summer day (May

September) by its meteorological conditions. Similar d ays 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 Fig ure 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 non

meteorological influences. Trends over the 13

- year period at most sites were found to be declin ing, with the exception of Detroit which showed fairly flat trends. C omparison 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, 2008 b ) . This metric reflects the variability from the 1 3

- year average in the number of days in the higher ozone concentration nodes (see Figure 13)

. Examination of these plots indicates :

• 

### 2002 and 2005 w

ere 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 fact ors (i.e., emission reductions).

In conclusion, a ll 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 meteoro logical variability is accounted for. The decreases are present whether seasonal average ozone, peak values (annual 4 th 

### highs), or a subset of high days with similar meteorology are considered . The consistency in results across models is a good indicatio n 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 o zone C ART groups (average ozone > 6 5 ppb) for seven Midwestern sites (199 5

– 20 0 7 )

Note: line represents linear best fit18 Figure 13 . Ozone c onduciveness i ndex (and number of high ozone days) f or s even M idwestern site (199 5

– 200 7 )19



### Pr ecursor S ensitivity : O zone i s 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/NO x ratios, typical of r ural environments ( with low NO x), ozone tends to be more responsive to reductions in NO x . Conversely, in areas with low VOC/NO x

ratios, typical of urban environments (with h i gh NOx) , ozone tends to be more responsive to VOC reductions.

An analysis of V OC and NO x 

### - limitation was conducted with the ozone MAPPER program, which is based on the Smog Production (SP) algorithm (Blanchard

, et al., 200 3 ) . 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 1 4 . As can be
seen, ozone is usually VOC

- l imited in cities and NOx

- limited in rural areas. ( Data from aircraft measurements suggest that ozone is usually NO x 

### - limited over Lake Michigan and away from urban centers on days when ozone in the urban centers is VOC

- limited. ) T he highest ozone days we re found t o be NO x 

### - limite d. 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 1 4 . Mean afternoon extent of reaction (1998

– 2002)20



### Blanchard (2004 and 2005 a ) examin ed weekend

- weekday differences in ozone and NO x 

### in the Midwest. All urban areas in th e s e two stud ies exhibited substantially lower (40

- 60%) weekend concentrations of NO x 

### compared to weekday concentrations. Despite lower weekend NO x 

### 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 1 5 ). This small but counterproductive change in local ozone concentrations suggests that local urban

- scale NO x 

### reductions alone may not be very effective.

Figure 1 5 . Weekday/w eekend d ifferences in 8

- h our o zone
– number of sites with weekend inc rease (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 (2005 a ) looked at the effe ct 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 re ductions.

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 w eekend changes in ozone precursor emissions cause both increases and decrease s 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

- sen sitive 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 Michiga n 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 P M 2. 5 

### impacts).

2.2 P M 2.5 

### In 1997, EPA adopted the PM 2. 5 

### standard s of 15 ug/ m 3 

### (annu al average) and 65 ug/m3 (24

- hour average). T he annual standard is attained if the 3

- year average of the annual average PM 2. 5 

### concentration is less than or equal to the level of the standard. The daily standard is attained if the 98th percentile of 24

- h our PM 2. 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 PM 2. 5 

### standard s to 15 ug/m 3 

### (annual average) and 35 ug/m 3 

### (24

- hour average).

Current C onditions : Maps of annual and 24

- hour PM 2. 5 

### design values for the 3

- year period 200 5

- 200 7 are shown in Figure 1 6 . The “hotter” colors represent higher concentrations , where red dots represent sites w ith design values above the annual standard

. Currently, there are 3 0 sites in vio lation of the annual PM 2. 5 

### standard.

Table 2 provides the annual PM 2. 5 

### concentrations and associated design values since 200 3 for several high monitoring sites throughout the region.22

Figure 1 6

. PM 2.5 d esign v alues

- a nnual a verage (top) and 24

- h o ur a verage (bottom) ( 200 5

- 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 Region24

When 

### EPA initially set the 2 4

- hour s tandard at 65 μg/m 3



### , it also adopted the following concentration ranges for its Air Quality Index (AQI) scale:

Good < 1 5 ug/m 3 Moderate 15

- 40 μg/m 3 Unhealthy for Sensitive Groups (USG) 40

- 65 μg/m 3 Unhealthy 65

- 150 μg/m 3



### Fig ure 1 7 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. Moder ate and USG levels can occur any time of the year.

