APPENDIX A

Air Quality System (AQS) Data

State and Local Air Monitoring Stations (SLAMS)
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

QUICKLOOK CRITERIA PARAMETERS

Report Request ID: 899506 Report Code: AMP450 Aug. 11, 2011

Selection Criteria Page 1

User ID: IDA

State County Site Parameter POC City AQCR UAR CBSA CSA

EPA

Region Method Duration Begin Date End Date

39

39

39

39

39

39

39

035

085

093

153

103

103

133

3002

0003

0004

0002

GEOGRAPHIC SELECTIONS

PROTOCOL SELECTIONS

Parameter

Classification Parameter Method Duration

QUICK LOOK 88101

SELECTED OPTIONS

EVENTS PROCESSING

MERGE PDF FILES

WORKFILE DELIMITER

Option Type Option Value

EXCLUDE REGIONALLY CONCURRED EVENTS

YES

,

SORT ORDER

1

2

3

4

5

6

7

Order Column

PARAMETER_CODE

STATE_CODE

COUNTY_CODE

SITE_ID

POC

DATES

EDT_ID

GLOBAL DATES

2008

Start Date End Date

2010

Tribal

Code

APPLICABLE STANDARDS

Standard Description

PM25 24-hour 2006

PM25 Annual 2006
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 1 of 7

EXCEPTIONAL DATA TYPES

EDT DESCRIPTION

0

1

2

5

NO EVENTS

EVENTS EXCLUDED

EVENTS INCLUDED

EVENTS WITH CONCURRENCE EXCLUDED
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 2 of 7

PM2.5 - Local Conditions (88101) Ohio Micrograms/cubic meter (LC) (105)

SITE ID

P

O

C PQAO CITY COUNTY ADDRESS YEAR METH #OBS

4TH

MAX CERT EDT

39-035-0027

39-035-0027

39-035-0034

39-035-0034

39-035-0034

39-035-0038

39-035-0038

39-035-0038

39-035-0038

39-035-0038

39-035-0038

39-035-0045

39-035-0045

39-035-0045

39-035-0060

39-035-0060

1

1

1

1

1

1

1

1

2

2

2

1

1

1

1

1

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

0229

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cleveland

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

2200 W 28TH 

ST.

2200 W 28TH 

ST.

891 E. 152 

ST.

891 E. 152 

ST.

891 E. 152 

ST.

2547 ST 

TIKHON

2547 ST 

TIKHON

2547 ST 

TIKHON

2547 ST 

TIKHON

2547 ST 

TIKHON

2547 ST 

TIKHON

4950 BROADWAY

AVE.

4950 BROADWAY

AVE.

4950 BROADWAY

AVE.

E. 14TH & 

ORANGE

E. 14TH & 

ORANGE

2008

2009

2008

2009

2010

2008

2009

2010

2008

2009

2010

2008

2009

2010

2008

2009

120

120

120

120

120

120

120

120

120

120

120

120

120

120

120

120

38.0

28.7

35.8

27.7

40.4

41.4

37.0

48.4

36.8

29.6

50.5

39.0

30.8

39.0

40.3

33.1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

24-HOUR 

98TH

PERCENTILE

VALUE

37.7

23.0

31.5

24.7

26.8

39.4

29.9

30.5

36.8

29.6

50.5

35.3

23.5

32.7

36.9

28.9

106

54

114

118

112

119

118

114

39

29

27

110

118

122

106

91

1ST

MAX

2ND

MAX

3RD

MAX

37.8

23.0

34.3

25.8

29.7

40.1

30.4

31.9

32.1

25.2

29.1

36.3

26.2

32.7

38.1

28.9

37.7

22.8

31.5

24.7

26.8

39.4

29.9

30.5

31.1

23.1

24.4

35.3

23.5

32.7

36.9

27.0

34.1

20.1

27.0

22.0

26.6

38.5

29.8

30.2

28.0

21.5

24.2

33.6

22.3

30.7

34.2

24.5

13.17 

10.61*

10.90 

10.20 

10.89 

14.08 

12.86 

13.97 

14.02*

12.66 

16.70 

13.67 

11.85 

13.25 

14.37*

12.28*

WTD

ARITH

MEAN
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 3 of 7

PM2.5 - Local Conditions (88101) Ohio Micrograms/cubic meter (LC) (105)

SITE ID

P

O

C PQAO CITY COUNTY ADDRESS YEAR METH #OBS

4TH

MAX CERT EDT

39-035-0060

39-035-0060

39-035-0060

39-035-0065

39-035-0065

39-035-0065

39-035-1002

39-035-1002

39-035-1002

39-085-0007

39-085-0007

39-085-0007

39-085-0007

39-085-3002

39-085-3002

39-093-3002

39-093-3002

1

2

2

1

1

1

1

1

1

1

1

2

2

1

2

1

1

0229

0229

0229

0229

0229

0229

0229

0229

0229

0595

0595

0595

0595

0595

0595

0807

0807

Cleveland

Cleveland

Cleveland

Newburgh 

Heights

Newburgh 

Heights

Newburgh 

Heights

Brook Park

Brook Park

Brook Park

Painesville

Painesville

Painesville

Painesville

Painesville

Painesville

Sheffield

Sheffield

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Cuyahoga

Lake

Lake

Lake

Lake

Lake

Lake

Lorain

Lorain

E. 14TH & 

ORANGE

E. 14TH & 

ORANGE

E. 14TH & 

ORANGE

4600 HARVARD 

AVE.

4600 HARVARD 

AVE.

4600 HARVARD 

AVE.

16900 HOLLAND

RD.

16900 HOLLAND

RD.

16900 HOLLAND

RD.

177 MAIN 

STREET

177 MAIN 

STREET

177 MAIN 

STREET

177 MAIN 

STREET

71 E HIGH

71 E HIGH

2180 LAKE 

BREEZE

2180 LAKE 

2010

2008

2009

2008

2009

2010

2008

2009

2010

2009

2010

2009

2010

2008

2008

2008

2009

120

120

120

120

120

120

120

120

120

120

120

120

120

120

120

120

120

52.6

40.0

22.1

38.4

32.8

40.0

34.3

26.9

32.0

27.0

33.2

28.4

32.1

32.0

35.5

35.5

28.9

Y

Y

Y

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

24-HOUR 

98TH

PERCENTILE

VALUE

30.9

40.0

22.1

33.8

28.9

27.3

30.1

20.5

26.5

19.8

26.9

19.4

24.1

27.5

35.5

32.1

21.5

114

42

2

118

118

110

109

107

108

112

120

58

60

110

50

117

118

1ST

MAX

2ND

MAX

3RD

MAX

31.0

27.2

10.6

36.0

30.3

28.6

30.2

24.7

27.4

21.7

30.4

19.4

24.1

28.0

31.9

34.3

24.5

30.9

27.0

33.8

28.9

27.3

30.1

20.5

26.5

19.8

26.9

19.3

21.7

27.5

27.7

32.1

21.5

30.6

26.5

32.5

26.6

27.3

29.0

20.2

25.8

19.7

25.6

19.0

18.9

26.7

26.8

26.6

21.5

13.70 

14.09 

16.35*

14.60 

12.39 

13.15*

12.03*

10.89*

11.33 

10.49 

10.39 

9.99 

10.64 

11.36*

12.51*

11.51 

9.89 

WTD

ARITH

MEAN
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 4 of 7

PM2.5 - Local Conditions (88101) Ohio Micrograms/cubic meter (LC) (105)

SITE ID

P

O

C PQAO CITY COUNTY ADDRESS YEAR METH #OBS

4TH

MAX CERT EDT

39-093-3002

39-093-3002

39-093-3002

39-093-3002

39-103-0003

39-103-0003

39-103-0003

39-103-0004

39-103-0004

39-133-0002

39-133-0002

39-133-0002

39-153-0017

39-153-0017

39-153-0017

39-153-0017

39-153-0017

39-153-0017

39-153-0017

39-153-0023

1

2

2

2

1

1

1

3

3

1

1

1

1

1

1

2

2

2

3

1

0807

0807

0807

0807

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

0012

Sheffield

Sheffield

Sheffield

Sheffield

Not in a city

Not in a city

Not in a city

Not in a city

Not in a city

Ravenna

Ravenna

Ravenna

Akron

Akron

Akron

Akron

Akron

Akron

Akron

Akron

Lorain

Lorain

Lorain

Lorain

Medina

Medina

Medina

Medina

Medina

Portage

Portage

Portage

Summit

Summit

Summit

Summit

Summit

Summit

Summit

Summit

BREEZE

2180 LAKE 

BREEZE

2180 LAKE 

BREEZE

2180 LAKE 

BREEZE

2180 LAKE 

BREEZE

6364 DEERVIEW

6364 DEERVIEW

6364 DEERVIEW

BALLASH ROAD

BALLASH ROAD

531 

WASHINGTON

531 

WASHINGTON

531 

WASHINGTON

80 BRITTAIN

80 BRITTAIN

80 BRITTAIN

80 BRITTAIN

80 BRITTAIN

80 BRITTAIN

80 BRITTAIN

660 W. 

EXCHANGE ST.

2010

2008

2009

2010

2008

2009

2010

2009

2010

2008

2009

2010

2008

2009

2010

2008

2009

2010

2010

2008

120

120

120

120

120

120

120

182

182

120

120

120

120

000

118

120

000

118

170

120

39.2

35.0

23.0

38.5

33.1

27.2

31.8

28.2

28.2

35.1

26.1

33.4

43.6

32.5

40.3

43.1

30.7

40.4

44.5

41.0

Y

Y

Y

Y

Y

Y

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

24-HOUR 

98TH

PERCENTILE

VALUE

24.4

35.0

19.9

25.3

30.3

25.7

28.8

22.4

22.0

29.4

23.8

31.9

37.6

25.2

31.9

34.2

25.9

32.1

35.1

32.7

118

55

59

59

113

116

116

2924

2160

116

115

115

118

327

351

53

60

55

6897

114

1ST

MAX

2ND

MAX

3RD

MAX

25.6

30.3

19.9

25.3

31.1

25.7

31.3

26.4

22.0

31.7

25.0

32.2

40.3

32.0

36.6

34.2

25.9

32.1

38.9

36.0

24.4

25.4

18.2

20.8

30.3

25.7

28.8

22.4

21.2

29.4

23.8

31.9

37.6

31.8

35.3

31.2

25.0

28.6

36.9

32.7

23.5

23.1

18.2

19.5

26.5

25.3

25.1

21.6

19.9

29.2

23.2

26.9

32.6

31.7

34.5

27.5

24.9

27.4

36.3

32.7

10.41 

11.06 

9.27 

10.29 

11.76 

10.83 

10.76 

11.46*

7.92*

12.12 

11.13 

11.18 

13.83 

12.56*

13.23 

13.61*

11.57 

14.11*

15.94*

12.89 

WTD

ARITH

MEAN
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 5 of 7

PM2.5 - Local Conditions (88101) Ohio Micrograms/cubic meter (LC) (105)

SITE ID

P

O

C PQAO CITY COUNTY ADDRESS YEAR METH #OBS

4TH

MAX CERT EDT

39-153-0023

39-153-0023

1

1

0012

0012

Akron

Akron

Summit

Summit

660 W. 

EXCHANGE ST.

660 W. 

EXCHANGE ST.

2009

2010

000

000

32.8

33.9

0

0

24-HOUR 

98TH

PERCENTILE

VALUE

24.8

30.2

114

110

1ST

MAX

2ND

MAX

3RD

MAX

27.9

33.0

24.8

30.2

24.4

28.6

11.41 

12.54 

WTD

ARITH

MEAN
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 6 of 7

88101

88101

88101

88101

88101

000

118

120

170

182

MULTIPLE METHODS

R & P Model 2025 PM2.5 Sequential w/WINS

Andersen RAAS2.5-300 PM2.5 SEQ w/WINS

Met One BAM-1020 Mass Monitor w/VSCC

Thermo Scientific TEOM 1405-DF Dichotomous FDM

MULTIPLE METHODS

GRAVIMETRIC

GRAVIMETRIC

Beta Attenuation

FDMS Gravimetric

PARAMETER

METHOD

CODE COLLECTION METHOD ANALYSIS METHOD

METHODS USED IN THIS REPORT
Aug. 11, 2011

Note: The * indicates that the mean does

not satisfy summary criteria.

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

AIR QUALITY SYSTEM

QUICK LOOK REPORT (AMP450)

Page 7 of 7

PQAOS USED IN THIS REPORT

PQAO AGENCY DESCRIPTION

0012

0229

0595

0807

Akron Regional Air Pollution Control Agency

Cleveland Air Pollution Control Agency

Lake County Health Department Division Air Pollution Control

Ohio EPA, Northeast District Office
Environmental

protection Agency

John R. Kasich, Governor

Mary Taylor, Lt. Governor

Scott J. Nally, Director

CERTIFIED MAIL

March 10, 2011

Cheryl L. Newton, Director

Air and Radiation Division

U.S.EPA, Region V

77 West Jackson Boulevard

Chicago, Illinois 60604

RE: 2010 SLAMS PM,.5 and Ozone Data Certification

Dear Ms. Newton:

Please find enclosed our SLAMS Report (AMP-450, AMP-450NC and AMP255) for calendar year 2010 as required in 40 CFR, Part 58, Section

58.15. The ambient concentration and the quality assurance data

have been completely submitted to the AQS database.

The remaining criteria pollutant data will be certified within the

month of April.

I certify that the Ozone and PM,., data in the report are accurate to

the best of our knowledge taking into consideration the quality

assurance findings and only to the extent of the activities

performed by Ohio EPA.

There were no incidents of air pollution that reached or exceeded

levels as specified by Section 51.151 which could cause significant

harm to the health of persons.

Sincerely,

Robert Hodanbosi

Chief, Division of Air Pollution Control

Enclosure

50 West Town Street, Su~e 700

PO. Box 1049

Columbus,OH 43216-1049

61416443020

61416443184 (fax)

www.epa.ohio.gov
APPENDIX B

Ohio 2005 SIP Base Year Inventory Discussion
1

OHIO 

2005 Base Year PM2.5 SIP Inventory 

Ohio Environmental Protection Agency 

Lazarus Government Center 

50 West Town Street, Suite 700 

Columbus Ohio 43215 

Prepared by Ohio EPA 

Division of Air Pollution Control 

Emissions Inventory Unit 

February 2008 
2

Table of Contents 

Section Page 

 Introduction ................................................................................................. 3 

1.0 Point Sources ............................................................................................. 4 

 

2.0 Area Sources .............................................................................................. 8 

3.0 Non Road Sources........................................................................15 

4.0 On Road Sources........................................................................................17 

5.0 MAR Sources..............................................................................43 

 

Table 

2-1 Categories included in the 2005 area source Inventory.............................8

2-2 Emission Factors used for Wood Burners included in area sources............11 

3-1 NMIM NCD Tables for Specific Years and States ........................................... 16

4-1 Federal Functional Class Categories............................................................23 

4-2 DVMT Summary by County.................................................................. .24 

4-3 Speed by Federal Functional Class .............................................................28

4-4 Link Group Codes & Associated BPR Curves .......................................... 29 

4-5 Mobile6 Inputs.................................................................................31 

4-6 LADCO Data Output............................................. .............39

4-7 County Summary Table of MPOs by County......................................... .40 

5-1 Number of operations and LTOs for 2005.............................................. 45 

5-2 EDMS Aircraft Emissions ..................................................................50 

5-3 Fleet Emission Factor Categories ........................................................51 

5-4 Pollutant by County (Sample) ............................................................. 54 

. 

Figure 

1........................................ Speed Comparison Graphs.....................................27 
3

2005 Base Year PM2.5 SIP Inventory for Ohio 

Introduction 

The State of Ohio has a number of counties with air quality data showing nonattainment for the following PM2.5 standard: 

• Annual Standard = 15.0 ug/m3 

The Clean Air Act Amendments (CAAA) requires all states to revise and submit State 

Implementation Plans (SIP) for areas which are classified as non-attainment of the 1997 

Fine Particle (PM2.5) National Ambient Air Quality Standards (NAAQS). The Federal 

Register ( Vol. 72, No. 79/ Wednesday, April 25, 2007) provides the emissions inventory 

rules and regulations for the PM2.5 Clean Air Fine Particle implementation rule. An 

electronic version of the document can be found at http://www.epa.gov/fedrgstr/EPAAIR/2007/April/Day-25/a6347.pdf. 

As part of the designation of non-attainment areas for PM2.5 standards, a new 

attainment demonstration SIP will be necessary. A key element in the overall SIP 

planning process is the need for an updated emissions inventory. This document 

presents the 2005 Base Year Particulate SIP Emissions Inventory for Ohio as required 

by the CAAA. It includes emissions for point, area, on-road mobile and non-road mobile 

for the State of Ohio. 

This technical report documents the procedures and the methodologies that were used 

in the development of daily emissions for all counties in Ohio. This report describes the 

following: 

1. Identification of stationary and mobile sources included in the 

inventory; 

2. Sources of data, and data collection methods used in the development 

of the inventory; 

3. Methods and procedures used to estimate emissions; and 

4. Assumptions considered in the development of the emissions 

inventories. 

The intent of this report is to describe how the inventory was prepared, and what 

information was considered in the inventory development. 

This document is comprised of 5 sections, one section for each inventory type. The 

biogenic inventory is not being discussed in this document because Ohio EPA did not 

participate in the generation of this inventory. Lake Michigan Air Directors Consortium 

(LADCO) ran EPA’s BEIS model in the Emission Modeling System (EMS) to generate 

Summer Weekday emissions for VOC and NOx. 
4

SECTION 1 

POINT SOURCES 

Emissions and source specific data for point sources are collected for the 2005 baseyear SIP inventory by the Ohio Environmental Protection Agency (Ohio EPA.) The 

primary source of data for point sources is facility reported STARShip files. STARShip 

is a software package developed by Ohio EPA, Division of Air Pollution Control (DAPC), 

to assist the regulated community in preparing and submitting a variety of electronic 

permit applications and reports to the DAPC. These data are reported by the Title V 

facilities annually as part of the emissions fee/inventory process conducted by Ohio 

EPA and include emissions, process rates, operating schedules, emissions control data 

and other relevant information. 

The STARShip files are electronically transferred to the DAPC and stored into the 

Division’s Oracle database, STARS. The files are reviewed by the local air agencies 

and Ohio EPA district and central office staff. After review, the data are imported into 

Excel and linked with an Access® database to further process the information into the 

federally approved National Emission Inventory (NEI) database format in version 3.0. 

The files are quality assured again using the United States Environmental Protection 

Agency’s (U.S.EPA) QA/QC software for format and content. The data is finally 

submitted to LADCO for emissions processing through the Emissions Modeling System. 

The State provided inventory for Electric Generating Units (EGU) is replaced with the 

Federal EGU inventory. The EGU inventory is compiled by U.S. EPA’s Acid Rain 

Program. It is based on facility reported emissions as measured by continuous 

emissions monitors. In conclusion, the final point source inventory is a hybrid of the 

federal EGU inventory and the state provided non-EGU units. 

A major distinction typically made in emissions inventories is that between point and 

area sources. In this inventory, point sources are sources for which individual records 

are maintained for that source. Such records are maintained for all Ohio Title V facilities 

(706 facilities statewide). The area source inventory accounts for facilities from nonTitle V facilities and calculates emissions information using surrogate emissions factors 

based on energy usage, population, employment records, or other reliable data. A 

more detailed discussion of the area source inventory is provided in Section 2. The 

point source inventory described herein is considered to be the most current and 

accurate source of emissions data available for 2005. 

1.1 Point Source Process Emissions 

Ohio EPA defines point source process emissions as those which occur at an 

identifiable Title V stationary stack or vent. Point source emissions not emitted from 

discrete stacks or vents are termed fugitive emissions and are discussed in Section 1.2. 
5

1.1.1 Source Identification and Data Collection 

The sources to be included in the 2005 base year inventory are identified using the Title 

V STARS database. Facility production and emissions data are included in this 

database. This information is facility-reported actual 2005 emissions. 

1.1.2 Non-reactive VOC Emissions Adjustments 

This section is primarily applicable for VOC pollutants. Sources are required to identify 

emissions of photochemically non-reactive Volatile Organic Compounds (VOC.) Based 

upon this information, those emissions have been specifically excluded from the 2005 

base line inventory in accordance with U.S. EPA’s “Recommended Policy on the 

Control of Volatile Organic Compounds.” A complete list of the compounds that U.S. 

EPA has identified as being photochemically non-reactive, and therefore not included in 

the inventory, are listed below: 

• Methane 

• Ethane 

• Methylene chloride 

• Methyl chloroform 

• Trichlorofluoromethane (CFC-11) 

• Dichlorodifluoromethane (CFC-12) 

• Chlorodifluoromethane (CFC-22) 

• Trifluoromethane (HFC-23) 

• Chlorofluoromethance (HCFC-31) 

• Difluoromethane (HFC-32) 

• Decafluoropentane (HFC-43-10mee) 

• Ethylfluoride (HFC-161) 

• Trichlorotrifluoroethane (CFC-113) 

• Dichlorotetrafluoroethane (CFC-114) 

• Chloropentafluoroethane (CFC-115) 

• 2,2-Dichloro-1,1,1-trifluoroethane (HCFC-123) 

• 1,1,2-Trifluoroethane (HCFC-123a) 

• 2-Chloro-1,1,1,2-tetrafluoroethane (HCFC-124) 

• Pentafluoroethane (HFC-125) 

• 1,1,2,2,-Tetrafluoroethane (HFC-134) 

• 1,1,1,2-Tetrafluoroethane (HFC-134a) 

• 1,1-Dichloro-1-fluoroethane (HCFC-141b) 

• 1-Chloro-1,1,-difluoroethane (HCFC-142b) 

• 1,1,1-Trifluoroethane (HFC-143a) 

• Fluoroethane (HCFC-151a) 

• 1,1-Difluoroethane (HFC-152a) 

• Pentafluoropropane (HFC-225ca) 

• Pentafluoropropane (HFC-225cb) 

• Hexafluoropropane (HFC-236ea) 

• Hexafluoropropane (HFC-236fa) 

• Pentafluoropropane (HFC-245ca) 

• Pentafluoropropane (HFC-245ea) 

• Pentafluoropropane (HFC-245eb) 
6

• Pentafluoropropane (HFC-245fa) 

• Pentafluorobutane (HFC-365mfc) 

• Parachlorobenzotrifluoride (PCBTF) 

• Methoxybutane 

• Nonaflourobutane 

• Heptafluoropropane ((CF3)2CFCF2OCH3) 

• Heptafluoropropane ((CF3)CFCF2OC2H5) 

• Perchloroethylene 

• Cyclic, branched or linear completely methylated siloxanes 

• Methyl acetate 

• Volatile methyl siloxanes 

• Acetone 

1.1.3 Emissions Estimation Methodologies 

Since source reported actual annual emissions are used in the 2005 base year 

inventory, no estimation methods are necessary. The reports are provided to LADCO in 

National Emissions Inventory Input Format (NIF) 3.0 format. LADCO imported and 

processed the NIF files in EMS and applied temporal and spatial profiles to the annual 

emissions to calculate weekday emissions rates. The final point source inventory is 

split into two separate reports, the Electric Generating Units (EGU) which is the U.S. 

EPA inventory for electric generating units and the non-EGU which is the state inventory 

minus the EGU units. 

1.2 Point Source Fugitive Emissions 

Another type of emissions data which is required to be filed from point sources is 

fugitive emissions. Before 1990, fugitive emissions were categorized as area sources 

due to the lack of detailed information available for fugitive sources. However, since 

these emissions are now electronically reported in the State’s ORACLE database, 

STARS, these emissions can be classified as point sources. 

1.3 References 

Getting Started: Emissions Inventory Methods for PM2.5 U.S. Environmental Protection 

Agency, Research Triangle Park, NC, September, 1999. 

Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter 

National Ambient Air Qaulity Standards (NAAQS) and Regional Haze Regulations, 

Office of Air Quality Planning & Standards Research, Triangle Park, NC. November 

2005. 

Compilation of Air Pollution Emission Factors, Fourth Edition and Supplements, AP-42. 

U.S. Environmental Protection Agency, Research Triangle Park, NC, September 1985. 
7

Preparing 2002 Regional PM2.5 Emissions QAQPS PM Inventory Conference Inventory 

Conference San Diego, 2003. 

Documentation for the 2002 Electric Generating Unit National Emissions Inventory 

(NEI). Eastern research group, Inc., 1600 Perimeter Park Drive, Morrisville, NC 27560 

and E.H. Pechan and Associates, Inc., 5528–B Hempstead Way 
8

SECTION 2 

AREA SOURCES 

Area sources are sources which are typically small, individual, numerous, and have not 

been inventoried as specific point, mobile, or biogenic sources. For inventory purposes, 

they are grouped with other like sources into categories that allow emissions to be 

calculated collectively using one methodology. Since area sources are traditionally 

defined at the county level, most methods are designed to estimate area source 

emissions at the county level. 

Ohio EPA has either used published Emission Inventory Improvement Program (EIIP)6

methodologies or selected other methodologies which are shared by other states. The 

decision of which methodology to use was largely based on Ohio’s data availability. 

Data which was not available on a county-level is estimated by assigning a percentage 

of the state’s total activity to each county based on the state’s population or employment 

information. If Ohio county specific activity data is available through Ohio EPA or other 

State Agencies, that data is used rather than allocating activity by percentage. Table 2-1 

lists the sources which emit PM2.5, NOx and SO2 along with the respective EFs used to 

calculate each pollutant. 

