16

Population Data 

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

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

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

states modified construction data but was overlooked. 

SCC Data 

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

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

equipment) in NONROAD2004. 

Fuel Data 

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

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

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

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

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

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

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

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

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

years. 

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

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

the appropriate versions of the tables for the selected years. 

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

States 

Years 

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

LADCO states countynrfile_NMIM05_rev, 

countyyear_NMIM05_rev, 

countyyearmonth_NMIM05_w

_05_12_18_rev, 

datasource_NMIM05_rev, 

gasoline_NMIM05_def 

SCC_NCD20060201_rev 

(used when NCD20060201 is 

used) 

countynrfile_NMIM05_rev, 

countyyear_NMIM05_rev , 

countyyearmonth_NMIM05_w_05_12_18_rev, 

datasource_NMIM05_rev, 

gasoline_NMIM05_w_05_12_18_rev 

SCC_NCD20060201_rev (used when 

NCD20060201 is used)
17

SECTION 4

ON-ROAD SOURCES 

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

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

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

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

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

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

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

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

4.1 Categories of Mobile Sources 

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

categories. 

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

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

transportation of goods and passengers on roads and streets 

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

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

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

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

transportation, ranging from construction equipment and farm tractors to 

lawnmowers and hand-held power weed choppers. 

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

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

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

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

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

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

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

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

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

them. 
18

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

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

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

4.1.1 Categories and Components of Mobile Source Emissions 

There are three categories of mobile source emissions: 

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

source’s engine 

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

its fuel system 

• Refueling emissions 

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

is running. 

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

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

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

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

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

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

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

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

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

during refueling. 

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

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

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

emissions as defined above. 

Refueling emissions have two subcomponents: 

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

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

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

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

displacement emissions: 

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

service station are being refilled; 
19

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

refueled. 

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

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

during or after refueling and evaporate away. 

4.2 Ohio On-Road Mobile Source Inventory 

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

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

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

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

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

the summer of 2005. 

4.2.1 Emission Inventories Developed with MOBILE6 Model 

MOBILE6 Overview: 

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

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

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

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

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

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

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

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

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

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

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

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

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

operation in each calendar year. 

4.2.2 MOBILE6 Defaults: 

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

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

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

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

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

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

conditions vary considerably. 

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

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

4.2.3 MOBILE6 Input Parameters 

• Calendar year

• Month (January, July) 

• Hourly Temperature 

• Altitude (high, low) 

• Weekend/weekday 

• Fuel characteristics (Reid vapor pressure, sulfur content, 

 oxygenate content, etc.) 

• Humidity and solar load 

• Registration (age) distribution by vehicle class 

• Annual mileage accumulation by vehicle class 

• Diesel sales fractions by vehicle class and model year 

• Average speed distribution by hour and roadway 

• Distribution of vehicle miles traveled by roadway type 

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

• Engine start soak time distribution by hour 

• Trip end distribution by hour 

• Average trip length distribution 

• Hot soak duration 

• Distribution of vehicle miles traveled by vehicle class 

• Full, partial, and multiple diurnal distribution by hour 

• Inspection and maintenance (I/M) program description 

• Anti-tampering inspection program description 

• Stage II refueling emissions inspection program description 

• Natural gas vehicle fractions 

• HC species output 

• Particle size cutoff 

• Emissions factors for PM and HAP 

• Output format specifications and selections

4.2.4 MOBILE6 References 

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

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

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

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

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

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

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

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

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

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

various versions was developed and published by Assessment & Modeling Division 

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

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

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

Technical Guidance on the Use of MOBILE6 for Emission Inventory 

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

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

adjunct to the M6 User’s Guide. 

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

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

TGD. 

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

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

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

Directors. 

4.3 Ohio’s Alternate Data for MOBILE6 

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

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

local data as possible. 

4.3.1 Vehicle Registration Distribution by Age 

Overview: 

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

emissions control requirement standard and the deterioration of the emission control 

technology. 

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

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

totals. 
22

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

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

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

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

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

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

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

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

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

categories. 

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

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

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

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

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

distribution data from the Vehicle Identification Numbers (VIN). 

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

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

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

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

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

average. 

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

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

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

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

Advantages: 

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

local area than the national default. 

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

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

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

over the national default approach.
23

4.3.2 Daily Vehicle Miles Traveled (DVMT) 

Overview: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Transportation’s Roadway Information Files and the annual Highway Performance 

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

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

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

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

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

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

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

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

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

systematic growth. 

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

roads functionally classified as collector or above. 

Table 4-1 Federal Functional Class Categories: 

01 - Rural Interstate 

02 - Rural Principal Arterial 

06 - Rural Minor Arterial 

07 - Rural Major Collector 

08 - Rural Minor Collector 

09 - Rural Local 

11 - Urban Interstate 

12 - Urban Freeway & Expressway 

14 - Urban Principal Arterial 

16 - Urban Minor Arterial 

17 - Urban Collector 

19 - Urban Local 
24

Table 4-2 DVMT County Summary: 

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

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

Disclaimer by ODOT: 

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

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

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

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

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

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

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

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

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

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

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

assume responsibility for any incorrectness that may occur. 

4.3.3 VMT From Travel Demand Models (TDM) 

Overview: 

Travel demand forecast modeling is performed by the Ohio Metropolitan Planning 

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

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

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

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

to assure consistent methods are used for transportation conformity analysis and 

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

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

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

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

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

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

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

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

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

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

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

from which VMT is computed. 

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

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

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

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

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

transportation planners to evaluate the relative emissions affect of improvements to 

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

roadway improvement projects air quality impacts of construction programs. 
26

4.3.4 Speed Distribution Profiles 

Overview: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

used for addressing the one hour standard. 
27

Figure 1 - Speed Comparison Graphs 

Speeds Comparison

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

FC01

FC06

FC08

FC11

FC14

FC17

Functional Class

Speed

Average Speed

1990 Analysis

Speeds

2001 Speed Study

Speeds Comparison

0

20

40

60

80

100

FC01FC06FC08

FC11

FC14FC17

RAMPS

Functional Class

Speed

Average Speed

Min. Avg Speed

Max. Avg. Speed

Gebhardt's Speeds

Spd Study Est

Speeds

Spd Study Greg Est

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

Functional Class memo. 

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

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

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

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

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

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

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

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

documented procedures for emission inventory preparation. This document suggests 

post processing travel demand model traffic assignment results to estimate average 

speeds. 

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

Table 4-3 Speed by Federal Functional Class 

Functional Class 1990 Analysis 2001 Speed Study 

 Speeds Speeds 

FC01 57.3 64.0 

FC02 45.3 53.0 

FC06 39.9 53.0 

FC07 35.1 43.1 

FC08 30.5 43.1 

FC09 28.0 43.1 

FC11 46.3 61.6 

FC12 43.3 61.6 

FC14 18.9 29.3 

FC16 19.6 29.3 

FC17 19.6 29.2 

FC19 19.6 23.8 

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

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

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

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

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

Miles Traveled (DVMT). 

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

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

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

4.3.5 Link Group Speed Curves: 

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

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

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

5.3.2 and to assure consistent methods are used for transportation conformity 

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

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

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

Table 2 shows these link group curves. 

Table 4-4 Link Group Codes & Associated BPR Curves 

Link 

Group 

Facility 

Type 

Free Flow 

Speed 

Areatype a b 

1 Freeway 75 Any 0.39 6.3 

2 Freeway 70 Any 0.32 7.0 

3 Freeway 65 Any 0.25 9.0 

4 Freeway 60 Any 0.18 8.5 

5 Freeway 55 Any 0.10 10.0 

6 Multi-Lane 60 Rural 0.09 6.0 

7 Multi-Lane 55 Rural 0.08 6.0 

8 Multi-Lane 50 Rural 0.07 6.0 

9 Multi-Lane 45 Rural 0.07 6.0 

10 2 Lane Any Rural 0.34 4.0 

10 Urban 

Street 

50 Suburban 0.34 4.0 

11 Urban 

Street 

50 Urban 0.74 5.0 

12 Urban 

Street 

50 CBD 1.16 6.0 

13 Urban 

Street 

40 Suburban 0.38 5.0 

14 Urban 

Street 

40 Urban 0.70 5.0 

15 Urban 

Street 

40 CBD 1.00 5.0 

16 Urban 

Street 

35 Suburban 0.96 5.0 

17 Urban 

Street 

35 Urban 1.00 5.0 

18 Urban 

Street 

35 CBD 1.40 5.0 

19 Urban 

Street 

30 Suburban 1.11 5.0 

20 Urban 

Street 

30 Urban 1.20 5.0 

21 Urban 

Street 

30 CBD 1.50 5.0 

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

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

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

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

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

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

(Microsoft Word document) 

4.4 Mobile6 Inputs: 

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

inventory. 

4.4.1 Ohio’s 2005 MOBILE6 Inputs 

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

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

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

continued between the parties. 

For historical reference: 

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

All, 

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

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

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

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

submitting the 2002 inventories to US EPA. 

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

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

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

DM 
31

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

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

of Air Pollution Control. 

Table 4-5 Mobile Inputs 

Cleveland-Akron 2005 Ozone M6.2 Inputs 

Includes the following counties: 

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

 

State Programs 

Input 

 

Stage II Refueling 93/3/86/86 

 

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

I/M Programs Yes 

Exclude Ashtabula County - No I/M program 

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

Model Years 1 1996 2050 

Vehicles 1 22222 21111111 1 

Stringency 1 30.0 

Compliance 1 98.0 

Waiver Rates 1 1.0 1.0 

Cutpoints 

Exemption Age 1 25 

Grace Period 1 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

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

Model Years 2 1996 2050 

Vehicles 2 22222 11111111 1 

Stringency 

Compliance 2 98.0 

Waiver Rates 2 1.0 1.0 

Cutpoints 

Exemption Age 2 25 

Grace Period 2 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

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

Model Years 3 1996 2003 

Vehicles 3 22222 21111111 1 

Stringency 3 30.0 

Compliance 3 98.0 

Waiver Rates 3 3.0 1.0 

Cutpoints 

Exemption Age 3 25 

Grace Period 3 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

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

Model Years 4 1975 1995 

Vehicles 4 22222 21111111 1 

Stringency 4 30.0 

Compliance 4 98.0 

Waiver Rates 4 3.0 1.0 

Cutpoints 

Exemption Age 4 25 

Grace Period 4 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 5 1998 2000 2 T/O LOADED/IDLE 

Model Years 5 1975 2000 

Vehicles 5 22222 21111111 1 

Stringency 5 30.0 

Compliance 5 98.0 

Waiver Rates 5 3.0 1.0 

Cutpoints 

Exemption Age 5 25 

Grace Period 5 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 6 1996 1997 2 T/O IM240 

Model Years 6 1975 1997 

Vehicles 6 22222 21111111 1 

Stringency 6 30.0 

Compliance 6 98.0 

Waiver Rates 6 3.0 1.0 

Cutpoints 6 CUTPOINT.D 

Exemption Age 6 25 

Grace Period 6 2 

NO TTC Credits 

Effectiveness 

DESC file 
33

 

Program 7 1996 2050 2 T/O GC 

Model Years 7 1975 1995 

Vehicles 7 22222 21111111 1 

Stringency 

Compliance 7 98.0 

Waiver Rates 7 3.0 1.0 

Cutpoints 

Exemption Age 7 25 

Grace Period 7 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Fuel Commands 

 

Fuel Program 1 

 

Oxygenated Fuels 0.00 0.42 0.00 0.036 2 

 

Fuel RVP 9 

 

Alternative Emission Regulations and Control Measures 

Rebuild Effects 0.1 

 

External Conditions Commands 

 

Calendar Year 2005 

 

Evaluation Month 7 

 

Min/Max Temperature National Climatic Data Center 

 

Vehicle Fleet Characteristic Commands 

 

Registration Distribution Variable 

 
34

Cincinnati-Dayton-Springfield 2005 Ozone M6.2 Inputs 

Includes the following counties: 

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

Warren

Indiana: Lawrenceburg Twp., Dearborn County

Kentucky: Boone, Campbell and Kenton counties

State Programs 

Input 

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

 

Stage II Refueling 93/3/86/86 

 

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

I/M Programs Yes 

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

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

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

Model Years 1 1996 2050 

Vehicles 1 22222 21111111 1 

Stringency 1 30.0 

Compliance 1 98.0 

Waiver Rates 1 1.0 1.0 

Cutpoints 

Exemption Age 1 25 

Grace Period 1 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

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

Model Years 2 1996 2050 

Vehicles 2 22222 11111111 1 

Stringency 

Compliance 2 98.0 

Waiver Rates 2 1.0 1.0 

Cutpoints 

Exemption Age 2 25 

Grace Period 2 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

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

Model Years 3 1996 2003 

Vehicles 3 22222 21111111 1 

Stringency 3 30.0 

Compliance 3 98.0 
35

Waiver Rates 3 3.0 1.0 

Cutpoints 

Exemption Age 3 25 

Grace Period 3 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

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

Model Years 4 1975 1995 

Vehicles 4 22222 21111111 1 

Stringency 4 30.0 

Compliance 4 98.0 

Waiver Rates 4 3.0 1.0 

Cutpoints 

Exemption Age 4 25 

Grace Period 4 4 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 5 1998 2000 2 T/O LOADED/IDLE 

Model Years 5 1975 2000 

Vehicles 5 22222 21111111 1 

Stringency 5 30.0 

Compliance 5 98.0 

Waiver Rates 5 3.0 1.0 

Cutpoints 

Exemption Age 5 25 

Grace Period 5 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 6 1996 1997 2 T/O IM240 

Model Years 6 1975 1997 

Vehicles 6 22222 21111111 1 

Stringency 6 30.0 

Compliance 6 98.0 

Waiver Rates 6 3.0 1.0 

Cutpoints 6 CUTPOINT.D 

Exemption Age 6 25 

Grace Period 6 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

Program 7 1996 2050 2 T/O GC 

Model Years 7 1975 1995 

Vehicles 7 22222 21111111 1 
36

Stringency 

Compliance 7 98.0 

Waiver Rates 7 3.0 1.0 

Cutpoints 

Exemption Age 7 25 

Grace Period 7 2 

NO TTC Credits 

Effectiveness 

DESC file 

 

 

Fuel Commands 

 

Fuel Program 1 

 

Oxygenated Fuels 0.00 0.42 0.00 0.036 2 

 

Fuel RVP 9 

 

Alternative Emission Regulations and Control Measures 

Rebuild Effects 0.1 

 

External Conditions Commands 

 

Calendar Year 2005 

 

Evaluation Month 7 

 

Min/Max Temperature National Climatic Data Center 

 

Vehicle Fleet Characteristic Commands 

 

Registration Distribution Variable 

 
37

 

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

Inputs 

Includes the following counties: 

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

Lucas,Madison, Mahoning, Pickaway, Trumball, Wood 

 

Fuel Commands 

Input 

 

Fuel Program 1 

Oxygenated Fuels 0.00 0.42 0.00 0.036 2 

Fuel RVP 9 

Alternative Emission Regulations and Control Measures 

 

Rebuild Effects 0.1 (0.30 for 2018) 

 

External Conditions Commands 

 

Calendar Year All 

 

Evaluation Month 7 

Min/Max Temperature National Climatic Data Center 

Vehicle Fleet Characteristic Commands 

Registration 

Distribution Variable 

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

Director’s Consortium): 

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

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

T3 and ConCEPT are described as follows. 
38

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

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

quality modeling to develop the necessary technical support for new State 

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

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

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

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

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

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

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

approximately twenty transportation networks using a variety of models, applies 

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

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

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

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

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

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

detailed on-road emission inventories. 

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

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

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

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

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

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

dimensional transformation approach, where the dimensions of the various 

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

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

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

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

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

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

The new CONCEPT (CONsolidated Community Emissions Processing Tool) 

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

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

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

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

the following features: 

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

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

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

modeling system. 

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

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

encourage user participation in the customizing the system for specific modeling 

requirements. 

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

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

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

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

The CONCEPT model includes modules for the major emissions source 

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

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

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

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

modeling community has already begun development of additional CONCEPT support 

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

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

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

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

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

janssen@ladco.org 

4.5.3 LADCO Ohio Data Outputs for 2005: 

The following LADCO outputs and documents can be found at: 

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

Table 4-6 LADCO Data Output 

Sta

te Network

T3 

Descripti

on DOC

M6 

Inpu

ts

VM

T vs 

HP

MS 

Exc

el

Average 

Day 

VMT 

(this 

should 

match 

conformi

ty 

inventory

)

Co

unt

y 

Em

issi

on

s 

Re

por

t

All 

Pollut

ants 

After 

Speci

ation

Dropped 

VMT

M6 

Run 

Summ

ary

Pollut

ant 

Totals 

(Short

)

Raw 

Summ

ary 

(pol,ve

h, 

etype)

Hourly 

Tempo

ral 

Summ

ary

Hourly 

Veh 

Mix 

Summ

ary

Hourly 

Speed 

Summ

ary 

(with 

volum

e/ 

capaci

ty) 

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

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

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

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

OH COLUMB

US

COLUMB

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

OH SPRING

FLD

SPRING

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

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

OH YNGSTO

WN

YNGSTO

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

OH STATEW

D

STATEW

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

Additional Documents on LADCO Web Page: 

“Comparison to EPA's Default Model NMIM”

“How we Build the 2005 Vmt/Networks”

“Spreadsheet/Graphics on Vehicle Mix”

“Background on the T3 Tool “ 

4.6 MPO Contact Table 

Table 4-7 County Summary Table of MPOs by County 

FIPS County Model Network MPO Contact Person 

39001 Adams Statewide 

39003 Allen Statewide 

Lima Allen County Regional 

Planning Commission 

Tom Mazur 

39005 Ashland Statewide 

39007 Ashtabula Statewide 

39009 Athens Statewide 

39011 Auglaize Statewide 

39013 Belmont Statewide 

Bel-O-Mar Regional Council 

and Interstate Planning 

Commission 

Rakesh Sharma 

39015 Brown Statewide 

39017 Butler Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser 

39019 Carroll Statewide 

39021 Champaign Statewide 

39023 Clark Springfield 

Coordinating Committee of 

the Clark County-Springfield 

Eric Ottoson 
41

Transportation Study 

39025 Clermont Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser 

39027 Clinton Statewide 

39029 Columbiana Statewide 

39031 Coshocton Statewide 

39033 Crawford Statewide 

39035 Cuyahoga Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39037 Darke Statewide 

39039 Defiance Statewide 

39041 Delaware Columbus 

Mid-Ohio Regional Planning 

Commission 

Nick Gill 

39043 Erie Statewide 

39045 Fairfield Statewide 

39047 Fayette Statewide 

39049 Franklin Columbus 

Mid-Ohio Regional Planning 

Commission 

Nick Gill 

39051 Fulton Statewide 

39053 Gallia Statewide 

39055 Geauga Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39057 Greene Cincinnat/Dayton 

Miami Valley Regional 

Planning Commission 

Ana Ramirez 

39059 Guernsey Statewide 

39061 Hamilton Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser 

39063 Hancock Statewide 

39065 Hardin Statewide 

39067 Harrison Statewide 

39069 Henry Statewide 

39071 Highland Statewide 

39073 Hocking Statewide 

39075 Holmes Statewide 

39077 Huron Statewide 

39079 Jackson Statewide 

39081 Jefferson Statewide 

Brooke-Hancock-Jefferson 

Transportation Study Policy 

Committee 

Mike Proprocki 

39083 Knox Statewide 

39085 Lake Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39087 Lawrence Statewide 

KYOVA Interstate Planning 

Commission 

39089 Licking Columbus 

Licking County Area 

Transportation Study 

Matthew Hill 

39091 Logan Statewide 

39093 Lorain Cleveland Northeast Ohio Areawide Bill Davis 
42

Coordinating Agency 

39095 Lucas Toledo 

Toledo Metropolitan Area 

Council of Governments 

Sujatha Mohanakrishnan 

39097 Madison Statewide 

39099 Mahoning Youngstown 

Eastgate Regional Council 

of Governments 

R.P. Samulka 

39101 Marion Statewide 

39103 Medina Cleveland 

Northeast Ohio Areawide 

Coordinating Agency 

Bill Davis 

39105 Meigs Statewide 

39107 Mercer Statewide 

39109 Miami Cincinnat/Dayton 

Miami Valley Regional 

Planning Commission 

Ana Ramirez 

39111 Monroe Statewide 

39113 Montgomery Cincinnat/Dayton 

Miami Valley Regional 

Planning Commission 

Ana Ramirez 

39115 Morgan Statewide 

39117 Morrow Statewide 

39119 Muskingum Statewide 

39121 Noble Statewide 

39123 Ottawa Statewide 

39125 Paulding Statewide 

39127 Perry Statewide 

39129 Pickaway Statewide 

39131 Pike Statewide 

39133 Portage Akron 

Akron Metropolitan Area 

Transportation Study 

Jason Segedy 

39135 Preble Statewide 

39137 Putnam Statewide 

39139 Richland Statewide 

Richland County Regional 

Planning Commission 

John Adams 

39141 Ross Statewide 

39143 Sandusky Statewide 

39145 Scioto Statewide 

39147 Seneca Statewide 

39149 Shelby Statewide 

39151 Stark Canton 

Stark County Regional 

Planning Commission 

Dan Slicker 

39153 Summit Akron 

Akron Metropolitan Area 

Transportation Study 

Jason Segedy 

39155 Trumbull 

Youngstown (partial 

county model 

coverage) 

Eastgate Regional Council 

of Governments 

R.P. Samulka 

39157 Tuscarawas Statewide 

39159 Union Statewide 

39161 Van Wert Statewide 

39163 Vinton Statewide 

39165 Warren Cincinnat/Dayton 

Ohio-Kentucky-Indiana 

Regional Council of 

Governments 

Andy Reser, OKI 

+ 
43

+ 

Miami Valley Regional 

Planning Commission 

Ana Ramirez, MVRPC 

39167 Washington Statewide 

Wood-Washington-Wirt 

Interstate Planning 

Commission 

39169 Wayne Statewide 

39171 Williams Statewide 

39173 Wood Toledo 

Toledo Metropolitan Area 

Council of Governments 

Sujatha Mohanakrishnan 

39175 Wyandot Statewide 

NOTE: Complete MPO information can be found at, 

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

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

SECTION 5

MARINE, AIRCRAFT and RAIL (MAR) SOURCES 

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

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

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

locomotive inventory is generated by Environ1

 under contract with LADCO and the 

aircraft inventory is generated by Ohio EPA. 

5.1 Marine Vessel Sources 

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

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

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

basis of the number of gallons of fuel consumed. 

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

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

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

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

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

subcategories are detailed in the complete inventory prepared and published by 

ENVIRON International Corporation: LADCO 2005 COMMERCIAL MARINE 

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

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

Ohio’s State Implementation Plan (SIP). 
44

5.2 Rail Sources 

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

Emission rates were derived from EPA documents provided as support documentation 

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

were based on rail activity. 

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

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

railroads. And Passenger rail: AMTRAK. 

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

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

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

Lindhjem, February, 2007. 

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

Ohio’s State Implementation Plan (SIP). 

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

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

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

5.3 Aircraft Sources 

INTRODUCTION: 

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

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

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

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

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

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

a description of an Access based aircraft emission calculator. 

COMPONENTS: 

 I. Ohio Airports: 

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

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

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

airports in Ohio. See Table 1. 
45

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

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

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

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

operations by two. These were totaled by county. 

Table 5-1 Number of operations and LTOs for 2005

County Airport Name ID Total Operations LTOs/Year 

Adams Alexander Salamon AMT 5210 2605 

Allen Allen County AOH 32500 16250 

Ashland Ashland County 3G4 49240 24620 

Ashtabula Ashtabula County HZY 16886 8443 

Ashtabula Germack 7D9 840 420 

Athens Ohio University UNI 51600 25800 

Auglaize Neil Armstrong AXV 29456 14728 

Belmont Barnesville-Bradfield 6G5 10150 5075 

Belmont Alderman 2P7 6150 3075 

Brown Brown County GEO 5157 2578.5 

Butler Butler County Regional HAO 61687 30843.5 

Butler Hook Field Municipal MWO 40050 20025 

Butler Miami University OXD 16708 8354 

Carroll Carroll County -Tolson TSO 34950 17475 

Carroll Parsons 5D6 2674 1337 

Champaign Grimes Field I74 23480 11740 

Champaign Weller 38I 300 150 

Clark Springfield-Beckley Municipal SGH 64033 32016.5 

Clark Mad River I54 15350 7675 

Clermont Clermont County I69 35741 17870.5 

Clinton Airborne Airpark ILN 52000 26000 

Clinton Clinton Field I66 29360 14680 

Clinton Hollister Field 2B6 161 80.5 

Columbiana Columbiana County Ø2G 31146 15573 

Columbiana Koons 8G8 2546 1273 

Coshocton Richard Downing I4Ø 19550 9775 

Coshocton Tri-City 8ØG 8085 4042.5 

Crawford Port Bucyrus 17G 24871 12435.5 

Crawford Galion Municipal GQQ 5216 2608 

Cuyahoga Burke Lakefront BKL 97100 48550 

Cuyahoga Cleveland-Hopkins International CLE 234356 117178 

Cuyahoga Cuyahoga County CGF 79774 39887 

Darke Darke County VES 9238 4619 

Defiance Defiance Memorial DFI 9130 4565 

Delaware Delaware Municipal DLZ 39300 19650 

Delaware Packer 5E9 3181 1590.5 

Erie Hinde 88D 1350 675 

Erie Kelleys Island 89D 25495 12747.5 

Erie Griffing-Sandusky SKY 112100 56050 
46

Erie Wakeman I64 17324 8662 

Fairfield Miller's Farm 7B4 360 180 

Fairfield Fairfield County LHQ 43066 21533 

Fayette Fayette County I23 29405 14702.5 

Franklin Ohio State University OSU 134459 67229.5 

Franklin Port Columbus International CMH 218438 109219 

Franklin Rickenbacker International LCK 96200 48100 

Franklin Bolton Field TZR 69149 34574.5 

Franklin Columbus Southwest Ø4I 11833 5916.5 

Franklin Darby Dan 6I6 11260 5630 

Fulton Fulton County USE 21123 10561.5 

Gallia Gallia-Meigs Regional GAS 12200 6100 

Geauga Gates 7D8 4200 2100 

Geauga Geauga County 7G8 5350 2675 

Greene Greene County - Lewis A. Jackson I19 37400 18700 

Greene Bloom 14I 100 50 

Guernsey Cambridge Municipal CDI 6040 3020 

Hamilton Lunken LUK 129430 64715 

Hamilton Blue Ash ISZ 35000 17500 

Hamilton Cincinnati West I67 30197 15098.5 

Hancock Bluffton 5G7 71980 35990 

Hancock Findlay FDY 19800 9900 

Hancock Priebe 7D5 3850 1925 

Hardin Ada ØD7 331 165.5 

Hardin Hardin County I95 6562 3281 

Hardin Elliott's Landing O74 1560 780 

Harrison Harrison County 8G6 11900 5950 

Henry Henry County 7W5 15637 7818.5 

Highland Highland County HOC 18325 9162.5 

Holmes Holmes County 1ØG 21400 10700 

Huron Huron County 5A1 10100 5050 

Huron Willard 8G1 2715 1357.5 

Jackson James A. Rhodes I43 6053 3026.5 

Jefferson Jefferson County Airpark 2G2 15969 7984.5 

Jefferson Eddie Dew Memorial 1G8 3540 1770 

Knox Knox County 4I3 20150 10075 

Knox Wynkoop 6G4 4691 2345.5 

Lake Concord Airpark 2G1 4510 2255 

Lake Willoughby Lost Nation Municipal LNN 45085 22542.5 

Lawrence Lawrence County Airpark HTW 41910 20955 

Licking Newark-Heath VTA 12457 6228.5 

Logan Bellefontaine Regional EDJ 8325 4162.5 

Lorain Columbia 4G8 5150 2575 

Lorain Elyria 1G1 14300 7150 

Lorain Lagrange 92D 1155 577.5 

Lorain Lorain County Regional LPR 62000 31000 

Lorain Reader-Botsford Airfield 67D 18700 9350 

Lucas Toledo Express TOL 94600 47300 
47

Madison Madison County UYF 41410 20705 

Mahoning Salem Airpark 38D 16920 8460 

Mahoning Tri-City 3G6 10555 5277.5 

Mahoning Elser Metro 4G4 49232 24616 

Mahoning Lansdowne Ø4G 750 375 

Marion Marion Municipal MNN 42650 21325 

Medina Medina Municipal 1G5 79685 39842.5 

Medina Wadsworth Municipal 3G3 41025 20512.5 

Medina Weltzien Skypark 15G 79130 39565 

Mercer Lakefield CQA 16212 8106 

Miami Hartzell Field I17 10200 5100 

Miami Troy Skypark 37I 4264 2132 

Miami Waco Field 1WF 0 0 

Monroe Monroe County 4G5 3324 1662 

Montgomery Brookville Air-Park I62 29359 14679.5 

Montgomery James M. Cox Dayton Intl DAY 134524 67262 

Montgomery Dayton Wright Brothers MGY 89045 44522.5 

Montgomery Moraine Airpark I73 12938 6469 

Montgomery Dahio Trotwood I44 1853 926.5 

Montgomery Phillipsburg 3I7 68000 34000 

Morgan Morgan County I71 5725 2862.5 

Morrow Morrow County 4I9 19108 9554 

Muskingum Zanesville Municipal ZZV 33312 16656 

Muskingum Parr 42I 16150 8075 

Noble Noble County - Mike Brienza Field I1Ø 5950 2975 

Ottawa Middle Bass-East Point 3W9 1300 650 

Ottawa Middle Bass Island 3T7 6500 3250 

Ottawa North Bass Island 3X5 1000 500 

Ottawa Carl R. Keller Field PCW 20890 10445 

Ottawa Put-In-Bay 3W2 15140 7570 

Paulding Paulding 2H8 2100 1050 

Perry Crooksville I84 400 200 

Perry Perry County I86 4550 2275 

Pickaway Pickaway County CYO 35450 17725 

Pickaway Clarks Dream Strip Ø3I 2770 1385 

Pike Pike County EOP 2012 1006 

Portage Freedom Air Field 7D6 1623 811.5 

Portage Farview 86D 3353 1676.5 

Portage Mills 7E3 1050 525 

Portage Portage County 29G 9621 4810.5 

Putnam Ruhe's R47 13250 6625 

Putnam Putnam County OWX 11910 5955 

Putnam Ohio Dusting Co. 6C2 2995 1497.5 

Richland Mansfield Lahm Regional MFD 57518 28759 

Richland Shelby Community 12G 2012 1006 

Ross Ross County RZT 50150 25075 

Sandusky Fremont 14G 37450 18725 

Sandusky Sandusky County Regional S24 6148 3074 
48

Scioto Greater Portsmouth Regional PMH 45830 22915 

Seneca Bandit Field 5D9 140 70 

Seneca Fostoria Metropolitan FZI 7900 3950 

Seneca Weiker 82D 320 160 

Seneca Seneca County 16G 60165 30082.5 

Shelby Sidney Municipal I12 20500 10250 

Stark Barber 2D1 13750 6875 

Stark Miller Airport 4G3 8000 4000 

Stark Beach City 2D7 6112 3056 

Summit Akron Fulton International AKR 26000 13000 

Summit Akron-Canton Regional CAK 120441 60220.5 

Summit Mayfield 1D4 450 225 

Summit Kent State University 1G3 72500 36250 

Trumbull Braceville 41N 425 212.5 

Trumbull Warren 62D 14738 7369 

Trumbull Youngstown-Warren Regional YNG 98298 49149 

Tuscarawas Harry Clever Field PHD 54880 27440 

Union Union County MRT 31886 15943 

Van Wert Van Wert County VNW 20516 10258 

Vinton Vinton County 22I 5225 2612.5 

Warren Warren County I68 24951 12475.5 

Warren Red Stewart Airfield 4ØI 16800 8400 

Wayne Wayne County BJJ 96520 48260 

Williams Williams County ØG6 10010 5005 

Wood Wood County 1GØ 27405 13702.5 

Wood Bordner 3D8 2200 1100 

Wood Deshler Municipal 6D7 2000 1000 

Wood Metcalf TDZ 90700 45350 

Wyandot Wyandot County 56D 7410 3705 

III. Aircraft Models and Number of LTO/yr: 

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

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

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

airport. 

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

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

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

of services/LTOs. 

 
49

TOTAL DEPARTURES PERFORMD

Aircraft Scheduled Non-Sched All 

Model Service Service Service 

A-318 1 1 

A319 384 384 

BOEING 717-200 3850 3850 

BOEING 727-100 1 1 

BOEING 727-200 5 5 

BOEING 737-100/200 74 74 

BOEING 737-200C 3 3 6 

BOEING 737-300 2 2 

BOEING 737-700/LR 212 212 

BOEING 737-800 5 5 

BOEING 757-200 4 4 

BOEING 767-300/ER 4 4 

CANADAIR RJ-100/ER 542 3 545 

CANADAIR RJ-700 2881 2881 

CONVAIR CV-580 3 3 

DASSAULT FALCON 7 7 

DHC8-100 DASH 8 2 2 

DOUGLAS DC-9-15F 13 13 

DOUGLAS DC-9-30 6 6 

EMBRAER-145 144 144 

RJ-200ER/RJ-440 5519 5519 

SAAB-FAIRCHD 340/B 1804 1804 

ALL TYPES 15342 130 15472 

IV. Emission Factors: 

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

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

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

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

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

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

to calculate pollutant concentrations in these environments. 
50

Table 5-2 EDMS Aircraft Emissions 

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

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

EDMS Aircraft Emissions/LTO by Aircraft Type 

EDMS 4.5 Emissions Inventory Report of 2005 Aircraft Inventory Emissions Factors

 

Year 2005 Lbs Emitted Per LTO 

Aircraft Type CO NOx HC VOC SO2 PM2.5 PM10 

A- 318 19.8 18.7 4.0 4.4 1.5 0.0 0.0 

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

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

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

A-319 19.8 18.7 4.0 4.4 1.5 0.0 0.0 

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

A-321 16.8 36.8 3.1 3.3 2.2 0.0 0.0 

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

AVROLINER RJ85 24.7 9.5 2.9 3.3 1.3 0.2 0.2 

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

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

BEECH KINGAIR C90 1.8 0.9 0.2 0.2 0.2 0.0 0.0 

717-200 11.7 23.4 0.0 0.0 1.5 0.0 0.0 

727-100 21.4 23.1 4.6 5.1 2.9 0.4 0.4 

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

727-200 19.6 27.3 2.9 3.1 3.3 1.1 1.1 

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

737-200C 13.9 17.4 6.8 7.5 2.0 0.9 0.9 

737-300 28.7 15.9 1.8 2.0 1.8 0.0 0.0 

737-400 26.5 18.5 1.5 1.5 1.8 0.0 0.0 

737-500 24.7 21.2 1.3 1.3 2.0 0.0 0.0 

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

737-800 15.7 27.1 1.5 1.8 2.0 0.7 0.7 

737-900 15.7 27.1 1.5 1.8 2.0 0.7 0.7 

747-100 252.6 108.5 106.7 116.8 7.1 0.4 0.4 

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

747-400 67.0 105.6 5.7 6.4 7.3 0.7 0.7 

757-200 24.7 35.7 2.0 2.2 2.6 0.4 0.4 

757-300 27.1 33.1 0.4 0.4 3.1 0.2 0.2 

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

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

777 32.8 85.1 5.1 5.7 4.4 0.4 0.4 

CANADAIR RJ100/ER 16.3 4.9 1.5 1.8 0.7 0.0 0.0 

CANADAIR RJ-700 12.6 9.3 0.0 0.0 1.1 0.0 0.0 

CESSNA 208 1.1 0.4 0.0 0.0 0.0 0.0 0.0 

CONVAIR CV-580 36.2 0.9 8.8 9.5 0.7 0.0 0.0 

DASSAULT FALCON 13.7 2.6 2.4 2.6 0.2 0.0 0.0 

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

DORNIER 328 JET 1.3 6.6 11.9 12.6 0.7 0.0 0.0 

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

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

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

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

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

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

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

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

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

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

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

EMBRAER-135 12.8 5.5 1.1 1.3 0.7 0.0 0.0 

EMBRAER-140 13.7 6.0 1.3 1.3 0.7 0.0 0.0 

EMBRAER-145 6.4 6.8 1.1 1.1 0.7 0.0 0.0 

EMBRAER-170 9.0 9.7 0.0 0.0 1.1 0.0 0.0 

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

JETSTREAM 41 4.6 2.0 0.4 0.7 0.2 0.0 0.0 

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

LEAR-25 75.2 0.7 7.9 8.4 0.2 0.0 0.0 

LOCKHEED L100-30 48.7 9.9 19.6 21.4 1.8 0.0 0.0 

MD-11 47.8 93.3 4.0 4.4 6.0 0.7 0.7 

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

MD-90 12.1 23.8 0.2 0.2 2.0 0.2 0.2 

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

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

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

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

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

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

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

eliminate double counting those LTOs. 