Figure 1 7 . Percent of d ays in AQI c ategories for PM 2. 5 (2002

- 2004)



### Data Variability : PM 2. 5 

### concentrations vary spatially, temporally, and chemically in the region. This variability is discussed furt her below.

On an annual basis, PM 2.5 

### exhibits a distinct and consistent spatial pattern. As seen in Figure 1 6 , a cross the Midwest, annual concentrations follow a gradient from low values (5

- 6 μg/m 3 

### ) in

no rthern and western areas ( Minnesota and northern W isconsin ) to high values (17

- 18 μg/m 3 

### ) in Ohio and along the Ohio River. In addition, c oncentrations in urban areas are higher than in upwind rural areas, indicating that local urban sources add a significant increment of 2

- 3 μg/m 3 

### to the regional backgro und of 12

- 14 μg/m 3 

### (see Figure 1 8 ).

Figure 1 8

. Regional (lighter shading) v. local components (darker shading) of a nnual a verage PM 2. 5 concentrations25



### Because monitoring for PM 2. 5 

### only began in earnest in 1999, after promulgation of the PM 2. 5 

### standar d

, limited data are available to assess trends

. T ime series based on federal reference method (FRM) PM 2. 5 

### - mass data show a downward trend in each state (see Figure 19 ) 7



### .

Figure 19

. PM 2. 5 t rends i n a nnual average (top) and d aily c oncentrations (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 PM 2. 5 

### trends was performed using the n onparametric Theil test for slope (Hollander and Wolfe, 1973). Trends were generally consistent around the region, for both PM

mass a nd for the individual components of mass. Figure 2 0 shows trends for PM 2. 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 sh ow 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 decrea se for this set of sites is

- 0.24 ug/m 3 

### /year.

Figure 2 0

. Annual t rends in PM 2. 5 m ass ( 1999

– 2006 )



### Seasonal trends show mostly similar patterns (Fig ure 2 1 ). Trends were downward at most sites and seasons, with overall seasonal averages varying b etween

- 0.15 to

- 0.56 ug/m 3 

### /year. The strongest and most significant decreases took place during the winter quarter (Jan uary

- March ). No statistically significant increasing trends were observed.27 Figure 2 1 . Seasonal t rend s in PM 2. 5 m ass ( 1999

– 2006 )



### PM 2.5 

### shows a slight variation from weekday to weekend , as seen in Figure 2 2

. Although m ost cities have slightly lower concentrations on the weekend , t he difference is usually less than 1 μg/m 3 

### . The re is a more pronounced w eekday/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 2 2 Day

- of

- w eek v ariability in PM 2. 5 (2002

- 2004)28



### In the Midwest, PM 2.5 

### is made u p 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 2 3 . Spatial map of P M 2. 5 chemical composition in the Midwest (2002

- 2003)



### The three major components vary spatially (Fig ure 23 ), including notable urban and rural differences (Figure 24). The components also vary seasonally (Figure 25). T hese patterns account for much of th e annual variability in PM 2.5 

### mass noted above.

Figure 2 4 . Average regional (lighter shading) v. local (darker shading) of PM 2. 5 che mical species29 Figure 2 5 Seasonal and s patial v ariability in PM 2. 5 c omponents30



### 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 for med 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 f ertilizer 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 co ncentrations 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 summe r . Like nitrate, organic carbon has both regional and local co mponents. 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 fr om biogenic sources.