Table 2-1 Categories in the 2005 PM2.5 SIP Area Source inventory 

Area Source NOx PM2.5 SO2 Section 

Commercial Natural 

Gas Combustion 94 lb/MMSCF 7.6 lb/MMSCF 0.6 lb/MMSCF 2.1 

Industrial Distillate Oil 

Combustion 20 lb/E3gal fuel 0.25 lb/E3gal fuel 42.6 lb/E3gal fuel 2.2 

Industrial Residual Oil 

Combustion 55 lb/E3gal fuel 4.67 lb/E3gal fuel 157 lb/E3gal fuel 2.2 

Industrial Natural Gas 

Combustion 94 lb/MMSCF 7.6 lb/MMSCF 0.6 lb/MMSCF 2.2 

Residential Coal 

Combustion 9.1 Lb/Ton Coal 3.8 Lb/Ton Coal 31 Lb/Ton Coal 2.3 

Residential Distillate 

Oil Combustion 18 lb/E3gal fuel 0.83 lb/E3gal fuel 42.6 lb/E3gal fuel 2.3 

Residential Natural 

Gas Combustion 94 lb/MMSCF 7.6 lb/MMSCF 0.6 lb/MMSCF 2.3 

Residential LPG 

Combustion 13 lb/E3gal fuel 0.17 lb/E3gal fuel 0.1 lb/E3gal fuel 2.3 

Human Cremation 1.01E+01 lb/Ton 

cremated 

0.0637 lb/Ton 

cremated NA 2.4 

Structure Fires 1.4 Lb/Ton 

burned 

10.8 Lb/Ton 

burned NA 2.5 

Outdoor Wood Boilers 2.8 Lb/Ton 2.76E+1 Lb/Ton 4 E-1 Lb/Ton 2.6 
9

Residential Wood 

Combustion 

Non-Certified 

Non-Catalytic 

Catalytic 

2.80E+00 lb/Ton 

2.80E+00 lb/Ton 

2.00E+00 lb/Ton

3.06E+01 lb/Ton 

1.96E+01 lb/Ton 

2.04E+01 lb/Ton 

4.00E-01 lb/Ton 

4.00E-01 lb/Ton 

4.00E-01 lb/Ton 

2.3 

2.1 Commercial Natural Gas Combustion (SCC 2103006000)

The 2005 total state-level commercial sector energy consumption is obtained from the 

Energy Information Administration (EIA)’s State Energy Data System (SEDS), and 

apportioned per county based on population4

. Emissions factors are given in table 2-1. 

The area source emissions are calculated based on an adjusted value by subtracting 

the emissions due to point sources. 

2.2 Industrial Fuel Combustion 

Industrial Distillate Oil Combustion (SCC 2102004000) 

Ohio’s fuel consumption is apportioned per county based on the county’s population4

. 

The area source NOX emissions are calculated and adjusted by subtracting the 

emissions due to point sources. A heating value of 140 MMBTU/1000 Gal is used and 

84,408 thousand gallons are consumed in 2005. [MMBTU stand for Million British 

Thermal Units]. Emissions factors are given in table 2-1. 

SO2 emissions were calculated using EF 42.6 Lb/1000 Gal. PM10 and PM2.5 emissions

are calculated using 1Lb/1000 Gal and 0.25 Lb/1000 Gal respectively. All Factors are 

obtained from AP-4219 

Industrial Residual Oil Combustion (SCC 2102005000) 

Ohio’s fuel consumption is apportioned per county based on the county’s population4

. 

The area source NOX emissions are calculated and adjusted by subtracting the 

emissions due to point sources. 54,652 thousand gallons14 are consumed in 2005 and 

a heating value of 140 MMBTU/1000 Gal is used. The SO2 emissions are calculated 

using EF 157 Lb/1000 Gal. PM10 and PM2.5 emissions are calculated using 7.17 Lb/1000 

Gal and 4.67 Lb/1000 Gal respectively. All factors are obtained from AP-4219 

Industrial Natural Gas Combustion (SCC 2102006000) 

Ohio’s fuel consumption is apportioned per county based on the county’s population4

. 

The area source NOX emissions are calculated and adjusted by subtracting the 

emissions due to point sources. 293,857 MMCF14 are consumed in 2005. The SO2
10

emissions are calculated using EF 0.6 Lb/MMBTU. The PM10 and PM2.5 emissions are 

calculated using 7.6 Lb/MMBTU. All factors were obtained from AP-4219 

2.3 Residential Fuel Combustion 

Residential Coal Combustion (SCC 2104001000) 

Ohio’s household consumption of coal is apportioned per county based on county 

population4

. NOX emissions are calculated using EF of 9.1 of coal. The SO2 emissions 

are calculated using EF 31Lb/1000 Gal. The PM10 and PM2.5 emissions are calculated 

using 6.2Lb/Ton and 3.8 Lb/Ton respectively. All factors were obtained from AP-4219 

Residential Distillate Oil Combustion (SCC 2104004000) 

Ohio’s household consumption of distillate oil is apportioned per county based on 

county population4. NOX emissions are calculated using EF of 18 lb/1000 gallons 

distillate fuel respectively. A heating value of 140 MMBTU/1000 Gal is used. The SO2

emissions are calculated using EF 42.6 Lb/1000 Gal. The PM10 and PM2.5 emissions

are calculated using 1.08Lb/1000 Gal and 0.83 Lb/1000 Gal respectively. All factors are 

obtained from AP-4219 

Residential Liquid Petroleum Gas Combustion (LPG) 

(SCC 2104007000) 

Ohio’s household consumption of LPG is apportioned per county based on county 

population4. NOX emissions are calculated using EF of 13 lb/1000 gallons LPG. The 

SO2 emissions are calculated using EF 0.1 Lb/1000 Gal. The PM10 and PM2.5 emissions

are calculated using 0.17Lb/1000 Gal. All factors are obtained from AP-4219 

Residential Natural Gas Combustion (SCC 2104006010) 

Ohio’s household consumption of LPG is apportioned per county based on county 

population4

. NOX emissions are calculated using EF of 94 lb/MMSCF. MMSCF stands 

for Million Standard Cubic Feet. This source also emits SO2 emissions which are 

calculated using EF 0.6 Lb/MMBTU. The PM10 and PM2.5 emissions are calculated 

using 7.6Lb/MMBTU. All factors are obtained from AP-4219 

Residential Wood Combustion 

NOX emissions from this area source are calculated for seven types of residential 

heating units that utilize wood for fuel. They are listed below with the appropriate SCC: 

Fireplaces without inserts 2104008001 

Fireplaces with inserts catalytic (non-U.S. EPA cert) 2104008002 

Fireplaces with inserts non -catalytic 2104008003 

Fireplaces with inserts catalytic (U.S. EPA cert) 2104008004 

Wood stoves – Conventional 2104008010 

Woodstoves – Catalytic 2104008030 

Wood stoves – Non catalytic 2104008050 
11

The number of Ohio homes with fireplaces are adjusted for those that burn wood. The 

following assumptions are applied to those adjusted homes: 

92 percent of wood combusted in non-certified units 

5.7 percent of wood combusted in non-catalytic units 

2.3 percent of wood combusted in catalytic units 

A state consumption value is applied which is apportioned to each county based on its 

population4

. Table 2-2 shows the EF used for each of the seven types of indoor wood 

burners which make-up this category: 

Table 2-2 Emission Factors Used for Wood Burners 

SCC 2104008002 2104008003 2104008004 

SCC 2104008010 2104008050 2104008030 

SCC 2104008001 

RAPIDS Code Non-Certified Non-Catalytic Catalytic Units 

SO2 4.00E-01 4.00E-01 4.00E-01 Lb/ton 

NOX 2.80E+00 2.80E+00 2.00E+00 Lb/ton 

PM2.5 3.06E+01 1.96E+01 2.04E+01 Lb/ton

To avoid double counting of wood consumption for fuel, this category is adjusted by 

subtracting the wood consumption from OWB to allow this category to account only for 

indoor wood burning emissions. 

Residential Wood combustion also emits SO2 which is calculated using EF 0.4 Lb/Ton 

EF. The PM2.5 emissions are calculated using EF as shown in table 2-2. All factors are 

obtained from AP-4219 

2.4 Human Cremation (SCC 2810060200) 

 

Not all Ohio counties possess a crematory so only those counties with crematories are 

used to calculate the number of cremations and their resulting NOx emissions. The 

2005 cremation data is obtained from the Ohio Department of Health, Vital Statistics17. 

It is estimated that 3% of deaths occur outside the State of Ohio with no available data 

to account for their disposition at the time this area source is being calculated. 

Therefore, those deaths are not accounted for in this category. 

The methodology does not offer an EF for NOx for this category nor is NOx required to 

be calculated for this area source. Ohio feels that it is a combustion source and NOx 

needs to be included in the inventory along with the other combustion sources. 

Through its Permits-to-Install for human cremation, Ohio has selected a NOx EF of 

10.13lb/ton cremated to calculate emissions from this area source. 
12

This source also emits PM, PM10, and PM2.5 emissions which are calculated using EF 

0.1 Lb/Ton, 0.071Lb/Ton and, 0.0637 Lb/Ton respectively. All factors are obtained from 

AP-4219

2.5 Structure Fires (SCC 2810030000) 

The Structure Fires category includes residential and commercial fires resulting from 

unintentional actions. Intentional fires, forest and wildfires, agricultural, and vehicle 

burning are not included in this area source. The State Fire Marshall Office, Fire 

Prevention Bureau15 provided data on the number of structure fires per county in 2005. 

This area source is considered a combustion source for NOX emissions which are 

calculated using EF 1.4 lb/ton burned. The residential and commercial structures fires 

for each county are tabulated and a fuel loading of 1.15 Ton/fire is applied. This source 

also emits PM, PM10, and PM2.5 emissions which are calculated using EF 10.8 Lb/Ton. 

This factor was obtained from AP-4219 

2.6 Outdoor Wood Boilers (SCC 2104008070) 

Outdoor Wood Boilers (OWB), are also known as outdoor water stoves and outdoor 

wood furnaces, are used as outdoor residential heaters. These boilers have wood 

burning fireboxes surrounded by a water reservoir vented by a chimney stack. The 

combustion of the wood in the firebox heats the water in the surrounding reservoir and 

the heated water is pumped to the residence. OWB units can also supply residential 

hot water. The water capacity ranges from 60 gallons to 764 gallons. The operational 

design creates long periods where the fire smolders and creosote is formed13. 

When the water circulating through the furnace reaches an upper set point, the air 

supply to the fire is cut-off, cooling the fire so the water will not overheat. The furnace 

operates in this "idle" mode until the water temperature hits a lower set point and the air 

supply is re-established. The OWB may be in idle mode far longer than in operating 

mode. This type of operating causes very poor combustion and heavy foul smoke. Most 

of the smoke emitted is fine condensed organic material that does not burn under cool, 

oxygen starved conditions. In addition, many owners burn green wood full of moisture 

which also causes poor combustion12. The smoke created from these outdoor wood 

burning units can contain several pollutants that are harmful to breathe, including fine 

particle pollution such as PM 2.5

11 in addition to NOx (research assisted by Deborah 

Lucas, DAPC intern, 2007) 

This new area source category has many unknowns and variables associated with it 

and Ohio does not possess accurate OWB unit sales data available to calculate 

emissions on the county level. Therefore, several assumptions are made in agreement 

with the Great Lake States in order to formulate a homogeneous inventory for the 

region. 
13

The assumptions are as follows: 

• 100% of wood combusted in non-certified units. 

• OWB units to be 90% in rural counties and 10% in urban counties 

• 11.68 cords of wood consumption per unit per year (includes heating efficiency of 

30-40%) 

• 5 months heating season = 3650 hours (24/7) 

• PM2.5 emissions factor (g/kg wood) =13.82 (Average of indoor and outdoor) or 

27.64 lb/ton of wood 

The agreed upon methodology requires that total number of OWB sold in Ohio be 

apportioned to each county based on rural or urban designation while observing the 9:1 

ratio in area sales. The guesstimated factors (see above) are applied to calculate the 

emissions from this outdoor wood burner. Total emissions obtained from this category 

are subtracted from the Residential Wood Combustion category to allow for accurate 

emissions from the indoor wood burning units. 

 

2.7 References 

1. Documentation For the 2002 Non-Point Source National Emission Inventory For 

CAPS and HAPS. U.S. Environmental Protection Agency. Emissions Factor and 

inventory Group. E.H Pechan and Associates, Inc. January 2004. 

2. United States Census Bureau. Population Division.

http://eire.census.gov/popest/data/counties/tables/CO-EST2002/CO-EST2002-01-

39.php (Accessed 2003) 

3. United States Census Bureau. County Business Patterns. 

http://censtats.census.gov/cbpnaic/cbpnaic.shtml 

(Accessed 2003) 

4. Ohio Department of Development, Office of Strategic Research. Population Projects 

by Age and Sex: 2005-2030. http://www.odod.ohio.gov/research/files/p200.htm 

(Accessed August 2006) 

5. Environmental Protection Agency (EPA). 1999 Base Year Non-point Source National 

Emission Inventory for Hazardous Air Pollutants, Research Triangle Park, NC. 

September 28, 2001. 

6. Environmental Protection Agency (EPA). STAPPA-ALAPCO-EPA Emission 

Inventory Improvement Program (EIIP). Volume III - Area Sources Preferred and 

Alternative Methods. November 1996. 

7. Documentation for the Draft 1999 National Emissions Inventory (Version 3.0) for 

Criteria Air Pollutants and Ammonia. Area Sources. E. H. Pechan & Associates, Inc. 

March 3, 2003 
14

8. U.S. Environmental Protection Agency, STAPPA-ALAPCO-EPA Emission Inventory 

Improvement Program (EIIP), Volume III - Area Sources Preferred and Alternative 

Methods, Chapter 8. September 1997. 

9. Spreadsheet formulation provided by “Buzz” David Asselmeier, Air Quality Planning 

Section, Inventory Group, Springfield, Illinois. 

10. Environmental Protection Agency (EPA). STAPPA-ALAPCO-EPA Emission 

Inventory Improvement Program (EIIP). Volume III - Area Sources Preferred and 

Alternative Methods. May 1997. 

11. (Environmental Chemistry.com) viewed on Feb 8th, 2007. 

[http://environmentalchemistry.com/yogi/environmental/200602outdoorwoodfurnaces.ht

ml] 

12. (Washington State, Department of Ecology) viewed on Feb 8th, 2007 

[http://www.ecy.wa.gov/programs/air/AOP_Permits/Boiler/Outdoor_Boilers_home.html] 

13. U.S. Environmental Protection Agency. Reducing Pollution from Outdoor WoodFired Hydronic Heaters. Fact Sheet

http://www.epa.gov/owhh/pdfs/fact_sheet_1_29_07.pdf (Accessed 1/2007) 

14. Industrial Sector Energy Price and Expenditure Estimates, 1970-2001, Ohio. 

Energy Information Administration. U. S. Department of Energy. 

http://www.eia.doe.gov/emeu/states/sep_prices/ind/pr_ind_oh.html

http://www.eia.doe.gov/emeu/aer/pdf/pages/sec1_27.pdf (Accessed 2003) 

15. State Fire Marshal Office. Fire Prevention Bureau, Columbus, Ohio. 

http://www.com.state.oh.us/odoc/sfm/firecont.htm (Accessed 2003) 

16. Documentation for the Draft 1999 National Emissions Inventory (Version 3.0) For 

Criteria Air Pollutants and Ammonia. Area Sources. E. H. Pechan & Associates, Inc. 

March 3, 2003 

17. Ohio Department of Health, Vital Statistics 

18. Environmental Protection Agency. STAPPA-ALAPCO-EPA Emission Inventory 

Improvement Program (EIIP). Volume X – Emissions Projections. December 1999. 

http://www.epa.gov/ttn/chief/eiip/techreport/volume10/x01.pdf 

(Accessed 2/2007) 

19. Environmental Protection Agency. AP-42. http://www.epa.gov/ttn/chief/ap42/ 
15

SECTION 3 

NON-ROAD SOURCES 

The non-road inventory is generated regionally by running U.S. EPA’s National Mobile 

Inventory Model (NMIM) model. The Wisconsin Department of Natural Resources 

undertook the responsibility of customizing the NMIM input files and submitting the 

output file in NIF format to LADCO and U.S. EPA. LADCO processed the NMIM files in 

their emissions model and generated daily emissions rates. Grant Heatherington from 

the Wisconsin Department of Natural Resources provided the following descriptions... 

The National Mobile Inventory Model (NMIM) developed by USEPA was used to 

estimate emissions for all other non-road mobile categories. NMIM consolidates nonroad mobile emissions and on-road emissions modeling into a single modeling system. 

Only the non-road emissions modeling portion of NMIM was used in the development of 

this emission’s inventory. NMIM uses the USEPA’s NONROAD model to calculate nonroad mobile emissions. The basic NONROAD algorithm for calculating emissions uses 

base year equipment populations, average load factors, available engine powers, 

activity hours and emission factors. Before NMIM was run, modifications and additions 

were made to the NMIM input data. 

a. Added emission factors for diesel tampers/rammers provided by E.H. 

Pechan & Associates, Inc. Diesel tampers/rammers are a type of 

construction equipment. 

b. Revised PM2.5 ratios in SCC table to correctly calculate PM2.5 diesel 

emissions. This error was introduced with NMIM2005 and didn’t exist in 

NMIM2004. 

c. Revised gasoline parameters using updates provided by the states and 

E.H. Pechan & Associates, Inc. Gasoline parameters include Reid Vapor 

Pressure (RVP), oxygenate content and sulfur content. 

The NMIM NEI NIF files are on the LADCO ftp site at: 

ftp://ftp.airtoxics.org/inv2005/nonroad/NMIM/Base_L_ph2/2005/ 

Revised NMIM2005 Input Data 

Emission Factor Data 

All States: Pechan revised the brake specific fuel consumption (BSFC) emission factor data to 

include diesel tampers/rammers (2270002006). The revised NMIM file is saved as 

revBSFC.EMF. 
16

Population Data 

For 26000.pop, replaced default file supplied with NMIM2005 with 26000_rev_NMIM05.pop that 

contains revised construction data missing from 26000.pop external file provided with 

NMIM2005. This Michigan construction data should have been added with the other LADCO 

states modified construction data but was overlooked. 

SCC Data 

The default SCC table of NCD20060201 is replaced by a version that contains corrections to the 

PM25fac field that earlier NCDs contained (i.e. changed from 0.92 to 0.97 for diesel non-road 

equipment) in NONROAD2004. 

Fuel Data 

LADCO States: Pechan revised four tables (countyyear, countyyearmonth, datasource and 

gasoline) in the National County Database (NCD) used by NMIM to incorporate new fuel data. 

AIR revised gasoline characteristics per instructions from the states. Also, gasoline 

characteristic revisions for 2005 provided by states were incorporated. Additional revisions 

were incorporated into 2002 data for non-road Stage 2 controls. Depending on the year being 

modeled, different versions of the revised tables are used. Also, the countynrfile, countyyear 

and datasource tables were revised to reference the new activity, allocation, growth, population 

and seasonality files described above. NCD tables with names ending in “def” are default 

versions of the table. See table below for the appropriate versions of the tables for the selected 

years. 

Non-LADCO States: The countynrfile, countyyear and datasource tables were revised to 

reference the new activity, allocation and seasonality files described above. See table below for 

the appropriate versions of the tables for the selected years. 

Table 3-1 NMIM National County Database Tables for Specific Years and States 

States 

Years 

1999 (WI only) 2002, 05, 07, 08, 09, 12 and 18 

LADCO states countynrfile_NMIM05_rev, 

countyyear_NMIM05_rev, 

countyyearmonth_NMIM05_w

_05_12_18_rev, 

datasource_NMIM05_rev, 

gasoline_NMIM05_def 

SCC_NCD20060201_rev 

(used when NCD20060201 is 

used) 

countynrfile_NMIM05_rev, 

countyyear_NMIM05_rev , 

countyyearmonth_NMIM05_w_05_12_18_rev, 

datasource_NMIM05_rev, 

gasoline_NMIM05_w_05_12_18_rev 

SCC_NCD20060201_rev (used when 

NCD20060201 is used)
17

SECTION 4 

ON-ROAD SOURCES 

A mobile source of air pollution is a self-propelled or portable emitter of air pollutants, 

and mobile source emissions are those generated by the engines or motors that power 

such sources. Most mobile sources, except jet or turboprop aircraft, are powered by 

internal combustion (IC) piston engines, and nearly all use liquid fuels. 

Gaseous fuels, such as compressed natural gas (CNG) or liquefied petroleum gas 

(LPG), had only a very small fraction of the motor fuel market in Ohio in 2005. Solid 

fuels have not been used by mobile sources in significant amounts since railroads 

retired their coal-powered steam locomotives in the 1950s. 

4.1 Categories of Mobile Sources 

For inventory and planning purposes, mobile sources are divided into two major 

categories. 

1. On-highway mobile sources (usually referred to as on-road), e.g., motor 

vehicles such as cars, vans, trucks, buses and motorcycles used for 

transportation of goods and passengers on roads and streets 

2. Off-highway (usually referred to as non-road) mobile sources including: 

• Modes of powered transportation that do not use roads, such as aircraft, 

trains, ships and boats, and motor vehicles used off-road. 

• Self-propelled or portable motorized machines or equipment not used for 

transportation, ranging from construction equipment and farm tractors to 

lawnmowers and hand-held power weed choppers. 

Mobile Sources: All on-road mobile sources are self-propelled. 

Non-road Mobile Sources: Some non-road mobile sources (e.g., farm tractors), are 

self-propelled, but many non-road sources are not. A gasoline-powered chain saw is a 

familiar example of a non-self-propelled non-road mobile source. 

Stationary Sources: Not all movable or portable emission sources are mobile sources, 

however. A small truck-portable cement or hot-mix asphalt plant, for example, may be 

set up near a construction or road-building site. Such plants are classified as stationary 

sources, not mobile sources for two reasons: (1) they may operate for weeks or months 

at a single location, and (2) the trucks that move the plants do not supply power for 

them. 
18

NOTE: Not all Internal Combustion engines (IC) or turbine engines are mobile sources. 

Fixed IC engines, such as those that power pipeline compressors or standby generators 

in electricity plants and elsewhere, are also classified as stationary sources. 

4.1.1 Categories and Components of Mobile Source Emissions 

There are three categories of mobile source emissions: 

• Exhaust or tailpipe emissions, which result from the combustion of fuel in the 

source’s engine 

• Evaporative emissions, which result from evaporation of fuel from the engine or 

its fuel system 

• Refueling emissions 

Exhaust Emissions: Are the result of fuel combustion and occur only when the engine 

is running. 

Evaporative emissions: Are Volatile Organic Compound (VOC) based only and are 

continuously emitted from an engine’s fuel system, whether the engine is running or not. 

Gasoline is a very volatile fuel, so total VOC emissions from gasoline powered vehicles 

have a large evaporative component. Diesel and jet fuels are of very low volatility, so 

evaporative emissions from diesel and turbine engines are a much smaller part of their 

total VOC emissions. Evaporative emissions for CNG or LPG powered vehicles are 

negligible because their fuel tanks and systems are of necessity, sealed. 

Evaporative and exhaust VOC emissions can be calculated separately for most mobile 

source categories. Evaporative emissions do not include VOC emissions that occur 

during refueling. 

Refueling Emissions: These emissions are the third category of mobile source 

emissions. Refueling emissions are entirely VOC. Although they result from the 

evaporation of fuel, they are distinct from, and not directly related to, evaporative 

emissions as defined above. 

Refueling emissions have two subcomponents: 

• Displacement emissions. These occur when new fuel is transferred into a partly 

filled tank--be it a service station storage tank, a portable fuel container (gas 

can), or a vehicle or engine’s fuel tank; displacing the air in the tank and forcing 

that vapor-rich air out the inlet pipe or other vent. There are two stages of 

displacement emissions: 

o “Stage I” emissions occur when the underground storage tanks at a 

service station are being refilled; 
19

o “Stage II” emissions occur when a motor vehicle (or gas can) is being 

refueled. 

NOTE: These emissions are covered in, “Area Sources,” section 3.6. 

• Spill emissions. These occur when drops of fuel drip or splash on the ground 

during or after refueling and evaporate away. 

4.2 Ohio On-Road Mobile Source Inventory 

The inventory of on-road mobile source emissions was developed in conjunction with 

the Ohio Department of Transportation (ODOT), Lake Michigan Air Director’s 

Consortium (LADCO), United States Environmental Protection Agency (USEPA), and 

the Ohio EPA (OEPA). Estimates of the amounts of NOx and VOC are reported by 

county in tons per day. Emissions are reported for a typical ozone season weekday in 

the summer of 2005. 

4.2.1 Emission Inventories Developed with MOBILE6 Model 

MOBILE6 Overview: 

MOBILE6 is a computer program that estimates hydrocarbon (HC), carbon monoxide 

(CO), oxides of nitrogen (NOx), exhaust particulate matter (which consists of several 

components), tire wear particulate matter, brake wear particulate matter, sulfur dioxide 

(SO2), ammonia (NH3), six hazardous air pollutant (HAP), and carbon dioxide (CO2) 

emission factors for gasoline-fueled and diesel highway motor vehicles, and for certain 

specialized vehicles such as natural-gas-fueled or electric vehicles that may replace 

them. The program uses the calculation procedures presented in technical reports 

posted on EPA's MOBILE6 Web page http://www.epa.gov/otaq/models.htm. 

MOBILE6 emission factor estimates depend on various conditions, such as ambient 

temperatures, travel speeds, operating modes, fuel volatility, and mileage accrual rates. 

Many of the variables affecting vehicle emissions can be specified by the user. 

MOBILE6 will estimate emission factors for any calendar year between 1952 and 2050, 

inclusive. Vehicles from the 25 most recent model years are considered to be in 

operation in each calendar year. 

4.2.2 MOBILE6 Defaults: 

MOBILE6 includes default values for a wide range of conditions that affect emissions. 

These defaults are designed to represent “national average” input data values. Users 

who desire a more precise estimate of local emissions can substitute information that 

more specifically reflects local conditions. Use of local input data will be particularly 

common when the local emission inventory is to be constructed from separate 
20

estimates of roadways, geographic areas, or times of day, in which fleet or traffic 

conditions vary considerably. 

A list of MOBILE6 input parameters is provided below. Most of these inputs are optional 

because the model will supply default values unless alternate data are provided. 

4.2.3 MOBILE6 Input Parameters 

• Calendar year

• Month (January, July) 

• Hourly Temperature 

• Altitude (high, low) 

• Weekend/weekday 

• Fuel characteristics (Reid vapor pressure, sulfur content, 

 oxygenate content, etc.) 

• Humidity and solar load 

• Registration (age) distribution by vehicle class 

• Annual mileage accumulation by vehicle class 

• Diesel sales fractions by vehicle class and model year 

• Average speed distribution by hour and roadway 

• Distribution of vehicle miles traveled by roadway type 

• Engine starts per day by vehicle class and distribution by hour 

• Engine start soak time distribution by hour 

• Trip end distribution by hour 

• Average trip length distribution 

• Hot soak duration 

• Distribution of vehicle miles traveled by vehicle class 

• Full, partial, and multiple diurnal distribution by hour 

• Inspection and maintenance (I/M) program description 

• Anti-tampering inspection program description 

• Stage II refueling emissions inspection program description 

• Natural gas vehicle fractions 

• HC species output 

• Particle size cutoff 

• Emissions factors for PM and HAP 

• Output format specifications and selections

4.2.4 MOBILE6 References 

The following publications provide much of the guidance for the preparation of the onhighway inventory. 