Table 5-3 Fleet Emission Factor Categories 

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

General, and Military. 

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

(Taken from : Table A-5) 

Pollutant Emission Factors (lbs/LTO) 

HC 1.234 

NOx 0.158 

CO 28.13 

SOx 0.015 

PM10 0.60333 

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

 
52

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

(Taken From: Table A-11) 

Pollutant Emission Factors (lbs/LTO) 

HC 0.394 

NOx 0.065 

CO 12.014 

SOx 0.01 

PM10 0.2367 

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

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

(Taken from: Table A-17) 

Pollutant Emission Factors (lbs/LTO

HC 1.234 

NOx 0.158 

CO 28.13 

SOx 0.015 

PM10 0.60333 

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

METHODOLOGY 

Introduction 

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

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

commercial air freight activity by aircraft model types, 

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

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

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

(LTO) cycles, the following assumptions were made: 

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

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

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

yearly LTO cycles by 365. 

 

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

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

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

were obtained by dividing these annual totals by 365. 

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

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

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

dividing these annual totals by 365. 

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

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

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

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

Emissions and Dispersion Modeling System. Commercial and air freight aircraft 

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

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

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

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

EDMS model simulations. 

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

aircraft emission factors based upon EPA alternative fleet average procedures were 

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

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

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

compounds was performed and based upon the EPA guidance. 

APPROACH 

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

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

the total number of operations per year. 

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

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

the total number of LTOs per year, per county. 

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

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

emissions. 
54

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

Local (General aviation local) 

Military (Military) 

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

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

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

double counting those LTOs. 

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

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

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

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

were used. 

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

Table 5-4 Pollutant by County (Sample)

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

ADAMS CO 2605 7.136986 24.552325 

ADAMS HC 2605 7.136986 0.977285 

ADAMS NOX 2605 7.136986 0.136045 

ADAMS PM10-PRI 2605 7.136986 0.513114825 

ADAMS PM25-PRI 2605 7.136986 0 

ADAMS SOX 2605 7.136986 0.0157875 

ADAMS VOC 2605 7.136986 0.96314234 

ALLEN CO 16250 44.52055 147.945065 

ALLEN HC 16250 44.52055 5.841097 

ALLEN NOX 16250 44.52055 0.827189 

ALLEN PM10-PRI 16250 44.52055 3.081191265 

ALLEN PM25-PRI 16250 44.52055 0 

ALLEN SOX 16250 44.52055 0.0984575 

ALLEN VOC 16250 44.52055 5.769568254 

ASHLAND CO 24620 67.45205 173.67794 

ASHLAND HC 24620 67.45205 6.19414 

ASHLAND NOX 24620 67.45205 0.94895 

ASHLAND PM10-PRI 24620 67.45205 3.532515 

ASHLAND PM25-PRI 24620 67.45205 0 

ASHLAND SOX 24620 67.45205 0.1311 

ASHLAND VOC 24620 67.45205 6.07175247 

ASHTABULA CO 8863 24.28219 78.115087 

ASHTABULA HC 8863 24.28219 3.042551 

ASHTABULA NOX 8863 24.28219 0.431593 
55

ASHTABULA PM10-PRI 8863 24.28219 1.623493455 

ASHTABULA PM25-PRI 8863 24.28219 0 

ASHTABULA SOX 8863 24.28219 0.0520325 

ASHTABULA VOC 8863 24.28219 2.99420213 

ATHENS CO 25800 70.68493 196.874985 

ATHENS HC 25800 70.68493 7.267183 

ATHENS NOX 25800 70.68493 1.081521 

ATHENS PM10-PRI 25800 70.68493 4.037266335 

ATHENS PM25-PRI 25800 70.68493 0 

ATHENS SOX 25800 70.68493 0.1420925 

ATHENS VOC 25800 70.68493 7.128180006 

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

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

DATA ERROR 

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

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

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

instead of LTOs....which made the inventory exactly twice as large as what it really was. 

ACCESS CALCULATOR 

Introduction: 

Our database programmer set up Access application to calculate Ohio’s aircraft 

emission inventory, and export those results to Excel. His utility allows for easy 

modification of the aircraft data to match future data scenarios. Output to Excel also 

allows for additional data manipulation and importation. 
56

Interface: 

Through this interface the following sets of data can be edited/updated: Airport activity, 

emission factors, specific airport emissions, state/county FIPS, detailed aircraft 

information, airport information, and aircraft SCCs. Once the final emissions have been 

calculated and summed, then the data can be export via the export function on the Main 

Menu. 
Mobile Source Emissions Inventory 

for Cincinnati PM2.5 Nonattainment 

Area 

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

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

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

developed in support of the PM2.5 State Implementation Plan 

August 2010 

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

for Air Quality and the Ohio Environmental Protection Agency by 

OKI Regional Council of Governments

Acknowledgments 

Title Mobile Source Emissions Inventory for Cincinnati PM2.5 Nonattainment 

Area 

Abstract This report was prepared for the Indiana Department of Environmental 

Management, the Kentucky Division for Air Quality and the Ohio 

Environmental Protection Agency. The Cincinnati PM2.5 nonattainment 

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

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

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

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

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

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

rates. 

Date August 2010 

Agency Ohio-Kentucky-Indiana Regional Council of Governments 

 Mark Policinski, Executive Director 

 Robert Koehler, P.E., Deputy Director 

 

Project Manager Andrew J. Reser, AICP 

Project Staff Harikishan Perugu, PTP 

 Larry Buckler 

 

 

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

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

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

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

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

not constitute a standard, specification, or regulation. 

MOBILE Source Emissions Inventory for the 

Cincinnati PM2.5 nonattainment area 

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

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

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

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

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

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

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

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

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

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

PM2.5 brakewear, NOx

 and SO2. 

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

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

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

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

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

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

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

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

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

emissions do not exceed the established budget. 

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

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

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

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

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

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

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

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

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

Year Pollutant Name DailyEmissions AnnualEmissions

Kentucky Portion of NA Area

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

Oxides of Nitrogen 39.10 13,496.54

Primary Exhaust PM2.5 - Total 1.36 466.23

Primary PM2.5 - Brakewear Particulate 0.16 54.04

Primary PM2.5 - Tirewear Particulate 0.05 17.52

Sulfur Dioxide (SO2) 0.12 41.46

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

Oxides of Nitrogen 37.91 13,114.20

Primary Exhaust PM2.5 - Total 1.64 562.84

Primary PM2.5 - Brakewear Particulate 0.18 62.10

Primary PM2.5 - Tirewear Particulate 0.06 20.70

Sulfur Dioxide (SO2) 0.12 42.74

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

Oxides of Nitrogen 29.33 10,141.52

Primary Exhaust PM2.5 - Total 1.19 407.74

Primary PM2.5 - Brakewear Particulate 0.20 68.38

Primary PM2.5 - Tirewear Particulate 0.07 22.68

Sulfur Dioxide (SO2) 0.13 45.36

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

Oxides of Nitrogen 20.18 6,996.21

Primary Exhaust PM2.5 - Total 0.78 267.30

Primary PM2.5 - Brakewear Particulate 0.23 77.94

Primary PM2.5 - Tirewear Particulate 0.08 25.88

Sulfur Dioxide (SO2) 0.15 50.50

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

Oxides of Nitrogen 15.78 5,480.81

Primary Exhaust PM2.5 - Total 0.59 202.15

Primary PM2.5 - Brakewear Particulate 0.27 91.15

Primary PM2.5 - Tirewear Particulate 0.09 30.09

Sulfur Dioxide (SO2) 0.16 56.28

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

Oxides of Nitrogen 12.75 4,435.96

Primary Exhaust PM2.5 - Total 0.43 146.79

Primary PM2.5 - Brakewear Particulate 0.28 96.84

Primary PM2.5 - Tirewear Particulate 0.09 31.74

Sulfur Dioxide (SO2) 0.17 58.63

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

Ohio/Indiana Portion of NA Area

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

Oxides of Nitrogen 168.89 58,423.36

Primary Exhaust PM2.5 - Total 5.74 1,979.63

Primary PM2.5 - Brakewear Particulate 0.65 223.20

Primary PM2.5 - Tirewear Particulate 0.20 69.67

Sulfur Dioxide (SO2) 0.48 165.35

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

Oxides of Nitrogen 148.02 51,357.02

Primary Exhaust PM2.5 - Total 4.85 1,675.04

Primary PM2.5 - Brakewear Particulate 0.80 273.84

Primary PM2.5 - Tirewear Particulate 0.25 85.37

Sulfur Dioxide (SO2) 0.54 185.13

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

Oxides of Nitrogen 135.95 47,061.53

Primary Exhaust PM2.5 - Total 5.54 1,904.61

Primary PM2.5 - Brakewear Particulate 0.85 290.00

Primary PM2.5 - Tirewear Particulate 0.27 91.52

Sulfur Dioxide (SO2) 0.53 182.01

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

Oxides of Nitrogen 89.45 31,064.21

Primary Exhaust PM2.5 - Total 3.57 1,227.86

Primary PM2.5 - Brakewear Particulate 0.82 280.25

Primary PM2.5 - Tirewear Particulate 0.26 90.54

Sulfur Dioxide (SO2) 0.53 182.69

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

Oxides of Nitrogen 70.34 24,451.43

Primary Exhaust PM2.5 - Total 2.78 958.57

Primary PM2.5 - Brakewear Particulate 0.90 307.39

Primary PM2.5 - Tirewear Particulate 0.29 99.03

Sulfur Dioxide (SO2) 0.57 195.09

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

Oxides of Nitrogen 55.50 18,911.05

Primary Exhaust PM2.5 - Total 2.10 705.30

Primary PM2.5 - Brakewear Particulate 0.96 320.17

Primary PM2.5 - Tirewear Particulate 0.31 102.89

Sulfur Dioxide (SO2) 0.60 199.14

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

County Year Pollutant Name DailyEmissions AnnualEmissions

Indiana

Dearborn NA

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

Oxides of Nitrogen 2.40 865.46

Primary Exhaust PM2.5 - Total 0.08 29.68

Primary PM2.5 - Brakewear Particulate 0.01 3.28

Primary PM2.5 - Tirewear Particulate 0.00 1.02

Sulfur Dioxide (SO2) 0.01 2.45

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

Oxides of Nitrogen 2.09 748.81

Primary Exhaust PM2.5 - Total 0.07 24.72

Primary PM2.5 - Brakewear Particulate 0.01 3.94

Primary PM2.5 - Tirewear Particulate 0.00 1.23

Sulfur Dioxide (SO2) 0.01 2.69

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

Oxides of Nitrogen 1.92 685.40

Primary Exhaust PM2.5 - Total 0.08 27.88

Primary PM2.5 - Brakewear Particulate 0.01 4.19

Primary PM2.5 - Tirewear Particulate 0.00 1.32

Sulfur Dioxide (SO2) 0.01 2.65

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

Oxides of Nitrogen 1.31 482.33

Primary Exhaust PM2.5 - Total 0.05 19.43

Primary PM2.5 - Brakewear Particulate 0.01 4.32

Primary PM2.5 - Tirewear Particulate 0.00 1.39

Sulfur Dioxide (SO2) 0.01 2.87

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

Oxides of Nitrogen 1.03 376.85

Primary Exhaust PM2.5 - Total 0.04 15.09

Primary PM2.5 - Brakewear Particulate 0.01 4.70

Primary PM2.5 - Tirewear Particulate 0.00 1.51

Sulfur Dioxide (SO2) 0.01 3.04

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

Oxides of Nitrogen 0.81 297.95

Primary Exhaust PM2.5 - Total 0.03 11.44

Primary PM2.5 - Brakewear Particulate 0.01 5.05

Primary PM2.5 - Tirewear Particulate 0.00 1.62

Sulfur Dioxide (SO2) 0.01 3.19

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

Kentucky

Boone

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

Oxides of Nitrogen 14.94 5,126.88

Primary Exhaust PM2.5 - Total 0.52 177.58

Primary PM2.5 - Brakewear Particulate 0.06 20.86

Primary PM2.5 - Tirewear Particulate 0.02 6.77

Sulfur Dioxide (SO2) 0.05 15.91

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

Oxides of Nitrogen 14.73 5,067.94

Primary Exhaust PM2.5 - Total 0.64 219.29

Primary PM2.5 - Brakewear Particulate 0.07 24.42

Primary PM2.5 - Tirewear Particulate 0.02 8.14

Sulfur Dioxide (SO2) 0.05 16.71

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

Oxides of Nitrogen 11.61 3,990.01

Primary Exhaust PM2.5 - Total 0.48 162.47

Primary PM2.5 - Brakewear Particulate 0.08 27.55

Primary PM2.5 - Tirewear Particulate 0.03 9.14

Sulfur Dioxide (SO2) 0.05 18.16

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

Oxides of Nitrogen 8.11 2,788.45

Primary Exhaust PM2.5 - Total 0.32 108.49

Primary PM2.5 - Brakewear Particulate 0.09 32.17

Primary PM2.5 - Tirewear Particulate 0.03 10.69

Sulfur Dioxide (SO2) 0.06 20.67

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

Oxides of Nitrogen 6.34 2,182.28

Primary Exhaust PM2.5 - Total 0.24 82.19

Primary PM2.5 - Brakewear Particulate 0.11 37.76

Primary PM2.5 - Tirewear Particulate 0.04 12.47

Sulfur Dioxide (SO2) 0.07 23.14

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

Oxides of Nitrogen 5.14 1,772.72

Primary Exhaust PM2.5 - Total 0.18 60.19

Primary PM2.5 - Brakewear Particulate 0.12 40.56

Primary PM2.5 - Tirewear Particulate 0.04 13.30

Sulfur Dioxide (SO2) 0.07 24.37

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

Campbell

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

Oxides of Nitrogen 8.87 3,041.21

Primary Exhaust PM2.5 - Total 0.31 104.22

Primary PM2.5 - Brakewear Particulate 0.04 12.14

Primary PM2.5 - Tirewear Particulate 0.01 3.94

Sulfur Dioxide (SO2) 0.03 9.30

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

Oxides of Nitrogen 8.63 2,988.33

Primary Exhaust PM2.5 - Total 0.37 127.73

Primary PM2.5 - Brakewear Particulate 0.04 14.05

Primary PM2.5 - Tirewear Particulate 0.01 4.68

Sulfur Dioxide (SO2) 0.03 9.69

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

Oxides of Nitrogen 6.61 2,287.81

Primary Exhaust PM2.5 - Total 0.27 91.36

Primary PM2.5 - Brakewear Particulate 0.04 15.26

Primary PM2.5 - Tirewear Particulate 0.01 5.06

Sulfur Dioxide (SO2) 0.03 10.15

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

Oxides of Nitrogen 4.55 1,570.14

Primary Exhaust PM2.5 - Total 0.17 59.30

Primary PM2.5 - Brakewear Particulate 0.05 17.31

Primary PM2.5 - Tirewear Particulate 0.02 5.75

Sulfur Dioxide (SO2) 0.03 11.21

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

Oxides of Nitrogen 3.52 1,216.21

Primary Exhaust PM2.5 - Total 0.13 44.14

Primary PM2.5 - Brakewear Particulate 0.06 19.90

Primary PM2.5 - Tirewear Particulate 0.02 6.57

Sulfur Dioxide (SO2) 0.04 12.28

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

Oxides of Nitrogen 2.84 985.28

Primary Exhaust PM2.5 - Total 0.09 32.07

Primary PM2.5 - Brakewear Particulate 0.06 21.10

Primary PM2.5 - Tirewear Particulate 0.02 6.92

Sulfur Dioxide (SO2) 0.04 12.77

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

Kenton

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

Oxides of Nitrogen 15.29 5,328.44

Primary Exhaust PM2.5 - Total 0.53 184.43

Primary PM2.5 - Brakewear Particulate 0.06 21.04

Primary PM2.5 - Tirewear Particulate 0.02 6.82

Sulfur Dioxide (SO2) 0.05 16.24

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

Oxides of Nitrogen 14.55 5,057.93

Primary Exhaust PM2.5 - Total 0.62 215.81

Primary PM2.5 - Brakewear Particulate 0.07 23.63

Primary PM2.5 - Tirewear Particulate 0.02 7.87

Sulfur Dioxide (SO2) 0.05 16.34

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

Oxides of Nitrogen 11.11 3,863.70

Primary Exhaust PM2.5 - Total 0.45 153.90

Primary PM2.5 - Brakewear Particulate 0.07 25.57

Primary PM2.5 - Tirewear Particulate 0.02 8.48

Sulfur Dioxide (SO2) 0.05 17.05

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

Oxides of Nitrogen 7.51 2,637.63

Primary Exhaust PM2.5 - Total 0.29 99.51

Primary PM2.5 - Brakewear Particulate 0.08 28.45

Primary PM2.5 - Tirewear Particulate 0.03 9.44

Sulfur Dioxide (SO2) 0.05 18.62

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

Oxides of Nitrogen 5.93 2,082.32

Primary Exhaust PM2.5 - Total 0.22 75.82

Primary PM2.5 - Brakewear Particulate 0.10 33.49

Primary PM2.5 - Tirewear Particulate 0.03 11.04

Sulfur Dioxide (SO2) 0.06 20.86

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

Oxides of Nitrogen 4.76 1,677.96

Primary Exhaust PM2.5 - Total 0.16 54.53

Primary PM2.5 - Brakewear Particulate 0.10 35.19

Primary PM2.5 - Tirewear Particulate 0.03 11.52

Sulfur Dioxide (SO2) 0.06 21.48

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

Ohio

Butler

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

Oxides of Nitrogen 32.00 10,910.37

Primary Exhaust PM2.5 - Total 1.06 361.06

Primary PM2.5 - Brakewear Particulate 0.12 40.31

Primary PM2.5 - Tirewear Particulate 0.04 12.60

Sulfur Dioxide (SO2) 0.09 30.01

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

Oxides of Nitrogen 28.56 9,803.70

Primary Exhaust PM2.5 - Total 0.91 311.45

Primary PM2.5 - Brakewear Particulate 0.15 50.45

Primary PM2.5 - Tirewear Particulate 0.05 15.74

Sulfur Dioxide (SO2) 0.10 34.25

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

Oxides of Nitrogen 26.50 9,074.89

Primary Exhaust PM2.5 - Total 1.05 356.91

Primary PM2.5 - Brakewear Particulate 0.16 53.99

Primary PM2.5 - Tirewear Particulate 0.05 17.06

Sulfur Dioxide (SO2) 0.10 34.00

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

Oxides of Nitrogen 17.64 6,064.61

Primary Exhaust PM2.5 - Total 0.68 231.78

Primary PM2.5 - Brakewear Particulate 0.16 52.42

Primary PM2.5 - Tirewear Particulate 0.05 16.96

Sulfur Dioxide (SO2) 0.10 34.28

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

Oxides of Nitrogen 13.98 4,813.27

Primary Exhaust PM2.5 - Total 0.54 182.29

Primary PM2.5 - Brakewear Particulate 0.17 57.91

Primary PM2.5 - Tirewear Particulate 0.06 18.68

Sulfur Dioxide (SO2) 0.11 36.85

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

Oxides of Nitrogen 11.13 3,757.91

Primary Exhaust PM2.5 - Total 0.41 135.39

Primary PM2.5 - Brakewear Particulate 0.19 60.81

Primary PM2.5 - Tirewear Particulate 0.06 19.56

Sulfur Dioxide (SO2) 0.12 37.90

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

Clermont

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

Oxides of Nitrogen 21.21 7,295.87

Primary Exhaust PM2.5 - Total 0.72 245.48

Primary PM2.5 - Brakewear Particulate 0.08 27.67

Primary PM2.5 - Tirewear Particulate 0.03 8.64

Sulfur Dioxide (SO2) 0.06 20.51

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

Oxides of Nitrogen 18.81 6,516.40

Primary Exhaust PM2.5 - Total 0.61 211.40

Primary PM2.5 - Brakewear Particulate 0.10 34.46

Primary PM2.5 - Tirewear Particulate 0.03 10.74

Sulfur Dioxide (SO2) 0.07 23.32

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

Oxides of Nitrogen 17.48 6,039.51

Primary Exhaust PM2.5 - Total 0.71 243.25

Primary PM2.5 - Brakewear Particulate 0.11 37.00

Primary PM2.5 - Tirewear Particulate 0.03 11.68

Sulfur Dioxide (SO2) 0.07 23.23

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

Oxides of Nitrogen 11.54 3,993.63

Primary Exhaust PM2.5 - Total 0.46 156.92

Primary PM2.5 - Brakewear Particulate 0.11 35.82

Primary PM2.5 - Tirewear Particulate 0.03 11.58

Sulfur Dioxide (SO2) 0.07 23.34

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

Oxides of Nitrogen 9.09 3,146.47

Primary Exhaust PM2.5 - Total 0.36 122.57

Primary PM2.5 - Brakewear Particulate 0.12 39.31

Primary PM2.5 - Tirewear Particulate 0.04 12.67

Sulfur Dioxide (SO2) 0.07 24.94

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

Oxides of Nitrogen 7.22 2,449.31

Primary Exhaust PM2.5 - Total 0.27 90.84

Primary PM2.5 - Brakewear Particulate 0.12 41.28

Primary PM2.5 - Tirewear Particulate 0.04 13.27

Sulfur Dioxide (SO2) 0.08 25.66

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

Hamilton

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

Oxides of Nitrogen 89.30 31,127.09

Primary Exhaust PM2.5 - Total 3.06 1,064.67

Primary PM2.5 - Brakewear Particulate 0.35 119.94

Primary PM2.5 - Tirewear Particulate 0.11 37.41

Sulfur Dioxide (SO2) 0.26 88.85

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

Oxides of Nitrogen 77.45 27,020.93

Primary Exhaust PM2.5 - Total 2.56 889.81

Primary PM2.5 - Brakewear Particulate 0.42 145.42

Primary PM2.5 - Tirewear Particulate 0.13 45.31

Sulfur Dioxide (SO2) 0.28 98.30

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

Oxides of Nitrogen 70.18 24,435.59

Primary Exhaust PM2.5 - Total 2.88 997.06

Primary PM2.5 - Brakewear Particulate 0.44 151.73

Primary PM2.5 - Tirewear Particulate 0.14 47.86

Sulfur Dioxide (SO2) 0.28 95.30

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

Oxides of Nitrogen 45.58 15,925.19

Primary Exhaust PM2.5 - Total 1.83 634.62

Primary PM2.5 - Brakewear Particulate 0.42 144.67

Primary PM2.5 - Tirewear Particulate 0.14 46.71

Sulfur Dioxide (SO2) 0.27 94.43

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

Oxides of Nitrogen 35.51 12,422.37

Primary Exhaust PM2.5 - Total 1.41 490.62

Primary PM2.5 - Brakewear Particulate 0.45 156.90

Primary PM2.5 - Tirewear Particulate 0.15 50.52

Sulfur Dioxide (SO2) 0.29 99.78

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

Oxides of Nitrogen 27.80 9,530.16

Primary Exhaust PM2.5 - Total 1.06 357.87

Primary PM2.5 - Brakewear Particulate 0.48 161.69

Primary PM2.5 - Tirewear Particulate 0.15 51.92

Sulfur Dioxide (SO2) 0.30 100.82

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

Warren

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

Oxides of Nitrogen 23.98 8,224.57

Primary Exhaust PM2.5 - Total 0.82 278.74

Primary PM2.5 - Brakewear Particulate 0.09 32.00

Primary PM2.5 - Tirewear Particulate 0.03 10.00

Sulfur Dioxide (SO2) 0.07 23.54

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

Oxides of Nitrogen 21.11 7,267.18

Primary Exhaust PM2.5 - Total 0.69 237.65

Primary PM2.5 - Brakewear Particulate 0.12 39.57

Primary PM2.5 - Tirewear Particulate 0.04 12.34

Sulfur Dioxide (SO2) 0.08 26.57

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

Oxides of Nitrogen 19.88 6,826.15

Primary Exhaust PM2.5 - Total 0.82 279.53

Primary PM2.5 - Brakewear Particulate 0.13 43.09

Primary PM2.5 - Tirewear Particulate 0.04 13.60

Sulfur Dioxide (SO2) 0.08 26.83

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

Oxides of Nitrogen 13.37 4,598.44

Primary Exhaust PM2.5 - Total 0.54 185.12

Primary PM2.5 - Brakewear Particulate 0.13 43.02

Primary PM2.5 - Tirewear Particulate 0.04 13.91

Sulfur Dioxide (SO2) 0.08 27.77

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

Oxides of Nitrogen 10.73 3,692.47

Primary Exhaust PM2.5 - Total 0.43 148.00

Primary PM2.5 - Brakewear Particulate 0.14 48.57

Primary PM2.5 - Tirewear Particulate 0.05 15.66

Sulfur Dioxide (SO2) 0.09 30.49

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

Oxides of Nitrogen 8.54 2,875.72

Primary Exhaust PM2.5 - Total 0.33 109.76

Primary PM2.5 - Brakewear Particulate 0.16 51.34

Primary PM2.5 - Tirewear Particulate 0.05 16.51

Sulfur Dioxide (SO2) 0.10 31.58

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

Emission Factor Model

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

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

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

MOVES are found in the Appendix. 

Table 3. 

MOVES Runspec [sic] Parameter Settings

MOVES Version 2009/12/21, MOVES default 

database 2010615111524

Scale County, Emission Rates

Time Span Time aggregation = Hour

1 month representing average annual 

temperatures 

All hours of day selected 

Weekdays only 

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

counties 

Vehicles/Equipment All source types, gasoline and diesel

Road Type All road types including off-network

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

Consumption 

Strategies none

General Output Units= grams, joules and miles

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

rates by road type and source use type. 

Advanced Performance none

Table 4 

County Data Manager Data Source

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

motor vehicle registration data. Default data used for source types 

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

54 used for KY. 

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

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

monthVMTFraction = default. dayVMTFraction=default,

hourVMTFraction=local. 

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

Fuel Formulation Default
Fuel Supply Default

Meteorology Data Local. Kentucky Division for Air Quality.

Ramp Fraction Local. Ramp emissions calculated outside of MOVES

Road Type Distribution Local. OKI travel demand model.

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

motor vehicle registration data. Default data used for source types 

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

54 used for KY. 

Average Speed Distribution Local. OKI travel demand model.

OKI Travel Demand Model 

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

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

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

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

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

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

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

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

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

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

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

equations. The following categories of planning data are utilized: 

• Population (household and group quarter) 

• Households 

• Household vehicles 

• Employment (by employment category and zone of work) 

• Labor force participation (by zone of residence) 

• Area type 

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

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

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

complete demographic overview of the region. 

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

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

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

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

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

planning assumptions. 
Population 

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

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

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

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

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

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

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

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

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

the zonal populations equals the control total. 

Households 

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

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

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

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

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

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

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

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

households for each TAZ. 

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

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

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

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

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

Household Vehicles 

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

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

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

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

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

per household has exceeded the number of drivers per household. 

Labor Force 

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

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

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

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

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

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

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

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

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

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

the zonal labor force counts equals the control total. 

Employment 

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

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

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

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

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

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

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

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

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

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

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

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

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

factors. 

Area Type 

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

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

Model Calibration

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

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

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

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

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

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

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

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

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

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

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

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

work performed by OKI. 

Local Inputs and Post-Model Processing

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

OKI 2003.

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

APPENDIX 

OKI Technical Documentation for Using 

EPA MOVES to Develop MOBILE Source 

Emissions 

August 2010 

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

1. Using MOVES 

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

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

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

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

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

Graphical User Interface of MOVES software. 

1.1 Description 

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

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

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

pollutants analyzed. 

1.2 Scale 

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

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

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

have used is “Emission Rates”. 

1.3 Time Spans 

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

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

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

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

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

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

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

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

hour were calculated and assigned the April month ID. 

1.4 Geographic Bounds 

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

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

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

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

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

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

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

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

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

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

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

different input databases for each year. 

1.5 Vehicles/Equipment 

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

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

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

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

fuel/vehicle type combinations. 

1.6 Road Type 

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

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

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

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

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

roadways. 

1.7 Pollutants and Processes 

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

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

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

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

Nitrogen are also selected. 

1.8 Miscellaneous 

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

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

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

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

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

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

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

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

sourceTypeID modelYearID fuelTypeID engTechID fuelEngFraction

42 1960 2 1 1 

42 1961 2 1 1 

42 1962 2 1 1 

42 1963 2 1 1 

42 1964 2 1 1 

42 1965 2 1 1 

42 1966 2 1 1 

42 1967 2 1 1 

42 1968 2 1 1 

42 1969 2 1 1 

42 1970 2 1 1 

42 1971 2 1 1 

42 1972 2 1 1 

42 1973 2 1 1 

42 1974 2 1 1 

42 1975 2 1 1 

42 1976 2 1 1 

42 1977 2 1 1 

42 1978 2 1 1 

42 1979 2 1 1 

42 1980 2 1 1 

42 1981 2 1 1 

42 1982 2 1 1 

42 1983 2 1 1 

42 1984 2 1 1 

42 1985 2 1 1 

42 1986 2 1 1 

42 1987 2 1 1 

42 1988 2 1 1 

42 1989 2 1 1 

42 1990 2 1 1 

42 1991 2 1 1 

42 1992 2 1 1 

42 1993 2 1 1 

42 1994 2 1 1 

42 1995 2 1 1 

42 1996 2 1 1 

42 1997 2 1 1 

42 1998 2 1 1 

42 1999 2 1 1 

42 2000 2 1 1 

42 2001 2 1 1 

42 2002 2 1 1 

42 2003 2 1 1 

42 2004 2 1 1 

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

42 2006 2 1 1 

42 2007 2 1 1 

42 2008 2 1 1 

42 2009 2 1 1 

42 2010 2 1 1 

42 2011 2 1 1 

42 2012 2 1 1 

42 2013 2 1 1 

42 2014 2 1 1 

42 2015 2 1 1 

42 2016 2 1 1 

42 2017 2 1 1 

42 2018 2 1 1 

42 2019 2 1 1 

42 2020 2 1 1 

42 2021 2 1 1 

42 2022 2 1 1 

42 2023 2 1 1 

42 2024 2 1 1 

42 2025 2 1 1 

42 2026 2 1 1 

42 2027 2 1 1 

42 2028 2 1 1 

42 2029 2 1 1 

42 2030 2 1 1 

42 2031 2 1 1 

42 2032 2 1 1 

42 2033 2 1 1 

42 2034 2 1 1 

42 2035 2 1 1 

42 2036 2 1 1 

42 2037 2 1 1 

42 2038 2 1 1 

42 2039 2 1 1 

42 2040 2 1 1 

42 2041 2 1 1 

42 2042 2 1 1 

42 2043 2 1 1 

42 2044 2 1 1 

42 2045 2 1 1 

42 2046 2 1 1 

42 2047 2 1 1 

42 2048 2 1 1 

42 2049 2 1 1 

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

1.9 Output 

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

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

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

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

different options for data aggregation. 

2. Data Importers 

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

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

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

format of MOVES software. 

2.1 Meteorology Data Importer 

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

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

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

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

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

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

Table 2 : Meteorology data obtained from KDAQ 

monthID zoneID hourID temperature relHumidity 

4 993900 1 47.5 72.9 

4 993900 2 46.4 75.8 

4 993900 3 45.5 77.9 

4 993900 4 44.8 79.4 

4 993900 5 44.3 80.7 

4 993900 6 43.7 82.1 

4 993900 7 43.2 83.3 

4 993900 8 43.6 82.3 

4 993900 9 46.1 76.4 

4 993900 10 50.1 67.0 

4 993900 11 54.2 57.8 

4 993900 12 57.7 50.9 

4 993900 13 60.8 45.7 

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

4 993900 15 63.1 42.2 

4 993900 16 63.2 42.0 

4 993900 17 62.8 42.6 

4 993900 18 61.7 44.3 

4 993900 19 59.7 47.6 

4 993900 20 57.1 52.2 

4 993900 21 54.5 57.2 

4 993900 22 52.2 62.1 

4 993900 23 50.6 65.9 

4 993900 24 49.1 69.4 

7 993900 1 69.3 69.5 

7 993900 2 68.1 72.4 

7 993900 3 67.1 74.8 

7 993900 4 66.4 76.6 

7 993900 5 65.9 78 

7 993900 6 65.3 79.7 

7 993900 7 64.8 81.1 

7 993900 8 65.2 79.9 

7 993900 9 67.8 73.1 

7 993900 10 72 63.4 

7 993900 11 76.2 55 

7 993900 12 79.8 48.8 

7 993900 13 83 44 

7 993900 14 84.7 41.6 

7 993900 15 85.3 40.8 

7 993900 16 85.5 40.6 

7 993900 17 85.1 41.2 

7 993900 18 83.9 42.8 

7 993900 19 81.8 45.8 

7 993900 20 79.1 49.9 

7 993900 21 76.4 54.6 

7 993900 22 74.1 59 

7 993900 23 72.5 62.3 

7 993900 24 70.8 65.9 

2.2 Source Type Population Importer 

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

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

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

county for 6 HPMS vehicle types. The KYTC data was converted to the 13 MOVES vehicle types 

based on the Ohio distribution. Same vehicle population was used for all analysis years. As per 

suggestions made by FHWA and KYTC, the Source Type Population has been forecasted for 

future years with +0.8 % per year. Similarly, the Source Type Populations has been estimated 

for past years. The MOVES default source type population for intercity bus, refuse trucks, 

motor homes and combination trucks was used. In addition, MOVES default source type 
18 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

population for passenger trucks and light commercial trucks was used for Kentucky. The 

MOVES default source type population was acquired from the MOVES activity output tables 

from county-level inventory runs. 

Table3 : Source Type Population for Ohio Custom Domain (2008) 

yearID sourceTypeID sourceTypePopulation

2008 11 68559 

2008 21 1191067 

2008 31 482420 

2008 32 15817 

2008 41 454 

2008 42 81 

2008 43 3651 

2008 51 409 

2008 52 366 

2008 53 361 

2008 54 4888 

2008 61 4839 

2008 62 5548 

Table 4: Kentucky Source Type population (acquired from KYTC) 

yearID sourceTypeID sourceTypePopulation

2008 11 7975 

2008 21 197009 

2008 31 120518 

2008 32 40263 

2008 41 127 

2008 42 21 

2008 43 977 

2008 51 115 

2008 52 761 

2008 53 751 

2008 54 1379 

2008 61 1580 

2008 62 1811 

2.3 Age Distribution Importer 

For emission calculation the MOVES need vehicle Age Distribution by Source Type. Vehicle Age 

Distribution is divided into 30 years based on vehicle model years. For each vehicle type, the 

distribution sum adds up to one. ODOT has obtained vehicle registration data from the Bureau 

of Motor Vehicles for all the counties in Ohio and processed them to convert into MOVES Age 

Distribution for 13 vehicle types. We have used the same Age Distribution for all year runs. All 
19 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

the vehicles older than 30 years are considered as 30-years old. Same age distribution is used 

for all analysis years. KYTC also provided similar information, but for the 6 HPMS types only. 

