## APPENDIX A

Air Quality System (AQS) Data

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

QUICKLOOK CRITERIA PARAMETERS

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

Selection Criteria Page 1 User ID: IDA

State County City

Site Parameter POC 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 2006Aug. 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 EXCLUDEDAug. 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 MEANAug. 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 MEANAug. 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 MEANAug. 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 MEANAug. 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 REPORTAug. 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

Introduc tion ................................................................................................. 3

1.0 Point Sources ............................................................................................. 4
2.0 Area S ources .............................................................................................. 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 Bu rners 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 Fa ctor 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 nu mber of counties with air quality data showing nonattainment for the following PM 2 

### . 5 

### standard:

• 

### Annual Standard = 15.0 ug/m3 The Clean Air Act Amendments (CAA A) requires all states to revise and submit State Implementation Plans (SIP) for areas which are classified as non-atta inment of the 1997 Fine Particle (PM 2 

### . 5 

### ) National Ambient Air Quality St andards (NAAQS). The Federal Register ( Vol. 72, No. 79/ Wednesday, April 25, 2007) provides the emissions inventory rules and regulations for the PM 2 

### . 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 PM 2 

### . 5 

### standards, a new attainment demonstration SIP will be necessary. A key el ement in the overall SIP planning process is the need for an updated emissions inventory. This document presents the 2005 Base Year Pa rticulate SIP Emissions Inv entory for Ohio as required by the CAAA. It includes emissions for poi nt, area, on-road mobi le and non-road mobile for the State of Ohio.
This technical report document s the procedures and the me thodologies that were used in the development of daily emissions for all counties in Ohio. This report describes the following:

1. Identification of st ationary and mobile sources included in the inventory; 2. Sources of data, and data collecti on methods used in the development of the inventory; 3. Methods and procedures used to estimate emissions; and 4. Assumptions considered in t he development of the emissions inventories. The intent of this report is to descri be 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 Em ission 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 Envir onmental 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 Ai r Pollution Control (DAPC), to assist the regulated community in prepar ing 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 emission s fee/inventory process conducted by Ohio EPA and include emissions, process rates, oper ating schedules, emissions control data and other relevant information. The STARShip files are electr onically transferred to the DAPC and stored into the Division’s Oracle database, STARS. The f iles 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 Em ission Inventory (NEI) database format in version 3.0. The files are quality assured ag ain using the United States Environmental Protection Agency’s (U.S.EPA) QA/QC so ftware for format and content. The data is finally submitted to LADCO for emissions processi ng through the Emissions Modeling System. The State provided inv entory for Electric Generating Un its (EGU) is replaced with the Federal EGU inventory. T he EGU inventory is compiled by U.S. EPA’s Acid Rain Program. It is based on facility report ed emissions as meas ured 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 emissi ons inventories is t hat between point and area sources. In this invent ory, 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 t he area source inventory is pr ovided in Section 2. The point source inventory descr ibed 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 Identificati on 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 fac ility-reported actual 2005 emissions.

1.1.2 Non-reactive VOC Emissions Adjustments This section is primarily appl icable for VOC pollutants. S ources are required to identify emissions of photochemically non-reactive Volatile Organic Compounds (VOC.) Based upon this information, those emissions have been specifically excl uded from the 2005 base line inventory in accordance with U.S. EPA’s “Recommended Policy on the Control of Volatile Organic Co mpounds.” 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 ((CF 3 

### ) 2 

### CFCF 2 

### OCH 3 

### )

• Heptafluoropropane ((CF 3 

### )CFCF 2 

### OC 2 

### H 5 

### )

• Perchloroethylene

• Cyclic, branched or linear completely methylated siloxanes

• Methyl acetate

• Volatile methyl siloxanes

• Acetone



### 1.1.3 Emissions Esti mation Methodologies Since source reported actual annual emi ssions 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 spat ial profiles to the annual emissions to calculate weekday emissions rate s. 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 a nd 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 r equired 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 availa ble for fugitive sources. However, since these emissions are now el ectronically reported in t he State’s ORACLE database, STARS, these emissions can be classified as point sources.

1.3 References Getting Started: Emissions Inve ntory Methods for PM2.5 U. S. Environmental Protection Agency, Research Triangle Park, NC, September, 1999. Emissions Inventory Guidance for Implem entation of Ozone and Particulate Matter National Ambient Air Qaulity Standards (NAAQ S) and Regional Haze Regulations, Office of Air Quality Planning & Standards Research, Triangle Park, NC. November 2005. Compilation of Air Pollution Emission Factors, Fourth Edit ion and Supplements, AP-42. U.S. Environmental Protecti on Agency, Research Triangle Park, NC, September 1985.

### 7 Preparing 2002 Regional PM2.5 Emissions QAQ PS PM Inventory Conference Inventory Conference San Diego, 2003.

Documentation for the 2002 El ectric Generating Unit Nati onal Emissions Inventory (NEI). Eastern research group, Inc., 1600 Pe rimeter 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, indivi dual, 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 usi ng 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 lar gely based on Ohio’s data availability. Data which was not available on a county-lev el is estimated by assigning a percentage of the state’s total activity to each county based on the state’s popul ation or employment information. If Ohio county specific activity data is available throu gh Ohio EPA or other State Agencies, that data is us ed rather than allocating acti vity by percentage. Table 2-1 lists the sources which emit PM 2.5 

### , NO x 

### and SO 2 

### along with the respective EFs used to calculate each pollutant.

Table 2-1 Categories in the 2005 PM 2 

### . 5 

### SIP Area Source inventory

Area Source NO x 

### PM 2.5 

### SO 2 

### 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 population 4 

### . Emissions factors are given in table 2-1. The area source emissions are calculated ba sed 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 population 4



### . The area source NO X 

### emissions are calculated and adjusted by subtracting the emissions due to point source s. 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 fact ors are given in table 2-1.

SO 2 

### emissions were calculated us ing EF 42.6 Lb/1000 Gal. PM 10 

### and PM 2.5 

### emissions are calculated using 1Lb/1000 Ga l and 0.25 Lb/1000 Gal respecti vely. All Factors are obtained from AP-42 19


### Industrial Residual Oil Combustion (SCC 2102005000) Ohio’s fuel consumption is apportioned per county based on the county’s population 4



### . The area source NO X 

### emissions are calculated and adjusted by subtracting the emissions due to point sour ces. 54,652 thousand gallons 14 

### are consumed in 2005 and a heating value of 140 MMBTU/1000 Gal is used. The SO 2 

### emissions are calculated using EF 157 Lb/1000 Gal. PM 10 

### and PM 2.5 

### emissions are calculated using 7.17 Lb/1000 Gal and 4.67 Lb/1000 Gal respectively. All factors are obtained from AP-42 19



### Industrial Natural Gas Combustion (S CC 2102006000) Ohio’s fuel consumption is apportioned per county based on the county’s population 4



### . The area source NO X 

### emissions are calculated and adjusted by subtracting the emissions due to point sources. 293,857 MMCF 14 

### are consumed in 2005. The SO 2

### 10 emissions are calculated usi ng EF 0.6 Lb/MMBTU. The PM 10 

### and PM 2.5 

### emissions are calculated using 7.6 Lb/MMBTU. All factors were obtained from AP-42 19



### 2.3 Residential Fuel Combustion Residential Coal Combustion (SCC 2104001000) Ohio’s household consumption of coal is apportioned per county based on county population 4 