EPA-450/4-81-026d (Revised), now EPA/450-R-92-009, Procedures for Emission 

Inventory Preparation, Volume IV: Mobile Sources, December 1992. Hereafter, 

“Procedures Vol. IV”. The 1992 version is still the definitive document on 
21

inventories. If a previous edition is referred to, the fact will be noted as, for example, 

“the 1989 Procedures Vol. IV” or “Volume IV, 1989 edition”. 

EPA420-R-03-010, User’s Guide to MOBILE6.1 and MOBILE6.2: Mobile Source 

Emission Factor Model, August 2003. This is the User’s Guide for the official 

MOBILE6.2.03 on-highway mobile source emission factor model and will usually be 

referred to as the M6.2 (or simply M6) User’s Guide (UG). The M6 model in its 

various versions was developed and published by Assessment & Modeling Division 

(AMD) of the National Vehicle & Fuels Emissions Laboratory (NVFEL) in Ann Arbor, 

Michigan. The NVFEL is part of USEPA Office of Transportation & Air Quality 

(OTAQ), formerly the Office of Mobile Sources (OMS). 

Technical Guidance on the Use of MOBILE6 for Emission Inventory 

Preparation, August 2004. Hereafter, the M6 “Technical Guidance [Document]” or 

“TGD”. The TGD is the primary source of guidance on M6 inputs and an invaluable 

adjunct to the M6 User’s Guide. 

USEPA document “Frequently Asked Questions on MOBILE6”, 16 January 

2002. Hereafter, [M6] “FAQ”. This document was published along with the M6 

TGD. 

USEPA memo, “Policy Guidance on the Use of MOBILE6 for SIP Development 

and Transportation Conformity”, dated 18 January 2002, from John Seitz, 

Director of OAQPS, and Margo Oge, Director of OTAQ, to Regional Air Division 

Directors. 

4.3 Ohio’s Alternate Data for MOBILE6 

Alternative data is state-specific data that is used in the Mobile6 runs. Using local data 

is preferred to using the default data in Mobile6. Efforts are made to collect as much 

local data as possible. 

4.3.1 Vehicle Registration Distribution by Age 

Overview: 

The vehicle age distribution determines the fraction of vehicles operating within each 

emissions control requirement standard and the deterioration of the emission control 

technology. 

Emission rates vary widely between new and older vehicles. Thus, even small changes 

in fleet age, particularly for older vehicles, may result in large changes in emission 

totals. 
22

The MOBILE6 model requires estimates of a distribution of registered vehicles by age 

and vehicle category for current and future years. MOBILE6 default values were 

developed using national level vehicle registration data by age and class for July 1, 

1996. EPA developed a methodology to convert the July 1, 1996 registration profile into 

a general registration distribution by age and by vehicle category for some 6 composite 

(gasoline and diesel) vehicle types plus motorcycles. To project future changes, EPA 

evaluated general sales growth and vehicle scrappage trends for the total light-duty 

vehicle in-use fleet and the total heavy-duty vehicle in-use fleet, and made minor 

adjustments, where possible, to reflect some of the differences between vehicle 

categories. 

Description: The MOBILE6 model requires estimates of a distribution of registered 

vehicles by age and vehicle category for current and future years. OEPA chose to use 

local vehicle registration data provided by the Ohio Bureau of Motor Vehicles (BMV) 

which was sent to the Lake Michigan Air Directors Consortium (LADCO) to develop 

these inputs. LADCO then contracted with a subcontractor to breakout the age 

distribution data from the Vehicle Identification Numbers (VIN). 

Note: It was learned during the course of this inventory that there were some 

discrepancies in the age distribution data. But it was too large of a project to reevaluate 

the data prior to this inventory. This will be corrected in the next inventory (2008). 

Method Applicability: This approach is most applicable in areas where there are 

significant differences in the local vehicle fleet age distribution relative to the national 

average. 

Data Sources and Procedures: This approach involves using local vehicle registration 

data. This is typically available at the county level, but may also be applied using 

statewide data from the state motor vehicle registration office. The fleet age should be 

representative of the vehicle fleet over the small urban or rural area under question. 

Advantages: 

• Uses locally specific registration data, which is likely more representative of the 

local area than the national default. 

• Requires minimal additional resources, particularly if data is readily available at 

the county or local level from the State department of motor vehicle registration. 

• Recommended by EPA and generally is encouraged as a preferred approach 

over the national default approach.
23

4.3.2 Daily Vehicle Miles Traveled (DVMT) 

Overview: 

In coordination with Ohio Metropolitan Planning Organizations (MPOs), the Ohio 

Department of Transportation (ODOT) provided Daily Vehicle Miles Traveled (DVMT) 

data and travel demand model (TDM) data. TDM data will be covered in another 

section. Because TDM results are used by the state and MPOs to forecast traffic for a 

variety of reasons, undergo rigorous calibration and validation checks, and are sensitive 

to roadway capacity/travel time improvements, the TDMs are considered the best tool 

for emissions forecasting. Therefore, the DVMT data discussed in this section is not 

used directly for all areas of Ohio. In counties where it is not used directly it is used for 

making rough emissions estimates where models do not exist or where time prohibits 

the use of TDMs. DVMT is a simple mechanism to measure how much traffic is flowing 

along a roadway during an average 24 hour period. This simple formula multiplies 

Average Annual Daily Traffic (AADT) by the length of the roadway. For example; if a 

roadway is 2 miles in length and the AADT is 4000 vehicles per day the DVMT would be 

computed by multiplying 2*4,000 =8,000 or 8,000 DVMT. 

County-By-County DVMT is computed using the State of Ohio, Department of 

Transportation’s Roadway Information Files and the annual Highway Performance 

Monitoring System (HPMS) Summary Reports. DVMT’s are computed for all of the 

Federal Functional Class (FC) categories within each of Ohio’s 88 counties. 

The AADT and Roadway length information provides a very accurate estimate of 

statewide total DVMT for The State Highway System (Interstate, US and State Routes). 

County total DVMT are consistent and considered a good source of county level DVMT 

for countywide emissions estimates. For roadways that are not part of the State 

Highway System, various representative counts were used, such as: railroad crossing 

counts, HPMS Sample Section Counts, etc. All traffic count data that was not collected 

during the current year has had statewide growth factors applied to account for 

systematic growth. 

Given the previously mentioned methodologies, the DVMT data is more accurate on 

roads functionally classified as collector or above. 

Table 4-1 Federal Functional Class Categories: 

01 - Rural Interstate 

02 - Rural Principal Arterial 

06 - Rural Minor Arterial 

07 - Rural Major Collector 

08 - Rural Minor Collector 

09 - Rural Local 

11 - Urban Interstate 

12 - Urban Freeway & Expressway 

14 - Urban Principal Arterial 

16 - Urban Minor Arterial 

17 - Urban Collector 

19 - Urban Local 
24

Table 4-2 DVMT County Summary: 

For PDF web based tables of 2005 DVMT by county see: 

http://www.dot.state.oh.us/techservsite/availpro/Road_%20Infor/KDVMT/vmt2005.pdf 

Disclaimer by ODOT: 

The above PDF web based tables contain the State of Ohio's adjusted county DVMT's 

and road mileage for the years 1990 - 2005. Please be aware that the numbers are 

estimates only. The factoring process used annual, estimated, and statewide ADT 

(Average Daily Traffic) growth factors, derived from the output of a limited number of 

traffic counting stations around the state. Although the growth factors are available by 

functional class, they are more reliable for major roads such as interstates or 

expressways, which are relatively well-sampled, than for local roads or collectors. The 

numbers also do no allow for periodic, large-scale functional reclassification actions 
25

which reassign selected roads or road segments from one functional class to another. 

The Ohio Department of Transportation therefore does not warrant the accuracy, 

completeness, or reliability of these estimates for your research. We also do not 

assume responsibility for any incorrectness that may occur. 

4.3.3 VMT From Travel Demand Models (TDM) 

Overview: 

Travel demand forecast modeling is performed by the Ohio Metropolitan Planning 

Organizations (MPOs) and ODOT for a multitude of purposes including the preparation 

of regional emissions estimates. The ODOT Office of Technical Services’ Modeling & 

Forecasting Section recommends that Ohio’s TDMs and ODOT’s conformity analysis 

methods be used to establish the roadway mobile source portion of Ohio’s SIP budget 

to assure consistent methods are used for transportation conformity analysis and 

budgets. Therefore, ODOT provided both MPO regional TDM runs and statewide TDM 

runs with associated data to OEPA and LADCO. The ODOT provided model run data 

for years 2002, 2005, 2009, 2012, and 2018. 

Data provided included loaded networks in both CSV format and GIS shape files, trip 

end summaries, zone boundary GIS shape files, intra-zonal trip VMT estimates, and 

VMT summaries for each of the loaded networks. Additional post processing data was 

provided including but not limited to metadata describing the loaded networks, Hourly 

distribution by functional class, speed profiles, day of week / weekend / monthly car and 

truck traffic profiles, 2009 & 2018 VMT RPO data sets, statewide VMT growth rates for 

local traffic, and a 2005 VMT summary comparison spreadsheet. It should be noted 

that among other things, the loaded TDM Networks contain distance and daily volumes 

from which VMT is computed. 

Network volumes are post processed to estimate VMT by hour of day. The hourly 

volumes and capacity, posted speed limit, and type of roadway for each roadway 

segment are then used to estimate average hourly speeds needed for MOBILE6 based 

emissions estimates. Modeling by segment by hour of day in this way makes emissions 

estimates more sensitive to the effects of roadway improvements. This allows 

transportation planners to evaluate the relative emissions affect of improvements to 

individual roadways as well as packages of improvements and the entire set of planned 

roadway improvement projects air quality impacts of construction programs. 
26

4.3.4 Speed Distribution Profiles 

Overview: 

ODOT provided speed distribution profiles to LADCO. A couple of different sets of 

speed distribution profiles were provided to OEPA and LADCO for their use, a table of 

space mean speeds by functional class for use with DVMTs and link group speed 

curves for post processing travel demand model traffic forecasts. Both sets of speed 

profiles are based on the same speed study conducted by ODOT. The speed study is 

documented in “Statewide Travel Time Study, May 2001 by Greg Giaimo, Ohio 

Department of Transportation”. When OEPA asked for speeds for use with MOBILE6 

for development of CERR, ODOT provided a set of speeds based on that statewide 

travel time study. Those speeds are documented in a technical memorandum titled 

“Estimation of Average Speed by Functional Class for MOBILE6 Runs” dated 

5/27/2004. Readers should refer to those documents for the details. Here the contents 

of the technical memo, Estimated Average Speed by Functional Class, are summarized. 

Space Mean Speeds by Functional Class for Use with County Level DVMT, HPMS: 

The memo states that the speeds which the Ohio Department of Transportation (ODOT) 

had been using prior to that date, 2004, for air quality analysis estimates based on 

County DVMT summaries, were developed by a former ODOT employee for addressing 

the one (1) hour standards conformity rules established due to 1990 emissions 

exceedances. No documentation was found in ODOT’s files on the origin of these 

average speed values or how they were estimated. In addition, EPA has requirements 

for using latest planning assumptions for air quality conformity analyses. Therefore, 

ODOT believed that it was in the state’s best interest to use the most recent available 

data to provide a new set of estimated average speeds consistent with those used for 

urban area travel demand models which were under development at that time. The 

memo contains comparisons of 2002 speed data obtained from traffic count equipment, 

automatic traffic recorders (ATRs) which collect data continuously throughout the year. 

It also contains comparisons of the new speeds with those used to address the one 

hour standard Gebhart’s. The graphs shown in figure 1, taken from the memo, illustrate 

the comparisons. The first graph compares time mean speeds from the ATRs with 

space mean speeds from ODOTs travel time study done in 2001and with the speeds 

used for addressing the one hour standard. 
27

Figure 1 - Speed Comparison Graphs 

Speeds Comparison

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

FC01

FC06

FC08

FC11

FC14

FC17

Functional Class

Speed

Average Speed

1990 Analysis

Speeds

2001 Speed Study

Speeds Comparison

0

20

40

60

80

100

FC01FC06FC08

FC11

FC14FC17

RAMPS

Functional Class

Speed

Average Speed

Min. Avg Speed

Max. Avg. Speed

Gebhardt's Speeds

Spd Study Est

Speeds

Spd Study Greg Est

The following caution statement was taken from the Estimation of Average Speed by 

Functional Class memo. 

CAUTION: It should be noted that speeds on facilities falling in any one of the federal 

functional classifications vary greatly between roadways, between hour of the day, and 

day of the week. So these provide only very rough estimates of speed and should be 

used with caution. In addition, it is expected that these average statewide speeds are 

higher than the average speeds in the non-attainment areas because the non-
28

attainment counties tend to be more populated and more congested. The document 

“Highway Vehicle Speed Estimation Procedures For Use in Emission Inventories”, 

September 1991 by Earl Ruiter of Cambridge Systematics Inc. is referenced by EPA’s 

documented procedures for emission inventory preparation. This document suggests 

post processing travel demand model traffic assignment results to estimate average 

speeds. 

Final space mean speeds that ODOT provided are summarized in the Table 1 below: 

Table 4-3 Speed by Federal Functional Class 

Functional Class 1990 Analysis 2001 Speed Study 

 Speeds Speeds 

FC01 57.3 64.0 

FC02 45.3 53.0 

FC06 39.9 53.0 

FC07 35.1 43.1 

FC08 30.5 43.1 

FC09 28.0 43.1 

FC11 46.3 61.6 

FC12 43.3 61.6 

FC14 18.9 29.3 

FC16 19.6 29.3 

FC17 19.6 29.2 

FC19 19.6 23.8 

It was decided by mutual agreement among individuals within the ODOT Office of 

Technical Services that these new space mean speed based average speed estimates 

were reproducible and defensible since they are well documented and should therefore 

be the speeds used with HPMS** VMT if any year 2002 emissions budget work is done 

using only the county level VMT summaries discussed in section 5.3.2 Daily Vehicle 

Miles Traveled (DVMT). 

** Note that HPMS VMT is a statewide VMT estimate and the county level DVMT 

summaries are consistent with the HPMS VMT so the county level DVMT summaries 

are loosely referred to as HPMS VMTs even though in fact they are not. 

4.3.5 Link Group Speed Curves: 

The ODOT Modeling & Forecasting Section recommends that Ohio’s travel demand 

forecasting models and ODOT’s conformity analysis methods be used to establish the 

roadway mobile source portion of Ohio’s SIP budget for reasons already mentioned in 

5.3.2 and to assure consistent methods are used for transportation conformity 

demonstration analyses and budgets. Therefore, ODOT provided travel demand model 

runs and the speed curves by link group that ODOT uses for the speed estimates within 
29

the post processing of travel demand model runs for estimating regional emissions. 

Table 2 shows these link group curves. 

Table 4-4 Link Group Codes & Associated BPR Curves 

Link 

Group 

Facility 

Type 

Free Flow 

Speed 

Areatype a b 

1 Freeway 75 Any 0.39 6.3 

2 Freeway 70 Any 0.32 7.0 

3 Freeway 65 Any 0.25 9.0 

4 Freeway 60 Any 0.18 8.5 

5 Freeway 55 Any 0.10 10.0 

6 Multi-Lane 60 Rural 0.09 6.0 

7 Multi-Lane 55 Rural 0.08 6.0 

8 Multi-Lane 50 Rural 0.07 6.0 

9 Multi-Lane 45 Rural 0.07 6.0 

10 2 Lane Any Rural 0.34 4.0 

10 Urban 

Street 

50 Suburban 0.34 4.0 

11 Urban 

Street 

50 Urban 0.74 5.0 

12 Urban 

Street 

50 CBD 1.16 6.0 

13 Urban 

Street 

40 Suburban 0.38 5.0 

14 Urban 

Street 

40 Urban 0.70 5.0 

15 Urban 

Street 

40 CBD 1.00 5.0 

16 Urban 

Street 

35 Suburban 0.96 5.0 

17 Urban 

Street 

35 Urban 1.00 5.0 

18 Urban 

Street 

35 CBD 1.40 5.0 

19 Urban 

Street 

30 Suburban 1.11 5.0 

20 Urban 

Street 

30 Urban 1.20 5.0 

21 Urban 

Street 

30 CBD 1.50 5.0 

Note: a and b are the BPR curve parameters for the equation 

T = T0 {1 + a * (V/C)^b] 
30

More complete details about emissions modeling process employed by ODOT may be 

found in ODOT documentations. The document titled “Congestion Management & Air 

Quality Analysis (CMAQ) Program Documentation” dated December 2005 may be 

obtained from the ODOT web site at www.dot.state.oh.us/urban/data/cmaq.doc 

(Microsoft Word document) 

4.4 Mobile6 Inputs: 

The following table contains the inputs supplied to LADCO to process our mobile 

inventory. 

4.4.1 Ohio’s 2005 MOBILE6 Inputs 

The following tables are the result of a joint meeting between Ohio EPA, ODOT, and 

MPOs from around the state. At that meeting Mobile6 inputs were discussed and 

efforts were made to verify the sources of data inputs for Moble6. Dialogue has 

continued between the parties. 

For historical reference: 

>>> Dave Moore <Dave.Moore1@dot.state.oh.us> 4/10/2006 2:13 PM >>> 

All, 

An air quality coordination meeting has been scheduled for April 27, 2006 at 10:00 AM at ODOT Central 

Office conference room GA. The primary purpose of this meeting is to discuss development of 2002 

mobile source inventories for use in developing the Ohio 2007 8-Hour Ozone SIP Attainment 

Demonstrations. See meeting agenda below. OEPA is working toward a June 15, 2006 schedule for 

submitting the 2002 inventories to US EPA. 

A key component of the meeting will be to review and confirm the MOBILE6.2 input parameters, by Ohio 

a/q area, for use in developing the 2002 mobile inventories. See draft template below. The Ohio MPO 

travel demand models will be used to generate the 2002 VMT inputs to MOBILE. Thanks, DM 

DM 
31

IMPORTANT NOTICE: The following tables are not to be used for inventory purposes 

as the data is subject to change. For the current input table, contact Ohio EPA, Division 

of Air Pollution Control. 

Table 4-5 Mobile Inputs 

Cleveland-Akron 2005 Ozone M6.2 Inputs 

Includes the following counties: 

Ashtabula, Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, Summit 

 

State Programs 

Input 

 

Stage II Refueling 93/3/86/86 

 

Anti-tampering Programs 96/78/50/22222/21111111/1/12/098./12111112 

I/M Programs Yes 

Exclude Ashtabula County - No I/M program 

Program 1 2004 2050 2 T/O OBD I/M 

Model Years 1 1996 2050 

Vehicles 1 22222 21111111 1 

Stringency 1 30.0 

Compliance 1 98.0 

Waiver Rates 1 1.0 1.0 

Cutpoints 

Exemption Age 1 25 

Grace Period 1 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 2 2004 2050 2 T/O EVAP OBD & GC 

Model Years 2 1996 2050 

Vehicles 2 22222 11111111 1 

Stringency 

Compliance 2 98.0 

Waiver Rates 2 1.0 1.0 

Cutpoints 

Exemption Age 2 25 

Grace Period 2 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 3 2001 2003 2 T/O ASM 2525 PHASE-IN 
32

Model Years 3 1996 2003 

Vehicles 3 22222 21111111 1 

Stringency 3 30.0 

Compliance 3 98.0 

Waiver Rates 3 3.0 1.0 

Cutpoints 

Exemption Age 3 25 

Grace Period 3 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 4 2001 2050 2 T/O ASM 2525 PHASE-IN 

Model Years 4 1975 1995 

Vehicles 4 22222 21111111 1 

Stringency 4 30.0 

Compliance 4 98.0 

Waiver Rates 4 3.0 1.0 

Cutpoints 

Exemption Age 4 25 

Grace Period 4 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 5 1998 2000 2 T/O LOADED/IDLE 

Model Years 5 1975 2000 

Vehicles 5 22222 21111111 1 

Stringency 5 30.0 

Compliance 5 98.0 

Waiver Rates 5 3.0 1.0 

Cutpoints 

Exemption Age 5 25 

Grace Period 5 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 6 1996 1997 2 T/O IM240 

Model Years 6 1975 1997 

Vehicles 6 22222 21111111 1 

Stringency 6 30.0 

Compliance 6 98.0 

Waiver Rates 6 3.0 1.0 

Cutpoints 6 CUTPOINT.D 

Exemption Age 6 25 

Grace Period 6 2 

NO TTC Credits 

Effectiveness 

DESC file 
33

 

Program 7 1996 2050 2 T/O GC 

Model Years 7 1975 1995 

Vehicles 7 22222 21111111 1 

Stringency 

Compliance 7 98.0 

Waiver Rates 7 3.0 1.0 

Cutpoints 

Exemption Age 7 25 

Grace Period 7 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Fuel Commands 

 

Fuel Program 1 

 

Oxygenated Fuels 0.00 0.42 0.00 0.036 2 

 

Fuel RVP 9 

 

Alternative Emission Regulations and Control Measures 

Rebuild Effects 0.1 

 

External Conditions Commands 

 

Calendar Year 2005 

 

Evaluation Month 7 

 

Min/Max Temperature National Climatic Data Center 

 

Vehicle Fleet Characteristic Commands 

 

Registration Distribution Variable 

 
34

Cincinnati-Dayton-Springfield 2005 Ozone M6.2 Inputs 

Includes the following counties: 

Ohio: Butler, Clark, Clermont, Clinton, Greene, Hamilton, Miami, Montgomery, 

Warren

Indiana: Lawrenceburg Twp., Dearborn County

Kentucky: Boone, Campbell and Kenton counties

State Programs 

Input 

Note: Indiana and Kentucky inputs may not coincide with Ohio inputs 

 

Stage II Refueling 93/3/86/86 

 

Anti-tampering Programs 96/78/05/22222/21111111/1/12/098./12111112 

I/M Programs Yes 

Excludes Clinton Co. and Miami Co., OH, Dearborn County, IN - No I/M program 

Note: I/M inputs for Kentucky counties are not included 

Program 1 2004 2050 2 T/O OBD I/M 

Model Years 1 1996 2050 

Vehicles 1 22222 21111111 1 

Stringency 1 30.0 

Compliance 1 98.0 

Waiver Rates 1 1.0 1.0 

Cutpoints 

Exemption Age 1 25 

Grace Period 1 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 2 2004 2050 2 T/O EVAP OBD & GC 

Model Years 2 1996 2050 

Vehicles 2 22222 11111111 1 

Stringency 

Compliance 2 98.0 

Waiver Rates 2 1.0 1.0 

Cutpoints 

Exemption Age 2 25 

Grace Period 2 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 3 2001 2003 2 T/O ASM 2525 PHASE-IN 

Model Years 3 1996 2003 

Vehicles 3 22222 21111111 1 

Stringency 3 30.0 

Compliance 3 98.0 
35

Waiver Rates 3 3.0 1.0 

Cutpoints 

Exemption Age 3 25 

Grace Period 3 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 4 2001 2050 2 T/O ASM 2525 PHASE-IN 

Model Years 4 1975 1995 

Vehicles 4 22222 21111111 1 

Stringency 4 30.0 

Compliance 4 98.0 

Waiver Rates 4 3.0 1.0 

Cutpoints 

Exemption Age 4 25 

Grace Period 4 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 5 1998 2000 2 T/O LOADED/IDLE 

Model Years 5 1975 2000 

Vehicles 5 22222 21111111 1 

Stringency 5 30.0 

Compliance 5 98.0 

Waiver Rates 5 3.0 1.0 

Cutpoints 

Exemption Age 5 25 

Grace Period 5 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 6 1996 1997 2 T/O IM240 

Model Years 6 1975 1997 

Vehicles 6 22222 21111111 1 

Stringency 6 30.0 

Compliance 6 98.0 

Waiver Rates 6 3.0 1.0 

Cutpoints 6 CUTPOINT.D 

Exemption Age 6 25 

Grace Period 6 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 7 1996 2050 2 T/O GC 

Model Years 7 1975 1995 

Vehicles 7 22222 21111111 1 
36

Stringency 

Compliance 7 98.0 

Waiver Rates 7 3.0 1.0 

Cutpoints 

Exemption Age 7 25 

Grace Period 7 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

 

Fuel Commands 

 

Fuel Program 1 

 

Oxygenated Fuels 0.00 0.42 0.00 0.036 2 

 

Fuel RVP 9 

 

Alternative Emission Regulations and Control Measures 

Rebuild Effects 0.1 

 

External Conditions Commands 

 

Calendar Year 2005 

 

Evaluation Month 7 

 

Min/Max Temperature National Climatic Data Center 

 

Vehicle Fleet Characteristic Commands 

 

Registration Distribution Variable 

 
37

 

Other Areas (excluding NOACA/AMATS and OKI/MVRPC) Ozone M6.2 

Inputs 

Includes the following counties: 

Ohio: Belmont, Columbiana, Delaware, Fairfield, Franklin, Jefferson, Knox, Licking,

Lucas,Madison, Mahoning, Pickaway, Trumball, Wood 

 

Fuel Commands 

Input 

 

Fuel Program 1 

Oxygenated Fuels 0.00 0.42 0.00 0.036 2 

Fuel RVP 9 

Alternative Emission Regulations and Control Measures 

 

Rebuild Effects 0.1 (0.30 for 2018) 

 

External Conditions Commands 

 

Calendar Year All 

 

Evaluation Month 7 

Min/Max Temperature National Climatic Data Center 

Vehicle Fleet Characteristic Commands 

Registration 

Distribution Variable 

4.5 Ohio’s Mobile Emission Data Processed by LADCO (Lake Michigan Air 

Director’s Consortium): 

2005 TDM and Mobile6 input data were provided to LADCO for processing. The data 

was processed by LADCO with T3 to prepare it as an input into the ConCEPT model. 