Precursor S ensitivity:

Data from the Midwest ammonia monitoring network were analyzed with thermodynamic equilibrium models to assess the effect of changes in precursor gas concentrations on PM 2.5 

### concentrations (Blanchard, 200 5 b ). These analyses indicate that particle formation responds in varying degrees to reductions in sulfate, nitric acid, and ammonia

. Based on Figure 2 6 , which shows PM 2.5 

### concentrations as a function of sulfate, nitric acid (HNO 3), and ammonia (NH3), several k ey findings should be noted:

• 

### P M 2. 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 PM 2.5 

### mass decreases.

• 

### PM 2. 5 

### mass is also sensitive to reductions in nitric acid and ammonia . The greatest PM 2. 5 

### decrease in response to nitric acid reductio ns occurs during the winter , when nitrate is a significant fraction of PM 2. 5



### .

• 

### Under conditions with lower sulfate levels (i.e., prox y of future year conditions), P M 2. 5 

### is more sensitive to reductions in nitric acid compared to reductions in ammonia.

• 

### Ammo nia 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) NO x lead to reductions , albeit non

propo rtional reductions, in PM

- nitrate (Blanchard, 200 4 )

. This result is consistent with analyses of continuous PM

- nitrate from several US cities, including St. Louis (Millstein, et al, 2007).31

Figure 2 6

. Predicted mean PM fine mass con centrations at Bondville, IL (top) and Detroit (Allen Park), MI (bottom) as functions of changes in sulfate, nitric acid (HNO3), and ammoni a (NH3)

Note: starting at the baseline values (represented by the red star), either moving downward (reductions in n itric acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM 2.5 values32



### Meteorology : PM 2 .5 

### concentrations are not as strongly influenced by meteorolog y 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 PM 2.5 

### . In the case of PM

, the reason is two

- fold: (1) stagnation and limited mixing under these conditions cause PM 2. 5 

### to build up, usually over several days, and (2) these conditions generally promote higher conversion of important precursors (SO 2 

### to SO 4 

### ) and higher emissions of some precursors, especially biogenic ca rbon. Wind direction is another strong determinant of PM 2.5 

### ; air transported from polluted source regions has higher concentrations.

Unlike ozone, PM 2.5 

### has occasional winter episodes. Conditions are similar to those for summer episodes, in that station ary high pressure and ( seasonally ) warm temperatures are usually factors. Winter episodes are also fueled by high humidity and low mixing heights.

PM 2. 5 

### chemical species show noticeable transport influences. T rajectory 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 2 7 shows results from an ensemble trajectory analysis of 17 rural eastern IMPROVE sites.

Figure 2 7

. Sulfate and n itrate s ource r egions based on e nsemble t rajectory a nalysis



### When these results are considered together with analyses of precursor sens itivity ( e.g., Fig ure 2 6 )

, 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 Mid west, 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 PM 2. 5 

### monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 200 8 ). Figure 2 8 summarizes the source contributions from these studies. The studies show that a large portion of PM 2. 5 

### mass consists of secondary, regional impacts, which cannot be attributed to indivi dual facilities or sources (e.g., secondary sulfate, secondary nitrate, and secondary organic aerosols). Nevertheless, wind analyses (e.g., Figure 2 7 ) provide information on likely source regions. Regional

- or national

- scale control programs may be the m ost effective way to deal with these impacts. EPA's C AIR , for example, will provide for substantial reductions in SO2 emissions over the eastern half of the U.S., which will reduce sulfate (and PM 2. 5 

### ) concentrations and improve visibility levels.

The stu dies also show that a smaller, yet significant portion of PM 2. 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 stat istical analysis methods h elp to identify local sources and quantify their impact. This information is valuable to s tates wishing to develop control programs to address local impacts. A combination of national/regional

- scale and local

- scale emission redu ctions 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 s ources, 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 7CoalCombustionSecondaryNitrateMobileIndustrialSoilBurningug/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

La w ndale Springfield

Tikhon Craig

Taft

Figure 2 8

. Major Source Contributions in the Midwest based on Hopke , 2005 (upper left), STI, 2006 (upper right), and STI, 200 8 (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 b y 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 imp airment re sults from manmade air pollution” . To impl ement this provision, in 1999, EPA adopted regulations to address regional haze visibility impairment (USEPA, 1999)