### . NO X 

### emissions are calculated using EF of 9.1 of coal. The SO 2 

### emissions are calculated using EF 31Lb/1000 Gal. The PM 10 

### and PM 2.5 

### emissions are calculated using 6.2Lb/Ton and 3.8 Lb/Ton respectively . All factors were obtained from AP-42 19



### Residential Distillate Oil Combustion (SCC 2104004000) Ohio’s household consumption of distillate oil is apportioned per county based on county population 4 

### . NO X 

### emissions are calculated us ing EF of 18 lb/1000 gallons distillate fuel respectively. A heating value of 140 MMBTU/1000 Gal is used. The SO 2 

### emissions are calculated using EF 42.6 Lb/1000 Gal. The PM 10 

### and PM 2.5 

### emissions are calculated using 1.08Lb/1000 Gal and 0.83 Lb/1000 Gal respec tively. All factors are obtained from AP-42 19


### Residential Liquid Petroleu m Gas Combustion (LPG) (SCC 2104007000) Ohio’s household consumption of LPG is apportioned per county based on county population 4 

### . NO X 

### emissions are calculated using EF of 13 lb/1000 gallons LPG. The SO 2 

### emissions are calculated usi ng EF 0.1 Lb/1000 Gal. The PM 10 

### and PM 2.5 

### emissions are calculated using 0.17Lb/1000 Gal. All factors are obtained from AP-42 19


### Residential Natural Gas Combustion (SCC 2104006010) Ohio’s household consumption of LPG is apportioned per county based on county population 4 

### . NO X 

### emissions are calculated using EF of 94 lb/MMSCF. MMSCF stands for Million Standard Cubic Feet. This source also emits SO 2 

### emissions which are calculated using EF 0.6 Lb/MMBTU. The PM 10 

### and PM 2.5 

### emissions are calculated using 7.6Lb/MMBTU. All fact ors are obtained from AP-42 19



### Residential Wood Combustion NO X 

### emissions from this area source are ca lculated 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 cataly tic (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 ar e adjusted for those t hat burn wood. The following assumptions are applie d to those adjusted homes: 92 percent of wood combust ed 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 population 4 

### . 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 210400 8003 2104008004 SCC 2104008010 210400 8050 2104008030 SCC 2104008001 RAPIDS Code Non-Certified N on-Catalytic Catalytic Units SO 2 

### 4.00E-01 4.00E-01 4.00E-01 Lb/ton NO X 

### 2.80E+00 2.80E+00 2.00E+00 Lb/ton PM 2.5 

### 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 SO 2 

### which is calculated using EF 0.4 Lb/Ton EF. The PM 2.5 

### emissions are calculated using EF as shown in table 2-2. All factors are obtained from AP-42 19



### 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 fr om the Ohio Department of Health, Vital Statistics 17



### . It is estimated that 3% of deaths occur outsi de 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 f eels that it is a combustion source and NOx needs to be included in the inventory al ong with the other com bustion sources. Through its Permits-to-Install for human crem ation, Ohio has se lected a NOx EF of 10.13lb/ton cremated to calculate emi ssions from this area source.

### 12 This source also emits PM, PM 10 

### , and PM 2.5 

### emissions which are calculated using EF 0.1 Lb/Ton, 0.071Lb/Ton and, 0.0637 Lb/Ton respec tively. All factors are obtained from AP-42 19



### 2.5 Structur e Fires (SCC 2810030000) The Structure Fires category includes resid ential and commercial fires resulting from unintentional actions. Intentional fires, fo rest and wildfires, agricultural, and vehicle burning are not included in this area source

. The State Fire Marshall Office, Fire Prevention Bureau 15 

### provided data on the number of st ructure fires per county in 2005.
This area source is consider ed a combustion source for NO X 

### emissions which are calculated using EF 1.4 lb/ton burned. The residential and commercial structures fires for each county are tabul ated and a fuel load ing of 1.15 Ton/fire is applied. This source also emits PM, PM 10 

### , and PM 2.5 

### emissions which are calculat ed using EF 10.8 Lb/Ton. This factor was obtained from AP-42 19



### 2.6 Outdoor W ood Boilers (SCC 2104008070) Outdoor Wood Boilers (OWB), are also known as outdoor water stoves and outdoor wood furnaces, are used as outdoor residenti al heaters. These boilers have wood burning fireboxes surrounded by a water reserv oir vented by a chimney stack. The combustion of the wood in t he 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 capacit y ranges from 60 gallons to 764 gallons. The operational design creates long periods where the fire smolders and creosote is formed 13



### . When the water circulating through the furn ace 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 te mperature 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 fi ne condensed organic material t hat does not burn under cool, oxygen starved conditions. In addition, many owners burn green wood full of moisture which also causes poor combustion 12



### . The smoke created from these outdoor wood burning units can contain severa l 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 uni t sales data available to calculate emissions on the county level. Therefore, several assu mptions 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)

• PM 2.5 

### emissions factor (g/kg wood) =13.82 (Average of indoor and outdoor) or 27.64 lb/ton of wood The agreed upon methodology requi res 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 factor s (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 Co mbustion category to allow for accurate emissions from the indoor wood burning units.

2.7 References 1. Documentation For the 2002 Non-Point S ource National Emission Inventory For CAPS and HAPS. U.S. Environmental Pr otection Agency. Emissions Factor and inventory Group. E.H Pe chan and Associates, Inc. January 2004.

2. United States Census Bureau. Population Division. http://eire.census.gov/popest/data/c ounties/tables/CO-EST 2002/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-ALAP CO-EPA Emission Inventory Improvement Progr am (EIIP). Volume III - Area Sources Preferred and Alternative Methods. November 1996.

7. Documentation for the Dr aft 1999 National Emissions In ventory (Version 3.0) for Criteria Air Pollutants and Amm onia. Area Sources. E. H. Pechan & Associates, Inc. March 3, 2003

### 14 8. U.S. Environmental Pr otection Agency, STAPPA-ALAPC O-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 Asse lmeier, Air Quality Planning Section, Inventory Group, Springfield, Illinois. 10. Environmental Protection Agen cy (EPA). STAPPA-ALAPCO-EPA Emission Inventory Improvement Progr am (EIIP). Volume III - Area Sources Preferred and Alternative Methods. May 1997. 11. (Environmental Chemistr y.com) viewed on Feb 8th, 2007. [http://environmentalchemistry.com/yogi /environmental/200602out doorwoodfurnaces.ht ml] 12. (Washington State, Department of Ecology) viewed on Feb 8th, 2007 [http://www.ecy.wa.gov/progr ams/air/AOP_Permits/Boiler /Outdoor_Boilers_home.html] 13. U.S. Environmental Protection Agen cy. 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 E nergy Price and Expenditure Estimates, 1970-2001, Ohio. Energy Information Administration. U. S. Department of Energy. http://www.eia.doe.gov/emeu/st ates/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 In ventory (Version 3.0) For Criteria Air Pollutants and Amm onia. Area Sources. E. H. Pechan & Associates, Inc. March 3, 2003 17. Ohio Department of Health, Vital Statistics 18. Environmental Protection Agency. STAPPA-ALAPCO-EPA Em ission 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 gener ated regionally by running U. S. EPA’s Nati onal Mobile Inventory Model (NMIM) model. The Wi sconsin 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 process ed the NMIM files in their emissions model and generat ed daily emissions rates. Grant Heatherington from the Wisconsin Department of Natural Reso urces provided the following descriptions... The National Mobile Inventory Model (NMIM) developed by USEPA was used to estimate emissions for all other non-road m obile categories. NMIM consolidates nonroad mobile emissions and on-road emissions modeling into a singl e 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 populati ons, average load factors, available engine powers, activity hours and emission factors. Before NMIM was run, modifications and additions were made to the NM IM input data.

a. Added emission factors for diesel ta mpers/rammers provided by E.H. Pechan & Associates, Inc. Diesel tampers/rammers are a type of construction equipment.

b. Revised PM 2.5 

### ratios in SCC table to correctly calculate PM 2 

### . 5 

### diesel emissions. This error was introduc ed with NMIM2005 and didn’t exist in NMIM2004.

c. Revised gasoline parameter s using updates provi ded by the states and E.H. Pechan & Associates, Inc. Ga soline parameters include Reid Vapor Pressure (RVP), oxygenate c ontent and sulfur content.