T3 and ConCEPT are described as follows. 
38

4.5.1 T3--Development of Link-Level Mobile Source Emission Inventories: 

Highly resolved emission inventories for on-road mobile sources are needed for air 

quality modeling to develop the necessary technical support for new State 

Implementation Plans (SIPs) for regional haze, fine particles, and ozone. Emissions for 

on-road motor vehicles are estimated using vehicle miles traveled, trip starts and ends, 

speed, and other activity data developed by State Agencies and Metropolitan Planning 

Organizations (MPOs) using transportation demand models (TDMs), and emission 

factors from EPA's MOBILE6 model. To support this modeling in the upper Midwest, 

ENVIRON, working with LADCO, State DOTs, and local MPOs, has developed a 

software tool (the TDM Transformation Tool, or "T3") that takes TDM output from 

approximately twenty transportation networks using a variety of models, applies 

appropriate data transformations, and outputs link- and county-level activity data in a 

uniform format for input to the CONCEPT emissions processing model (a new 

emissions processing model also developed with funding from LADCO). In a parallel 

effort, analyses of extensive automatic traffic recorder (ATR) data collected by State 

DOTs were conducted to develop temporal profiles (hour of day, day of week, and 

month of year) of vehicle counts and vehicle mix by roadway type for developing the 

detailed on-road emission inventories. 

T3 provides a conduit from the projections of traffic demand modelers regarding vehicle 

types, road networks, and vehicle activity to the activity data required by emissions 

modelers. The primary goals of T3 are to provide an easy mechanism for incorporating 

TDM model outputs in as "raw" a format as possible, while simultaneously providing a 

great degree of flexibility in representing the TDM projections in terms acceptable to 

most air quality models. These goals have been achieved through the use of a 

dimensional transformation approach, where the dimensions of the various 

transformations are user-defined - hence the name of the tool. 

By Stella Shepard, Alison K. Pollack, John Haasbeek, ENVIRON International 

Corporation, 101 Rowland Way, Suite 220, Novato, CA. 94945 

& Mark Janssen, Lake Michigan Air Directors Consortium (LADCO), 2250 E. Devon 

Avenue # 250, Des Plaines, IL 60018, janssen@ladco.org 

4.5.2 ConCEPT--Consolidated Community Emissions Processing Tool an OpenSource Tool for the Emissions Modeling Community: 

The new CONCEPT (CONsolidated Community Emissions Processing Tool) 

Emissions Processor is now available for use by the emissions modeling community. 

Developed as joint project between Alpine Geophysics, LLC and ENVIRON Corporation, 

with Midwest RPO and joint RPO funding, the CONCEPT model combines the best 

attributes of current emissions modeling systems into an open source model highlighting 

the following features: 

• Open Source. Written primarily in PostgreSQL, the software required for running 
39

CONCEPT is in the public domain. The model itself is GNU Public License (GPL) 

compliant and users are encouraged to make additions and enhancements to the 

modeling system. 

• Transparent. The database structure of the model makes the system easy to 

understand, and the modeling codes themselves are extremely well documented to 

encourage user participation in the customizing the system for specific modeling 

requirements. 

• Quality Control. The CONCEPT model structure and implementation allows for 

multiple levels of QA analysis during every step of the emissions calculation process. 

Using the database structures, an emissions modeler can easily trace a process or 

facility and review the calculation procedures and assumptions for any emissions value. 

The CONCEPT model includes modules for the major emissions source 

categories: area source, point source, on-road motor vehicles, non-road motor vehicles 

and biogenic emissions, as well as a number of supporting modules, including spatial 

allocation factor development, speciation profile development, growth and control for 

point and area sources, and CEM point source emissions handling. The emissions 

modeling community has already begun development of additional CONCEPT support 

modules including CEM preprocessing software, graphical QA tools, and an interface to 

the traffic demand models for on-road motor vehicle emissions estimation. 

By Cyndi Loomis, James G. Wilkinson, Alpine Geophysics, LLC, & John Haasbeek, 

Alison Pollack, ENVIRON Corporation. & Mark Janssen, Lake Michigan Air Directors 

Consortium (LADCO), 2250 E. Devon Avenue # 250, Des Plaines, IL 60018, 

janssen@ladco.org 

4.5.3 LADCO Ohio Data Outputs for 2005: 

The following LADCO outputs and documents can be found at: 

www.ladco.org/tech/emis/net05/index.html 

Table 4-6 LADCO Data Output 

Sta

te Network

T3 

Descripti

on DOC

M6 

Inpu

ts

VM

T vs 

HP

MS 

Exc

el

Average 

Day 

VMT 

(this 

should 

match 

conformi

ty 

inventory

)

Co

unt

y 

Em

issi

on

s 

Re

por

t

All 

Pollut

ants 

After 

Speci

ation

Dropped 

VMT

M6 

Run 

Summ

ary

Pollut

ant 

Totals 

(Short

)

Raw 

Summ

ary 

(pol,ve

h, 

etype)

Hourly 

Tempo

ral 

Summ

ary

Hourly 

Veh 

Mix 

Summ

ary

Hourly 

Speed 

Summ

ary 

(with 

volum

e/ 

capaci

ty) 

OH AKRON AKRON OH OH AKRON 1 1 1 1 1 1 1 1 1
40

OH CANTON CANTON OH OH CANTON 1 1 1 1 1 1 1 1 1

OH CINCI CINCI OH OH CINCI 1 1 1 1 1 1 1 1 1

OH CLEVE CLEVE OH OH CLEVE 1 1 1 1 1 1 1 1 1

OH COLUMB

US

COLUMB

US OH OH COLUMBUS 1 1 1 1 1 1 1 1 1

OH SPRING

FLD

SPRING

FLD OH OH SPRINGFLD 1 1 1 1 1 1 1 1 1

OH TOLEDO TOLEDO OH OH TOLEDO 1 1 1 1 1 1 1 1 1

OH YNGSTO

WN

YNGSTO

WN OH OH YNGSTOWN 1 1 1 1 1 1 1 1 1

OH STATEW

D

STATEW

D OH OH STATEWD 1 1 1 1 1 1 1 1 1

Additional Documents on LADCO Web Page: 

“Comparison to EPA's Default Model NMIM”

“How we Build the 2005 Vmt/Networks”

“Spreadsheet/Graphics on Vehicle Mix”

“Background on the T3 Tool “ 

4.6 MPO Contact Table 

Table 4-7 County Summary Table of MPOs by County 

FIPS County Model Network MPO Contact Person 

39001 Adams Statewide 

39003 Allen Statewide 

Lima Allen County Regional 

Planning Commission 

Tom Mazur 

39005 Ashland Statewide 

39007 Ashtabula Statewide 

39009 Athens Statewide 

39011 Auglaize Statewide 

39013 Belmont Statewide 

Bel-O-Mar Regional Council 

and Interstate Planning 

Commission 

Rakesh Sharma 

39015 Brown Statewide 

39017 Butler Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser 

39019 Carroll Statewide 

39021 Champaign Statewide 

39023 Clark Springfield 

Coordinating Committee of 

the Clark County-Springfield 

Eric Ottoson 
41

Transportation Study 

39025 Clermont Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser 

39027 Clinton Statewide 

39029 Columbiana Statewide 

39031 Coshocton Statewide 

39033 Crawford Statewide 

39035 Cuyahoga Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39037 Darke Statewide 

39039 Defiance Statewide 

39041 Delaware Columbus 

Mid-Ohio Regional Planning 

Commission 

Nick Gill 

39043 Erie Statewide 

39045 Fairfield Statewide 

39047 Fayette Statewide 

39049 Franklin Columbus 

Mid-Ohio Regional Planning 

Commission 

Nick Gill 

39051 Fulton Statewide 

39053 Gallia Statewide 

39055 Geauga Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39057 Greene Cincinnat/Dayton 

Miami Valley Regional 

Planning Commission 

Ana Ramirez 

39059 Guernsey Statewide 

39061 Hamilton Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser 

39063 Hancock Statewide 

39065 Hardin Statewide 

39067 Harrison Statewide 

39069 Henry Statewide 

39071 Highland Statewide 

39073 Hocking Statewide 

39075 Holmes Statewide 

39077 Huron Statewide 

39079 Jackson Statewide 

39081 Jefferson Statewide 

Brooke-Hancock-Jefferson 

Transportation Study Policy 

Committee 

Mike Proprocki 

39083 Knox Statewide 

39085 Lake Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39087 Lawrence Statewide 

KYOVA Interstate Planning 

Commission 

39089 Licking Columbus 

Licking County Area 

Transportation Study 

Matthew Hill 

39091 Logan Statewide 

39093 Lorain Cleveland Northeast Ohio Areawide Bill Davis 
42

Coordinating Agency 

39095 Lucas Toledo 

Toledo Metropolitan Area 

Council of Governments 

Sujatha Mohanakrishnan 

39097 Madison Statewide 

39099 Mahoning Youngstown 

Eastgate Regional Council 

of Governments 

R.P. Samulka 

39101 Marion Statewide 

39103 Medina Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39105 Meigs Statewide 

39107 Mercer Statewide 

39109 Miami Cincinnat/Dayton 

Miami Valley Regional 

Planning Commission 

Ana Ramirez 

39111 Monroe Statewide 

39113 Montgomery Cincinnat/Dayton 

Miami Valley Regional 

Planning Commission 

Ana Ramirez 

39115 Morgan Statewide 

39117 Morrow Statewide 

39119 Muskingum Statewide 

39121 Noble Statewide 

39123 Ottawa Statewide 

39125 Paulding Statewide 

39127 Perry Statewide 

39129 Pickaway Statewide 

39131 Pike Statewide 

39133 Portage Akron 

Akron Metropolitan Area 

Transportation Study 

Jason Segedy 

39135 Preble Statewide 

39137 Putnam Statewide 

39139 Richland Statewide 

Richland County Regional 

Planning Commission 

John Adams 

39141 Ross Statewide 

39143 Sandusky Statewide 

39145 Scioto Statewide 

39147 Seneca Statewide 

39149 Shelby Statewide 

39151 Stark Canton 

Stark County Regional 

Planning Commission 

Dan Slicker 

39153 Summit Akron 

Akron Metropolitan Area 

Transportation Study 

Jason Segedy 

39155 Trumbull 

Youngstown (partial 

county model 

coverage) 

Eastgate Regional Council 

of Governments 

R.P. Samulka 

39157 Tuscarawas Statewide 

39159 Union Statewide 

39161 Van Wert Statewide 

39163 Vinton Statewide 

39165 Warren Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser, OKI 

+ 
43

+ 

Miami Valley Regional 

Planning Commission 

Ana Ramirez, MVRPC 

39167 Washington Statewide 

Wood-Washington-Wirt 

Interstate Planning 

Commission 

39169 Wayne Statewide 

39171 Williams Statewide 

39173 Wood Toledo 

Toledo Metropolitan Area 

Council of Governments 

Sujatha Mohanakrishnan 

39175 Wyandot Statewide 

NOTE: Complete MPO information can be found at, 

www.dot.state.oh.us/urban/mpomap.htm and at 

www.dot.state.oh.us/urban/mpolist.htm#Cleveland . 

SECTION 5 

MARINE, AIRCRAFT and RAIL (MAR) SOURCES 

MAR sources are non-road sources which are significant enough in terms of emissions 

to be considered separately from the rest of the non-road sources. The MAR inventory 

consists of commercial marines, aircraft and locomotive sources. The marine and 

locomotive inventory is generated by Environ1

 under contract with LADCO and the 

aircraft inventory is generated by Ohio EPA. 

5.1 Marine Vessel Sources 

The approach to commercial marine emission estimates needed to be flexible because 

the activity data was available in many formats. Emission estimates were determined 

either by multiplying engine power, load factor, hours per year of operation, or on the 

basis of the number of gallons of fuel consumed. 

Emissions were determined for ten subclasses of vessel types: Deep draft vessels 

(DDV) at port, DDV mid-late, push boats (rivers/lakes), tugs, ferries, other special 

(excursion) vessels, support vessels, dredges, commercial fishing, and military vessels 

(Coast Guard). These were linked to various Ohio lakes and rivers. 

Because of the large variety of methodologies employed, inventory tables for the ten 

subcategories are detailed in the complete inventory prepared and published by 

ENVIRON International Corporation: LADCO 2005 COMMERCIAL MARINE 

EMISSIONS, by Christian E. Lindhjem, March, 2007. 

Emission totals produced by ENVIRON were provided to LADCO to submit to EPA for 

Ohio’s State Implementation Plan (SIP). 
44

5.2 Rail Sources 

The primary activity unit used to determine emissions is gallons of fuel consumed. 

Emission rates were derived from EPA documents provided as support documentation 

for the 1997 locomotive emission standards (EPA, 1997). Gallons of fuel consumed 

were based on rail activity. 

Rail activity was broken down into four Source Category Codes (SCC). Class I, linehaul rail: Large interstate railroad companies like Union Pacific and Norfolk Southern. 

Class I,II, III, switching rail: Yard operations. Class II, III line haul: Regional and local 

railroads. And Passenger rail: AMTRAK. 

Class I, line-haul represents 84.3% of fuel used and the largest emission’s category. 

The complete emission’s inventory was prepared and published by ENVIRON 

International Corporation: LADCO 2005 LOCOMOTIVE EMISSIONS, by Christian E. 

Lindhjem, February, 2007. 

Emission totals produced by ENVIRON were provided to LADCO to submit to EPA for 

Ohio’s State Implementation Plan (SIP). 

EPA. 1997: "Locomotive Emission Standards." Regulatory Support Document, United States 

Environmental Protection Agency, Office of Mobile Sources, April. And EPA 1997, Emission Factors for 

Locomotives," Environmental Protection Agency, EPA420-F-97-051, December. 

5.3 Aircraft Sources 

INTRODUCTION: 

 The aircraft emission’s inventory is derived by taking the number of Landings and 

Take Offs (LTOs) per year and multiplying by an emission factor. In the Ohio inventory 

when specific aircraft models and engine type emission factors are known they were 

used. For the rest of the inventory the emission factors came from USEPA’s fleet 

average emissions data. Those results are then compiled as tons per year per pollutant 

by county. The following describes the components, methodology, and concludes with 

a description of an Access based aircraft emission calculator. 

COMPONENTS: 

 I. Ohio Airports: 

 A list of both towered and non-towered airports in Ohio is obtained from the Ohio 

Department of Transportation. See: www.dot.state.oh.us/aviation/ In conversation 

with ODOT two individuals stated that the 164 airports listed covered over 90% of the 

airports in Ohio. See Table 1. 
45

 II. Number of Operations/LTOs by Airport/County: 

 The ODOT list contained the number of operations a year per airport. An 

operation is either a landing or a take-off. A Landing and Take Off (LTO) is required for 

FAA EDMS calculations. LTOs were derived simply by dividing the number of 

operations by two. These were totaled by county. 

Table 5-1 Number of operations and LTOs for 2005

County Airport Name ID Total Operations LTOs/Year 

Adams Alexander Salamon AMT 5210 2605 

Allen Allen County AOH 32500 16250 

Ashland Ashland County 3G4 49240 24620 

Ashtabula Ashtabula County HZY 16886 8443 

Ashtabula Germack 7D9 840 420 

Athens Ohio University UNI 51600 25800 

Auglaize Neil Armstrong AXV 29456 14728 

Belmont Barnesville-Bradfield 6G5 10150 5075 

Belmont Alderman 2P7 6150 3075 

Brown Brown County GEO 5157 2578.5 

Butler Butler County Regional HAO 61687 30843.5 

Butler Hook Field Municipal MWO 40050 20025 

Butler Miami University OXD 16708 8354 

Carroll Carroll County -Tolson TSO 34950 17475 

Carroll Parsons 5D6 2674 1337 

Champaign Grimes Field I74 23480 11740 

Champaign Weller 38I 300 150 

Clark Springfield-Beckley Municipal SGH 64033 32016.5 

Clark Mad River I54 15350 7675 

Clermont Clermont County I69 35741 17870.5 

Clinton Airborne Airpark ILN 52000 26000 

Clinton Clinton Field I66 29360 14680 

Clinton Hollister Field 2B6 161 80.5 

Columbiana Columbiana County Ø2G 31146 15573 

Columbiana Koons 8G8 2546 1273 

Coshocton Richard Downing I4Ø 19550 9775 

Coshocton Tri-City 8ØG 8085 4042.5 

Crawford Port Bucyrus 17G 24871 12435.5 

Crawford Galion Municipal GQQ 5216 2608 

Cuyahoga Burke Lakefront BKL 97100 48550 

Cuyahoga Cleveland-Hopkins International CLE 234356 117178 

Cuyahoga Cuyahoga County CGF 79774 39887 

Darke Darke County VES 9238 4619 

Defiance Defiance Memorial DFI 9130 4565 

Delaware Delaware Municipal DLZ 39300 19650 

Delaware Packer 5E9 3181 1590.5 

Erie Hinde 88D 1350 675 

Erie Kelleys Island 89D 25495 12747.5 

Erie Griffing-Sandusky SKY 112100 56050 
46

Erie Wakeman I64 17324 8662 

Fairfield Miller's Farm 7B4 360 180 

Fairfield Fairfield County LHQ 43066 21533 

Fayette Fayette County I23 29405 14702.5 

Franklin Ohio State University OSU 134459 67229.5 

Franklin Port Columbus International CMH 218438 109219 

Franklin Rickenbacker International LCK 96200 48100 

Franklin Bolton Field TZR 69149 34574.5 

Franklin Columbus Southwest Ø4I 11833 5916.5 

Franklin Darby Dan 6I6 11260 5630 

Fulton Fulton County USE 21123 10561.5 

Gallia Gallia-Meigs Regional GAS 12200 6100 

Geauga Gates 7D8 4200 2100 

Geauga Geauga County 7G8 5350 2675 

Greene Greene County - Lewis A. Jackson I19 37400 18700 

Greene Bloom 14I 100 50 

Guernsey Cambridge Municipal CDI 6040 3020 

Hamilton Lunken LUK 129430 64715 

Hamilton Blue Ash ISZ 35000 17500 

Hamilton Cincinnati West I67 30197 15098.5 

Hancock Bluffton 5G7 71980 35990 

Hancock Findlay FDY 19800 9900 

Hancock Priebe 7D5 3850 1925 

Hardin Ada ØD7 331 165.5 

Hardin Hardin County I95 6562 3281 

Hardin Elliott's Landing O74 1560 780 

Harrison Harrison County 8G6 11900 5950 

Henry Henry County 7W5 15637 7818.5 

Highland Highland County HOC 18325 9162.5 

Holmes Holmes County 1ØG 21400 10700 

Huron Huron County 5A1 10100 5050 

Huron Willard 8G1 2715 1357.5 

Jackson James A. Rhodes I43 6053 3026.5 

Jefferson Jefferson County Airpark 2G2 15969 7984.5 

Jefferson Eddie Dew Memorial 1G8 3540 1770 

Knox Knox County 4I3 20150 10075 

Knox Wynkoop 6G4 4691 2345.5 

Lake Concord Airpark 2G1 4510 2255 

Lake Willoughby Lost Nation Municipal LNN 45085 22542.5 

Lawrence Lawrence County Airpark HTW 41910 20955 

Licking Newark-Heath VTA 12457 6228.5 

Logan Bellefontaine Regional EDJ 8325 4162.5 

Lorain Columbia 4G8 5150 2575 

Lorain Elyria 1G1 14300 7150 

Lorain Lagrange 92D 1155 577.5 

Lorain Lorain County Regional LPR 62000 31000 

Lorain Reader-Botsford Airfield 67D 18700 9350 

Lucas Toledo Express TOL 94600 47300 
47

Madison Madison County UYF 41410 20705 

Mahoning Salem Airpark 38D 16920 8460 

Mahoning Tri-City 3G6 10555 5277.5 

Mahoning Elser Metro 4G4 49232 24616 

Mahoning Lansdowne Ø4G 750 375 

Marion Marion Municipal MNN 42650 21325 

Medina Medina Municipal 1G5 79685 39842.5 

Medina Wadsworth Municipal 3G3 41025 20512.5 

Medina Weltzien Skypark 15G 79130 39565 

Mercer Lakefield CQA 16212 8106 

Miami Hartzell Field I17 10200 5100 

Miami Troy Skypark 37I 4264 2132 

Miami Waco Field 1WF 0 0 

Monroe Monroe County 4G5 3324 1662 

Montgomery Brookville Air-Park I62 29359 14679.5 

Montgomery James M. Cox Dayton Intl DAY 134524 67262 

Montgomery Dayton Wright Brothers MGY 89045 44522.5 

Montgomery Moraine Airpark I73 12938 6469 

Montgomery Dahio Trotwood I44 1853 926.5 

Montgomery Phillipsburg 3I7 68000 34000 

Morgan Morgan County I71 5725 2862.5 

Morrow Morrow County 4I9 19108 9554 

Muskingum Zanesville Municipal ZZV 33312 16656 

Muskingum Parr 42I 16150 8075 

Noble Noble County - Mike Brienza Field I1Ø 5950 2975 

Ottawa Middle Bass-East Point 3W9 1300 650 

Ottawa Middle Bass Island 3T7 6500 3250 

Ottawa North Bass Island 3X5 1000 500 

Ottawa Carl R. Keller Field PCW 20890 10445 

Ottawa Put-In-Bay 3W2 15140 7570 

Paulding Paulding 2H8 2100 1050 

Perry Crooksville I84 400 200 

Perry Perry County I86 4550 2275 

Pickaway Pickaway County CYO 35450 17725 

Pickaway Clarks Dream Strip Ø3I 2770 1385 

Pike Pike County EOP 2012 1006 

Portage Freedom Air Field 7D6 1623 811.5 

Portage Farview 86D 3353 1676.5 

Portage Mills 7E3 1050 525 

Portage Portage County 29G 9621 4810.5 

Putnam Ruhe's R47 13250 6625 

Putnam Putnam County OWX 11910 5955 

Putnam Ohio Dusting Co. 6C2 2995 1497.5 

Richland Mansfield Lahm Regional MFD 57518 28759 

Richland Shelby Community 12G 2012 1006 

Ross Ross County RZT 50150 25075 

Sandusky Fremont 14G 37450 18725 

Sandusky Sandusky County Regional S24 6148 3074 
48

Scioto Greater Portsmouth Regional PMH 45830 22915 

Seneca Bandit Field 5D9 140 70 

Seneca Fostoria Metropolitan FZI 7900 3950 

Seneca Weiker 82D 320 160 

Seneca Seneca County 16G 60165 30082.5 

Shelby Sidney Municipal I12 20500 10250 

Stark Barber 2D1 13750 6875 

Stark Miller Airport 4G3 8000 4000 

Stark Beach City 2D7 6112 3056 

Summit Akron Fulton International AKR 26000 13000 

Summit Akron-Canton Regional CAK 120441 60220.5 

Summit Mayfield 1D4 450 225 

Summit Kent State University 1G3 72500 36250 

Trumbull Braceville 41N 425 212.5 

Trumbull Warren 62D 14738 7369 

Trumbull Youngstown-Warren Regional YNG 98298 49149 

Tuscarawas Harry Clever Field PHD 54880 27440 

Union Union County MRT 31886 15943 

Van Wert Van Wert County VNW 20516 10258 

Vinton Vinton County 22I 5225 2612.5 

Warren Warren County I68 24951 12475.5 

Warren Red Stewart Airfield 4ØI 16800 8400 

Wayne Wayne County BJJ 96520 48260 

Williams Williams County ØG6 10010 5005 

Wood Wood County 1GØ 27405 13702.5 

Wood Bordner 3D8 2200 1100 

Wood Deshler Municipal 6D7 2000 1000 

Wood Metcalf TDZ 90700 45350 

Wyandot Wyandot County 56D 7410 3705 

III. Aircraft Models and Number of LTO/yr: 

Specific aircraft models by airport (generally the larger airports) is obtained from “Table 

7” provided by the United States Department of Transportation, Office of Airline 

Information. This provided the number of LTO’s for each aircraft model per year per 

airport. 

Table 2. Sample from “Table 7.” This is the all community total of aircraft models for 

the Akron/Canton area. “All Service” departures were used as the number of LTOs per 

year for that model. Listed in the original table are aircraft model by airport and number 

of services/LTOs. 

 
49

TOTAL DEPARTURES PERFORMD 

Aircraft Scheduled Non-Sched All 

Model Service Service Service 

A-318 1 1 

A319 384 384 

BOEING 717-200 3850 3850 

BOEING 727-100 1 1 

BOEING 727-200 5 5 

BOEING 737-100/200 74 74 

BOEING 737-200C 3 3 6 

BOEING 737-300 2 2 

BOEING 737-700/LR 212 212 

BOEING 737-800 5 5 

BOEING 757-200 4 4 

BOEING 767-300/ER 4 4 

CANADAIR RJ-100/ER 542 3 545 

CANADAIR RJ-700 2881 2881 

CONVAIR CV-580 3 3 

DASSAULT FALCON 7 7 

DHC8-100 DASH 8 2 2 

DOUGLAS DC-9-15F 13 13 

DOUGLAS DC-9-30 6 6 

EMBRAER-145 144 144 

RJ-200ER/RJ-440 5519 5519 

SAAB-FAIRCHD 340/B 1804 1804 

ALL TYPES 15342 130 15472 

IV. Emission Factors: 

 Where there was specific aircraft model data the emission factors were derived 

using the FAA’s Emission Dispersion Modeling System (EDMS). EDMS is a combined 

emissions and dispersion model for assessing air quality at civilian airports and military 

air bases. The model was developed by the Federal Aviation Administration (FAA) in 

cooperation with the United States Air Force (USAF). The model is used to produce an 

inventory of emissions generated by sources on and around the airport or air base, and 

to calculate pollutant concentrations in these environments. 
50

Table 5-2 EDMS Aircraft Emissions 

Emissions provided by Michigan Department of Environmental Quality. (The few 

aircraft in Ohio not included in the table had emissions derived by EDMS 5.0 in-house.) 

