### 16 Population Data

For 26000.pop, replaced default file supplied with NMIM2005 with 26000_rev_NMIM05.pop that contains revised construction data missing from 26000.pop external file provided with NMIM2005. This Michigan construction data should have been added with the other LADCO states modified construction data but was overlooked.



### SCC Data

The default SCC table of NCD20060201 is replaced by a version that contains corrections to the PM25fac field that earlier NCDs contained (i.e. changed from 0.92 to 0.97 for diesel non-road equipment) in NONROAD2004.



### Fuel Data

LADCO States: Pechan revised four tables (countyyear, countyyearmonth, datasource and gasoline) in the National County Database (NCD) used by NMIM to incorporate new fuel data. AIR revised gasoline characteristics per instructions from the states. Also, gasoline characteristic revisions for 2005 provided by states were incorporated. Additional revisions were incorporated into 2002 data for non-road Stage 2 controls. Depending on the year being modeled, different versions of the revised tables are used. Also, the countynrfile, countyyear and datasource tables were revised to reference the new activity, allocation, growth, population and seasonality files described above. NCD tables with names ending in “def” are default versions of the table. See table below for the appropriate versions of the tables for the selected years.

Non-LADCO States: The countynrfile, countyyear and datasource tables were revised to reference the new activity, allocation and seasonality files described above. See table below for the appropriate versions of the tables for the selected years.



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

States Years 1999 (WI only) 2002, 05, 07, 08, 09, 12 and 18 LADCO states countynrfile_NMIM05_rev, countyyear_NMIM05_rev, countyyearmonth_NMIM05_w _05_12_18_rev, datasource_NMIM05_rev, gasoline_NMIM05_def SCC_NCD20060201_rev (used when NCD20060201 is used) countynrfile_NMIM05_rev, countyyear_NMIM05_rev , countyyearmonth_NMIM05_w_05_12_18_rev, datasource_NMIM05_rev, gasoline_NMIM05_w_05_12_18_rev SCC_NCD20060201_rev (used when NCD20060201 is used)

### 17 

## SECTION 4

ON-ROAD SOURCES

A mobile source of air pollution is a self-p ropelled or portable emi tter of air pollutants, and mobile source emissions are those genera ted by the engines or motors that power such sources. Most mobile sources, exce pt jet or turboprop ai rcraft, are powered by internal combustion (IC) piston engines, and nearly all use liquid fuels.

Gaseous fuels, such as compressed natur al gas (CNG) or li quefied petroleum gas (LPG), had only a very small fraction of the motor fuel market in Ohio in 2005. Solid fuels have not been used by mobile sources in significant amounts since railroads retired their coal-powered steam locomotives in the 1950s.

4.1 Categories of Mobile Sources For inventory and planning pur poses, mobile sources ar e divided into two major categories.

1. On-highway mobile sources (usually referred to as on-road ), e.g., motor vehicles such as cars, vans, trucks, buses and motorcycles used for transportation of goods and pa ssengers on roads and streets

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

• Modes of powered transportation that do not use roads, such as aircraft, trains, ships and boats, and motor vehicles used off-road.

• Self-propelled or portable motoriz ed machines or equipment not used for transportation, ranging from constructi on equipment and farm tractors to lawnmowers and hand-held power weed choppers.

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

Non-road Mobile Sources: Some non-road mobile source s (e.g., farm tractors), are self-propelled, but many non-road sources ar e not. A gasoline-powe red chain saw is a familiar example of a non-self-pr opelled non-road mobile source.

Stationary Sources: Not all movable or portable emi ssion sources are mobile sources, however. A small truck-portable cement or hot-mix asphalt plant, for example, may be set up near a construction or road-building site . Such plants are classified as stationary sources, not mobile sources for two reasons: (1) they may operate for weeks or months at a single location, and (2) the trucks that move the plants do not supply power for them.

### 18 NOTE: Not all Internal Combustion engines (IC) or turbine engines are mobile sources. Fixed IC engines, such as those that pow er pipeline compressors or standby generators in electricity plants and elsewhere, are al so classified as stationary sources.

4.1.1 Categories and Componen ts of Mobile Source Emissions

There are three categories of mobile source emissions:

• Exhaust or tailpipe emissions , which result from the combustion of fuel in the source’s engine

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

• Refueling emissions

Exhaust Emissions: Are the result of fuel combustion and occu r only when the engine is running.

Evaporative emissions: Are Volatile Organic Compou nd (VOC) based only and are continuously emitted from an engine’s fuel syst em, whether the engine is running or not. Gasoline is a very volatile fuel, so total VOC emissions from gasoline powered vehicles have a large evaporative component. Diesel and je t fuels are of very low volatility, so evaporative emissions from diesel and turbi ne engines are a much smaller part of their total VOC emissions. Evaporative emission s for CNG or LPG powered vehicles are negligible because their fuel tanks and systems are of necessity, sealed.

Evaporative and exhaust VOC emissions can be calculated separately for most mobile source categories. Evaporative emissions do not include VOC emissions that occur during refueling.

Refueling Emissions: These emissions are the third category of mobile source emissions. Refueling emissi ons are entirely VOC. Alth ough they result from the evaporation of fuel, they ar e distinct from, and not dire ctly related to, evaporative emissions as defined above.

Refueling emissions have two subcomponents:

• Displacement emissions . These occur when new fuel is transferred into a partly filled tank--be it a service station stor age tank, a portable fuel container (gas can), or a vehicle or engine’s fuel tank; displacing the air in the tank and forcing that vapor-rich air out the inlet pipe or other vent. There are two stages of displacement emissions:

o “ Stage I ” emissions occur when the underground storage tanks at a service station are being refilled;

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

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

• Spill emissions . These occur when drops of fuel drip or splash on the ground during or after refueling and evaporate away.

4.2 Ohio On-Road Mobile Source Inventory

The inventory of on-road mobile source emissions was developed in conjunction with the Ohio Department of Transportation (ODOT), Lake Michigan Air Director’s Consortium (LADCO), Unit ed States Environmental Protection Agency (USEPA), and the Ohio EPA (OEPA). Estimates of the amounts of NOx and VO C are reported by county in tons per day. Emissions are repor ted for a typical ozone season weekday in the summer of 2005.

4.2.1 Emission Inventories Developed with MOBILE6 Model

MOBILE6 Overview:

MOBILE6 is a computer program that es timates hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NO x 

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

### ), ammonia (NH 3 

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

### ) emission factors for gasoline-f ueled and diesel highway moto r vehicles, and for certain specialized vehicles such as natural-gas-fuel ed or electric vehicles that may replace them. The program uses the calculation procedures presented in technical reports posted on EPA's MOBILE6 Web page http ://www.epa.gov/otaq/models.htm.

MOBILE6 emission factor estimates depend on various conditions, such as ambient temperatures, travel speeds, operating modes, fuel volatility, and mileage accrual rates. Many of the variables affe cting vehicle emissions can be specified by the user. MOBILE6 will estimate emission factors fo r any calendar year between 1952 and 2050, inclusive. Vehicles from the 25 most re cent model years are considered to be in operation in each calendar year.

4.2.2 MOBILE6 Defaults:

MOBILE6 includes default values for a wide range of conditions that affect emissions. These defaults are designed to represent “ national average” input data values. Users who desire a more precise estimate of local emissions can substitute information that more specifically reflects local conditions. Use of local input data will be particularly common when the local emission inventor y is to be constructed from separate

### 20 estimates of roadways, geographic areas, or ti mes of day, in which fleet or traffic conditions vary considerably.

A list of MOBILE6 input parameters is provi ded below. Most of these inputs are optional because the model will supply default values unless alternate data are provided.

4.2.3 MOBILE6 Input Parameters

• Calendar year

• Month (January, July)

• Hourly Temperature

• Altitude (high, low)

• Weekend/weekday

• Fuel characteristics (Reid va por pressure, sulfur content, oxygenate content, etc.)

• Humidity and solar load

• Registration (age) distribution by vehicle class

• Annual mileage accumulation by vehicle class

• Diesel sales fractions by vehicle class and model year

• Average speed distribution by hour and roadway

• Distribution of vehicle miles traveled by roadway type

• Engine starts per day by vehi cle class and distribution by hour

• Engine start soak time distribution by hour

• Trip end distribution by hour

• Average trip length distribution

• Hot soak duration

• Distribution of vehicle miles traveled by vehicle class

• Full, partial, and multiple diurnal distribution by hour

• Inspection and maintenance ( I/M) program description

• Anti-tampering inspection program description

• Stage II refueling emissions inspection program description

• Natural gas vehicle fractions

• HC species output

• Particle size cutoff

• Emissions factors for PM and HAP

• 

### Output format specific ations and selections

4.2.4 MOBILE6 References

The following publications prov ide much of the guidance fo r the preparation of the onhighway inventory.

EPA-450/4-81-026d (Revised), now EPA/450-R-92-009, Procedures for Emission Inventory Preparation , Volume IV: Mobile Sources , December 1992. Hereafter, “Procedures Vol. IV”. The 1992 vers ion is still the definitive document on

### 21 inventories. If a previous edition is referr ed to, the fact will be not ed as, for example, “the 1989 Procedures Vol. IV” or “Volume IV, 1989 edition”.

EPA420-R-03-010, User’s Guide to MOBILE6.1 and MOBILE6.2: Mobile Source Emission Factor Model , August 2003. This is the User’s Guide for the official MOBILE6.2.03 on-highway mobile source em ission factor model and will usually be referred to as the M6.2 (or simply M6) User’s Guide (UG). The M6 model in its various versions was developed and publis hed by Assessment & Modeling Division (AMD) of the National Vehicle & Fuels Em issions Laboratory (NVFEL) in Ann Arbor, Michigan. The NVFEL is part of USE PA Office of Transportation & Air Quality (OTAQ), formerly the Office of Mobile Sources (OMS).

Technical Guidance on the Use of MOBILE6 for Emission Inventory Preparation , August 2004. Hereafter, the M6 “Technical Guidance [Document]” or “TGD”. The TGD is the pr imary source of guidance on M6 inputs and an invaluable adjunct to the M6 User’s Guide.

USEPA document “Frequently Asked Questions on MOBILE6” , 16 January 2002. Hereafter, [M6] “FAQ”. This document was published along with the M6 TGD.

USEPA memo, “Policy Guidance on the Use of MOBILE6 for SIP Development and Transportation Conformity” , dated 18 January 2002, from John Seitz, Director of OAQPS, and Mar go Oge, Director of OTAQ, to Regional Air Division Directors.

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

4.3.1 Vehicle Registra tion Distribution by Age

Overview:

The vehicle age distribution determines the fr action of vehicles operating within each emissions control requirement standard and the deterioration of the emission control technology.

Emission rates vary widely between new and older vehicles. Thus, even small changes in fleet age, particularly for older vehicles , may result in large changes in emission totals.

### 22 The MOBILE6 model requires esti mates of a distribution of r egistered vehicles by age and vehicle category for current and future years. MOBILE6 default values were developed using national level vehicle registration data by age and class for July 1, 1996. EPA developed a methodology to convert t he July 1, 1996 registration profile into a general registration distribut ion by age and by vehicle ca tegory for some 6 composite (gasoline and diesel) vehicle types plus motorcycles. To project future changes, EPA evaluated general sales growth and vehicle scrappage trends for the total light-duty vehicle in-use fleet and the total heavy-dut y vehicle in-use fleet, and made minor adjustments, where possible, to reflect some of the differences between vehicle categories.

Description: The MOBILE6 model requires estima tes of a distribution of registered vehicles by age and vehicle category for curr ent and future years. OEPA chose to use local vehicle registration data provided by the Ohio Bureau of Motor Vehicles (BMV) which was sent to the Lake Michigan Air Dir ectors Consortium (LADCO) to develop these inputs. LADCO then contracted with a subcontractor to breakout the age distribution data from the Vehicle Identification Num bers (VIN).

Note: It was learned during the course of this inventory that there were some discrepancies in the age distribution data. But it was too large of a project to reevaluate the data prior to this invent ory. This will be corrected in the next inventory (2008).

Method Applicability: This approach is most applic able in areas where there are significant differences in the local vehicle fleet age distribution rela tive to the national average.

Data Sources and Procedures: This approach involves using local vehicle registration data. This is typically available at the county level, but may also be applied using statewide data from the stat e motor vehicle registration office. The fleet age should be representative of the vehicle fleet over the small urban or rural area under question.

Advantages:

• Uses locally specific registration data, which is likely more representative of the local area than t he national default.

• Requires minimal additional resources, par ticularly if data is readily available at the county or local level fr om the State departm ent of motor vehicle registration.

• Recommended by EPA and generally is encouraged as a preferred approach over the national default approach.

### 23 4.3.2 Daily Vehicle Miles Traveled (DVMT)

Overview:

In coordination with Ohio Metropolitan Pl anning Organizations (MPOs), the Ohio Department of Transportation (ODOT) provi ded Daily Vehicle Miles Traveled (DVMT) data and travel demand model (TDM) data. TDM data will be covered in another section. Because TDM results are used by t he state and MPOs to fo recast traffic for a variety of reasons, undergo rigorous calibrati on and validation checks, and are sensitive to roadway capacity/travel time improvement s, the TDMs are considered the best tool for emissions forecasting. Therefore, the DVMT data discussed in this section is not used directly for all areas of Ohio. In countie s where it is not used directly it is used for making rough emissions estimates where model s do not exist or where time prohibits the use of TDMs. DVMT is a simple mechan ism to measure how much traffic is flowing along a roadway during an average 24 hour peri od. This simple formula multiplies Average Annual Daily Traffic (AADT) by the length of the roadway. For example; if a roadway is 2 miles in length and the AADT is 4000 vehicles per day the DVMT would be computed by multiplying 2* 4,000 =8,000 or 8,000 DVMT.

County-By-County DVMT is computed usin g the State of Ohio, Department of Transportation’s Roadway Information F iles and the annual Highway Performance Monitoring System (HPMS) Summary Reports . DVMT’s are computed for all of the Federal Functional Class (FC) categories within each of Ohio’s 88 counties.

The AADT and Roadway length information prov ides a very accurate estimate of statewide total DVMT for The State Highway S ystem (Interstate, US and State Routes). County total DVMT are consistent and cons idered a good source of county level DVMT for countywide emissions estimates. For roadways that are not part of the State Highway System, various representative count s were used, such as: railroad crossing counts, HPMS Sample Section Counts, etc. All traffic count data that was not collected during the current year has had statewide growth factors applied to account for systematic growth.

Given the previously mentioned methodologies, the DVMT data is more accurate on roads functionally classified as collector or above.

Table 4-1 Federal Func tional Class Categories:

01 - Rural Interstate 02 - Rural Principal Arterial 06 - Rural Minor Arterial 07 - Rural Major Collector 08 - Rural Minor Collector 09 - Rural Local 11 - Urban Interstate 12 - Urban Freeway & Expressway 14 - Urban Principal Arterial 16 - Urban Minor Arterial 17 - Urban Collector 19 - Urban Local

### 24 Table 4-2 DVMT County Summary:

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

http://www.dot.state.oh.us /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, deriv ed from the output of a limited number of traffic counting stations around the state. Although the growth factors are available by functional class, they are more reliable for major roads such as interstates or expressways, which are relatively well-sampl ed, than for local roads or collectors. The numbers also do no allow for periodic, large- scale functional reclassification actions

### 25 which reassign selected roads or road segm ents from one functional class to another.

The Ohio Department of Transportation t herefore does not warr ant the accuracy, completeness, or reliability of these estimates for your research. We also do not assume responsibility for any incorrectness that may occur.

4.3.3 VMT From Travel Demand Models (TDM)

Overview:

Travel demand forecast modeling is perform ed by the Ohio Metropolitan Planning Organizations (MPOs) and ODOT for a multit ude of purposes including the preparation of regional emissions estimate s. The ODOT Office of Te chnical Services’ Modeling & Forecasting Section recommends that Ohio ’s TDMs and ODOT’s conformity analysis methods be used to establish the roadway mob ile source portion of Ohio’s SIP budget to assure consistent methods are used for transportation conformity analysis and budgets. Therefore, ODOT provided both MPO regional TDM runs and statewide TDM runs with associated data to OEPA and LADC O. The ODOT provided model run data for years 2002, 2005, 2009, 2012, and 2018.

Data provided included loaded networks in both CSV format and GIS shape files, trip end summaries, zone boundary GIS shape files, intra-zonal trip VMT estimates, and VMT summaries for each of the loaded networ ks. Additional post processing data was provided including but not lim ited to metadata describing the loaded networks, Hourly distribution by functional cla ss, speed profiles, day of week / weekend / monthly car and truck traffic profiles, 2009 & 2018 VMT RPO data sets, state wide VMT growth rates for local traffic, and a 2005 VMT summary comparison spreadsheet. It should be noted that among other things, the loaded TDM Netw orks contain distance and daily volumes from which VMT is computed.

Network volumes are post processed to esti mate VMT by hour of day. The hourly volumes and capacity, posted speed limi t, and type of roadway for each roadway segment are then used to estimate aver age hourly speeds needed for MOBILE6 based emissions estimates. Modelin g by segment by hour of day in this way makes emissions estimates more sensitive to the effects of roadway improvements. This allows transportation planners to evaluate the relati ve emissions affect of improvements to individual roadways as well as packages of improvements and the entire set of planned roadway improvement projec ts air quality impacts of construction programs.

### 26 4.3.4 Speed Di stribution Profiles

Overview:

ODOT provided speed distribution profiles to LADCO. A couple of different sets of speed distribution profiles were provided to OEPA and LADCO for their use, a table of space mean speeds by functional class for use with DVMTs and link group speed curves for post processing travel demand model traffic forecasts. Both sets of speed profiles are based on the same speed study c onducted by ODOT. The speed study is documented in “Statewide Travel Time Study, May 2001 by Greg Giaimo, Ohio Department of Transportation ”. When OEPA asked for s peeds for use with MOBILE6 for development of CERR, ODOT provided a set of speeds based on that statewide travel time study. Those speeds are doc umented in a technical memorandum titled “Estimation of Average Speed by Functional Class for MOBILE6 Runs ” dated 5/27/2004. Readers should refer to those docum ents for the details. Here the contents of the technical memo, Estimated Average Speed by Functional Class, are summarized.