The NMIM NEI NIF files are on the LADCO ftp site at: ftp://ftp.airtoxics.org/inv 2005/nonroad/NMIM/B ase_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-p ropelled or portable emi tter of air pollutants, and mobile source emissions are those genera ted by the engines or motors that power such sources. Most mobile sources, exce pt jet or turboprop ai rcraft, are powered by internal combustion (IC) piston engines, and nearly all use liquid fuels. Gaseous fuels, such as compressed natur al gas (CNG) or li quefied 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 pur poses, mobile sources ar e 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 pa ssengers 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 motoriz ed machines or equipment not used for transportation, ranging from constructi on equipment and farm tractors to lawnmowers and hand-held power weed choppers.

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

Non-road Mobile Sources: Some non-road mobile source s (e.g., farm tractors), are self-propelled, but many non-road sources ar e not. A gasoline-powe red chain saw is a familiar example of a non-self-pr opelled non-road mobile source. Stationary Sources: Not all movable or portable emi ssion 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 pow er pipeline compressors or standby generators in electricity plants and elsewhere, are al so classified as stationary sources. 4.1.1 Categories and Componen ts 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 occu r only when the engine is running. Evaporative emissions: Are Volatile Organic Compou nd (VOC) based only and are continuously emitted from an engine’s fuel syst em, 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 je t fuels are of very low volatility, so evaporative emissions from diesel and turbi ne engines are a much smaller part of their total VOC emissions. Evaporative emission s 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 emissi ons are entirely VOC. Alth ough they result from the evaporation of fuel, they ar e distinct from, and not dire ctly 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 stor age 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), Unit ed States Environmental Protection Agency (USEPA), and the Ohio EPA (OEPA). Estimates of the amounts of NOx and VO C are reported by county in tons per day. Emissions are repor ted 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 es timates hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NO x 

### ), exhaust particulate matter (which consists of several components), tire wear particula te matter, brake wear particu late matter, sulfur dioxide (SO 2 

### ), ammonia (NH 3 

### ), six hazardous air pollutant (HAP), and carbon dioxide (CO 2 

### ) emission factors for gasoline-f ueled and diesel highway moto r vehicles, and for certain specialized vehicles such as natural-gas-fuel ed 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 affe cting vehicle emissions can be specified by the user. MOBILE6 will estimate emission factors fo r any calendar year between 1952 and 2050, inclusive. Vehicles from the 25 most re cent 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 inventor y is to be constructed from separate

### 20 estimates of roadways, geographic areas, or ti mes of day, in which fleet or traffic conditions vary considerably. A list of MOBILE6 input parameters is provi ded 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 va por 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 vehi cle 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 specific ations and selections
4.2.4 MOBILE6 References
The following publications prov ide much of the guidance fo r 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 vers ion is still the definitive document on

### 21 inventories. If a previous edition is referr ed to, the fact will be not ed 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 em ission 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 publis hed by Assessment & Modeling Division (AMD) of the National Vehicle & Fuels Em issions Laboratory (NVFEL) in Ann Arbor, Michigan. The NVFEL is part of USE PA 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 pr imary 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 Mar go Oge, Director of OTAQ, to Regional Air Division Directors.

4.3 Ohio’s Alternate Data for MOBILE6 Alternative data is state-specif ic data that is used in the M obile6 runs. Using local data is preferred to using the default data in Mobi le6. Efforts are made to collect as much local data as possible.

4.3.1 Vehicle Registra tion Distribution by Age

Overview: The vehicle age distribution determines the fr action 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 esti mates of a distribution of r egistered 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 t he July 1, 1996 registration profile into a general registration distribut ion by age and by vehicle ca tegory 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-dut y vehicle in-use fleet, and made minor adjustments, where possible, to reflect some of the differences between vehicle categories. Description: The MOBILE6 model requires estima tes of a distribution of registered vehicles by age and vehicle category for curr ent 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 Dir ectors Consortium (LADCO) to develop these inputs. LADCO then contracted with a subcontractor to breakout the age distribution data from the Vehicle Identification Num bers (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 invent ory. This will be corrected in the next inventory (2008). Method Applicability: This approach is most applic able in areas where there are significant differences in the local vehicle fleet age distribution rela tive 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 stat e 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 t he national default.

• Requires minimal additional resources, par ticularly if data is readily available at the county or local level fr om the State departm ent 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 Pl anning Organizations (MPOs), the Ohio Department of Transportation (ODOT) provi ded 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 t he state and MPOs to fo recast traffic for a variety of reasons, undergo rigorous calibrati on and validation checks, and are sensitive to roadway capacity/travel time improvement s, 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 countie s where it is not used directly it is used for making rough emissions estimates where model s do not exist or where time prohibits the use of TDMs. DVMT is a simple mechan ism to measure how much traffic is flowing along a roadway during an average 24 hour peri od. 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 usin g the State of Ohio, Department of Transportation’s Roadway Information F iles 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 prov ides a very accurate estimate of statewide total DVMT for The State Highway S ystem (Interstate, US and State Routes). County total DVMT are consistent and cons idered a good source of county level DVMT for countywide emissions estimates. For roadways that are not part of the State Highway System, various representative count s 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 Func tional 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/te chservsite/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, deriv ed 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-sampl ed, 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 segm ents from one functional class to another. The Ohio Department of Transportation t herefore does not warr ant 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 perform ed by the Ohio Metropolitan Planning Organizations (MPOs) and ODOT for a multit ude of purposes including the preparation of regional emissions estimate s. The ODOT Office of Te chnical Services’ Modeling & Forecasting Section recommends that Ohio ’s TDMs and ODOT’s conformity analysis methods be used to establish the roadway mob ile 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 LADC O. 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 networ ks. Additional post processing data was provided including but not lim ited to metadata describing the loaded networks, Hourly distribution by functional cla ss, speed profiles, day of week / weekend / monthly car and truck traffic profiles, 2009 & 2018 VMT RPO data sets, state wide VMT growth rates for local traffic, and a 2005 VMT summary comparison spreadsheet. It should be noted that among other things, the loaded TDM Netw orks contain distance and daily volumes from which VMT is computed. Network volumes are post processed to esti mate VMT by hour of day. The hourly volumes and capacity, posted speed limi t, and type of roadway for each roadway segment are then used to estimate aver age hourly speeds needed for MOBILE6 based emissions estimates. Modelin g 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 relati ve emissions affect of improvements to individual roadways as well as packages of improvements and the entire set of planned roadway improvement projec ts air quality impacts of construction programs.