EDMS Aircraft Emissions/LTO by Aircraft Type 

EDMS 4.5 Emissions Inventory Report of 2005 Aircraft Inventory Emissions Factors 

 

Year 2005 Lbs Emitted Per LTO 

Aircraft Type CO NOx HC VOC SO2 PM2.5 PM10 

A- 318 19.8 18.7 4.0 4.4 1.5 0.0 0.0 

A-300-600/R/CF/RCF 27.1 56.4 2.0 2.2 4.0 0.2 0.2 

A-300B/C/F-100/200 30.2 52.5 3.5 3.7 3.3 0.2 0.2 

A-310-200C/F 32.6 52.5 7.3 7.9 3.3 0.2 0.2 

A-319 19.8 18.7 4.0 4.4 1.5 0.0 0.0 

A320-100/200 13.7 19.8 1.3 1.3 1.8 0.2 0.2 

A-321 16.8 36.8 3.1 3.3 2.2 0.0 0.0 

A-330-200 29.8 61.5 0.4 0.4 4.4 0.2 0.2 

AVROLINER RJ85 24.7 9.5 2.9 3.3 1.3 0.2 0.2 

BAE-146-300 24.7 9.0 3.1 3.3 1.3 0.2 0.2 

BEECH 1900 A/B/C 11.0 1.1 3.3 3.5 0.2 0.0 0.0 

BEECH KINGAIR C90 1.8 0.9 0.2 0.2 0.2 0.0 0.0 

717-200 11.7 23.4 0.0 0.0 1.5 0.0 0.0 

727-100 21.4 23.1 4.6 5.1 2.9 0.4 0.4 

727-100C/QC 44.5 19.8 4.6 5.1 2.4 1.1 1.1 

727-200 19.6 27.3 2.9 3.1 3.3 1.1 1.1 

737-100/200 14.1 16.1 2.2 2.4 2.0 0.7 0.7 

737-200C 13.9 17.4 6.8 7.5 2.0 0.9 0.9 

737-300 28.7 15.9 1.8 2.0 1.8 0.0 0.0 

737-400 26.5 18.5 1.5 1.5 1.8 0.0 0.0 

737-500 24.7 21.2 1.3 1.3 2.0 0.0 0.0 

737-700/LR 17.6 20.1 2.0 2.0 1.8 0.4 0.4 

737-800 15.7 27.1 1.5 1.8 2.0 0.7 0.7 

737-900 15.7 27.1 1.5 1.8 2.0 0.7 0.7 

747-100 252.6 108.5 106.7 116.8 7.1 0.4 0.4 

747-200/300 60.6 104.7 7.1 7.7 6.8 0.7 0.7 

747-400 67.0 105.6 5.7 6.4 7.3 0.7 0.7 

757-200 24.7 35.7 2.0 2.2 2.6 0.4 0.4 

757-300 27.1 33.1 0.4 0.4 3.1 0.2 0.2 

767-200/ER 32.6 52.5 7.3 7.9 3.3 0.2 0.2 

767-300/ER 32.0 62.2 2.6 2.9 4.0 0.4 0.4 

777 32.8 85.1 5.1 5.7 4.4 0.4 0.4 

CANADAIR RJ100/ER 16.3 4.9 1.5 1.8 0.7 0.0 0.0 

CANADAIR RJ-700 12.6 9.3 0.0 0.0 1.1 0.0 0.0 

CESSNA 208 1.1 0.4 0.0 0.0 0.0 0.0 0.0 

CONVAIR CV-580 36.2 0.9 8.8 9.5 0.7 0.0 0.0 

DASSAULT FALCON 13.7 2.6 2.4 2.6 0.2 0.0 0.0 

DHC8-100 DASH 8 5.1 3.1 0.0 0.0 0.4 0.0 0.0 

DORNIER 328 JET 1.3 6.6 11.9 12.6 0.7 0.0 0.0 

DOUGLAS DC-10-10 102.5 76.7 38.6 42.1 4.2 0.0 0.0 

DOUGLAS DC-10-30 45.4 78.7 5.3 5.7 5.1 0.4 0.4 
51

DOUGLAS DC-10-40 131.8 81.8 30.2 33.1 6.0 0.7 0.7 

DOUGLAS DC-8-63 263.5 25.6 219.1 239.9 4.2 5.1 5.1 

DOUGLAS DC-8-71 53.6 34.6 3.1 3.3 3.7 0.0 0.0 

DOUGLAS DC-8-73 53.6 34.6 3.1 3.3 3.7 0.0 0.0 

DOUGLAS DC-9-10 14.1 14.6 3.7 4.0 1.8 0.2 0.2 

DOUGLAS DC-9-15F 14.1 14.6 3.7 4.0 1.8 0.2 0.2 

DOUGLAS DC-9-30 14.1 14.6 3.7 4.0 1.8 0.2 0.2 

DOUGLAS DC-9-40 39.7 16.5 10.8 11.9 2.0 1.3 1.3 

DOUGLAS DC-9-50 12.6 20.1 1.8 1.8 2.2 0.9 0.9 

EMBRAER-135 12.8 5.5 1.1 1.3 0.7 0.0 0.0 

EMBRAER-140 13.7 6.0 1.3 1.3 0.7 0.0 0.0 

EMBRAER-145 6.4 6.8 1.1 1.1 0.7 0.0 0.0 

EMBRAER-170 9.0 9.7 0.0 0.0 1.1 0.0 0.0 

F28-4000/6000 76.7 10.4 77.2 84.4 1.5 0.0 0.0 

JETSTREAM 41 4.6 2.0 0.4 0.7 0.2 0.0 0.0 

L-101101/100/200 33.3 112.0 6.2 6.8 5.7 1.3 1.3 

LEAR-25 75.2 0.7 7.9 8.4 0.2 0.0 0.0 

LOCKHEED L100-30 48.7 9.9 19.6 21.4 1.8 0.0 0.0 

MD-11 47.8 93.3 4.0 4.4 6.0 0.7 0.7 

MD-80, 1, 2, 3, 7, 8 16.3 20.3 0.0 0.0 2.2 0.2 0.2 

MD-90 12.1 23.8 0.2 0.2 2.0 0.2 0.2 

RJ-200ER/RJ-440 16.3 4.9 1.5 1.8 0.7 0.0 0.0 

SAAB-FAIRCHD 340/B 4.2 1.5 1.5 1.5 0.2 0.0 0.0 

NOTE: Where specific aircraft model data was not available fleet emissions were used. 

EPA default fleet average emission factors were taken from “Documentation for Aircraft, 

Commercial Marine Vessel, ,Locomotive, and Other Non-road Components of the 

National Emissions Inventory. 2005, see Appendix A, Aircraft Emission Estimation 

Methodology.” Specific model LTOs were subtracted from county LTO totals to 

eliminate double counting those LTOs. 

Table 5-3 Fleet Emission Factor Categories 

Fleet emission factors were broken down into three categories. Itinerant General, Local 

General, and Military. 

Table 5-3a Fleet Average Emission Factors for Itinerant General Aircraft. 

(Taken from : Table A-5) 

Pollutant Emission Factors (lbs/LTO) 

HC 1.234 

NOx 0.158 

CO 28.13 

SOx 0.015 

PM10 0.60333 

Note: Air taxi HC emissions * VOC/HC (0.9914) conversion factor = air taxi VOC estimate

 
52

Table 5-3b Fleet Average Emission Factors for Local General Aircraft. 

(Taken From: Table A-11) 

Pollutant Emission Factors (lbs/LTO) 

HC 0.394 

NOx 0.065 

CO 12.014 

SOx 0.01 

PM10 0.2367 

Note: GA HC emissions * VOC/HC(0.9708) conversion factor = GA VOC estimate 

Table 5-3c Fleet Average Emission Factors for Military Aircraft. 

(Taken from: Table A-17) 

Pollutant Emission Factors (lbs/LTO

HC 1.234 

NOx 0.158 

CO 28.13 

SOx 0.015 

PM10 0.60333 

Note: Military HC emissions * VOC/HC(1.1046) conversion factor = Military VOC estimate 

METHODOLOGY 

Introduction 

The following information was considered in the development of emission estimates: 

1. Commercial scheduled and non-scheduled aircraft air carrier activity and 

commercial air freight activity by aircraft model types, 

2. General aviation and air taxi annual local and itinerant operations for year 2005, 

3. Military annual local and itinerant operations for year 2005. 

Due to the need to have aircraft operations information expressed as landing/take off 

(LTO) cycles, the following assumptions were made: 

1. For commercial aircraft and commercial air freight activity, the number of annual 

aircraft annual LTO cycles was assumed to be equal to the number of 
53

departures. The daily LTO cycle frequency was then obtained by dividing the 

yearly LTO cycles by 365. 

 

2. For general aircraft annual local and itinerant airport operations, each respective 

operations total was divided by 2 to obtain the corresponding year local and 

itinerant LTO cycles. The expected daily local and itinerant LTO cycles then 

were obtained by dividing these annual totals by 365. 

3. For military annual local and itinerant operations, each respective operations total 

was divided by 2 to obtain the corresponding year local and itinerant LTO cycles. 

The expected military daily local and itinerant LTO cycles then were obtained by 

dividing these annual totals by 365. 

Airport LTO cycles were further categorized into commercial aircraft by plane and 

engine type, general aviation itinerant aircraft of unknown aircraft type, general aviation 

local aircraft of unknown aircraft type, and military aircraft. This was necessary in order 

to utilize the U.S. Department of Transportation, Federal Aviation Administration EDMS 

Emissions and Dispersion Modeling System. Commercial and air freight aircraft 

emission factors per LTO cycle were determined using EDMS for each commercial 

aircraft type models where possible were used at each towered airport. Default 

commercial aircraft engine type, and Environmental Protection Agency (EPA) default 

time in mode values for takeoff, approach, and landing roll times were used in the 

EDMS model simulations. 

For those aircraft types that could not be determined using the EDMS emissions model, 

aircraft emission factors based upon EPA alternative fleet average procedures were 

then used to estimate their emissions. These included general aviation and air taxi 

itinerant aircraft of unknown aircraft type, general aviation local aircraft of unknown 

aircraft type, and military aircraft. Conversion from total hydrocarbons to volatile organic 

compounds was performed and based upon the EPA guidance. 

APPROACH 

1. A list of more than 90% of the airports was obtained from the Ohio Department of 

Transportation. These were classed by airport, county, aircraft flight classification, and 

the total number of operations per year. 

2. The number of operations (a landing or a take off) were then divided by two giving 

the number of LTOs per year per airport. These airports were combined by county for 

the total number of LTOs per year, per county. 

3. In dialog with ODOT it was determined that the following Ohio flight groups of aircraft 

be combined to match the three categories used by the USEPA in calculating 

emissions. 
54

Itinerant (General, air carrier, commuter, air taxi, general aviation itinerant) 

Local (General aviation local) 

Military (Military) 

4. LTOs for specific models of aircraft per airport were taken from the FAA Table 7. 

See Table 2 above. These were then combined to give the number of LTOs per aircraft 

model per county. Specific model LTOs were subtracted from county totals to avoid 

double counting those LTOs. 

5. Emission factors were determined from the FAA’s EDMS program for specific aircraft 

model and engine type. See IV. Emission factors above. The aircraft emission table 

provided by Michigan has the emission factors for most of the aircraft flown in Ohio. . 

Where specific aircraft model data was not available USEPA average fleet emissions 

were used. 

6. Emission factors times LTOs by county yielded tons per year per county. 

Table 5-4 Pollutant by County (Sample)

County POLLUTANT ACTIVITY(LTOS/YEAR) ACTIVITY(LTOS/DAY) EMISSIONS(TON/YEAR) 

ADAMS CO 2605 7.136986 24.552325 

ADAMS HC 2605 7.136986 0.977285 

ADAMS NOX 2605 7.136986 0.136045 

ADAMS PM10-PRI 2605 7.136986 0.513114825 

ADAMS PM25-PRI 2605 7.136986 0 

ADAMS SOX 2605 7.136986 0.0157875 

ADAMS VOC 2605 7.136986 0.96314234 

ALLEN CO 16250 44.52055 147.945065 

ALLEN HC 16250 44.52055 5.841097 

ALLEN NOX 16250 44.52055 0.827189 

ALLEN PM10-PRI 16250 44.52055 3.081191265 

ALLEN PM25-PRI 16250 44.52055 0 

ALLEN SOX 16250 44.52055 0.0984575 

ALLEN VOC 16250 44.52055 5.769568254 

ASHLAND CO 24620 67.45205 173.67794 

ASHLAND HC 24620 67.45205 6.19414 

ASHLAND NOX 24620 67.45205 0.94895 

ASHLAND PM10-PRI 24620 67.45205 3.532515 

ASHLAND PM25-PRI 24620 67.45205 0 

ASHLAND SOX 24620 67.45205 0.1311 

ASHLAND VOC 24620 67.45205 6.07175247 

ASHTABULA CO 8863 24.28219 78.115087 

ASHTABULA HC 8863 24.28219 3.042551 

ASHTABULA NOX 8863 24.28219 0.431593 
55

ASHTABULA PM10-PRI 8863 24.28219 1.623493455 

ASHTABULA PM25-PRI 8863 24.28219 0 

ASHTABULA SOX 8863 24.28219 0.0520325 

ASHTABULA VOC 8863 24.28219 2.99420213 

ATHENS CO 25800 70.68493 196.874985 

ATHENS HC 25800 70.68493 7.267183 

ATHENS NOX 25800 70.68493 1.081521 

ATHENS PM10-PRI 25800 70.68493 4.037266335 

ATHENS PM25-PRI 25800 70.68493 0 

ATHENS SOX 25800 70.68493 0.1420925 

ATHENS VOC 25800 70.68493 7.128180006 

7. Emissions were then summed by pollutants in each county by SCC aircraft category 

type so data could be provided to LADCO in the EPA prescribed NEI – NIF format. 

DATA ERROR 

The first aircraft emission inventory submitted to LADCO in April, 2007 contained an 

error. The inventory submitted in May, 2007 has the error corrected. The error was the 

result of a carry-over function in Access that picked up the number of operations as 

instead of LTOs....which made the inventory exactly twice as large as what it really was. 

ACCESS CALCULATOR 

Introduction: 

Our database programmer set up Access application to calculate Ohio’s aircraft 

emission inventory, and export those results to Excel. His utility allows for easy 

modification of the aircraft data to match future data scenarios. Output to Excel also 

allows for additional data manipulation and importation. 
56

Interface: 

Through this interface the following sets of data can be edited/updated: Airport activity, 

emission factors, specific airport emissions, state/county FIPS, detailed aircraft 

information, airport information, and aircraft SCCs. Once the final emissions have been 

calculated and summed, then the data can be export via the export function on the Main 

Menu. 
APPENDIX C

Mobile Source Emission Inventory (MOVES)
Technical Memorandum 

Northeast Ohio (AMATS, NOACA, Ashtabula) 

1997 Annual NAAQS PM Fine Attainment SIP Inventory 

Mobile Source Emissions (PM 2.5 and NOx) 

May 2011 

By AMATS, NOACA and ODOT 

In Coordination with the Ohio Environmental Protection Agency 

 
Table of Contents 

1. Overview ............................................................................................................... 1 

2. Urban Travel Demand Models ............................................................................. 5

3. Emission Factor Generation ................................................................................. 5

4. Post Processing ................................................................................................... 22

5. Multiple MPO Coordination Issues .................................................................... 24

Appendix A Interagency Consultation Documentation 

Appendix B Post Processing Default Distributions 
1 

1. Overview 

This memorandum is intended to document the USEPA MOVES based mobile source air quality 

analyses performed by the Ohio Department of Transportation (ODOT) for the Akron 

Metropolitan Area Transportation Study (AMATS) and Ashtabula Township areas, and by the 

Northeast Ohio Areawide Coordinating Agency for the counties of Cuyahoga, Lake, Lorain and 

Medina in its area. These analyses were conducted in support of Ohio EPA’s intent to submit a 

redesignation package State Implementation Plan (SIP) submittal for the 1997 Annual PM2.5

NAAQS. 

The designated northeast Ohio nonattainment areas for the 1997 Annual PM2.5 NAAQS includes 

the counties of Cuyahoga, Lake, Lorain, Medina, Portage, and Summit; and Ashtabula Township 

in Ashtabula County. USEPA has recently proposed to redesignate these counties to attainment 

based on several years of monitoring data (76 FR 28393). Despite the potential change in 

attainment status, Ohio EPA must still continue its efforts to assemble the necessary 

redesignation submittals. 

The inventories include county based, annual totals of PM2.5 and Oxides of Nitrogen (NOX) for 

the base year 2005, the attainment year 2008, the interim year 2015, and the maintenance period 

horizon year 2022. 

Latest Planning Assumptions 

These annual PM2.5 inventory runs meet the latest planning assumption requirement. As 

discussed in Section 2 of this report, the travel forecast modeling processes used to develop 

vehicle miles of travel (VMT) for the AMATS and NOACA areas is calibrated using the latest 

population and land use data available and are validated using corresponding traffic count data. 

Currently, the travel demand models are validated to year 2005. 

U.S. EPA’s most recent emissions software, MOVES, is used for all mobile source emission 

analyses with MOVES inputs and output needs being established at various interagency 

consultation meetings and e-mails between November 2010 and May 2011 (See Appendix A). 

It was also established that annual emission estimates would be based on a single-season 

approach. Since travel demand models produce average daily conditions, the daily emissions 

estimates are multiplied by 365 days to produce annual emissions estimates expressed in tons per 

year. 

Finally, the regional emissions analysis includes emissions for Direct PM2.5 (exhaust, brake, and 

tire wear), and Oxides of Nitrogen (NOX).Sulfur Dioxide (SO2), Volatile Organic Compounds 

(VOCs) and Ammonia (NH3) are considered insignificant at this time. SO2 emissions are shown 

to support the assertion that they are insignificant. 
2 

On Road Mobile Source Emissions Inventory Summary Tables 1 to 4 present a summary of emissions by county as well as the entire nonattainment area for NOX, PM2.

calendar years 2005, 2008, 2015, and 2022. They also include annual VMT totals. The remainder of this docum

assumptions behind the analyses. 

Table 1 – Northeast Ohio On-Road Mobile Source Emissions 

 2005 Base 

COUNTY NOX (tons/year) PM2.5 (tons/year) SO2 (tons/year) AAshtabula Twp. 294.66 11.50 5.07 Cuyahoga 36,506.02 1,247.92 845.65 Lake 8,067.92 282.95 176.88 Lorain 9,181.08 319.21 199.86 Medina 9,280.85 319.21 207.56 Portage 5,707.22 187.71 96.74 Summit 17,377.28 611.78 319.72 

TOTAL 86,415.03 2,980.28 1,851.48 
3 

Table 2 – Northeast Ohio On-Road Mobile Source Emissions 

 2008 Attainment COUNTY NOX (tons/year) PM2.5 (tons/year) SO2 (tons/year) AAshtabula Twp. 232.83 10.04 1.57 Cuyahoga 28,227.20 1,040.28 253.16 Lake 6,425 241.78 51.94 Lorain 7,354.77 274.43 60.17 Medina 7,603.83 317.43 57.65 Portage 4,630.98 160.90 30.52 Summit 13,609.82 511.43 100.66 

TOTAL 68,084.64 2,556.29 555.66 

Table 3 – Northeast Ohio On-Road Mobile Source Emissions 

 2015 Interim COUNTY NOX (tons/year) PM2.5 (tons/year) SO2 (tons/year) AAshtabula Twp. 107.68 5.37 0.66 Cuyahoga 12,371.38 430.01 76.36 Lake 2,840.39 122.02 15.91 Lorain 3,456.51 143.34 18.51 Medina 3,306.35 152.50 17.63 Portage 2,264.50 87.54 13.23 Summit 6,170.32 251.70 42.76 

TOTAL 30,517.14 1,192.47 185.06 
4 

Table 4 – Northeast Ohio On-Road Mobile Source Emissions 

 2022 Maintenance COUNTY NOX (tons/year) PM2.5 (tons/year) SO2 (tons/year) AAshtabula Twp. 54.49 3.03 0.62 Cuyahoga 6,065.83 320.94 68.07 Lake 1,417.15 69.42 14.45 Lorain 1,723.02 81.69 16.83 Medina 1,677.58 78.48 16.83 Portage 1,014.35 49.45 7.40 Summit 3,059.45 162.98 39.57 

TOTAL 15,011.86 765.99 163.77 
5 

2. Urban Travel Demand Models

NOACA maintains a PC-based regional travel demand forecasting model on the Citilabs CUBE 

platform for use in its urban transportation planning process. This model employs the traditional 

four step modeling process to project existing and future traffic volumes and travel patterns on 

the regional transportation networks. The four step process consists of trip generation, trip 

distribution, mode split, and route assignment. Output from the model is link-by-link directional 

volumes for four time periods: AM peak, Midday, PM peak, and Night and is added together to 

create 24-hour traffic volumes for the existing or future regional transportation networks. 

The Ohio Department of Transportation (ODOT) holds the transportation model for the AMATS 

area. AMATS prepares and submits networks for its planning area to ODOT. ODOT prepares 

networks for Ashtabula County. ODOT's modeling is also run on a PC-based CUBE platform. 

These models are uniquely suited to perform emission analyses. The modeling process identifies 

growth in vehicle miles of travel and changes in regional travel patterns resulting from the 

projects that are proposed in these areas’ transportation plans and programs. 

Detailed information about NOACA’s travel demand forecast model can be found here: 

http://www.noaca.org/traveldemandmemo.pdf

Landuse and Socio-economic Data 

The areas’ socio-economic model variables reflect the current and expected future regional land 

uses as best known to staff. 

Socio-economic variables were developed for all areas based on 2000 Census data and 2030 

county-level Ohio Department of Development population projections. Minor adjustments to 

2005 data have been made in an effort to have this data correspond to 2005 estimates released by 

the US Census Bureau. 

 

Until new 2010 Census based projections are developed by the Ohio Department of 

Development (ODOD), the current data represents the best available for this effort. 

3. Emission Factor Generation

Base and Attainment Years 

Using MOVES, emission factor files were generated for base year-2005, attainment year-2008, 

interim year 2015, and maintenance year 2022. Assumptions for these runs include an I/M 

program in all but Ashtabula Township. Programs and corresponding MOVES parameters were 

developed in consultation with OEPA. 
6 

Technical Details 

Table 7 summarizes the settings used in the MOVES run specification file and the MOVES 

County-Data Manager. All inputs for the region’s analyses are included in the CD provides to 

Ohio EPA as part of this document submittal. Further information about specific inputs that are 

not using default values is also discussed below. 

Table 7 – MOVES Inputs 

RunSpec Parameter Settings 

MOVES Version 2010/08/26 

Scale County 

MOVES Modeling Technique Emission Factor Method 

Rates per Distance 

Rates per Vehicle 

Time Span Time Aggregation: Hour 

1 Month representing average annual temperatures 

All hours of day selected 

16 speed bins 

Weekdays only 

Geographic Bounds Cuyahoga, Lake, Lorain, Medina, Portage and Summit 

Counties; Ashtabula Township 

Vehicles/Equipment All related source types, gasoline and diesel; CNG-Transit 

Buses for base year-2005 and attainment year-2008 

Road Type All road types including off-network 

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

Strategies Alternative Vehicle Fuels and Technologies for interim year 

2015, and maintenance year 2022 to offset impact of CNG 

General Output Units = grams, joules and miles 

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

type and source use type. 

Advance Performance None 

County Data Manager Sources 

Source Type Population Combination of local and default data 

Local BMV Vehicle Registration Total 

Local BMV population for motorcycle (11), moped (11), bus 

(41, 42,43), mobile home (54), house vehicle (54) 

MOVES default fractions for the rest (21,31,32,51,52,53,61,62) 

Future year growth rate based on TFM’s household growth rates. 

Vehicle Type VMT Travel Demand Forecast Model’s daily VMT 

EPA annual VMT converter 
7 

Hourly VMT fractions - ODOT Hourly Percentage 

I/M Program I/M program information supplied by Ohio EPA 

Fuel Formulation Default 

Fuel Supply Default 

Metereology Data Local data obtained from NOAA National Climatic Data Center. 

Data will consist of monthly high and low temperatures and 

daily relative humidity for 2005. 

Ramp Fraction Travel Demand Forecast Model 

Road Type Distribution ODOT’s 2005 DVMT by FC 

Age Distribution Local data ODOT from 2009 motor vehicle registration 

The same age distribution will be used for all analysis years 

Average Speed Distribution Default as a place holder 

Zone Activity Default with necessary modification as prescribed in MOVES 

userguide. 
8 

Temperature and Relative Humidity 

Temperatures used for the single season approach are representative of 12 months in 2005 based 

on NOAA data from the National Climate Data Center website. The month of April was used as 

the representative month. Data for Cleveland Hopkins International Airport (CLE) was used for 

NOACA’s area. Data for Akron-Canton Airport (CAK) was used for AMATS and Ashtabula 

Twp. Areas. To get the correct fomat for MOVES, the data was entered into a spreadsheet 

provided by EPA which was designed to convert Mobile6 data to MOVES. The average annual 

hourly temperature and relative humidity distribution profile for Cuyahoga, Lake, Lorain, and 

Medina Counties can be seen in Table 8a. Table 8b portrays the data for Portage and Summit 

Counties; and Ashtabula Twp. 

Table 8a – Temperature and Relative Humidity Data for NOACA portion of area

Hour Average Temperature 

Average 

Relative Humidity 

1 42.1033 68 

2 40.9717 73 

3 40.0825 75 

4 39.4358 76 

5 38.9508 76 

6 38.3850 77 

7 37.9000 70 

8 38.3042 68 

9 40.7292 63 

10 44.6092 58 

11 48.5700 58 

12 51.9650 56 

13 54.9558 53 

14 56.5725 54 

15 57.1383 52 

16 57.3000 52 

17 56.8958 52 

18 55.7642 52 

19 53.8242 53 

20 51.3183 56 

21 48.8125 57 

22 46.6300 61 

23 45.0942 64 

24 43.5583 66 

 
9 

Table 8b – Temperature and Relative Humidity Data for AMATS portion of area

Hour Average Temperature 

Average 

Relative Humidity 

1 44.9033 76 

2 43.7717 78 

3 42.8825 79 

4 42.2358 81 

5 41.7508 82 

6 41.1850 83 

7 40.7000 82 

8 41.1042 79 

9 43.5292 74 

10 47.4092 71 

11 51.3700 68 

12 54.7650 63 

13 57.7558 61 

14 59.3725 58 

15 59.9383 56 

16 60.1000 55 

17 59.6958 57 

18 58.5642 60 

19 56.6242 65 

20 54.1183 69 

21 51.6125 71 

22 49.4300 73 

23 47.8942 74 

24 46.3583 75 

 
10 

Ramp Fraction 

Ramp fractions were derived using the base year travel demand model VHT fractions. Ramp 

fractions can be seen in Table 9. Base year fractions were kept the same for future years. 