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

The memo states that the s peeds which the Ohio Department of Transportation (ODOT) had been using prior to that date, 2004, fo r air quality analysis estimates based on County DVMT summaries, were developed by a former ODOT employee for addressing the one (1) hour standards conformity ru les established due to 1990 emissions exceedances. No documentation was found in ODOT’s files on the origin of these average speed values or how they were esti mated. In addition, EPA has requirements for using latest planning assumptions for ai r quality conformity analyses. Therefore, ODOT believed that it was in the state’s best interest to use the mo st recent available data to provide a new set of estimated av erage speeds consistent with those used for urban area travel demand models which were under development at that time. The memo contains comparisons of 2002 speed dat a obtained from traf fic count equipment, automatic traffic recorders (ATRs) which co llect data continuously throughout the year. It also contains comparisons of the new speeds with those used to address the one hour standard Gebhart’s. The graphs shown in figure 1, taken from the memo, illustrate the comparisons. The first graph compares time mean speeds from the ATRs with space mean speeds from ODOTs travel time study done in 2001and with the speeds used for addressing the one hour standard.

### 27 Figure 1 - Speed Comparison Graphs

Speeds Comparison

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

FC01 FC 0

6 F C0 8 F C1 1 FC1 4 FC1 7



### Functional ClassSpeed Average Speed

1990 Analysis Speeds 2001 Speed Study



## Speeds Comparison

0 20 40 60 80 100

FC 0

1 FC 06 F C08 F C11 FC14 FC 1

7

R

AMPS



### Functional ClassSpeed 

### Average Speed

Min. Avg Speed

Max. Avg. Speed

Gebhardt's Speeds

Spd Study Est Speeds Spd Study Greg Est



### The following caution statem ent 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 bet ween roadways, between hour of the day, and day of the week. So these provide only very rough estimates of speed and should be used with caution. In addition, it is expec ted that these average statewide speeds are higher than the average speeds in t he non-attainment areas because the non-

### 28 attainment counties tend to be more popul ated and more congested. The document “Highway Vehicle Speed Es timation Procedures For Use in Emission Inventories”, September 1991 by Earl Ruiter of Cambridge Systematics Inc. is referenced by EPA’s documented procedures for emission inventor y preparation. This document suggests post processing travel demand model traffic assignment results to estimate average speeds.

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

Table 4-3 Speed by Federal Functional Class

Functional Class 1990 Analysis 2001 Speed Study Speeds Speeds FC01 57.3 64.0 FC02 45.3 53.0 FC06 39.9 53.0 FC07 35.1 43.1 FC08 30.5 43.1 FC09 28.0 43.1 FC11 46.3 61.6 FC12 43.3 61.6 FC14 18.9 29.3 FC16 19.6 29.3 FC17 19.6 29.2 FC19 19.6 23.8

It was decided by mutual agreement among individuals within the ODOT Office of Technical Services that these new s pace mean speed based average speed estimates were reproducible and defensib le since they are well docu mented and should therefore be the speeds used with HPMS** VMT if any year 2002 emissions budget work is done using only the county level VMT summaries discussed in section 5.3.2 Daily Vehicle Miles Traveled (DVMT).

** Note that HPMS VMT is a statewide VMT estimate and the county level DVMT summaries are consistent with the HPMS VMT so the county level DVMT summaries are loosely referred to as HPMS VMTs even though in fact they are not.

4.3.5 Link Group Speed Curves:

The ODOT Modeling & Forecasting Section recommends that Ohio’s travel demand forecasting models and ODOT’s conformity anal ysis methods be used to establish the roadway mobile source portion of Ohio’s SIP budget for reasons already mentioned in 5.3.2 and to assure consistent methods are used for transportation conformity demonstration analyses and budget s. Therefore, ODOT pr ovided travel demand model runs and the speed curves by link group that ODOT uses for the speed estimates within

### 29 the post processing of travel demand model runs for estimating regional emissions. Table 2 shows these link group curves.

Table 4-4 Link Group Cod es & Associated BPR Curves

Link Group Facility Type Free Flow Speed Areatype a b

1 Freeway 75 Any 0.39 6.3 2 Freeway 70 Any 0.32 7.0 3 Freeway 65 Any 0.25 9.0 4 Freeway 60 Any 0.18 8.5 5 Freeway 55 Any 0.10 10.0 6 Multi-Lane 60 Rural 0.09 6.0 7 Multi-Lane 55 Rural 0.08 6.0 8 Multi-Lane 50 Rural 0.07 6.0 9 Multi-Lane 45 Rural 0.07 6.0 10 2 Lane Any Rural 0.34 4.0 10 Urban Street 50 Suburban 0.34 4.0

11 Urban Street 50 Urban 0.74 5.0

12 Urban Street 50 CBD 1.16 6.0

13 Urban Street 40 Suburban 0.38 5.0

14 Urban Street 40 Urban 0.70 5.0

15 Urban Street 40 CBD 1.00 5.0

16 Urban Street 35 Suburban 0.96 5.0

17 Urban Street 35 Urban 1.00 5.0

18 Urban Street 35 CBD 1.40 5.0

19 Urban Street 30 Suburban 1.11 5.0

20 Urban Street 30 Urban 1.20 5.0

21 Urban Street 30 CBD 1.50 5.0

Note: a and b are the BPR curv e parameters for the equation

T = T 0 

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

### 30 More complete details about emissions mode ling process employed by ODOT may be found in ODOT documentations. The docum ent titled “Congestion Management & Air Quality Analysis (CMAQ) Program Docu mentation” dated December 2005 may be obtained from the ODOT web site at www.dot.state.oh.us/urban/data/cmaq.doc (Microsoft Word document)

4.4 Mobile6 Inputs:

The following table contains the inputs s upplied to LADCO to process our mobile inventory.

4.4.1 Ohio’s 2005 MOBILE6 Inputs The following tables are the result of a joint meeting between Ohio EPA, ODOT, and MPOs from around the state. At that meeting Mobile6 inputs were discussed and efforts were made to verify the sources of data inputs for Moble6. Dialogue has continued between the parties.

For historical reference:

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

All,

An air quality coordination meeting has been scheduled for April 27, 2006 at 10:00 AM at ODOT Central Office conference room GA. The primary purpose of this meeting is to discuss development of 2002 mobile source inventories for use in developi ng the Ohio 2007 8-Hour Ozone SIP Attainment Demonstrations. See meeting agen da below. OEPA is working to ward a June 15, 2006 schedule for submitting the 2002 invent ories to US EPA.

A key component of the meeting will be to review a nd confirm the MOBILE6.2 i nput parameters, by Ohio a/q area, for use in developing the 2002 mobile inve ntories. See draft templa te below. The Ohio MPO travel demand models will be used to generate t he 2002 VMT inputs to MOBILE. Thanks, DM DM

### 31 IMPORTANT NOTICE: The following tables are not to be used for inventory purposes as the data is subject to change. For the cu rrent input table, contact Ohio EPA, Division of Air Pollution Control.

Table 4-5 Mobile Inputs



## Cleveland-Akron 2005 Ozone M6.2 Inputs
Includes the following counties: Ashtabula, Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, Summit



### State Programs Input

Stage II Refueling 93/3/86/86

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

I/M Programs Yes Exclude Ashtabula County - No I/M program Program 1 2004 2050 2 T/O OBD I/M Model Years 1 1996 2050 Vehicles 1 22222 21111111 1 Stringency 1 30.0 Compliance 1 98.0 Waiver Rates 1 1.0 1.0 Cutpoints Exemption Age 1 25 Grace Period 1 4 NO TTC Credits Effectiveness DESC file

Program 2 2004 2050 2 T/O EVAP OBD & GC Model Years 2 1996 2050 Vehicles 2 22222 11111111 1 Stringency Compliance 2 98.0 Waiver Rates 2 1.0 1.0 Cutpoints Exemption Age 2 25 Grace Period 2 4 NO TTC Credits Effectiveness DESC file

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

### 32 Model Years 3 1996 2003 Vehicles 3 22222 21111111 1 Stringency 3 30.0 Compliance 3 98.0 Waiver Rates 3 3.0 1.0 Cutpoints Exemption Age 3 25 Grace Period 3 2 NO TTC Credits Effectiveness DESC file

Program 4 2001 2050 2 T/O ASM 2525 PHASE-IN Model Years 4 1975 1995 Vehicles 4 22222 21111111 1 Stringency 4 30.0 Compliance 4 98.0 Waiver Rates 4 3.0 1.0 Cutpoints Exemption Age 4 25 Grace Period 4 4 NO TTC Credits Effectiveness DESC file

Program 5 1998 2000 2 T/O LOADED/IDLE Model Years 5 1975 2000 Vehicles 5 22222 21111111 1 Stringency 5 30.0 Compliance 5 98.0 Waiver Rates 5 3.0 1.0 Cutpoints Exemption Age 5 25 Grace Period 5 2 NO TTC Credits Effectiveness DESC file

Program 6 1996 1997 2 T/O IM240 Model Years 6 1975 1997 Vehicles 6 22222 21111111 1 Stringency 6 30.0 Compliance 6 98.0 Waiver Rates 6 3.0 1.0 Cutpoints 6 CUTPOINT.D Exemption Age 6 25 Grace Period 6 2 NO TTC Credits Effectiveness DESC file

### 33 Program 7 1996 2050 2 T/O GC Model Years 7 1975 1995 Vehicles 7 22222 21111111 1 Stringency Compliance 7 98.0 Waiver Rates 7 3.0 1.0 Cutpoints Exemption Age 7 25 Grace Period 7 2 NO TTC Credits Effectiveness DESC file



### Fuel Commands

Fuel Program 1

Oxygenated Fuels 0.00 0.42 0.00 0.036 2

Fuel RVP 9



### Alternative Emission Regulati ons and Control Measures

Rebuild Effects 0.1



### External Conditions Commands

Calendar Year 2005

Evaluation Month 7

Min/Max Temperature National Climatic Data Center



### Vehicle Fleet Characteristic Commands

Registration Distribution Variable

### 34 

## Cincinnati-Dayton-Springfield 2005 Ozone M6.2 Inputs
Includes the following counties: Ohio : Butler, Clark, Clermont, Clinton, Greene, Hamilton, Miami, Montgomery, Warren Indiana : Lawrenceburg Twp., Dearborn County Kentucky : Boone, Campbell and Kenton counties



### State Programs Input Note: Indiana and Kentucky inputs may not coincide with Ohio inputs

Stage II Refueling 93/3/86/86

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

I/M Programs Yes Excludes Clinton Co. and Miami Co., OH, Dearborn County, IN - No I/M program Note: I/M inputs for Kentucky counties are not included Program 1 2004 2050 2 T/O OBD I/M Model Years 1 1996 2050 Vehicles 1 22222 21111111 1 Stringency 1 30.0 Compliance 1 98.0 Waiver Rates 1 1.0 1.0 Cutpoints Exemption Age 1 25 Grace Period 1 2 NO TTC Credits Effectiveness DESC file

Program 2 2004 2050 2 T/O EVAP OBD & GC Model Years 2 1996 2050 Vehicles 2 22222 11111111 1 Stringency Compliance 2 98.0 Waiver Rates 2 1.0 1.0 Cutpoints Exemption Age 2 25 Grace Period 2 2 NO TTC Credits Effectiveness DESC file

Program 3 2001 2003 2 T/O ASM 2525 PHASE-IN Model Years 3 1996 2003 Vehicles 3 22222 21111111 1 Stringency 3 30.0 Compliance 3 98.0

### 35 Waiver Rates 3 3.0 1.0 Cutpoints Exemption Age 3 25 Grace Period 3 2 NO TTC Credits Effectiveness DESC file

Program 4 2001 2050 2 T/O ASM 2525 PHASE-IN Model Years 4 1975 1995 Vehicles 4 22222 21111111 1 Stringency 4 30.0 Compliance 4 98.0 Waiver Rates 4 3.0 1.0 Cutpoints Exemption Age 4 25 Grace Period 4 4 NO TTC Credits Effectiveness DESC file

Program 5 1998 2000 2 T/O LOADED/IDLE Model Years 5 1975 2000 Vehicles 5 22222 21111111 1 Stringency 5 30.0 Compliance 5 98.0 Waiver Rates 5 3.0 1.0 Cutpoints Exemption Age 5 25 Grace Period 5 2 NO TTC Credits Effectiveness DESC file

Program 6 1996 1997 2 T/O IM240 Model Years 6 1975 1997 Vehicles 6 22222 21111111 1 Stringency 6 30.0 Compliance 6 98.0 Waiver Rates 6 3.0 1.0 Cutpoints 6 CUTPOINT.D Exemption Age 6 25 Grace Period 6 2 NO TTC Credits Effectiveness DESC file

Program 7 1996 2050 2 T/O GC Model Years 7 1975 1995 Vehicles 7 22222 21111111 1

### 36 Stringency Compliance 7 98.0 Waiver Rates 7 3.0 1.0 Cutpoints Exemption Age 7 25 Grace Period 7 2 NO TTC Credits Effectiveness DESC file



### Fuel Commands

Fuel Program 1

Oxygenated Fuels 0.00 0.42 0.00 0.036 2

Fuel RVP 9



### Alternative Emission Regulati ons and Control Measures

Rebuild Effects 0.1



### External Conditions Commands

Calendar Year 2005

Evaluation Month 7

Min/Max Temperature National Climatic Data Center



### Vehicle Fleet Characteristic Commands

Registration Distribution Variable

### 37 

## Other Areas ( excluding NOACA/AMATS 

## and OKI/MVRPC 

## ) Ozone M6.2 Inputs
Includes the following counties: Ohio : Belmont, Columbiana, Delaware, Fairfield, Franklin, Jefferson, Knox, Licking, Lucas,Madison, Mahoning, Pickaway, Trumball, Wood



### Fuel Commands Input

Fuel Program 1

Oxygenated Fuels 0.00 0.42 0.00 0.036 2

Fuel RVP 9



### Alternative Emission Regulati ons and Control Measures

Rebuild Effects 0.1 (0.30 for 2018)



### External Conditions Commands

Calendar Year All

Evaluation Month 7

Min/Max Temperature National Climatic Data Center



### Vehicle Fleet Characteristic Commands

Registration Distribution Variable



### 4.5 Ohio’s Mobile Emission Data Processed by LADCO (Lake Michigan Air Director’s Consortium):

2005 TDM and Mobile6 input data were provided to LADCO for processing. The data was processed by LADCO with T3 to prepare it as an input into the ConCEPT model. T3 and ConCEPT are described as follows.

### 38 4.5.1 T3--Development of Link-Level Mobile Source Emission Inventories:

Highly resolved emission inventories for on- road mobile sources are needed for air quality modeling to develop the necessary technical support for new State Implementation Plans (SIPs) fo r regional haze, fine particles , and ozone. Emissions for on-road motor vehicles are estimated using vehi cle miles traveled, tr ip starts and ends, speed, and other activity data developed by State Agencies and Metropolitan Planning Organizations (MPOs) using transportati on demand models (TDMs), and emission factors from EPA's MOBILE6 model. To s upport this modeling in the upper Midwest, ENVIRON, working with LADCO, State DOTs, and local MPOs, has developed a software tool (the TDM Transformation Tool, or "T3") that takes TDM output from approximately twenty transportation networ ks using a variety of models, applies appropriate data transformations , and outputs link- and county-le vel activity data in a uniform format for input to the CONCEPT emissions processing model (a new emissions processing model also developed wit h funding from LADCO). In a parallel effort, analyses of extensive automatic tra ffic recorder (ATR) dat a collected by State DOTs were conducted to develop temporal profiles (hour of day , day of week, and month of year) of vehicle counts and vehicl e mix by roadway type for developing the detailed on-road emissi on inventories.

T3 provides a conduit from the projections of traffic demand model ers regarding vehicle types, road networks, and vehicle activity to the activity data required by emissions modelers. The primary goals of T3 are to provide an easy mechanism for incorporating TDM model outputs in as "raw" a format as possible, while simult aneously providing a great degree of flexibility in representing t he TDM projections in terms acceptable to most air quality models. These goal s have been achieved through the use of a dimensional transformation approach, w here the dimensions of the various transformations are user-defined - hence the name of the tool.

By Stella Shepard, Alison K. Pollack, John Haasbeek, ENVIRON International Corporation, 101 Rowland Way, Su ite 220, Novato, CA. 94945 & Mark Janssen, Lake Michigan Air Directors Consortium (LADCO ), 2250 E. Devon Avenue # 250, Des Plaines, IL 60018, janssen@ladco.org

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

The new CONCEPT (CONsolidated Communi ty Emissions Processing Tool) Emissions Processor is now available for use by the emissions modeling community. Developed as joint project between Alpine Geophysics, LLC and ENVIRON Corporation, with Midwest RPO and joint RPO funding, the CONCEPT model combines the best attributes of current emissions modeling syste ms into an open source model highlighting the following features:

• Open Source. Writt en primarily in PostgreSQL, th e software required for running

### 39 CONCEPT is in the public dom ain. The model itself is GNU Public License (GPL) compliant and users are encouraged to make additions and enhancements to the modeling system.

• Transparent. The database structur e of the model makes the system easy to understand, and the modeling codes themselv es are extremely well documented to encourage user participation in the customizing the system for specific modeling requirements.

• Quality Control. The CONCEPT model structure an d implementation allows for multiple levels of QA analysis during every step of the emissions calculation process. Using the database structures, an emissions modeler can easily trace a process or facility and review the calculat ion procedures and assumptions for any emissions value.

The CONCEPT model includes modules for the major emissions source categories: area source, point source, on-ro ad motor vehicles, non-road motor vehicles and biogenic emissions, as well as a num ber of supporting modules, including spatial allocation factor development, speciation pr ofile development, growth and control for point and area sources, and CEM point sour ce emissions handling. The emissions modeling community has alr eady begun development of addi tional CONCEPT support modules including CEM preproce ssing software, graphical QA tools, and an interface to the traffic demand models for on-road mo tor vehicle emissions estimation.