### 26 4.3.4 Speed Di stribution 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 c onducted 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 s peeds for use with MOBILE6 for development of CERR, ODOT provided a set of speeds based on that statewide travel time study. Those speeds are doc umented in a technical memorandum titled “Estimation of Average Speed by Func tional Class for MOBILE6 Runs” dated 5/27/2004. Readers should refer to those docum ents 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 U se with County Level DVMT, HPMS:

The memo states that the s peeds which the Ohio Department of Transportation (ODOT) had been using prior to that date, 2004, fo r air quality analysis estimates based on County DVMT summaries, were developed by a former ODOT employee for addressing the one (1) hour standards conformity ru les 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 esti mated. In addition, EPA has requirements for using latest planning assumptions for ai r quality conformity analyses. Therefore, ODOT believed that it was in the state’s best interest to use the mo st recent available data to provide a new set of estimated av erage 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 dat a obtained from traf fic count equipment, automatic traffic recorders (ATRs) which co llect 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 FC 0

6 F C0 8 F C1 1 FC1 4 FC1 7



### Functional ClassSpeed Average Speed
1990 Analysis Speeds 2001 Speed Study



## Speeds Comparison

0 20 40 60 80 100

FC 0

1 FC 06 F C08 F C11 FC14 FC 1

7

R

AMPS



### Functional ClassSpeed 

### 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 Esti mation 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 bet ween 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 expec ted that these average statewide speeds are higher than the average speeds in t he non-attainment areas because the non-

### 28 attainment counties tend to be more popul ated and more congested. The document “Highway Vehicle Speed Es timation Procedures For Use in Emission Inventories”, September 1991 by Earl Ruiter of Cambridge Systematics Inc. is referenced by EPA’s documented procedures for emission inventor y preparation. This document suggests post processing travel demand model traffic assignment results to estimate average speeds.
Final space mean speeds that ODOT provi ded 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 s pace mean speed based average speed estimates were reproducible and defensib le since they are well docu mented 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 anal ysis 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 budget s. Therefore, ODOT pr ovided 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 Cod es & 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 curv e parameters for the equation

T = T 0 

### {1 + a * (V/C)^b]

### 30 More complete details about emissions mode ling process employed by ODOT may be found in ODOT documentations. The docum ent titled “Congestion Management & Air Quality Analysis (CMAQ) Program Docu mentation” 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 s upplied 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.s tate.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 developi ng the Ohio 2007 8-Hour Ozone SIP Attainment Demonstrations. See meeting agen da below. OEPA is working to ward a June 15, 2006 schedule for submitting the 2002 invent ories to US EPA. A key component of the meeting will be to review a nd confirm the MOBILE6.2 i nput parameters, by Ohio a/q area, for use in developing the 2002 mobile inve ntories. See draft templa te below. The Ohio MPO travel demand models will be used to generate t he 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 cu rrent 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 Regulati ons 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 Regulati ons 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 Regulati ons 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) fo r regional haze, fine particles , and ozone. Emissions for on-road motor vehicles are estimated using vehi cle miles traveled, tr ip starts and ends, speed, and other activity data developed by State Agencies and Metropolitan Planning Organizations (MPOs) using transportati on demand models (TDMs), and emission factors from EPA's MOBILE6 model. To s upport 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 networ ks using a variety of models, applies appropriate data transformations , and outputs link- and county-le vel activity data in a uniform format for input to the CONCEPT emissions processing model (a new emissions processing model also developed wit h funding from LADCO). In a parallel effort, analyses of extensive automatic tra ffic recorder (ATR) dat a collected by State DOTs were conducted to develop temporal profiles (hour of day , day of week, and month of year) of vehicle counts and vehicl e mix by roadway type for developing the detailed on-road emissi on inventories. T3 provides a conduit from the projections of traffic demand model ers 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 simult aneously providing a great degree of flexibility in representing t he TDM projections in terms acceptable to most air quality models. These goal s have been achieved through the use of a dimensional transformation approach, w here 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, Su ite 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 Emi ssions Modeling Community: The new CONCEPT (CONsolidated Communi ty 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 syste ms into an open source model highlighting the following features: • Open Source. Writt en primarily in PostgreSQL, th e software required for running

### 39 CONCEPT is in the public dom ain. 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 structur e of the model makes the system easy to understand, and the modeling codes themselv es are extremely well documented to encourage user participation in the customizing the system for specific modeling requirements. • Quality Control. The CONCEPT model structure an d 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 calculat ion procedures and assumptions for any emissions value. The CONCEPT model includes modules for the major emissions source categories: area source, point source, on-ro ad motor vehicles, non-road motor vehicles and biogenic emissions, as well as a num ber of supporting modules, including spatial allocation factor development, speciation pr ofile development, growth and control for point and area sources, and CEM point sour ce emissions handling. The emissions modeling community has alr eady begun development of addi tional CONCEPT support modules including CEM preproce ssing software, graphical QA tools, and an interface to the traffic demand models for on-road mo tor 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 Av enue # 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 T otals (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 COLUMB US 1 1 1 1 1 1 1 1 1

OH SPRING FLD SPRING FLD OH OH SPRING FLD 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 YNGSTO WN 1 1 1 1 1 1 1 1 1

OH STATEW D STATEW D OH OH STATEW D 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 Ta ble 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 sour ces which are significant enoug h 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 Environ 1 

### under contract with LADCO and the aircraft inventory is generated by Ohio EPA.

5.1 Marine Vessel Sources The approach to commercial marine emi ssion estimates needed to be flexible because the activity data was available in many fo rmats. Emission esti mates were determined either by multiplying engine power, load factor, hours per ye ar of operation, or on the basis of the number of gall ons 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 methodologie s employed, inventory tables for the ten subcategories are detailed in the comp lete inventory prepared and published by ENVIRON Internati onal Corporation: LADCO 2005 COMMERCIAL MARINE EMISSIONS , by Christian E. Li ndhjem, 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 em issions is gallons of fuel consumed. Emission rates were derived from EPA doc uments 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 larges t 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, O ffice of Mobile Sources, April. And EPA 1997, Emission Factors for Locomotives," Environmental Protecti on 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 fact or. 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 t hen compiled as tons per year per pollutant by county. The following de scribes the components, met hodology, and concludes with a description of an Access based aircraft emission calculator.

COMPONENTS: I. Ohio Airports: A list of both towered and non-towered airpor ts 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 16 4 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 deriv ed 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 Stat e 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-W arren 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 t he larger airports) is obtained from “Table 7” provided by the United States Departm ent 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” departure s were used as the number of LTOs per year for that model. Listed in the original table are airc raft 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 ai r quality at civilian airports and military air bases. The model was developed by the Federal Aviation Administration (FAA) in cooperation with the Unit ed States Air Force (USAF). The model is used to produce an inventory of emissions generated by source s 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 0.0BEECH 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 0.4CANADAIR 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 emissi on factors were taken from “Documentation for Aircraft, Commercial Marine Vessel, ,Locomotive, and Other Non-road Components of the National Emissions Inventory. 2005, s ee 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 Emissi on Factor Categories

Fleet emission factors were br oken down into three categories. Itinerant General, Local General, and Military.

Table 5-3a Fleet Average Emission Factor s 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 Factor s 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 Fa ctors 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 ai rcraft air carrier activity and commercial air freight activity by aircraft model types,

2. General aviation and air taxi annual loca l and itinerant operations for year 2005, 3. Military annual local and iti nerant operations for year 2005. Due to the need to have aircraft operations in formation expressed as landing/take off (LTO) cycles, the following assumptions were made:

1. For commercial aircraft and commercial ai r freight activity, t he number of annual aircraft annual LTO cycles was assu med 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 iti nerant airport operations, each respective operations total was divided by 2 to obt ain 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 operat ions, each respective operations total was divided by 2 to obtain the correspondi ng year local and itinerant LTO cycles. The expected military daily local and iti nerant LTO cycles then were obtained by dividing these annual totals by 365. Airport LTO cycles were further categoriz ed 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 Av iation Administration EDMS Emissions and Dispersion Modeling System . Commercial and air freight aircraft emission factors per LTO cycle were det ermined using EDMS for each commercial aircraft type models where possible were used at each towered airport. Default commercial aircraft engine type, and Enviro nmental 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 alter native 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 fr om the Ohio Department of Transportation. These were classed by airport, county, aircraft flight classification, and the total number of op erations per year. 2. The number of operations (a landing or a take off) we re 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 categorie s used by the USEPA in calculating emissions.