For Kentucky, identical age distributions are used within each HPMS vehicle type. 

Table 5 : Ohio Custom Domain Age distribution 

Source

TypeID

yearID ageID ageFraction

11 2008 0 0.0019 

11 2008 1 0.0191 

11 2008 2 0.0531 

11 2008 3 0.0688 

11 2008 4 0.0773 

11 2008 5 0.0737 

11 2008 6 0.0611 

11 2008 7 0.0780 

11 2008 8 0.0636 

11 2008 9 0.0537 

11 2008 10 0.0435 

11 2008 11 0.0359 

11 2008 12 0.0282 

11 2008 13 0.0230 

11 2008 14 0.0220 

11 2008 15 0.0183 

11 2008 16 0.0160 

11 2008 17 0.0146 

11 2008 18 0.0097 

11 2008 19 0.0080 

11 2008 20 0.0072 

11 2008 21 0.0086 

11 2008 22 0.0084 

11 2008 23 0.0121 

11 2008 24 0.0171 

11 2008 25 0.0179 

11 2008 26 0.0137 

11 2008 27 0.0171 

11 2008 28 0.0249 

11 2008 29 0.0172 

11 2008 30 0.0862 

21 2008 0 0.0121 

21 2008 1 0.0331 

21 2008 2 0.0440 

21 2008 3 0.0528 

21 2008 4 0.0534 

21 2008 5 0.0566 

21 2008 6 0.0570 

21 2008 7 0.0592 

21 2008 8 0.0591 

21 2008 9 0.0542 

21 2008 10 0.0590 

21 2008 11 0.0568 

21 2008 12 0.0507 

21 2008 13 0.0499 

21 2008 14 0.0438 

21 2008 15 0.0453 

21 2008 16 0.0368 

21 2008 17 0.0308 

21 2008 18 0.0261 

21 2008 19 0.0207 

21 2008 20 0.0165 

21 2008 21 0.0132 

21 2008 22 0.0095 

21 2008 23 0.0073 

21 2008 24 0.0059 

21 2008 25 0.0043 

21 2008 26 0.0033 

21 2008 27 0.0017 

21 2008 28 0.0011 

21 2008 29 0.0010 

21 2008 30 0.0346 

31 2008 0 0.0103 

31 2008 1 0.0279 

31 2008 2 0.0502 

31 2008 3 0.0570 

31 2008 4 0.0659 

31 2008 5 0.0806 

31 2008 6 0.0796 

31 2008 7 0.0733 

31 2008 8 0.0727 

31 2008 9 0.0599 

31 2008 10 0.0625 

31 2008 11 0.0603 

31 2008 12 0.0516 

31 2008 13 0.0432 

31 2008 14 0.0380 

31 2008 15 0.0386 

31 2008 16 0.0302 

31 2008 17 0.0260 

31 2008 18 0.0165 

31 2008 19 0.0125 

31 2008 20 0.0093 
20 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

31 2008 21 0.0084 

31 2008 22 0.0067 

31 2008 23 0.0051 

31 2008 24 0.0037 

31 2008 25 0.0025 

31 2008 26 0.0017 

31 2008 27 0.0009 

31 2008 28 0.0004 

31 2008 29 0.0002 

31 2008 30 0.0041 

32 2008 0 0.0178 

32 2008 1 0.0459 

32 2008 2 0.0871 

32 2008 3 0.0699 

32 2008 4 0.0707 

32 2008 5 0.0357 

32 2008 6 0.0355 

32 2008 7 0.0369 

32 2008 8 0.0366 

32 2008 9 0.0407 

32 2008 10 0.0491 

32 2008 11 0.0547 

32 2008 12 0.0427 

32 2008 13 0.0413 

32 2008 14 0.0383 

32 2008 15 0.0602 

32 2008 16 0.0476 

32 2008 17 0.0381 

32 2008 18 0.0304 

32 2008 19 0.0181 

32 2008 20 0.0212 

32 2008 21 0.0184 

32 2008 22 0.0135 

32 2008 23 0.0134 

32 2008 24 0.0095 

32 2008 25 0.0070 

32 2008 26 0.0054 

32 2008 27 0.0021 

32 2008 28 0.0014 

32 2008 29 0.0008 

32 2008 30 0.0100 

41 2008 0 0.0000 

41 2008 1 0.0309 

41 2008 2 0.0884 

41 2008 3 0.0890 

41 2008 4 0.0768 

41 2008 5 0.0746 

41 2008 6 0.0967 

41 2008 7 0.0635 

41 2008 8 0.0486 

41 2008 9 0.0801 

41 2008 10 0.0751 

41 2008 11 0.0624 

41 2008 12 0.0254 

41 2008 13 0.0271 

41 2008 14 0.0188 

41 2008 15 0.0193 

41 2008 16 0.0133 

41 2008 17 0.0177 

41 2008 18 0.0094 

41 2008 19 0.0177 

41 2008 20 0.0171 

41 2008 21 0.0099 

41 2008 22 0.0039 

41 2008 23 0.0055 

41 2008 24 0.0061 

41 2008 25 0.0011 

41 2008 26 0.0033 

41 2008 27 0.0033 

41 2008 28 0.0028 

41 2008 29 0.0017 

41 2008 30 0.0105 

42 2008 0 0.0000 

42 2008 1 0.0366 

42 2008 2 0.1098 

42 2008 3 0.0366 

42 2008 4 0.1585 

42 2008 5 0.0366 

42 2008 6 0.0610 

42 2008 7 0.0610 

42 2008 8 0.0244 

42 2008 9 0.1098 

42 2008 10 0.0366 

42 2008 11 0.0976 

42 2008 12 0.0366 

42 2008 13 0.0244 

42 2008 14 0.0244 

42 2008 15 0.0122 

42 2008 16 0.0244 

42 2008 17 0.0244 

42 2008 18 0.0366 

42 2008 19 0.0000 

42 2008 20 0.0000 

42 2008 21 0.0122 

42 2008 22 0.0000 

42 2008 23 0.0000 

42 2008 24 0.0000 

42 2008 25 0.0000 
21 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

42 2008 26 0.0122 

42 2008 27 0.0000 

42 2008 28 0.0000 

42 2008 29 0.0122 

42 2008 30 0.0122 

43 2008 0 0.0905 

43 2008 1 0.0302 

43 2008 2 0.0549 

43 2008 3 0.0467 

43 2008 4 0.0592 

43 2008 5 0.0723 

43 2008 6 0.0481 

43 2008 7 0.0334 

43 2008 8 0.0668 

43 2008 9 0.0647 

43 2008 10 0.0842 

43 2008 11 0.0864 

43 2008 12 0.0473 

43 2008 13 0.0500 

43 2008 14 0.0242 

43 2008 15 0.0185 

43 2008 16 0.0106 

43 2008 17 0.0228 

43 2008 18 0.0109 

43 2008 19 0.0130 

43 2008 20 0.0125 

43 2008 21 0.0092 

43 2008 22 0.0062 

43 2008 23 0.0079 

43 2008 24 0.0090 

43 2008 25 0.0035 

43 2008 26 0.0030 

43 2008 27 0.0011 

43 2008 28 0.0027 

43 2008 29 0.0016 

43 2008 30 0.0087 

51 2008 0 0.0054 

51 2008 1 0.0488 

51 2008 2 0.0623 

51 2008 3 0.0705 

51 2008 4 0.0867 

51 2008 5 0.0434 

51 2008 6 0.0434 

51 2008 7 0.0542 

51 2008 8 0.0542 

51 2008 9 0.0759 

51 2008 10 0.0217 

51 2008 11 0.0407 

51 2008 12 0.0786 

51 2008 13 0.0542 

51 2008 14 0.0515 

51 2008 15 0.0678 

51 2008 16 0.0325 

51 2008 17 0.0081 

51 2008 18 0.0163 

51 2008 19 0.0027 

51 2008 20 0.0081 

51 2008 21 0.0000 

51 2008 22 0.0027 

51 2008 23 0.0027 

51 2008 24 0.0136 

51 2008 25 0.0000 

51 2008 26 0.0000 

51 2008 27 0.0000 

51 2008 28 0.0027 

51 2008 29 0.0000 

51 2008 30 0.0515 

52 2008 0 0.0054 

52 2008 1 0.0488 

52 2008 2 0.0623 

52 2008 3 0.0705 

52 2008 4 0.0867 

52 2008 5 0.0434 

52 2008 6 0.0434 

52 2008 7 0.0542 

52 2008 8 0.0542 

52 2008 9 0.0759 

52 2008 10 0.0217 

52 2008 11 0.0407 

52 2008 12 0.0786 

52 2008 13 0.0542 

52 2008 14 0.0515 

52 2008 15 0.0678 

52 2008 16 0.0325 

52 2008 17 0.0081 

52 2008 18 0.0163 

52 2008 19 0.0027 

52 2008 20 0.0081 

52 2008 21 0.0000 

52 2008 22 0.0027 

52 2008 23 0.0027 

52 2008 24 0.0136 

52 2008 25 0.0000 

52 2008 26 0.0000 

52 2008 27 0.0000 

52 2008 28 0.0027 

52 2008 29 0.0000 

52 2008 30 0.0515 
22 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

53 2008 0 0.0000 

53 2008 1 0.0062 

53 2008 2 0.0373 

53 2008 3 0.0093 

53 2008 4 0.0280 

53 2008 5 0.0342 

53 2008 6 0.0186 

53 2008 7 0.0186 

53 2008 8 0.0124 

53 2008 9 0.0155 

53 2008 10 0.0217 

53 2008 11 0.0373 

53 2008 12 0.0093 

53 2008 13 0.0311 

53 2008 14 0.0217 

53 2008 15 0.0373 

53 2008 16 0.0217 

53 2008 17 0.0342 

53 2008 18 0.0124 

53 2008 19 0.0186 

53 2008 20 0.0248 

53 2008 21 0.0373 

53 2008 22 0.0186 

53 2008 23 0.0248 

53 2008 24 0.0062 

53 2008 25 0.0373 

53 2008 26 0.0155 

53 2008 27 0.0186 

53 2008 28 0.0217 

53 2008 29 0.0186 

53 2008 30 0.3509 

54 2008 0 0.0077 

54 2008 1 0.0170 

54 2008 2 0.0377 

54 2008 3 0.0424 

54 2008 4 0.0471 

54 2008 5 0.0579 

54 2008 6 0.0552 

54 2008 7 0.0485 

54 2008 8 0.0406 

54 2008 9 0.0439 

54 2008 10 0.0505 

54 2008 11 0.0539 

54 2008 12 0.0435 

54 2008 13 0.0360 

54 2008 14 0.0348 

54 2008 15 0.0375 

54 2008 16 0.0303 

54 2008 17 0.0231 

54 2008 18 0.0196 

54 2008 19 0.0150 

54 2008 20 0.0183 

54 2008 21 0.0208 

54 2008 22 0.0218 

54 2008 23 0.0217 

54 2008 24 0.0186 

54 2008 25 0.0173 

54 2008 26 0.0163 

54 2008 27 0.0118 

54 2008 28 0.0084 

54 2008 29 0.0059 

54 2008 30 0.0968 

61 2008 0 0.0030 

61 2008 1 0.0167 

61 2008 2 0.0334 

61 2008 3 0.0393 

61 2008 4 0.0506 

61 2008 5 0.0530 

61 2008 6 0.0620 

61 2008 7 0.0625 

61 2008 8 0.0562 

61 2008 9 0.0551 

61 2008 10 0.0595 

61 2008 11 0.0569 

61 2008 12 0.0458 

61 2008 13 0.0493 

61 2008 14 0.0380 

61 2008 15 0.0435 

61 2008 16 0.0425 

61 2008 17 0.0312 

61 2008 18 0.0262 

61 2008 19 0.0235 

61 2008 20 0.0201 

61 2008 21 0.0225 

61 2008 22 0.0212 

61 2008 23 0.0141 

61 2008 24 0.0137 

61 2008 25 0.0096 

61 2008 26 0.0069 

61 2008 27 0.0039 

61 2008 28 0.0030 

61 2008 29 0.0027 

61 2008 30 0.0343 

62 2008 0 0.0078 

62 2008 1 0.0232 

62 2008 2 0.0307 

62 2008 3 0.0907 

62 2008 4 0.0721 
23 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

62 2008 5 0.0808 

62 2008 6 0.0564 

62 2008 7 0.0520 

62 2008 8 0.0360 

62 2008 9 0.0552 

62 2008 10 0.1019 

62 2008 11 0.0813 

62 2008 12 0.0603 

62 2008 13 0.0425 

62 2008 14 0.0439 

62 2008 15 0.0442 

62 2008 16 0.0273 

62 2008 17 0.0202 

62 2008 18 0.0122 

62 2008 19 0.0101 

62 2008 20 0.0103 

62 2008 21 0.0080 

62 2008 22 0.0079 

62 2008 23 0.0058 

62 2008 24 0.0050 

62 2008 25 0.0036 

62 2008 26 0.0038 

62 2008 27 0.0001 

62 2008 28 0.0012 

62 2008 29 0.0010 

62 2008 30 0.0046 

Table 6 : Kentucky Custom Domain Age 

distribution 

Source

TypeID

yearID ageID ageFraction

11 2008 0 0.0020 

11 2008 1 0.0323 

11 2008 2 0.0606 

11 2008 3 0.0826 

11 2008 4 0.0831 

11 2008 5 0.0774 

11 2008 6 0.0667 

11 2008 7 0.0830 

11 2008 8 0.0650 

11 2008 9 0.0495 

11 2008 10 0.0424 

11 2008 11 0.0345 

11 2008 12 0.0287 

11 2008 13 0.0214 

11 2008 14 0.0240 

11 2008 15 0.0208 

11 2008 16 0.0138 

11 2008 17 0.0129 

11 2008 18 0.0092 

11 2008 19 0.0051 

11 2008 20 0.0052 

11 2008 21 0.0058 

11 2008 22 0.0078 

11 2008 23 0.0108 

11 2008 24 0.0153 

11 2008 25 0.0168 

11 2008 26 0.0124 

11 2008 27 0.0160 

11 2008 28 0.0228 

11 2008 29 0.0152 

11 2008 30 0.0568 

21 2008 0 0.0118 

21 2008 1 0.0665 

21 2008 2 0.0596 

21 2008 3 0.0642 

21 2008 4 0.0611 

21 2008 5 0.0705 

21 2008 6 0.0694 

21 2008 7 0.0699 

21 2008 8 0.0719 

21 2008 9 0.0619 

21 2008 10 0.0633 

21 2008 11 0.0591 

21 2008 12 0.0490 

21 2008 13 0.0442 

21 2008 14 0.0348 

21 2008 15 0.0318 

21 2008 16 0.0241 

21 2008 17 0.0191 

21 2008 18 0.0142 

21 2008 19 0.0111 

21 2008 20 0.0088 

21 2008 21 0.0066 

21 2008 22 0.0049 

21 2008 23 0.0039 

21 2008 24 0.0028 

21 2008 25 0.0024 
24 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

21 2008 26 0.0018 

21 2008 27 0.0009 

21 2008 28 0.0005 

21 2008 29 0.0005 

21 2008 30 0.0094 

31 2008 0 0.0000 

31 2008 1 0.0000 

31 2008 2 0.0000 

31 2008 3 0.0000 

31 2008 4 0.0238 

31 2008 5 0.0119 

31 2008 6 0.0119 

31 2008 7 0.0119 

31 2008 8 0.0119 

31 2008 9 0.0000 

31 2008 10 0.0238 

31 2008 11 0.0357 

31 2008 12 0.0119 

31 2008 13 0.0952 

31 2008 14 0.0833 

31 2008 15 0.0595 

31 2008 16 0.1071 

31 2008 17 0.0357 

31 2008 18 0.0357 

31 2008 19 0.0357 

31 2008 20 0.0119 

31 2008 21 0.0476 

31 2008 22 0.0238 

31 2008 23 0.0119 

31 2008 24 0.0119 

31 2008 25 0.0595 

31 2008 26 0.0357 

31 2008 27 0.0000 

31 2008 28 0.0238 

31 2008 29 0.0238 

31 2008 30 0.1548 

32 2008 0 0.0000 

32 2008 1 0.0000 

32 2008 2 0.0000 

32 2008 3 0.0000 

32 2008 4 0.0238 

32 2008 5 0.0119 

32 2008 6 0.0119 

32 2008 7 0.0119 

32 2008 8 0.0119 

32 2008 9 0.0000 

32 2008 10 0.0238 

32 2008 11 0.0357 

32 2008 12 0.0119 

32 2008 13 0.0952 

32 2008 14 0.0833 

32 2008 15 0.0595 

32 2008 16 0.1071 

32 2008 17 0.0357 

32 2008 18 0.0357 

32 2008 19 0.0357 

32 2008 20 0.0119 

32 2008 21 0.0476 

32 2008 22 0.0238 

32 2008 23 0.0119 

32 2008 24 0.0119 

32 2008 25 0.0595 

32 2008 26 0.0357 

32 2008 27 0.0000 

32 2008 28 0.0238 

32 2008 29 0.0238 

32 2008 30 0.1548 

41 2008 0 0.0455 

41 2008 1 0.1136 

41 2008 2 0.0000 

41 2008 3 0.0114 

41 2008 4 0.0227 

41 2008 5 0.0000 

41 2008 6 0.0000 

41 2008 7 0.0114 

41 2008 8 0.0114 

41 2008 9 0.0227 

41 2008 10 0.0114 

41 2008 11 0.0568 

41 2008 12 0.1250 

41 2008 13 0.0227 

41 2008 14 0.0000 

41 2008 15 0.0341 

41 2008 16 0.0341 

41 2008 17 0.0682 

41 2008 18 0.0455 

41 2008 19 0.0909 

41 2008 20 0.0568 

41 2008 21 0.0455 

41 2008 22 0.0341 

41 2008 23 0.0455 

41 2008 24 0.0227 

41 2008 25 0.0227 

41 2008 26 0.0114 

41 2008 27 0.0000 

41 2008 28 0.0227 

41 2008 29 0.0000 

41 2008 30 0.0114 
25 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

42 2008 0 0.0455 

42 2008 1 0.1136 

42 2008 2 0.0000 

42 2008 3 0.0114 

42 2008 4 0.0227 

42 2008 5 0.0000 

42 2008 6 0.0000 

42 2008 7 0.0114 

42 2008 8 0.0114 

42 2008 9 0.0227 

42 2008 10 0.0114 

42 2008 11 0.0568 

42 2008 12 0.1250 

42 2008 13 0.0227 

42 2008 14 0.0000 

42 2008 15 0.0341 

42 2008 16 0.0341 

42 2008 17 0.0682 

42 2008 18 0.0455 

42 2008 19 0.0909 

42 2008 20 0.0568 

42 2008 21 0.0455 

42 2008 22 0.0341 

42 2008 23 0.0455 

42 2008 24 0.0227 

42 2008 25 0.0227 

42 2008 26 0.0114 

42 2008 27 0.0000 

42 2008 28 0.0227 

42 2008 29 0.0000 

42 2008 30 0.0114 

43 2008 0 0.0455 

43 2008 1 0.1136 

43 2008 2 0.0000 

43 2008 3 0.0114 

43 2008 4 0.0227 

43 2008 5 0.0000 

43 2008 6 0.0000 

43 2008 7 0.0114 

43 2008 8 0.0114 

43 2008 9 0.0227 

43 2008 10 0.0114 

43 2008 11 0.0568 

43 2008 12 0.1250 

43 2008 13 0.0227 

43 2008 14 0.0000 

43 2008 15 0.0341 

43 2008 16 0.0341 

43 2008 17 0.0682 

43 2008 18 0.0455 

43 2008 19 0.0909 

43 2008 20 0.0568 

43 2008 21 0.0455 

43 2008 22 0.0341 

43 2008 23 0.0455 

43 2008 24 0.0227 

43 2008 25 0.0227 

43 2008 26 0.0114 

43 2008 27 0.0000 

43 2008 28 0.0227 

43 2008 29 0.0000 

43 2008 30 0.0114 

51 2008 0 0.0025 

51 2008 1 0.0200 

51 2008 2 0.0386 

51 2008 3 0.0436 

51 2008 4 0.0495 

51 2008 5 0.0579 

51 2008 6 0.0667 

51 2008 7 0.0698 

51 2008 8 0.0620 

51 2008 9 0.0611 

51 2008 10 0.0675 

51 2008 11 0.0619 

51 2008 12 0.0508 

51 2008 13 0.0529 

51 2008 14 0.0397 

51 2008 15 0.0397 

51 2008 16 0.0375 

51 2008 17 0.0276 

51 2008 18 0.0204 

51 2008 19 0.0184 

51 2008 20 0.0158 

51 2008 21 0.0174 

51 2008 22 0.0152 

51 2008 23 0.0108 

51 2008 24 0.0108 

51 2008 25 0.0071 

51 2008 26 0.0052 

51 2008 27 0.0031 

51 2008 28 0.0021 

51 2008 29 0.0021 

51 2008 30 0.0220 

52 2008 0 0.0025 

52 2008 1 0.0200 

52 2008 2 0.0386 

52 2008 3 0.0436 

52 2008 4 0.0495 
26 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

52 2008 5 0.0579 

52 2008 6 0.0667 

52 2008 7 0.0698 

52 2008 8 0.0620 

52 2008 9 0.0611 

52 2008 10 0.0675 

52 2008 11 0.0619 

52 2008 12 0.0508 

52 2008 13 0.0529 

52 2008 14 0.0397 

52 2008 15 0.0397 

52 2008 16 0.0375 

52 2008 17 0.0276 

52 2008 18 0.0204 

52 2008 19 0.0184 

52 2008 20 0.0158 

52 2008 21 0.0174 

52 2008 22 0.0152 

52 2008 23 0.0108 

52 2008 24 0.0108 

52 2008 25 0.0071 

52 2008 26 0.0052 

52 2008 27 0.0031 

52 2008 28 0.0021 

52 2008 29 0.0021 

52 2008 30 0.0220 

53 2008 0 0.0025 

53 2008 1 0.0200 

53 2008 2 0.0386 

53 2008 3 0.0436 

53 2008 4 0.0495 

53 2008 5 0.0579 

53 2008 6 0.0667 

53 2008 7 0.0698 

53 2008 8 0.0620 

53 2008 9 0.0611 

53 2008 10 0.0675 

53 2008 11 0.0619 

53 2008 12 0.0508 

53 2008 13 0.0529 

53 2008 14 0.0397 

53 2008 15 0.0397 

53 2008 16 0.0375 

53 2008 17 0.0276 

53 2008 18 0.0204 

53 2008 19 0.0184 

53 2008 20 0.0158 

53 2008 21 0.0174 

53 2008 22 0.0152 

53 2008 23 0.0108 

53 2008 24 0.0108 

53 2008 25 0.0071 

53 2008 26 0.0052 

53 2008 27 0.0031 

53 2008 28 0.0021 

53 2008 29 0.0021 

53 2008 30 0.0220 

54 2008 0 0.0025 

54 2008 1 0.0200 

54 2008 2 0.0386 

54 2008 3 0.0436 

54 2008 4 0.0495 

54 2008 5 0.0579 

54 2008 6 0.0667 

54 2008 7 0.0698 

54 2008 8 0.0620 

54 2008 9 0.0611 

54 2008 10 0.0675 

54 2008 11 0.0619 

54 2008 12 0.0508 

54 2008 13 0.0529 

54 2008 14 0.0397 

54 2008 15 0.0397 

54 2008 16 0.0375 

54 2008 17 0.0276 

54 2008 18 0.0204 

54 2008 19 0.0184 

54 2008 20 0.0158 

54 2008 21 0.0174 

54 2008 22 0.0152 

54 2008 23 0.0108 

54 2008 24 0.0108 

54 2008 25 0.0071 

54 2008 26 0.0052 

54 2008 27 0.0031 

54 2008 28 0.0021 

54 2008 29 0.0021 

54 2008 30 0.0220 

61 2008 0 0.0000 

61 2008 1 0.0064 

61 2008 2 0.0295 

61 2008 3 0.0205 

61 2008 4 0.0321 

61 2008 5 0.0346 

61 2008 6 0.0423 

61 2008 7 0.0308 

61 2008 8 0.0269 

61 2008 9 0.0179 
27 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

61 2008 10 0.0462 

61 2008 11 0.0410 

61 2008 12 0.0359 

61 2008 13 0.0513 

61 2008 14 0.0333 

61 2008 15 0.0359 

61 2008 16 0.0423 

61 2008 17 0.0269 

61 2008 18 0.0295 

61 2008 19 0.0231 

61 2008 20 0.0385 

61 2008 21 0.0397 

61 2008 22 0.0333 

61 2008 23 0.0346 

61 2008 24 0.0295 

61 2008 25 0.0192 

61 2008 26 0.0346 

61 2008 27 0.0128 

61 2008 28 0.0141 

61 2008 29 0.0128 

61 2008 30 0.1244 

62 2008 0 0.0000 

62 2008 1 0.0064 

62 2008 2 0.0295 

62 2008 3 0.0205 

62 2008 4 0.0321 

62 2008 5 0.0346 

62 2008 6 0.0423 

62 2008 7 0.0308 

62 2008 8 0.0269 

62 2008 9 0.0179 

62 2008 10 0.0462 

62 2008 11 0.0410 

62 2008 12 0.0359 

62 2008 13 0.0513 

62 2008 14 0.0333 

62 2008 15 0.0359 

62 2008 16 0.0423 

62 2008 17 0.0269 

62 2008 18 0.0295 

62 2008 19 0.0231 

62 2008 20 0.0385 

62 2008 21 0.0397 

62 2008 22 0.0333 

62 2008 23 0.0346 

62 2008 24 0.0295 

62 2008 25 0.0192 

62 2008 26 0.0346 

62 2008 27 0.0128 

62 2008 28 0.0141 

62 2008 29 0.0128 

62 2008 30 0.1244 
28 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2.4 Vehicle Type VMT and VMT Fractions 

This option is useful to import the annual VMT by source type into MOVES format. It has input 

option as HPMS Base Year VMT, for which we can either use HPMS data or the Travel Demand 

Model output. We have used annual VMT calculated from the OKI Regional Travel Demand 

Model. There are options like the Month VMT fraction, Day VMT fraction and Hour VMT 

fraction, which are useful for calculating emissions for different time periods. We have used 

default Monthly VMT distribution factors provided in the VMT Converter provided by EPA. 

Hourly distribution factors are developed from traffic count data collected in the region and the 

same set of Hourly Distribution Factors are used for all vehicle types and road types. OKI model 

could only predict VMT of two different vehicle types’ autos and trucks. So, we have distributed 

total Annual VMT based on vehicle population in the region. 

Table 7 : Annual VMT for Ohio Custom Domain from OKI travel demand model for 2005 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2005 67065022 0 

20 2005 7405961237 0 

30 2005 4943917030 0 

40 2005 24512225 0 

50 2005 334351024 0 

60 2005 567810955 0 

Table 8 :Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2005 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2005 16658465 0 

20 2005 1815341688 0 

30 2005 1209494070 0 

40 2005 5968488 0 

50 2005 82065068 0 

60 2005 160708291 0 

Table 9: Annual VMT for Ohio Custom Domain from OKI travel demand model for 2008 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 69438850 0 

20 2008 7668102136 0 

30 2008 5118911580 0 

40 2008 25379858 0 

50 2008 346185690 0 

60 2008 587909153 0 
29 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 10 : Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2008 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 17342291 0 

20 2008 1889861055 0 

30 2008 1259143528 0 

40 2008 6213493 0 

50 2008 85433821 0 

60 2008 167305330 0 

Table 9: Annual VMT for Ohio Custom Domain from OKI travel demand model for 2011 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 69438850 0 

20 2008 7668102136 0 

30 2008 5118911580 0 

40 2008 25379858 0 

50 2008 346185689 0 

60 2008 587909153 0 

Table 11: Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2011 

Table 12 :Annual VMT for Ohio Custom Domain from OKI travel demand model for 2015 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2015 73413634 0 

20 2015 8107035747 0 

30 2015 5411925719 0 

40 2015 26832639 0 

50 2015 366001875 0 

60 2015 621561950 0 

Table 13: Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2015 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2015 19903870 0 

20 2015 2169006811 0 

30 2015 1445127874 0 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2011 18163152 0 

20 2011 1979313603 0 

30 2011 1318742406 0 

40 2011 6507596 0 

50 2011 89477649 0 

60 2011 175224371 0 
30 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

40 2015 7131270 0 

50 2015 98052996 0 

60 2015 192017502 0 

Table 14:Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2018 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2018 76802311 0 

20 2018 8481245879 0 

30 2018 5661733109 0 

40 2018 28071199 0 

50 2018 382896041 0 

60 2018 650252434 0 

Table 13:Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2018 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2018 21077884 0 

20 2018 2296944011 0 

30 2018 1530367631 0 

40 2018 7551902 0 

50 2018 103836577 0 

60 2018 203343507 0 

Table 14 : Annual VMT for Ohio Custom Domain from OKI travel demand model for 2021 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2021 79128218 0 

20 2021 8738094842 0 

30 2021 5833194979 0 

40 2021 28921317 0 

50 2021 394491797 0 

60 2021 669944902 0 

Table 15 : Annual VMT for Kentucky Custom Domain from OKI travel demand model for 2021 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2021 21702396 0

20 2021 2364999583 0

30 2021 1575710506 0

40 2021 7775656 0

50 2021 106913124 0

60 2021 209368320 0
31 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 16 : Default Monthly VMT Distribution(year 2008) 

sourceTypeID isLeapYear monthID monthVMTFraction

11 Y 1 0.072904 

11 Y 2 0.072023 

11 Y 3 0.081529 

11 Y 4 0.082098 

11 Y 5 0.087286 

11 Y 6 0.088052 

11 Y 7 0.092096 

11 Y 8 0.093198 

11 Y 9 0.08447 

11 Y 10 0.086301 

11 Y 11 0.080029 

11 Y 12 0.080015 

21 Y 1 0.072904 

21 Y 2 0.072023 

21 Y 3 0.081529 

21 Y 4 0.082098 

21 Y 5 0.087286 

21 Y 6 0.088052 

21 Y 7 0.092096 

21 Y 8 0.093198 

21 Y 9 0.08447 

21 Y 10 0.086301 

21 Y 11 0.080029 

21 Y 12 0.080015 

31 Y 1 0.072904 

31 Y 2 0.072023 

Table 17 : Default Daily VMT distribution ( same for all Source Types and all years) 

sourceTypeID monthID roadTypeID dayID dayVMTFraction

11 1 1 2 0.237635

11 1 1 5 0.762365 

11 1 2 2 0.237635 

11 1 2 5 0.762365 

11 1 3 2 0.237635 

11 1 3 5 0.762365 

11 1 4 2 0.237635 

11 1 4 5 0.762365 

11 1 5 2 0.237635 

11 1 5 5 0.762365 
32 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 18 : Hourly VMT Distribution from local data 

sourceTypeID roadTypeID dayID hourID hourVMTFraction

11 1 2 1 0.021474 

11 1 2 2 0.014443 

11 1 2 3 0.010968 

11 1 2 4 0.007495 

11 1 2 5 0.006839 

11 1 2 6 0.010359 

11 1 2 7 0.01843 

11 1 2 8 0.026812 

11 1 2 9 0.036385 

11 1 2 10 0.047541 

11 1 2 11 0.057466 

11 1 2 12 0.065079 

11 1 2 13 0.071323 

11 1 2 14 0.071492 

11 1 2 15 0.071723 

11 1 2 16 0.072006 

11 1 2 17 0.071149 

11 1 2 18 0.067887 

11 1 2 19 0.061772 

11 1 2 20 0.051688 

11 1 2 21 0.042866 

11 1 2 22 0.03803 

11 1 2 23 0.032207 

11 1 2 24 0.024568 

2.5 Average Speed Distribution Importer 

This importer allows the user to input average speed data specific to vehicle type, road type, 

and time of day/ type of day. The MOVES model defines 16 “speed bins” which describe the 

average driving speed on each road type. Unlike MOBILE 6.2 model, which uses VMT-based 

speed distribution, MOVES use fraction of driving time in each speed bin for each vehicle type, 

for each road type, and for each hour. Thus, for each combination of vehicle type, road type, 

and hour/day type, the fractions will add to one. We have used OKI travel model to calculate 

average speed distribution based on VHT. However, this input is ignored by MOVES when we 

are running emission rate runs† (See Table 27). 

2.6 Road Type Distribution Importer 

User supplied vehicle-miles-traveled data by road type is used as an input in this importer. OKI 

travel demand model can calculate the VMT distribution by functional class, which is further 
33 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

processed to obtain road type VMT distribution. But, our model could not predict off network 

VMT, which is assumed as zero. However, this input is also ignored by MOVES when we are 

running Emission Rate runs † (see Table 26). 

2.7 Ramp Fraction Importer 

This option allows the user to modify the fraction of ramp driving time on selected road types. 

But, in the current version of MOVES model, there is no capability to model Emission Rates for 

Ramps. To circumvent this problem, FHWA has suggested a temporary solution. This solution 

discussed in the Section 3. 

2.8 Fuel Formulation Importer and Fuel Supply Importer 

Fuel formulation importer allows the user to select an existing fuel in the MOVES database and 

change its properties, or create a new fuel formulation with different fuel properties. But we 

have used only default fuels available in MOVES default database. Fuel supply importer allows 

the user to assign existing fuels to counties, months, and years, and the associated market 

share for each fuel. We have used default fuel supply from MOVES default database. And same 

type of fuel is used for Whole Custom Domain. 

Table 19 : Fuel supply data for Ohio Custom Domain ( same for all years) 

countyID fuelYearID monthGroupID fuelFormulationID marketShare marketShareCV

99390 2008 1 3982 1 

99390 2008 1 20011 1 

99390 2008 2 3982 1 

99390 2008 2 20011 1 

99390 2008 3 3982 1 

99390 2008 3 20011 1 

99390 2008 4 3982 1 

99390 2008 4 20011 1 

99390 2008 5 3982 1 

99390 2008 5 20011 1 

99390 2008 6 3982 1 

99390 2008 6 20011 1 

99390 2008 7 3982 1 

99390 2008 7 20011 1 

99390 2008 8 3982 1 

99390 2008 8 20011 1 

99390 2008 9 3982 1 

99390 2008 9 20011 1 

99390 2008 10 3982 1 

99390 2008 10 20011 1 

99390 2008 11 3982 1 

99390 2008 11 20011 1 
34 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

99390 2008 12 3982 1 

99390 2008 12 20011 1 

Table 20: Fuel supply data for Kentucky Custom Domain (same for all years) 

countyID fuelYearID monthGroupID fuelFormulationID marketShare marketShareCV

99210 2012 1 3982 1 

99210 2012 1 20011 1 

99210 2012 2 3982 1 

99210 2012 2 20011 1 

99210 2012 3 3982 1 

99210 2012 3 20011 1 

99210 2012 4 3982 1 

99210 2012 4 20011 1 

99210 2012 5 3982 1 

99210 2012 5 20011 1 

99210 2012 6 3982 1 

99210 2012 6 20011 1 

99210 2012 7 3982 1 

99210 2012 7 20011 1 

99210 2012 8 3982 1 

99210 2012 8 20011 1 

99210 2012 9 3982 1 

99210 2012 9 20011 1 

99210 2012 10 3982 1 

99210 2012 10 20011 1 

99210 2012 11 3982 1 

99210 2012 11 20011 1 

99210 2012 12 3982 1 

99210 2012 12 20011 1 

2.9 I/M Importer 

The I/M Importer allows the user to import information describing the inspection and 

maintenance programs. In the default database there is an option, whether to use default I/M 

program or not. We choose no I/M program for all of the Runspecs[sic] in the whole region. 