By Cyndi Loomis, James G. Wilkinson, Alpine Geophysics, LLC, & John Haasbeek, Alison Pollack, ENVIRON Corporation. & Mark Janssen, Lake Michigan Air Directors Consortium (LADCO), 2250 E. Devon Av enue # 250, Des Plaines, IL 60018, janssen@ladco.org

4.5.3 LADCO Ohio Data Outputs for 2005:

The following LADCO outputs and documents can be found at: www.ladco.org/tech/e mis/net05/index.html

Table 4-6 LADCO Data Output

Sta te Network T3 Descripti on DOC M6 Inpu ts VM T vs HP MS Exc el Average Day VMT (this should match conformi ty inventory ) Co unt y Em issi

on

s Re por t All Pollut ants After Speci ation Dropped VMT M6 Run Summ ary Pollut ant T otals (Short ) Raw Summ ary (pol,ve h, etype) Hourly Tempo ral Summ ary Hourly Veh Mix Summ ary Hourly Speed Summ ary (with volum e/ capaci ty)

OH AKRON AKRON OH OH AKRON 1 1 1 1 1 1 1 1 1

### 40 OH CANTON CANTON OH OH CANTON 1 1 1 1 1 1 1 1 1

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

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

OH COLUMB US COLUMB US OH OH COLUMB US 1 1 1 1 1 1 1 1 1

OH SPRING FLD SPRING FLD OH OH SPRING FLD 1 1 1 1 1 1 1 1 1

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

OH YNGSTO WN YNGSTO WN OH OH YNGSTO WN 1 1 1 1 1 1 1 1 1

OH STATEW D STATEW D OH OH STATEW D 1 1 1 1 1 1 1 1 1



### Additional Documents on LADCO Web Page:

“Comparison to EPA's Default Model NMIM”

“How we Build the 2005 Vmt/Networks”

“Spreadsheet/Graphics on Vehicle Mix”

“Background on the T3 Tool “

4.6 MPO Contact Table

Table 4-7 County Summary Ta ble of MPOs by County

FIPS County Model Network MPO Contact Person 39001 Adams Statewide

39003 Allen Statewide Lima Allen County Regional Planning Commission Tom Mazur

39005 Ashland Statewide 39007 Ashtabula Statewide 39009 Athens Statewide 39011 Auglaize Statewide

39013 Belmont Statewide Bel-O-Mar Regional Council and Interstate Planning Commission Rakesh Sharma

39015 Brown Statewide

39017 Butler Cincinnat/Dayton Ohio-Kentucky-Indiana Regional Council of Governments Andy Reser

39019 Carroll Statewide 39021 Champaign Statewide

39023 Clark Springfield Coordinating Committee of the Clark County-Springfield Eric Ottoson

### 41 Transportation Study

39025 Clermont Cincinnat/Dayton Ohio-Kentucky-Indiana Regional Council of Governments Andy Reser

39027 Clinton Statewide 39029 Columbiana Statewide 39031 Coshocton Statewide 39033 Crawford Statewide

39035 Cuyahoga Cleveland Northeast Ohio Areawide Coordinating Agency Bill Davis

39037 Darke Statewide 39039 Defiance Statewide

39041 Delaware Columbus Mid-Ohio Regional Planning Commission Nick Gill

39043 Erie Statewide 39045 Fairfield Statewide 39047 Fayette Statewide

39049 Franklin Columbus Mid-Ohio Regional Planning Commission Nick Gill

39051 Fulton Statewide 39053 Gallia Statewide

39055 Geauga Cleveland Northeast Ohio Areawide Coordinating Agency Bill Davis

39057 Greene Cincinnat/Dayton Miami Valley Regional Planning Commission Ana Ramirez

39059 Guernsey Statewide

39061 Hamilton Cincinnat/Dayton Ohio-Kentucky-Indiana Regional Council of Governments Andy Reser

39063 Hancock Statewide 39065 Hardin Statewide 39067 Harrison Statewide 39069 Henry Statewide 39071 Highland Statewide 39073 Hocking Statewide 39075 Holmes Statewide 39077 Huron Statewide 39079 Jackson Statewide

39081 Jefferson Statewide Brooke-Hancock-Jefferson Transportation Study Policy Committee Mike Proprocki

39083 Knox Statewide

39085 Lake Cleveland Northeast Ohio Areawide Coordinating Agency Bill Davis

39087 Lawrence Statewide KYOVA Interstate Planning Commission

39089 Licking Columbus Licking County Area Transportation Study Matthew Hill

39091 Logan Statewide
39093 Lorain Cleveland Northeast Ohio Areawide Bill Davis

### 42 Coordinating Agency

39095 Lucas Toledo Toledo Metropolitan Area Council of Governments Sujatha Mohanakrishnan

39097 Madison Statewide

39099 Mahoning Youngstown Eastgate Regional Council of Governments R.P. Samulka

39101 Marion Statewide

39103 Medina Cleveland Northeast Ohio Areawide Coordinating Agency Bill Davis

39105 Meigs Statewide 39107 Mercer Statewide

39109 Miami Cincinnat/Dayton Miami Valley Regional Planning Commission Ana Ramirez

39111 Monroe Statewide

39113 Montgomery Cincinnat/Dayton Miami Valley Regional Planning Commission Ana Ramirez

39115 Morgan Statewide 39117 Morrow Statewide 39119 Muskingum Statewide 39121 Noble Statewide 39123 Ottawa Statewide 39125 Paulding Statewide 39127 Perry Statewide 39129 Pickaway Statewide 39131 Pike Statewide

39133 Portage Akron Akron Metropolitan Area Transportation Study Jason Segedy

39135 Preble Statewide 39137 Putnam Statewide

39139 Richland Statewide Richland County Regional Planning Commission John Adams

39141 Ross Statewide 39143 Sandusky Statewide 39145 Scioto Statewide 39147 Seneca Statewide 39149 Shelby Statewide

39151 Stark Canton Stark County Regional Planning Commission Dan Slicker

39153 Summit Akron Akron Metropolitan Area Transportation Study Jason Segedy

39155 Trumbull Youngstown (partial county model

coverage) Eastgate Regional Council of Governments R.P. Samulka

39157 Tuscarawas Statewide 39159 Union Statewide 39161 Van Wert Statewide 39163 Vinton Statewide

39165 Warren Cincinnat/Dayton Ohio-Kentucky-Indiana Regional Council of Governments Andy Reser, OKI

+

### 43 + Miami Valley Regional Planning Commission Ana Ramirez, MVRPC

39167 Washington Statewide Wood-Washington-Wirt Interstate Planning Commission 39169 Wayne Statewide 39171 Williams Statewide

39173 Wood Toledo Toledo Metropolitan Area Council of Governments Sujatha Mohanakrishnan

39175 Wyandot Statewide 

### NOTE: Complete MPO information can be found at, www.dot.state.oh.us/urban/mpomap.htm and at www.dot.state.oh.us/urban/mpolist.htm#Cleveland

.



## SECTION 5

MARINE, AIRCRAFT and RAIL (MAR) SOURCES

MAR sources are non-road sour ces which are significant enoug h in terms of emissions to be considered separately from the rest of the non-road sources. The MAR inventory consists of commercial marines, aircraft and locomotive sources. The marine and locomotive inventory is generated by Environ 1 

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

5.1 Marine Vessel Sources
The approach to commercial marine emi ssion estimates needed to be flexible because the activity data was available in many fo rmats. Emission esti mates were determined either by multiplying engine power, load factor, hours per ye ar of operation, or on the basis of the number of gall ons of fuel consumed.

Emissions were determined for ten subclasses of vessel types: Deep draft vessels (DDV) at port, DDV mid-late, push boats (rivers /lakes), tugs, ferries, other special (excursion) vessels, support vessels, dredges, commercial fishing, and military vessels (Coast Guard). These were linked to various Ohio lakes and rivers.

Because of the large variety of methodologie s employed, inventory tables for the ten subcategories are detailed in the comp lete inventory prepared and published by ENVIRON Internati onal Corporation: LADCO 2005 COMMERCIAL MARINE EMISSIONS , by Christian E. Li ndhjem, March, 2007.

Emission totals produced by ENVIRON were provided to LADCO to submit to EPA for Ohio’s State Implementation Plan (SIP).

### 44 5.2 Rail Sources

The primary activity unit used to determine em issions is gallons of fuel consumed. Emission rates were derived from EPA doc uments provided as support documentation for the 1997 locomotive emission standards (EPA, 1997). Gallons of fuel consumed were based on rail activity.

Rail activity was broken down into four Source Category Codes (SCC). Class I, linehaul rail: Large interstate railroad companies like Union Pacific and Norfolk Southern. Class I,II, III, switching rail: Yard operations. Class II, III line haul: Regional and local railroads. And Passenger rail: AMTRAK.

Class I, line-haul represents 84.3% of fuel used and the larges t emission’s category. The complete emission’s inventory was prepared and published by ENVIRON International Corporation: LADCO 2005 LOCOMOTIVE EMISSIONS , by Christian E. Lindhjem, February, 2007.

Emission totals produced by ENVIRON were provided to LADCO to submit to EPA for Ohio’s State Implementation Plan (SIP).

EPA. 1997: "Locomotive Emission Standards." Regulatory Support Document, United States Environmental Protection Agency, O ffice of Mobile Sources, April. And EPA 1997, Emission Factors for Locomotives," Environmental Protecti on Agency, EPA420-F-97-051, December.



### 5.3 Aircraft Sources

INTRODUCTION:

The aircraft emission’s inventory is derived by taking the number of Landings and Take Offs (LTOs) per year and multiplying by an emission fact or. In the Ohio inventory when specific aircraft models and engine type emission factors are known they were used. For the rest of the inventory the emission factors came from USEPA’s fleet average emissions data. Those results are t hen compiled as tons per year per pollutant by county. The following de scribes the components, met hodology, and concludes with a description of an Access based aircraft emission calculator.

COMPONENTS:

I. Ohio Airports: A list of both towered and non-towered airpor ts in Ohio is obtained from the Ohio Department of Transportation. See: www.dot.state.oh.us/aviation/ In conversation with ODOT two individuals stated that the 16 4 airports listed covered over 90% of the airports in Ohio. See Table 1.

### 45 II. Number of Operations/LTOs by Airport/County: The ODOT list contained the number of operations a year per airport. An operation is either a landing or a take-off. A Landing and Take Off (LTO) is required for FAA EDMS calculations. LTOs were deriv ed simply by dividing the number of operations by two. These were totaled by county.

Table 5-1 Number of operations and LTOs for 2005

County Airport Name ID Total Operations LTOs/Year

Adams Alexander Salamon AMT 5210 2605
Allen Allen County AOH 32500 16250
Ashland Ashland County 3G4 49240 24620
Ashtabula Ashtabula County HZY 16886 8443

Ashtabula Germack 7D9 840 420
Athens Ohio University UNI 51600 25800
Auglaize Neil Armstrong AXV 29456 14728

Belmont Barnesville-Bradfield 6G5 10150 5075

Belmont Alderman 2P7 6150 3075
Brown Brown County GEO 5157 2578.5
Butler Butler County Regional HAO 61687 30843.5
Butler Hook Field Municipal MWO 40050 20025
Butler Miami University OXD 16708 8354
Carroll Carroll County -Tolson TSO 34950 17475

Carroll Parsons 5D6 2674 1337
Champaign Grimes Field I74 23480 11740
Champaign Weller 38I 300 150
Clark Springfield-Beckley Municipal SGH 64033 32016.5

Clark Mad River I54 15350 7675
Clermont Clermont County I69 35741 17870.5
Clinton Airborne Airpark ILN 52000 26000

Clinton Clinton Field I66 29360 14680

Clinton Hollister Field 2B6 161 80.5
Columbiana Columbiana County Ø2G 31146 15573

Columbiana Koons 8G8 2546 1273
Coshocton Richard Downing I4Ø 19550 9775
Coshocton Tri-City 8ØG 8085 4042.5
Crawford Port Bucyrus 17G 24871 12435.5
Crawford Galion Municipal GQQ 5216 2608
Cuyahoga Burke Lakefront BKL 97100 48550
Cuyahoga Cleveland-Hopkins International CLE 234356 117178
Cuyahoga Cuyahoga County CGF 79774 39887
Darke Darke County VES 9238 4619

Defiance Defiance Memorial DFI 9130 4565
Delaware Delaware Municipal DLZ 39300 19650

Delaware Packer 5E9 3181 1590.5

Erie Hinde 88D 1350 675
Erie Kelleys Island 89D 25495 12747.5
Erie Griffing-Sandusky SKY 112100 56050

### 46 Erie Wakeman I64 17324 8662

Fairfield Miller's Farm 7B4 360 180
Fairfield Fairfield County LHQ 43066 21533
Fayette Fayette County I23 29405 14702.5

Franklin Ohio Stat e University OSU 134459 67229.5

Franklin Port Columbus International CMH 218438 109219

Franklin Rickenbacker International LCK 96200 48100

Franklin Bolton Field TZR 69149 34574.5

Franklin Columbus Southwest Ø4I 11833 5916.5
Franklin Darby Dan 6I6 11260 5630
Fulton Fulton County USE 21123 10561.5
Gallia Gallia-Meigs Regional GAS 12200 6100
Geauga Gates 7D8 4200 2100
Geauga Geauga County 7G8 5350 2675
Greene Greene County - Lewis A. Jackson I19 37400 18700

Greene Bloom 14I 100 50
Guernsey Cambridge Municipal CDI 6040 3020

Hamilton Lunken LUK 129430 64715

Hamilton Blue Ash ISZ 35000 17500

Hamilton Cincinnati West I67 30197 15098.5

Hancock Bluffton 5G7 71980 35990
Hancock Findlay FDY 19800 9900

Hancock Priebe 7D5 3850 1925

Hardin Ada ØD7 331 165.5
Hardin Hardin County I95 6562 3281
Hardin Elliott's Landing O74 1560 780
Harrison Harrison County 8G6 11900 5950
Henry Henry County 7W5 15637 7818.5
Highland Highland County HOC 18325 9162.5
Holmes Holmes County 1ØG 21400 10700
Huron Huron County 5A1 10100 5050

Huron Willard 8G1 2715 1357.5

Jackson James A. Rhodes I43 6053 3026.5
Jefferson Jefferson County Airpark 2G2 15969 7984.5

Jefferson Eddie Dew Memorial 1G8 3540 1770
Knox Knox County 4I3 20150 10075
Knox Wynkoop 6G4 4691 2345.5
Lake Concord Airpark 2G1 4510 2255
Lake Willoughby Lost Nation Municipal LNN 45085 22542.5
Lawrence Lawrence County Airpark HTW 41910 20955
Licking Newark-Heath VTA 12457 6228.5
Logan Bellefontaine Regional EDJ 8325 4162.5

Lorain Columbia 4G8 5150 2575
Lorain Elyria 1G1 14300 7150
Lorain Lagrange 92D 1155 577.5
Lorain Lorain County Regional LPR 62000 31000

Lorain Reader-Botsford Airfield 67D 18700 9350
Lucas Toledo Express TOL 94600 47300

### 47 Madison Madison County UYF 41410 20705
Mahoning Salem Airpark 38D 16920 8460
Mahoning Tri-City 3G6 10555 5277.5
Mahoning Elser Metro 4G4 49232 24616
Mahoning Lansdowne Ø4G 750 375
Marion Marion Municipal MNN 42650 21325

Medina Medina Municipal 1G5 79685 39842.5
Medina Wadsworth Municipal 3G3 41025 20512.5
Medina Weltzien Skypark 15G 79130 39565

Mercer Lakefield CQA 16212 8106

Miami Hartzell Field I17 10200 5100
Miami Troy Skypark 37I 4264 2132

Miami Waco Field 1WF 0 0
Monroe Monroe County 4G5 3324 1662
Montgomery Brookville Air-Park I62 29359 14679.5
Montgomery James M. Cox Dayton Intl DAY 134524 67262
Montgomery Dayton Wright Brothers MGY 89045 44522.5
Montgomery Moraine Airpark I73 12938 6469
Montgomery Dahio Trotwood I44 1853 926.5
Montgomery Phillipsburg 3I7 68000 34000
Morgan Morgan County I71 5725 2862.5
Morrow Morrow County 4I9 19108 9554
Muskingum Zanesville Municipal ZZV 33312 16656
Muskingum Parr 42I 16150 8075
Noble Noble County - Mike Brienza Field I1Ø 5950 2975

Ottawa Middle Bass-East Point 3W9 1300 650

Ottawa Middle Bass Island 3T7 6500 3250

Ottawa North Bass Island 3X5 1000 500

Ottawa Carl R. Keller Field PCW 20890 10445
Ottawa Put-In-Bay 3W2 15140 7570
Paulding Paulding 2H8 2100 1050
Perry Crooksville I84 400 200
Perry Perry County I86 4550 2275
Pickaway Pickaway County CYO 35450 17725
Pickaway Clarks Dream Strip Ø3I 2770 1385
Pike Pike County EOP 2012 1006
Portage Freedom Air Field 7D6 1623 811.5
Portage Farview 86D 3353 1676.5
Portage Mills 7E3 1050 525
Portage Portage County 29G 9621 4810.5

Putnam Ruhe's R47 13250 6625
Putnam Putnam County OWX 11910 5955
Putnam Ohio Dusting Co. 6C2 2995 1497.5
Richland Mansfield Lahm Regional MFD 57518 28759
Richland Shelby Community 12G 2012 1006
Ross Ross County RZT 50150 25075
Sandusky Fremont 14G 37450 18725
Sandusky Sandusky County Regional S24 6148 3074

### 48 Scioto Greater Portsmouth Regional PMH 45830 22915

Seneca Bandit Field 5D9 140 70
Seneca Fostoria Metropolitan FZI 7900 3950

Seneca Weiker 82D 320 160
Seneca Seneca County 16G 60165 30082.5
Shelby Sidney Municipal I12 20500 10250

Stark Barber 2D1 13750 6875
Stark Miller Airport 4G3 8000 4000
Stark Beach City 2D7 6112 3056

Summit Akron Fulton International AKR 26000 13000
Summit Akron-Canton Regional CAK 120441 60220.5
Summit Mayfield 1D4 450 225
Summit Kent State University 1G3 72500 36250

Trumbull Braceville 41N 425 212.5

Trumbull Warren 62D 14738 7369
Trumbull Youngstown-W arren Regional YNG 98298 49149
Tuscarawas Harry Clever Field PHD 54880 27440
Union Union County MRT 31886 15943
Van Wert Van Wert County VNW 20516 10258
Vinton Vinton County 22I 5225 2612.5
Warren Warren County I68 24951 12475.5

Warren Red Stewart Airfield 4ØI 16800 8400
Wayne Wayne County BJJ 96520 48260
Williams Williams County ØG6 10010 5005
Wood Wood County 1GØ 27405 13702.5

Wood Bordner 3D8 2200 1100
Wood Deshler Municipal 6D7 2000 1000

Wood Metcalf TDZ 90700 45350
Wyandot Wyandot County 56D 7410 3705



### III. Aircraft Models and Number of LTO/yr:

Specific aircraft models by airport (generally t he larger airports) is obtained from “Table 7” provided by the United States Departm ent of Transportation, Office of Airline Information. This provided the number of LTO’s for each aircraft model per year per airport.