### 54 Itinerant (General, air carrier, commuter, ai r taxi, general aviation itinerant)

Local (General aviation local) Military (Military)

4. LTOs for specific models of aircraft per airport were ta ken from the FAA Table 7. See Table 2 above. These were then combined to gi ve 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 det ermined from the FAA’s EDMS pr ogram 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 fl own in Ohio. . Where specific aircraft model data was not available USEPA average fleet emissions were used. 6. Emission factors times LTOs by count y yielded tons per y ear 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 pollut ants 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 submitt ed 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 func tion in Access that picked up the number of operations as instead of LTOs....which made the in ventory exactly twice as lar ge as what it really was.

ACCESS CALCULATOR

Introduction: Our database programmer set up Access applic ation 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 futu re data scenarios. Ou tput 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 emissi ons, state/county FIPS, detailed aircraft information, airport informati on, and aircraft SCCs. Once the final emissions have been calculated and summed, then the data can be expo rt 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 Dema nd Models ............................................................................. 5 3. Emission Factor Generation ................................................................................. 5 4. Post Proce ssing ................................................................................................... 22 5. Multiple MPO Coordi nation Issu es .................................................................... 24

Appendix A Interagency Consulta tion Documentation Appendix B Post Processing Default Distributions1 

## 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 Trans portation Study (AMATS) and Ashtabul a Township areas, and by the Northeast Ohio Areawide Coordinating Agency fo r the counties of Cuyahoga, Lake, Lorain and Medina in its area. These analyses were conduc ted in support of Ohio EPA’s intent to submit a redesignation package State Implementation Plan (SIP) submittal for the 1997 Annual PM 2.5 

### NAAQS.
The designated northeast Ohio nonattai nment areas for the 1997 Annual PM 2.5 

### 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 c ounties to attainment based on several years of monitoring data ( 76 FR 28393). Despite the potential change in attainment status, Ohio EPA must still contin ue its efforts to assemble the necessary redesignation submittals.

The inventories include count y based, annual totals of PM 2.5 

### and Oxides of Nitrogen (NO X 

### ) for the base year 2005, the attainment year 2008, th e interim year 2015, and the maintenance period horizon year 2022.
Latest Planning Assumptions

These annual PM 2.5 

### 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 a nd NOACA areas is calibrated using the latest population and land use data available and are vali dated using corresponding traffic count data. Currently, the travel demand mode ls are validated to year 2005.
U.S. EPA’s most recent emissions software, M OVES, is used for all mobile source emission analyses with MOVES inputs and output need s 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 annua l emissions estimates expressed in tons per year. Finally, the regional emissions analys is includes emissions for Direct PM 2.5 

### (exhaust, brake, and tire wear), and Oxid es of Nitrogen (NO X 

### ).Sulfur Dioxide (SO 2 

### ), Volatile Organic Compounds (VOCs) and Ammonia (NH 3 

### ) are considered insignificant at this time. SO 2 

### emissions are shown to support the assertion that they are insignificant.2On Road Mobile Source Emissions Inventory SummaryTables 1 to 4 present a summary of emissions by county as well as the entire nonattainment area for NOX, PM2.5 and SO2forcalendar years 2005, 2008, 2015, and 2022. They also include annual VMT totals. The remainder of this document focuses on theassumptions behind the analyses.Table 1 – Northeast Ohio On-Road Mobile Source Emissions2005 BaseCOUNTY NOX(tons/year) PM2.5(tons/year) SO2(tons/year) Annual VMTAshtabula Twp. 294.66 11.50 5.07 79,693,005Cuyahoga 36,506.02 1,247.92 845.65 10,605,503,693Lake 8,067.92 282.95 176.88 2,112,119,403Lorain 9,181.08 319.21 199.86 2,468,649,943Medina 9,280.85 319.21 207.56 2,100,347,788Portage 5,707.22 187.71 96.74 1,761,085,222Summit 17,377.28 611.78 319.72 5,505,977,908TOTAL86,415.032,980.28 1,851.48 24,633,376,9623Table 2 – Northeast Ohio On-Road Mobile Source Emissions2008 AttainmentCOUNTY NOX(tons/year) PM2.5(tons/year) SO2(tons/year) Annual VMTAshtabula Twp. 232.83 10.04 1.57 80,604,775Cuyahoga 28,227.20 1,040.28 253.16 10,644,066,125Lake 6,425 241.78 51.94 2,154,120,865Lorain 7,354.77 274.43 60.17 2,519,904,703Medina 7,603.83 317.43 57.65 2,228,836,730Portage 4,630.98 160.90 30.52 1,811,101,624Summit 13,609.82 511.43 100.66 5,635,781,676TOTAL 68,084.64 2,556.29 555.66 25,074,416,498Table 3 – Northeast Ohio On-Road Mobile Source Emissions2015 InterimCOUNTY NOX(tons/year) PM2.5(tons/year) SO2(tons/year) Annual VMTAshtabula Twp. 107.68 5.37 0.66 82,946,980Cuyahoga 12,371.38 430.01 76.36 10,586,598,518Lake 2,840.39 122.02 15.91 2,178,585,720Lorain 3,456.51 143.34 18.51 2,550,296,428Medina 3,306.35 152.50 17.63 2,308,164,735Portage 2,264.50 87.54 13.23 1,935,234,601Summit 6,170.32 251.70 42.76 5,992,078,124TOTAL 30,517.14 1,192.47 185.06 25,633,905,1064Table 4 – Northeast Ohio On-Road Mobile Source Emissions2022 MaintenanceCOUNTY NOX(tons/year) PM2.5(tons/year) SO2(tons/year) Annual VMTAshtabula Twp. 54.49 3.03 0.62 85,561,840Cuyahoga 6,065.83 320.94 68.07 10,685,893,665Lake 1,417.15 69.42 14.45 2,250,259,310Lorain 1,723.02 81.69 16.83 2,632,524,905Medina 1,677.58 78.48 16.83 2,489,638,720Portage 1,014.35 49.45 7.40 2,036,373,863Summit 3,059.45 162.98 39.57 6,246,851,092TOTAL 15,011.86 765.99 163.77 26,427,103,3955 

## 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 a nd future traffic volumes and travel patterns on the regional transportation networ ks. The four step process cons ists of trip generation, trip distribution, mode split, and route assignment. Out put 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 patter ns 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 fo r 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 represen ts the best available for this effort.