2.10 Zone Road Activity Importer 

The Zone Road Activity Importer is used only if the Custom Domain option is chosen in the 

County Domain Manager. We have used value 1 for SHOallocfactor for each road type which 

means that all of the VMT input by the users is assigned to custom domain. 
35 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 21 : Kentucky Custom Domain Zone road activity data (same for all years) 

zoneID roadTypeID SHOAllocFactor 

992100 1 1 

992100 2 1 

992100 3 1 

992100 4 1 

992100 5 1 

Table 22 : Ohio Custom Domain Zone road activity data (same for all years) 

zoneID roadTypeID SHOAllocFactor 

993900 1 1 

993900 2 1 

993900 3 1 

993900 4 1 

993900 5 1 

3. Ramp Inventory Runs 

As discussed earlier, current version of the MOVES model cannot calculate Emission Rates for 

Ramps. To deal with this problem, FHWA has suggested an approach. The steps involved in this 

method are: (a) Calculating Emission Inventory for Urban Restricted and Rural Restricted road 

types keeping Ramp fraction as 1 (b) Finding out total VMT of Urban Restricted and Rural 

Restricted road types using MOVESactivityoutput option (c) Calculation Emission Rates for 

Ramps through dividing Emission Inventory with VMT ( d) Finally, using the Emission Rates in 

post processing for calculating regional Emission Inventory. 

Table 23 :Ramp fraction Input 

roadTypeID rampFraction

2 1

4 1
36 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

4. Post-Processing of MOVES Output 

4.1 Linking SQL tables to Microsoft Access 

Microsoft Access 2007 was used for the post-processing. An ODBC connection with the MOVES 

output directory was established. Information on how to link or import SQL tables to Access 

can be found in the MOVES Users Guide. 

4.2 Creating Emission Rate Lookup Tables 

The ratepervehicle and rateperdistance SQL tables, one set for each state (Kentucky and Ohio) 

and analysis year, were imported into Access. Ohio emission rates are used for the 

nonattainment portion of Dearborn County Indiana. Rateperprofile output was not generated 

by MOVES because evaporative output was not selected (i.e. VOC). Tables were renamed with 

state and analysis year in the format OH_20xxrateperdistance. All rateperdistance tables were 

merged with a Union query. The SQL commands are shown in Figure 3.1. ratepervehicle tables 

were merged in the same manner. 

 

Table 24 :Rateperdistance Union Query 

SELECT * 

FROM OH_2008rateperdistance 

WHERE MOVESRunID = (select max (MOVESRunID) from OH_2008rateperdistance) AND 

pollutantID = 3 Or MOVESRunID = (select max (MOVESRunID) from 

OH_2008rateperdistance) AND pollutantID=110 Or MOVESRunID = (select max 

(MOVESRunID) from OH_2008rateperdistance) AND pollutantID=116 Or MOVESRunID = 

(select max (MOVESRunID) from OH_2008rateperdistance) AND pollutantID=117 Or 

MOVESRunID = (select max (MOVESRunID) from OH_2008rateperdistance) AND 

pollutantID = 31 

UNION ALL select * 

FROM OH_2011rateperdistance 

WHERE ..... (repeated for each file) 

“Rateperdistance_state” and “Ratepervehicle_state” tables were created from the union query 

output using a Make Table query. Emission rates for each process were summed by pollutant 

and a stateID field is created. The SQL commands for creating the “Rateperdistance_state” 

table are shown in Table 25. Unique index fields were identified for each of the two tables. 

Indexes facilitate more efficient data processing. 

Table 25: Rateperdistance_State Query 

SELECT Val(Mid([LinkID],3,2)) AS StateID, Union_rateperdistance_state.yearID, 

Union_rateperdistance_state.monthID, Union_rateperdistance_state.linkID, 

Union_rateperdistance_state.hourID, Union_rateperdistance_state.sourceTypeID, 

Union_rateperdistance_state.roadTypeID, 

Union_rateperdistance_state.avgSpeedBinID, 

Union_rateperdistance_state.pollutantID, 

Sum(Union_rateperdistance_state.ratePerDistance) AS SumOfratePerDistance INTO 

rateperdistance_state 
37 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

FROM Union_rateperdistance_state 

GROUP BY Val(Mid([LinkID],3,2)), Union_rateperdistance_state.yearID, 

Union_rateperdistance_state.monthID, Union_rateperdistance_state.linkID, 

Union_rateperdistance_state.hourID, Union_rateperdistance_state.sourceTypeID, 

Union_rateperdistance_state.roadTypeID, 

Union_rateperdistance_state.avgSpeedBinID, 

Union_rateperdistance_state.pollutantID 

ORDER BY Union_rateperdistance_state.linkID, 

Union_rateperdistance_state.hourID, Union_rateperdistance_state.pollutantID; 

4.1 Creating a VMT Table by County 

The VMT table includes Daily VMT by county by analysis year from the OKI Travel Demand 

Model (TDM). Summer factors and applied by functional class to create Summer VMT. 

Seasonal factors by functional class are contained in the report, “OKI Travel Demand 

Forecasting Model, Update of Hourly and Seasonal Factors as Used in Air Quality Impact 

Calculations”, September 2001. Annual VMT is calculated by using EPA’s VMT converter to 

grow daily VMT to annual VMT. In order to accommodate an error in MOVES 2010, all VMT 

values are exclusive of ramp VMT. Ramp VMT and emission are added in later in the process. 

In order to apply the emission rates, it is necessary to factor the county VMT by source type, 

hour, road type and speed bin. 

Table 26 : VMT and Source Type Population by County and Year 

County Daily VMT Summer 

VMT 

Annual VMT yearID SourceType

Population 

stateID

Boone 3924117 4186006 1273226984 2005 129823 21

Boone 4076584 4355527 1350001557 2008 134028 21

Boone 4383716 4681593 1448879510 2011 136181 21

Boone 4950741 5276742 1628041303 2015 140590 21

Boone 5260102 5597287 1729595179 2018 143991 21

Boone 5478224 5826768 1800571708 2021 147476 21

Campbell 2286217 2437698 741790605 2005 86065 21

Campbell 2339542 2495174 774762729 2008 88853 21

Campbell 2421600 2582758 800372702 2011 90279 21

Campbell 2663159 2844504 875774499 2015 93204 21

Campbell 2771476 2958827 911300109 2018 95458 21

Campbell 2849127 3041704 936445364 2021 97768 21

Kenton 3927743 4182042 1274091658 2005 148193 21

Kenton 3927332 4185652 1300575265 2008 152992 21

Kenton 4049886 4327836 1338544021 2011 155451 21

Kenton 4341124 4614242 1427569992 2015 160484 21

Kenton 4629694 4880614 1522308203 2018 164368 21

Kenton 4715306 5006383 1549817345 2021 168343 21

Butler 578641 7804476 196737836 2005 24915 39
38 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Butler 587582 8133554 199777880 2008 25722 39

Butler 605620 8454053 205910800 2011 26135 39

Butler 657778 8768598 223644400 2015 26982 39

Butler 684361 9232457 232682740 2018 27635 39

Butler 706828 9592567 240321520 2021 28303 39

Clermont 7452286 5391578 2469166037 2005 401759 39

Clermont 7745685 5599530 2598059212 2008 414771 39

Clermont 8050701 5841102 2693716250 2011 421434 39

Clermont 8361487 6035155 2792188144 2015 435082 39

Clermont 8806042 6314640 2940849935 2018 445608 39

Clermont 9150031 6562428 2966037449 2021 456389 39

Dearborn NA 5083331 599761 1684259908 2005 232380 39

Dearborn NA 5262489 613027 1765145113 2008 239906 39

Dearborn NA 5489545 631914 1836768820 2011 243760 39

Dearborn NA 5687698 685272 1899318043 2015 251654 39

Dearborn NA 5952603 712461 1987920583 2018 257742 39

Dearborn NA 6186441 735862 2005371969 2021 263978 39

Hamilton 21859452 23170766 7241529618 2005 862422 39

Hamilton 22124503 23447460 7421005221 2008 890352 39

Hamilton 22426021 23803187 7503612070 2011 904655 39

Hamilton 22849494 24259554 7630232069 2015 933953 39

Hamilton 23630554 25096560 7891617279 2018 956548 39

Hamilton 24098698 25596996 7811737549 2021 979689 39

Warren 5884216 6263010 1949617151 2005 233106 39

Warren 6057338 6464217 2031753523 2008 240655 39

Warren 6406284 6835660 2143504189 2011 244521 39

Warren 6842828 7279441 2285055662 2015 252440 39

Warren 7368035 7836746 2460615706 2018 258547 39

Warren 7707500 8194596 2498432370 2021 264802 39

4.2 Source type population and source type VMT distribution 

A combination of local and MOVES default data were used for the source type populations. The 

source type VMT fractions are based on the ratio of MOVES default source type population and 

MOVES default source type VMT. It is assumed that the growth rate of source type populations 

is equal to the regional annual household growth rate of 0.8%. Source type VMT fractions are 

the same for all analysis years. 
39 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 27: Base Year Source Type Population and VMT Fraction 

stateID sourceTypeID sourceType

Population 

sourceTypeFraction sourceTypeVMTFraction

39 11 69121 0.038559 0.005026

39 21 1200827 0.669872 0.555019

39 31 486373 0.271319 0.277725

39 32 15947 0.008896 0.092783

39 41 458 0.000255 0.000754

39 42 82 0.000046 0.000225

39 43 3681 0.002053 0.000858

39 51 0 0.000000 0.000644

39 52 369 0.000206 0.020527

39 53 364 0.000203 0.002663

39 54 4928 0.002749 0.001224

39 61 4879 0.002722 0.017977

39 62 5593 0.003120 0.024576

21 11 8040 0.021370 0.005063

21 21 198623 0.527931 0.551736

21 31 121506 0.322958 0.275546

21 32 40593 0.107894 0.092055

21 41 128 0.000340 0.000745

21 42 21 0.000056 0.000222

21 43 985 0.002618 0.000847

21 51 0 0.000000 0.000641

21 52 767 0.002039 0.020433

21 53 757 0.002012 0.002650

21 54 1390 0.003695 0.001218

21 61 1593 0.004234 0.020634

21 62 1826 0.004853 0.028210

4.3 Hourly distribution 

MOVES default hourly distribution by source type was used during the post-processing. 

4.4 Road type distribution 

Road type VMT fractions by source type are default values, except for passenger cars (source 

type 21) and passenger trucks (source type 31). VMT fractions from the OKI TDM are used for 

passenger cars and passenger trucks. 
40 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 28: Base Year Source Type Population and VMT Fraction

sourceTypeID roadTypeID roadTypeVMTFraction stateID

21 1 0 21

21 2 0.0952 21

21 3 0.0818 21

21 4 0.4741 21

21 5 0.3489 21

31 1 0 21

31 2 0.0952 21

31 3 0.0818 21

31 4 0.4741 21

31 5 0.3489 21

21 1 0 39

21 2 0.0436 39

21 3 0.1256 39

21 4 0.4143 39

21 5 0.4165 39

31 1 0 39

31 2 0.0436 39

31 3 0.1256 39

31 4 0.4143 39

31 5 0.4165 39

4.5 Average speed distribution 

Average speed fractions for each of the 16 speed bins are provided by the OKI TDM. The 

average speed fractions vary by state, year, road type and hour. 

Table 29: Average Speed Distribution (Example: only road type 2, year 2011, Ohio values shown) 

roadTypeID hourID avgSpeedBinID avgSpeedFraction YearID stateID

2 1 1 0.00000000 2011 39

2 1 2 0.00000000 2011 39

2 1 3 0.00000000 2011 39

2 1 4 0.00000000 2011 39

2 1 5 0.00000000 2011 39

2 1 6 0.00000000 2011 39

2 1 7 0.00000000 2011 39

2 1 8 0.00000000 2011 39
41 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 1 9 0.00000000 2011 39

2 1 10 0.11922233 2011 39

2 1 11 0.12547629 2011 39

2 1 12 0.19752816 2011 39

2 1 13 0.00589550 2011 39

2 1 14 0.00000000 2011 39

2 1 15 0.55187773 2011 39

2 1 16 0.00000000 2011 39

2 2 1 0.00000000 2011 39

2 2 2 0.00000000 2011 39

2 2 3 0.00000000 2011 39

2 2 4 0.00000000 2011 39

2 2 5 0.00000000 2011 39

2 2 6 0.00000000 2011 39

2 2 7 0.00000000 2011 39

2 2 8 0.00000000 2011 39

2 2 9 0.00000000 2011 39

2 2 10 0.11922233 2011 39

2 2 11 0.12547629 2011 39

2 2 12 0.19752816 2011 39

2 2 13 0.00589550 2011 39

2 2 14 0.00000000 2011 39

2 2 15 0.55187773 2011 39

2 2 16 0.00000000 2011 39

2 3 1 0.00000000 2011 39

2 3 2 0.00000000 2011 39

2 3 3 0.00000000 2011 39

2 3 4 0.00000000 2011 39

2 3 5 0.00000000 2011 39

2 3 6 0.00000000 2011 39

2 3 7 0.00000000 2011 39

2 3 8 0.00000000 2011 39

2 3 9 0.00000000 2011 39

2 3 10 0.11922233 2011 39

2 3 11 0.12547629 2011 39

2 3 12 0.19752816 2011 39

2 3 13 0.00589550 2011 39

2 3 14 0.06436270 2011 39
42 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 3 15 0.48751503 2011 39

2 3 16 0.00000000 2011 39

2 4 1 0.00000000 2011 39

2 4 2 0.00000000 2011 39

2 4 3 0.00000000 2011 39

2 4 4 0.00000000 2011 39

2 4 5 0.00000000 2011 39

2 4 6 0.00000000 2011 39

2 4 7 0.00000000 2011 39

2 4 8 0.00000000 2011 39

2 4 9 0.00000000 2011 39

2 4 10 0.11922233 2011 39

2 4 11 0.12547629 2011 39

2 4 12 0.19752816 2011 39

2 4 13 0.00589550 2011 39

2 4 14 0.12369152 2011 39

2 4 15 0.42818621 2011 39

2 4 16 0.00000000 2011 39

2 5 1 0.00000000 2011 39

2 5 2 0.00000000 2011 39

2 5 3 0.00000000 2011 39

2 5 4 0.00000000 2011 39

2 5 5 0.00000000 2011 39

2 5 6 0.00000000 2011 39

2 5 7 0.00000000 2011 39

2 5 8 0.00000000 2011 39

2 5 9 0.00000000 2011 39

2 5 10 0.11922233 2011 39

2 5 11 0.12547629 2011 39

2 5 12 0.26189086 2011 39

2 5 13 0.09782827 2011 39

2 5 14 0.06250896 2011 39

2 5 15 0.33307330 2011 39

2 5 16 0.00000000 2011 39

2 6 1 0.00000000 2011 39

2 6 2 0.12369152 2011 39

2 6 3 0.03260396 2011 39

2 6 4 0.03085494 2011 39
43 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 6 5 0.01601927 2011 39

2 6 6 0.01563475 2011 39

2 6 7 0.00000000 2011 39

2 6 8 0.00000000 2011 39

2 6 9 0.00000000 2011 39

2 6 10 0.11922233 2011 39

2 6 11 0.12547629 2011 39

2 6 12 0.19752816 2011 39

2 6 13 0.00589550 2011 39

2 6 14 0.00278921 2011 39

2 6 15 0.33028409 2011 39

2 6 16 0.00000000 2011 39

2 7 1 0.21880443 2011 39

2 7 2 0.00000000 2011 39

2 7 3 0.00278921 2011 39

2 7 4 0.00000000 2011 39

2 7 5 0.01131028 2011 39

2 7 6 0.03548550 2011 39

2 7 7 0.03519436 2011 39

2 7 8 0.03514937 2011 39

2 7 9 0.00617093 2011 39

2 7 10 0.08564905 2011 39

2 7 11 0.14691446 2011 39

2 7 12 0.13064235 2011 39

2 7 13 0.00589550 2011 39

2 7 14 0.14352342 2011 39

2 7 15 0.14247115 2011 39

2 7 16 0.00000000 2011 39

2 8 1 0.21880443 2011 39

2 8 2 0.00278921 2011 39

2 8 3 0.01131028 2011 39

2 8 4 0.01356074 2011 39

2 8 5 0.05711912 2011 39

2 8 6 0.03514937 2011 39

2 8 7 0.02902228 2011 39

2 8 8 0.01193860 2011 39

2 8 9 0.07229727 2011 39

2 8 10 0.05473480 2011 39
44 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 8 11 0.08493157 2011 39

2 8 12 0.11645227 2011 39

2 8 13 0.04360641 2011 39

2 8 14 0.20648097 2011 39

2 8 15 0.04180267 2011 39

2 8 16 0.00000000 2011 39

2 9 1 0.21880443 2011 39

2 9 2 0.00000000 2011 39

2 9 3 0.00000000 2011 39

2 9 4 0.00000000 2011 39

2 9 5 0.00000000 2011 39

2 9 6 0.00278921 2011 39

2 9 7 0.00000000 2011 39

2 9 8 0.04679577 2011 39

2 9 9 0.03519436 2011 39

2 9 10 0.08369185 2011 39

2 9 11 0.14590244 2011 39

2 9 12 0.15349370 2011 39

2 9 13 0.02733367 2011 39

2 9 14 0.03771092 2011 39

2 9 15 0.24828365 2011 39

2 9 16 0.00000000 2011 39

2 10 1 0.15629548 2011 39

2 10 2 0.04687421 2011 39

2 10 3 0.01563475 2011 39

2 10 4 0.00000000 2011 39

2 10 5 0.00000000 2011 39

2 10 6 0.00000000 2011 39

2 10 7 0.00000000 2011 39

2 10 8 0.00000000 2011 39

2 10 9 0.00000000 2011 39

2 10 10 0.11922233 2011 39

2 10 11 0.13957578 2011 39

2 10 12 0.18621788 2011 39

2 10 13 0.00589550 2011 39

2 10 14 0.04428952 2011 39

2 10 15 0.28599457 2011 39

2 10 16 0.00000000 2011 39
45 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 11 1 0.06436270 2011 39

2 11 2 0.12278771 2011 39

2 11 3 0.01601927 2011 39

2 11 4 0.01563475 2011 39

2 11 5 0.00000000 2011 39

2 11 6 0.00000000 2011 39

2 11 7 0.00000000 2011 39

2 11 8 0.00000000 2011 39

2 11 9 0.00000000 2011 39

2 11 10 0.11922233 2011 39

2 11 11 0.13678656 2011 39

2 11 12 0.18900709 2011 39

2 11 13 0.00589550 2011 39

2 11 14 0.02285135 2011 39

2 11 15 0.30743274 2011 39

2 11 16 0.00000000 2011 39

2 12 1 0.06436270 2011 39

2 12 2 0.09193278 2011 39

2 12 3 0.04687421 2011 39

2 12 4 0.01563475 2011 39

2 12 5 0.00000000 2011 39

2 12 6 0.00000000 2011 39

2 12 7 0.00000000 2011 39

2 12 8 0.00000000 2011 39

2 12 9 0.00000000 2011 39

2 12 10 0.11922233 2011 39

2 12 11 0.12547629 2011 39

2 12 12 0.20031737 2011 39

2 12 13 0.00589550 2011 39

2 12 14 0.02285135 2011 39

2 12 15 0.30743274 2011 39

2 12 16 0.00000000 2011 39

2 13 1 0.06436270 2011 39

2 13 2 0.09193278 2011 39

2 13 3 0.04687421 2011 39

2 13 4 0.01563475 2011 39

2 13 5 0.00000000 2011 39

2 13 6 0.00000000 2011 39
46 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 13 7 0.00000000 2011 39

2 13 8 0.00000000 2011 39

2 13 9 0.00000000 2011 39

2 13 10 0.11922233 2011 39

2 13 11 0.12547629 2011 39

2 13 12 0.19752816 2011 39

2 13 13 0.00868471 2011 39

2 13 14 0.00000000 2011 39

2 13 15 0.33028409 2011 39

2 13 16 0.00000000 2011 39

2 14 1 0.06436270 2011 39

2 14 2 0.09193278 2011 39

2 14 3 0.03085494 2011 39

2 14 4 0.03165402 2011 39

2 14 5 0.00000000 2011 39

2 14 6 0.00000000 2011 39

2 14 7 0.00000000 2011 39

2 14 8 0.00000000 2011 39

2 14 9 0.00000000 2011 39

2 14 10 0.11922233 2011 39

2 14 11 0.12547629 2011 39

2 14 12 0.19752816 2011 39

2 14 13 0.00868471 2011 39

2 14 14 0.00000000 2011 39

2 14 15 0.33028409 2011 39

2 14 16 0.00000000 2011 39

2 15 1 0.06436270 2011 39

2 15 2 0.09193278 2011 39

2 15 3 0.04687421 2011 39

2 15 4 0.01563475 2011 39

2 15 5 0.00000000 2011 39

2 15 6 0.00000000 2011 39

2 15 7 0.00000000 2011 39

2 15 8 0.00000000 2011 39

2 15 9 0.00000000 2011 39

2 15 10 0.11922233 2011 39

2 15 11 0.12547629 2011 39

2 15 12 0.19752816 2011 39
47 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 15 13 0.00868471 2011 39

2 15 14 0.02285135 2011 39

2 15 15 0.30743274 2011 39

2 15 16 0.00000000 2011 39

2 16 1 0.12369152 2011 39

2 16 2 0.07947816 2011 39

2 16 3 0.01563475 2011 39

2 16 4 0.00000000 2011 39

2 16 5 0.00000000 2011 39

2 16 6 0.00000000 2011 39

2 16 7 0.00000000 2011 39

2 16 8 0.00000000 2011 39

2 16 9 0.00000000 2011 39

2 16 10 0.11922233 2011 39

2 16 11 0.13957578 2011 39

2 16 12 0.18621788 2011 39

2 16 13 0.00589550 2011 39

2 16 14 0.04428952 2011 39

2 16 15 0.28599457 2011 39

2 16 16 0.00000000 2011 39

2 17 1 0.20316968 2011 39

2 17 2 0.01563475 2011 39

2 17 3 0.00000000 2011 39

2 17 4 0.00000000 2011 39

2 17 5 0.00000000 2011 39

2 17 6 0.00000000 2011 39

2 17 7 0.00278921 2011 39

2 17 8 0.02487101 2011 39

2 17 9 0.05711912 2011 39

2 17 10 0.05722137 2011 39

2 17 11 0.15811770 2011 39

2 17 12 0.14489757 2011 39

2 17 13 0.05018502 2011 39

2 17 14 0.00000000 2011 39

2 17 15 0.28599457 2011 39

2 17 16 0.00000000 2011 39

2 18 1 0.15629548 2011 39

2 18 2 0.04687421 2011 39
48 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 18 3 0.01563475 2011 39

2 18 4 0.00000000 2011 39

2 18 5 0.00000000 2011 39

2 18 6 0.00000000 2011 39

2 18 7 0.00000000 2011 39

2 18 8 0.00000000 2011 39

2 18 9 0.00000000 2011 39

2 18 10 0.11922233 2011 39

2 18 11 0.13957578 2011 39

2 18 12 0.18621788 2011 39

2 18 13 0.00589550 2011 39

2 18 14 0.04428952 2011 39

2 18 15 0.28599457 2011 39

2 18 16 0.00000000 2011 39

2 19 1 0.00000000 2011 39

2 19 2 0.00000000 2011 39

2 19 3 0.00000000 2011 39

2 19 4 0.00000000 2011 39

2 19 5 0.06436270 2011 39

2 19 6 0.00000000 2011 39

2 19 7 0.05932882 2011 39

2 19 8 0.03260396 2011 39

2 19 9 0.00000000 2011 39

2 19 10 0.15007727 2011 39

2 19 11 0.14149556 2011 39

2 19 12 0.21316291 2011 39

2 19 13 0.00589550 2011 39

2 19 14 0.00000000 2011 39

2 19 15 0.33307330 2011 39

2 19 16 0.00000000 2011 39

2 20 1 0.00000000 2011 39

2 20 2 0.00000000 2011 39

2 20 3 0.00000000 2011 39

2 20 4 0.00000000 2011 39

2 20 5 0.00000000 2011 39

2 20 6 0.00000000 2011 39

2 20 7 0.00000000 2011 39

2 20 8 0.00000000 2011 39
49 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 20 9 0.00000000 2011 39

2 20 10 0.11922233 2011 39

2 20 11 0.12547629 2011 39

2 20 12 0.19752816 2011 39

2 20 13 0.00589550 2011 39

2 20 14 0.15629548 2011 39

2 20 15 0.39558226 2011 39

2 20 16 0.00000000 2011 39

2 21 1 0.00000000 2011 39

2 21 2 0.00000000 2011 39

2 21 3 0.00000000 2011 39

2 21 4 0.00000000 2011 39

2 21 5 0.00000000 2011 39

2 21 6 0.00000000 2011 39

2 21 7 0.00000000 2011 39

2 21 8 0.00000000 2011 39

2 21 9 0.00000000 2011 39

2 21 10 0.11922233 2011 39

2 21 11 0.12547629 2011 39

2 21 12 0.19752816 2011 39

2 21 13 0.00589550 2011 39

2 21 14 0.00000000 2011 39

2 21 15 0.55187773 2011 39

2 21 16 0.00000000 2011 39

2 22 1 0.00000000 2011 39

2 22 2 0.00000000 2011 39

2 22 3 0.00000000 2011 39

2 22 4 0.00000000 2011 39

2 22 5 0.00000000 2011 39

2 22 6 0.00000000 2011 39

2 22 7 0.00000000 2011 39

2 22 8 0.00000000 2011 39

2 22 9 0.00000000 2011 39

2 22 10 0.11922233 2011 39

2 22 11 0.12547629 2011 39

2 22 12 0.19752816 2011 39

2 22 13 0.00589550 2011 39

2 22 14 0.00000000 2011 39
50 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

2 22 15 0.55187773 2011 39

2 22 16 0.00000000 2011 39

2 23 1 0.00000000 2011 39

2 23 2 0.00000000 2011 39

2 23 3 0.00000000 2011 39

2 23 4 0.00000000 2011 39

2 23 5 0.00000000 2011 39

2 23 6 0.00000000 2011 39

2 23 7 0.00000000 2011 39

2 23 8 0.00000000 2011 39

2 23 9 0.00000000 2011 39

2 23 10 0.11922233 2011 39

2 23 11 0.12547629 2011 39

2 23 12 0.19752816 2011 39

2 23 13 0.00589550 2011 39

2 23 14 0.00000000 2011 39

2 23 15 0.55187773 2011 39

2 23 16 0.00000000 2011 39

2 24 1 0.00000000 2011 39

2 24 2 0.00000000 2011 39

2 24 3 0.00000000 2011 39

2 24 4 0.00000000 2011 39

2 24 5 0.00000000 2011 39

2 24 6 0.00000000 2011 39

2 24 7 0.00000000 2011 39

2 24 8 0.00000000 2011 39

2 24 9 0.00000000 2011 39

2 24 10 0.11922233 2011 39

2 24 11 0.12547629 2011 39

2 24 12 0.19752816 2011 39

2 24 13 0.00589550 2011 39

2 24 14 0.00000000 2011 39

2 24 15 0.55187773 2011 39

2 24 16 0.00000000 2011 39
51 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

4.6 Creating a VMT Table by County, Year, Source Type, Hour, Road Type, and 

Average Speed Bin 

The ‘CountyVMT’ query creates a County VMT Table by source type, hour, road type and 

average speed utilizing the VMT distribution factors described in 4.2, 4.3, 4.4 and 4.5. The SQL 

commands for this query are shown in Table 30. 

Table 30 :County VMT Table Query 

SELECT VMT.yearID, VMT.State, roadtypedistribution1.stateID, VMT.County, 

roadtypedistribution1.sourceTypeID, hourvmtfraction.hourID, 

roadtypedistribution1.roadTypeID, avgSpeedDistribution.avgSpeedBinID, 

sourecetypepopulation.sourceTypeFraction, hourvmtfraction.hourVMTFraction, 

roadtypedistribution1.roadTypeVMTFraction, 

avgSpeedDistribution.avgSpeedFraction, First(VMT.[Annual VMT]) AS 

[FirstOfAnnual VMT], First(VMT.[Summer VMT]) AS [FirstOfSummer VMT], 

[FirstOfSummer 

VMT]*[hourVMTFraction]*[sourceTypeFraction]*[roadTypeVMTFraction]*[avgSpeedFr

action] AS DailyVMT, [FirstOfAnnual 

VMT]*[sourceTypeFraction]*[hourVMTFraction]*[roadTypeVMTFraction]*[avgSpeedFr

action] AS AnnualizedVMT INTO CountyVMT_Table 

FROM (((avgSpeedDistribution INNER JOIN hourvmtfraction ON 

(avgSpeedDistribution.hourDayID = hourvmtfraction.hourID) AND 

(avgSpeedDistribution.roadTypeID = hourvmtfraction.roadTypeID) AND 

(avgSpeedDistribution.sourceTypeID = hourvmtfraction.sourceTypeID)) INNER 

JOIN roadtypedistribution1 ON (avgSpeedDistribution.stateID = 

roadtypedistribution1.stateID) AND (avgSpeedDistribution.roadTypeID = 

roadtypedistribution1.roadTypeID) AND (avgSpeedDistribution.sourceTypeID = 

roadtypedistribution1.sourceTypeID)) INNER JOIN sourecetypepopulation ON 

(avgSpeedDistribution.sourceTypeID = sourecetypepopulation.sourceTypeID) AND 

(avgSpeedDistribution.stateID = sourecetypepopulation.stateID)) INNER JOIN 

VMT ON (avgSpeedDistribution.YearID = VMT.yearID) AND 

(avgSpeedDistribution.stateID = VMT.stateID) 

GROUP BY VMT.yearID, VMT.State, roadtypedistribution1.stateID, VMT.County, 

roadtypedistribution1.sourceTypeID, hourvmtfraction.hourID, 

roadtypedistribution1.roadTypeID, avgSpeedDistribution.avgSpeedBinID, 

sourecetypepopulation.sourceTypeFraction, hourvmtfraction.hourVMTFraction, 

roadtypedistribution1.roadTypeVMTFraction, 

avgSpeedDistribution.avgSpeedFraction 

HAVING (((avgSpeedDistribution.avgSpeedFraction)>0)) 

ORDER BY VMT.yearID, roadtypedistribution1.stateID, VMT.County, 

hourvmtfraction.hourID; 

5. Combining VMT and Emission Rates; Calculating Total Emissions 

5.1 Summarizing Distance-based Emissions by Source Type 

The daily VMT and annual VMT in each county, year, hour, source type, road type, and speed 

bin is multiplied by the appropriate rate per distance for each pollutant. This query is shown in 

Table 31. 
52 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

Table 31 :Emissions distance Query 

SELECT CountyVMT_Table.stateID, CountyVMT_Table.State, 

CountyVMT_Table.County, CountyVMT_Table.yearID, 

rateperdistance_state.monthID, CountyVMT_Table.hourID, 

rateperdistance_state.sourceTypeID, CountyVMT_Table.roadTypeID, 

CountyVMT_Table.avgSpeedBinID, rateperdistance_state.pollutantID, 

CountyVMT_Table.DailyVMT, CountyVMT_Table.AnnualizedVMT, 

rateperdistance_state.SumOfratePerDistance, [DailyVMT]*[SumOfratePerDistance] 

AS EmissionsDist, [AnnualizedVMT]*[SumOfratePerDistance] AS 

AnnualEmissionsDist 

FROM rateperdistance_state INNER JOIN CountyVMT_Table ON 

(rateperdistance_state.avgSpeedBinID = CountyVMT_Table.avgSpeedBinID) AND 

(rateperdistance_state.roadTypeID = CountyVMT_Table.roadTypeID) AND 

(rateperdistance_state.sourceTypeID = CountyVMT_Table.sourceTypeID) AND 

(rateperdistance_state.hourID = CountyVMT_Table.hourID) AND 

(rateperdistance_state.StateID = CountyVMT_Table.stateID) AND 

(rateperdistance_state.yearID = CountyVMT_Table.yearID) 

GROUP BY CountyVMT_Table.stateID, CountyVMT_Table.State, 

CountyVMT_Table.County, CountyVMT_Table.yearID, 

rateperdistance_state.monthID, CountyVMT_Table.hourID, 

rateperdistance_state.sourceTypeID, CountyVMT_Table.roadTypeID, 

CountyVMT_Table.avgSpeedBinID, rateperdistance_state.pollutantID, 

CountyVMT_Table.DailyVMT, CountyVMT_Table.AnnualizedVMT, 

rateperdistance_state.SumOfratePerDistance; 

A second query further summarizes the emissions by source type. This is necessary in order to 

combine with vehicle-based emissions that are independent of road type and speed. 

5.2 Summarizing Vehicle-based Emissions by Source type 

The source population for each county, year, hour, and source type is multiplied by the rate per 

vehicle for each pollutant. This query is shown in Table 32. 

 

Table 32: Emissions Vehicle Query 

SELECT VMT.stateID, VMT.County, ratepervehicle_state.yearID, 

ratepervehicle_state.monthID, ratepervehicle_state.hourID, 

ratepervehicle_state.sourceTypeID, sourecetypepopulation.sourceTypeFraction, 

VMT.SourceTypePopulation, ratepervehicle_state.pollutantID, 

ratepervehicle_state.SumOfratePerVehicle, First(VMT.BudgetAreaPop) AS 

FirstOfBudgetAreaPop, 

((Nz([VMT]![sourceTypePopulation]*[sourceTypeFraction],0)/24)) AS STPop, 

Nz([VMT]![sourceTypePopulation]*[sourceTypeFraction]*[SumOfratePerVehicle],0) 

AS emissionsVehicle, 

Nz(([VMT]![sourceTypePopulation]*[sourceTypeFraction]*[SumOfratePerVehicle])*

365,0) AS AnnualemissionsVehicle 

FROM sourecetypepopulation INNER JOIN (ratepervehicle_state INNER JOIN VMT ON 

(ratepervehicle_state.yearID = VMT.yearID) AND (ratepervehicle_state.StateID 

= VMT.stateID)) ON (sourecetypepopulation.sourceTypeID = 

ratepervehicle_state.sourceTypeID) AND (sourecetypepopulation.stateID = 

ratepervehicle_state.StateID) 

GROUP BY VMT.stateID, VMT.County, ratepervehicle_state.yearID, 

ratepervehicle_state.monthID, ratepervehicle_state.hourID, 

ratepervehicle_state.sourceTypeID, sourecetypepopulation.sourceTypeFraction, 

VMT.SourceTypePopulation, ratepervehicle_state.pollutantID, 

ratepervehicle_state.SumOfratePerVehicle; 
53 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

5.3 Ramp Emissions 

Ramp emission rates, calculated as discussed in Section 3, are multiplied by ramp VMT in each 

county, year and source type. This query is shown in Table 33.

Table 33: Ramp Emissions Query

SELECT VMT.stateID, VMT.County, VMT.yearID, hourvmtfraction.hourID, hourvmtfraction.sourceTypeID, 

hourvmtfraction.hourVMTFraction, ramp_rate.pollutantID, VMT.[Ramp VMT], ([Ramp 

VMT]*[hourVMTFraction])/13 AS HourlyRampVMT, ramp_rate.ramprate, [HourlyRampVMT]*[ramprate] 

AS RampEmissions, ([HourlyRampVMT]*[ramprate])*340 AS RampEmissionsAnnual 

FROM hourvmtfraction INNER JOIN (VMT INNER JOIN ramp_rate ON (VMT.stateID = ramp_rate.StateID) 

AND (VMT.yearID = ramp_rate.yearID)) ON hourvmtfraction.hourID = ramp_rate.hourID 

WHERE (((hourvmtfraction.roadTypeID)=4))

ORDER BY VMT.stateID, VMT.County, VMT.yearID, hourvmtfraction.hourID, 

hourvmtfraction.sourceTypeID, ramp_rate.pollutantID; 

5.4 Summarizing Results 

Distance-based emissions by source type, vehicle-based emissions by source type, and ramp emissions 

by source type are summed by county, year and pollutant. This query is shown below. This is also 

where criteria may be set for limiting the results by state, county, year or pollutant. A sum of VMT and 

source type population is also useful as a verification that all steps were run properly. The appropriate 

monthID criteria should be set here. The annual average temperature profile is contained in April 

(monthID=4). July (monthID=7) should be used for summer weekday emissions. 