Table 9 – Ramp Fractions 

County roadTypeID roadDesc rampFraction 

Cuyahoga 2 Rural Restricted Access 0 

4 Urban Restricted Access 0.116 

Lake 2 Rural Restricted Access 0.092 

4 Urban Restricted Access 0.092 

Lorain 2 Rural Restricted Access 0.107 

4 Urban Restricted Access 0.107 

Medina 2 Rural Restricted Access 0.058 

4 Urban Restricted Access 0.058 

Portage 2 Rural Restricted Access 0.014 

4 Urban Restricted Access 0.184 

Summit 2 Rural Restricted Access 0.005 

4 Urban Restricted Access 0.091 

Ashtabula 2 Rural Restricted Access 0 

4 Urban Restricted Access 0.184 
11 

Source Type Population Source type population is based on a combination of local and MOVES default data. Local data was obtained froMotor Vehicles Motor Vehicle Registrations by county and vehicle type data for the base year 2005 and intermedMOVES default source type population data was obtained from the national level MOVES inventory runs for ea

OBMV’s vehicle registration data was used as a control total for all source type population and a sub total for IntTransit Bus (42), and School Bus (43). The same data was also used to assign source type population of Motorcy

Home (54). For the rest of source types 21, 31, 32, 51, 52, 53, 61 and 62, the fraction of each source type from thdata was used and adjusted to match up the control total for source type population.Future year source type growtravel demand model’s annual household growth rate from year 2005 to year 2022 for corresponding analysis yesource type population for the analyzed counties in 2005. 

Table 10 – Source Type Population for year 2005 

sourceTypeID sourceTypeName Cuyahoga Lake Lorain Medina Portage Su

11 Motorcycle 24,754 7,960 10,241 6,585 7,158 21 Passenger Car 579,339 121,433 150,152 89,819 84,140 31 Passenger Truck 362,540 76,735 95,416 58,943 60,169 32 Light Commercial Truck 121,119 25,636 31,877 19,692 2,413 41 Intercity Bus 289 61 29 16 33 42 Transit Bus 742 37 17 9 17 43 School Bus 2,003 422 198 108 198 51 Refuse truck 314 73 92 73 64 52 Single Unit Short-haul Truck 17,464 4,049 5,122 4,059 2,651 53 Single Unit Long-haul Truck 1,770 410 519 411 338 54 Motor Home 5,121 780 1,354 901 2,537 61 Comb Short-haul Truck 3,758 1,043 1,083 1,336 1,283 62 Comb Long-haul Truck 3,870 1,075 1,116 1,376 1,476 
12 

I/M Program 

I/M program information was supplied by Ohio EPA. The I/M program was applied to all 

analysis years for every geography with the exception of Ashtabula Township. The I/M program 

MOVES inputs reflect: 

1) ASM 2525 Final Cutpoints, for model years up to 1995 

2) Two-mode, 2500 RPM/Idle Test, for model years 1996 & newer 

OBD II in Ohio is not an exhaust emission test and is not considered, since the pollutants under 

current consideration are only SO2, NOX and PM2.5. The compliance rate and failure rates are 

obtained from Ohio EPA and compliance factors are calculated. These are applied for all 

counties under consideration and for all model years. 

Vehicle Age Distribution 

Vehicle age distribution information was derived using ODPS vehicle registration data (2009). 

The data was given to OEPA who supplied a VIN decoder that allowed ODOT to create 

correctly formatted MOVES inputs. A different age distribution file is used for each county. 

Table 11 provides a sample distribution for Cuyahoga County. The distributions for other areas 

can be found in the electronic input submittals. The same age distributions were used for all 

analysis years. 
13 

Table 11 – Vehicle Age Distribution for 

Cuyahoga County 

yearid sourcetypeid ageid ageFraction

2005 11 30 0.106925 

2005 11 29 0.018783 

2005 11 28 0.026115 

2005 11 27 0.014845 

2005 11 26 0.011632 

2005 11 25 0.01317 

2005 11 24 0.014596 

2005 11 23 0.00998 

2005 11 22 0.007536 

2005 11 21 0.009369 

2005 11 20 0.009074 

2005 11 19 0.008758 

2005 11 18 0.010636 

2005 11 17 0.013827 

2005 11 16 0.017312 

2005 11 15 0.021 

2005 11 14 0.02272 

2005 11 13 0.021951 

2005 11 12 0.02582 

2005 11 11 0.03761 

2005 11 10 0.044965 

2005 11 9 0.05413 

2005 11 8 0.060081 

2005 11 7 0.075515 

2005 11 6 0.063725 

2005 11 5 0.066418 

2005 11 4 0.076963 

2005 11 3 0.068658 

2005 11 2 0.044852 

2005 11 1 0.021362 

2005 11 0 0.001675 

2005 21 30 0.031927 

2005 21 29 0.001002 

2005 21 28 0.001082 

2005 21 27 0.001608 

2005 21 26 0.002483 

2005 21 25 0.003053 

2005 21 24 0.003691 

2005 21 23 0.005082 

2005 21 22 0.006714 

2005 21 21 0.010246 

yearid sourcetypeid ageid ageFraction

2005 21 20 0.013223 

2005 21 19 0.017294 

2005 21 18 0.023152 

2005 21 17 0.028475 

2005 21 16 0.034352 

2005 21 15 0.044328 

2005 21 14 0.040195 

2005 21 13 0.048119 

2005 21 12 0.050547 

2005 21 11 0.056333 

2005 21 10 0.057401 

2005 21 9 0.054497 

2005 21 8 0.058427 

2005 21 7 0.059745 

2005 21 6 0.059884 

2005 21 5 0.060329 

2005 21 4 0.058016 

2005 21 3 0.060513 

2005 21 2 0.05262 

2005 21 1 0.04119 

2005 21 0 0.014472 

2005 31 30 0.002999 

2005 31 29 0.000209 

2005 31 28 0.000285 

2005 31 27 0.000524 

2005 31 26 0.000797 

2005 31 25 0.001189 

2005 31 24 0.001723 

2005 31 23 0.002477 

2005 31 22 0.003177 

2005 31 21 0.00491 

2005 31 20 0.006233 

2005 31 19 0.008686 

2005 31 18 0.011764 

2005 31 17 0.02159 

2005 31 16 0.026281 

2005 31 15 0.03281 

2005 31 14 0.031973 

2005 31 13 0.036449 

2005 31 12 0.043884 

2005 31 11 0.049373 

2005 31 10 0.053384 

2005 31 9 0.052496 

2005 31 8 0.068594 
14 

yearid sourcetypeid ageid ageFraction

2005 31 7 0.073487 

2005 31 6 0.079743 

2005 31 5 0.092016 

2005 31 4 0.082702 

2005 31 3 0.07979 

2005 31 2 0.07549 

2005 31 1 0.04345 

2005 31 0 0.011516 

2005 32 30 0.006296 

2005 32 29 0.000508 

2005 32 28 0.000914 

2005 32 27 0.001219 

2005 32 26 0.002844 

2005 32 25 0.002945 

2005 32 24 0.002945 

2005 32 23 0.004367 

2005 32 22 0.005078 

2005 32 21 0.008531 

2005 32 20 0.010358 

2005 32 19 0.009851 

2005 32 18 0.014522 

2005 32 17 0.024576 

2005 32 16 0.03727 

2005 32 15 0.054737 

2005 32 14 0.036458 

2005 32 13 0.043668 

2005 32 12 0.041637 

2005 32 11 0.048746 

2005 32 10 0.055753 

2005 32 9 0.040317 

2005 32 8 0.038997 

2005 32 7 0.03788 

2005 32 6 0.029349 

2005 32 5 0.029958 

2005 32 4 0.086016 

2005 32 3 0.091398 

2005 32 2 0.13923 

2005 32 1 0.063166 

2005 32 0 0.030466 

2005 41 30 0.002855 

2005 41 29 0.001428 

2005 41 28 0.001428 

2005 41 27 0.001428 

2005 41 26 0.000714 

yearid sourcetypeid ageid ageFraction

2005 41 25 0.00571 

2005 41 24 0 

2005 41 23 0.003569 

2005 41 22 0.009279 

2005 41 21 0.003569 

2005 41 20 0.007852 

2005 41 19 0.010707 

2005 41 18 0.017131 

2005 41 17 0.009993 

2005 41 16 0.009279 

2005 41 15 0.0207 

2005 41 14 0.019986 

2005 41 13 0.0207 

2005 41 12 0.024982 

2005 41 11 0.042827 

2005 41 10 0.071378 

2005 41 9 0.06424 

2005 41 8 0.059957 

2005 41 7 0.097787 

2005 41 6 0.114918 

2005 41 5 0.063526 

2005 41 4 0.114918 

2005 41 3 0.086367 

2005 41 2 0.044254 

2005 41 1 0.068522 

2005 41 0 0 

2005 42 30 0.010638 

2005 42 29 0 

2005 42 28 0 

2005 42 27 0 

2005 42 26 0.005319 

2005 42 25 0 

2005 42 24 0 

2005 42 23 0 

2005 42 22 0 

2005 42 21 0.005319 

2005 42 20 0 

2005 42 19 0 

2005 42 18 0 

2005 42 17 0 

2005 42 16 0.021277 

2005 42 15 0.005319 

2005 42 14 0.010638 

2005 42 13 0.005319 
15 

yearid sourcetypeid ageid ageFraction

2005 42 12 0.047872 

2005 42 11 0.079787 

2005 42 10 0.026596 

2005 42 9 0.058511 

2005 42 8 0.079787 

2005 42 7 0.079787 

2005 42 6 0.079787 

2005 42 5 0.074468 

2005 42 4 0.170213 

2005 42 3 0.074468 

2005 42 2 0.12766 

2005 42 1 0.037234 

2005 42 0 0 

2005 43 30 0.000585 

2005 43 29 0 

2005 43 28 0 

2005 43 27 0.001171 

2005 43 26 0.001171 

2005 43 25 0.005855 

2005 43 24 0.002342 

2005 43 23 0.007611 

2005 43 22 0.008197 

2005 43 21 0.005855 

2005 43 20 0.008197 

2005 43 19 0.014052 

2005 43 18 0.007611 

2005 43 17 0.009368 

2005 43 16 0.009953 

2005 43 15 0.011124 

2005 43 14 0.010539 

2005 43 13 0.044496 

2005 43 12 0.050937 

2005 43 11 0.088993 

2005 43 10 0.104801 

2005 43 9 0.067916 

2005 43 8 0.100703 

2005 43 7 0.058548 

2005 43 6 0.050351 

2005 43 5 0.052693 

2005 43 4 0.052693 

2005 43 3 0.0726 

2005 43 2 0.049766 

2005 43 1 0.059719 

2005 43 0 0.042155 

yearid sourcetypeid ageid ageFraction

2005 51 30 0.014851 

2005 51 29 0.002475 

2005 51 28 0 

2005 51 27 0 

2005 51 26 0 

2005 51 25 0.002475 

2005 51 24 0.00495 

2005 51 23 0.012376 

2005 51 22 0 

2005 51 21 0.00495 

2005 51 20 0.00495 

2005 51 19 0.00495 

2005 51 18 0.007426 

2005 51 17 0.012376 

2005 51 16 0.024752 

2005 51 15 0.066832 

2005 51 14 0.044554 

2005 51 13 0.027228 

2005 51 12 0.044554 

2005 51 11 0.027228 

2005 51 10 0.044554 

2005 51 9 0.044554 

2005 51 8 0.059406 

2005 51 7 0.09901 

2005 51 6 0.032178 

2005 51 5 0.071782 

2005 51 4 0.128713 

2005 51 3 0.071782 

2005 51 2 0.094059 

2005 51 1 0.04703 

2005 51 0 0 

2005 52 30 0.014851 

2005 52 29 0.002475 

2005 52 28 0 

2005 52 27 0 

2005 52 26 0 

2005 52 25 0.002475 

2005 52 24 0.00495 

2005 52 23 0.012376 

2005 52 22 0 

2005 52 21 0.00495 

2005 52 20 0.00495 

2005 52 19 0.00495 

2005 52 18 0.007426 
16 

yearid sourcetypeid ageid ageFraction

2005 52 17 0.012376 

2005 52 16 0.024752 

2005 52 15 0.066832 

2005 52 14 0.044554 

2005 52 13 0.027228 

2005 52 12 0.044554 

2005 52 11 0.027228 

2005 52 10 0.044554 

2005 52 9 0.044554 

2005 52 8 0.059406 

2005 52 7 0.09901 

2005 52 6 0.032178 

2005 52 5 0.071782 

2005 52 4 0.128713 

2005 52 3 0.071782 

2005 52 2 0.094059 

2005 52 1 0.04703 

2005 52 0 0 

2005 53 30 0.096774 

2005 53 29 0 

2005 53 28 0 

2005 53 27 0 

2005 53 26 0.064516 

2005 53 25 0.032258 

2005 53 24 0.032258 

2005 53 23 0.032258 

2005 53 22 0.064516 

2005 53 21 0.032258 

2005 53 20 0.064516 

2005 53 19 0.032258 

2005 53 18 0 

2005 53 17 0.032258 

2005 53 16 0.064516 

2005 53 15 0.032258 

2005 53 14 0.193548 

2005 53 13 0.096774 

2005 53 12 0.064516 

2005 53 11 0 

2005 53 10 0 

2005 53 9 0 

2005 53 8 0 

2005 53 7 0 

2005 53 6 0.032258 

2005 53 5 0 

yearid sourcetypeid ageid ageFraction

2005 53 4 0 

2005 53 3 0 

2005 53 2 0 

2005 53 1 0 

2005 53 0 0.032258 

2005 54 30 0.155541 

2005 54 29 0.007973 

2005 54 28 0.012568 

2005 54 27 0.014595 

2005 54 26 0.023378 

2005 54 25 0.021622 

2005 54 24 0.02473 

2005 54 23 0.024865 

2005 54 22 0.026486 

2005 54 21 0.028514 

2005 54 20 0.023649 

2005 54 19 0.017838 

2005 54 18 0.023514 

2005 54 17 0.027568 

2005 54 16 0.032432 

2005 54 15 0.037838 

2005 54 14 0.032973 

2005 54 13 0.034054 

2005 54 12 0.036351 

2005 54 11 0.045 

2005 54 10 0.045811 

2005 54 9 0.032297 

2005 54 8 0.034595 

2005 54 7 0.037027 

2005 54 6 0.038378 

2005 54 5 0.039054 

2005 54 4 0.039595 

2005 54 3 0.034595 

2005 54 2 0.028784 

2005 54 1 0.014865 

2005 54 0 0.003514 

2005 61 30 0.026394 

2005 61 29 0.001795 

2005 61 28 0.001969 

2005 61 27 0.0027 

2005 61 26 0.004162 

2005 61 25 0.005771 

2005 61 24 0.007714 

2005 61 23 0.007951 
17 

yearid sourcetypeid ageid ageFraction

2005 61 22 0.01355 

2005 61 21 0.017698 

2005 61 20 0.017692 

2005 61 19 0.018667 

2005 61 18 0.023951 

2005 61 17 0.030517 

2005 61 16 0.044041 

2005 61 15 0.043611 

2005 61 14 0.038558 

2005 61 13 0.047786 

2005 61 12 0.044426 

2005 61 11 0.056475 

2005 61 10 0.062624 

2005 61 9 0.052544 

2005 61 8 0.054525 

2005 61 7 0.065875 

2005 61 6 0.064946 

2005 61 5 0.065997 

2005 61 4 0.06129 

2005 61 3 0.052723 

2005 61 2 0.042316 

2005 61 1 0.018436 

2005 61 0 0.003296 

2005 62 30 0.001821 

2005 62 29 0 

2005 62 28 0 

2005 62 27 0.00091 

2005 62 26 0.000759 

2005 62 25 0.002731 

2005 62 24 0.001517 

2005 62 23 0.001972 

2005 62 22 0.00349 

2005 62 21 0.005159 

2005 62 20 0.0044 

2005 62 19 0.003945 

2005 62 18 0.007131 

2005 62 17 0.016083 

2005 62 16 0.021393 

2005 62 15 0.028827 

2005 62 14 0.034593 

2005 62 13 0.025338 

2005 62 12 0.059172 

2005 62 11 0.080413 

2005 62 10 0.107419 

yearid sourcetypeid ageid ageFraction

2005 62 9 0.075861 

2005 62 8 0.04582 

2005 62 7 0.047944 

2005 62 6 0.099833 

2005 62 5 0.090882 

2005 62 4 0.057047 

2005 62 3 0.112729 

2005 62 2 0.017145 

2005 62 1 0.038993 

2005 62 0 0.006676 
18 

Road Type Distribution 

Road type distribution is based on the ODOT’s 2005 daily vehicle miles of travel (DVMT) by 

functional classification (FC). These inputs vary by county. A sample road type distribution 

input for Cuyahoga County can be seen in Table 12. Cuyahoga County does not contain rural 

road types 2 and 3. The distributions for other areas can be found in the electronic input 

submittals. 

 

Table 12 – Road Type Distribution for Cuyahoga County 

sourceTypeID roadTypeID roadTypeVMTFraction 

11 1 0 

11 2 0 

11 3 0 

11 4 0.4761 

11 5 0.5239 

21 1 0 

21 2 0 

21 3 0 

21 4 0.4761 

21 5 0.5239 

31 1 0 

31 2 0 

31 3 0 

31 4 0.4761 

31 5 0.5239 

32 1 0 

32 2 0 

32 3 0 

32 4 0.4761 

32 5 0.5239 

41 1 0 

41 2 0 

41 3 0 

41 4 0.4761 

41 5 0.5239 

42 1 0 

42 2 0 

42 3 0 

42 4 0.4761 

42 5 0.5239 

43 1 0 

43 2 0 
19 

sourceTypeID roadTypeID roadTypeVMTFraction 

43 3 0 

43 4 0.4761 

43 5 0.5239 

51 1 0 

51 2 0 

51 3 0 

51 4 0.4761 

51 5 0.5239 

52 1 0 

52 2 0 

52 3 0 

52 4 0.4761 

52 5 0.5239 

53 1 0 

53 2 0 

53 3 0 

53 4 0.4761 

53 5 0.5239 

54 1 0 

54 2 0 

54 3 0 

54 4 0.4761 

54 5 0.5239 

61 1 0 

61 2 0 

61 3 0 

61 4 0.4761 

61 5 0.5239 

62 1 0 

62 2 0 

62 3 0 

62 4 0.4761 

62 5 0.5239 

Vehicle Type VMT and VMT Fractions

VMT by MOVES vehicle types is subdivided into four sections namely HPMS base year VMT, 

monthly VMT fractions, daily VMT fractions and hourly VMT fractions. For NOACA’s 

counties, HPMS base year VMT was derived by using the converter tool, US EPA’s AADVMT 

Calculator_HPMS.xls. The converter takes average annual daily VMT (AADVMT) and 

generates MOVES input data based on default factors. AADVMT is computed by using total 

daily VMT obtained from travel demand model and fractioning it with the MOVES default 

distance travelled by each source type to come up vehicle type VMTs. The same method was 
20 

used to generate for other analysis years. ODOT uses weigh in motion (WIM) data to develop 

inputs for the counties it models. The inputs for each geographic area can be seen in the supplied 

input files. Sample HPMS base year VMT for 2005 is provided in Table 13. 

Table 13 – Yearly HPMS VMT for 2005 

HPMS 

VType ID Cuyahoga Lake Lorain Medina Portage Summit Ashtabula 

TWP 

10 32,922,024 6,679,430 7,643,257 6,670,080 7,077,843 22,229,559 320,426 

20 5,535,142,837 1,090,354,599 1,260,262,447 989,168,530 1,262,277,469 3,964,466,531 57,145,446 

30 3,276,696,376 651,498,431 757,091,949 613,540,515 389,472,936 1,223,227,424 17,632,101 

40 13,744,581 3,270,549 3,993,067 4,965,027 3,980,932 12,503,013 180,224 

50 206,032,254 44,685,476 52,797,748 54,841,123 29,429,299 92,429,337 1,332,314 

60 316,106,345 81,519,583 79,496,762 128,111,534 69,870,561 219,444,226 3,163,159 
21 

Output Emission Factors 

Table 14 shows the first record in a MOVES sample output (rate per distance) emission file for 

year 2005. For any given month, day of week, hour of the day, pollutant, and process; the rate 

per distance varies by road type, and speed bin. Rates per distance emissions are applied to link 

and intrazonal VMT. 

Table 14 – Sample Emission File (Rate per Distance) for year 2005 

Heading

: 

MOVESScenarioI

D 

MOVESRunI

D yearID monthID dayID hourID 

Record: Cuyahoga 1 2005 4 5 24 

Heading

: linkID pollutantID processID 

sourceTypeI

D SCC fuelTypeID 

Record: 990180516 117 10 0 0 

Heading

: modelYearID roadTypeID 

avgSpeedBinI

D temperature 

relHumidit

y 

ratePerDistanc

e 

Record: 0 5 16 43.5583 66 0.000879331 

Table 15 shows the first record in a MOVES sample output (rate per vehicle) emission file for 

year 2005. The rate per vehicle varies for any combinations of month, day of week, hour of the 

day, pollutant, and process. Rates per vehicle emissions are applied to the vehicle souce type 

population. 

Table 15 – Sample Emission File (Rate per Vehicle) for year 2005 

Heading: MOVESScenarioID MOVESRunID yearID monthID dayID 

Record: Cuyahoga 1 2005 4 5 

Heading: hourID zoneID pollutantID processID sourceTypeID 

Record: 24 990180 115 90 0 

Heading: SCC fuelTypeID modelYearID temperature ratePerVehicle 

Record: 0 0 43.5583 0.0000446782 

All output files are available in the electronic submittal. 
22 

4. Post Processing

Total emissions were computed with the aid of several custom programs by ODOT. The process 

uses data on daily and directional traffic distributions as well as more up to date volume/delay 

functions from the 2000 Highway Capacity Manual (HCM). This process, described below and 

illustrated in Figure 4, also uses rewritten code able to handle the newer model network formats 

and MOVES generated emission factors. 

The first step in the the process involves running ODOT postcms.exe to calculate hourly link 

volumes based on the percentage of the daily volume (travel demand model output) determined 

by a link’s facility and area type. Link speeds from the travel demand model are not used in the 

analysis. The speeds are estimated as a post-process to the model based on HCM methods using 

a link’s volume-to-capacity ratio and link group code. The daily to hourly volume conversion 

percentages and speed tables can be seen in Appendix B. 

The second step (mmoves.exe) uses a combination of the MOVES emission factors and the 

hourly link volumes that are output of the postcms.exe program. The hourly volumes are 

multiplied by the MOVES emission factor for the corresponding hour of day, speed bin, and 

roadtype to calculate emissions for every network link for each hour. The final link on road 

vehicle emissions for the area is the sum of all individual link-hour emissions. 

The third step, (mvehicle.exe), calculates vehicle-based emissions for each source type for each 

hour of the day. The vehicle source type is based on a combination of local and default data. 

The final vehicle emissions for each county are the sum of all individual hourly emissions for all 

vehicle types. 

Intrazonal trips do not get loaded onto the network, so the fourth step in the process requires a 

separate method to account for those trips that use local roads to travel within a zone. The 

mintra.exe program uses intrazonal trips to estimate VMT using the area in square miles and 

intrazonal trips of each zone. The zone is assumed circular and the radius of the circle is used as 

the average trip length for these intrazonal trips. Intrazonal emissions are then calculated by 

combining MOVES generated emissions with estimated intrazonal VMT. The emission rates are 

the same as those used to calculated link based emissions. 

The final step is to summarize link, vehicle, and intrazonal emissions for each county, pollutant, 

and analyzed year, and to multiply annual average daily emissions by 365 to produce an annual 

estimate. Summary emissions for each pollutant, county, and scenario year in northeast Ohio 

were previously displayed in tables 1-4. 
23 

Figure 4 – Emission Calculation Process

MOVES

TFM

ODOT

CMAQ

NOACA

AQ

ODOT

MMOVES

ODOT

MVEHICLE

ODOT

MINTRA

Emission Factor

Rate/Distance

Emission Factor

Rate/Vehicle

24HR NET

Speed 

Calculator

LNKGRP

CMAQ NET

CMAQ Report

HOURLY.CSV

Intrazonal Trips 24HR Vehicle Trips 4 Periods Vehicle Trip Matrices

4 Periods 

Assigned NET

VHT

VMT

4 Congested 

SPEED

EMISS

OUTP

Ton/d

TAZ Area (Sq.mi)

2. ODOT’s CMAQ POST-PROCESSOR

3. INTRASOZNAL TRIP CALCULATION 4. ODOT’s MOVES 

POST-PROCESSOR

1. NOACA AQPOST-PROCESSOR

Ramp Fractions

Average

Speed Distribution

Road Type 

VMT fractions

Vehicle Type

Annual VMTs

Fuel supply & 

formulation

I/M Program

Age Distribution

Meteorology

Zone Activity Source Type Population

FINA

EMISS

Ton/Y
24 

5. Multiple MPO Coordination Issues

AMATS, NOACA, ODOT, and OEPA have a long history of working together in air quality 

issues. All parties have have had an opportunity to review and approve this summary document. 
Appendix A 

Interagency Consultation Documentation 
‐‐‐‐‐Original Message‐‐‐‐‐ 

From: Carolina Prado [mailto:Carolina.Prado@epa.state.oh.us]

Sent: Thursday, October 21, 2010 9:00 AM

To: Jividen, Phyllis; Nino.Brunello@dot.state.oh.us; Bill Davis

Cc: Jennifer Hunter

Subject: Cleveland‐Akron‐Lorain area PM2.5 redesignation effort

Nino, Bill and Phyllis ,

Ohio EPA is starting to work on PM2.5 redesignations in all Ohio's

nonattainment areas, and Jennifer Hunter and I would like to have a small

conference call with you and start discussion on the mobile emission

modeling portion of the redesignation effort in the Cleveland‐Akron‐Lorain

area.

Since historically, NOACA has modeled a portion of the area and ODOT and

AMATS another, it would be useful to agree and be consistent among what's

modeled and the assumptions used, as well as to have a sense of timing and

other inputs needed.

We would like to schedule a conference call on Monday October, 25 at 10:00

AM.

We will be calling you so please verify the following information:

Nino: (614) 752‐5742

Bill: (216) 241‐2414, Ext. 251

Phyllis: (330) 375‐2436

Please let us know if this time/day will work for you ASAP so we can make

changes accordingly. The other available day we have is Friday October 29.

Thanks,

Carolina M. Prado

State Implementation Plan (SIP) Development Ohio EPA, Division of Air

Pollution Control 614‐644‐2310 Carolina.Prado@epa.state.oh.us  

******************************************************************************* 

‐‐‐‐‐Original Message‐‐‐‐‐ 

From: Carolina Prado [mailto:Carolina.Prado@epa.state.oh.us]

Sent: Friday, October 29, 2010 11:05 AM

To: PJividen@akronohio.gov; Leigh.Oesterling@dot.gov;

Nino.Brunello@dot.state.oh.us; Patricia Morris; Jennifer Hunter; B Davis

Subject: Cleveland‐Akron‐Lorain area PM2.5 redesignation effort

Interagency Consultancy Group,
On Monday, November 15, 2010 from 10:30 AM to 12:30PM,  we will have an

Interagency Consultation Group call to agree on parameters and timing for

the PM2.5 redesignation for the Cleveland‐Akron‐Lorain area PM2.5

nonattainment area.  The Cleveland‐Akron‐Lorain area includes the counties

of Ashtabula (partial), Lake, Cuyahoga, Lorain, Medina, Summit and

Portage.  For the purpose of redesignations and maintenance plans we have

to include emissions/projections for all these areas.