## 3. Emission Factor Generation

Base and Attainment Years
Using MOVES, emission factor files were ge nerated for base year-2005, attainment year-2008, interim year 2015, and maintenance year 2022. A ssumptions for these runs include an I/M program in all but Ashtabula Township. Progr ams and corresponding MOVES parameters were developed in consultation with OEPA.6 

### Technical Details
Table 7 summarizes the settings used in th e 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. Fu rther 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, Lo rain, Medina, Portage and Summit Counties; Ashtabula Township Vehicles/Equipment All related source ty pes, 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 NO x 

### , All PM 2.5 

### categories, SO 2 

### , 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 Combinatio n 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 converter7 

### Hourly VMT fractions - ODOT Hourly Percentage I/M Program I/M program info rmation supplied by Ohio EPA Fuel Formulation Default Fuel Supply Default Metereology Data Local data obtained fr om 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 moto r 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 Ho pkins International Airport (CLE) was used for NOACA’s area. Data for Akron-Canton Airpor t (CAK) was used for AMATS and Ashtabula Twp. Areas. To get the correct fomat for MOVE S, the data was entered into a spreadsheet provided by EPA which was designe d 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. Ta ble 8b portrays the data for Portage and Summit Counties; and Ashtabula Twp.
Table 8a – Temperature and Relative Humi dity 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 669 

### Table 8b – Temperature and Relative Humi dity 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 

### 7510 

### Ramp Fraction
Ramp fractions were derived using the base ye ar travel demand model VHT fractions. Ramp fractions can be seen in Table 9. Base year fractions were ke pt 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 0.18411Source Type PopulationSource type population is based on a combination of local and MOVES default data. Localdata was obtained from ODPS Bureau ofMotor Vehicles Motor Vehicle Registrationsby county and vehicle type data for the base year 2005 and intermediate year 2008. TheMOVES default source type population datawas obtained from the national level MOVES inventory runs for each county.OBMV’s vehicle registration data was usedas a control total for all source type population and a sub totalfor Intercity Bus (41),Transit Bus (42), and School Bus (43). The same data was also used to assign source type population of Motorcycle (11) and MotorHome (54). For the rest of source types 21, 31, 32, 51, 52, 53, 61 and62, the fraction of each source type from the MOVES defaultdata was used and adjusted to match up the control total for source type population.Future year source type growth rate is based ontravel demand model’s annual household growthrate from year 2005 to year 2022 for corresponding analysis years. Table 10 showssource type population for the analyzed counties in 2005.Table 10 – Source Type Population for year 2005sourceTypeID sourceTypeName CuyahogaLake Lorain Medina Portage SummitAshtabulaTwp11 Motorcycle24,754 7,960 10,241 6,585 7,158 17,354 59321 Passenger Car579,339 121,433 150,152 89,819 84,140 277,131 7,50631 Passenger Truck362,540 76,735 95,416 58,943 60,169 190,713 5,40432 Light Commercial Truck121,119 25,636 31,877 19,692 2,413 7,648 21741 Intercity Bus289 61 29 16 33 139 1042 Transit Bus742 37 17 9 17 74 543 School Bus2,003 422 198 108 198 849 6251 Refuse truck314 73 92 73 64 154 652 Single Unit Short-haul Truck17,464 4,049 5,122 4,059 2,651 6,355 24653 Single Unit Long-haul Truck1,770 410 519 411 338 812 3254 Motor Home5,121 780 1,354 901 2,537 4,821 2961 Comb Short-haul Truck3,758 1,043 1,083 1,336 1,283 2,293 11862 Comb Long-haul Truck3,870 1,075 1,116 1,376 1,476 2,637 13612 

### I/M Program
I/M program information was supplied by Ohio EPA. The I/M program was applied to all analysis years for every geogra phy 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, NO X 

### and PM 2.5 

### . The compliance rate and failure rates are obtained from Ohio EPA and compliance factor s are calculated. These are applied for all counties under consideration and for all model years.

Vehicle Age Distribution
Vehicle age distribution informa tion was derived using ODPS vehi cle 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 Cuya hoga 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 0.06859414 

### 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 0.00531915 

### 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 0.00742616 

### 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 0.00795117 

### 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 0.00667618 

### Road Type Distribution
Road type distribution is base d on the ODOT’s 2005 daily vehicle miles of travel (DVMT) by functional classification (FC). Th ese inputs vary by county. A sa mple road type distribution input for Cuyahoga County can be seen in Ta ble 12. Cuyahoga County does not contain rural road types 2 and 3. The distributions for othe r 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 2 019 

### 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 f our 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 take s 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 mode l and fractioning it with the MOVES default distance travelled by each source type to come up vehicle type VMTs. The same method was20 

### 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 3,163,15921 

### 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 distan ce 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 pe r vehicle) emission file for year 2005. The rate per vehicle varies for any co mbinations of month, da y of week, hour of the day, pollutant, and process. Rates per vehicle em issions 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 directiona l 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 em ission factors.
The first step in the the process involves r unning ODOT postcms.exe to calculate hourly link volumes based on the percentage of the daily vo lume (travel demand mode l output) determined by a link’s facility and area type. Link speeds fro m the travel demand model are not used in the analysis. The speeds are estimated as a post-pr ocess 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 combin ation of the MOVES emission factors and the hourly link volumes that are out put of the postcms.exe progra m. The hourly volumes are multiplied by the MOVES emission factor fo r 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 su m of all individual link-hour emissions.
The third step, (mvehicle.exe), calculates vehicl e-based emissions for each source type for each hour of the day. The vehicle source type is base d on a combination of local and default data. The final vehicle emissions for each county are th e sum of all individual hourly emissions for all vehicle types.
Intrazonal trips do not get loaded onto the networ k, so the fourth step in the process requires a separate method to account for those trips that us e local roads to travel within a zone. The mintra.exe program uses intrazonal trips to es timate VMT using the area in square miles and intrazonal trips of each zone . The zone is assumed circular and th e 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 estim ated intrazonal VMT. The emission rates are the same as those used to calculated link based emissions.
The final step is to summarize link, vehicle, a nd intrazonal emissions for each county, pollutant, and analyzed year, and to multiply annual aver age 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 1-4.23Figure 4 – Emission Calculation ProcessMOVESTFMODOTCMAQNOACAAQODOTMMOVESODOTMVEHICLEODOTMINTRAEmission FactorRate/DistanceEmission FactorRate/Vehicle24HR NETSpeedCalculatorLNKGRPCMAQ NETCMAQ ReportHOURLY.CSVIntrazonal Trips24HRVehicle Trips4 PeriodsVehicle TripMatrices4 PeriodsAssigned NETVHTVMT4 CongestedSPEEDEMISSIONOUTPUTTon/dayTAZ Area (Sq.mi)2. ODOT’s CMAQ POST-PROCESSOR3. INTRASOZNAL TRIP CALCULATION4. ODOT’s MOVESPOST-PROCESSOR1. NOACA AQPOST-PROCESSORRamp FractionsAverageSpeed DistributionRoad TypeVMT fractionsVehicle TypeAnnual VMTsFuel supply &formulationI/M ProgramAge DistributionMeteorologyZone ActivitySource TypePopulationFINALEMISSIONTon/Year24 