Table 2. Sample from “Table 7.” This is the all community total of aircraft models for the Akron/Canton area. “All Service” departure s were used as the number of LTOs per year for that model. Listed in the original table are airc raft model by airport and number of services/LTOs.

### 49 **TOTAL DEPARTURES PERFORMD**

**Aircraft Scheduled Non-Sched All Model Service Service Service** 

A-318 1 1 A319 384 384 BOEING 717-200 3850 3850 BOEING 727-100 1 1 BOEING 727-200 5 5 BOEING 737-100/200 74 74 BOEING 737-200C 3 3 6 BOEING 737-300 2 2 BOEING 737-700/LR 212 212 BOEING 737-800 5 5 BOEING 757-200 4 4 BOEING 767-300/ER 4 4 CANADAIR RJ-100/ER 542 3 545 CANADAIR RJ-700 2881 2881 CONVAIR CV-580 3 3 DASSAULT FALCON 7 7 DHC8-100 DASH 8 2 2 DOUGLAS DC-9-15F 13 13 DOUGLAS DC-9-30 6 6 EMBRAER-145 144 144 RJ-200ER/RJ-440 5519 5519 SAAB-FAIRCHD 340/B 1804 1804 ALL TYPES 15342 130 15472

IV. Emission Factors: Where there was specific aircraft model data the emission factors were derived using the FAA’s Emission Dispersion Modeling System (EDMS). EDMS is a combined emissions and dispersion model for assessing ai r quality at civilian airports and military air bases. The model was developed by the Federal Aviation Administration (FAA) in cooperation with the Unit ed States Air Force (USAF). The model is used to produce an inventory of emissions generated by source s on and around the airport or air base, and to calculate pollutant concentrations in these environments.

### 50 Table 5-2 EDMS Aircraft Emissions Emissions provided by Michigan Department of Environmental Quality. (The few aircraft in Ohio not included in the table had emissions derived by EDMS 5.0 in-house.)

EDMS Aircraft Emissions/LTO by Aircraft Type EDMS 4.5 Emissions Inventory Report of 2005 Aircraft Inventory Emissions Factors

Year 2005 Lbs Emitted Per LTO Aircraft Type CO NOx HC VOC SO2 PM2.5 PM10

A- 318 19.8 18.7 4.0 4.4 1.5 0.0 0.0

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

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

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

A-319 19.8 18.7 4.0 4.4 1.5 0.0 0.0

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

A-321 16.8 36.8 3.1 3.3 2.2 0.0 0.0

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

AVROLINER RJ85 24.7 9.5 2.9 3.3 1.3 0.2 0.2

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

BEECH 1900 A/B/C 11.0 1.1 3.3 3.5 0.2 0.0 0.0
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 emissi on factors were taken from “Documentation for Aircraft, Commercial Marine Vessel, ,Locomotive, and Other Non-road Components of the National Emissions Inventory. 2005, s ee Appendix A, Aircraft Emission Estimation Methodology.” Specific model LTOs were subtracted from county LTO totals to eliminate double counting those LTOs.

Table 5-3 Fleet Emissi on Factor Categories

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

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

(Taken from : Table A-5) Pollutant Emission Factors (lbs/LTO) HC 1.234 NOx 0.158 CO 28.13 SOx 0.015 PM10 0.60333

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

### 52 Table 5-3b Fleet Average Emission Factor s for Local General Aircraft.

(Taken From: Table A-11) Pollutant Emission Factors (lbs/LTO) HC 0.394 NOx 0.065 CO 12.014 SOx 0.01 PM10 0.2367

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

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

(Taken from: Table A-17) Pollutant Emission Factors (lbs/LTO HC 1.234 NOx 0.158 CO 28.13 SOx 0.015 PM10 0.60333

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

METHODOLOGY

Introduction

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

1. Commercial scheduled and non-scheduled ai rcraft air carrier activity and commercial air freight activity by aircraft model types,

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

3. Military annual local and iti nerant operations for year 2005.

Due to the need to have aircraft operations in formation expressed as landing/take off (LTO) cycles, the following assumptions were made:

1. For commercial aircraft and commercial ai r freight activity, t he number of annual aircraft annual LTO cycles was assu med to be equal to the number of

### 53 departures. The daily LTO cycle frequency was then obtained by dividing the yearly LTO cycles by 365.

2. For general aircraft annual local and iti nerant airport operations, each respective operations total was divided by 2 to obt ain the corresponding year local and itinerant LTO cycles. The expected daily local and itinerant LTO cycles then were obtained by dividing these annual totals by 365.

3. For military annual local and itinerant operat ions, each respective operations total was divided by 2 to obtain the correspondi ng year local and itinerant LTO cycles. The expected military daily local and iti nerant LTO cycles then were obtained by dividing these annual totals by 365.

Airport LTO cycles were further categoriz ed into commercial aircraft by plane and engine type, general aviation itinerant aircraft of unknown aircraft type, general aviation local aircraft of unknown aircraft type, and military aircraft. This was necessary in order to utilize the U.S. Department of Transportation, Federal Av iation Administration EDMS Emissions and Dispersion Modeling System . Commercial and air freight aircraft emission factors per LTO cycle were det ermined using EDMS for each commercial aircraft type models where possible were used at each towered airport. Default commercial aircraft engine type, and Enviro nmental Protection Agency (EPA) default time in mode values for takeoff, approach, and landing roll times were used in the EDMS model simulations.

For those aircraft types that could not be determined using the EDMS emissions model, aircraft emission factors based upon EPA alter native fleet average procedures were then used to estimate their emissions. These included general aviation and air taxi itinerant aircraft of unknown aircraft type, general aviation local aircraft of unknown aircraft type, and military aircraft. Conversion from total hydrocarbons to volatile organic compounds was performed and based upon the EPA guidance

.



### APPROACH

1. A list of more than 90% of the airports was obtained fr om the Ohio Department of Transportation. These were classed by airport, county, aircraft flight classification, and the total number of op erations per year.

2. The number of oper ations (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 categorie s used by the USEPA in calculating emissions.

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

Local (General aviation local)

Military (Military)

4. LTOs for specific models of aircraft per airport were ta ken from the FAA Table 7. See Table 2 above. These were then combined to gi ve the number of LTOs per aircraft model per county. Specific model LTOs were subtracted from county totals to avoid double counting those LTOs.

5. Emission factors were det ermined from the FAA’s EDMS pr ogram for specific aircraft model and engine type. See IV. Emission factors above. The aircraft emission table provided by Michigan has the emission factors for most of the aircraft fl own in Ohio. . Where specific aircraft model data was not available USEPA average fleet emissions were used.

6. Emission factors times LTOs by count y yielded tons per y ear per county.

Table 5-4 Pollutant by County (Sample)

County POLLUTANT ACTIVITY(LTOS/YEAR) ACTIVITY(LTOS/DAY) EMISSIONS(TON/YEAR) ADAMS CO 2605 7.136986 24.552325 ADAMS HC 2605 7.136986 0.977285 ADAMS NOX 2605 7.136986 0.136045 ADAMS PM10-PRI 2605 7.136986 0.513114825 ADAMS PM25-PRI 2605 7.136986 0 ADAMS SOX 2605 7.136986 0.0157875 ADAMS VOC 2605 7.136986 0.96314234 ALLEN CO 16250 44.52055 147.945065 ALLEN HC 16250 44.52055 5.841097 ALLEN NOX 16250 44.52055 0.827189 ALLEN PM10-PRI 16250 44.52055 3.081191265 ALLEN PM25-PRI 16250 44.52055 0 ALLEN SOX 16250 44.52055 0.0984575 ALLEN VOC 16250 44.52055 5.769568254 ASHLAND CO 24620 67.45205 173.67794 ASHLAND HC 24620 67.45205 6.19414 ASHLAND NOX 24620 67.45205 0.94895 ASHLAND PM10-PRI 24620 67.45205 3.532515 ASHLAND PM25-PRI 24620 67.45205 0 ASHLAND SOX 24620 67.45205 0.1311 ASHLAND VOC 24620 67.45205 6.07175247 ASHTABULA CO 8863 24.28219 78.115087 ASHTABULA HC 8863 24.28219 3.042551 ASHTABULA NOX 8863 24.28219 0.431593

### 55 ASHTABULA PM10-PRI 8863 24.28219 1.623493455 ASHTABULA PM25-PRI 8863 24.28219 0 ASHTABULA SOX 8863 24.28219 0.0520325 ASHTABULA VOC 8863 24.28219 2.99420213 ATHENS CO 25800 70.68493 196.874985 ATHENS HC 25800 70.68493 7.267183 ATHENS NOX 25800 70.68493 1.081521 ATHENS PM10-PRI 25800 70.68493 4.037266335 ATHENS PM25-PRI 25800 70.68493 0 ATHENS SOX 25800 70.68493 0.1420925 ATHENS VOC 25800 70.68493 7.128180006



### 7. Emissions were then summed by pollut ants in each county by SCC aircraft category type so data could be provided to LADCO in the EPA prescribed NEI – NIF format.

DATA ERROR

The first aircraft emission inventory submitt ed to LADCO in April, 2007 contained an error. The inventory submitted in May, 2007 has the error corrected. The error was the result of a carry-over func tion in Access that picked up the number of operations as instead of LTOs....which made the in ventory exactly twice as lar ge as what it really was.

ACCESS CALCULATOR

Introduction:

Our database programmer set up Access applic ation to calculate Ohio’s aircraft emission inventory, and export those results to Excel. His utility allows for easy modification of the aircraft data to match futu re data scenarios. Ou tput to Excel also allows for additional data manipulation and importation.

### 56 Interface:

Through this interface the following sets of data can be edited/updated: Airport activity, emission factors, specific airport emissi ons, state/county FIPS, detailed aircraft information, airport informati on, and aircraft SCCs. Once the final emissions have been calculated and summed, then the data can be expo rt via the export function on the Main Menu

.

# Mobile Source Emissions Inventory for Cincinnati PM2.5 Nonattainment

Area

Includes a portion of Dearborn County, Indiana, the counties of Boone, Campbell, Kenton in Kentucky, and the counties of Butler, Cle rmont, Hamilton, and Warren in Ohio. Emission estimates for the Year 2005, 2008, 2011, 2015, 2018, and 2021 developed in support of the PM2.5 State Implementat ion Plan

August 2010

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

for Air Quality and the Ohio Environmental Protecti on Agency by

OKI Regional Council of Governments

### Acknowledgments

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

Area

Abstract This report was prepared for the Indiana Department of Environmental Management, the Kentucky Division for Air Quality a nd the Ohio Environmental Protection Agency. The Cincinnati PM 2.5 nonattainment area includes a portion of Dearborn County Indiana, the counties of Boone, Campbell, Kenton in Kentucky, and the counties of B utler, Clermont, Hamilton, and Warren in Ohio. This report includes emission estimates for the years 2005, 2008, 2011, 2015, 2018 and 2021 was generated to support
the attainment SIPs for the annual PM2.5 standard. EPA’s Motor Vehicle Emission Simulation (MOVES) 2010 was used to genera te the emission

rates.

Date August 2010

Agency Ohio-Kentucky-Indiana Regional Council of Governme nts Mark Policinski, Executive Director Robert Koehler, P.E., Deputy Director

Project Manager

Andrew J. Reser, AICP

Project Staff

Harikishan Perugu, PTP Larry Buckler

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

# MOBILE Source Emissions Inventory for the Cincinnati PM2.5 nonattainment area

This report was prepared for the Indiana Department of Environmental Management, the Kentucky Division for Air Quality and the Ohio Environmental Protection Agency. The Cincinnati PM2.5 nonattainment area includes a portion of Dearborn C ounty Indiana, the counties of Boone, Campbell, Kenton in Kentucky, and the counties of Butler, Cle rmont, Hamilton, and Warren in Ohio. This report includes emission estimates for the years 2005, 200 8, 2011, 2015, 2018 and 2021 was generated to support the attainment SIPs for the annual PM2.5 st andard. EPA’s Motor Vehicle Emissions Simulator (MOVES) 2010 model was used to generate the vehicle emission rates. In December 2009, MOVES replaced MOBILE6.2 as the EPA’s official emission f actor model. Technical details on OKI’s use of MOVES can be found in the Appendix. The OKI travel demand model version 7.6 was used to generate VMT and speed estimates. MOVES emission rates wer e generated for direct PM2.5, PM2.5 tirewear, PM2.5 brakewear, NO x and SO 2

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

Table 1 shows daily and annual mobile source emissi ons for the combined Indiana and Ohio portions of the nonattainment area, as well as the Kentucky por tion of the nonattainment area. Separate MVEB’s are typically designated for these two areas. Alth ough official federal guidance on the use of MOVES for PM2.5 SIP development was not available at the time of this analysis, the Federal Highway Administration (FHWA) along with state and local ai r quality staff were consulted periodically throughout the development of these emissions. An additional safety margin should be added to the MVEB’s due uncertainty with growth assumptions util ized in the OKI travel demand model and uncertainty regarding the use of MOVES. Daily and annual mobile source emissions for each county in
the nonattainment area are shown in Table 2.

## Table 1. Mobile Source Emissions for the Cincinnati PM2.5 Nonattainment Area (tons)

Year Pollutant Name DailyEmissions AnnualEmissions 

### Kentucky Portion of NA Area

**2005** Annual VMT: 3,289,109,202 Daily VMT: 9,621,110 Vehicle Population: 364,081
**Oxides of Nitrogen** 39.10 13,496.54 **Primary Exhaust PM2.5 - Total** 1.36 466.23 **Primary PM2.5 - Brakewear Particulate** 0.16 54.04 **Primary PM2.5 - Tirewear Particulate** 0.05 17.52 **Sulfur Dioxide (SO2)** 0.12 41.46

**2008** Annual VMT: 3,425,339,505 Daily VMT: 9,991,179 Vehicle Population: 375,873
**Oxides of Nitrogen** 37.91 13,114.20 **Primary Exhaust PM2.5 - Total** 1.64 562.84 **Primary PM2.5 - Brakewear Particulate** 0.18 62.10 **Primary PM2.5 - Tirewear Particulate** 0.06 20.70 **Sulfur Dioxide (SO2)** 0.12 42.74

**2011** Annual VMT: 3,587,796,186 Daily VMT: 10,490,143 Vehicle Population: 381,911
**Oxides of Nitrogen** 29.33 10,141.52 **Primary Exhaust PM2.5 - Total** 1.19 407.74 **Primary PM2.5 - Brakewear Particulate** 0.20 68.38 **Primary PM2.5 - Tirewear Particulate** 0.07 22.68 **Sulfur Dioxide (SO2)** 0.13 45.36

**2015** Annual VMT: 3,931,385,741 Daily VMT: 11,495,496 Vehicle Population: 394,278
**Oxides of Nitrogen** 20.18 6,996.21 **Primary Exhaust PM2.5 - Total** 0.78 267.30 **Primary PM2.5 - Brakewear Particulate** 0.23 77.94 **Primary PM2.5 - Tirewear Particulate** 0.08 25.88 **Sulfur Dioxide (SO2)** 0.15 50.50

**2018** Annual VMT: 4,163,203,435 Daily VMT: 12,173,549 Vehicle Population: 403,817
**Oxides of Nitrogen** 15.78 5,480.81 **Primary Exhaust PM2.5 - Total** 0.59 202.15 **Primary PM2.5 - Brakewear Particulate** 0.27 91.15 **Primary PM2.5 - Tirewear Particulate** 0.09 30.09 **Sulfur Dioxide (SO2)** 0.16 56.28

**2021** Annual VMT: 4,286,834,360 Daily VMT: 12,534,236 Vehicle Population: 413,587
**Oxides of Nitrogen** 12.75 4,435.96 **Primary Exhaust PM2.5 - Total** 0.43 146.79 **Primary PM2.5 - Brakewear Particulate** 0.28 96.84 **Primary PM2.5 - Tirewear Particulate** 0.09 31.74 **Sulfur Dioxide (SO2)** 0.17 58.63

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

### Ohio/Indiana Portion of NA Area

**2005** Annual VMT: 13,541,324,003 Daily VMT: 39,564,030 Vehicle Population: 1,754,582
**Oxides of Nitrogen** 168.89 58,423.36 **Primary Exhaust PM2.5 - Total** 5.74 1,979.63 **Primary PM2.5 - Brakewear Particulate** 0.65 223.20 **Primary PM2.5 - Tirewear Particulate** 0.20 69.67 **Sulfur Dioxide (SO2)** 0.48 165.35