. EPA’s rule requires s tates to “make reasonable progress toward meeting the national goal” . Spe cifically, states must

establish reasonable progress goals , which provide for improved visibility on the most impaired (20% worst) days sufficient to achieve natu ral 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, w hich is expressed in terms of visual range, light extinction (1/Mm)

, or deciviews (dv), depends not just on the t otal PM 2. 5 

### mass concentration, but also on the chemical composition of the particles and meteorological conditions.

Current C onditions : A map of the average l ight extinction values for the most impaired (20% worst) visibility days for the 5

- year baseline period ( 2000

- 2004 ) is shown in Figure 2 9

.

Figure 2 9 . Baseline Visibility Levels for 20% Worst Days (2000

– 2004) , units: Mm - 136



### 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

. Th e new IMPROVE equation was used to calculate updated baseline values.

Second, d ue 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” dat a 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 the se days to be exclu ded from the baseline calculations. However, the light extinction due to the remaining measured species is significant ( i.e., above the 80 th 

### percentile). It makes sense to include these days in the baseline calculations, because they are largely dominate d 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 i n the baseline calculation results in a s mall

, but measurab le

, effect on the baseline values (i.e., v alues 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 19.8 6 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 v isibility m etrics (deciviews) for n orthern Class I a reas

Old IMPROVE Equation (Cite: VIEWS, November 2005) 20% Worst Days

2000 2001 2002 2003 2004 Baseline Value 2018

UR I Value Natural

Conditions Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.0 9
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 Natur al 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

UR I 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 2 1.97 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 Ken s ki, 200 7 adjustment for missing days

UR I

= uniform rate of improvement38



### 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 EP A 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 upda ted 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 PM 2. 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 c omposition 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 PM 2. 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 PM 2. 5 

### mass and chemical species for rural IMPROVE (and IMPROVE

- protocol) sites in the ea stern U.S. showed a high degree of correlation between PM 2. 5 

### - mass , sulfate, and nitrate levels (see Figure 3 1 ). The Class I sites in northern Michigan and northern Minnesota , in particular, are highly correlated for PM 2. 5 

### mass, sulfates, and org anic carbo n mass (AER, 2004)

.39

Figure 3 1

. Correlations a mong IMPROVE (and IMPROVE

- protocol) m onitoring s ites in Eastern U.S.



### Lo ng

- term trends at Boundary Waters (the only regional site with a sufficient data record) show significant decreases in total PM 2. 5 

### (

- 0.005 ug/year) and SO4 (

- 0.04 ug/year) and an increase in NO3 (+0.01 ug/year). These PM 2. 5 

### and SO4 trends are generally consistent with long

- term trends at other IMPROVE sites in the eastern U.S. , which have shown widespre ad decreases in SO4 and PM 2. 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. P oor visibility days occur throughout the year, as indicated in Fig ure 3 2

. (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 3 3 (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, sugge sting the importance of different sources and meteorological conditions at different times of the year.40

Figure 3 2 . Daily light extinction values for 20% worst days at Boundary Waters (2000

– 2004)41 Figure 3 3 . Monthly average light extinction values for northern Class I areas42



### Precursor S ensitivity: Results from two analyses using t hermodynamic equilibrium model s provide information on the effect of changes in precursor concentrations on PM 2.5 

### concentrations (and, in turn , visibility levels) in the northern Class I areas . First, a preliminary analysis using data collected at Seney indicate d that PM 2.5 

### 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, 2005 b )

. F igure 34 shows PM 2.5 

### co ncentrations 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 PM 2.5 

### concentrations. Thus, reductions in sulfate, nitric acid, and ammonia will lower PM 2.5 

### concentrations and improve visibility in the northern Class I areas.

Figure 3 4 . Predicted PM 2.5 mass concentrations at Great River Bluffs, MN as functions of changes in sulfate, nitric acid, and ammonia



### Meteorology and T

ransport : The role of meteorology in haze is complex. Wind speed and wind direction govern the movement of air masses from polluted areas to the cl eaner 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 a nd 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 understan d 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 3 5