## 5. Multiple MPO Coordination Issues

AMATS, NOACA, ODOT, and OEPA have a long hi story 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 DistributionsHOUR 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2021 22 23PCTADTURB 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 7.9 5.8 4.2 3.42.9 2.2 1.5URB 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 8.1 6.2 4.8 4.03.0 1.9 1.3RUR 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 7.0 5.6 4.5 3.83.2 2.5 2.0RUR 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 7.7 5.6 4.2 3.52.8 1.9 1.3PCTADT TRKURB 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 4.6 4.1 3.7 3.43.1 2.8 2.4URB 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 5.0 3.7 2.8 2.31.9 1.5 1.3RUR 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 4.9 4.6 4.3 4.03.8 3.5 3.1RUR 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 4.6 3.8 3.1 2.62.3 2.1 1.7PCTDIRURB FWY 38 40 40 46 56 64 70 70 68 62 58 52 52 52 50 46 38 38 46 52 4642 42 40URB ART 44 46 44 48 54 62 66 68 64 56 54 52 50 50 50 46 40 38 46 52 4846 46 46RUR FWY 44 46 48 54 60 68 68 64 58 54 52 50 50 52 52 48 42 40 44 48 4844 46 44RUR ART 40 42 44 48 58 66 72 68 60 56 54 50 50 50 50 46 40 38 46 50 4644 44 44LOS 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 10.625 11.25 11.875 12.513.125 13.75 14.375SPEEDVCcurve1 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 47.7 41.2 34.9 28.923.7 19.2 15.5curve2 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 47 40.5 33.9 27.722.2 17.6 13.8curve3 65 65 65 65 65 65 65 65 65 64.9 64.8 64.4 63.8 62.6 60.5 57 52 45.4 37.8 29.9 22.716.7 12.1 8.6curve4 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 46.1 40.3 33.8 27.321.3 16.2 12.2curve5 55 55 55 55 55 55 55 55 55 55 55 54.9 54.7 54.3 53.6 52.3 50 46.5 41.5 35.3 28.521.9 16.1 11.5curve6 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 53.1 50.7 47.9 44.741.1 37.3 33.4curve7 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 49.3 47.3 44.9 42.139 35.7 32.2curve8 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 45.4 43.8 41.8 39.536.8 33.9 30.9curve9 45 45 45 45 45 45 45 45 45 44.9 44.8 44.7 44.4 44.1 43.6 43 42.1 40.9 39.4 37.6 35.533.1 30.5 27.8curve10 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 34.9 32.4 29.8 27.324.9 22.6 20.4curve11 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 25 21.4 18.2 15.312.9 10.8 9curve12 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 18.7 14.9 11.8 9.27.2 5.7 4.5curve13 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 26.4 23.7 21.1 18.516.1 13.9 12curve14 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 20.5 17.7 15.1 12.810.7 9 7.6curve15 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 17 14.3 11.9 9.98.2 6.8 5.6curve16 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 15.2 12.8 10.7 8.97.4 6.1 5.1curve17 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 14.9 12.5 10.4 8.67.2 5.9 4.9curve18 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 12.1 9.9 8.1 6.65.4 4.4 3.6curve19 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 12 10 8.3 6.85.6 4.6 3.8curve20 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 11.4 9.5 7.8 6.45.3 4.3 3.6curve21 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 9.9 8.1 6.6 5.44.4 3.6 2.9VC 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 TO DETERMINE EXCEEDANCE)BASE RUR2 FWYA 0.00 0.00 0.00B 0.30 0.00 0.25C 0.50 0.10 0.40D 0.70 0.30 0.60E 0.90 0.50 0.80F 1.00 1.00 1.00F+ 1.10 1.10 1.10F++ 1.30 1.30 1.30SPEED 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 AREANUM LOS DEFINITION1 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 AREA4 E RURAL NON MPO COUNTIESPEAK SPREADING MODEL INFO (SET MAX ITERATIONS TO 0 TO DISABLE PEAK SPREADING)MAX VC RATIO FWY: 1.30MAX VC RATIO ART: 1.30MAX ITERATIONS : 1000TRUCK PCE: 2.00AQ SEASON FACTOR: 1.00MODEL 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 00 0CLS BEG 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0CLS END 23 23 0 0 0 0 0 0 0 0 0 0 0 00 0CLS NUM 1 3 0 0 0 0 0 0 0 0 0 0 0 00 0

# APPENDIX D

LADCD Technical Support Document

LADCD Technical Support Document (supplemental)

# Regional Quality Analyses for Ozone, PM 2

. 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, PM 2

. 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 nonattainm ent area is located in northwest Ohio and includes the following counties: Cu yahoga, Lake, Lorain, Medina, Portage and Summit.

The area has twelve 1 

### monitors measuring PM 2.5 

### concentrations, which are overseen by the Ohio EPA and operated by Local Air Agencies and Ohio EPA’s Northeast District Office 2 

### . A listing of the design values based on the 3-year average of the annual mean concentrations from 2008 through 2010 is s hown in Table 1. The design values calculated for the Cleveland-Akron-Lor ain area shows that the annual PM 2.5 

### 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/a irs/airsaqs/index.htm

1 The redesignation and maintenance plan document re fers to thirteen monitors. The thirteenth monitor was permanently shut down in 2009 as a part of Oh io’s approved monitoring network. Therefore, this monitor is not included in this analysis. 2 Monitors in Cuyahoga County are operated by the Cl eveland Department of Public Health & Welfare; the monitor in Lake County are operated by Lake County General Health Di strict; monitors Medina, Portage, and Summit Counties are operated by the Ak ron 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 PM 2.5 

### 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/m 3 

### . Moreover, in accordance with the CAA Amendments, three complete years of monitoring data are required to demonstrate atta inment 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, si te 39-035-0060 has only 48% and 63% capture in the second and third quarter of 2009, re spectively. Site 39-035-0065 has only 74% capture in the third quarter of 2010, and si te 39-035-1002 has only 71% capture in the first quarter of 2008 and 73% capture in the third quarter of 20 09. These monitoring sites experienced instrument malfunctions during the low perce ntage capture periods with a total of 27 missing 1-in-3 PM2.5 FR M runs in site 39-035-0060 (in 2009), a total of 8 missing 1-in-3 PM 2.5 

### FRM runs in site 39-035-0065 (in 2010), and a total of 8 missing 1-in-3 PM 2.5 

### FRM runs and a total of 9 missing 1-in-3 PM 2.5 

### 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 mult iple imputations. Imputing mi ssing 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 m easurements (this approach is us ually better than excluding subjects with incomplete data). Most methods for accounting for having incomplete data can be complex; the bootstr apping method however, is an easy method to implement even though the computations ca n be slow. To use the bootstrap, to correctly estimate variances of regression coe fficients, 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 regr ession model is used for imputation, the process involves adding a rand om residual to the “best gue ss” 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 peri od. 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 redesi gnation absent this a nalysis. 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 met hods 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 t hat 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 combination 3 

### has

3 Site D vs. Site A or Site D vs. Site B or Site D vs. Si te 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 Si te 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. Si te 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. Si te 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 Si te 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 wh ich to impute missing data for site D, site E, and site F we determine the correlation betwe en 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 (R 2 

### ) is useful because it descri bes the degree of relationship between two variables 4 

### (variables or site conc entrations in all sites). R 2 

### is only a descriptive statistics. R oughly speaking, we asso ciate a high value of R 2 

### with a good fit of the regression line and a ssociate a low value of R 2 

### with a poor fit. The mean of the quarterly dat a 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 statistica lly significant as the correla tion, or as the mean of the percentage captured, pairing the site data see ks to reduce variability in order to make more precise comparisons with fewer obs ervations. 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 R 2 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 ex actly 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 LQuarterlyDataCapture2003‐2010 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 re lationship among sites) from which to impute missing data for all three sites (D, E, and F). From the mean of the quar terly 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 completeness 5 