As you all know we have to use MOVES to model our mobile emission for

PM2.5, NOx, and SO2, and to fulfill our redesignation/maintenance plan,

the following years will need mobile inventories ‐ 2005 base nonattainment

year, 2008 attainment year, 2015 interim (BUDGET) year and 2022

maintenance (BUDGET) year.  During this call we would like to agree and be

consistent among what's modeled and the assumptions used, as well as to

have a sense of timing and other inputs needed.

Please call  614‐387‐7405  

If you think I am forgetting somebody, please forward this email.

Thanks,  

Carolina M. Prado

State Implementation Plan (SIP) Development Ohio EPA, Division of Air

Pollution Control 614‐644‐2310

******************************************************************************* 

‐‐‐‐‐Original Message‐‐‐‐‐ 

From: Carolina Prado [mailto:Carolina.Prado@epa.state.oh.us]  

Sent: Monday, December 20, 2010 12:43 PM

To: PJividen@akronohio.gov; Leigh.Oesterling@dot.gov; Dave Moore;

Nino.Brunello@dot.state.oh.us; PatriciaMorris; B Davis

Cc: Jennifer Hunter

Subject: RE: NEXT: Cleveland‐Akron‐Lorain area PM2.5 redesignation effort

You are right.

This is our schedule:

1. ODOT is expected to submit all post‐processor information to NOACA (for

the 4 counties in its jurisdiction, Lake, Cuyahoga, Lorain and Medina) by

January 10, 2011.

2. ODOT and NOACA will submit the final mobile emission numbers (for Lake,

Cuyahoga, Lorain and Medina, Portage, Summit and Ashtabula‐partial

counties)  to the rest of the IAC group by the end of January 2011

3. ODOT and NOACA will submit to IAC group in a document the final mobile

emission number for review and comment. February 1, 2011.
4. IAC group call on Feb 7, 2011, discuss final mobile emission numbers

for the 7 county area, make changes if needed, and propose budgets for

2015 and 2022.

Thanks

>>> B Davis <BDavis@mpo.noaca.org> 12/20/2010 12:35 PM >>>

I believe ODOT's deadline for providing NOACA with the post‐processor was

January 10th?

Bill Davis

NOACA

(216)‐241‐2414, Ext. 251

  

******************************************************************************* 

‐‐‐‐‐Original Message‐‐‐‐‐ 

From: Carolina Prado [mailto:Carolina.Prado@epa.state.oh.us]  

Sent: Monday, December 20, 2010 12:13 PM

To: PJividen@akronohio.gov; Leigh.Oesterling@dot.gov; Dave Moore;

Nino.Brunello@dot.state.oh.us; Patricia Morris; B Davis

Cc: Jennifer Hunter

Subject: NEXT: Cleveland‐Akron‐Lorain area PM2.5 redesignation effort

Thanks for all your input on today's call.

Our next call will be on February 7, 2011 from 11:00AM to 12:30PM EST.

Please call 614‐387‐7405

During the call we will discuss the final mobile emission numbers for the

7 county area, make changes if needed, and propose budgets for 2015 and

2022.

As discussed during today's call, ODOT is expected to submit all post‐

processor information to NOACA (for the 4 counties in its jurisdiction,

Lake, Cuyahoga, Lorain and Medina) by January 1, 2011.

ODOT and NOACA will submit the final mobile emission numbers (for Lake,

Cuyahoga, Lorain and Medina, Portage, Summit and Ashtabula‐partial

counties)  to the rest of the IAC group by the end of January 2011 so that

by the Feb 7, 2011 meeting, we all had time to review the numbers.

Thanks,  

Carolina M. Prado

State Implementation Plan (SIP) Development

Ohio EPA, Division of Air Pollution Control

614-644-2310

******************************************************************************* 
‐‐‐‐‐Original Message‐‐‐‐‐ 

From: Carolina Prado [mailto:Carolina.Prado@epa.state.oh.us]  

Sent: Wednesday, May 11, 2011 12:42 PM

To: Nino.Brunello@dot.state.oh.us; Chandra Parasa; NGILL@morpc.org; B

Davis; E Kang; Ana Ramirez

Cc: Jennifer Hunter

Subject: Clarification: Mobile emission estimate

Importance: High

** High Priority **

Hi everybody,

Thanks for reworking the post‐processor information and thanks for

committing to a fast turn around. We really appreciate all your effort!

I just want to clarify a couple of issues so we don't waste more time.

For all our redesignation documents we are stating that "...Mobile

source emissions were calculated from MOVES2010 ‐produced emission

factors. Only PM2.5 and NOx necessitate emissions inventory analysis. As

documented in Ohio EPA’s attainment demonstration SIP, Ohio EPA in

consultation with U.S. EPA determined mobile sources are insignificant

contributors for SO2. Consistent with Ohio EPA’s attainment

demonstration, Ohio EPA continues to consider mobile source SO2 to be an

insignificant contributor to fine particles for this nonattainment

area...."

However we still need to show SO2 emission estimations per county, so

that USEPA can agree with us that SO2 is indeed insignificant.

So we will need SO2 mobile emission as well (not only PM2.5 and NOx),

just like what you did previously for Dayton and Columbus, for example.

We also need the Annual VMT.

We should have something like this for every county:

Hamilton County, Ohio Emissions Estimations for On‐Road Mobile Sources

2005200820152021PM2.5 (tpy)1,222.0201,080.540826.00571.48NOx

(tpy)31,127.0927,020.9315,925.199,530.16SO2

(tpy)88.8598.3094.43100.82Annual

VMT7,241,536,8127,421,012,5947,630,239,6507,811,745,310

  

Please let us know if you have any question!

Thanks!!

Carolina

Carolina M. Prado

State Implementation Plan (SIP) Development

Ohio EPA, Division of Air Pollution Control

614‐644‐2310

Carolina.Prado@epa.state.oh.us  
Appendix B 

Post Processing Default Distributions 
HOUR 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 

21 22 23 PCTADT URB FWY 0.9 0.6 0.5 0.6 0.9 2.2 5.2 7.3 6.4 5.2 4.9 5.1 5.3 5.5 6.1 7.2 8.0 2.9 2.2 1.5 URB ART 0.7 0.4 0.3 0.3 0.6 1.5 3.5 5.7 5.5 5.1 5.3 6.2 6.5 6.4 6.8 7.6 8.2 3.0 1.9 1.3 RUR FWY 1.4 1.1 0.9 1.0 1.3 2.2 3.7 5.2 5.4 5.4 5.6 5.6 5.7 6.0 6.5 7.1 7.5 3.2 2.5 2.0 RUR ART 0.8 0.5 0.4 0.5 1.0 2.4 4.8 6.2 5.5 5.3 5.5 5.8 6.0 6.0 6.7 7.6 8.1 2.8 1.9 1.3 PCTADT TRK URB FWY 2.1 1.9 1.8 2.0 2.4 3.0 3.9 4.6 5.3 6.0 6.3 6.4 6.4 6.4 6.3 5.8 5.2 3.1 2.8 2.4 URB ART 1.1 0.9 1.0 1.2 1.6 2.3 3.9 5.9 6.9 6.7 7.1 7.6 7.4 7.2 7.4 7.2 6.0 1.9 1.5 1.3 RUR FWY 2.6 2.2 2.1 2.3 2.6 3.1 3.5 4.0 4.5 5.1 5.6 5.8 5.8 5.8 5.8 5.6 5.3 3.8 3.5 3.1 RUR ART 1.5 1.3 1.4 1.6 2.2 3.0 4.2 5.3 6.1 6.7 7.0 7.1 7.0 6.9 6.8 6.3 5.5 2.3 2.1 1.7 PCTDIR URB FWY 38 40 40 46 56 64 70 70 68 62 58 52 52 52 50 46 38 42 42 40 URB ART 44 46 44 48 54 62 66 68 64 56 54 52 50 50 50 46 40 46 46 46 RUR FWY 44 46 48 54 60 68 68 64 58 54 52 50 50 52 52 48 42 44 46 44 RUR ART 40 42 44 48 58 66 72 68 60 56 54 50 50 50 50 46 40 44 44 44 LOS E VC 0 0.625 1.25 1.875 2.5 3.125 3.75 4.375 5 5.625 6.25 6.875 7.5 8.125 8.75 9.375 10 10.13.125 13.75 14.375 SPEEDVC curve1 75 75 75 75 75 75 74.9 74.8 74.6 74.2 73.5 72.3 70.5 67.8 64.2 59.5 54 423.7 19.2 15.5 curve2 70 70 70 70 70 70 70 69.9 69.8 69.6 69.2 68.4 67.1 65.1 62.2 58.2 53 22.2 17.6 13.8 curve3 65 65 65 65 65 65 65 65 65 64.9 64.8 64.4 63.8 62.6 60.5 57 52 416.7 12.1 8.6 curve4 60 60 60 60 60 60 60 60 60 59.9 59.8 59.6 59.1 58.2 56.7 54.3 50.8 421.3 16.2 12.2 curve5 55 55 55 55 55 55 55 55 55 55 55 54.9 54.7 54.3 53.6 52.3 50 421.9 16.1 11.5 curve6 60 60 60 60 60 60 60 60 59.9 59.8 59.7 59.4 59.1 58.5 57.7 56.5 55 541.1 37.3 33.4 curve7 55 55 55 55 55 55 55 55 54.9 54.9 54.7 54.5 54.2 53.8 53.1 52.2 50.9 439 35.7 32.2 
curve8 50 50 50 50 50 50 50 50 49.9 49.9 49.8 49.6 49.4 49 48.5 47.7 46.7 4

36.8 33.9 30.9 curve9 45 45 45 45 45 45 45 45 45 44.9 44.8 44.7 44.4 44.1 43.6 43 42.1 433.1 30.5 27.8 curve10 50 50 50 50 49.9 49.8 49.7 49.4 49 48.4 47.5 46.5 45.1 43.5 41.7 39.6 37.3 324.9 22.6 20.4 curve11 50 50 50 50 50 49.9 49.7 49.4 48.9 48 46.7 44.9 42.5 39.6 36.2 32.6 28.7 12.9 10.8 9 curve12 50 50 50 50 50 49.9 49.8 49.6 49.1 48.2 46.8 44.5 41.4 37.5 32.9 28 23.1 17.2 5.7 4.5 curve13 40 40 40 40 40 40 39.9 39.8 39.5 39.2 38.6 37.8 36.7 35.3 33.5 31.4 29 216.1 13.9 12 curve14 40 40 40 40 40 39.9 39.8 39.6 39.1 38.5 37.5 36.1 34.3 32.1 29.4 26.5 23.5 210.7 9 7.6 curve15 40 40 40 40 40 39.9 39.7 39.4 38.8 37.9 36.5 34.7 32.3 29.5 26.4 23.2 20 8.2 6.8 5.6 curve16 35 35 35 35 35 34.9 34.8 34.5 34 33.2 32.1 30.5 28.5 26.1 23.5 20.6 17.9 17.4 6.1 5.1 curve17 35 35 35 35 35 34.9 34.7 34.4 33.9 33.1 32 30.3 28.3 25.8 23.1 20.3 17.5 17.2 5.9 4.9 curve18 35 35 35 35 35 34.9 34.6 34.2 33.5 32.4 30.9 28.8 26.3 23.4 20.4 17.4 14.6 15.4 4.4 3.6 curve19 30 30 30 30 30 29.9 29.8 29.5 29 28.2 27.1 25.6 23.7 21.5 19.1 16.6 14.2 5.6 4.6 3.8 curve20 30 30 30 30 30 29.9 29.7 29.4 28.9 28.1 26.9 25.3 23.4 21.1 18.6 16.1 13.6 15.3 4.3 3.6 curve21 30 30 30 30 30 29.9 29.7 29.3 28.7 27.7 26.2 24.4 22.1 19.6 17 14.4 12 4.4 3.6 2.9 VC RATIO TO LOS CONVERSION (VALUE SHOWN IS LOWER LIMIT FOR THAT LOS)(URBAN ROADS USE SPEED BREAKS BELOW FOR LOS DETERMINATION) (ALL USE THE BASE VC'S  BASE RUR2 FWY A 0.00 0.00 0.00 B 0.30 0.00 0.25 C 0.50 0.10 0.40 D 0.70 0.30 0.60 E 0.90 0.50 0.80 F 1.00 1.00 1.00 F+ 1.10 1.10 1.10 F++ 1.30 1.30 1.30 SPEED VC RATIO BREAKS FOR URBAN STREETS (HIGHEST SPEED FOR GIVEN LOS & FF SPEED) FFS B C D E F >47 42. 34. 27. 21. 16. >37 35. 28. 22. 17. 13. >32 30. 24. 18. 14. 10. <33 25. 19. 13. 9. 7. 
LEVEL OF SERVICE THRESHOLD BY AREA 

NUM LOS DEFINITION 1 F CINCINNATI,CLEVELAND,COLUMBUS CENTRAL MPO COUNTIES (CUY,FRA,HAM) 2 E OTHER TMA MPOS (AKRON,CANTON,DAYTON,TOLEDO,YOUNGSTOWN + NON-CENTRAL COUNTIES FROM 1) 3 E OTHER MPOS & PARTS OF AREAS 1 & 2 OUTSIDE URBANIZED AREA 4 E RURAL NON MPO COUNTIES PEAK SPREADING MODEL INFO (SET MAX ITERATIONS TO 0 TO DISABLE PEAK SPREADING) MAX VC RATIO FWY: 1.30 MAX VC RATIO ART: 1.30 MAX ITERATIONS : 1000 TRUCK PCE: 2.00 AQ SEASON FACTOR: 1.00 MODEL CLASS PARAMETERS (MAX 4 CLASSES, HOURS 0-23 W/ NO OVERLAP IN CLASS, ALLOCATE ENTIRE CLASS AS TRUCK(1) OR NOT(0)) CLS TRK 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CLS BEG 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CLS END 23 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CLS NUM 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
APPENDIX D

LADCD Technical Support Document

LADCD Technical Support Document (supplemental)
Regional Quality Analyses for

Ozone, PM2.5 and Regional Haze:

Final Technical Support Document

April 25, 2008

States of Illinois, Indiana, Michigan, Ohio,

and Wisconsin

The document can be found at:

.b!!P://www.epa.ohio.g.QYLportals/27/SIP/AQPendix 0 -

TSD Version IV April 25 2008 FINAL.QQ.f
Regional Quality Analyses for

Ozone, PM2.5 and Regional Haze:

Technical Support Document Supplement

September 12, 2008

States of Illinois, Indiana, Michigan, Ohio and

Wisconsin

The document can be found at:

.b.!!P://www.epa.ohio.g.QYLportals/27/SIP/AQ,Pendix%20 0-

2 TSD SUJ2.plement without CAIR modeling FINAL.lli!f
APPENDIX E

Missing Monitoring Data Imputation Analysis
Missing Monitoring Data Imputation Analysis 

Monitoring Sites 39-035-0060, 39-035-0065, and 39-035-1002 

Cuyahoga County, Ohio 

Ohio Environmental Protection Agency 

Lazarus Government Center 

50 West Town Street, Suite 700 

Columbus Ohio 43215 

Prepared by Ohio EPA 

Division of Air Pollution Control 

State Implementation Plan Section 

August 2011 
2 

Cleveland-Akron-Lorain PM2.5 Nonattainment Area 

Monitor Missing Data Analysis

The current Cleveland-Akron-Lorain nonattainment area is located in northwest Ohio 

and includes the following counties: Cuyahoga, Lake, Lorain, Medina, Portage and 

Summit. 

The area has twelve1 monitors measuring PM2.5 concentrations, which are overseen by 

the Ohio EPA and operated by Local Air Agencies and Ohio EPA’s Northeast District 

Office2. A listing of the design values based on the 3-year average of the annual mean 

concentrations from 2008 through 2010 is shown in Table 1. The design values 

calculated for the Cleveland-Akron-Lorain area shows that the annual PM2.5 NAAQS has 

been attained. 

Table 1 - Monitoring Data for the Cleveland-Akron-Lorain area for 2008 – 2010 

 Annual Standard 

Site County 

Year Average 

2008 2009 2010 2008-2010 

39-035-0034 

Cuyahoga 

10.9 10.2 10.9 10.7 

39-035-0038 14.1 12.8 14.0 13.6 

39-035-0045 13.7 11.8 13.3 12.9 

39-035-0060 14.1 12.3 13.7 13.4 

39-035-0065 14.6 12.4 13.2 13.4 

39-035-1002 12.0 10.9 11.3 11.4 

39-085-0007 Lake 10.4 10.4 10.8 

39-085-3002 11.5 

39-093-3002 Lorain 11.4 9.9 10.4 10.6 

39-103-0003 Medina 11.8 10.8 10.8 11.1 

39-133-0002 Portage 12.1 11.1 11.2 11.5 

39-153-0017 Summit 13.8 12.6 13.4 13.3 

39-153-0023 12.9 11.4 12.5 12.3 

 Less than 75% capture in at least one quarter 

Source: U.S. EPA Air Quality System (AQS); http://www.epa.gov/ttn/airs/airsaqs/index.htm

 

1 The redesignation and maintenance plan document refers to thirteen monitors. The thirteenth monitor 

was permanently shut down in 2009 as a part of Ohio’s approved monitoring network. Therefore, this 

monitor is not included in this analysis. 

2 Monitors in Cuyahoga County are operated by the Cleveland Department of Public Health & Welfare; 

the monitor in Lake County are operated by Lake County General Health District; monitors Medina, 

Portage, and Summit Counties are operated by the Akron Regional Air Quality Management District; and 

the monitor in Lorain County is operated by Ohio EPA’s Northeast District Office.
3 

However, based on Section 107(d)(3)(E)(i) of the Clean Air Act (CAA) the PM2.5 

monitoring has to show that the 3-year average of the annual mean values, based on 

data from all monitoring sites in the area or its affected downwind environs, are below 

15.0/m3. Moreover, in accordance with the CAA Amendments, three complete years of 

monitoring data are required to demonstrate attainment at a monitoring site. In addition, 

U.S. EPA regulations require at least 75% data capture in each quarter of a consecutive 

3-year period in order for a design value to be valid. 

Table 1 shows that three monitor sites in Cuyahoga County, site 39-035-0060, site 39-

035-0065, and site 39-035-1002, did not comply with the 75% data capture requirement 

for the 2008-2010 period. Specifically, site 39-035-0060 has only 48% and 63% capture 

in the second and third quarter of 2009, respectively. Site 39-035-0065 has only 74% 

capture in the third quarter of 2010, and site 39-035-1002 has only 71% capture in the 

first quarter of 2008 and 73% capture in the third quarter of 2009. These monitoring 

sites experienced instrument malfunctions during the low percentage capture periods 

with a total of 27 missing 1-in-3 PM2.5 FRM runs in site 39-035-0060 (in 2009), a total 

of 8 missing 1-in-3 PM2.5 FRM runs in site 39-035-0065 (in 2010), and a total of 8 

missing 1-in-3 PM2.5 FRM runs and a total of 9 missing 1-in-3 PM2.5 FRM runs in site 39-

035-1002 (in both 2008 and 2009 respectively). 

In order to comply with U.S.EPA’s 75% capture requirements, Ohio EPA prepared a 

statistical analysis using multiple imputations. Imputing missing values for sites 39-035-

0060, 39-035-0065, and 39-035-1002 and then doing an ordinary analysis as if the 

imputed values were real measurements (this approach is usually better than excluding 

subjects with incomplete data). Most methods for accounting for having incomplete data 

can be complex; the bootstrapping method however, is an easy method to implement 

even though the computations can be slow. To use the bootstrap, to correctly estimate 

variances of regression coefficients, one must repeat the imputation process and the 

model fitting perhaps 1000 times using a resampling procedure. 

Multiple imputations use random draws from the conditional distribution of the target 

variable given the other variables. When a regression model is used for imputation, the 

process involves adding a random residual to the “best guess” for missing values, to 

yield the same conditional variance as the original variable. To properly account for 

variability due to unknown values, the imputation will be repeated 1000 times. 

Imputing missing data for Cleveland-Akron-Lorain PM2.5 nonattainment area 

For simplification purposes we will refer to the sites in the Cleveland-Akron-Lorain 

nonattainment area by the Site letter denoted in the second column of the table below. 
4 

1. Cleveland-Akron-Lorain Annual PM2.5 Design Value History 

Table 2 – Historic Design Values for the Cleveland-Akron-Lorain area from 1999 

to 2010

Site Site County 

Annual Design Value 

1999-

2001 

2000-

2002 

2001-

2003 

2002-

2004 

2003-

2005 

2004-

2006 

2005-

2007 

2006-

2008 

2007-

2009 

2008-

2010 

39-035-0034 A 

Cuyahoga 

15.3 15.0 14.2 13.4 14.1 13.5 13.8 12.0 11.6 10.7 

39-035-0038 B 20.3 19.2 18.4 17.6 18.1 17.2 16.8 15.1 14.4 13.6 

39-035-0045 C 16.7 17.5 16.7 16.0 17.0 16.2 16.2 14.4 13.6 12.9 

39-035-0060 D 18.6 18.2 17.5 17.0 17.7 16.9 16.8 15.0 14.1 13.4 

39-035-0065 E 17.6 17.0 16.0 15.5 16.5 15.6 15.8 14.5 14.3 13.4 

39-035-1002 F 15.1 15.0 14.6 14.1 14.6 13.9 13.9 12.3 12.1 11.4 

39-085-0007 G Lake 11.9 10.8 

39-085-3002 12.3 

39-093-3002 H Lorain 12.5 12.8 12.6 13.0 11.9 11.4 10.6 

39-103-0003 I Medina 13.3 12.1 11.8 11.1 

39-133-0002 J Portage 15.3 15.2 14.2 13.3 13.4 13.2 13.6 12.6 12.3 11.5 

39-153-0017 K Summit 17.4 17.0 16.6 15.7 15.6 15.0 14.9 14.0 13.7 13.3 

39-153-0023 L 16.2 16.3 15.6 15.0 14.6 14.1 14.1 13.1 12.7 12.3 

 incomplete data (quarter with <75% capture) 

 violating DV 

From Table 2, all twelve sites have design values (DV) that meet the PM2.5 annual 

standard since the 2007-2009 period. However, sites D, E, and F have not achieved 

clean data in at least on quarter from 2008-2010, and; therefore the entire 

nonattainment area is not eligible for redesignation absent this analysis. As mentioned 

before, the lack of clean data is due to the low percentage of data capture in one or 

more quarters between 2008 and 2010. 

Multiple imputations and bootstrapping methods will help to generate values for the 

missing data to determine 2008, 2009, and 2010 completeness and recalculate the 

2008-2010 DV. 

2. Correlation, Quarterly Data Capture, and Data Site Pairing 

Linear regression analyzes the relationship between two variables, X and Y. For each 

subject, there is a known X and Y and we want to find the best straight line through the 

data. The goal of linear regression is to find the line that best predicts Y from X. Linear 

regression does this by finding the line that minimizes the sum of the squares of the 

vertical distances of the points from the line. To determine which site combination3

 has 

 

3 Site D vs. Site A or Site D vs. Site B or Site D vs. Site C or Site D vs. Site G or Site D vs. Site H or Site 

D vs. Site I or Site D vs. Site J or Site D vs. Site K or Site D vs. Site L. 

Site E vs. Site A or Site E vs. Site B or Site E vs. Site C or Site E vs. Site G or Site E vs. Site H or Site E 

vs. Site I or Site E vs. Site J or Site E vs. Site K or Site E vs. Site L. 

And Site F vs. Site A or Site F vs. Site B or Site F vs. Site C or Site F vs. Site G or Site F vs. Site H or Site 

F vs. Site I or Site F vs. Site J or Site F vs. Site K or Site F vs. Site L. 

Note that we do not compare Site D vs. Site E, Site D vs. Site F nor Site E vs. Site F since these sites are 

incomplete and would provide a biased relationship. 
5 

the best data (best fit) from which to impute missing data for site D, site E, and site F we 

determine the correlation between all the sites and site D, the correlation between all 

the sites and site E and the correlation between all the sites and site F. 

The correlation value (R2

) is useful because it describes the degree of relationship 

between two variables4

 (variables or site concentrations in all sites). R2 is only a 

descriptive statistics. Roughly speaking, we associate a high value of R2

 with a good fit 

of the regression line and associate a low value of R2 with a poor fit. 

The mean of the quarterly data captured (the mean of the percentage captured) will 

allow verifying the central tendency of each site, which will help to determine what site 

has more data completeness to impute for sites D, E, and F. 

Finally, although not as statistically significant as the correlation, or as the mean of the 

percentage captured, pairing the site data seeks to reduce variability in order to make 

more precise comparisons with fewer observations. Pairing the data will help to 

determine which site has more data when paired with D, E, and F. 

Below are the results for site D vs. the rest of the sites, site E vs. the rest of the sites, 

and for sites F vs. the rest of the sites. 

Table 3 – Correlation Matrix for site D, site E, and site F against all other sites. 