. The o range areas are where the air is most likely to come from, and the green
areas are where the ai r 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 3 5 . Composite back trajectories f or 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, 200 8 ). Based on this information, the most important contributing states are Michigan, Minnesota, and Wisconsin, as well as Missouri, North Dakota, Iow a, Indiana and Illinois (see, for example, Figure 3 5 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 f rom 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 usi ng monitoring data from Boundary Waters and statistical analysis methods (DRI, 2005). The study shows that a large portion of PM 2.5 

### mass consists of secondary, regional impacts, which cannot be attributed to individual facilities or sources (e.g., seconda ry sulfate, secondary nitrate, and secondary organic aerosols). Industrial

sources contribute about 3

- 4% and mobile sources about 4

- 7 % to PM 2.5 

### mass.

A special study was performed in Seney to identify sources of organic carbon ( Sheesley, et al, 2004 ). As seen in Figure 3 6 , t he highest PM 2.5 

### 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 b ec ause 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 3 6 . Monthly concentrations of PM 2.5 species (top ) , and secondary and b iogenic

- 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 PM 2.5 

### chemical speciation data were examined for days with high organic carbon and elemental carbon conc entrations, 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 wildfi res 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 ca rbon 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 dec iding 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 candida te control programs.

The modeling analyses wer e conducted in accordance with EPA’s modeling guidelines (EPA, 2007 a ). Further details of the modeling are provided in two protocol documents : LADCO, 2007a and LADCO, 2007b.

This section reviews the develop ment and evaluation of the modeling system used for the multi

pollutant analyses. Application of the modeling system (i.e., attainment demonstration for ozone and PM 2. 5 

### , and reasonable progress assessment for haze) is covered in the following sections.

3.1 Selection of Base Year Two base year s 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 ye ar. 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 200 2 was more severe compared to 200 5 . 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 f or ozone and PM 2.5 

### , and reasonable progress goals for regional haze, several future years are of interest:

2008 Planning year for oz one basic nonattainment areas (attainment date 2009) 8 

### 2009 Planning year for ozone moderate nonattainment areas and PM 2.5 

### nonattainment areas (attainment date 2010) 2012 Planning year for ozone moderate nonattainment areas and PM 2.5 

### nonattainment

areas , with 3

- yea r extension (attainment date 2013) 2018 First milestone year for regional haze planning

8 According to USE PA’ 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, 2007 c ). 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 att ainment 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 w as conducted (e.g., 20 12 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 nest ed grids, process analysis, source apportionment, and plume

- in

grid), 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 wi th 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 w as used for this modeling. A subset of the RPO domain was

us ed for the LADCO modeling. For PM 2. 5 

### and haze , the large eastern U.S. grid at 36 km (see box on right side of Figure 3 6 ) was used . A PM 2. 5 

### sensitivity run was also performed for this domain at 12 km. For ozone , the smaller grid at 12 km (see shaded port ion of the box on the right side of Figure 3 7 ) w as used for most model runs . An ozone sensitivity run was also performed with a 4km sub

- grid over the Lake Michigan a rea 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 3 7

. Modeling grids

– RPO domain (left) and LADCO modeling domain (right) 

## 12 km

36 km48



### 3.5 Model Inputs : Meteorology Meteorological inputs w ere derived using the Fifth

- Generation NCAR/Penn State Meteorological Model (MM5)

– version 3.6.3 for the year s 2001

– 2003, and version 3.7 for the year 2 005

. The MM5 modeling domains are consistent with the National RPO grid projections (see Figure 3 8 )

.

Figure 3 8 . MM5 m odeling 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 n on

- 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 200 5.

Model performance was assessed quantitatively wi th the METSTAT tool from Environ . The metrics used to quantify model performance include mean observat ion, mean prediction, bias

, gross error, root mean square error, and index of agreement. Model perfor mance metrics were calculated for several sub

- region s of the modeling domain (Figure 3 9 ) and represent hourly spatial averages of multiple monitor locations. Additional analysis of rainfall is done on a monthly basis.