### from where to impute missing va lues. In addition, from the dat a 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%8Linear Regression and Correlation for Site D vs. Site BRegressionStatisticsMultipleR0.9568RSquare0.9154AdjustedRSquare0.9153StandardError2.5039Observations815CoefficientsStandardErrortStatP‐valueLower95%Upper95%Intercept1.5655664050.1740871868.9930019541.66443E‐181.2238530731.907279737XVariable10.8804423840.0093877893.7860016500.8620152410.898869529ANOVAdfSSMSFSignificanceFRegression155145.6536555145.653658795.8141060Residual8135097.1309626.269533779Total81460242.784619Linear Regression and Correlation for Site E vs. Site CRegressionStatisticsMultipleR0.9415RSquare0.8864AdjustedRSquare0.8863StandardError2.5592Observations756CoefficientsStandardErrortStatP‐valueLower95%Upper95%Intercept0.9325873610.2022236994.6116620564.69183E‐060.5355989421.32957578XVariable10.9441520340.01230643476.7201990700.9199930870.968310981ANOVAdfSSMSFSignificanceFRegression138549.3921138549.392115885.9889450Residual7544938.2086716.549348371Total75543487.6007810Linear Regression and Correlation for Site F vs. Site ARegressionStatisticsMultipleR0.9383RSquare0.8804AdjustedRSquare0.8802StandardError2.6246Observations731CoefficientsStandardErrortStatP‐valueLower95%Upper95%Intercept‐0.310956210.198704325‐1.564919200.118035816‐0.7010572060.079144775XVariable10.9917643970.01353958473.2492536200.9651831691.018345625ANOVAdfSSMSFSignificanceFRegression136959.0257936959.025795365.4531550Residual7295021.5944536.888332583Total73041980.62025

### 11 Finally, based on the above results, it was determined that Site B, Site C and Site A have the best data, statistically signific ant, (greater correlation, adequate data completeness, and adequate paired data) from which to imput e missing data for Site D, Site E, and Site F, respectively. Theref ore, we will use multiple imputations and bootstrapping methods to generate t he 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 randoml y applies real residuals from the linear regression to the imputed current-period Si te D values. A 1000 bootst rap runs adds a random residual to the “best guess” missing values yielding the same conditional variance as the original variable. A summary of the bootstrappi ng statistics is pr esented 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 regre ssion from Site D vs. Site B:

Site D concentration = Intercept + x Variable * Site B concentration + bootstrapping residual

Where: Intercept = 1.56556640501966 (f rom 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 da ta 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 a nd 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

‐ 2010 2004 2005 2006 2007 2008 2009 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 randoml y 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 stat istics 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 regre ssion from Site E vs. Site C:

Site E concentration = Intercept + x Variable * Site C concentration + bootstrapping residual

Where: Intercept = 0.932587360923463 (f rom linear regression) x Variable = 0.94415203423236 (f rom linear regression) Bootstrapping residual = 0. 00185132118436311Site C concentration = values in Site C After applying the above equati on 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.

### 13 Table 9 – Comparison Between Original a nd 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

‐ 2010 2004 2005 2006 2007 2008 2009 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 randoml y 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 stat istics 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 regre ssion from Site F vs. Site A:

Site F concentration = Intercept + x Variable * Site A concentration + bootstrapping residual

Where: Intercept = -0.310956215392546 (f rom linear regression) x Variable = 0.991764397262166 (from linear regression) Bootstrapping resi dual = 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 mi ssing data. Also the “new” site (with imputed values) shows a “new” passing DV for 2008-2010.

### 14 Table 11 – Comparison Between Original a nd 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

‐ 2010 2004 2005 2006 2007 2008 2009 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 analysi s has generated the mo st statistically significant results yielding to the “best” missing data statisti cally possible. This analysis should also satisfy US EPA requirements in terms of total percentage data capture and design values under the PM 2.5 

### annual standard (15.0μg/m 3 

### ).

### 15 References:
Brownstone, David and Robert Valleta. 20 00. Draft for JEP Econometrics Symposium. “The bootstrap and multiple imputations”. Harrel, Frank E. Jr. 2001. Spri nger Series in Statistics. “R egression modeling strategies: with applications to linear models, logist ic regression, and survival analysis”. Mooney, Christopher Z., Robert D. Duval, Robert Duva l. 1993. Sage Publications Inc. “Bootstrapping: a nonparametri c approach to statistical in ference” 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 PM 2.5 

### 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 Environment al Protection Agency (U.S. EPA) revise the current air quality designation for the Cleveland-Akron-Lorain nonattainment area, including Ashtabula, Cu yahoga, Lake, Lorain, Medina, Portage and Summit counties to attainment with respect to the 1997 annual PM 2.5 

### national ambient air quality standard (NAAQS). Fine particle pollution or PM 2.5 

### is a mixture of microscopic solids and liquid droplets suspended in air that is 2.5 micrometers in diameter and smaller – 1/30 th 

### the diameter of a human hair. PM 2.5 

### can be emitted directly or formed secondarily in the atmosphere. Air qualit y monitoring data collected between 2008 and 2010 in the region demonstrate a ttainment of the NAAQS and t here is evidence that the improved air quality is due to permanent, enfor ceable emission reductions. In addition, existing requirements are suffici ent to maintain the 1997 annual PM 2.5 

### standard in this area at least ten years into the future. Computer models show that existing stat e 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 ex isting emission inventory information and projections of future emissions as the dem onstration of the ability to maintain the NAAQS in the Cleveland-Akron- Lorain area in the future.

The Cleveland-Akron-Lorain ar ea is currently designated as nonattainment for the 1997 annual PM 2.5 

### standard. As part of an acceptable maintenance plan, Ohio EPA is required to develop a contingency plan to prov ide for additional emission reductions if a violation of the NAAQS is monitored afte r the area has been redes ignated. The plan which Ohio EPA is proposing to USEPA as part of this redesignation contains reductions which will help alleviate the ambi ent problem until a re vised 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 PM 2.5 

### NAAQS and revise the maintenance plan. This request will document that existing enforceable control measures are responsible for the obs erved 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 allo w public comment and to satisfy USEPA requirements for public involvement in SIP re lated activities. This notice addresses Ohio EPA’s reliance on the emission projections as evidence of attainment and maintenance and the commitment to instit ute contingency measures if ambient exceedances or violations trigger the contingency plan r equirements. 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 conduc ted as follows September 23, 2011 at 2:30 PM, at the Twinsburg Library, 10050 Rav enna Road, Twinsburg, Ohio 44087.
All interested persons are entitled to att end 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 circumst ances 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.

### 1


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### 5 In re: OHIO ENVIRONMENTAL PROTECTION AGENCY
PUBLIC HEARING

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### Division of Air Pollution Control: Cleveland, Akron, Lorain PM 2.5 7 Redesignation Public Hearing

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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. 16
17
18 APPEARANCES:
19 Kristopher Weiss, Public Involvement Coordinator
20 Sarah VanderWie1en, Environmental Specialist II
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## fINCUN-MANCINI

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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 1


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# P38 e3

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

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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 My name is Amy MS. WAINRIGHT: Wainright. A-m-y, W-a-i-n-r-i-g-h-t. 1


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25 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



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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



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# P88 e7

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

Is Thanks very much. 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. 1

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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

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25 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.

# =~~C-.~ COURT REPORTER

FINCUN-MANCINI COURT REPORTERS 1801 East Ninth Street Suite 1720 Cleveland, Ohio 44114 (216) 696-2272 (216) 696-2275 FAX



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### Response to Comments PM 2.5 

### Redesignation Request for the 1997 PM 2.5 

### Annual Standard for the Cleveland, Akron, Lorain Area Agency Contact for this Package Division Contact: (Carolina Prado, Division of Air Po llution 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 PM 2.5 

### Nonattainment Area. This document summarizes the comments and questions received at the public hearing and during the associated co mment period, which ended on September 28, 2011. Ohio EPA review ed 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 doc ument, the questions are grouped by topic and organized in a consistent format. The name of the commenter follo ws the comment in parentheses.

Ohio EPA received oral testimony fr om the Northeast Ohio Areawide Coordinating Agency (NOACA) in support of the redesignation request for the 1997 PM 2.5 

### Annual Standard for the Clevela nd, Akron, Lorain Area. NOACA’s testimony can be found in this submittal.

End of comments