Table 34:Results Query 

SELECT EmissionsDistance_bySourceType.State, EmissionsDistance_bySourceType.County, 

EmissionsDistance_bySourceType.yearID, EmissionsDistance_bySourceType.pollutantName, 

Sum(EmissionsDistance_bySourceType.SumOfDailyVMT) AS SumOfSumOfDailyVMT, 

Sum(RampEmissions_Query.HourlyRampVMT) AS SumOfHourlyRampVMT, 

Sum(EmissionsDistance_bySourceType.SumOfAnnualizedVMT) AS SumOfSumOfAnnualizedVMT, 

First(EmissionsVehicle_Query.SourceTypePopulation) AS FirstOfSourceTypePopulation, 

Sum(EmissionsDistance_bySourceType.SumOfEmissionsDist) AS SumOfSumOfEmissionsDist, 

Sum(Nz([emissionsVehicle],0)) AS EmissionsVeh, Sum(RampEmissions_Query.RampEmissions) AS 

SumOfRampEmissions, Sum(EmissionsDistance_bySourceType.SumOfAnnualEmissionsDist) AS 

SumOfSumOfAnnualEmissionsDist, Sum(Nz([AnnualemissionsVehicle],0)) AS AnnualEmissionsVeh, 

Sum(RampEmissions_Query.RampEmissionsAnnual) AS SumOfRampEmissionsAnnual, 

(([SumOfSumOfEmissionsDist]+[EmissionsVeh]+[SumOfHourlyRampVMT])/1000)*0.001102 AS 

DailyEmissionsTONS, 

(([SumOfSumOfAnnualEmissionsDist]+[AnnualEmissionsVeh]+[SumOfRampEmissionsAnnual])/1000)*0.0

01102 AS AnnualEmissionsTONS, 

Sum([RampEmissions_Query]![HourlyRampVMT]+[EmissionsDistance_bySourceType]![SumOfDailyVMT]

) AS AllVMT 
54 Appendix: OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source Emissions

FROM (EmissionsDistance_bySourceType LEFT JOIN EmissionsVehicle_Query ON 

(EmissionsDistance_bySourceType.pollutantID = EmissionsVehicle_Query.pollutantID) AND 

(EmissionsDistance_bySourceType.sourceTypeID = EmissionsVehicle_Query.sourceTypeID) AND 

(EmissionsDistance_bySourceType.hourID = EmissionsVehicle_Query.hourID) AND 

(EmissionsDistance_bySourceType.monthID = EmissionsVehicle_Query.monthID) AND 

(EmissionsDistance_bySourceType.yearID = EmissionsVehicle_Query.yearID) AND 

(EmissionsDistance_bySourceType.County = EmissionsVehicle_Query.County)) INNER JOIN 

RampEmissions_Query ON (EmissionsDistance_bySourceType.County = RampEmissions_Query.County) 

AND (EmissionsDistance_bySourceType.yearID = RampEmissions_Query.yearID) AND 

(EmissionsDistance_bySourceType.hourID = RampEmissions_Query.hourID) AND 

(EmissionsDistance_bySourceType.sourceTypeID = RampEmissions_Query.sourceTypeID) AND 

(EmissionsDistance_bySourceType.pollutantID = RampEmissions_Query.pollutantID) 

GROUP BY EmissionsDistance_bySourceType.State, EmissionsDistance_bySourceType.County, 

EmissionsDistance_bySourceType.yearID, EmissionsDistance_bySourceType.pollutantName, 

EmissionsDistance_bySourceType.monthID, EmissionsDistance_bySourceType.pollutantID 

HAVING (((EmissionsDistance_bySourceType.monthID)=4)); 
Regional Air Quality Analyses for

Ozone, PM2.5, and Regional Haze: 

Final Technical Support Document

April 25, 2008

States of Illinois, Indiana, Michigan, Ohio, and Wisconsin
ii

Table of Contents

Section-Title Page

Executive Summary iii

1.0 Introduction 1

1.1 SIP Requirements 1

1.2 Organization 2

1.3 Technical Work: Overview 3

2.0 Ambient Data Analyses 4

2.1 Ozone 4

2.2 PM2.5 21

2.3 Regional Haze 35

3.0 Air Quality Modeling 46

3.1 Selection of Base Year 46

3.2 Future Years of Interest 46

3.3 Modeling System 47

3.4 Domain/Grid Resolution 47

3.5 Model Inputs: Meteorology 48

3.6 Model Inputs: Emissions 51

3.7 Base Year Modeling Results 60

4.0 Attainment Demonstration for Ozone and PM2.5 71

4.1 Future Year Modeling Results 71

4.2 Supplemental Analyses 82

4.3 Weight of Evidence Determination for Ozone 82

4.4 Weight of Evidence Determination for PM2.5 90

5.0 Reasonable Progress Assessment for Regional Haze 93

5.1 Class I Areas Impacted 93

5.2 Future Year Modeling Results 96

5.3 Weight of Evidence Determination for Regional Haze 104

6.0 Summary 111

7.0 References 114

Appendix I Ozone and PM2.5 Modeling Results

Appendix II Ozone Source Apportionment Modeling Results

Appendix III PM2.5Source Apportionment Modeling Results

Appendix IV Haze Source Apportionment Modeling Results
iii

EXECUTIVE SUMMARY

States in the upper Midwest face a number of air quality challenges. More than 50 counties are 

currently classified as nonattainment for the 8-hour ozone standard and 60 for the fine particle 

(PM2.5) standard (1997 versions). A map of these nonattainment areas is provided in the figure 

below. In addition, visibility impairment due to regional haze is a problem in the larger national 

parks and wilderness areas (i.e., Class I areas). There are 156 Class I areas in the U.S., 

including two in northern Michigan.

Figure i. Current nonattainment counties for ozone (left) and PM2.5 (right)

To support the development of State Implementation Plans (SIPs) for ozone, PM2.5, and 

regional haze in the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin, technical 

analyses were conducted by the Lake Michigan Air Directors Consortium (LADCO), its member 

states, and various contractors. The analyses include preparation of regional emissions 

inventories and meteorological data, evaluation and application of regional chemical transport 

models, and collection and analysis of ambient monitoring data. 

Monitoring data were analyzed to produce a conceptual understanding of the air quality 

problems. Key findings of the analyses include:

Ozone

• Current monitoring data (2005-2007) show about 20 sites in violation of the 8-hour 

ozone standard of 85 parts per billion (ppb). Historical ozone data show a steady 

downward trend over the past 15 years, especially since 2001-2003, due likely to 

federal and state emission control programs.

• Ozone concentrations are strongly influenced by meteorological conditions, with 

more high ozone days and higher ozone levels during summers with above normal 

temperatures.
iv

• Inter- and intra-regional transport of ozone and ozone precursors affects many 

portions of the five states, and is the principal cause of nonattainment in some areas 

far from population or industrial centers. 

PM2.5

• Current monitoring data (2005-2007) show 30 sites in violation of the annual PM2.5

standard of 15 ug/m3. Nonattainment sites are characterized by an elevated 

regional background (about 12 – 14 ug/m3) and a significant local (urban) increment 

(about 2 – 3 ug/m3). Historical PM2.5 data show a slight downward trend since 

deployment of the PM2.5 monitoring network in 1999.

• PM2.5 concentrations are also influenced by meteorology, but the relationship is 

more complex and less well understood compared to ozone.

• On an annual average basis, PM2.5 chemical composition consists mostly of sulfate, 

nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data (2000-2004) show visibility levels in the Class I areas in 

northern Michigan are on the order of 22 – 24 deciviews. The goal of EPA’s visibility 

program is to achieve natural conditions, which is about 12 deciviews for these 

Class I areas, by the year 2064.

• Visibility impairment is dominated by sulfate and nitrate.

Air quality models were applied to support the regional planning efforts. Two base years were 

used in the modeling analyses: 2002 and 2005. Basecase modeling was conducted to evaluate 

model performance (i.e., assess the model's ability to reproduce observed concentrations). This 

exercise was intended to build confidence in the model prior to its use in examining control 

strategies. Model performance for ozone and PM2.5 was found to be generally acceptable.

Future year strategy modeling was conducted to determine whether existing (“on the books”) 

controls would be sufficient to provide for attainment of the standards for ozone and PM2.5 and if 

not, then what additional emission reductions would be necessary for attainment. Based on the 

modeling and other supplemental analyses, the following general conclusions can be made:

• Existing controls are expected to produce significant improvement in ozone and 

PM2.5 concentrations and visibility levels.

• The choice of the base year affects the future year model projections. A key 

difference between the base years of 2002 and 2005 is meteorology. 2002 was 

more ozone conducive than 2005. The choice of which base year to use as the 

basis for the SIP is a policy decision (i.e., how much safeguard to incorporate).

• Modeling suggests that most sites are expected to meet the current 8-hour ozone 

standard by the applicable attainment date, except for sites in western Michigan 

and, possibly, in eastern Wisconsin and northeastern Ohio.
v

• Modeling suggests that most sites are expected to meet the current PM2.5

standard by the applicable attainment date, except for sites in Detroit, Cleveland, 

and Granite City.

The regional modeling for PM2.5 does not include air quality benefits expected 

from local controls. States are conducting local-scale analyses and will use

these results, in conjunction with the regional-scale modeling, to support their 

attainment demonstrations for PM2.5.

• These findings of residual nonattainment for ozone and PM2.5 are supported by 

current (2005 – 2007) monitoring data which show significant nonattainment in 

the region (e.g., peak ozone design values on the order of 90 – 93 ppb, and peak 

PM2.5 design values on the order of 16 - 17 ug/m3). It is unlikely that sufficient 

emission reductions will occur in the next couple of years to provide for 

attainment at all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual 

future year meteorology (e.g., if the weather conditions are consistent with [or 

less severe than] 2005, then attainment is likely) and actual future year 

emissions (e.g., if the emission reductions associated with the existing controls 

are achieved, then attainment is likely). If either of these conditions is not met, 

then attainment may be less likely.

• Modeling suggests that the new PM2.5 24-hour standard and the new lower 

ozone standard will not be met at several sites, even by 2018, with existing 

controls.

• Visibility levels in a few Class I areas in the eastern U.S. are expected to be 

greater than (less improved than) the uniform rate of visibility improvement 

values in 2018 based on existing controls, including those in northern Michigan 

and some in the northeastern U.S. Visibility levels in many other Class I areas in 

the eastern U.S. are expected to be less than (more improved than) the uniform 

rate of visibility improvement values in 2018. These results, along with 

information on the costs of compliance, time necessary for compliance, energy 

and non air quality environmental impacts of compliance, and remaining useful 

life of existing sources, should be considered by the states in setting reasonable 

progress goals for regional haze.
1

Section 1.0 Introduction

This Technical Support Document summarizes the final air quality analyses conducted by the 

Lake Michigan Directors Consortium (LADCO)1and its contractors to support the development 

of State Implementation Plans (SIPs) for ozone, fine particles (PM2.5 ), and regional haze in the 

States of Illinois, Indiana, Michigan, Ohio, and Wisconsin. The analyses include preparation of 

regional emissions inventories and meteorological modeling data for two base years (2002 and 

2005), evaluation and application of regional chemical transport models, and analysis of 

ambient monitoring data. 

Two aspects of the analyses should be emphasized. First, a regional, multi-pollutant approach 

was taken in addressing ozone, PM2.5, and haze for technical reasons (e.g., commonality in

precursors, emission sources, atmospheric processes, transport influences, and geographic 

areas of concern), and practical reasons (e.g., more efficient use of program resources). 

Furthermore, EPA has consistently encouraged multi-pollutant planning in its rule for the haze 

program (64 FR 35719), and its implementation guidance for ozone (70 FR 71663) and PM2.5

(72 FR 20609). Second, a weight-of-evidence approach was taken in considering the results of 

the various analyses (i.e., two sets of modeling results -- one for a 2002 base year and one for a 

2005 base year -- and ambient data analyses) in order to provide a more robust assessment of 

expected future year air quality.

The report is organized in the following sections. This Introduction provides an overview of 

regulatory requirements and background information on regional planning. Section 2 reviews 

the ambient monitoring data and presents a conceptual model of ozone, PM2.5, and haze for the 

region. Section 3 discusses the air quality modeling analyses, including development of the key 

model inputs (emissions inventory and meteorological data), and basecase model performance 

evaluation. A modeled attainment demonstration for ozone and PM2.5 is presented in Section 4, 

along with relevant data analyses considered as part of the weight-of-evidence determination. 

Section 5 documents the reasonable progress assessment for regional haze, along with

relevant data analyses considered as part of the weight-of-evidence determination. Finally, key 

study findings are reviewed and summarized in Section 6.

1.1 SIP Requirements

For ozone, EPA promulgated designations on April 15, 2004 (69 FR 23858, April 30, 2004). In 

the 5-state region, more than 100 counties were designated as nonattainment.

2

 The

designations became effective on June 15, 2004. SIPs for ozone were due no later than three 

years from the effective date of the nonattainment designations (i.e., by June 2007). The

attainment date for ozone varies as a function of nonattainment classification. For the region, 

the attainment dates are either June 2007 (marginal nonattainment areas), June 2009 (basic 

nonattainment areas), or June 2010 (moderate nonattainment areas).

 

1

 A sub-entity of LADCO, known as the Midwest Regional Planning Organization (MRPO), is responsible 

for the regional haze activities of the multi-state organization.

2

 Based on more recent air quality data, many counties in Indiana, Michigan, and Ohio were 

subsequently redesignated as attainment. As of December 31, 2007, there are 53 counties designated 

as nonattainment in the region.
2

For PM2.5, EPA promulgated designations on December 17, 2004 (70 FR 944, January 5, 2005). 

In the 5-state region, 70 counties were designated as nonattainment.3 The designations became 

effective on April 5, 2005. SIPs for PM2.5 are due no later than three years from the effective 

date of the nonattainment designations (per section 172(b) of the Clean Air Act) (i.e., by April 

2008) and for haze no later than three years after the date on which the Administrator 

promulgated the PM2.5 designations (per the Omnibus Appropriations Act of 2004) (i.e., by 

December 2007). The applicable attainment date for PM2.5 nonattainment areas is five years 

from the date of the nonattainment designation (i.e., by April 2010). 

For haze, the Clean Air Act sets “as a national goal the prevention of any future, and the 

remedying of any existing, impairment of visibility in Class I areas which impairment results from 

manmade air pollution.” There are 156 Class I areas, including two in northern Michigan: Isle 

Royale National Park and Seney National Wildlife Refuge4. EPA’s visibility rule (64 FR 35714, 

July 1, 1999) requires reasonable progress in achieving “natural conditions” by the year 2064. 

As noted above, the first regional haze SIP was due in December 2007 and must address the 

initial 10-year implementation period (i.e., reasonable progress by the year 2018). SIP 

requirements (pursuant to 40 CFR 51.308(d)) include setting reasonable progress goals, 

determining baseline conditions, determining natural conditions, providing a long-term control 

strategy, providing a monitoring strategy (air quality and emissions), and establishing BART 

emissions limitations and associated compliance schedule. 

1.2 Organization

LADCO was established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. The 

four states and EPA signed a Memorandum of Agreement (MOA) that initiated the Lake 

Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee the study. 

Additional MOAs were signed by the States in 1991 (to establish the Lake Michigan Ozone 

Control Program), January 2000 (to broaden LADCO’s responsibilities), and June 2004 (to 

update LADCO’s mission and reaffirm the commitment to regional planning). In March 2004, 

Ohio joined LADCO. LADCO consists of a Board of Directors (i.e., the State Air Directors), a 

technical staff, and various workgroups. The main purposes of LADCO are to provide technical 

assessments for and assistance to its member states, and to provide a forum for its member 

states to discuss regional air quality issues. 

MRPO is a similar entity led by the five LADCO States and involves the federally recognized 

tribes in Michigan and Wisconsin, EPA, and Federal Land Managers (i.e., National Park

Service, U.S. Fish & Wildlife Agency, and U.S. Forest Service). In October 2000, the States of 

Illinois, Indiana, Michigan, Ohio, and Wisconsin signed an MOA that established the MRPO. An 

operating principles document for MRPO, which describe the roles and responsibilities of states, 

tribes, federal agencies, and stakeholders, was issued in March 2001. MRPO has a similar 

purpose as LADCO, but is focused on visibility impairment due to regional haze in the Federal 

Class I areas located inside the borders of the five states, and the impact of emissions from the 

five states on visibility impairment due to regional haze in the Federal Class I areas located

outside the borders of the five states. MRPO works cooperatively with the Regional Planning 

Organizations (RPOs) representing other parts of the country. The RPOs sponsored several

 

3

 USEPA subsequently adjusted the final designations, which resulted in 63 counties in the region being 

designated as nonattainment (70 FR 19844, April 15, 2005).

4

 Although Rainbow Lake in northern Wisconsin is also a Class I area, the visibility rule does not apply

because the Federal Land Manager determined that visibility is not an air quality related value there.
3

joint projects and, with assistance by EPA, maintain regular contact on technical and policy 

matters.

1.3 Technical Work: Overview

To ensure the reliability and effectiveness of its planning process, LADCO has made data 

collection and analysis a priority. More than $7M in RPO grant funds were used for special 

purpose monitoring, preparing and improving emissions inventories, and conducting air quality 

analyses5. An overview of the technical work is provided below.

Monitoring: Numerous monitoring projects were conducted to supplement on-going state and 

local air pollution monitoring. These projects include rural monitoring (e.g., comprehensive 

sampling in the Seney National Wildlife Refuge and in Bondville, IL); urban monitoring (e.g., 

continuation of the St. Louis Supersite); aloft (aircraft) measurements; regional ammonia 

monitoring; and organic speciation sampling in Seney, Bondville, and five urban areas.

Emissions: Baseyear emissions inventories were prepared for 2002 and 2005. States provided 

point source and area source emissions data, and MOBILE6 input files and mobile source 

activity data. LADCO and its contractors developed the emissions data for other source 

categories (e.g., select nonroad sources, ammonia, fires, and biogenics) and processed the 

data for input into an air quality model. To support control strategy modeling, future year 

inventories were prepared. The future years of interest include 2008 (planning year to address 

the 2009 attainment year for basic ozone nonattainment ares), 2009 (planning year to address 

the 2010 attainment year for PM2.5 and moderate ozone nonattainment areas), 2012 (planning

to address a 2013 alternative attainment date), and 2018 (first milestone year for regional haze).

Air Quality Analyses: The weight-of-evidence approach relies on data analysis and modeling. 

Air quality data analyses were used to provide both a conceptual model (i.e., a qualitative 

description of the ozone, PM2.5, and regional haze problems) and supplemental information for 

the attainment demonstration. Given uncertainties in emissions inventories and modeling, 

especially for PM2.5, these data analyses are a necessary part of the overall technical support.

Modeling includes baseyear analyses for 2002 and 2005 to evaluate model performance and 

future year strategy analyses to assess candidate control strategies. The analyses were 

conducted in accordance with EPA’s modeling guidelines (EPA, 2007a). The PM/haze 

modeling covers the full calendar year (2002 and 2005) for an eastern U.S. 36 km domain, while 

the ozone modeling focuses on the summer period (2002 and 2005) for a Midwest 12 km 

subdomain. The same model (CAMx) was used for ozone, PM2.5, and regional haze.

 

5

 Since 1999, MRPO has received almost $10M in RPO grant funds from USEPA.
4

Section 2.0 Ambient Data Analyses

An extensive network of air quality monitors in the 5-state region provides data for ozone (and 

its precursors), PM2.5 (both total mass and individual chemical species), and visibility. These 

data are used to determine attainment/nonattainment designations, support SIP development, 

and provide air quality information to public (see, for example, www.airnow.gov).

Analyses of the data were conducted to produce a conceptual model, which is a qualitative 

summary of the physical, chemical, and meteorological processes that control the formation and 

distribution of pollutants in a given region. This section reviews the relevant data analyses and 

describes our understanding of ozone, PM2.5, and regional haze with respect to current 

conditions, data variability (spatial, temporal, and chemical), influence of meteorology (including 

transport patterns), precursor sensitivity, and source culpability.

2.1 Ozone

In 1979, EPA adopted an ozone standard of 0.12 ppm, averaged over a 1-hour period. This 

standard is attained when the number of days per calendar year with maximum hourly average 

concentrations above 0.12 ppm is equal to or less than 1.0, averaged over a 3-year period, 

which generally reflects a design value (i.e., the 4th highest daily 1-hour value over a 3-year 

period) less than 0.12 ppm.

In 1997, EPA tightened the ozone standard to 0.08 ppm, averaged over an 8-hour period6. The 

standard is attained if the 3-year average of the 4th-highest daily maximum 8-hour average 

ozone concentrations (i.e., the design value) measured at each monitor within an area is less 

than 0.08 ppm (or 85 ppb). 

Current Conditions: A map of the 8-hour ozone design values at each monitoring site in the 

region for the 3-year period 2005-2007 is shown in Figure 1. The “hotter” colors represent 

higher concentrations, where yellow and orange dots represent sites with design values above 

the standard. Currently, there are 19 sites in violation of the 8-hour ozone NAAQS in the 5-state 

region, including sites in the Lake Michigan area, Detroit, Cleveland, Cincinnati, and Columbus.

Table 1 provides the 4th-highest daily 8-hour ozone values and the associated design values 

since 2001 for several high monitoring sites throughout the region.

 

6

 On March 12, 2008, USEPA further tightened the 8-hour ozone standard to increase public health 

protection and prevent environmental damage from ground-level ozone. USEPA set the primary (health) 

standard and secondary (welfare) standard at the same level: 0.075 ppm (75 ppb), averaged over an 8-

hour period.
5

Figure 1. 8-hour ozone design values (2005-2007)
Key Sites

'01 '02 '03 '04 '05 '06 '07 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

Lake Michigan Area

Chiwaukee 99 116 88 78 93 79 85 101 94 86 83 85

Racine 92 111 82 69 95 71 77 95 87 82 78 81

Milwaukee-Bayside 93 99 92 73 93 73 83 94 88 86 79 83

Harrington Beach 102 93 99 72 94 72 84 98 88 88 79 83

Manitowoc 97 83 92 74 95 78 85 90 83 87 82 86

Sheboygan 102 105 93 78 97 83 88 100 92 89 86 89

Kewaunee 90 92 97 73 88 76 85 93 87 86 79 83

Door County 95 95 93 78 101 79 92 94 88 90 86 90

Hammond 90 101 81 67 87 75 77 90 83 78 76 79

Whiting 64 88 81 88 77 85

Michigan City 90 107 82 70 84 75 73 93 86 78 76 77

Ogden Dunes 85 101 77 69 90 70 84 87 82 78 76 81

Holland 92 105 96 79 94 91 94 97 93 89 88 93

Jenison 86 93 91 69 86 83 88 90 84 82 79 85

Muskegon 95 96 94 70 90 90 86 95 86 84 83 88

Indianapolis Area

Noblesville 88 101 101 75 87 77 84 96 92 87 79 82

Fortville 89 101 92 72 80 75 81 94 88 81 75 78

Fort B. Harrison 87 100 91 73 80 76 83 92 88 81 76 79

Detroit Area

New Haven 95 95 102 81 88 78 93 97 92 90 82 86

Warren 94 92 101 71 89 78 91 95 88 87 79 86

Port Huron 84 100 87 74 88 78 89 90 87 83 80 85

Cleveland Area

Ashtabula (Conneaut) 97 103 99 81 93 86 92 99 94 91 86 90

Notre Dame (Geauga) 99 115 97 75 88 70 68 103 95 86 77 75

Eastlake (Lake) 89 104 92 79 97 83 74 95 91 89 86 84

Akron (Summit) 98 103 89 77 89 77 91 96 89 85 81 85

Cincinnati Area

Wilmington (Clinton) 93 99 96 78 83 81 82 96 91 85 80 82

Sycamore (Hamilton) 88 100 93 76 89 81 90 93 89 86 82 86

Hamilton (Butler) 83 100 94 75 86 79 91 92 89 85 80 85

Middleton (Butler) 87 98 83 76 88 76 91 89 85 82 80 85

Lebanon (Warren) 85 98 95 81 92 86 88 92 91 89 86 88

Columbus Area

London (Madison) 84 97 90 75 81 76 83 90 87 82 77 80

New Albany (Franklin) 90 103 94 78 92 82 87 95 91 88 84 87

Franklin (Franklin) 83 99 84 73 86 79 79 88 85 81 79 81

Ohio Other Areas

Marietta (Washington) 85 95 80 77 88 81 86 86 84 81 82 85

St. Louis Area

W. Alton (MO) 85 99 91 77 89 91 89 91 89 85 85 89

Orchard (MO) 88 98 90 76 92 92 83 92 88 86 86 89

Sunset Hills (MO) 88 98 88 70 89 80 89 91 85 82 79 86

Arnold (MO) 86 93 82 70 92 79 87 87 81 81 80 86

Margaretta (MO) 80 98 90 72 91 76 91 89 86 84 79 86

Maryland Heights (MO) 88 84 94 88

4th High 8-hour Value Design Values

Table 1. Ozone Data for Select Sites in 5-State Region
7

Meteorology and Transport: Most pollutants exhibit some dependence on meteorological 

factors, especially wind direction, because that governs which sources are upwind and thus 

most influential on a given sample. Ozone is even more dependent, since its production is 

driven by high temperatures and sunlight, as well as precursor concentrations (see, for 

example, Figure 2). 

Figure 2. Number of hot days and 8-hour “exceedance” days in 5-state region

Qualitatively, ozone episodes in the region are associated with hot weather, clear skies 

(sometimes hazy), low wind speeds, high solar radiation, and southerly to southwesterly winds. 

These conditions are often a result of a slow-moving high pressure system to the east of the 

region. The relative importance of various meteorological factors is discussed later in this 

section.

Transport of ozone (and its precursors) is a significant factor and occurs on several spatial 

scales. Regionally, over a multi-day period, somewhat stagnant summertime conditions can 

lead to the build-up in ozone and ozone precursor concentrations over a large spatial area. This

pollutant air mass can be advected long distances, resulting in elevated ozone levels in 

locations far downwind. An example of such an episode is shown in Figure 3. 

Figure 3. Example of elevated regional ozone concentrations (June 23 – 25, 2005)

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8-

hour standard
8

Locally, emissions from urban areas add to the regional background leading to ozone 

concentration hot spots downwind. Depending on the synoptic wind patterns (and local landlake breezes), different downwind areas are affected (see, for example, Figure 4).

Figure 4. Examples of recent high ozone days in the Lake Michigan area

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8-

hour standard

Aloft (aircraft) measurements in the Lake Michigan area also provide evidence of elevated 

regional background concentrations and “plumes” from urban areas. For one example summer 

day (August 20, 2003 – see Figure 5), the incoming background ozone levels were on the order 

of 80 – 100 ppb and the downwind ozone levels over Lake Michigan were on the order of 100 -

150 ppb (STI, 2004).

Figure 5. Aircraft ozone measurements over Lake Michigan (left) and along upwind boundary 

(right) – August 20, 2003 (Note: aircraft measurements reflect instantaneous values)
9

As discussed in Section 4, residual nonattainment is projected in at least one area in the 5-state 

region –i.e., western Michigan. To understand the source regions likely impacting high ozone 

concentrations in western Michigan and estimate the impact of these source regions, two simple 

transport-related analyses were performed.

First, back trajectories were constructed using the HYSPLIT model for high ozone days (8-hour 

peak > 80 ppb) during the period 2002-2006 in western Michigan to characterize general

transport patterns. Composite trajectory plots for all high ozone days based on data from three 

sites (Cass County, Holland, and Muskegon) are provided in Figure 6. The plots point back to 

areas located to the south-southwest (especially, northeastern Illinois and northwestern Indiana) 

as being upwind on these high ozone days.

Figure 6 Back trajectory analysis showing upwind areas associated with high ozone

concentrations

Second, to assess the impact from Chicago/NW Indiana, Blanchard (2005a) compared ozone 

concentrations upwind (Braidwood, IL), within Chicago (ten sites in the City), and downwind 

(Holland and Muskegon) for days in 1999 – 2002 with southwesterly winds - i.e., transport 

towards western Michigan. Figure 7 shows the distribution of daily peak 8-hour ozone 

concentrations by day-of-week, with a line connecting the mean values. The difference 

between day-of-week mean values at downwind and upwind sites indicates that Chicago/NW 

Indiana contributes about 10-15 ppb to downwind ozone levels.
10

Figure 7. Mean day-of-week peak 8-hour ozone concentrations at sites upwind, within, and 

downwind of Chicago, 1999 – 2002 (southwesterly wind days)

Based on this information, the following key findings related to transport can be made:

• Ozone transport is a problem affecting many portions of the eastern U.S. The Lake 

Michigan area (and other areas in the LADCO region) both receive high levels of 

incoming (transported) ozone and ozone precursors from upwind source areas on many 

hot summer days, and contribute to the high levels of ozone and ozone precursors 

affecting downwind receptor areas.

• The presence of a large body of water (i.e., Lake Michigan) influences for the formation 

and transport of ozone in the Lake Michigan area. Depending on large-scale synoptic 

winds and local-scale lake breezes, different parts of the area experience high ozone 

concentrations. For example, under southerly flow, high ozone can occur in eastern 

Wisconsin, and under southwesterly flow, high ozone can occur in western Michigan. 

• Downwind shoreline areas around Lake Michigan are affected by both regional transport 

of ozone and subregional transport from major cities in the Lake Michigan area. 

Counties along the western shore of Michigan (from Benton Harbor to Traverse City, and 

even as far north as the Upper Peninsula) are impacted by high levels of incoming 

(transported) ozone.
11

Data Variability: Since 1980, considerable progress has been made to meet the previous 1-

hour ozone standard. Figure 8 shows the decline in both the 1-hour and 8-hour design values 

for the 5-state LADCO region over the last 25 years. 

Figure 8 Ozone design value trends in 5-State region

The trend is more dramatic for the higher ozone sites in the 5-state region (see Figure 9). This 

plot shows a pronounced downward trend in the design value since the 2001-2003 period, due, 

in part, to the very low 4th high values in 2004.

 

Figure 9. Trend in ozone design values and 4th high values for higher ozone sites in region

The improvement in ozone concentrations is also seen in the decrease in the number of sites

measuring nonattainment over the past 15 years in the Lake Michigan area (see Figure 10).

75

85

95

105

'95-'97 '96-'98 '97-'99 '98-'00 '99-'01 '00-'02 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

65

75

85

95

105

115

'95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07

Design Values

4

th High Values
12

 

Figure 10. Ozone design value maps for 1995-1997, 2000-2002, and 2005-2007
13

Given the effect of meteorology on ambient ozone levels, year-to-year variations in meteorology 

can make it difficult to assess trends in ozone air quality. Two approaches were considered to 

adjust ozone trends for meteorological influences: an air quality-meteorology statistical model 

developed by EPA (i.e., Cox method), and statistical grouping of meteorological variables 

performed by LADCO (i.e., Classification and Regression Trees, or CART).

Cox Method: This method uses a statistical model to ‘remove’ the annual effect of meteorology 

on ozone (Cox and Chu, 1993). A regression model was fit to the 1997-2007 data to relate daily 

peak 8-hour ozone concentrations to six daily meteorological variables plus seasonal and 

annual factors (Kenski, 2008a). Meteorological variables included were daily maximum 

temperature, mid-day average relative humidity, morning and afternoon wind speed and wind 

direction. The model is then used to predict 4th high ozone values. By holding the 

meteorological effects constant, the long term trend can be examined independently of 

meteorology. Presumably, any trend reflects changes in emissions of ozone precursors. 

Figure 11a shows the meteorologically-adjusted 4th high ozone concentrations for several 

monitors near major urban areas in the region. The plots indicate a general downward trend 

since the late 1990s for most cities, indicating that recent emission reductions have had a 

positive effect in improving ozone air quality. 

A similar model was run to examine meteorologically adjusted trends in seasonal average 

ozone. This model incorporates more meteorological variables, including rain and long-distance 

transport (direction and distance). Model development was documented in Camalier et al., 

2007. The seasonal average trends are shown in Figure 11b. Trends determined by seasonal

model for the same set of sites examined above are consistent with those developed by the 4

th

high model.
14

Chiwaukee, WI Sheboygan, WI

Cleveland (Ashtabula), OH Cincinnati (Sycamore), OH

Detroit (New Haven), MI St. Louis, MO

Indianapolis, IN

Figure 11a. Trends in meteorologically 

adjusted 4th high 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)
15

Chiwaukee, WI Sheboygan, WI

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Cleveland (Ashtabula), OH Cincinnati (Sycamore), OH

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Detroit (New Haven), MI St. Louis, MO

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Indianapolis, IN

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Figure 11b. Trends in seasonal 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)
16

CART: Classification and Regression Tree (CART) analysis is another statistical technique 

which partitions data sets into similar groups (Breiman et al., 1984). CART analysis was 

performed using data for the period 1995-2007 for 22 selected ozone monitors with current 8-

hour design values close to or above the standard (Kenski, 2008b). The CART model searches 

through 60 meteorological variables to determine which are most efficient in predicting ozone. 

Although the exact selection of predictive variables changes from site to site, the most common 

predictors were temperature, wind direction, and relative humidity. Only occasionally were

upper air variables, transport time or distance, lake breeze, or other variables significant. (Note, 

the ozone and meteorological data for the CART analysis are the same as used in the EPA/Cox 

analysis.)

For each monitor, regression trees were developed that classify each summer day (MaySeptember) by its meteorological conditions. Similar days are assigned to nodes, which are 

equivalent to branches of the regression tree. Ozone time series for the higher concentration

nodes are plotted for select sites in Figure 12. By grouping days with similar meteorology, the 

influence of meteorological variability on the trend in ozone concentrations is partially removed; 

the remaining trend is presumed to be due to trends in precursor emissions or other nonmeteorological influences. Trends over the 13-year period at most sites were found to be 

declining, with the exception of Detroit which showed fairly flat trends. Comparison of the 

average of the high concentration node values for 2001-2003 v. 2005-2007 showed an 

improvement of about 5 ppb across all sites (even Detroit).

The effect of meteorology was further examined by using an ozone conduciveness index

(Kenski, 2008b). This metric reflects the variability from the 13-year average in the number of 

days in the higher ozone concentration nodes (see Figure 13). Examination of these plots 

indicates:

• 2002 and 2005 were both above normal, with 2002 tending to be more severe; and

• 2001-2003 and 2005-2007 were both above normal, with no clear pattern in which 

period was more severe (i.e., ozone conduciveness values were similar at most sites, 

2001-2003 values were higher at a few sites, and 2005-2007 values were higher at a 

few sites).

Given the similarity in ozone conduciveness between 2001-2003 and 2005-2007, the 

improvement in ozone levels noted above is presumed to be due to non-meteorological factors 

(i.e., emission reductions).

In conclusion, all three statistical approaches (CART and the two nonlinear regression models) 

show a similar result; ozone in the urban areas of the LADCO region has declined during the 

1997-2007 period, even when meteorological variability is accounted for. The decreases are 

present whether seasonal average ozone, peak values (annual 4th highs), or a subset of high 

days with similar meteorology are considered. The consistency in results across models is a 

good indication that these trends reflect impacts of emission control programs.
17

Chiwaukee, WI Sheboygan, WI

Cleveland (Ashtabula), OH Cincinnati (Sycamore), OH

Detroit (New Haven), MI St. Louis, MO

Indianapolis, IN

Figure 12. Trends for higher ozone CART 

groups (average ozone > 65 ppb) for seven 

Midwestern sites (1995 – 2007)

Note: line represents linear best fit
18

Figure 13. Ozone conduciveness index (and 

number of high ozone days) for seven 

Midwestern site (1995 – 2007)
19

Precursor Sensitivity: Ozone is formed from the reactions of hydrocarbons and nitrogen oxides 

under meteorological conditions that are conducive to such reactions (i.e., warm temperatures 

and strong sunlight). In areas with high VOC/NOx ratios, typical of rural environments (with low 

NOx), ozone tends to be more responsive to reductions in NOx. Conversely, in areas with low 

VOC/NOx ratios, typical of urban environments (with high NOx), ozone tends to be more 

responsive to VOC reductions. 