A B C G H I J K L 

D 0.8841 0.9154 0.8965 0.8387 0.8461 0.8165 0.7663 0.8217 0.8079

E 0.8763 0.8484 0.8864 0.7833 0.8366 0.8002 0.7628 0.7922 0.7877

F 0.8804 0.8136 0.8700 0.8449 0.8528 0.8410 0.7976 0.8143 0.8233

 

4 An R2 value of 0.0 means that knowing X does not help to predict Y, there is no linear relationship 

between X and Y. When R2 equals 1.0, all points lie exactly on a straight line with no scatter; knowing X 

predicts Y perfectly. 
6 

Table 4 - Quarterly Data Capture 

      Monitoring Sites

      A B C D E F G H I J K L Quarterly Data Capture 2003‐2010

2003 Q1 100% 94% 100% 90% 87% 93%   ‐  83%   ‐  87% 86% 90%

2003 Q2 100% 97% 100% 93% 87% 97%   ‐  97%   ‐  90% 89% 88%

2003 Q3 100% 97% 94% 94% 100% 87%  ‐  90%  ‐  87% 91% 91%

2003 Q4 90% 72% 100% 93% 93% 97%   ‐  87%   ‐  100% 90% 97%

2004 Q1 97% 90% 94% 97% 94% 94%   ‐  94%   ‐  90% 98% 84%

2004 Q2 87% 87% 90% 100% 97% 93%  ‐  100% ‐  97% 92% 92%

2004 Q3 97% 86% 100% 100% 100% 100%  ‐  90%   ‐  97% 96% 99%

2004 Q4 97% 98% 97% 97% 93% 93%   ‐  90%   ‐  90% 98% 97%

2005 Q1 95% 96% 86% 100% 81% 81%  ‐  93%  ‐  97% 92% 80%

2005 Q2 100% 94% 93% 100% 93% 100%  ‐  87% 94% 100% 94% 100%

2005 Q3 100% 97% 93% 83% 100% 93%   ‐  83% 100% 100% 100% 100%

2005 Q4 100% 94% 100% 97% 100% 100% ‐  97% 77% 81% 90% 94%

2006 Q1 87% 87% 100% 93% 93% 100% 85% 97% 90% 97% 100% 100%

2006 Q2 100% 100% 100% 87% 93% 100% 93% 90% 67% 93% 100% 93%

2006 Q3 93% 100% 100% 94% 93% 67% 94% 90% 100% 97% 100% 90%

2006 Q4 94% 100% 94% 100% 100% 88% 90% 94% 94% 100% 97% 97%

2007 Q1 95% 93% 85% 80% 75% 85% 97% 63% 90% 93% 97% 100%

2007 Q2 100% 90% 87% 90% 100% 87% 97% 93% 80% 87% 100% 100%

2007 Q3 90% 77% 100% 100% 100% 94% 97% 94% 100% 97% 100% 97%

2007 Q4 90% 57% 97% 97% 100% 93% 93% 93% 93% 97% 87% 93%

2008 Q1 84% 97% 90% 77% 94% 71% 90% 94% 94% 97% 87% 97%

2008 Q2 100% 100% 97% 100% 100% 90% 100% 97% 100% 100% 100% 90%

2008 Q3 97% 97% 84% 97% 94% 97% 100% 100% 90% 100% 100% 94%

2008 Q4 93% 97% 90% 97% 100% 100% 93% 97% 87% 83% 100% 93%

2009 Q1 93% 93% 97% 90% 93% 87% 90% 100% 97% 93% 77% 93%

2009 Q2 100% 100% 94% 48% 97% 94% 100% 100% 90% 100% 97% 90%

2009 Q3 93% 97% 97% 63% 97% 73% 100% 100% 100% 87% 91% 97%

2009 Q4 100% 100% 100% 97% 100% 97% 87% 94% 94% 97% 100% 94%

2010 Q1 100% 90% 100% 83% 100% 87% 97% 93% 90% 87% 99% 90%

2010 Q2 83% 97% 97% 100% 97% 80% 100% 100% 93% 97% 100% 90%

2010 Q3 94% 87% 100% 97% 74% 100% 100% 100% 100% 97% 100% 97%

2010 Q4 90% 100% 94% 94% 90% 87% 100% 100% 97% 97% 100% 84%

MEAN 95% 93% 95% 92% 94% 91% 95% 93% 92% 94% 95% 93%
7 

Table 5 - Paired Data Site 

 30 - Quarter look (2003 Q1 - 2010 Q4) 

Site Site pairs Q1 pairs Q2 pairs Q3 pairs Q4 Total 

D A 170 179 185 190 724 

D B 193 209 206 207 815 

D C 167 178 187 196 728 

D G 100 121 136 130 487 

D H 180 205 206 214 805 

D I 110 140 161 161 572 

D J 190 208 213 217 828 

D K 187 205 219 220 831 

D L 192 203 213 221 829 

E A 178 193 192 191 754 

E B 179 195 185 177 736 

E C 178 189 193 196 756 

E G 98 126 124 117 465 

E H 164 192 186 185 727 

E I 109 132 138 134 513 

E J 173 193 191 190 747 

E K 172 191 197 192 752 

E L 174 188 194 189 745 

F A 177 182 184 188 731 

F B 170 186 176 174 706 

F C 171 178 184 195 728 

F G 90 113 119 113 435 

F H 157 182 181 182 702 

F I 100 121 132 132 485 

F J 166 181 184 185 716 

F K 167 180 188 188 723 

F L 168 177 184 187 716 

Table 3 shows that the correlation between D and B is 0.9154, the correlation between 

E and C is 0.8864, and the correlation between F and A is 0.8804. These correlations 

show the best data (stronger relationship among sites) from which to impute missing 

data for all three sites (D, E, and F). From the mean of the quarterly data captured 

(Table 4), we can observe that sites A, B and C (the sites that show higher correlation 

with the sites with missing values – D, E, and F) have adequate data completeness5

from where to impute missing values. In addition, from the data site pairing (Table 5) it 

can be observed that sites D vs. B, E vs. C, and F vs. C have enough paired data (in 

each quarter and in total) to establish a good relationship. 

 

5 Site A = 95%, Site B = 93%, and Site C = 95% 
Linear Regression and Correlation for Site D vs. Site B 

Regression Stati

Multiple

R 

R Square Adjusted

R Square 

Standard Error Observations 

Coefficients Standard Error t Stat

P‐value Lower 95% Upper 95%

Intercept 1.565566405 0.174087186 8.993001954 1.66443E‐18 1.223853073 1.907279737X Variable 1 0.880442384 0.00938778 93.78600165

0 0.862015241 0.898869529

ANOVA

df SS MS

F Significance

F

Regression 1 55145.65365 55145.65365 8795.814106

0

Residual 813 5097.130962 6.269533779Total 814 60242.78461
Linear Regression and Correlation for Site E vs. Site C 

Regression Statis

Multiple

R 

R Square Adjusted

R Square 

Standard Error Observations 

Coefficients Standard Error t Stat

P‐value Lower 95% Upper 95%

Intercept 0.932587361 0.202223699 4.611662056 4.69183E‐06 0.535598942 1.32957578X Variable 1 0.944152034 0.012306434 76.72019907

0 0.919993087 0.968310981

ANOVA

df SS MS

F Significance

F

Regression 1 38549.39211 38549.39211 5885.988945

0

Residual 754 4938.208671 6.549348371Total 755 43487.60078
Linear Regression and Correlation for Site F vs. Site A 

Regression Sta

Multiple

R 

R Square Adjusted

R Square 

Standard Error Observations 

Coefficients Standard Error t Stat

P‐value Lower 95% Upper 95%

Intercept  ‐0.31095621 0.198704325 ‐1.56491920 0.118035816 ‐0.701057206 0.079144775X Variable 1 0.991764397 0.013539584 73.24925362

0 0.965183169 1.018345625

ANOVA

df SS MS

F Significance

F

Regression 1 36959.02579 36959.02579 5365.453155

0

Residual 729 5021.594453 6.888332583Total 730 41980.62025
11 

Finally, based on the above results, it was determined that Site B, Site C and Site A 

have the best data, statistically significant, (greater correlation, adequate data 

completeness, and adequate paired data) from which to impute missing data for Site D, 

Site E, and Site F, respectively. Therefore, we will use multiple imputations and 

bootstrapping methods to generate the necessary data from Site B, Site C and Site A to 

impute on Site D, Site E, and Site F missing values. 

3. Bootstrapping and Imputation 

3.1. Site D and Site B 

The bootstrapping method randomly applies real residuals from the linear regression to 

the imputed current-period Site D values. A 1000 bootstrap runs adds a random 

residual to the “best guess” missing values yielding the same conditional variance as 

the original variable. A summary of the bootstrapping statistics is presented in Table 6, 

where the bootstrapping residual is 0.00238. 

Table 6 - Bootstrapping Summary Statistics 

Average 0.0023787420

SD 0.0849651846

Max 0.2365176245

Min -0.3681799770

Finally to impute the missing data using the bootstrapping residual we use the following 

equation, based on the linear regression from Site D vs. Site B: 

Site D concentration = Intercept + x Variable * Site B concentration + 

bootstrapping residual 

Where: 

Intercept = 1.56556640501966 (from linear regression) 

x Variable = 0.880442384961355 (from linear regression) 

Bootstrapping residual = 0.0023787420 

Site B concentration = values in Site B 

After applying the above equation to all missing data in Site D, we recalculated the 

design values based on the 3-year average of the annual mean concentrations for all 

existing years in Site D (Site 39-035-0060). Table 7 shows Site 39-035-0060 before and 

after the imputation of missing data. Also the “new” site (with imputed values) shows a 

“new” passing DV for 2008-2010. 
12 

Table 7 – Comparison Between Original and Imputed Values for Site 39-035-0060 

from 2004 to 2010 

                           Annual Design Value

   Site Year 2004‐

2006

2005‐

2007

2006‐

2008

2007‐

2009

2008‐

   2004 2005 2006 2007 2008 2009 2010 2010

OLD 39-035-0060 16.4 19.4 15.0 15.9 14.1 12.3 13.7 16.9 16.8 15.0 14.1 13.4

NEW 39-035-0060 15.6 18.5 14.6 15.4 13.8 12.5 13.6 16.2 16.2 14.6 13.9 13.3

   incomplete data (quarter with <75% capture)                     

3.2. Site E and Site C 

The bootstrapping method randomly applies real residuals from the linear regression to 

the imputed current-period Site E values. A 1000 bootstrap runs adds a random residual 

to the “best guess” missing values yielding the same conditional variance as the original 

variable. A summary of the bootstrapping statistics is presented in Table 8, where the 

bootstrapping residual is 0.00185. 

Table 8 - Bootstrapping Summary Statistics 

Average 0.0018513212

SD 0.0900055812

Max 0.2707392673

Min -0.2713173739

Finally to impute the missing data using the bootstrapping residual we use the following 

equation, based on the linear regression from Site E vs. Site C: 

Site E concentration = Intercept + x Variable * Site C concentration + 

bootstrapping residual 

Where: 

Intercept = 0.932587360923463 (from linear regression) 

x Variable = 0.94415203423236 (from linear regression) 

Bootstrapping residual = 0.00185132118436311Site C concentration = values in Site C 

After applying the above equation to all missing data in Site E, we recalculated the 

design values based on the 3-year average of the annual mean concentrations for all 

existing years in Site E (Site 39-035-0065). Table 9 shows Site 39-035-0065 before and 

after the imputation of missing data. Also the “new” site (with imputed values) shows a 

“new” passing DV for 2008-2010. 
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Table 9 – Comparison Between Original and Imputed Values for Site 39-035-0065 

from 2004 to 2010 

                           Annual Design Value

   Site Year 2004‐

2006

2005‐

2007

2006‐

2008

2007‐

2009

2008‐

   2004 2005 2006 2007 2008 2009 2010 2010

OLD 39-035-0065 15.2 18.6 13.1 15.8 14.6 12.4 13.2 15.6 15.8 14.5 14.3 13.4

NEW 39-035-0065 15.2 18.4 13.2 15.7 14.8 12.4 13.4 15.6 15.8 14.6 14.3 13.5

   incomplete data (quarter with <75% capture)                     

3.3. Site F and Site A 

The bootstrapping method randomly applies real residuals from the linear regression to 

the imputed current-period Site F values. A 1000 bootstrap runs adds a random residual 

to the “best guess” missing values yielding the same conditional variance as the original 

variable. A summary of the bootstrapping statistics is presented in Table 10, where the 

bootstrapping residual is 0.00648. 

Table 10 - Bootstrapping Summary Statistics 

Average 0.0064810934

SD 0.0984385889

Max 0.3018532840

Min -0.3655841335

Finally to impute the missing data using the bootstrapping residual we use the following 

equation, based on the linear regression from Site F vs. Site A: 

Site F concentration = Intercept + x Variable * Site A concentration + 

bootstrapping residual 

Where: 

Intercept = -0.310956215392546 (from linear regression) 

x Variable = 0.991764397262166 (from linear regression) 

Bootstrapping residual = 0.00648109339124171 

Site A concentration = values in Site A 

After applying the above equation to all missing data in Site F, we recalculated the 

design values based on the 3-year average of the annual mean concentrations for all 

existing years in Site F (Site 39-035-1002). Table 11 shows Site 39-035-1002 before 

and after the imputation of missing data. Also the “new” site (with imputed values) 

shows a “new” passing DV for 2008-2010. 
14 

Table 11 – Comparison Between Original and Imputed Values for Site 39-035-1002 

from 2004 to 2010 

                           Annual Design Value

   Site Year 2004‐

2006

2005‐

2007

2006‐

2008

2007‐

2009

2008‐

   2004 2005 2006 2007 2008 2009 2010 2010

OLD 39-035-1002 13.2 16.8 11.6 13.4 12.0 10.9 11.3 13.9 13.9 12.3 12.1 11.4

NEW 39-035-1002 13.1 16.7 11.0 13.3 11.1 10.6 11.1 13.6 13.7 11.8 11.6 10.9

   incomplete data (quarter with <75% capture)                     

Ohio EPA believes that the above analysis has generated the most statistically 

significant results yielding to the “best” missing data statistically possible. This analysis 

should also satisfy US EPA requirements in terms of total percentage data capture and 

design values under the PM2.5 annual standard (15.0μg/m3

). 

 
15 

References: 

Brownstone, David and Robert Valleta. 2000. Draft for JEP Econometrics Symposium. 

“The bootstrap and multiple imputations”. 

Harrel, Frank E. Jr. 2001. Springer Series in Statistics. “Regression modeling strategies: 

with applications to linear models, logistic regression, and survival analysis”. 

Mooney, Christopher Z., Robert D. Duval, Robert Duval. 1993. Sage Publications Inc. 

“Bootstrapping: a nonparametric approach to statistical inference” Issues 94-95. 
APPENDIX F

Public Participation

Public Notice

Public Comments

Hearing Transcript

Responsive Summary to Comments
Public Notice 

Ohio Environmental Protection Agency 

Redesignation and Maintenance Plan for the 

Cleveland-Akron-Lorain, OH-KY-WV Annual PM2.5 Nonattainment Area 

Ashtabula, Cuyahoga, Lake, Lorain, Medina, Portage and Summit Counties 

Notice is hereby given that the Director of the Ohio Environmental Protection Agency, 

(Ohio EPA) is requesting that the United States Environmental Protection Agency (U.S. 

EPA) revise the current air quality designation for the Cleveland-Akron-Lorain 

nonattainment area, including Ashtabula, Cuyahoga, Lake, Lorain, Medina, Portage and 

Summit counties to attainment with respect to the 1997 annual PM2.5 national ambient 

air quality standard (NAAQS). Fine particle pollution or PM2.5 is a mixture of microscopic 

solids and liquid droplets suspended in air that is 2.5 micrometers in diameter and 

smaller – 1/30th the diameter of a human hair. PM2.5 can be emitted directly or formed 

secondarily in the atmosphere. Air quality monitoring data collected between 2008 and 

2010 in the region demonstrate attainment of the NAAQS and there is evidence that the 

improved air quality is due to permanent, enforceable emission reductions. In addition, 

existing requirements are sufficient to maintain the 1997 annual PM2.5 standard in this 

area at least ten years into the future. 

Computer models show that existing state and federal emission reduction requirements 

are sufficient to attain and maintain the NAAQS in the Cleveland-Akron-Lorain area. 

Therefore, Ohio EPA proposes to utilize existing emission inventory information and 

projections of future emissions as the demonstration of the ability to maintain the 

NAAQS in the Cleveland-Akron-Lorain area in the future. 

The Cleveland-Akron-Lorain area is currently designated as nonattainment for the 1997 

annual PM2.5 standard. As part of an acceptable maintenance plan, Ohio EPA is 

required to develop a contingency plan to provide for additional emission reductions if a 

violation of the NAAQS is monitored after the area has been redesignated. The plan 

which Ohio EPA is proposing to USEPA as part of this redesignation contains 

reductions which will help alleviate the ambient problem until a revised SIP can be 

developed. 

The State of Ohio proposes to: 

1. Request the U.S. EPA redesignate the Cleveland-Akron-Lorain area to 

attainment with respect to the 1997 annual PM2.5 NAAQS and revise the 

maintenance plan. This request will document that existing enforceable control 

measures are responsible for the observed improvement in air quality. 

2. Designate existing controls as sufficient to maintain the NAAQS into the future. 

3. Commit to the proposed contingency plan. 
These actions must be noticed to allow public comment and to satisfy USEPA 

requirements for public involvement in SIP related activities. This notice addresses Ohio 

EPA’s reliance on the emission projections as evidence of attainment and maintenance 

and the commitment to institute contingency measures if ambient exceedances or 

violations trigger the contingency plan requirements. Written comments will be received 

on or before September 23, 2011 at the following address: 

E-mail: Carolina.Prado@epa.state.oh.us

Mailing address: Carolina Prado 

Ohio Environmental Protection Agency, DAPC 

Lazarus Government Center 

P.O. Box 1049 

Columbus, Ohio 43216-1049 

Pursuant to Section 119.03 of the Ohio Revised Code, a public hearing on this 

redesignation request will be conducted as follows September 23, 2011 at 2:30 PM, at 

the Twinsburg Library, 10050 Ravenna Road, Twinsburg, Ohio 44087. 

All interested persons are entitled to attend or be represented at the hearings and give 

written or oral comments on these changes. All oral comments presented at the 

hearing, and all written statements submitted at the hearing or to the above address by 

the close of business on September 23, 2011 will be considered by Ohio EPA prior to 

final action on this redesignation. Written statements submitted after September 23, 

2011 may be considered as time and circumstances permit, but will not be part of the 

official record of the hearing. 

This redesignation and maintenance request is available on Ohio EPA DAPC’s Web 

page for electronic downloading at: http://www.epa.ohio.gov/dapc/SIP/annual.aspx. 

Questions regarding accessing the web site should be directed to Arunee Niamlarb at 

614-728-1342; other questions or comments about this document should be directed to 

either Carolina Prado, (614)-644-2310, Carolina.Prado@epa.state.oh.us or Jennifer 

Hunter at (614) 644-3696, Jennifer.Hunter@epa.state.oh.us or mailed to Carolina Prado 

or Jennifer Hunter at the above address. 
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5 In re:

OHIO ENVIRONMENTAL PROTECTION AGENCY

PUBLIC HEARING

- - - - - - - - - - - - _.

P88e1

6 Division of Air Pollution Control:

Cleveland, Akron, Lorain PM 2.5

7 Redesignation Public Hearing

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9 - - - - - - - - - - - - - - - - _.

10

11 Transcript of proceedings before the Ohio

12 Environmental Protection Agency, taken at

13 Twinsburg Public Library, 10050 Ravenna Road,

14 Twinsburg, Ohio 44087, on Friday,

15 September 23, 2011, commencing at 2:30 p.m.

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17

18 APPEARANCES:

19 Kristopher Weiss, Public Involvement Coordinator

20 Sarah VanderWie1en, Environmental Specialist II

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fINCUN-MANCINI -- M COURT REPORTER&>

(216) 696-22T2
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reductions.

coordinator in the Ohio EPA's Public Interest

NAAQS.

Cleveland, Akron, Lorain, nonattainment area,

I'm

Well, good

I'm a public involvement

PROCEEDINGS

MR. WEISS:

is evidence that the improved air quality is

All interested persons are entitled to

afternoon. Thanks for coming out.

which includes, Ashtabula, Cuyahoga, Lake,

Lorain, Medina, Portage and Summit counties

with respect to the 1997 annual PM 2.5

national ambient air quality standard, or

Air quality monitoring data collected

between 2008 and 2010 in this region

demonstrate attainment of the NAAQS and there

Kristopher Weiss.

due to permanent, enforceable emission

Center and I'll moderate today's public

hearing.

Thanks for taking the time to attend.

The purpose of this hearing is to obtain

comments from any interested person regarding

Ohio EPA's request that U.S. EPA revise the

current air-quality designation for the

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attend or be represented, and to present oral

and/or written comments concerning the

proposed agenda. All written and oral

comments received as part of the official

record will be considered by the Director of

Ohio EPA.

To be included in the official record

written comments must be received by Ohio EPA

by the close of business today, September 23,

2011. These comments can be filed with me

today or e-mailed to

carolina.prado@epa.state.oh.us. The spelling

is C-a-r-o-I-i-n-a, P-r-a-d-o at

epa.state.oh.us.

All written comments submitted for the

record receive the same consideration as oral

testimony given today. Written statements

submitted after today may be considered as

time and circumstances permit but will not be

part of the official record for this hearing.

If you wish to present oral testimony

and have not already signed the registration

sheet, please do so now. The sheet is

available at the registration table. People

will be called in the order in which they have

r'INCUN-ML\NONI -- Tl:IE COURT REPORTER&>

(216) 696-2272
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registered.

There is no cross-examination of

environmental planner at NOACA and I'm also

legal counsel there. But it's in my role as

environmental planner that I worked on the PM

2.5 SIP issues with the help of Ed Fasco and

have registered to testify at this hearing and

will give each person an opportunity to

testify. And the first person is Amy

Wainright. Thank you.

And can you just say your name and

spell it?

speakers or representatives of Ohio EPA in

public hearings of this type. Ohio EPA

hearings such as this afford citizens the

opportunity to provide comments on the

official record. Therefore, we will not be

able to answer questions during the hearing.

However, members of the panel may ask

clarifying questions of the person testifying

to ensured the record is as complete and

accurate as possible.

I will now read the names of those who

I'm an

MS. WAINRIGHT: My name is Amy

Wainright. A-m-y, W-a-i-n-r-i-g-h-t.

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many others who served on NOACA's air quality

task force.

In 2006 we took an entire year working

with Ohio EPA through a Memorandum of

Understanding to tackle the fine particulate

issues for Northeast Ohio, and we looked at

all of the different things that caused fine

particles and we came up with a multitude of

different solutions that we thought could be

implementing in order to try to solve the

problem. And eventually the task force

narrowed it down to some pieces that were they

thought reasonable and possible and sent all

of them to the Ohio EPA in the form of a NOACA

governing board resolution.

And we're just really delighted and

grateful to our partners at Ohio EPA that so

many things have been successful in taking

care of this problem. We're glad that the

State of Ohio has done the work that it has

done with coal fired power plants and we're

very glad that U.S. EPA took the sulfur out of

diesel fuel in 2007 and decided that diesel

tractor trailer rigs would now have air

pollution controls on all of their new

FlNCUN-MANCINI -- M COURT mpORTER&

('216) 696-'2'272
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engines. And the reason that we were glad is

because a lot of these things are issues that

we at the local level cannot control. We

don't have any way to help to solve our NAAQS

problems without partners like Ohio EPA and

u.S. EPA. And so I felt that everybody pulled

together in order to help the region to attain

the NAAQS. And NOACA continues to do

voluntary measures that address fine

particles. We are doing an anti-idling

campaign. We have given away more than 500

aluminum anti-idling signs that are being put

up all throughout the counties and they are

put up in both public sector locations and

private sector locations, hoping that people

will no longer waste gasoline and diesel fuel

by idling their engines when they're not going

anywhere. And we've helped a number of local

municipalities adopt ordinances to make it

illegal to idle engines when nobody is doing

anything.

At my agency we also do transportation

planning. So we try every year to work on

traffic congestion and traffic signal

synchronization so that there isn't wasted

FINCUN-ML\NCINI -- TIre COURT REPORTER8>

('216) 696-2272
P88e7

it.

I know that the data also shows we'll

know, it's been a long road but thank you for

but I want to thank you so much for -- you

Thanks very much. Is

So I feel that it has worked

MR. WEISS:

there anybody else who would like to testify

all the work that was done.

really sure that we were going to make the

goal, and we did and it's wonderful.

I'd be happy to answer any questions

in order to help us meet this goal.

I got to tell you in 2006 we weren't

citizenry, the trucking fleet, the businesses

public, the efforts of the utilities, the

We certainly have nothing but good

things to say on the record. The president of

NOACA this year is Medina County Commissioner

Steve Hambley and he requested that I come

fuel and PM 2.5 ending up in the air just due

here today and mention on the record how proud

we are of our own efforts, the efforts of the

out very well in this instance.

end up with good numbers from the daily

standard for PM 2.5 and we're all glad to see

to traffic jams.

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(Off the record.)

at this time?

(No response.)

comments can be submitted through the close of

Okay. Seeing that

Okay. We'll go ahead

MR. WEISS:

MR. WEISS:

there are no further requests for testimony

I'll remind you once again that written

now 3:00 and the hearing is adjourned.

(Hearing adjourned at 3:00 p.m.)

business today. Thanks for coming out. It's

and go off the record and see if anybody else

comes to testify.

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fINCUN-MANCINI -- TIiE COURT REPORTER&

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1 State of Ohio,

SS:

2 County of Cuyahoga.

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C E R T I F I CAT E

This certifies that the foregoing is a true

and correct transcript of the proceedings had

before the State of Ohio, Environmental

Protection Agency, in Twinsburg, Ohio, on

Friday, September 23, 2011 commencing at

2:30 p.m.

In Re:

Division of Air Pollution Control

Cleveland, Akron, Lorain PM 2.5

Redesignation Public Hearing.

COURT =~~C-.~ REPORTER

FINCUN-MANCINI COURT REPORTERS

1801 East Ninth Street

Suite 1720

Cleveland, Ohio 44114

(216) 696-2272

(216) 696-2275 FAX

FINCUN-MANCINI -- TIm COURT mpORTER&>

(216) 696-2272
Response to Comments 

PM2.5 Redesignation Request for the 1997 PM2.5 Annual Standard 

for the Cleveland, Akron, Lorain Area 

Agency Contact for this Package

Division Contact: (Carolina Prado, Division of Air Pollution Control, 614-644-2310, 

Carolina.Prado@epa.state.oh.us) 

Ohio EPA held a public hearing in Twinsburg, OH on September 28, 2011, regarding 

the Redesignation Request and Maintenance Plan for the Cleveland, Akron, Lorain 

PM2.5 Nonattainment Area. This document summarizes the comments and questions 

received at the public hearing and during the associated comment period, which ended 

on September 28, 2011. Ohio EPA reviewed and considered all comments received 

during the public comment period. 

By law, Ohio EPA has authority to consider specific issues related to protection of the 

environment and public health. 

In an effort to help you review this document, the questions are grouped by topic and 

organized in a consistent format. The name of the commenter follows the comment in 

parentheses. 

Ohio EPA received oral testimony from the Northeast Ohio Areawide 

Coordinating Agency (NOACA) in support of the redesignation request for the 

1997 PM2.5 Annual Standard for the Cleveland, Akron, Lorain Area. NOACA’s 

testimony can be found in this submittal. 

End of comments