**2008** Annual VMT: 14,015,754,874 Daily VMT: 40,858,751 Vehicle Population: 1,811,406
**Oxides of Nitrogen** 148.02 51,357.02 **Primary Exhaust PM2.5 - Total** 4.85 1,675.04 **Primary PM2.5 - Brakewear Particulate** 0.80 273.84 **Primary PM2.5 - Tirewear Particulate** 0.25 85.37 **Sulfur Dioxide (SO2)** 0.54 185.13

**2011** Annual VMT: 14,383,526,419 Daily VMT: 42,044,841 Vehicle Population: 1,840,505
**Oxides of Nitrogen** 135.95 47,061.53 **Primary Exhaust PM2.5 - Total** 5.54 1,904.61 **Primary PM2.5 - Brakewear Particulate** 0.85 290.00 **Primary PM2.5 - Tirewear Particulate** 0.27 91.52 **Sulfur Dioxide (SO2)** 0.53 182.01

**2015** Annual VMT: 14,830,453,053 Daily VMT: 43,316,281 Vehicle Population: 1,900,111
**Oxides of Nitrogen** 89.45 31,064.21 **Primary Exhaust PM2.5 - Total** 3.57 1,227.86 **Primary PM2.5 - Brakewear Particulate** 0.82 280.25 **Primary PM2.5 - Tirewear Particulate** 0.26 90.54 **Sulfur Dioxide (SO2)** 0.53 182.69

**2018** Annual VMT: 15,513,701,656 Daily VMT: 45,314,292 Vehicle Population: 1,946,080
**Oxides of Nitrogen** 70.34 24,451.43 **Primary Exhaust PM2.5 - Total** 2.78 958.57 **Primary PM2.5 - Brakewear Particulate** 0.90 307.39 **Primary PM2.5 - Tirewear Particulate** 0.29 99.03 **Sulfur Dioxide (SO2)** 0.57 195.09

**2021** Annual VMT: 15,521,916,278 Daily VMT: 46,689,707 Vehicle Population: 1,993,161
**Oxides of Nitrogen** 55.50 18,911.05 **Primary Exhaust PM2.5 - Total** 2.10 705.30 **Primary PM2.5 - Brakewear Particulate** 0.96 320.17 **Primary PM2.5 - Tirewear Particulate** 0.31 102.89 **Sulfur Dioxide (SO2)** 0.60 199.14

17-Aug-10 Page 2 of 2 MOVES 2010 and OKI Travel Demand Model v7.6

## Table 2. Mobile Source Emissions by County for the Cincinnati PM2.5 Nonattainment Area (tons)

County Year Pollutant Name DailyEmissions AnnualEmissions 

## Indiana

Dearborn NA

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

O xides of Nitrogen 2.40 865.46
Primary Exhaust PM2.5 - Total 0.08 29.68
Primary PM2.5 - Brakewear Particulate 0.01 3.28
Primary PM2.5 - Tirewear Particulate 0.00 1.02
Sulfur Dioxide (SO2) 0.01 2.45

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

O xides of Nitrogen 2.09 748.81
Primary Exhaust PM2.5 - Total 0.07 24.72
Primary PM2.5 - Brakewear Particulate 0.01 3.94
Primary PM2.5 - Tirewear Particulate 0.00 1.23
Sulfur Dioxide (SO2) 0.01 2.69

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

O xides of Nitrogen 1.92 685.40
Primary Exhaust PM2.5 - Total 0.08 27.88
Primary PM2.5 - Brakewear Particulate 0.01 4.19
Primary PM2.5 - Tirewear Particulate 0.00 1.32
Sulfur Dioxide (SO2) 0.01 2.65

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

O xides of Nitrogen 1.31 482.33
Primary Exhaust PM2.5 - Total 0.05 19.43
Primary PM2.5 - Brakewear Particulate 0.01 4.32
Primary PM2.5 - Tirewear Particulate 0.00 1.39
Sulfur Dioxide (SO2) 0.01 2.87

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

O xides of Nitrogen 1.03 376.85
Primary Exhaust PM2.5 - Total 0.04 15.09
Primary PM2.5 - Brakewear Particulate 0.01 4.70
Primary PM2.5 - Tirewear Particulate 0.00 1.51
Sulfur Dioxide (SO2) 0.01 3.04

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

O xides of Nitrogen 0.81 297.95
Primary Exhaust PM2.5 - Total 0.03 11.44
Primary PM2.5 - Brakewear Particulate 0.01 5.05
Primary PM2.5 - Tirewear Particulate 0.00 1.62
Sulfur Dioxide (SO2) 0.01 3.19

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

## Kentucky

Boone

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

O xides of Nitrogen 14.94 5,126.88
Primary Exhaust PM2.5 - Total 0.52 177.58
Primary PM2.5 - Brakewear Particulate 0.06 20.86
Primary PM2.5 - Tirewear Particulate 0.02 6.77
Sulfur Dioxide (SO2) 0.05 15.91

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

O xides of Nitrogen 14.73 5,067.94
Primary Exhaust PM2.5 - Total 0.64 219.29
Primary PM2.5 - Brakewear Particulate 0.07 24.42
Primary PM2.5 - Tirewear Particulate 0.02 8.14
Sulfur Dioxide (SO2) 0.05 16.71

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

O xides of Nitrogen 11.61 3,990.01
Primary Exhaust PM2.5 - Total 0.48 162.47
Primary PM2.5 - Brakewear Particulate 0.08 27.55
Primary PM2.5 - Tirewear Particulate 0.03 9.14
Sulfur Dioxide (SO2) 0.05 18.16

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

O xides of Nitrogen 8.11 2,788.45
Primary Exhaust PM2.5 - Total 0.32 108.49
Primary PM2.5 - Brakewear Particulate 0.09 32.17
Primary PM2.5 - Tirewear Particulate 0.03 10.69
Sulfur Dioxide (SO2) 0.06 20.67

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

O xides of Nitrogen 6.34 2,182.28
Primary Exhaust PM2.5 - Total 0.24 82.19
Primary PM2.5 - Brakewear Particulate 0.11 37.76
Primary PM2.5 - Tirewear Particulate 0.04 12.47
Sulfur Dioxide (SO2) 0.07 23.14

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

O xides of Nitrogen 5.14 1,772.72
Primary Exhaust PM2.5 - Total 0.18 60.19
Primary PM2.5 - Brakewear Particulate 0.12 40.56
Primary PM2.5 - Tirewear Particulate 0.04 13.30
Sulfur Dioxide (SO2) 0.07 24.37

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

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

O xides of Nitrogen 8.87 3,041.21
Primary Exhaust PM2.5 - Total 0.31 104.22
Primary PM2.5 - Brakewear Particulate 0.04 12.14
Primary PM2.5 - Tirewear Particulate 0.01 3.94
Sulfur Dioxide (SO2) 0.03 9.30

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

O xides of Nitrogen 8.63 2,988.33
Primary Exhaust PM2.5 - Total 0.37 127.73
Primary PM2.5 - Brakewear Particulate 0.04 14.05
Primary PM2.5 - Tirewear Particulate 0.01 4.68
Sulfur Dioxide (SO2) 0.03 9.69

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

O xides of Nitrogen 6.61 2,287.81
Primary Exhaust PM2.5 - Total 0.27 91.36
Primary PM2.5 - Brakewear Particulate 0.04 15.26
Primary PM2.5 - Tirewear Particulate 0.01 5.06
Sulfur Dioxide (SO2) 0.03 10.15

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

O xides of Nitrogen 4.55 1,570.14
Primary Exhaust PM2.5 - Total 0.17 59.30
Primary PM2.5 - Brakewear Particulate 0.05 17.31
Primary PM2.5 - Tirewear Particulate 0.02 5.75
Sulfur Dioxide (SO2) 0.03 11.21

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

O xides of Nitrogen 3.52 1,216.21
Primary Exhaust PM2.5 - Total 0.13 44.14
Primary PM2.5 - Brakewear Particulate 0.06 19.90
Primary PM2.5 - Tirewear Particulate 0.02 6.57
Sulfur Dioxide (SO2) 0.04 12.28

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

O xides of Nitrogen 2.84 985.28
Primary Exhaust PM2.5 - Total 0.09 32.07
Primary PM2.5 - Brakewear Particulate 0.06 21.10
Primary PM2.5 - Tirewear Particulate 0.02 6.92
Sulfur Dioxide (SO2) 0.04 12.77

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

Kenton

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

O xides of Nitrogen 15.29 5,328.44
Primary Exhaust PM2.5 - Total 0.53 184.43
Primary PM2.5 - Brakewear Particulate 0.06 21.04
Primary PM2.5 - Tirewear Particulate 0.02 6.82
Sulfur Dioxide (SO2) 0.05 16.24

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

O xides of Nitrogen 14.55 5,057.93
Primary Exhaust PM2.5 - Total 0.62 215.81
Primary PM2.5 - Brakewear Particulate 0.07 23.63
Primary PM2.5 - Tirewear Particulate 0.02 7.87
Sulfur Dioxide (SO2) 0.05 16.34

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

O xides of Nitrogen 11.11 3,863.70
Primary Exhaust PM2.5 - Total 0.45 153.90
Primary PM2.5 - Brakewear Particulate 0.07 25.57
Primary PM2.5 - Tirewear Particulate 0.02 8.48
Sulfur Dioxide (SO2) 0.05 17.05

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

O xides of Nitrogen 7.51 2,637.63
Primary Exhaust PM2.5 - Total 0.29 99.51
Primary PM2.5 - Brakewear Particulate 0.08 28.45
Primary PM2.5 - Tirewear Particulate 0.03 9.44
Sulfur Dioxide (SO2) 0.05 18.62

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

O xides of Nitrogen 5.93 2,082.32
Primary Exhaust PM2.5 - Total 0.22 75.82
Primary PM2.5 - Brakewear Particulate 0.10 33.49
Primary PM2.5 - Tirewear Particulate 0.03 11.04
Sulfur Dioxide (SO2) 0.06 20.86

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

O xides of Nitrogen 4.76 1,677.96
Primary Exhaust PM2.5 - Total 0.16 54.53
Primary PM2.5 - Brakewear Particulate 0.10 35.19
Primary PM2.5 - Tirewear Particulate 0.03 11.52
Sulfur Dioxide (SO2) 0.06 21.48

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

## Ohio

Butler

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

O xides of Nitrogen 32.00 10,910.37
Primary Exhaust PM2.5 - Total 1.06 361.06
Primary PM2.5 - Brakewear Particulate 0.12 40.31
Primary PM2.5 - Tirewear Particulate 0.04 12.60
Sulfur Dioxide (SO2) 0.09 30.01

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

O xides of Nitrogen 28.56 9,803.70
Primary Exhaust PM2.5 - Total 0.91 311.45
Primary PM2.5 - Brakewear Particulate 0.15 50.45
Primary PM2.5 - Tirewear Particulate 0.05 15.74
Sulfur Dioxide (SO2) 0.10 34.25

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

O xides of Nitrogen 26.50 9,074.89
Primary Exhaust PM2.5 - Total 1.05 356.91
Primary PM2.5 - Brakewear Particulate 0.16 53.99
Primary PM2.5 - Tirewear Particulate 0.05 17.06
Sulfur Dioxide (SO2) 0.10 34.00

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

O xides of Nitrogen 17.64 6,064.61
Primary Exhaust PM2.5 - Total 0.68 231.78
Primary PM2.5 - Brakewear Particulate 0.16 52.42
Primary PM2.5 - Tirewear Particulate 0.05 16.96
Sulfur Dioxide (SO2) 0.10 34.28

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

O xides of Nitrogen 13.98 4,813.27
Primary Exhaust PM2.5 - Total 0.54 182.29
Primary PM2.5 - Brakewear Particulate 0.17 57.91
Primary PM2.5 - Tirewear Particulate 0.06 18.68
Sulfur Dioxide (SO2) 0.11 36.85

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

O xides of Nitrogen 11.13 3,757.91
Primary Exhaust PM2.5 - Total 0.41 135.39
Primary PM2.5 - Brakewear Particulate 0.19 60.81
Primary PM2.5 - Tirewear Particulate 0.06 19.56
Sulfur Dioxide (SO2) 0.12 37.90

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

Clermont

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

O xides of Nitrogen 21.21 7,295.87
Primary Exhaust PM2.5 - Total 0.72 245.48
Primary PM2.5 - Brakewear Particulate 0.08 27.67
Primary PM2.5 - Tirewear Particulate 0.03 8.64
Sulfur Dioxide (SO2) 0.06 20.51

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

O xides of Nitrogen 18.81 6,516.40
Primary Exhaust PM2.5 - Total 0.61 211.40
Primary PM2.5 - Brakewear Particulate 0.10 34.46
Primary PM2.5 - Tirewear Particulate 0.03 10.74
Sulfur Dioxide (SO2) 0.07 23.32

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

O xides of Nitrogen 17.48 6,039.51
Primary Exhaust PM2.5 - Total 0.71 243.25
Primary PM2.5 - Brakewear Particulate 0.11 37.00
Primary PM2.5 - Tirewear Particulate 0.03 11.68
Sulfur Dioxide (SO2) 0.07 23.23

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

O xides of Nitrogen 11.54 3,993.63
Primary Exhaust PM2.5 - Total 0.46 156.92
Primary PM2.5 - Brakewear Particulate 0.11 35.82
Primary PM2.5 - Tirewear Particulate 0.03 11.58
Sulfur Dioxide (SO2) 0.07 23.34

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

O xides of Nitrogen 9.09 3,146.47
Primary Exhaust PM2.5 - Total 0.36 122.57
Primary PM2.5 - Brakewear Particulate 0.12 39.31
Primary PM2.5 - Tirewear Particulate 0.04 12.67
Sulfur Dioxide (SO2) 0.07 24.94

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

O xides of Nitrogen 7.22 2,449.31
Primary Exhaust PM2.5 - Total 0.27 90.84
Primary PM2.5 - Brakewear Particulate 0.12 41.28
Primary PM2.5 - Tirewear Particulate 0.04 13.27
Sulfur Dioxide (SO2) 0.08 25.66

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

Hamilton

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

O xides of Nitrogen 89.30 31,127.09
Primary Exhaust PM2.5 - Total 3.06 1,064.67
Primary PM2.5 - Brakewear Particulate 0.35 119.94
Primary PM2.5 - Tirewear Particulate 0.11 37.41
Sulfur Dioxide (SO2) 0.26 88.85

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

O xides of Nitrogen 77.45 27,020.93
Primary Exhaust PM2.5 - Total 2.56 889.81
Primary PM2.5 - Brakewear Particulate 0.42 145.42
Primary PM2.5 - Tirewear Particulate 0.13 45.31
Sulfur Dioxide (SO2) 0.28 98.30

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

O xides of Nitrogen 70.18 24,435.59
Primary Exhaust PM2.5 - Total 2.88 997.06
Primary PM2.5 - Brakewear Particulate 0.44 151.73
Primary PM2.5 - Tirewear Particulate 0.14 47.86
Sulfur Dioxide (SO2) 0.28 95.30

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

O xides of Nitrogen 45.58 15,925.19
Primary Exhaust PM2.5 - Total 1.83 634.62
Primary PM2.5 - Brakewear Particulate 0.42 144.67
Primary PM2.5 - Tirewear Particulate 0.14 46.71
Sulfur Dioxide (SO2) 0.27 94.43

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

O xides of Nitrogen 35.51 12,422.37
Primary Exhaust PM2.5 - Total 1.41 490.62
Primary PM2.5 - Brakewear Particulate 0.45 156.90
Primary PM2.5 - Tirewear Particulate 0.15 50.52
Sulfur Dioxide (SO2) 0.29 99.78

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

O xides of Nitrogen 27.80 9,530.16
Primary Exhaust PM2.5 - Total 1.06 357.87
Primary PM2.5 - Brakewear Particulate 0.48 161.69
Primary PM2.5 - Tirewear Particulate 0.15 51.92
Sulfur Dioxide (SO2) 0.30 100.82

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

Warren

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

O xides of Nitrogen 23.98 8,224.57
Primary Exhaust PM2.5 - Total 0.82 278.74
Primary PM2.5 - Brakewear Particulate 0.09 32.00
Primary PM2.5 - Tirewear Particulate 0.03 10.00
Sulfur Dioxide (SO2) 0.07 23.54

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

O xides of Nitrogen 21.11 7,267.18
Primary Exhaust PM2.5 - Total 0.69 237.65
Primary PM2.5 - Brakewear Particulate 0.12 39.57
Primary PM2.5 - Tirewear Particulate 0.04 12.34
Sulfur Dioxide (SO2) 0.08 26.57

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

O xides of Nitrogen 19.88 6,826.15
Primary Exhaust PM2.5 - Total 0.82 279.53
Primary PM2.5 - Brakewear Particulate 0.13 43.09
Primary PM2.5 - Tirewear Particulate 0.04 13.60
Sulfur Dioxide (SO2) 0.08 26.83

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

O xides of Nitrogen 13.37 4,598.44
Primary Exhaust PM2.5 - Total 0.54 185.12
Primary PM2.5 - Brakewear Particulate 0.13 43.02
Primary PM2.5 - Tirewear Particulate 0.04 13.91
Sulfur Dioxide (SO2) 0.08 27.77

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

O xides of Nitrogen 10.73 3,692.47
Primary Exhaust PM2.5 - Total 0.43 148.00
Primary PM2.5 - Brakewear Particulate 0.14 48.57
Primary PM2.5 - Tirewear Particulate 0.05 15.66
Sulfur Dioxide (SO2) 0.09 30.49

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

O xides of Nitrogen 8.54 2,875.72
Primary Exhaust PM2.5 - Total 0.33 109.76
Primary PM2.5 - Brakewear Particulate 0.16 51.34
Primary PM2.5 - Tirewear Particulate 0.05 16.51
Sulfur Dioxide (SO2) 0.10 31.58

17-Aug-10 Page 8 of 8 MOVES 2010 and OKI Travel Demand Model v7.6

### Mobile Source Emission Forecast Process

Emission Factor Model

OKI’s conformity assessment utilized U.S.EPA’s emis sions model MOVES 2010 to develop emission factors for SO2, NO X and PM2.5. Table 3 summarizes the settings used i n the MOVES run specification
file. Table 4 lists the data used in the MOVES Cou nty-Data Manager. Further details on the use of MOVES are found in the Appendix.