An analysis of VOC and NOx-limitation was conducted with the ozone MAPPER program, which 

is based on the Smog Production (SP) algorithm (Blanchard, et al., 2003). The “Extent of 

Reaction” parameter in the SP algorithm provides an indication of VOC and NOx sensitivity:

Extent Range Precursor Sensitivity

< 0.6 VOC-sensitive

0.6 – 0.8 Transitional

> 0.8 NOx-sensitive

A map of the Extent of Reaction values for high ozone days is provided in Figure 14. As can be 

seen, ozone is usually VOC-limited in cities and NOx-limited in rural areas. (Data from aircraft 

measurements suggest that ozone is usually NOx-limited over Lake Michigan and away from 

urban centers on days when ozone in the urban centers is VOC-limited.) The highest ozone 

days were found to be NOx-limited. This analysis suggests that a NOx reduction strategy would 

be effective in reducing ozone levels. Examination of day-of-week concentrations, however,

raises some question about the effectiveness of NOx reductions.

Figure 14. Mean afternoon extent of reaction (1998 – 2002)
20

Blanchard (2004 and 2005a) examined weekend-weekday differences in ozone and NOx in the 

Midwest. All urban areas in these two studies exhibited substantially lower (40-60%) weekend 

concentrations of NOx compared to weekday concentrations. Despite lower weekend NOx

concentrations, weekend ozone concentrations were not lower; in fact, most urban sites had 

higher concentrations of ozone, although the increase was generally not statistically significant 

(see Figure 15). This small but counterproductive change in local ozone concentrations 

suggests that local urban-scale NOx reductions alone may not be very effective. 

Figure 15. Weekday/weekend differences in 8-hour ozone – number of sites with weekend 

increase (positive values) v. number of sites with weekend decreases (negative values)

Two additional analyses, however, demonstrate the positive effect of NOx emission reductions 

on downwind ozone concentrations. First, Blanchard (2005a) looked at the effect of changes in 

precursor emissions in Chicago on downwind ozone levels in western Michigan. For the 

transport days of interest (i.e., southwesterly flow during the summers of 1999 – 2002), mean 

NOx concentrations in Chicago are about 50% lower and mean ozone concentrations at the 

(downwind) western Michigan sites are about 1.5 – 5.2 ppb (3 – 8 %) lower on Sunday 

compared to Wednesday. This degree of change in downwind ozone levels suggests a 

positive, albeit non-linear response to urban area emission reductions.

Second, Environ (2007a) examined the effect of differences in day-of-week emissions in 

southeastern Michigan on downwind ozone levels. This modeling study found that weekend 

changes in ozone precursor emissions cause both increases and decreases in Southeast 

Michigan ozone, depending upon location and time:
21

• Weekend increases in 8-hour maximum ozone occur in and immediately downwind of 

the Detroit urban area (i.e., in VOC-sensitive areas).

• Weekend decreases in 8-hour maximum ozone occur outside and downwind of the 

Detroit urban area (i.e., in NOx-sensitive areas).

• At the location of the peak 8-hour ozone downwind of Detroit, ozone was lower on 

weekends than weekdays.

• Ozone benefits (reductions) due to weekend emission changes in Southeast Michigan 

can be transported downwind for hundreds of miles.

• Southeast Michigan benefits from lower ozone transported into the region on Saturday

through Monday because of weekend emission changes in upwind areas.

In summary, these analyses suggest that urban VOC reductions and regional (urban and rural) 

NOx reductions will be effective in lowering ozone concentrations. Local NOx reductions can 

lead to local ozone increases (i.e., NOx disbenefits), but this effect does not appear to pose a 

problem with respect to attainment of the standard. It should also be noted that urban VOC and 

regional NOx reductions are likely to have multi-pollutant benefits (e.g., both lower ozone and 

PM2.5 impacts).

2.2 PM2.5

In 1997, EPA adopted the PM2.5 standards of 15 ug/m

3

 (annual average) and 65 ug/m3 (24-hour 

average). The annual standard is attained if the 3-year average of the annual average PM2.5

concentration is less than or equal to the level of the standard. The daily standard is attained if 

the 98th percentile of 24-hour PM2.5 concentrations in a year, averaged over three years, is less 

than or equal to the level of the standard.

In 2006, EPA revised the PM2.5 standards to 15 ug/m3 (annual average) and 35 ug/m3 (24-hour 

average). 

Current Conditions: Maps of annual and 24-hour PM2.5 design values for the 3-year period 

2005-2007 are shown in Figure 16. The “hotter” colors represent higher concentrations, where 

red dots represent sites with design values above the annual standard. Currently, there are 30

sites in violation of the annual PM2.5 standard.

Table 2 provides the annual PM2.5 concentrations and associated design values since 2003 for 

several high monitoring sites throughout the region.
22

Figure 16. PM2.5 design values - annual average (top) and 24-hour average (bottom) (2005-2007)
2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07

Average 

w/ 2007

Average

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6

Indy - Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2

Indy - W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0

Indy - Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.0 15.3 17.0 16.2 16.2 16.5 16.7

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.4 13.6 14.7 15.4 14.9 14.9 15.1 16.0

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 15.9 16.2 16.3

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.4 15.2 15.7

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6

Annual Average Conc. Design Values

Table 2. PM2.5 Data for Select Sites in 5-State Region
24

When EPA initially set the 24-hour standard at 65 μg/m3, it also adopted the following 

concentration ranges for its Air Quality Index (AQI) scale:

Good < 15 ug/m3

Moderate 15-40 μg/m3

Unhealthy for Sensitive Groups (USG) 40-65 μg/m3

Unhealthy 65-150 μg/m3

Figure 17 shows the frequency of these AQI categories for major metropolitan areas in the 

region. Daily average concentrations are often in the moderate range and occasionally in the 

USG range. Moderate and USG levels can occur any time of the year. 

Figure 17. Percent of days in AQI categories for PM2.5 (2002-2004)

Data Variability: PM2.5 concentrations vary spatially, temporally, and chemically in the region. 

This variability is discussed further below.

On an annual basis, PM2.5 exhibits a distinct and consistent spatial pattern. As seen in Figure 

16, across the Midwest, annual concentrations follow a gradient from low values (5-6 μg/m3) in 

northern and western areas (Minnesota and northern Wisconsin) to high values (17-18 μg/m3) in 

Ohio and along the Ohio River. In addition, concentrations in urban areas are higher than in 

upwind rural areas, indicating that local urban sources add a significant increment of 2-3 μg/m3

to the regional background of 12 - 14 μg/m3 (see Figure 18). 

Figure 18. Regional (lighter shading) v. local components (darker shading) of annual average PM2.5 

concentrations
25

Because monitoring for PM2.5 only began in earnest in 1999, after promulgation of the PM2.5

standard, limited data are available to assess trends. Time series based on federal reference 

method (FRM) PM2.5-mass data show a downward trend in each state (see Figure 19)

7

.

Figure 19. PM2.5 trends in annual average (top) and daily concentrations (bottom)

 

7

 Despite the general downward trend since 1999, all states experienced an increase during 2005. 

Further analyses are underway to understand this increase (e.g., examination of meteorological and 

emissions effects).
26

A statistical analysis of PM2.5 trends was performed using the nonparametric Theil test for slope 

(Hollander and Wolfe, 1973). Trends were generally consistent around the region, for both PM 

mass and for the individual components of mass. Figure 20 shows trends for PM2.5 based on 

FRM data at sites with six or more years of data since 1999. The size and direction of each 

arrow shows the size and direction of the trend for each site; solid arrows show statistically 

significant trends and open arrows show trends that are not significant. Region-wide decreases 

are widespread and consistent; all sites had decreasing concentration trends (13 of the 38 were 

statistically significant). The average decrease for this set of sites is -0.24 ug/m3/year. 

Figure 20. Annual trends in PM2.5 mass (1999 – 2006)

Seasonal trends show mostly similar patterns (Figure 21). Trends were downward at most sites 

and seasons, with overall seasonal averages varying between -0.15 to -0.56 ug/m3/year. The 

strongest and most significant decreases took place during the winter quarter (January - March). 

No statistically significant increasing trends were observed.
27

Figure 21. Seasonal trends in PM2.5 mass (1999 – 2006)

PM2.5 shows a slight variation from weekday to weekend, as seen in Figure 22. Although most 

cities have slightly lower concentrations on the weekend, the difference is usually less than 1 

μg/m3. There is a more pronounced weekday/weekend difference at monitoring sites that are 

strongly source-influenced. Rural monitors tend to show less of a weekday/weekend pattern 

than urban monitors.

Figure 22 Day-of-week variability in PM2.5 (2002-2004)
28

In the Midwest, PM2.5 is made up of mostly ammonium sulfate, ammonium nitrate, and organic 

carbon in approximately equal proportions on an annual average basis. Elemental carbon and 

crustal matter (also referred to as soil) contribute less than 5% each. 

Figure 23. Spatial map of PM2.5 chemical composition in the Midwest (2002-2003)

The three major components vary spatially (Figure 23), including notable urban and rural 

differences (Figure 24). The components also vary seasonally (Figure 25). These patterns 

account for much of the annual variability in PM2.5 mass noted above.

Figure 24. Average regional (lighter shading) v. local (darker shading) of PM2.5 chemical species
29

Figure 25 Seasonal and spatial variability in PM2.5 components
30

Ammonium sulfate peaks in the summer and is highest in the southern and eastern parts of the 

Midwest, closest to the Ohio River Valley. Sulfate is primarily a regional pollutant; 

concentrations are similar in rural and urban areas and highly correlated over large distances. It 

is formed when sulfuric acid (an oxidation product of sulfur dioxide) and ammonia react in the 

atmosphere, especially in cloud droplets. Coal combustion is the primary source of sulfur 

dioxide; ammonia is emitted primarily from animal husbandry operations and fertilizer use.

Ammonium nitrate has almost the opposite spatial and seasonal pattern, with the highest 

concentrations occurring in the winter and in the northern parts of the region. Nitrate seems to 

have both regional and local sources, because urban concentrations are higher than rural 

upwind concentrations. Ammonium nitrate forms when nitric acid reacts with ammonia, a 

process that is enhanced when temperatures are low and humidity is high. Nitric acid is a 

product of the oxidation of nitric oxide, a pollutant that is emitted by combustion processes.

Organic carbon is more consistent from season to season and city to city, although 

concentrations are generally slightly higher in the summer. Like nitrate, organic carbon has 

both regional and local components. Particulate organic carbon can be emitted directly from 

cars and other fuel combustion sources or formed in a secondary process as volatile organic 

gases react and condense. In rural areas, summer organic carbon has significant contributions 

from biogenic sources.

Precursor Sensitivity: Data from the Midwest ammonia monitoring network were analyzed with 

thermodynamic equilibrium models to assess the effect of changes in precursor gas 

concentrations on PM2.5 concentrations (Blanchard, 2005b). These analyses indicate that 

particle formation responds in varying degrees to reductions in sulfate, nitric acid, and ammonia. 

Based on Figure 26, which shows PM2.5 concentrations as a function of sulfate, nitric acid 

(HNO3), and ammonia (NH3), several key findings should be noted: 

• PM2.5 mass is sensitive to reductions in sulfate at all times of the year and all parts of the 

region. Even though sulfate reductions cause more ammonia to be available to form 

ammonium nitrate (PM-nitrate increases slightly when sulfate is reduced), this increase 

is generally offset by the sulfate reductions, such that PM2.5 mass decreases.

• PM2.5 mass is also sensitive to reductions in nitric acid and ammonia. The greatest PM2.5

decrease in response to nitric acid reductions occurs during the winter, when nitrate is a

significant fraction of PM2.5.

• Under conditions with lower sulfate levels (i.e., proxy of future year conditions), PM2.5 is 

more sensitive to reductions in nitric acid compared to reductions in ammonia.

• Ammonia becomes more limiting as one moves from west to east across the region.

Examination of weekend/weekday difference in PM-nitrate and NOx concentrations in the 

Midwest demonstrate that reductions in local (urban) NOx lead to reductions, albeit nonproportional reductions, in PM-nitrate (Blanchard, 2004). This result is consistent with analyses 

of continuous PM-nitrate from several US cities, including St. Louis (Millstein, et al, 2007). 
31

Figure 26. Predicted mean PM fine mass concentrations at Bondville, IL (top) and Detroit (Allen Park), MI 

(bottom) as functions of changes in sulfate, nitric acid (HNO3), and ammonia (NH3)

Note: starting at the baseline values (represented by the red star), either moving downward (reductions in nitric 

acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM2.5 values
32

Meteorology: PM2.5 concentrations are not as strongly influenced by meteorology as ozone, but 

the two pollutants share some similar meteorological dependencies. In the summer, conditions 

that are conducive to ozone (hot temperatures, stagnant air masses, and low wind speeds due 

to stationary high pressure systems) also frequently give rise to high PM2.5. In the case of PM,

the reason is two-fold: (1) stagnation and limited mixing under these conditions cause PM2.5 to 

build up, usually over several days, and (2) these conditions generally promote higher 

conversion of important precursors (SO2 to SO4) and higher emissions of some precursors, 

especially biogenic carbon. Wind direction is another strong determinant of PM2.5; air 

transported from polluted source regions has higher concentrations.

Unlike ozone, PM2.5 has occasional winter episodes. Conditions are similar to those for summer 

episodes, in that stationary high pressure and (seasonally) warm temperatures are usually 

factors. Winter episodes are also fueled by high humidity and low mixing heights. 

PM2.5 chemical species show noticeable transport influences. Trajectory analyses have 

demonstrated that high PM-sulfate is associated with air masses that traveled through the 

sulfate-rich Ohio River Valley (Poirot, et al, 2002 and Kenski, 2004). Likewise, high PM-nitrate 

is associated with air masses that traveled through the ammonia-rich Midwest. Figure 27

shows results from an ensemble trajectory analysis of 17 rural eastern IMPROVE sites. 

Figure 27. Sulfate and nitrate source regions based on ensemble trajectory analysis

When these results are considered together with analyses of precursor sensitivity (e.g., Figure 

26), one possible conclusion is that ammonia control in the Midwest could be effective at 

reducing nitrate concentrations. The thermodynamic equilibrium modeling shows that ammonia 

reductions would reduce PM concentrations in the Midwest, but that nitric acid reductions are 

more effective when the probable reductions in future sulfate levels are considered. 
33

Source Culpability: Three source apportionment studies were performed using speciated PM2.5

monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 2008). 

Figure 28 summarizes the source contributions from these studies. The studies show that a

large portion of PM2.5 mass consists of secondary, regional impacts, which cannot be attributed 

to individual facilities or sources (e.g., secondary sulfate, secondary nitrate, and secondary 

organic aerosols). Nevertheless, wind analyses (e.g., Figure 27) provide information on likely 

source regions. Regional- or national-scale control programs may be the most effective way to 

deal with these impacts. EPA's CAIR, for example, will provide for substantial reductions in 

SO2 emissions over the eastern half of the U.S., which will reduce sulfate (and PM2.5) 

concentrations and improve visibility levels.

The studies also show that a smaller, yet significant portion of PM2.5 mass is due to emissions 

from nearby (local) sources. Local (urban) excesses occur in many urban areas for organic and 

elemental carbon, crustal matter, and, in some cases, sulfate. The statistical analysis methods 

help to identify local sources and quantify their impact. This information is valuable to states 

wishing to develop control programs to address local impacts. A combination of 

national/regional-scale and local-scale emission reductions may be necessary to provide for 

attainment.

The carbon sources are not easily identified in complex urban environments. LADCO’s Urban 

Organics Study (STI, 2006) identified four major sources of organic carbon: mobile sources, 

burning, industrial sources, and secondary organic aerosols. Additional sampling and analysis 

is underway in Cleveland and Detroit to provide further information on sources of organic 

carbon.
34

0

1

2

3

4

5

6

7

Coal

Combustion

Secondary

Nitrate

Mobile

Industrial

Soil

Burning

ug/m3

Northbrook (Jan 03-Mar 05)

Cincinnati (Dec 03-Mar 05)

Indianapolis (Jan 02-Mar 05)

Allen Park (2002-2004)

Dearborn (Jun 02-May 05)

Bondville (2002-2004)

St. Louis (2004)

Granite City (2002-2004)

Tikhon (Jan 03-Jun05)

Orange (Apr 01-Mar 05)

Lorain (Feb 02-Dec04)

 

0

2

4

6

8

10

sulfate

nitrate

OM

diesel

soil

steel

ind. zinc

ind. copper

mixed ind.

Allen Park

Dearborn

Law ndale

Springfield

Tikhon

Craig

Taft

Figure 28. Major Source Contributions in the Midwest based on Hopke, 2005 (upper left), STI, 2006 (upper right), and STI, 2008 (lower left)

(Note: the labeling of similar source types varies between studies – e.g., organic carbon/mobile sources are named gasoline and diesel by 

Hopke, mobile by STI 2006, and OM and diesel by STI 2008)
35

2.3 Haze

Section 169A of the Clean Air Act sets as a national goal “the prevention of any future, and the 

remedying of any existing, impairment of visibility in mandatory Class I Federal areas which 

impairment results from manmade air pollution”. To implement this provision, in 1999, EPA 

adopted regulations to address regional haze visibility impairment (USEPA, 1999). EPA’s rule 

requires states to “make reasonable progress toward meeting the national goal”. Specifically, 

states must establish reasonable progress goals, which provide for improved visibility on the 

most impaired (20% worst) days sufficient to achieve natural conditions by the year 2064, and 

for no degradation on the least impaired (20% best) days.

The primary cause of impaired visibility in the Class I areas is pollution by fine particles that 

scatter light. The degree of impairment, which is expressed in terms of visual range, light 

extinction (1/Mm), or deciviews (dv), depends not just on the total PM2.5 mass concentration, but 

also on the chemical composition of the particles and meteorological conditions.

Current Conditions: A map of the average light extinction values for the most impaired (20% 

worst) visibility days for the 5-year baseline period (2000-2004) is shown in Figure 29. 

Figure 29. Baseline Visibility Levels for 20% Worst Days (2000 – 2004), units: Mm-1
36

Initially, the baseline (2000 – 2004) visibility condition values were derived using the average for 

the 20% worst and 20% best days for each year, as reported on the VIEWS website: 

http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx . These values were 

calculated using the original IMPROVE equation for reconstructed light extinction.

Three changes were made to the baseline calculations to produce a new set of values. First, 

the reconstructed light extinction equation was revised by the IMPROVE Steering Committee in 

2005. The new IMPROVE equation was used to calculate updated baseline values. 

Second, due to sampler problems, the 2002-2004 data for Boundary Waters were invalid for 

certain chemical species. (Note, sulfate and nitrate data were valid.) A “substituted” data set 

was developed by using values from Voyageurs for the invalid species.

Third, LADCO identified a number of days during 2000-2004 where data capture at the Class I

monitors was incomplete (Kenski, 2007b). The missing data cause these days to be excluded 

from the baseline calculations. However, the light extinction due to the remaining measured 

species is significant (i.e., above the 80th percentile). It makes sense to include these days in 

the baseline calculations, because they are largely dominated by anthropogenic sources. (Only 

one of these days is driven by high organic carbon, which might indicate non-anthropogenic 

aerosol from wildfires.) As seen in Table 3, inclusion of these days in the baseline calculation

results in a small, but measurable, effect on the baseline values (i.e., values increase from 0.2 

to 0.8 dv).

Table 3. Average of 20% worst days, with and without missing data days

Average Worst Day 

DV, per RHR

Average Worst Day DV,

with Missing Data Days

Difference

BOWA 19.59 19.86 0.27

ISLE 20.74 21.59 0.85

SENE 24.16 24.38 0.22

VOYA 19.27 19.48 0.21

A summary of the initial and updated baseline values for the Class I areas in northern Michigan 

and northern Minnesota are presented in Table 4. The updated baseline values reflect the most 

current, complete understanding of visibility impairing effects and, as such, will be used for SIP 

planning purposes.
37

Table 4. Summary of visibility metrics (deciviews) for northern Class I areas

Old IMPROVE Equation (Cite: VIEWS, November 2005)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural 

Conditions

Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.09

BWCA 19.85 19.99 19.68 19.73 17.65 19.38 17.47 11.21

Isle Royale 20.00 22.00 20.80 19.50 19.10 20.28 18.17 11.22

Seney 22.60 24.90 24.00 23.80 22.60 23.58 20.73 11.37

20% Best Days

2000 2001 2002 2003 2004

Baseline

Value

Natural

Conditions

Voyageurs 6.30 6.20 6.70 7.00 5.40 6.32 3.41

BWCA 5.90 6.52 6.93 6.67 5.61 6.33 3.53

Isle Royale 5.70 6.40 6.40 6.30 5.30 6.02 3.54

Seney 5.80 6.10 7.30 7.50 5.80 6.50 3.69

New IMPROVE Equation (Cite: VIEWS, March 2006)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural

Conditions

Voyageurs 19.55 18.57 20.14 20.25 18.87 19.48 17.74 12.05

BWCA 20.20 20.04 20.76 20.13 18.18 19.86 17.94 11.61

Isle Royale 20.53 23.07 21.97 22.35 20.02 21.59 19.43 12.36

Seney 22.94 25.91 25.38 24.48 23.15 24.37 21.64 12.65

20% Best Days

2000 2001 2002 2003 2004

Baseline

Value

Natural

Conditions

Voyageurs 7.01 7.12 7.53 7.68 6.37 7.14 4.26

BWCA 6.00 6.92 7.00 6.45 5.77 6.43 3.42

Isle Royale 6.49 7.16 7.07 6.99 6.12 6.77 3.72

Seney 6.50 6.78 7.82 8.01 6.58 7.14 3.73

Notes: (1) BWCA values for 2002 - 2004 reflect "substituted" data.

 (2) New IMPROVE equation values include Kenski, 2007 adjustment for missing days

 URI = uniform rate of improvement
38

As noted above, the goal of the visibility program is to achieve natural conditions. Initially, the 

natural conditions values for each Class I area were taken directly from EPA guidance (EPA, 

2003). These values were calculated using the original IMPROVE equation. This equation was 

revised by the IMPROVE Steering Committee in 2005, and the new IMPROVE equation was 

used to calculate updated natural conditions values. The updated values are reported on the 

VIEWS website.

A summary of the initial and updated natural conditions values are presented in Table 4. The 

updated natural conditions values (based on the new IMPROVE equation) will be used for SIP 

planning purposes.

Data Variability: For the four northern Class I areas, the most important PM2.5 chemical species 

are ammonium sulfate, ammonium nitrate, and organic carbon. The contribution of these 

species on the 20% best and 20% worst visibility days (based on 2000 – 2004 data) is provided 

in Figure 30. For the 20% worst visibility days, the contributions are: sulfate = 35-55%, nitrate = 

25-30%, and organic carbon = 12-22%. Although the chemical composition is similar, sulfate 

increases in importance from west to east and concentrations are highest at Seney (the 

easternmost site). It should also be noted that sulfate and nitrate contribute more to light 

extinction than to PM2.5 mass because of their hygroscopic properties.

20% Best Days 20% Worst Days 

0

20

40

60

80

100

120

VOYA BOWA ISRO Seney VOYA BOWA ISRO Seney

sea salt

soil

elem. carbon

coarse mass

organic carbon

nitrate

sulfate

Figure 30. Chemical composition of light extinction for 20% best visibility days (left) and 20% 

worst visibility days (right) in terms of Mm-1

Analysis of PM2.5 mass and chemical species for rural IMPROVE (and IMPROVE-protocol) sites 

in the eastern U.S. showed a high degree of correlation between PM2.5-mass, sulfate, and 

nitrate levels (see Figure 31). The Class I sites in northern Michigan and northern Minnesota, in 

particular, are highly correlated for PM2.5 mass, sulfates, and organic carbon mass (AER, 2004).
39

Figure 31. Correlations among IMPROVE (and IMPROVE-protocol) monitoring sites in Eastern U.S.

Long-term trends at Boundary Waters (the only regional site with a sufficient data record) show 

significant decreases in total PM2.5 (-0.005 ug/year) and SO4 (-0.04 ug/year) and an increase in 

NO3 (+0.01 ug/year). These PM2.5 and SO4 trends are generally consistent with long-term 

trends at other IMPROVE sites in the eastern U.S., which have shown widespread decreases in 

SO4 and PM2.5 (DeBell, et al, 2006). Detecting changes in nitrate has been hampered by 

uncertainties in the IMPROVE data for particular years and, thus, this estimate should be 

considered tentative. 

Haze in the Midwest Class I areas has no strong seasonal pattern. Poor visibility days occur 

throughout the year, as indicated in Figure 32. (Note, in contrast, other parts of the country, 

such as Shenandoah National Park in Virginia, show a strong tendency for the worst air quality 

days to occur in the summer months.) This figure and Figure 33 (which presents the monthly 

average light extinction values based on all sampling days) also show that sulfate and organic 

carbon concentrations are higher in the summer, and nitrate concentrations are higher in the 

winter, suggesting the importance of different sources and meteorological conditions at different 

times of the year.
40

Figure 32. Daily light extinction values for 20% worst days at Boundary Waters (2000 – 2004)
41

Figure 33. Monthly average light extinction values for northern Class I areas
42

Precursor Sensitivity: Results from two analyses using thermodynamic equilibrium models 

provide information on the effect of changes in precursor concentrations on PM2.5

concentrations (and, in turn, visibility levels) in the northern Class I areas. First, a preliminary 

analysis using data collected at Seney indicated that PM2.5 there is most sensitive to reductions 

in sulfate, but is also sensitive to reductions in nitric acid (Blanchard, 2004). 

Second, an analysis was performed using data from the Midwest ammonia monitoring network

for a site in Minnesota -- Great River Bluffs, which is the closest ammonia monitoring site to the 

northern Class I areas (Blanchard, 2005b). Figure 34 shows PM2.5 concentrations as a function 

of sulfate, nitric acid (HNO3), and ammonia (NH3). Reductions in sulfate (i.e., movement to the 

left of baseline value [represented by the red star]), as well as reductions in nitric acid (i.e., 

movement downward) and NH3 (i.e., movement to the left), result in lower PM2.5 concentrations. 

Thus, reductions in sulfate, nitric acid, and ammonia will lower PM2.5 concentrations and 

improve visibility in the northern Class I areas.

Figure 34. Predicted PM2.5 mass concentrations at Great River Bluffs, MN as functions of changes 

in sulfate, nitric acid, and ammonia

Meteorology and Transport: The role of meteorology in haze is complex. Wind speed and wind 

direction govern the movement of air masses from polluted areas to the cleaner wilderness 

areas. As noted above, increasing humidity increases the efficiency with which sulfate and 

nitrate aerosols scatter light. Temperature and humidity together govern whether ammonium 

nitrate can form from its precursor gases, nitric acid and ammonia. Temperature and sunlight 

also play an indirect role in emissions of biogenic organic species that condense to form 

particulate organic matter; emissions increase in the summer daylight hours. 

Trajectory analyses were performed to understand transport patterns for the 20% worst and 

20% best visibility days. The composite results for the four northern Class I areas are provided 

in Figure 35. The orange areas are where the air is most likely to come from, and the green 

areas are where the air is least likely to come from. As can be seen, bad air days are generally 

associated with transport from regions located to the south, and good air days with transport 

from Canada. 
43

Figure 35. Composite back trajectories for light extinction- 20% best visibility days (left) and 

20% worst visibility days (right) (2000 – 2005)

Source Culpability: Air quality data analyses (including the trajectory analyses above) and 

dispersion modeling were used to provide information on source region and source sector 

contributions to regional haze in the northern Class I areas (see MRPO, 2008). Based on this 

information, the most important contributing states are Michigan, Minnesota, and Wisconsin, as 

well as Missouri, North Dakota, Iowa, Indiana and Illinois (see, for example, Figure 35 above). 

The most important contributing pollutants and source sectors are SO2 emissions from 

electrical generating units (EGUs) and certain non-EGUs, which lead to sulfate formation, and 

NOx emissions from a variety of source types (e.g., motor vehicles), which lead to nitrate 

formation. Ammonia emissions from livestock waste and fertilizer applications are also 

important, especially for nitrate formation.

A source apportionment study was performed using monitoring data from Boundary Waters and 

statistical analysis methods (DRI, 2005). The study shows that a large portion of PM2.5 mass 

consists of secondary, regional impacts, which cannot be attributed to individual facilities or 

sources (e.g., secondary sulfate, secondary nitrate, and secondary organic aerosols). Industrial 

sources contribute about 3-4% and mobile sources about 4-7% to PM2.5 mass. 

A special study was performed in Seney to identify sources of organic carbon (Sheesley, et al, 

2004). As seen in Figure 36, the highest PM2.5 concentrations occurred during the summer, 

with organic carbon being the dominant species. The higher summer organic carbon 

concentrations were attributed mostly to secondary organic aerosols of biogenic origin because 

of the lack of primary emission markers, and concentrations of know biogenic-related species 

(e.g., pinonic acid – see Figure 36) were also high during the summer.
44

Figure 36. Monthly concentrations of PM2.5 species (top), and secondary and biogenic-related 

organic carbon species in Seney (bottom)
45

Although the Seney study showed that biomass burning was a relatively small contributor to 

organic carbon on an annual average basis, episodic impacts are apparent (see, for example, 

high organic carbon days in Figure 32). To assess further whether burning is a significant 

contributor to visibility impairment in the northern Class I areas, the PM2.5 chemical speciation 

data were examined for days with high organic carbon and elemental carbon concentrations, 

which are indicative of biomass burning impacts. Only a handful of such days were identified:

Table 5. Days with high OC and EC concentrations in northern Class I areas

Site 2000 2001 2002 2003 2004

Voyageurs --- --- Jun 1 Aug 25 Jul 17

Jun 28

Jul 19

Boundary Waters --- --- Jun 28 Aug 25 Jul 17

Jul 19

Isle Royale --- --- Jun 1 Aug 25 ---

Jun 28

Seney --- --- Jun 28 --- ---

Back trajectories on these days point mostly to wildfires in Canada. Elimination of these high 

organic carbon concentration days has a small effect in lowering the baseline visibility levels in 

the northern Class I areas (i.e., Minnesota Class I areas change by about 0.3 deciviews and 

Michigan Class I areas change by less than 0.2 deciviews). This suggests that fire activity, 

although significant on a few days, is on average a relatively small contributor to visibility 

impairment in the northern Class I areas.

In summary, these analyses show that organic carbon in the northern Class I is largely 

uncontrollable.
46

Section 3.0 Air Quality Modeling

Air quality models are relied on by federal and state regulatory agencies to support their 

planning efforts. Used properly, models can assist policy makers in deciding which control 

programs are most effective in improving air quality, and meeting specific goals and objectives. 

For example, models can be used to conduct “what if” analyses, which provide information for 

policy makers on the effectiveness of candidate control programs.

The modeling analyses were conducted in accordance with EPA’s modeling guidelines (EPA, 

2007a). Further details of the modeling are provided in two protocol documents: LADCO, 2007a 

and LADCO, 2007b. 

This section reviews the development and evaluation of the modeling system used for the multipollutant analyses. Application of the modeling system (i.e., attainment demonstration for ozone 

and PM2.5, and reasonable progress assessment for haze) is covered in the following sections.

3.1 Selection of Base Year

Two base years were used in the modeling analyses: 2002 and 2005. EPA’s modeling 

guidance recommends using 2002 as the baseline inventory year, but also allows for use of an 

alternative baseline inventory year, especially a more recent year. Initially, LADCO conducted 

modeling with a 2002 base year (i.e., Base K/Round 4 modeling, which was completed in 2006). 

A decision was subsequently made to conduct modeling with a 2005 base year (i.e., Base 

M/Round 5, which was completed in 2007). As discussed in the previous section, 2002 and 

2005 both had above normal ozone conducive conditions, although 2002 was more severe 

compared to 2005. Examination of multiple base years provides for a more complete technical 

assessment. Both sets of model runs are discussed in this document. 

3.2 Future Years of Interest

To address the multiple attainment requirements for ozone and PM2.5, and reasonable progress 

goals for regional haze, several future years are of interest:

2008 Planning year for ozone basic nonattainment areas (attainment date 2009)8

2009 Planning year for ozone moderate nonattainment areas and PM2.5 nonattainment 

areas (attainment date 2010)

2012 Planning year for ozone moderate nonattainment areas and PM2.5 nonattainment 

areas, with 3-year extension (attainment date 2013)

2018 First milestone year for regional haze planning

 

8

According to USEPA’s ozone implementation rule (USEPA, 2005), emission reductions needed for 

attainment must be implemented by the beginning of the ozone season immediately preceding the area’s 

attainment date. The PM2.5 implementation rule contains similar provisions – i.e., emission reductions 

should be in place by the beginning of the year preceding the attainment date (USEPA, 2007c). The logic 

for requiring emissions reductions by the year (or season) immediately preceding the attainment year 

follows from language in the Clean Air Act, and the ability for an area to receive up to two 1-year 

extensions. Therefore, emissions in the year preceding the attainment year should be at a level that is 

consistent with attainment. It also follows that the year preceding the attainment year should be modeled 

for attainment planning purposes.
47

Detailed emissions inventories were developed for 2009 and 2018. To support modeling for 

other future years, less rigorous emissions processing was conducted (e.g., 2012 emissions 

were estimated for several source sectors by interpolating between 2009 and 2018 emissions).

3.3 Modeling System

The air quality analyses were conducted with the CAMx model, with emissions and meteorology 

generated using EMS (and CONCEPT) and MM5, respectively. The selection of CAMx as the 

primary model is based on several factors: performance, operator considerations (e.g., ease of 

application and resource requirements), technical support and documentation, model 

extensions (e.g., 2-way nested grids, process analysis, source apportionment, and plume-ingrid), and model science. CAMx model set-up for Base M and Base K is summarized below:

Base M (2005) Base K (2002)

• CAMx v4.50 * CAMx 4.30

• CB05 gas phase chemistry * CB-IV with updated gas-phase chemistry

• SOA chemistry updates * No SOA chemistry updates

• AERMOD dry deposition scheme * Wesley-based dry deposition

• ISORROPIA inorganic chemistry • ISORROPIA inorganic chemistry

• SOAP organic chemistry • SOAP organic chemistry

• RADM aqueous phase chemistry • RADM aqueous phase chemistry

• PPM horizontal transport • PPM horizontal transport

3.4 Domain/Grid Resolution

The National RPO grid projection was used for this modeling. A subset of the RPO domain was 

used for the LADCO modeling. For PM2.5 and haze, the large eastern U.S. grid at 36 km (see 

box on right side of Figure 36) was used. A PM2.5 sensitivity run was also performed for this 

domain at 12 km. For ozone, the smaller grid at 12 km (see shaded portion of the box on the 

right side of Figure 37) was used for most model runs. An ozone sensitivity run was also 

performed with a 4km sub-grid over the Lake Michigan area and Detroit/Cleveland.

The vertical resolution in the air quality model consists of 16 layers extending up to 15 km, with 

higher resolution in the boundary layer. 

Figure 37. Modeling grids – RPO domain (left) and LADCO modeling domain (right)

12 km

36 km
48

3.5 Model Inputs: Meteorology

Meteorological inputs were derived using the Fifth-Generation NCAR/Penn State Meteorological 

Model (MM5) – version 3.6.3 for the years 2001–2003, and version 3.7 for the year 2005. The 

MM5 modeling domains are consistent with the National RPO grid projections (see Figure 38). 

Figure 38. MM5 modeling domain for 2001-2003 (left) and 2005 (right)

The annual 2002 36 km MM5 simulation was completed by Iowa DNR. The 36/12 km 2-way 

nested simulation for the summers of 2001, 2002, and 2003 were conducted jointly by Illinois 

EPA and LADCO. The 36 km non-summer portion of the annual 2003 simulation was conducted 

by Wisconsin DNR. The annual 2005 36/12 km (and summer season 4 km) MM5 modeling was 

completed by Alpine Geophysics. Wisconsin DNR also completed 36/12 km MM5 runs for the 

summer season of 2005.