Figure 3 9 . Sub

- domains used for m odel performance for 2001

- 2003 (left) and 2005 (right)



### A summary of the performance evaluation results for the meteorological model ing is provided below. Further details are provided in two summary reports (LADCO, 2005 and LADCO, 2007 c ).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 mod el performance

.

Figure 40

. Daily temperature bias for 2002 (left ) and 2005 ( right ) with hotter colors (yellow/orange/red) representing overestimates and cooler colors (blues) represen ting 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 Field

s : The win d 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

- pr edicted 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 go od agreement spatially and in terms of magnitude in the winter, fall, and early spring months. There are , however, large over

predictions of rainfall in the late spring and summer months (see Figure 4 1 )

. These over

predictions are seen spatially and in ma gnitude 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 dep

osition in the photochemical model. CAMx does not explicitly use the convective and

non

- convective rainfall output by MM5, but estimates wet scavenging by hydromete o rs using cloud, ice, snow, and rain water mixing ratios output by MM5. Nevertheless, this could have an effect on model performance for PM 2. 5 

### , as discussed in Section 3.7, and may warrant further attention.50

Figure 4 1

. 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 E mission 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 provide d in two summary reports (LADCO, 2006 a and LADCO, 200 8 a ) 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 [comme rcial marine, air craft, and railroads]), emissions were prepared using data supplied by the LADCO States and other RPOs

.

Base Year Emissions : S tate and source sector emission summaries for 2002 ( Base K) and 2005 ( Base M) are compared in Figure 4 2

. A ddit ional detail is provided in Table s 6 a (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 4 2 . Base K and Base M e missions for 5

- s tate LADCO r egion by state (top) and source sector (bottom) , u nits: 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 723Heat 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 (weekda y, 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 p lanning 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 (Envir o n, 2008 ) . CONCEPT was run with meteorological data for a July and January weekday, Saturday, and Sunday (July 15

– 17 and January 16

– 18). A sp atial plot of emissions is provided in Figure 4 3

.

VOC Emissions NOx Emissions

Figure 4 3

. M otor v ehicle 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, u pdated local data for railro ads and commercial marine w ere prepared by a contractor (Environ , 2007 b, 2007 c )

. Table 7 compares the Base M 2005 and Base K 2002 emissions. Compared to 2002, t he new 2005 emissions reflect substantially lower commercial marine emissions and lower locomo tive NOx emissions.

Table 7 . Locomotive and c ommercial m arine e missions 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, E MS was run by LADCO using data supplied by the LADCO States to produce weekday, Saturday, and Sunday emissions for e ach month. For 2005,

s pecial attention was given to two source categories: industrial adhesi ve and sealant solvents (which were dropped from the inventory to avoid double

- counting) and outdoor wood boilers (which were added to the inventory)

.

Point Sourc es : 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, t he annual and summer season emissions were temporalized for modeling purposes using profiles prepared by Scott Edick (Michigan DEQ) based on CEM data

. Biogenic s: For Base M, a contractor (Alpine) provided an updated version of the CONCEPT/MEGAN biogenics model. Compared to the previous (EMS/BIOME) emissions, t here is more regional isoprene using MEGA N compared to the BIOME estimates used for Base K (see Figure 4 4 ). Also, with the secondary organic aerosol updates to the CAMx air quality model, Base M includes emissions for monoterpenes and sesquiterpenes, which are pre

cursors of secondary PM 2. 5 

### organ ic carbon mass

.

Figure 4 4 . Isoprene emissions for Base M (left) v. Base K (right)



### Ammonia : For Base M, t he 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 4 5

. A spatial plot of emissi ons is provided in Figure 4 6 , 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 4 5 . Average daily ammonia emissions for Midwest States by month (2005)

- (units: average daily emissions

– tons per day)56 Figure 4 6

. 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 par ameters) 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 4 7

. Additional plots and emission reports are available on the LADCO website ( http://www.ladco.org/tech/emis/basem/canada/index.htm ).

Figure 4 7

. Canadian point source emissions for SO2 (left) and NOx (rig ht)