Table 3.

MOVES Runspec [sic] Parameter Settings
MOVES Version 2009/12/21, MOVES default database 2010615111524 Scale County, Emission Rates Time Span Time aggregation = Hour 1 month representing average annual temperatures All hours of day selected Weekdays only Geographic Bounds 2 Custom Domains

– 4 Ohio counties, 3 Kentucky
counties Vehicles/Equipment All source types, gasoline and diesel Road Type All road types including off

- network Pollutants and Processes NO x , All PM2.5 categories, SO 2 , Total Energy Consumption Strategies none General Output Units= grams, joules and miles Output Emissions Time = hour, Location =county, on

- road emission rates by road type and source use type. Advanced Performance none

Table 4

County Data Manager Data Source Source Type Population Local and default. Local data (2010) from KYTC an d ODOT from motor vehicle registration data. Default data used for source types 41, 61 and 62. In addition , default data for sour ce types 31, 32 and 54 used for KY. Vehicle Type VMT Local and defa ult. HPMSVTypeYear VMT=daily VMT from OKI travel demand model with EPA’s daily to annual VMT convert er applied. monthVMTFraction = default. dayVMTFraction=default, hourVMTFraction=local. I/M Programs Default modified to reflect discontinued I/M progra m Fuel Formulation DefaultFuel Supply Default

Meteorology Data Local. Kentucky Division for Air Quality. Ramp Fraction Local. Ramp emissions calculated outside of MOVES Road Type Distribution Local. OKI travel demand model. Age Distribution Local and default. Local data (2010) from KYTC an d ODOT from motor vehicle registration data. Default data used for source types 41, 61 and 62. In addition , default data for sour ce types 31, 32 and 54 used for KY. Average Speed Distribution Loca l. OKI travel demand model.

OKI Travel Demand Model

Transportation system performance was estimated usi ng the OKI Travel Demand Model Version 7.6. The OKI Travel Demand Model is composed of TRANPLAN programs, CUBE Voyager programs and a series of FORTRAN programs written by OKI. It is a state of the practice model that uses the standard 4 phase sequential modeling approach of trip generati on, distribution, modal choice and assignment. The model uses demographic and land use data and capaci ty and free-flow speed characteristics for each roadway segment in the network to produce a “loaded ” highway network with forecasted traffic volumes with revised speeds based on specified spee d/capacity relationships.

Travel analysis zones are the basic geographic unit for estimating travel in the OKI model. The OKI re gion is subdivided into 1608 traffic analysis zones to p ermit detail as well as manageability. A variety o f socioeconomic data items are used in the OKI transp ortation planning process. These data are used primarily to forecast future travel patterns by ser ving as independent variables in OKI trip generatio n equations. The following categories of planning dat a are utilized:

• Population (household and group quarter)

• Households

• Household vehicles

• Employment (by employment category and zone of work )

• Labor force participation (by zone of residence)

• Area type The principal data requirements of the OKI travel d emand forecasting model are population and employment. From these variables, other characteris tics including households, labor force, and persona l vehicles may be derived. Chapter 5 of OKI 2030 Regional Transportation Plan 2008 Update provides a complete demographic overview of the region.

OKI utilizes both base year (2005) and future year data (2010, 2020 and 2030) in the planning process. Planning data are maintained at the Traffic Analysi s Zone (TAZ) level, and originate in the 2000 Census of Population and Housing. Base year 2005 and future y ear data for each variable are developed through various methods. More detailed explanation of base year and future year data generation for each of the above-mentioned categories of planning data fol lows. All of the variables represent the latest OK I planning assumptions.

### Population

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

As a tri-state regional planning agency, OKI uses c ounty level projections as prepared by the respecti ve state data centers (Ohio Department of Development Office of Strategic Research, Kentucky State Data Center and Indiana Business Research Center) as con trol totals. The most current projections (years 2005 to 2030) were released by the Ohio and Indiana state data centers in 2003 and the Kentucky State Data Center in 2004. Population projections at the zonal level are calculated by multiplying household size by the projected zonal households. Household s ize is factored so that, in each county, the sum of the zonal populations equals the control total.



### Households Base Year Data: Household data for base year 2005 originates with the 2000 Census of Population and Housing. Utilizing the geographic information syste m ArcMap, household data at the zonal level for 200 0 was derived from the area proportion allocation of block level households. Year 2000 household data was updated to 2005 with residential building permi ts issued between January 2000 and December 2004. The residential building locations were geoco ded in ArcMap, then aggregated to the TAZs. The housing unit totals for each TAZ were converted to h ouseholds by applying a vacancy rate, an adjustment for permitted but unbuilt units, and sub tracting demolitions (where data was available). These households were then added to the year Census 2000 zonal household total to arrive at 2005
households for each TAZ.

Future Year Data: The preparation of household proj ections was accomplished by calculating the number of households for a projected county populat ion using ratios of householders to total population by age specific cohorts derived from the 2000 Census for each analysis year. Disaggregation to TAZs was determined by historical trends, existin g and future land use, topography, flood plain information, availability of land, local knowledge and other factors.



### Household Vehicles Base and Future Year Data: Base and future year ho usehold vehicle data were obtained from the 2000 Census of Population and Housing. The 2000 Census i s the only source of household vehicle data available at the block group level. Average vehicle s per household were calculated for block groups th en applied to the TAZs associated with each block group . The 2005, 2010, 2020 and 2030 vehicles per
household level was held at the 2000 level based on the fact that, since 2002, the number of vehicles per household has exceeded the number of drivers pe r household.



### Labor Force Base and Future Year Data: The OKI labor force is a function of the population as determined by a lab or force participation ratio (the number of employed p ersons in the labor force per persons 16 and over). Household data for base year 2005 originates with t he 2000 Census of Population and Housing. Utilizingthe geographic information system ArcMap, household data at the zonal level for 2000 was derived

from the area proportion allocation of block group level employed labor force. The labor force projections for 2005, 2010, 2020 and 2030 were base d on the most recent projections of national labor force participation rates by age and sex cohorts fr om the U.S. Department of Labor, Bureau of Labor Statistics for each of those years. These rates wer e then applied to the projected county age/sex coho rts and adjusted to eliminate the unemployed to arrive at a county employed labor force control total. Employed labor force at the zonal level is calculat ed by multiplying the labor force participation rat e by the zonal population. The labor force participation rate is adjusted so that, in each county, the sum of the zonal labor force counts equals the control tot al.



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

Future Year Data: For future year employment proje ction, calculation was first made of the employment at the regional level. At the regional l evel, employment is a calculation of the region’s employed labor force minus workers who live in the region but commute out to work, plus workers who live outside the region but commute in to work. The regional total was disaggregated first to the coun ty level based on historic trends and expected changes in the county’s share of the region’s employment and then to the TAZ level. Disaggregation to TAZs was determined by historical trends, existing and future land use, topography, flood plain informatio n, availability of land, local knowledge and other
factors.



### Area Type Base and Future Year Data: For each analysis year, each TAZ is assigned an area type designation as CBD, Urban, Suburban or Rural based on population a nd employment densities.

Model Calibration

OKI’s Travel Demand Model has been validated to obs erved traffic volumes for the model base year 2005. The modeling network encompasses the entire ozone nonattainment area with the exception of Clinton County, Ohio. The modeling network also in cludes Greene, Miami and Montgomery counties in Ohio and the remainder of Dearborn County Indiana. The difference between estimated vehicle miles traveled (VMT) and 2005 observed VMT is less than 1 %. A highway screenline analysis compares the
screenline observed and simulated traffic volume di screpancies with the ODOT standard of maximum desirable deviation. The comparison shows that the model performs at a satisfactory level and all the
errors were under the ODOT curve. Further informat ion can be found in OKI’s 2007 report, “ OKI/MVRPC Travel Demand Model Methodology/ Validation Report ”. For the calibration, OKI used over 3000 traffic counts collected through 2006 by the Ohio Departmen t of Transportation (ODOT), the KentuckyTransportation Cabinet, many county and local gover nments, transportation engineering consultants,

and OKI. These traffic counts cover nearly 50% per cent of the links in the OKI portion of the modelin g network. The methodology provides consistency with past emission inventory and conformity analysis work performed by OKI.

Local Inputs and Post-Model Processing

OKI incorporates a variety of sources of local data to both improve and confirm the accuracy of VMT, a s well as other travel-related parameters. Free flow speeds used on the highway and transit networks ar e based on travel time studies performed locally. Th e OKI post-processing program, IMPACT, uses the loaded highway network to generate VMT by hour, VMT by speed distribution and VMT by facility type. These tables are then included as input into MOVES. Two separate sets of VMT tables are generated: one for the four Ohio counties plus Dearborn County Indiana, and a second for the three Kentucky counties. The VMT by hour tables utilize hourly tr affic distribution and directional split factors fo r different roadway types as developed by OKI. The ma in source of the data was the permanent traffic counting stations located throughout the OKI region for the years of 1998-2002. This data was supplemented with data collected at coverage count stations (locations with counts taken on only onetwo days). The stations were classified by area ty pe: urban and rural, and functional classification: freeway, arterial and collector. Speeds representin g various “loaded” conditions (with traffic volumes ) are estimated using techniques from the 1997 Highwa y Capacity Manual. This permits the estimation of speeds as conditions vary from hour to hour on the different facility types throughout the region. Th e IMPACT program performs the appropriate summation b y area and roadway type as well as regional totals. OKI has also developed seasonal conversion factors to adjust traffic volumes to summer
conditions. The factors were derived from local da ta collected at permanent traffic counting stations during 1994-1997 utilizing the average daily traffi c monthly conversion factors for June, July and Aug ust. Further information on OKI’s IMPACT program is docu mented in the report, “Travel Demand Model Summary Reporting and Impact Summary Reporting: OKI /MVRPC Travel Demand Model User’s Guide”,

OKI 2003.

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



# APPENDIX

OKI Technical Documentation for Using EPA MOVES to Develop MOBILE Source

Emissions
August 2010

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



## 1. Using MOVES

To determine specific emission profiles and invento ry, user has to define the input data like area, time span, type of vehicles, road types, fuel types, emission producing processes etc. These data are stored in an XML file which is calle d Runspec [sic]. Using graphical user interface user can modify all these attributes of Runspec [si c]. In the following sections, how input data is entered and modified is explained. All these input options are found in the navigation panel of Graphical User Interface of MOVES software.



### 1.1 Description

This input tells about the specifications of the Ru nspec [sic] and it is useful to distinguish between the Runspecs [sic]. We can also explain the brief overview of the particular Runspec [sic]. In all of our current Runspecs [sic], we hav e details such as analysis years, area and pollutants analyzed.



### 1.2 Scale

In this option, we need to specify about the Domai n/Scale and Calculation type. The Domain specifies the level of default data we need to use for analysis and also the scale of the analysis. We have considered the County scale for Ohio Custom Domain and the calculation type we have used is “Emission Rates”.



### 1.3 Time Spans

This input panel has different time-related input d ata like time aggregation level, year of analysis, month of analysis, whether analysis day i s Weekday or Weekend, and hours of analysis. In all of our runs, time aggregation leve l is considered as hour, which is the most disaggregated level possible in MOVES and it is als o specified in the technical guidance† for all SIP runs. We have used different years of analysis (i.e. 2005, 2008, 2011, 2015, 2018, and 2021). We have used two different months, July and April. Ozone season daily analysis is done using July temperatures. Annual analysis uses one 24-hou r set of average annual temperatures. The annual average minimum temperature, maximum tempera ture and humidity values for each hour were calculated and assigned the April month I D.


### 1.4 Geographic Bounds

In this input type, we need to specify about region of analysis (eg. Nation, State, Custom Domain). We have created a separate input database through combining four Ohio counties namely, Hamilton, Butler, Clermont and Warren. Upon selecting the custom domain, MOVES will consider this region as separate Generic Count y. The state ID is fixed as 99 and we have assigned an arbitrary CountyID 390 for Ohio to dist inguish between default county codes. User also need to provide a fraction geographic phase in area, in this case we do not have any phase

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



### in area fraction and we also provided average barom etric pressure to identify whether it is low altitude area or high altitude area (the barometric pressures are averages of all constituent counties). Since we do not have I/M program in the region the refueling program adjustment fraction and refueling spill program adjustment fra ctions are assigned as 0.00. In this input panel we also need to specify the Domain Input Data bases. For all of our runs we have defined different input databases for each year.


### 1.5 Vehicles/Equipment

In MOVES [sic], user also needs to provide the diff erent type of vehicles considered for analysis in the region. MOVES [sic] provide us with 13 diffe rent types of vehicles or equipment and four different fuel types and we need to select appropri ate fuel and vehicle combinations. In MOVES [sic] vehicle types are called SourceUseTypes [sic] . We have considered all possible types of fuel/vehicle type combinations.


### 1.6 Road Type

Next input panel is about type of roadways in the r egion. There are five types of road types available in MOVES, since OKI travel demand model c ould not predict the VMT in parking lots (off network) only four road types are considered. These road types are relatively simple and are based on area type, whether it is urban or rura l. All expressways and freeways are considered as restricted roadways and all other roa d types are considered as unrestricted roadways.



### 1.7 Pollutants and Processes

There are different pollutants and corresponding pr ocesses are available in MOVES. A separate panel is available for selecting different pollutan ts and processes. In these particular set of runs, total PM2.5 emissions are selected with an addition of sulfur dioxide. To perform calculation of PM2.5 it is also required to select Total energy co nsumption. In addition to PM2.5, Oxides of Nitrogen are also selected.



### 1.8 Miscellaneous

Further, if we have information about future or pre sent Alternative Vehicle Fuels & Technologies, on-road retrofit and rate of progress information that can be given as input to the Runspec [sic]. If we do not specify future Alte rnative Vehicle Fuel & Technologies, MOVES [sic] is going to assume default alternative fuels. So, we have modified default AVFT through importing new AVFT strategy file which includes the re would not be any change in transit bus fuels. MOVES [sic] also provide us the options whe ther we would like to save the MOVESactivityoutput [sic] and MOVESOutput [sic] dat abases or not.

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

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

sourceTypeID modelYearID fuelTypeID engTechID fuelEngFraction

42 1960 2 1 1
42 1961 2 1 1
42 1962 2 1 1
42 1963 2 1 1
42 1964 2 1 1
42 1965 2 1 1
42 1966 2 1 1
42 1967 2 1 1
42 1968 2 1 1
42 1969 2 1 1
42 1970 2 1 1
42 1971 2 1 1
42 1972 2 1 1
42 1973 2 1 1
42 1974 2 1 1
42 1975 2 1 1
42 1976 2 1 1
42 1977 2 1 1
42 1978 2 1 1
42 1979 2 1 1
42 1980 2 1 1
42 1981 2 1 1
42 1982 2 1 1
42 1983 2 1 1
42 1984 2 1 1
42 1985 2 1 1
42 1986 2 1 1
42 1987 2 1 1
42 1988 2 1 1
42 1989 2 1 1
42 1990 2 1 1
42 1991 2 1 1
42 1992 2 1 1
42 1993 2 1 1
42 1994 2 1 1
42 1995 2 1 1
42 1996 2 1 1
42 1997 2 1 1
42 1998 2 1 1
42 1999 2 1 1
42 2000 2 1 1
42 2001 2 1 1
42 2002 2 1 1
42 2003 2 1 1
42 2004 2 1 1
42 2005 2 1 1

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

42 2006 2 1 1
42 2007 2 1 1
42 2008 2 1 1
42 2009 2 1 1
42 2010 2 1 1
42 2011 2 1 1
42 2012 2 1 1
42 2013 2 1 1
42 2014 2 1 1
42 2015 2 1 1
42 2016 2 1 1
42 2017 2 1 1
42 2018 2 1 1
42 2019 2 1 1
42 2020 2 1 1
42 2021 2 1 1
42 2022 2 1 1
42 2023 2 1 1
42 2024 2 1 1
42 2025 2 1 1
42 2026 2 1 1
42 2027 2 1 1
42 2028 2 1 1
42 2029 2 1 1
42 2030 2 1 1
42 2031 2 1 1
42 2032 2 1 1
42 2033 2 1 1
42 2034 2 1 1
42 2035 2 1 1
42 2036 2 1 1
42 2037 2 1 1
42 2038 2 1 1
42 2039 2 1 1
42 2040 2 1 1
42 2041 2 1 1
42 2042 2 1 1
42 2043 2 1 1
42 2044 2 1 1
42 2045 2 1 1
42 2046 2 1 1
42 2047 2 1 1
42 2048 2 1 1
42 2049 2 1 1
42 2050 2 1 1

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



### 1.9 Output

In MOVES we need to specify the output database an d need to create new database for each new Runspec [sic]. We also have options like specif ying the units for emission rates and energy consumption. These options are available in the Gen eral Output panel. There is one more option available within the output which is called output emissions detail, which provides user different options for data aggregation.



## 2. Data Importers

In order to enter local data into Runspec [sic], we need to use pre processing option in the MOVES. We can select either Data Importer or County Importer for Custom Domain option. These Importers convert the data in excel format to MySQL tables. This is the preferred input format of MOVES software.



### 2.1 Meteorology Data Importer

In this type of Importer, meteorology data is impor ted a MOVES input format. This dataset has different data items like month ID, Zone ID, hour I D, Temperature and Relative Humidity. For OKI region and Ohio portion runs we have used tempe rature data obtained from the Kentucky Division for Air Quality (KDAQ). Even though ODOT has provided the temperature data (collected from local airports), KDAQ data appeared to be more applicable. In the data set, April Meteorology data is replaced with annual average te mperatures and relative humidity.