Model performance was assessed quantitatively with the METSTAT tool from Environ. The 

metrics used to quantify model performance include mean observation, mean prediction, bias, 

gross error, root mean square error, and index of agreement. Model performance metrics were 

calculated for several sub-regions of the modeling domain (Figure 39) and represent hourly 

spatial averages of multiple monitor locations. Additional analysis of rainfall is done on a 

monthly basis.

Figure 39. Sub-domains used for model performance for 2001-2003 (left) and 2005 (right)

A summary of the performance evaluation results for the meteorological modeling is provided 

below. Further details are provided in two summary reports (LADCO, 2005 and LADCO, 2007c).
49

Temperature: The biggest issue with the performance in the upper Midwest is the existence of a 

cool diurnal temperature bias in the winter and warm temperature bias over night during the 

summer (see Figure 40). These features are common to other annual MM5 simulations for the 

central United States and do not appear to adversely affect model performance. 

Figure 40. Daily temperature bias for 2002 (left) and 2005 (right) with hotter colors 

(yellow/orange/red) representing overestimates and cooler colors (blues) representing 

underestimates

Note: months are represented from left to right (January to December) and days are represented 

from top to bottom (1 to 30(31) – i.e., upper left hand corner is January 1 and lower right hand 

corner is December 31

Wind Fields: The wind fields are generally good. Wind speed bias is less than 0.5 m/sec and 

wind speed error is consistently between 1.0 and 1.5 m/sec. Wind direction error is generally 

within 15-30 degrees.

Mixing Ratio: The mixing ratio (a measure of humidity) is over-predicted in the late spring and 

summer months, and mixing ratio error is highest during this period. There is little bias and 

error during the cooler months when there is less moisture in the air.

Rainfall: The modeled and observed rainfall totals show good agreement spatially and in

terms of magnitude in the winter, fall, and early spring months. There are, however, large overpredictions of rainfall in the late spring and summer months (see Figure 41). These overpredictions are seen spatially and in magnitude over the entire domain, particularly in the 

Southeast United States, and are likely due to excessive convective rainfall being predicted in 

MM5. This over-prediction of rainfall in MM5 does not necessarily translate into over-prediction 

of wet deposition in the photochemical model. CAMx does not explicitly use the convective and 

non-convective rainfall output by MM5, but estimates wet scavenging by hydrometeors using 

cloud, ice, snow, and rain water mixing ratios output by MM5. Nevertheless, this could have an 

effect on model performance for PM2.5, as discussed in Section 3.7, and may warrant further 

attention.
50

Figure 41. Comparison of observed (left column) and modeled (right column) monthly rainfall for 

July 2002 (top) and July 2005 (bottom)
51

3.6 Model Inputs: Emissions

Emission inventories were prepared for two base years: 2002 (Base K) and 2005 (Base M), and 

several future years: 2008, 2009, 2012, and 2018. Further details of the emission inventories 

are provided in two summary reports (LADCO, 2006a and LADCO, 2008a) and the following 

pages of the LADCO web site:

http://www.ladco.org/tech/emis/basek/BaseK_Reports.htm

http://www.ladco.org/tech/emis/r5/round5_reports.htm

For on-road, nonroad, ammonia, and biogenic sources, emissions were estimated by models. 

For the other sectors (point sources, area sources, and MAR [commercial marine, aircraft, and 

railroads]), emissions were prepared using data supplied by the LADCO States and other 

RPOs.

Base Year Emissions: State and source sector emission summaries for 2002 (Base K) and 

2005 (Base M) are compared in Figure 42. Additional detail is provided in Tables 6a (all sectors 

– tons per day) and 6b (EGUs – tons per year). 

VOC NOx SO2

 

0

2000

4000

6000

8000

10000

12000

2002

(K)

2005

(M)

2002

(K)

2005

(M)

2002

(K)

2005

(M)

WI

OH

MI

IN

IL

0

2000

4000

6000

8000

10000

12000

2002

(K)

2005

(M)

2002

(K)

2005

(M)

2002

(K)

2005

(M)

Area

Nonroad+MAR

Onroad

Non-EGU

EGU

Figure 42. Base K and Base M emissions for 5-state LADCO region by state (top) and source 

sector (bottom), units: tons per summer weekday

A summary of the base year emissions by sector for the LADCO States is provided below.
VOC Base M BaseK Base M BaseK BaseK Base M NOx Base M BaseK Base M BaseK BaseK Base M SOX Base M BaseK Base M BaseK BaseK Base M PM2.5 Base M BaseK Base M BaseK BaseK Base M

July 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018

Nonroad

IL 224 321 164 257 149 130 213 324 333 263 275 224 154 155 31 33 5 5 0.6 0.4 0.4 30 24 14

IN 125 195 94 160 95 95 128 178 191 142 158 141 141 89 17 19 3 3 3 0.3 0.2 17 13 7

MI 348 414 307 350 276 222 271 205 239 159 197 133 93 112 19 22 3 3 0.5 0.3 0.3 22 18 11

OH 222 356 161 294 145 126 238 253 304 195 246 162 109 135 23 29 4 5 0.5 0.3 0.4 27 22 13

WI 214 238 194 203 175 140 157 145 157 114 129 97 69 77 13 15 2 2 0.3 0.2 0.2 14 12 7

5-State Total 1133 1524 920 1264 840 713 1007 1105 1224 873 1005 757 566 568 103 118 17 18 4.9 1.5 1.5 110 89 52

U.S. Total 8463 9815 5442 8448 5244 6581 6041 9060 6057 8120 5832 5100 505 654 117 153 104 13 573 750 475

MAR

IL 10 11 10 10 10 10 6 277 246 201 228 195 186 165 0 22 0 19 0 0 17 7 6 4

IN 5 5 5 5 5 5 3 123 93 89 87 87 84 65 0.2 8 0.2 7 0.2 0.2 6 2 2 2

MI 7 7 7 7 7 8 7 114 87 112 82 111 110 65 0.6 21 0.7 14 0.7 0.8 8 3 3 2

OH 8 7 8 7 8 8 5 177 134 128 126 126 122 94 0.4 14 0.3 12 0.3 0.3 10 4 4 2

WI 4 4 4 4 4 4 3 79 58 59 54 59 57 41 12.7 8 9.5 6 9.5 8.7 5 2 2 1

5-State Total 34 34 34 33 34 35 24 770 618 589 577 578 559 430 13.9 73 10.7 58 10.7 10 46 18 17 11

U.S. Total 307 317 321 157 329 346 334 4968 4515 4002 1813 3964 3919 3812 620 512 509 122 509 503 290 147 57 165

OtherArea

IL 679 675 688 594 700 738 582 62 48 68 48 70 73 49 11 11 12 16 12 13 16 40 64 69

IN 354 391 365 358 373 398 384 62 56 65 58 67 69 59 158 32 150 32 151 153 32 2 2 2

MI 518 652 516 562 520 541 549 49 49 52 50 53 54 51 71 29 68 29 68 68 28 111 114 120

OH 546 604 550 506 558 593 487 50 93 59 108 60 62 108 22 6 34 15 35 35 14 19 35 34

WI 458 315 467 290 474 506 293 32 37 34 37 34 35 37 9 17 9 13 10 10 13 11 12 12

5-State Total 2555 2637 2586 2310 2625 2776 2295 255 283 278 301 284 293 304 271 95 273 105 276 279 103 183 227 237

U.S. Total 17876 21093 18638 18683 20512 24300 3856 4899 4100 4220 4418 5357 2075 2947 2062 2559 2189 2709 2735 2621 2570

On-Road

IL 446 341 314 268 260 197 151 890 748 578 528 474 300 201 9 4 3 13 10 6

IN 405 282 237 235 193 150 138 703 541 425 402 313 187 173 11 3 2 9 7 2

MI 522 351 335 269 303 217 163 926 722 680 501 619 385 204 14 4 3 12 9 3

OH 574 680 365 424 340 238 242 1035 934 609 693 512 270 274 18 4 4 16 12 4

WI 238 175 144 119 117 88 68 481 457 303 322 226 118 138 9 2 2 8 6 2

5-State Total 2185 1829 1395 1315 1213 890 762 4035 3402 2595 2446 2144 1260 990 61 17 14 58 44 17

U.S. Total 14263 7825 23499 13170

EGU

IL 9 7 8 6 8 9 7 712 305 227 275 244 231 224 1310 1158 944 958 789 810 869 13 34 77

IN 6 6 6 6 7 6 6 830 393 406 370 424 283 255 2499 2614 1267 1033 1263 1048 1036 16 73 74

MI 12 6 11 4 11 12 4 448 393 218 242 219 247 243 1103 1251 1022 667 1031 1058 725 15 25 29

OH 5 4 6 5 7 7 6 1139 408 330 280 322 271 285 3131 3405 1463 1326 994 701 983 28 94 80

WI 3 5 3 2 4 4 3 293 213 146 165 139 147 177 602 545 512 460 492 500 435 0 22 25

5-State Total 35 28 34 23 37 38 26 3422 1712 1327 1332 1348 1179 1184 8645 8973 5208 4444 4569 4117 4048 72 248 285

U.S. Total 214 140 195 124 197 215 138 14371 10316 7746 7274 7721 7007 6095 31839 34545 20163 16903 17629 14727 14133 685 1131 1571

Non-EGU

IL 313 221 286 218 305 350 258 356 330 334 218 338 343 235 373 423 251 335 257 249 346 16 17 19

IN 150 130 160 137 170 199 167 238 179 212 175 216 225 178 292 218 270 216 274 290 180 35 36 44

MI 123 116 115 119 122 139 140 216 240 208 242 214 229 271 162 158 166 148 171 185 163 20 21 25

OH 77 84 75 87 79 90 104 177 175 157 166 160 167 178 240 289 231 288 210 216 293 27 28 33

WI 88 84 97 87 104 120 106 98 97 91 93 92 94 81 163 156 154 152 155 156 85 0 0.1 0.1

5-State Total 751 635 733 648 780 898 775 1085 1021 1002 894 1020 1058 943 1230 1244 1072 1139 1067 1096 1067 98 102 121

U.S. Total 4087 3877 4409 4700 5378 6446 6730 6129 6435 6952 5759 5630 6093 6340 6970 1444 1777

IL 1681 1576 1470 1353 1432 1434 1217 2621 2010 1671 1572 1545 1287 1029 1725 1656 1212 1337 1059 1072 1251 119 155 189

IN 1045 1009 867 901 843 853 826 2134 1453 1339 1250 1248 989 819 2966 2902 1690 1294 1691 1492 1256 81 133 131

MI 1530 1546 1291 1311 1239 1139 1134 1958 1730 1429 1314 1349 1118 946 1356 1495 1260 865 1271 1312 927 183 190 190

OH 1432 1735 1165 1323 1137 1062 1082 2831 2048 1478 1619 1342 1001 1074 3416 3761 1732 1650 1240 953 1304 121 195 166

WI 1005 821 909 705 878 862 630 1128 1019 747 800 647 520 551 800 750 687 635 667 675 540 35 54 47

5-State Total 6693 6687 5702 5593 5529 5350 4889 10672 8260 6664 6555 6131 4915 4419 10263 10564 6581 5781 5928 5504 5280 539 727 723
Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35

IPM 2.1.9 241,000 73,000

1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107

IPM3.0 - will do 140,296 0.214 62,990 0.096

IPM3.0 - may do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45

IPM 2.1.9 377,000 95,000

1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120

IPM3.0 - will do 417,000 0.552 94,000 0.125

IPM3.0 - may do 417,000 0.552 94,000 0.125

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35

IPM 2.1.9 399,000 100,000

1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158

IPM3.0 - will do 244,151 0.484 79,962 0.158

IPM3.0 - may do 244,151 0.484 79,962 0.158

OH 1,306,296,589 2001 - 2003 (average) 1,144,484 1.75 353,255 0.54

IPM 2.1.9 216,000 84,000

1,628,081,545 IPM3.0 (base) 316,883 0.389 96,103 0.118

IPM3.0 - will do 348,000 101,000

IPM3.0 - may do 348,000 101,000

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36

IPM 2.1.9 155,000 46,000

675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167

IPM3.0 - will do 150,340 0.445 55,019 0.163

IPM3.0 - may do 62,439 0.185 46,154 0.137

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40

IPM 2.1.9 147,000 51,000

534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224

IPM3.0 - will do 115,938 0.434 59,994 0.224

IPM3.0 - may do 100,762 0.377 58,748 0.220

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42

IPM 2.1.9 86,000 42,000

447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186

IPM3.0 - will do 54,315 0.243 49,488 0.221

IPM3.0 - may do 51,290 0.229 39,085 0.175

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37

IPM 2.1.9 281,000 78,000

893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163

IPM3.0 - will do 237,600 0.532 72,950 0.163

IPM3.0 - may do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45

IPM 2.1.9 109,000 72,000

342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258

IPM3.0 - will do 56,175 0.328 58,850 0.343

IPM3.0 - may do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80

IPM 2.1.9 12,000 15,000

44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114

IPM3.0 - will do 4,464 0.199 2,548 0.114

IPM3.0 - may do 4,464 0.199 2,548 0.114

Table 6b. EGU Emissions for Midwest States (2018)
54

On-road Sources: For 2002, EMS was run by LADCO using VMT and MOBILE6 inputs supplied 

by the LADCO States. EMS was run to generate 36 days (weekday, Saturday, Sunday for each 

month) at 36 km, and 9 days (weekday, Saturday, Sunday for June – August) at 12 km. For 

2005, CONCEPT was run by a contractor (Environ) using transportation data (e.g., VMT and 

vehicle speeds) supplied by the state and local planning agencies in the LADCO States and 

Minnesota for 24 networks. These data were first processed with T3 (Travel Demand Modeling 

[TDM] Transformation Tool) to provide input files for CONCEPT to calculate link-specific, hourly 

emission estimates (Environ, 2008). CONCEPT was run with meteorological data for a July and 

January weekday, Saturday, and Sunday (July 15 – 17 and January 16 – 18). A spatial plot of 

emissions is provided in Figure 43.

VOC Emissions NOx Emissions

Figure 43. Motor vehicle emissions for VOC (left) and NOx (right) for a July weekday (2005)

Off-road Sources: For 2002 and 2005, NMIM and NMIM2005, respectively, were run by 

Wisconsin DNR. Additional off-road sectors (i.e., commercial marine, aircraft, and railroads 

[MAR]) were handled separately. Local data for agricultural equipment, construction equipment, 

commercial marine, recreational marine, and railroads were prepared by contractors (Environ, 

2004, and E.H. Pechan, 2004). For Base M, updated local data for railroads and commercial 

marine were prepared by a contractor (Environ, 2007b, 2007c). Table 7 compares the Base M

2005 and Base K 2002 emissions. Compared to 2002, the new 2005 emissions reflect 

substantially lower commercial marine emissions and lower locomotive NOx emissions.

Table 7. Locomotive and commercial marine emissions for the five LADCO States (2002 v. 2005)

Railroads (TPY) Commercial Marine (TPY)

2002 2005 2002 2005

VOC 7,890 7,625 1,562 828

CO 20,121 20,017 8,823 6,727

NOx 182,226 145,132 64,441 42,336

PM 5,049 4,845 3,113 1,413

SO2 12,274 12,173 25,929 8,637

NH3 86 85 ---- ----
55

Area Sources: For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO 

States to produce weekday, Saturday, and Sunday emissions for each month. For 2005, 

special attention was given to two source categories: industrial adhesive and sealant solvents 

(which were dropped from the inventory to avoid double-counting) and outdoor wood boilers

(which were added to the inventory).

Point Sources: For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO 

States to produce weekday, Saturday, and Sunday emissions for each month. For EGUs, the 

annual and summer season emissions were temporalized for modeling purposes using profiles 

prepared by Scott Edick (Michigan DEQ) based on CEM data. 

 

Biogenics: For Base M, a contractor (Alpine) provided an updated version of the 

CONCEPT/MEGAN biogenics model. Compared to the previous (EMS/BIOME) emissions, 

there is more regional isoprene using MEGAN compared to the BIOME estimates used for Base 

K (see Figure 44). Also, with the secondary organic aerosol updates to the CAMx air quality 

model, Base M includes emissions for monoterpenes and sesquiterpenes, which are precursors of secondary PM2.5 organic carbon mass.

Figure 44. Isoprene emissions for Base M (left) v. Base K (right)

Ammonia: For Base M, the CMU-based 2002 (Base K) ammonia emissions were projected to 

2005 using growth factors from the Round 4 emissions modeling. These emissions were then 

adjusted by applying temporal factors by month based on the process-based ammonia 

emissions model (Zhang, et al, 2005, and Mansell, et al, 2005). A plot of average daily 

emissions by state and month is provided in Figure 45. A spatial plot of emissions is provided in 

Figure 46, which shows high emissions densities in the central U.S.

0

300

600

900

1200

1500

1800

Jan Fed March Apr May Jun Jul Aug Sept Oct Nov Dec

Illinois Indiana Iowa Michigan

Minnesota Ohio Wisconsin

Figure 45. Average daily ammonia emissions for Midwest States by month (2005) - (units: average 

daily emissions – tons per day)
56

Figure 46. Ammonia emissions for a July weekday (2005) – 12 km modeling domain

Canadian Emissions: For Base M, Scott Edick (Michigan DEQ) processed the 2005 Canadian 

National Pollutant Release Inventory, Version 1.0 (NPRI). Specifically, a subset of the NPRI 

data (emissions and stack parameters) relevant to the air quality modeling were reformatted. 

The resulting emissions represent a significant improvement in the base year emissions. 

A spatial plot of point source SO2 and NOx emissions is provided in Figure 47. Additional plots 

and emission reports are available on the LADCO website 

(http://www.ladco.org/tech/emis/basem/canada/index.htm). 

Figure 47. Canadian point source emissions for SO2 (left) and NOx (right)
57

Fires: For Base K, a contractor (EC/R, 2004) developed a 2001, 2002, and 2003 fire emissions 

inventory for eight Midwest States (five LADCO states plus Iowa, Minnesota, and Missouri), 

including emissions from wild fires, prescribed fires, and agricultural burns. Projected emissions 

were also developed for 2010 and 2018 assuming “no smoke management” and “optimal smoke 

management” scenarios. An early model sensitivity run showed very little difference in modeled 

PM2.5 concentrations. Consequently, the fire emissions were not included in subsequent 

modeling runs (i.e., they were not in the Base K or Base M modeling inventories).

Future Year Emissions: Complete emission inventories were developed for several future years: 

Base K – 2009, 2012, and 2018, and Base M – 2009 and 2018. In addition, 2008 (Base K and 

Base M) and 2012 (Base M) proxy inventories were estimated based on the 2009 and 2018 

data. (Note, the EGU emissions for the Base M 2012 inventory were based on EPA’s IPM3.0 

modeling.)

Source sector emission summaries for the base years and future years are shown in Figure 48. 

Additional detail is provided in Tables 6a and 6b. 

VOC NOx SO2

0

3000

6000

9000

12000

2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018

Area

On-road

Nonroad

Non-EGU

EGU

Figure 48. Base year and future year emissions for 5-State LADCO Region (TPD, July weekday)

For on-road, and nonroad, the future year emissions were estimated by models (i.e., 

EMS/CONCEPT and NMIM, respectively). One adjustment was made to the 2009 and 2018 

motor vehicle emission files prepared by Environ with CONCEPT. To reflect newer 

transportation modeling conducted by CATS for the Chicago area, emissions were increased by 

9% in 2009 and 2018. The 2005 base year and adjusted 2009 and 2018 motor vehicle 

emissions are provided in Table 8.
58

Table 8. Motor Vehicle Emissions Produced by CONCEPT Modeling (July weekday – tons per day)

Year State Sum of CO Sum of TOG Sum of NOx Sum of PM2.5 Sum of SO2 Sum of NH3 Sum of VMT

2005 IL 3,684.3 341.5 748.2 12.9 9.6 35.9 344,087,819.6

IN 3,384.9 282.0 541.1 8.9 11.1 25.7 245,537,231.9

MI 4,210.3 351.9 722.0 12.4 13.9 35.3 340,834,025.9

MN 2,569.1 218.7 380.5 6.3 7.6 17.7 170,024,599.7

OH 6,113.4 679.8 933.6 16.2 18.8 36.5 360,521,068.6

WI 2,206.0 175.1 457.5 7.8 9.2 19.7 189,123,964.3

 Total 22,168.0 2,049.0 3,782.9 64.5 70.2 170.8 1,650,128,709.9

2009 IL 2,824.4 268.0 527.8 10.1 4.2 38.9 372,132,591.1

IN 2,839.5 234.9 401.9 6.7 2.8 26.1 249,817,026.3

MI 3,172.0 269.2 500.9 9.2 4.0 37.1 356,347,010.5

MN 2,256.8 206.3 307.5 5.1 2.3 21.5 204,443,017.8

OH 4,619.2 423.7 693.5 11.8 4.7 39.5 387,428,127.2

WI 1,673.4 119.4 322.1 5.7 2.3 20.6 197,729,964.9

 Total 17,385.3 1,521.5 2,753.6 48.7 20.3 183.6 1,767,897,737.8

2018 IL 2,084.7 151.5 200.7 6.3 3.7 43.1 413,887,887.3

IN 2,217.3 138.4 173.0 4.4 2.6 30.2 288,042,232.1

MI 2,434.3 163.5 204.1 5.9 3.6 40.5 388,128,431.8

MN 1,799.6 123.1 137.1 3.6 2.2 24.9 237,022,213.7

OH 3,361.5 242.5 274.1 6.8 4.0 43.1 421,694,093.4

WI 1,255.5 68.4 138.5 3.9 2.0 22.2 218,277,167.5

 Total 13,152.9 887.5 1,127.5 30.8 18.1 203.9 1,967,052,025.8
59

For EGUs, future year emissions were based on IPM2.1.9 modeling completed by the RPOs in 

July 2005 Base K and IPM3.0 completed by EPA in February 2007 for Base M. Several CAIR 

scenarios were assumed:

Base K

1a: IPM2.1.9, with full trading and banking

1b: IPM2.1.9, with restricted trading (compliance with state-specific emission budgets) and full trading

1d: IPM2.1.9, with restricted trading (compliance with state-specific emission budgets)

Base M

5a: EPA’s IPM3.0 was assumed as the future year base for EGUs.

5b: EPA’s IPM3.0, with several “will do” adjustments identified by the States. These adjustments should 

reflect a legally binding commitment (e.g., signed contract, consent decree, or operating permit). 

5c: EPA’s IPM3.0, with several “may do” adjustments identified by the States. These adjustments reflect 

less rigorous criteria, but should still be some type of public reality (e.g., BART determination or press 

announcement).

For other sectors (area, MAR, and non-EGU point sources), the future year emissions for the 

LADCO States were derived by applying growth and control factors to the base year inventory. 

These factors were developed by a contractor (E.H. Pechan, 2005 and E.H. Pechan, 2007). 

For the non-LADCO States, future year emission files were based on data from other RPOs.

Growth factors were based initially on EGAS (version 5.0), and were subsequently modified (for 

select, priority categories) by examining emissions activity data. Due to a lack of information on 

future year conditions, the biogenic VOC and NOx emissions, and all Canadian emissions were 

assumed to remain the constant between the base year and future years.

A “base” control scenario was prepared for each future year based on the following “on the 

books” controls:

On-Highway Mobile Sources

• Federal Motor Vehicle Emission Control Program, low-sulfur gasoline and ultra-low sulfur diesel fuel

• Inspection - maintenance programs, including IL’s vehicle emissions tests (NE IL), IN’s vehicle 

emissions testing program (NW IN), OH’s E-check program (NE OH), and WI’s vehicle inspection 

program (SE WI) – note: a special emissions modeling run was done for the Cincinnati/Dayton area to 

reflect the removal of the state’s E-check program and inclusion of low RVP gasoline

• Reformulated gasoline, including in Chicago-Gary,-Lake County, IL,IN; and Milwaukee, Racine, WI

Off-Highway Mobile Sources

• Federal control programs incorporated into NONROAD model (e.g., nonroad diesel rule), plus the 

evaporative Large Spark Ignition and Recreational Vehicle standards

• Heavy-duty diesel (2007) engine standard/Low sulfur fuel

• Federal railroad/locomotive standards

• Federal commercial marine vessel engine standards

Area Sources (Base M only)

• Consumer solvents

• AIM coatings

• Aerosol coatings

• Portable fuel containers

Power Plants

• Title IV (Phases I and II)

• NOx SIP Call

• Clean Air Interstate Rule
60

Other Point Sources

• VOC 2-, 4-, 7-, and 10-year MACT standards

• Combustion turbine MACT

Other controls included in the modeling include: consent decrees (refineries, ethanol plants, and 

ALCOA)9, NOx RACT in Illinois and Ohio10, and BART for a few non-EGU sources in Indiana 

and Wisconsin.

For Base K, several additional control scenarios were considered:

Scenario 2 – “base” controls plus additional controls recommended in LADCO White 

Papers for stationary and mobile sources

Scenario 3 – Scenario 2 plus additional White Papers for stationary and mobile sources

Scenario 4 – “base” controls plus additional candidate control measures under

discussion by State Commissioners

Scenario 5 – “base” controls plus additional candidate control measures identified by the 

LADCO Project Team

3.7 Basecase Modeling Results

The purpose of the basecase modeling is to evaluate model performance (i.e., assess the 

model's ability to reproduce the observed concentrations). The model performance evaluation 

focused on the magnitude, spatial pattern, and temporal of modeled and measured 

concentrations. This exercise was intended to assess whether, and to what degree, confidence 

in the model is warranted (and to assess whether model improvements are necessary).

Model performance was assessed by comparing modeled and monitored concentrations. 

Graphical (e.g., side-by-side spatial plots, time series plots, and scatter plots) and statistical 

analyses were conducted. No rigid acceptance/rejection criteria were used for this study. 

Instead, the statistical guidelines recommended by EPA and other modeling studies (e.g., 

modeling by the other RPOs) were used to assess the reasonableness of the results. The 

model performance results presented here describe how well the model replicates observed 

ozone and PM2.5 concentrations after a series of iterative improvements to model inputs.

Ozone: Spatial plots are provided for high ozone periods in June 2002 and June 2005 (see 

Figures 49a and 49b). The plots show that the model is doing a reasonable job of reproducing 

the magnitude, day-to-day variation, and spatial pattern of ozone concentrations. There is a 

tendency, however, to underestimate the magnitude of regional ozone levels. This is more 

apparent with the 2002 modeling; the regional concentrations in the 2005 modeling agree better 

with observations due to model and inventory improvements.

 

9

 E.H. Pechan’s original control file included control factors for three sources in Wayne County, MI. 

These control factors were not applied in the regional-scale modeling to avoid double-counting with the 

State’s local-scale analysis for PM2.5 

10 NOx RACT in Wisconsin is included in the 2005 basecase (and EGU “will do” scenario). NOx RACT in 

Indiana was not included in the modeling inventory.
61

 

 

Figure 49a. Modeled (top) v. monitored (bottom) 8-hour ozone concentrations: June 20 – 25, 2002
62

 

 

Figure 49b Modeled (top) v. monitored (bottom) 8-hour ozone concentrations: June 23– 28 2005
63

Standard model performance statistics were generated for the entire 12 km domain, and by day 

and by monitoring site. The domain-wide mean normalized bias for the 2005 base year is 

similar to that for the 2002 base year and is generally within 30% (see Figure 50). 

Figure 50. Mean bias for summer 2005 (Base M) and summer 2002 (Base K)

Station-average metrics (over the entire summer) are shown in Figure 51. The bias results 

further demonstrate the model’s tendency to underestimate absolute ozone concentrations.

Figure 51. Mean bias (left) and gross error (right) for summer 2005
64

A limited 4 km ozone analysis was performed by LADCO to address the effect of grid spacing. 

For this modeling, 4 km grids were placed over Lake Michigan and the Detroit-Cleveland area

(see Figure 52). Model inputs included 4 km emissions developed by LADCO (consistent with 

Base K/Round 4) and the 4 km meteorology developed by Alpine Geophysics. 

Figure 52. 4 km grids for Lake Michigan region and Detroit-Cleveland region

Hourly time series plots were prepared for several monitors (see Figure 53). The results are 

similar at 12 km and 4 km, with some site-by-site and day-by-day differences.

Figure 53. Ozone time series plots for 12 km and 4 km modeling (June 17-29, 2002)
65

An additional diagnostic analysis was performed to assess the response of the modeling system 

to changes in emissions (Baker and Kenski, 2007). Specifically, the 2002-to-2005 change in 

observed ozone concentrations was compared to the change in modeled ozone concentrations

based on the 95th percentile(and above) concentration values for each monitor. This analysis 

was also done with the inclusion of model performance criteria which eliminated poorly 

performing days (i.e., error > 35%). The results show good agreement in the modeled and 

monitored ozone concentration changes (e.g., ozone improves by about 9-10 ppb between 

2002 and 2005 according to the model and the measurements) – see Figure 54. This provides 

further support for using the model to develop ozone control strategies.

Figure 54. Comparison of change in predicted and observed ozone concentrations (2002 v. 2005) 
66

PM2.5: Time series plots of the monthly average mean bias and annual fractional bias for Base 

M and Base K are shown in Figure 55. As can be seen, Base M model performance for most 

species is fair (i.e., close to “no bias” throughout most of the year), with two main exceptions. 

First, the Base M and Base K results for organic carbon are poor, suggesting the need for more 

work on primary organic carbon emissions. Second, the Base M results for sulfate, while 

acceptable (i.e., bias values are within 35%), are not as good as the Base K results (e.g., 

noticeable underprediction during the summer months). 

Figure 55. PM2.5 Model performance - monthly average mean bias and annual fractional bias for 

Base M (left column) and Base K (right column)

Base M Base K
67

Two analyses were undertaken to understand sulfate model performance for 2005:

• Assess Meteorological Influences: The MM5 model performance evaluation showed that 

rainfall is over-predicted by MM5 over most of the domain during the summer months 

(LADCO, 2007c). Because CAMx does not explicitly use the rainfall output by MM5, this 

may or may not result in over-prediction sulfate wet deposition (and under-prediction of 

sulfate concentrations). A sensitivity run was performed with no wet deposition for July, 

August, and September. The resulting model performance (see green line in Figure 56)

showed a noticeable difference from the basecase (i.e., higher sulfate concentrations), 

and suggests that further evaluation of MM5 precipitation fields may be warranted.

• Assess Emissions Influences: The major contributor to sulfate concentrations in the 

region is SO2 emitted from EGUs. The basecase modeling inventory for EGUs is based 

on annual emissions, which were allocated to a typical weekday, Saturday, and Sunday 

by month using CEM-based temporal profiles. A sensitivity run was performed using 

day-specific emissions. The resulting model performance (see purple line in Figure 56)

showed little difference from the basecase.

Figure 56. Monthly sulfate bias for Base M (MRPO EGU) v. two sensitivity analyses (Note: positive 

values indicate over-prediction, negative values indicate under-prediction)

To assess the effect of the wet deposition issue on future year modeled values, another 

sensitivity run was conducted with no wet deposition in Quarters 2-3 for the base year 

(2005) and 2018. The resulting future year values were only slightly different from the 

current base strategy run. In general, the future year values (without wet deposition) 

were a little higher (+0.15 ug/m3 or less) in the Ohio Valley and a little lower (-.10 ug/m3

of less) in the Great Lakes region. This sensitivity run provides a bound for sulfate wet 

deposition issue in terms of the attainment test, given that having no wet deposition is 

unrealistic. The results suggest that even with an improved wet deposition treatment, 

the Base M strategy results are not expected to change very much.

Time series plots of daily sulfate, nitrate, elemental carbon, and organic carbon concentrations 

for three Midwestern locations are presented in Figures 57 (2002) and 58 (2005). These results 

are consistent with the model performance statistics (i.e., good agreement for sulfates and 

nitrates and poor agreement [large underprediction] for organic carbon).

Monthly Sulfate Bias (ug/m3)

-3

-2

-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12

MRPO EGU MOG daily EGU No Wet Deposition
68

 SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 57. Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005
69

 SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 58. Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005
70

In summary, model performance for ozone and PM2.5 is generally acceptable and can be 

characterized as follows:

Ozone

• Good agreement between modeled and monitored concentration for higher 

concentration levels (> 60 ppb) – i.e., bias within 30%

• Regional modeled concentrations appear to be underestimated in the 2002 base 

year, but show better agreement (with monitored data) in the 2005 base year due to 

model and inventory improvements.

• Day-to-day and hour-to-hour variation in and spatial patterns of modeled 

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to 

reductions in precursor emissions.

PM2.5

• Good agreement in the magnitude of fine particle mass, but some species are 

overestimated and some are underestimated (during periods of the year when it is 

important)

• Sulfates: good agreement in the 2002 base year, but underestimated in 

the summer in the 2005 base year due probably to meteorological factors

• Nitrates: slightly overestimated in the winter in the 2002 base year, but 

good agreement in the 2005 base year as a result of model and inventory 

improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base 

years due likely to missing primary organic carbon emissions and, 

possibly, other factors (e.g., grid resolution and model chemistry).

• Temporal variation and spatial patterns of modeled concentrations are consistent 

with monitored data

Several observations should be noted on the implications of these model performance findings 

on the attainment modeling presented in the following section. First, it has been demonstrated 

that model performance overall is acceptable and, thus, the model can be used for air quality 

planning purposes. Second, consistent with EPA guidance, the model is used in a relative 

sense to project future year values. EPA suggests that this approach “should reduce some of 

the uncertainty attendant with using absolute model predictions alone” (EPA, 2007a). 

Furthermore, the attainment modeling is supplemented by additional information to provide a 

weight of evidence determination.
71

Section 4.0 Attainment Demonstration for Ozone and PM2./5

Air quality modeling and other information were used to determine whether existing (“on the 

books”) controls would be sufficient to provide for attainment of the NAAQS for ozone and PM2.5

and if not, then what additional emission reductions would be necessary for attainment. 

Traditionally, attainment demonstrations involved a “bright line” test in which a single modeled 

value was compared to the ambient standard. To provide a more robust assessment of 

expected future year air quality, EPA’s modeling guidelines call for consideration of 

supplemental information. This section summarizes the results of the primary (guideline) 

modeling analysis and a weight of evidence determination based on the modeling results and 

other supplemental analyses.

4.1 Future Year Modeling Results

The purpose of the future year modeling is to assess the effectiveness of existing and possible 

additional control programs. The model was used in a relative sense to project future year 

values, which are then compared to the standard to determine attainment/nonattainment. 

Specifically, the modeling test consists of the following steps:

(1) Calculate base year design values: For ozone and PM2.5, the base year design 

values were derived by averaging the three 3-year periods centered on the 

emissions base year:

2002 base year: 2000-2002, 2001-2003, and 2002-2004

2005 base year: 2003-2005, 2004-2006, and 2005-200711

(2) Estimate the expected change in air quality: For each grid cell, a relative 

reduction factor (RRF) is calculated by taking the ratio of the future year and 

baseline modeling results. 

(3) Calculate future year values: For each grid cell (with a monitor), the RRFs are 

multiplied by the base year design values to project the future year values

(4) Assess attainment: Future year values are compared to the NAAQS to assess 

attainment or nonattainment.

A comparison of the 2002 and 2005 base year design values for ozone and PM2.5 is provided in 

Figure 59. In general, the figure shows that the 2005 base year design values are much lower 

than the 2002 base year design values, especially for ozone.