Table 2 : Meteorology data obtained from KDAQ

monthID zoneID hourID temperature relHumidity

4 993900 1 47.5 72.9
4 993900 2 46.4 75.8
4 993900 3 45.5 77.9
4 993900 4 44.8 79.4
4 993900 5 44.3 80.7
4 993900 6 43.7 82.1
4 993900 7 43.2 83.3
4 993900 8 43.6 82.3
4 993900 9 46.1 76.4
4 993900 10 50.1 67.0
4 993900 11 54.2 57.8
4 993900 12 57.7 50.9
4 993900 13 60.8 45.7
4 993900 14 62.5 43.1

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

4 993900 15 63.1 42.2
4 993900 16 63.2 42.0
4 993900 17 62.8 42.6
4 993900 18 61.7 44.3
4 993900 19 59.7 47.6
4 993900 20 57.1 52.2
4 993900 21 54.5 57.2
4 993900 22 52.2 62.1
4 993900 23 50.6 65.9
4 993900 24 49.1 69.4
7 993900 1 69.3 69.5
7 993900 2 68.1 72.4
7 993900 3 67.1 74.8
7 993900 4 66.4 76.6
7 993900 5 65.9 78
7 993900 6 65.3 79.7
7 993900 7 64.8 81.1
7 993900 8 65.2 79.9
7 993900 9 67.8 73.1
7 993900 10 72 63.4
7 993900 11 76.2 55
7 993900 12 79.8 48.8
7 993900 13 83 44
7 993900 14 84.7 41.6
7 993900 15 85.3 40.8
7 993900 16 85.5 40.6
7 993900 17 85.1 41.2
7 993900 18 83.9 42.8
7 993900 19 81.8 45.8
7 993900 20 79.1 49.9
7 993900 21 76.4 54.6
7 993900 22 74.1 59
7 993900 23 72.5 62.3
7 993900 24 70.8 65.9



### 2.2 Source Type Population Importer

This importer imports vehicle type, and registered vehicle population in the region into MOVES input databases. ODOT has provided us with the regi stered vehicle population in each county in the region for 13 MOVES vehicle types. KYTC has pro vided registered vehicle population by county for 6 HPMS vehicle types. The KYTC data was converted to the 13 MOVES vehicle types based on the Ohio distribution. Same vehicle popul ation was used for all analysis years. As per suggestions made by FHWA and KYTC, the Source Type Population has been forecasted for future years with +0.8 % per year. Similarly, the S ource Type Populations has been estimated for past years. The MOVES default source type popu lation for intercity bus, refuse trucks, motor homes and combination trucks was used. In ad dition, MOVES default source type

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



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

Table3 : Source Type Population for Ohio Custom Dom ain (2008)

yearID sourceTypeID sourceTypePopulation

2008 11 68559
2008 21 1191067
2008 31 482420
2008 32 15817
2008 41 454
2008 42 81
2008 43 3651
2008 51 409
2008 52 366
2008 53 361
2008 54 4888
2008 61 4839
2008 62 5548

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

yearID sourceTypeID sourceTypePopulation

2008 11 7975
2008 21 197009
2008 31 120518
2008 32 40263
2008 41 127
2008 42 21
2008 43 977
2008 51 115
2008 52 761
2008 53 751
2008 54 1379
2008 61 1580
2008 62 1811



### 2.3 Age Distribution Importer

For emission calculation the MOVES need vehicle Age Distribution by Source Type. Vehicle Age Distribution is divided into 30 years based on vehi cle model years. For each vehicle type, the distribution sum adds up to one. ODOT has obtained vehicle registration data from the Bureau of Motor Vehicles for all the counties in Ohio and processed them to convert into MOVES Age Distribution for 13 vehicle types. We have used the same Age Distribution for all year runs. All

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



### the vehicles older than 30 years are considered as 30-years old. Same age distribution is used for all analysis years. KYTC also provided similar information, but for the 6 HPMS types only. For Kentucky, identical age distributions are used within each HPMS vehicle type.

Table 5 : Ohio Custom Domain Age distribution

Source TypeID yearID ageID ageFraction

11 2008 0 0.0019
11 2008 1 0.0191
11 2008 2 0.0531
11 2008 3 0.0688
11 2008 4 0.0773
11 2008 5 0.0737
11 2008 6 0.0611
11 2008 7 0.0780
11 2008 8 0.0636
11 2008 9 0.0537
11 2008 10 0.0435
11 2008 11 0.0359
11 2008 12 0.0282
11 2008 13 0.0230
11 2008 14 0.0220
11 2008 15 0.0183
11 2008 16 0.0160
11 2008 17 0.0146
11 2008 18 0.0097
11 2008 19 0.0080
11 2008 20 0.0072
11 2008 21 0.0086
11 2008 22 0.0084
11 2008 23 0.0121
11 2008 24 0.0171
11 2008 25 0.0179
11 2008 26 0.0137
11 2008 27 0.0171
11 2008 28 0.0249
11 2008 29 0.0172
11 2008 30 0.0862
21 2008 0 0.0121
21 2008 1 0.0331
21 2008 2 0.0440
21 2008 3 0.0528
21 2008 4 0.0534
21 2008 5 0.0566
21 2008 6 0.0570
21 2008 7 0.0592 21 2008 8 0.0591
21 2008 9 0.0542
21 2008 10 0.0590
21 2008 11 0.0568
21 2008 12 0.0507
21 2008 13 0.0499
21 2008 14 0.0438
21 2008 15 0.0453
21 2008 16 0.0368
21 2008 17 0.0308
21 2008 18 0.0261
21 2008 19 0.0207
21 2008 20 0.0165
21 2008 21 0.0132
21 2008 22 0.0095
21 2008 23 0.0073
21 2008 24 0.0059
21 2008 25 0.0043
21 2008 26 0.0033
21 2008 27 0.0017
21 2008 28 0.0011
21 2008 29 0.0010
21 2008 30 0.0346
31 2008 0 0.0103
31 2008 1 0.0279
31 2008 2 0.0502
31 2008 3 0.0570
31 2008 4 0.0659
31 2008 5 0.0806
31 2008 6 0.0796
31 2008 7 0.0733
31 2008 8 0.0727
31 2008 9 0.0599
31 2008 10 0.0625
31 2008 11 0.0603
31 2008 12 0.0516
31 2008 13 0.0432
31 2008 14 0.0380
31 2008 15 0.0386
31 2008 16 0.0302
31 2008 17 0.0260
31 2008 18 0.0165
31 2008 19 0.0125
31 2008 20 0.0093

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

31 2008 21 0.0084
31 2008 22 0.0067
31 2008 23 0.0051
31 2008 24 0.0037
31 2008 25 0.0025
31 2008 26 0.0017
31 2008 27 0.0009
31 2008 28 0.0004
31 2008 29 0.0002
31 2008 30 0.0041
32 2008 0 0.0178
32 2008 1 0.0459
32 2008 2 0.0871
32 2008 3 0.0699
32 2008 4 0.0707
32 2008 5 0.0357
32 2008 6 0.0355
32 2008 7 0.0369
32 2008 8 0.0366
32 2008 9 0.0407
32 2008 10 0.0491
32 2008 11 0.0547
32 2008 12 0.0427
32 2008 13 0.0413
32 2008 14 0.0383
32 2008 15 0.0602
32 2008 16 0.0476
32 2008 17 0.0381
32 2008 18 0.0304
32 2008 19 0.0181
32 2008 20 0.0212
32 2008 21 0.0184
32 2008 22 0.0135
32 2008 23 0.0134
32 2008 24 0.0095
32 2008 25 0.0070
32 2008 26 0.0054
32 2008 27 0.0021
32 2008 28 0.0014
32 2008 29 0.0008
32 2008 30 0.0100
41 2008 0 0.0000
41 2008 1 0.0309
41 2008 2 0.0884
41 2008 3 0.0890
41 2008 4 0.0768
41 2008 5 0.0746
41 2008 6 0.0967
41 2008 7 0.0635 41 2008 8 0.0486
41 2008 9 0.0801
41 2008 10 0.0751
41 2008 11 0.0624
41 2008 12 0.0254
41 2008 13 0.0271
41 2008 14 0.0188
41 2008 15 0.0193
41 2008 16 0.0133
41 2008 17 0.0177
41 2008 18 0.0094
41 2008 19 0.0177
41 2008 20 0.0171
41 2008 21 0.0099
41 2008 22 0.0039
41 2008 23 0.0055
41 2008 24 0.0061
41 2008 25 0.0011
41 2008 26 0.0033
41 2008 27 0.0033
41 2008 28 0.0028
41 2008 29 0.0017
41 2008 30 0.0105
42 2008 0 0.0000
42 2008 1 0.0366
42 2008 2 0.1098
42 2008 3 0.0366
42 2008 4 0.1585
42 2008 5 0.0366
42 2008 6 0.0610
42 2008 7 0.0610
42 2008 8 0.0244
42 2008 9 0.1098
42 2008 10 0.0366
42 2008 11 0.0976
42 2008 12 0.0366
42 2008 13 0.0244
42 2008 14 0.0244
42 2008 15 0.0122
42 2008 16 0.0244
42 2008 17 0.0244
42 2008 18 0.0366
42 2008 19 0.0000
42 2008 20 0.0000
42 2008 21 0.0122
42 2008 22 0.0000
42 2008 23 0.0000
42 2008 24 0.0000
42 2008 25 0.0000

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

42 2008 26 0.0122
42 2008 27 0.0000
42 2008 28 0.0000
42 2008 29 0.0122
42 2008 30 0.0122
43 2008 0 0.0905
43 2008 1 0.0302
43 2008 2 0.0549
43 2008 3 0.0467
43 2008 4 0.0592
43 2008 5 0.0723
43 2008 6 0.0481
43 2008 7 0.0334
43 2008 8 0.0668
43 2008 9 0.0647
43 2008 10 0.0842
43 2008 11 0.0864
43 2008 12 0.0473
43 2008 13 0.0500
43 2008 14 0.0242
43 2008 15 0.0185
43 2008 16 0.0106
43 2008 17 0.0228
43 2008 18 0.0109
43 2008 19 0.0130
43 2008 20 0.0125
43 2008 21 0.0092
43 2008 22 0.0062
43 2008 23 0.0079
43 2008 24 0.0090
43 2008 25 0.0035
43 2008 26 0.0030
43 2008 27 0.0011
43 2008 28 0.0027
43 2008 29 0.0016
43 2008 30 0.0087
51 2008 0 0.0054
51 2008 1 0.0488
51 2008 2 0.0623
51 2008 3 0.0705
51 2008 4 0.0867
51 2008 5 0.0434
51 2008 6 0.0434
51 2008 7 0.0542
51 2008 8 0.0542
51 2008 9 0.0759
51 2008 10 0.0217
51 2008 11 0.0407
51 2008 12 0.0786 51 2008 13 0.0542
51 2008 14 0.0515
51 2008 15 0.0678
51 2008 16 0.0325
51 2008 17 0.0081
51 2008 18 0.0163
51 2008 19 0.0027
51 2008 20 0.0081
51 2008 21 0.0000
51 2008 22 0.0027
51 2008 23 0.0027
51 2008 24 0.0136
51 2008 25 0.0000
51 2008 26 0.0000
51 2008 27 0.0000
51 2008 28 0.0027
51 2008 29 0.0000
51 2008 30 0.0515
52 2008 0 0.0054
52 2008 1 0.0488
52 2008 2 0.0623
52 2008 3 0.0705
52 2008 4 0.0867
52 2008 5 0.0434
52 2008 6 0.0434
52 2008 7 0.0542
52 2008 8 0.0542
52 2008 9 0.0759
52 2008 10 0.0217
52 2008 11 0.0407
52 2008 12 0.0786
52 2008 13 0.0542
52 2008 14 0.0515
52 2008 15 0.0678
52 2008 16 0.0325
52 2008 17 0.0081
52 2008 18 0.0163
52 2008 19 0.0027
52 2008 20 0.0081
52 2008 21 0.0000
52 2008 22 0.0027
52 2008 23 0.0027
52 2008 24 0.0136
52 2008 25 0.0000
52 2008 26 0.0000
52 2008 27 0.0000
52 2008 28 0.0027
52 2008 29 0.0000
52 2008 30 0.0515

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

53 2008 0 0.0000
53 2008 1 0.0062
53 2008 2 0.0373
53 2008 3 0.0093
53 2008 4 0.0280
53 2008 5 0.0342
53 2008 6 0.0186
53 2008 7 0.0186
53 2008 8 0.0124
53 2008 9 0.0155
53 2008 10 0.0217
53 2008 11 0.0373
53 2008 12 0.0093
53 2008 13 0.0311
53 2008 14 0.0217
53 2008 15 0.0373
53 2008 16 0.0217
53 2008 17 0.0342
53 2008 18 0.0124
53 2008 19 0.0186
53 2008 20 0.0248
53 2008 21 0.0373
53 2008 22 0.0186
53 2008 23 0.0248
53 2008 24 0.0062
53 2008 25 0.0373
53 2008 26 0.0155
53 2008 27 0.0186
53 2008 28 0.0217
53 2008 29 0.0186
53 2008 30 0.3509
54 2008 0 0.0077
54 2008 1 0.0170
54 2008 2 0.0377
54 2008 3 0.0424
54 2008 4 0.0471
54 2008 5 0.0579
54 2008 6 0.0552
54 2008 7 0.0485
54 2008 8 0.0406
54 2008 9 0.0439
54 2008 10 0.0505
54 2008 11 0.0539
54 2008 12 0.0435
54 2008 13 0.0360
54 2008 14 0.0348
54 2008 15 0.0375
54 2008 16 0.0303
54 2008 17 0.0231 54 2008 18 0.0196
54 2008 19 0.0150
54 2008 20 0.0183
54 2008 21 0.0208
54 2008 22 0.0218
54 2008 23 0.0217
54 2008 24 0.0186
54 2008 25 0.0173
54 2008 26 0.0163
54 2008 27 0.0118
54 2008 28 0.0084
54 2008 29 0.0059
54 2008 30 0.0968
61 2008 0 0.0030
61 2008 1 0.0167
61 2008 2 0.0334
61 2008 3 0.0393
61 2008 4 0.0506
61 2008 5 0.0530
61 2008 6 0.0620
61 2008 7 0.0625
61 2008 8 0.0562
61 2008 9 0.0551
61 2008 10 0.0595
61 2008 11 0.0569
61 2008 12 0.0458
61 2008 13 0.0493
61 2008 14 0.0380
61 2008 15 0.0435
61 2008 16 0.0425
61 2008 17 0.0312
61 2008 18 0.0262
61 2008 19 0.0235
61 2008 20 0.0201
61 2008 21 0.0225
61 2008 22 0.0212
61 2008 23 0.0141
61 2008 24 0.0137
61 2008 25 0.0096
61 2008 26 0.0069
61 2008 27 0.0039
61 2008 28 0.0030
61 2008 29 0.0027
61 2008 30 0.0343
62 2008 0 0.0078
62 2008 1 0.0232
62 2008 2 0.0307
62 2008 3 0.0907
62 2008 4 0.0721

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

62 2008 5 0.0808
62 2008 6 0.0564
62 2008 7 0.0520
62 2008 8 0.0360
62 2008 9 0.0552
62 2008 10 0.1019
62 2008 11 0.0813
62 2008 12 0.0603
62 2008 13 0.0425
62 2008 14 0.0439
62 2008 15 0.0442
62 2008 16 0.0273
62 2008 17 0.0202
62 2008 18 0.0122
62 2008 19 0.0101 62 2008 20 0.0103
62 2008 21 0.0080
62 2008 22 0.0079
62 2008 23 0.0058
62 2008 24 0.0050
62 2008 25 0.0036
62 2008 26 0.0038
62 2008 27 0.0001
62 2008 28 0.0012
62 2008 29 0.0010
62 2008 30 0.0046

Table 6 : Kentucky Custom Domain Age distribution

Source TypeID yearID ageID ageFraction

11 2008 0 0.0020
11 2008 1 0.0323
11 2008 2 0.0606
11 2008 3 0.0826
11 2008 4 0.0831
11 2008 5 0.0774
11 2008 6 0.0667
11 2008 7 0.0830
11 2008 8 0.0650
11 2008 9 0.0495
11 2008 10 0.0424
11 2008 11 0.0345
11 2008 12 0.0287
11 2008 13 0.0214
11 2008 14 0.0240
11 2008 15 0.0208
11 2008 16 0.0138
11 2008 17 0.0129
11 2008 18 0.0092
11 2008 19 0.0051
11 2008 20 0.0052
11 2008 21 0.0058
11 2008 22 0.0078
11 2008 23 0.0108
11 2008 24 0.0153 11 2008 25 0.0168
11 2008 26 0.0124
11 2008 27 0.0160
11 2008 28 0.0228
11 2008 29 0.0152
11 2008 30 0.0568
21 2008 0 0.0118
21 2008 1 0.0665
21 2008 2 0.0596
21 2008 3 0.0642
21 2008 4 0.0611
21 2008 5 0.0705
21 2008 6 0.0694
21 2008 7 0.0699
21 2008 8 0.0719
21 2008 9 0.0619
21 2008 10 0.0633
21 2008 11 0.0591
21 2008 12 0.0490
21 2008 13 0.0442
21 2008 14 0.0348
21 2008 15 0.0318
21 2008 16 0.0241
21 2008 17 0.0191
21 2008 18 0.0142
21 2008 19 0.0111
21 2008 20 0.0088
21 2008 21 0.0066
21 2008 22 0.0049
21 2008 23 0.0039
21 2008 24 0.0028
21 2008 25 0.0024