 

11 A handful of source-oriented PM2.5 monitors in Illinois and Indiana were excluded from the annual 

attainment test, because these monitors are not to be used to judging attainment of the annual standard.
72

Figure 59. 2002 v. 2005 base year design values for ozone (top) and PM2.5 (bottom)

2002 2005

Statistical Summary

# Sites > NAAQS 93 9

Peak Value 99.0 ppb 90.0 ppb

Ave Exceedance Amount 7 ppb 2 ppb

2002 2005

Statistical Summary

# Sites > NAAQS 58 41

Peak Value 19.3 ug/m3 17.7 ug/m3

Ave Exceedance Amount 1.2 ug/m3 0.9 ug/m3
73

Ozone results are provided for those grid cells with ozone monitors. The RRF calculation 

considers all nearby grid cells (i.e., 3x3 for 12 km modeling) and a threshold of 85 ppb. (If there 

were less than 10 days above this value, then the threshold was lowered until either there were 

10 days or the threshold reached 70 ppb.) PM2.5 results are provided for those grid cells with 

FRM (PM2.5-mass) monitors. Spatial mapping was performed to extrapolate PM2.5-speciation 

data from STN and IMPROVE sites to FRM sites. RRF values for PM2.5 were derived as a 

function of quarter and chemical species.

Additional, hot-spot modeling will be performed by the states for certain PM2.5 nonattainment 

areas (e.g., Detroit, Cleveland, and Granite City) to address primary emissions from local point 

sources which may not be adequately accounted for by the regional grid modeling. This 

modeling will consist of Gaussian dispersion modeling (e.g., AERMOD) performed in 

accordance with EPA’s modeling guidance (see Section 5.3 of the April 2007 guidance 

document). Further analyses will need to be undertaken to determine how to best combine the 

regional modeling and the hot-spot modeling. This could mean some adjustment to the model 

results presented in this document to reflect better the regional component. 

The ozone and PM2.5 modeling results are provided in Appendix I for select monitors (high 

concentration sites) in the 5-state region for the following future years of interest: 2008 (ozone 

only), 2009, 2012, and 2018. (Note, RRF values for ozone, and for PM2.5 by season and 

chemical species are also included in Appendix I for key monitoring sites.) A summary of the 

modeling results is provided in Table 9 (ozone) and Table 10 (PM2.5), and spatial maps of the 

Base M future year concentrations are provided in Figures 60-62.
Key Sites 2018

Round 5 Round 4 Round 5 Round 4 Round 5 Round 4 Round 5

Lake Michigan Area

Chiwaukee 550590019 82.0 93.0 82.3 92.0 80.9 90.3 76.2

Racine 551010017 77.6 85.9 77.5 84.9 76.1 82.9 71.2

Milwaukee-Bayside 550190085 79.6 85.4 79.8 84.9 78.0 82.3 72.7

Harrington Beach 550890009 80.0 86.7 80.1 85.4 78.3 82.9 72.5

Manitowoc 550710007 81.3 80.3 80.8 78.9 78.6 76.3 72.5

Sheboygan 551170006 84.4 90.0 84.0 88.9 81.8 86.4 75.4

Kewaunee 550610002 78.9 82.5 78.1 81.0 75.9 79.1 69.9

Door County 550290004 84.8 83.6 83.9 81.8 81.5 79.3 74.7

Hammond 180892008 75.4 86.9 75.4 86.6 74.6 86.3 71.6

Whiting 180890030 77.0 77.0 76.2 73.1

Michigan City 180910005 74.2 87.4 73.9 86.5 72.5 85.4 68.1

Ogden Dunes 181270020 75.7 82.3 75.6 82.8 74.5 82.0 70.8

Holland 260050003 85.6 84.9 85.3 83.4 82.8 81.0 76.1

Jenison 261390005 77.9 78.7 77.1 77.6 74.5 75.5 68.7

Muskegon 261210039 80.8 82.7 80.5 81.5 78.0 79.4 71.9

Indianapolis Area

Noblesville 189571001 78.0 85.2 78.1 83.7 75.6 82.0 68.7

Fortville 180590003 73.9 85.1 73.9 83.8 71.4 82.1 65.1

Fort B. Harrison 180970050 74.8 84.8 75.1 83.7 73.2 82.4 69.1

Detroit Area

New Haven 260990009 82.7 86.3 81.4 85.3 80.2 83.5 76.1

Warren 260991003 82.5 84.3 81.3 83.3 80.7 81.9 77.6

Port Huron 261470005 79.0 80.5 77.5 79.1 75.5 77.0 70.9

Cleveland Area

Ashtabula 390071001 84.9 84.7 83.4 82.7 81.0 80.2 75.1

Geauga 390550004 75.7 90.3 74.7 88.8 72.7 86.2 67.3

Eastlake 390850003 82.8 84.2 81.9 82.8 80.5 80.6 76.2

Akron 391530020 79.3 83.0 78.1 81.4 75.6 78.5 68.7

Cincinnati Area

Wilmington 390271002 77.8 84.8 77.5 83.5 74.9 81.1 68.3

Sycamore 390610006 81.7 85.4 81.9 84.7 80.3 82.9 74.6

Lebanon 391650007 83.6 80.1 83.0 79.0 80.7 77.0 74.2

Columbus Area

London 390970007 75.4 79.9 75.0 78.4 72.6 76.5 66.3

New Albany 390490029 82.4 84.1 81.8 82.6 79.6 80.2 73.0

Franklin 290490028 77.0 77.7 75.9 76.5 74.1 74.7 69.0

St. Louis Area

W. Alton (MO) 291831002 82.4 86.1 81.0 85.2 78.6 84.0 74.9

Orchard (MO) 291831004 83.3 83.3 82.0 82.2 80.0 80.4 76.2

Sunset Hills (MO) 291890004 79.5 82.8 78.7 81.9 77.1 80.6 73.9

Arnold (MO) 290990012 78.7 78.4 77.2 77.4 75.6 75.8 72.0

Margaretta (MO) 295100086 79.8 84.0 79.3 83.4 77.9 82.5 74.4

Maryland Heights (MO) 291890014 84.5 83.4 81.7 78.1

2008 2009 2012

Table 9. Summary of Ozone Modeling Results
County Site ID Site Round 5 Round4 Round 5 Round4 Round 5 Round4

Cook 170310022 Chicago - Washington HS 14.1 14.8 14.0 14.6 13.9 14.4

Cook 170310052 Chicago - Mayfair 14.4 15.8 14.2 15.5 13.9 15.0

Cook 170310057 Chicago - Springfield 13.9 14.5 13.8 14.3 13.7 14.1

Cook 170310076 Chicago - Lawndale 13.8 14.5 13.7 14.3 13.6 14.1

Cook 170312001 Blue Island 13.7 14.5 13.6 14.3 13.4 14.1

Cook 170313301 Summit 14.2 14.8 14.0 14.6 13.9 14.4

Cook 170316005 Cicero 14.4 15.3 14.3 15.1 14.2 14.9

Madison 171191007 Granite City 15.1 16.0 14.9 15.8 14.3 15.5

St. Clair 171630010 E. St. Louis 14.1 14.9 13.9 14.7 13.4 14.5

Clark 180190005 Jeffersonville 13.8 15.5 13.7 15.0 13.4 14.4

Dubois 180372001 Jasper 12.4 13.8 12.2 13.5 11.8 13.0

Lake 180890031 Gary 13.0 12.8 12.4

Marion 180970078 Indy-Washington Park 12.8 14.5 12.6 14.2 12.0 13.7

Marion 180970083 Indy- Michigan Street 13.4 14.8 13.1 14.9 12.6 14.0

Wayne 261630001 Allen Park 13.0 14.5 12.8 14.1 12.4 13.3

Wayne 261630015 Southwest HS 14.2 15.8 13.9 15.3 13.5 14.4

Wayne 261630016 Linwood 13.1 14.1 12.8 13.7 12.5 13.0

Wayne 261630033 Dearborn 15.8 17.7 15.5 17.1 15.1 16.1

Wayne 261630036 Wyandotte 13.1 15.1 12.8 14.7 12.5 13.9

Butler 390170003 Middleton 13.5 14.2 13.2 13.7 12.8 13.1

Butler 390170016 Fairfield 13.1 13.5 12.9 12.9 12.5 12.2

Cuyahoga 390350027 Cleveland-28th Street 13.5 14.4 13.2 13.8 12.7 12.9

Cuyahoga 390350038 Cleveland-St. Tikhon 15.2 16.1 14.8 15.4 14.3 14.4

Cuyahoga 390350045 Cleveland-Broadway 14.4 14.6 14.0 14.0 13.5 13.1

Cuyahoga 390350060 Cleveland-GT Craig 15.0 15.3 14.6 14.7 14.1 13.7

Cuyahoga 390350065 Newburg Hts - Harvard Ave 14.0 14.1 13.6 13.5 13.1 12.6

Franklin 390490024 Columbus - Fairgrounds 12.9 14.6 12.6 14.0 12.0 13.0

Franklin 390490025 Columbus - Ann Street 12.7 14.1 12.4 13.5 11.9 12.5

Franklin 390490081 Columbus - Maple Canyon 11.7 14.0 11.4 13.4 10.9 12.5

Hamilton 390610014 Cincinnati - Seymour 14.5 15.5 14.3 14.8 13.8 14.0

Hamilton 390610040 Cincinnati - Taft Ave 12.8 13.6 12.6 13.0 12.2 12.3

Hamilton 390610042 Cincinnati - 8th Ave 14.0 14.6 13.8 14.0 13.4 13.2

Hamilton 390610043 Sharonville 12.9 13.6 12.7 13.0 12.3 12.2

Hamilton 390617001 Norwood 13.4 14.2 13.2 13.6 12.8 12.8

Hamilton 390618001 St. Bernard 14.7 15.2 14.4 14.6 14.0 13.8

Jefferson 390810016 Steubenville 12.8 16.3 12.5 15.9 12.7 16.2

Jefferson 390811001 Mingo Junction 13.5 15.5 13.2 15.0 13.4 15.3

Lawrence 390870010 Ironton 12.8 14.2 12.5 13.7 12.3 13.2

Montgomery 391130032 Dayton 13.2 13.7 12.9 13.2 12.4 12.3

Scioto 391450013 New Boston 12.1 15.4 11.9 14.8 11.6 14.2

Stark 391510017 Canton - Dueber 14.0 15.0 13.6 14.3 13.3 13.6

Stark 391510020 Canton - Market 12.6 13.6 12.3 13.0 11.9 12.2

Summit 391530017 Akron - Brittain 13.0 14.4 12.7 13.6 12.3 12.9

Summit 391530023 Akron - W. Exchange 12.3 13.6 12.0 13.0 11.5 12.2

2009 2012 2018

Table 10. Summary of PM2.5 Modeling Results
76

 2005 (observed) 2009 

2012 2018

Figure 60. Observed base year and projected future year design values for ozone – Base M
77

 2005 (observed) 2009 

2012 2018

Figure 61. Observed base year and projected future year design values for PM2.5 (annual average)–Base M
78

 2005 (observed) 2009 

2012 2018

Figure 62. Observed base year and projected future year design values for PM2.5 (24-hr average)-Base M
79

The number of monitors with design values above the standard are as follows:

Table 11. Number of sites above standard

 Ozone (8 hour: 85 ppb)

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

 IL 3 0 0 0 0 0 0 0

 IN 22 0 0 0 0 0 0 0

 MI 15 3 1 1 0 0 0 0

 OH 40 4 1 0 1 0 0 0

 WI 13 2 4 0 3 0 1 0

Total 93 9 6 1 4 0 1 0

PM2.5 (Annual: 15 ug/m3)

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

 IL 11 7 3 1 3 0 2 0

 IN 10 6 1 0 1 0 0 0

 MI 6 2 3 1 2 1 0 0

 OH 31 26 7 1 4 0 1 1

 WI 0 0 0 0 0 0 2 0

Total 58 41 14 3 10 1 5 1

The modeling results above reflect the “base” controls identified in Section 3.6, with EGU 

emissions based on IPM modeling (i.e., Round 4 – IPM2.1.9, and Round 5 – IPM3.0). In 

addition, two sets of alternative future year EGU emissions were examined in Round 5. First, 

alternative control assumptions were provided for several facilities by the states (i.e., “will do” 

and “may do” scenarios). In general, these scenarios produced a small change in future year 

ozone and PM2.5 concentrations (i.e., about 0.1 ug/m

3

 for PM2.5 and 0.1-0.2 ppb for ozone). 

Second, EPA suggested adjustments to the 2010 IPM emissions to reflect 2009 conditions. The 

revised (2009) SO2 emissions represent a 5-6% increase in domainwide SO2 emissions. The 

increased SO2 emissions result in slightly greater annual average PM2.5 concentrations (on the 

order of 0.1 – 0.2 ug/m3), but do not produce any new residual nonattainment areas.
80

The limited 4 km ozone modeling (based on Base K) performed by LADCO included a future 

year analysis for 2009. The figure below shows the 2009 values with 12 km and 4 km grid 

spacing for the LADCO modeling and similar modeling conducted by a stakeholder group

(Midwest Ozone Group).

70

75

80

85

90

95

Cook

Lake

LaPorte

Porter

Allegan

Muskegon

Kenosha

Milwaukee

Ozaukee

Racine

Sheboygan

MOG-12km MOG-4km LADCO-12km LADCO-4km

70

75

80

85

90

95

New Haven Warren Geauga Eastlake Portage Akron

MOG-12km MOG-4km LADCO-12km LADCO-4km

Figure 63. Future year (2009) values for Lake Michigan area (top) and Detroit-Cleveland region

(bottom)

These results show that the 12 km and 4 km values are similar, with the most notable changes 

in northwestern Indiana and northeastern Illinois (e.g., 4 km values are as much as 4 ppb lower 

than 12 km values). The differences in the southern part of the Lake Michigan area are 

plausible, given the tight emissions gradient there (i.e., finer grid resolution appears to provide 

more appropriate representation). 

In light of these findings, 12 km grid spacing can continue to be used for ozone modeling, but 

the Base K/Round 4 results for northwestern Indiana/northeastern Illinois should be viewed with 

caution (i.e., probably 1 – 4 ppb too high).
81

In summary, the ozone modeling provides the following information for the nonattainment areas 

in the region (see Table 12):

Table 12. Ozone Nonattainment Areas in the LADCO Region (as of December 31, 2007)

Area Name Category

Number of

Counties

Attainment 

Deadline

Detroit-Ann Arbor, MI Marginal 8 2007

Chicago-Gary-Lake County, IL-IN Moderate 10 2010

Cleveland-Akron-Lorain, OH Moderate 8 2010

Milwaukee-Racine, WI Moderate 6 2010

Sheboygan, WI Moderate 1 2010

St Louis, MO-IL Moderate 4 2010

Allegan Co, MI Subpart 1 1 2009

Cincinnati-Hamilton, OH-KY-IN Subpart 1 6 2009

Columbus, OH Subpart 1 6 2009

Door Co, WI Subpart 1 1 2009

Kewaunee Co, WI Subpart 1 1 2009

Manitowoc Co, WI Subpart 1 1 2009

53

Marginal Areas (2007 attainment date): No modeling was conducted for the 2006 SIP planning 

year. Rather, 2005 – 2007 air quality data are available to determine attainment.

Basic (Subpart 1) Areas (2009 attainment date): The modeling results for the 2008 SIP planning 

year show:

• Base K: all areas in attainment, except Cincinnati and Indianapolis

• Base M: all areas in attainment, except Holland (Allegan County) 

Moderate Areas (2010 attainment date): The modeling results for the 2009 SIP planning year 

show:

• Base K: all areas still in nonattainment

• Base M: all areas in attainment

The PM2.5 modeling results show:

• Base K: all areas in attainment, except for Chicago, Cincinnati, Cleveland, Detroit, 

Granite City (IL), Louisville, Portsmouth (OH), and Steubenville

• Base M: all areas in attainment, except for Cleveland, Detroit, and Granite City (IL)

With respect to the new lower 8-hour ozone standard, the modeling about 30 sites in 2012 and 

5 sites in 2018 with design values greater than 75 ppb. With respect to the new lower 24-hour 

PM2.5 standard, the modeling shows 13 sites in 2012 and 10 in 2018 with design values greater 

than 35 ug/m3.
82

4.2 Supplemental Analyses

EPA’s modeling guidelines recommend that attainment demonstrations consist of a primary 

(guideline) modeling analysis and supplemental analyses. Three basic types of supplemental 

analyses are recommended:

• additional modeling

• analyses of trends in ambient air quality and emissions, and

• observational models and diagnostic analyses

Furthermore, according to EPA’s guidelines, if the future year modeled values are “close” to the 

standard (i.e., 82 – 87 ppb for ozone and 14.5 – 15.5 ug/m3 for PM2.5), then the results of the 

primary modeling should be reviewed along with the supplemental information in a “weight of 

evidence” assessment of whether each area is likely to achieve timely attainment. 

A WOE determination for ozone and PM2.5 is provided in the following sections. Special 

attention is given to the following areas with future year modeled values that exceed or are 

“close” to the ambient standard (see Appendix I):

 Ozone PM2.5

Lake Michigan area Chicago, IL

Cleveland, OH Cleveland, OH

Cincinnati, OH Cincinnati, OH

Granite City, IL

Detroit, MI

4.3 Weight-of-Evidence Determination for Ozone

The WOE determination for ozone consists of the primary modeling and other supplemental 

analyses (some of which were discussed in Section 2). A summary of this information is 

provided below.

Primary (Guideline) Modeling: The guideline modeling is presented in Section 4.1. Key findings 

from this modeling include:

• Base M regional modeling shows attainment by 2008 and 2009 at all sites, except 

Holland (MI), and attainment at all sites by 2012.

• Base K modeling results reflect generally higher future year values, and show more 

sites in nonattainment compared to the Base M modeling. The difference in the two 

modeling analyses is due mostly to lower base year design values in Base M.

• Base K and Base M modeling analyses are considered “SIP quality”, so the 

attainment demonstration for ozone should reflect a weight-of-evidence approach, 

with consideration of monitoring based information.

• Base M modeling also shows that the proposed lower 8-hour standard will not be 

met at many sites, even by 2018, with existing controls.

Additional Modeling: Four additional modeling analyses were considered: (1) re-examination of 

the primary modeling to estimate attainment probabilities, (2) remodeling with different 

assumptions, (3) an unmonitored area analysis, and (4) EPA’s latest regional ozone modeling. 

Each of these analyses is described below.
83

First, the primary modeling results (which were initially processed using EPA’s attainment test) 

were re-examined to estimate the probability of attaining the ozone standard (Lopez, 2007, and 

LADCO, 2008b). Seven estimates of future year ozone concentrations were calculated based 

on model-based RRFs and appropriate monitor-based concentrations for each year between 

2001 and 2007. RRF values for 2001, 2003, 2004, 2006, and 2007 were derived based on the 

2002 and 2005 modeling results. Monitor-based concentrations reflect 4th high values, design 

values, or average of three design values centered on the year in question. The probability of 

attainment was determined as the percentage of these seven estimates below the standard. 

The results indicate that sites in the Lake Michigan area (Chiwaukee, Sheboygan, Holland, 

Muskegon), Cleveland (Ashtabula), and St. Louis (W Alton) have a fairly low probability of 

attainment by 2009 (i.e., about 50% or less).

Second, the primary modeling analysis was redone with different types of assumptions for 

calculating base year design values (i.e., using the 3-year period centered on base year, and 

using the highest 3-year period that includes the base year), and for calculating RRFs (i.e.,

using all days with base year modeled value > 70 ppb, and using all days with base year 

modeled value > 85 ppb, with at least 10 days and “acceptable” model performance). The 

results for several high concentration sites are presented in Tables 13a and 13b for 2009. The 

different modeling assumptions produce eight estimates of future year ozone concentrations. 

The highest estimates are associated with base year design values representing the 3-year 

average for 2001-2003, and the lowest estimates are associated with base year design values 

representing the 3-year average 2004-2006. The different RRF approaches produce little 

change in future year ozone concentrations. This suggests that future year concentration 

estimates are most sensitive to the choice of the base year and the methodology used to derive 

the base year design values.

Third, EPA’s modeling guidelines recommend that an “unmonitored area analysis” be included 

as a supplemental analysis, particularly in nonattainment areas where the monitoring network 

just meets or minimally exceeds the size of the network required to report data to EPA’s Air 

Quality System. The purpose of this analysis is to identify areas where future year values are 

predicted to be greater than the NAAQS. 

Based on examination of the spatial plots in Figures 49a and 49b, the most notable areas of 

high modeled ozone concentrations are over the Great Lakes. Over-water monitoring, however, 

is not required by EPA12. A cursory analysis of unmonitored areas for ozone was performed by 

LADCO using an earlier version of the 2002 base year modeling (i.e, Base I) (Baker, 2005). 

Base year and future year “observed” values were derived for unmonitored grid cells using the 

absolute modeled concentrations (in all grid cells) and the observed values (in monitored grid 

cells). A spatial map of the estimated 2009 values is provided in Figure 64. As can be seen, 

there are very few (over land) grid cells where additional monitors may be desirable. This 

indicates that the current modeling analysis, which focuses on monitored locations, is 

addressing areas of high ozone throughout the region.

 

12 Air quality measurements over Lake Michigan were collected by LADCO previously to understand 

ozone transport in the area (see, for example, Figure 5). Due to cut-backs in USEPA funding, however, 

these measurements were discontinued in 2003.
84

Table 13a. Primary and Additional Ozone Modeling Results – Lake Michigan and Cleveland Areas (2009)

2009 Modeling Results Lake Michigan Area Cleveland Area

Chiwaukee Harr.Beach Sheboygan DoorCounty Holland Hammond MichiganCity Ashtabula Geauga Eastlake

550590019 550890009 551170006 550290004 260050003 180892008 180910005 390071001 390550004 390850003

Attainment Test

(based on EPA guidance-2002 baseyear)

Base Year Design Value

(average of three 3-year periods)

98.3 93.0 97.0 91.0 94.0 88.3 90.3 95.7 99.0 92.7

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Future Year Design Value 91.9 85.4 88.9 81.8 83.5 86.5 86.5 82.8 88.8 82.9

Attainment Test

(based on EPA guidance-2005 baseyear)

Base Year Design Value

(average of three 3-year periods)

84.7 83.3 88.0 88.7 90.0 77.7 77.0 89.0 79.3 86.3

RRF (all days > 85 ppb, or at least 10 days) 0.972 0.961 0.955 0.946 0.948 0.971 0.960 0.937 0.942 0.949

Future Year Design Value 82.3 80.1 84.0 83.9 85.3 75.4 73.9 83.4 74.7 81.9

Weight of Evidence

(alternative approaches-2002baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2002)

101.0 98.0 100.0 94.0 97.0 90.0 93.0 99.0 103.0 95.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2002 )

101.0 98.0 100.0 94.0 97.0 92.0 93.0 99.0 103 95.0

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Alt 1 - Future Year Projected Value 94.4 90.0 91.6 84.5 86.1 88.2 89.1 85.6 92.4 84.9

Alt 2 - Future Year Projected Value 94.4 90.0 91.6 84.5 86.1 90.2 89.1 85.6 92.4 84.9

Alt 1 - RRF (all days > 70 ppb) 0.933 0.918 0.912 0.907 0.893 0.969 0.947 0.876 0.907 0.900

Alt 1 - Future Year Projected Value 94.2 90.0 91.2 85.3 86.6 87.2 88.1 86.7 93.4 85.5

Alt 2 - Future Year Projected Value 94.2 90.0 91.2 85.3 86.6 89.1 88.1 86.7 93.4 85.5

Alt 2 - RRF (all days > 85 ppb, or at least 10 

days; with acceptable model performance)

0.945 0.904 0.910 0.904 0.887 0.976 0.964 0.866 0.896 0.894

Alt 1 - Future Year Projected Value 95.4 88.6 91.0 85.0 86.0 87.8 89.7 85.7 92.3 84.9

Alt 2 - Future Year Projected Value 95.4 88.6 91.0 85.0 86.0 89.8 89.7 85.7 92.3 84.9

Weight of Evidence

(alternative approaches-2005baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2005)

83.0 79.0 86.0 86.0 88.0 76.0 76.0 86.0 77.0 86.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2005)

86.0 88.0 89.0 90.0 93.0 79.0 78.0 91.0 86.0 89.0

Alt 1 - Future Year Projected Value 80.7 75.9 82.1 81.4 83.4 73.8 73.0 80.6 72.5 81.6

Alt 2 - Future Year Projected Value 83.6 84.6 85.0 85.1 88.2 76.7 74.9 85.3 81.0 84.5
85

Table 13b. Primary and Additional Ozone Modeling Results – Cincinnati, Columbus, St. Louis, Indianapolis, and Detroit (2009)

2009 Modeling Results Cincinnati Area Columbus St. Louis Area Indianapolis Area Detroit Area

Wilmington Lebanon Sycamore NewAlbany W. Alton OrchardFarm Noblesville Fortville New Haven

390271002 39165007 390610006 390490029 291831002 291831004 180571001 18059003 260990009

Attainment Test

(based on EPA guidance-2002 baseyear)

Base Year Design Value

(average of three 3-year periods)

94.3 90.7 90.7 94.0 90.0 90.0 93.7 91.3 92.3

RRF (all days > 85 ppb, or at least 10 days) 0.885 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Future Year Design Value 83.5 82.4 85.1 83.5 85.2 82.3 83.8 83.8 85.3

Attainment Test

(based on EPA guidance-2005 baseyear)

Base Year Design Value

(average of three 3-year periods)

82.3 87.7 84.3 86.3 86.3 87.0 83.3 78.7 86.0

RRF (all days > 85 ppb, or at least 10 days) 0.941 0.947 0.967 0.947 0.938 0.942 0.945 0.947 0.947

Future Year Design Value 77.4 83.1 81.5 81.7 80.9 82.0 78.7 74.5 81.4

Weight of Evidence

(alternative approaches-2002baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2002)

96.0 92.0 93.0 95.0 91.0 92.0 96.0 94.0 97.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2002 )

96.0 92.0 93.0 96.0 91.0 92.0 96.0 94.0 97.0

RRF (all days > 85 ppb, or at least 10 days) 0.885 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Alt 1 - Future Year Projected Value 85.0 83.5 87.2 84.4 86.2 84.1 85.8 86.3 89.6

Alt 2 - Future Year Projected Value 85.0 83.5 87.2 85.2 86.2 84.1 85.8 86.3 89.6

Alt 1 - RRF (all days > 70 ppb) 0.885 0.914 0.940 0.901 0.945 0.911 0.912 0.907 0.918

Alt 1 - Future Year Projected Value 85.0 84.1 87.4 85.6 86.0 83.8 87.6 85.3 89.0

Alt 2 - Future Year Projected Value 85.0 84.1 87.4 86.5 86.0 83.8 87.6 85.3 89.0

Alt 2 - RRF (all days > 85 ppb, or at least 10 days; 

with acceptable model performance)

0.880 0.911 0.940 0.886 0.951 0.913 0.894 0.916 0.935

Alt 1 - Future Year Projected Value 84.5 83.8 87.4 84.2 86.5 84.0 85.8 86.1 90.7

Alt 2 - Future Year Projected Value 84.5 83.8 87.4 85.1 86.5 84.0 85.8 86.1 90.7

Weight of Evidence

(alternative approaches-2005baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2005)

80.0 86.0 81.0 84.0 85.0 86.0 80.0 76.0 82.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2005)

85.0 89.0 86.0 88.0 89.0 89.0 87.0 81.0 90.0

Alt 1 - Future Year Projected Value 75.3 81.4 78.3 79.5 79.7 81.0 75.6 72.0 77.7

Alt 2 - Future Year Projected Value 80.0 84.3 83.2 83.3 83.5 83.8 82.2 76.7 85.2
86

Figure 64. Estimated Future Year Values (unmonitored grid cells)

Finally, EPA’s latest regional ozone modeling was considered as corroborative information. 

This modeling was performed as part of the June 2007 proposal to revise the ozone standard 

(EPA, 2007b). EPA applied the CMAQ model with 2001 meteorology to first estimate ozone 

levels in 2020 based on the current standard and national rules in effect or proposed (i.e., the 

baseline), and then to evaluate strategies for attaining a more stringent (70 ppb) primary 

standard. Baseline (2020) ozone levels were predicted to be below the current standard in 481 

of the 491 counties with ozone monitors. Of the 10 counties predicted to be above the 

standard, there is one county in the LADCO region (i.e., Kenosha County, WI at 86 ppb). This 

result is consistent with LADCO’s Base K modeling for 2018 (i.e., Kenosha County, WI at 86.7 

ppb), which is not surprising given that EPA’s modeling and LADCO’s Base K modeling have a

similar base year (2001 v. 2002).

Analysis of Trends: EPA’s modeling guidelines note that while air quality models are generally 

the most appropriate tools for assessing the expected impacts of a change in emissions, it may 

also be possible to extrapolate future trends based on measured historical trends of air quality 

and emissions. To do so, USEPA’s guidance suggests that ambient trends should first be 

normalized to account for year-to-year variations in meteorological conditions (EPA, 2002). 

Meterologically-adjusted 4th high 8-hour ozone concentrations were derived using the air quality 

– meteorological regression model developed by EPA (i.e., Cox method – see Section 2.1). 

The historical trend in these met-adjusted ozone concentrations were extrapolated to estimate 

future year ozone concentrations based on historical and projected trends in precursor 

emissions. Both VOC and NOx emissions affect ozone concentrations. Given that observationbased methods show that urban areas in the region are generally VOC-limited and rural areas 

in the region are NOx-limited (see Section 2.1), urban VOC emissions and regional NOx 

emissions are considered important. The trends in urban VOC and regional NOx emissions 

were calculated to produce appropriate weighting factors. 

The resulting 2009 and 2012 ozone values are provided in Figure 65, along with the primary 

and alternative modeling ozone values for key sites in the Lake Michigan, Cleveland, and 

Cincinnati areas. The results reflect a fairly wide scatter, but, on balance, the supplemental 

information is supportive of the primary modeling results (i.e., sites in the Lake Michigan area 

and Cleveland are expected to be close to the standard).
87

Sheboygan

75

80

85

90

95

100

2002 2005 2009 2012

Holland

75

80

85

90

95

100

2002 2005 2009 2012

Sycamore

75

80

85

90

95

100

2002 2005 2009 2012

Ashtabula

75

80

85

90

95

100

2002 2005 2009 2012

Figure 65. Estimates of Future Year Ozone Concentrations – Lake Michigan Area (Sheboygan and Holland), Cincinnati (Sycamore), and 

Cleveland (Ashtabula)

Note: Primary (guideline) modeling values (Base K and Base M results) are represented by large red diamonds, additional modeling 

values by small black circles, and trends-based values by small pink squares
88

Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

MAPPER) is presented in Section 3. The key findings from this modeling are that most urban 

areas are VOC-limited and rural areas are NOx-limited.

The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information on source region (and source sector) impacts (Baker, 2007a). 

Specifically, the model estimated the impact of 18 geographic source regions (which are 

identified in Figure 66) and 6 source sectors (EGU point, non-EGU point, on-road, off-road, 

area, and biogenic sources) at ozone monitoring sites in the region.

 

Figure 66. Source regions (left) and key monitoring sites (right) for ozone modeling analysis

Modeling results for 2009 (Base M) and 2012 (Base K) are provided in Appendix II for several 

key monitoring sites. For each monitoring site, there are two graphs: one showing sector-level 

contributions, and one showing source region and sector-level contributions in terms of 

percentages. (Note, in the sector-level graph, the contributions from NOx emissions are shown 

in blue, and from VOC emissions in green.)

The sector-level results (see, for example, Figure 67) show that on-road and nonroad NOx 

emissions generally have the largest contributions at the key monitor locations (> 15% each). 

EGU and non-EGU NOx emissions are also important contributors (> 10% each). The source 

group contributions vary by receptor location due to emissions inventory differences. 

Figure 67. Source-sector results for Holland (left) and Ashtabula (right) monitors – 2009 (Base M)

Kenosha County

Cook County

Indianapolis

Holland

St. Louis

Detroit

Cincinnati

Door County

Cleveland
89

The source region results (see, for example, Figure 68) show that while nearby areas generally 

have the highest impacts (e.g., the northeastern IL/northwestern IN/southeastern WI

nonattainment area contributes 25-35% to high sites in the Lake Michigan area, and Cleveland 

nonattainment counties contribute 20-25% to high sites in northeastern Ohio), there is an even 

larger regional impact (i.e., contribution from other states).

Figure 68. Source-region results for Holland (left) and Ashtabula (right) monitors – 2009 (Base M)

Summary: Air quality modeling and other supplemental analyses were performed to estimate 

future year ozone concentrations. Based on this information, the following general conclusions 

can be made:

• Existing (“on the books”) controls are expected to produce significant 

improvement in ozone air quality.

• The choice of the base year affects the future year model projections. A key 

difference between the base years of 2002 and 2005 is meteorology. As noted 

above, 2002 was more ozone conducive than 2005. The choice of which base 

year to use as the basis for the SIP is a policy decision (i.e., how much safeguard 

to incorporate).

• Most sites are expected to meet the current 8-hour standard by the applicable 

attainment date, except, for sites in western Michigan and, possibly, in eastern 

Wisconsin and northeastern Ohio.

• Current monitoring data show significant nonattainment in these areas (e.g., 

peak design values on the order of 90 – 93 ppb). It is not clear whether sufficient 

emission reductions will occur in the next couple of years to provide for 

attainment.

• Attainment by the applicable attainment date is dependent on actual future year 

meteorology (e.g., if the weather conditions are consistent with [or less severe 

than] 2005, then attainment is likely) and actual future year emissions (e.g., if the 

emission reductions associated with the existing controls are achieved, then 

attainment is likely). On the other hand, if either of these conditions is not met, 

then attainment may be less likely.
90

4.3 Weight-of-Evidence Determination for PM2.5

The WOE determination for PM2.5 consists of the primary modeling and other supplemental 

analyses. A summary of this information is provided below.

Primary (Guideline) Modeling: The results of the guideline modeling are presented in Section 

4.1. Key findings from this modeling include:

• Base M regional modeling shows attainment by 2009 at all sites, except Detroit, 

Cleveland, and Granite City, and attainment at all sites by 2012, except for Detroit 

and Granite City.

The regional modeling for PM2.5 does not reflect any air quality benefit expected 

from local controls. States are conducting local-scale analyses and will use these 

results, in conjunction with the regional-scale modeling, to support their attainment 

demonstrations for PM2.5

• Base K modeling results reflect generally higher future year values, and show more 

sites in nonattainment in 2009 and 2012 compared to the Base M modeling. The 

difference in the two modeling analyses is due mostly to lower base year design 

values in Base M.

• Base K and Base M modeling analyses are considered “SIP quality”, so the 

attainment demonstration for PM2.5 should reflect a weight-of-evidence approach, 

with consideration of monitoring based information.

• Base M modeling also shows that the new PM2.5 24-hour standard will not be met at 

many sites, even by 2018, with existing controls.

Additional Modeling: EPA’s latest regional PM2.5 modeling was considered as corroborative 

information. This modeling was performed as part of the September 2006 revision to the PM2.5

standard (USEPA, 2006). EPA applied the CMAQ model with 2001 meteorology to estimate 

PM2.5 levels in 2015 and 2020 first with national rules in effect or proposed, and then with 

additional controls to attain the current standard (15 ug/m3 annual/65 ug/m3 daily). Additional 

analyses were performed to evaluate strategies for attaining more stringent standards in 2020 

(15/35, and 14/35). Baseline (2015) PM2.5 levels were predicted to be above the current 

standard in four counties in the LADCO region: Madison County, IL at 15.2 ug/m3, Wayne 

County, MI at 17.4, Cuyahoga County, OH at 15.4, and Scioto County, OH at 15.6. These 

results are consistent with LADCO’s Base K modeling for 2012/2018, which is not surprising 

given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v. 

2002).

Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

application of thermodynamic equilibrium models) is presented in Section 3. The key findings 

from this modeling are that PM2.5 mass is sensitive to reductions in sulfate, nitric acid, and 

ammonia concentrations. Even though sulfate reductions cause more ammonia to be available 

to form ammonium nitrate (PM-nitrate increases slightly when sulfate is reduced), this increase 

is generally offset by the sulfate reductions, such that PM2.5 mass decreases. Under conditions 

with lower sulfate levels (i.e., proxy of future year conditions), PM2.5 is more sensitive to 

reductions in nitric acid compared to reductions in ammonia.