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

21 2008 26 0.0018
21 2008 27 0.0009
21 2008 28 0.0005
21 2008 29 0.0005
21 2008 30 0.0094
31 2008 0 0.0000
31 2008 1 0.0000
31 2008 2 0.0000
31 2008 3 0.0000
31 2008 4 0.0238
31 2008 5 0.0119
31 2008 6 0.0119
31 2008 7 0.0119
31 2008 8 0.0119
31 2008 9 0.0000
31 2008 10 0.0238
31 2008 11 0.0357
31 2008 12 0.0119
31 2008 13 0.0952
31 2008 14 0.0833
31 2008 15 0.0595
31 2008 16 0.1071
31 2008 17 0.0357
31 2008 18 0.0357
31 2008 19 0.0357
31 2008 20 0.0119
31 2008 21 0.0476
31 2008 22 0.0238
31 2008 23 0.0119
31 2008 24 0.0119
31 2008 25 0.0595
31 2008 26 0.0357
31 2008 27 0.0000
31 2008 28 0.0238
31 2008 29 0.0238
31 2008 30 0.1548
32 2008 0 0.0000
32 2008 1 0.0000
32 2008 2 0.0000
32 2008 3 0.0000
32 2008 4 0.0238
32 2008 5 0.0119
32 2008 6 0.0119
32 2008 7 0.0119
32 2008 8 0.0119
32 2008 9 0.0000
32 2008 10 0.0238
32 2008 11 0.0357
32 2008 12 0.0119 32 2008 13 0.0952
32 2008 14 0.0833
32 2008 15 0.0595
32 2008 16 0.1071
32 2008 17 0.0357
32 2008 18 0.0357
32 2008 19 0.0357
32 2008 20 0.0119
32 2008 21 0.0476
32 2008 22 0.0238
32 2008 23 0.0119
32 2008 24 0.0119
32 2008 25 0.0595
32 2008 26 0.0357
32 2008 27 0.0000
32 2008 28 0.0238
32 2008 29 0.0238
32 2008 30 0.1548
41 2008 0 0.0455
41 2008 1 0.1136
41 2008 2 0.0000
41 2008 3 0.0114
41 2008 4 0.0227
41 2008 5 0.0000
41 2008 6 0.0000
41 2008 7 0.0114
41 2008 8 0.0114
41 2008 9 0.0227
41 2008 10 0.0114
41 2008 11 0.0568
41 2008 12 0.1250
41 2008 13 0.0227
41 2008 14 0.0000
41 2008 15 0.0341
41 2008 16 0.0341
41 2008 17 0.0682
41 2008 18 0.0455
41 2008 19 0.0909
41 2008 20 0.0568
41 2008 21 0.0455
41 2008 22 0.0341
41 2008 23 0.0455
41 2008 24 0.0227
41 2008 25 0.0227
41 2008 26 0.0114
41 2008 27 0.0000
41 2008 28 0.0227
41 2008 29 0.0000
41 2008 30 0.0114

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

42 2008 0 0.0455
42 2008 1 0.1136
42 2008 2 0.0000
42 2008 3 0.0114
42 2008 4 0.0227
42 2008 5 0.0000
42 2008 6 0.0000
42 2008 7 0.0114
42 2008 8 0.0114
42 2008 9 0.0227
42 2008 10 0.0114
42 2008 11 0.0568
42 2008 12 0.1250
42 2008 13 0.0227
42 2008 14 0.0000
42 2008 15 0.0341
42 2008 16 0.0341
42 2008 17 0.0682
42 2008 18 0.0455
42 2008 19 0.0909
42 2008 20 0.0568
42 2008 21 0.0455
42 2008 22 0.0341
42 2008 23 0.0455
42 2008 24 0.0227
42 2008 25 0.0227
42 2008 26 0.0114
42 2008 27 0.0000
42 2008 28 0.0227
42 2008 29 0.0000
42 2008 30 0.0114
43 2008 0 0.0455
43 2008 1 0.1136
43 2008 2 0.0000
43 2008 3 0.0114
43 2008 4 0.0227
43 2008 5 0.0000
43 2008 6 0.0000
43 2008 7 0.0114
43 2008 8 0.0114
43 2008 9 0.0227
43 2008 10 0.0114
43 2008 11 0.0568
43 2008 12 0.1250
43 2008 13 0.0227
43 2008 14 0.0000
43 2008 15 0.0341
43 2008 16 0.0341
43 2008 17 0.0682 43 2008 18 0.0455
43 2008 19 0.0909
43 2008 20 0.0568
43 2008 21 0.0455
43 2008 22 0.0341
43 2008 23 0.0455
43 2008 24 0.0227
43 2008 25 0.0227
43 2008 26 0.0114
43 2008 27 0.0000
43 2008 28 0.0227
43 2008 29 0.0000
43 2008 30 0.0114
51 2008 0 0.0025
51 2008 1 0.0200
51 2008 2 0.0386
51 2008 3 0.0436
51 2008 4 0.0495
51 2008 5 0.0579
51 2008 6 0.0667
51 2008 7 0.0698
51 2008 8 0.0620
51 2008 9 0.0611
51 2008 10 0.0675
51 2008 11 0.0619
51 2008 12 0.0508
51 2008 13 0.0529
51 2008 14 0.0397
51 2008 15 0.0397
51 2008 16 0.0375
51 2008 17 0.0276
51 2008 18 0.0204
51 2008 19 0.0184
51 2008 20 0.0158
51 2008 21 0.0174
51 2008 22 0.0152
51 2008 23 0.0108
51 2008 24 0.0108
51 2008 25 0.0071
51 2008 26 0.0052
51 2008 27 0.0031
51 2008 28 0.0021
51 2008 29 0.0021
51 2008 30 0.0220
52 2008 0 0.0025
52 2008 1 0.0200
52 2008 2 0.0386
52 2008 3 0.0436
52 2008 4 0.0495

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

52 2008 5 0.0579
52 2008 6 0.0667
52 2008 7 0.0698
52 2008 8 0.0620
52 2008 9 0.0611
52 2008 10 0.0675
52 2008 11 0.0619
52 2008 12 0.0508
52 2008 13 0.0529
52 2008 14 0.0397
52 2008 15 0.0397
52 2008 16 0.0375
52 2008 17 0.0276
52 2008 18 0.0204
52 2008 19 0.0184
52 2008 20 0.0158
52 2008 21 0.0174
52 2008 22 0.0152
52 2008 23 0.0108
52 2008 24 0.0108
52 2008 25 0.0071
52 2008 26 0.0052
52 2008 27 0.0031
52 2008 28 0.0021
52 2008 29 0.0021
52 2008 30 0.0220
53 2008 0 0.0025
53 2008 1 0.0200
53 2008 2 0.0386
53 2008 3 0.0436
53 2008 4 0.0495
53 2008 5 0.0579
53 2008 6 0.0667
53 2008 7 0.0698
53 2008 8 0.0620
53 2008 9 0.0611
53 2008 10 0.0675
53 2008 11 0.0619
53 2008 12 0.0508
53 2008 13 0.0529
53 2008 14 0.0397
53 2008 15 0.0397
53 2008 16 0.0375
53 2008 17 0.0276
53 2008 18 0.0204
53 2008 19 0.0184
53 2008 20 0.0158
53 2008 21 0.0174
53 2008 22 0.0152 53 2008 23 0.0108
53 2008 24 0.0108
53 2008 25 0.0071
53 2008 26 0.0052
53 2008 27 0.0031
53 2008 28 0.0021
53 2008 29 0.0021
53 2008 30 0.0220
54 2008 0 0.0025
54 2008 1 0.0200
54 2008 2 0.0386
54 2008 3 0.0436
54 2008 4 0.0495
54 2008 5 0.0579
54 2008 6 0.0667
54 2008 7 0.0698
54 2008 8 0.0620
54 2008 9 0.0611
54 2008 10 0.0675
54 2008 11 0.0619
54 2008 12 0.0508
54 2008 13 0.0529
54 2008 14 0.0397
54 2008 15 0.0397
54 2008 16 0.0375
54 2008 17 0.0276
54 2008 18 0.0204
54 2008 19 0.0184
54 2008 20 0.0158
54 2008 21 0.0174
54 2008 22 0.0152
54 2008 23 0.0108
54 2008 24 0.0108
54 2008 25 0.0071
54 2008 26 0.0052
54 2008 27 0.0031
54 2008 28 0.0021
54 2008 29 0.0021
54 2008 30 0.0220
61 2008 0 0.0000
61 2008 1 0.0064
61 2008 2 0.0295
61 2008 3 0.0205
61 2008 4 0.0321
61 2008 5 0.0346
61 2008 6 0.0423
61 2008 7 0.0308
61 2008 8 0.0269
61 2008 9 0.0179

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

61 2008 10 0.0462
61 2008 11 0.0410
61 2008 12 0.0359
61 2008 13 0.0513
61 2008 14 0.0333
61 2008 15 0.0359
61 2008 16 0.0423
61 2008 17 0.0269
61 2008 18 0.0295
61 2008 19 0.0231
61 2008 20 0.0385
61 2008 21 0.0397
61 2008 22 0.0333
61 2008 23 0.0346
61 2008 24 0.0295
61 2008 25 0.0192
61 2008 26 0.0346
61 2008 27 0.0128
61 2008 28 0.0141
61 2008 29 0.0128
61 2008 30 0.1244
62 2008 0 0.0000
62 2008 1 0.0064
62 2008 2 0.0295
62 2008 3 0.0205
62 2008 4 0.0321
62 2008 5 0.0346
62 2008 6 0.0423
62 2008 7 0.0308
62 2008 8 0.0269
62 2008 9 0.0179
62 2008 10 0.0462
62 2008 11 0.0410
62 2008 12 0.0359
62 2008 13 0.0513
62 2008 14 0.0333
62 2008 15 0.0359
62 2008 16 0.0423
62 2008 17 0.0269
62 2008 18 0.0295
62 2008 19 0.0231
62 2008 20 0.0385
62 2008 21 0.0397
62 2008 22 0.0333
62 2008 23 0.0346
62 2008 24 0.0295
62 2008 25 0.0192
62 2008 26 0.0346
62 2008 27 0.0128 62 2008 28 0.0141
62 2008 29 0.0128
62 2008 30 0.1244

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



### 2.4 Vehicle Type VMT and VMT Fractions

This option is useful to import the annual VMT by s ource type into MOVES format. It has input option as HPMS Base Year VMT, for which we can eith er use HPMS data or the Travel Demand Model output. We have used annual VMT calculated fr om the OKI Regional Travel Demand Model. There are options like the Month VMT fractio n, Day VMT fraction and Hour VMT fraction, which are useful for calculating emission s for different time periods. We have used default Monthly VMT distribution factors provided i n the VMT Converter provided by EPA. Hourly distribution factors are developed from traf fic count data collected in the region and the same set of Hourly Distribution Factors are used fo r all vehicle types and road types. OKI model could only predict VMT of two different vehicle typ es’ autos and trucks. So, we have distributed total Annual VMT based on vehicle population in the region.

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2005 67065022 0
20 2005 7405961237 0
30 2005 4943917030 0
40 2005 24512225 0
50 2005 334351024 0
60 2005 567810955 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2005 16658465 0
20 2005 1815341688 0
30 2005 1209494070 0
40 2005 5968488 0
50 2005 82065068 0
60 2005 160708291 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 69438850 0
20 2008 7668102136 0
30 2008 5118911580 0
40 2008 25379858 0
50 2008 346185690 0
60 2008 587909153 0

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

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 17342291 0
20 2008 1889861055 0
30 2008 1259143528 0
40 2008 6213493 0
50 2008 85433821 0
60 2008 167305330 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2008 69438850 0
20 2008 7668102136 0
30 2008 5118911580 0
40 2008 25379858 0
50 2008 346185689 0
60 2008 587909153 0

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

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2015 73413634 0
20 2015 8107035747 0
30 2015 5411925719 0
40 2015 26832639 0
50 2015 366001875 0
60 2015 621561950 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2015 19903870 0
20 2015 2169006811 0
30 2015 1445127874 0 HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2011 18163152 0
20 2011 1979313603 0
30 2011 1318742406 0
40 2011 6507596 0
50 2011 89477649 0
60 2011 175224371 0

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

40 2015 7131270 0
50 2015 98052996 0
60 2015 192017502 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2018 76802311 0
20 2018 8481245879 0
30 2018 5661733109 0
40 2018 28071199 0
50 2018 382896041 0
60 2018 650252434 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2018 21077884 0
20 2018 2296944011 0
30 2018 1530367631 0
40 2018 7551902 0
50 2018 103836577 0
60 2018 203343507 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2021 79128218 0
20 2021 8738094842 0
30 2021 5833194979 0
40 2021 28921317 0
50 2021 394491797 0
60 2021 669944902 0

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

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT

10 2021 21702396 0
20 2021 2364999583 0
30 2021 1575710506 0 40 2021 7775656 0
50 2021 106913124 0
60 2021 209368320 0

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

Table 16 : Default Monthly VMT Distribution(year 20 08)

sourceTypeID isLeapYear monthID monthVMTFraction

11 Y 1 0.072904
11 Y 2 0.072023
11 Y 3 0.081529
11 Y 4 0.082098
11 Y 5 0.087286
11 Y 6 0.088052
11 Y 7 0.092096
11 Y 8 0.093198
11 Y 9 0.08447
11 Y 10 0.086301
11 Y 11 0.080029
11 Y 12 0.080015
21 Y 1 0.072904
21 Y 2 0.072023
21 Y 3 0.081529
21 Y 4 0.082098
21 Y 5 0.087286
21 Y 6 0.088052
21 Y 7 0.092096
21 Y 8 0.093198
21 Y 9 0.08447
21 Y 10 0.086301
21 Y 11 0.080029
21 Y 12 0.080015
31 Y 1 0.072904
31 Y 2 0.072023

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

sourceTypeID monthID roadTypeID dayID dayVMTFraction

11 1 1 2 0.237635
11 1 1 5 0.762365
11 1 2 2 0.237635
11 1 2 5 0.762365
11 1 3 2 0.237635
11 1 3 5 0.762365
11 1 4 2 0.237635
11 1 4 5 0.762365
11 1 5 2 0.237635
11 1 5 5 0.762365

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

Table 18 : Hourly VMT Distribution from local data

sourceTypeID roadTypeID dayID hourID hourVMTFraction

11 1 2 1 0.021474
11 1 2 2 0.014443
11 1 2 3 0.010968
11 1 2 4 0.007495
11 1 2 5 0.006839
11 1 2 6 0.010359
11 1 2 7 0.01843
11 1 2 8 0.026812
11 1 2 9 0.036385
11 1 2 10 0.047541
11 1 2 11 0.057466
11 1 2 12 0.065079
11 1 2 13 0.071323
11 1 2 14 0.071492
11 1 2 15 0.071723
11 1 2 16 0.072006
11 1 2 17 0.071149
11 1 2 18 0.067887
11 1 2 19 0.061772
11 1 2 20 0.051688
11 1 2 21 0.042866
11 1 2 22 0.03803
11 1 2 23 0.032207
11 1 2 24 0.024568



### 2.5 Average Speed Distribution Importer

This importer allows the user to input average spee d data specific to vehicle type, road type, and time of day/ type of day. The MOVES model defin es 16 “speed bins” which describe the average driving speed on each road type. Unlike MOB ILE 6.2 model, which uses VMT-based speed distribution, MOVES use fraction of driving t ime in each speed bin for each vehicle type, for each road type, and for each hour. Thus, for ea ch combination of vehicle type, road type, and hour/day type, the fractions will add to one. We have used OKI travel model to calculate average speed distribution based on VHT. However, t his input is ignored by MOVES when we are running emission rate runs† (See Table 27).



### 2.6 Road Type Distribution Importer

User supplied vehicle-miles-traveled data by road t ype is used as an input in this importer. OKI travel demand model can calculate the VMT distribut ion by functional class, which is further

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



### processed to obtain road type VMT distribution. But , our model could not predict off network VMT, which is assumed as zero. However, this input is also ignored by MOVES when we are running Emission Rate runs † (see Table 26).



### 2.7 Ramp Fraction Importer

This option allows the user to modify the fraction of ramp driving time on selected road types. But, in the current version of MOVES model, there i s no capability to model Emission Rates for Ramps. To circumvent this problem, FHWA has suggest ed a temporary solution. This solution discussed in the Section 3.


### 2.8 Fuel Formulation Importer and Fuel Supply Import er

Fuel formulation importer allows the user to select an existing fuel in the MOVES database and change its properties, or create a new fuel formula tion with different fuel properties. But we have used only default fuels available in MOVES def ault database. Fuel supply importer allows the user to assign existing fuels to counties, mont hs, and years, and the associated market share for each fuel. We have used default fuel supp ly from MOVES default database. And same type of fuel is used for Whole Custom Domain.

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

countyID fuelYearID monthGroupID fuelFormulationID marketShare marketShareCV

99390 2008 1 3982 1
99390 2008 1 20011 1
99390 2008 2 3982 1
99390 2008 2 20011 1
99390 2008 3 3982 1
99390 2008 3 20011 1
99390 2008 4 3982 1
99390 2008 4 20011 1
99390 2008 5 3982 1
99390 2008 5 20011 1
99390 2008 6 3982 1
99390 2008 6 20011 1
99390 2008 7 3982 1
99390 2008 7 20011 1
99390 2008 8 3982 1
99390 2008 8 20011 1
99390 2008 9 3982 1
99390 2008 9 20011 1
99390 2008 10 3982 1
99390 2008 10 20011 1
99390 2008 11 3982 1
99390 2008 11 20011 1

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

99390 2008 12 3982 1
99390 2008 12 20011 1

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

countyID fuelYearID monthGroupID fuelFormulationID marketShare marketShareCV

99210 2012 1 3982 1
99210 2012 1 20011 1
99210 2012 2 3982 1
99210 2012 2 20011 1
99210 2012 3 3982 1
99210 2012 3 20011 1
99210 2012 4 3982 1
99210 2012 4 20011 1
99210 2012 5 3982 1
99210 2012 5 20011 1
99210 2012 6 3982 1
99210 2012 6 20011 1
99210 2012 7 3982 1
99210 2012 7 20011 1
99210 2012 8 3982 1
99210 2012 8 20011 1
99210 2012 9 3982 1
99210 2012 9 20011 1
99210 2012 10 3982 1
99210 2012 10 20011 1
99210 2012 11 3982 1
99210 2012 11 20011 1
99210 2012 12 3982 1
99210 2012 12 20011 1



### 2.9 I/M Importer

The I/M Importer allows the user to import informat ion describing the inspection and maintenance programs. In the default database there is an option, whether to use default I/M program or not. We choose no I/M program for all o f the Runspecs[sic] in the whole region.



### 2.10 Zone Road Activity Importer

The Zone Road Activity Importer is used only if the Custom Domain option is chosen in the County Domain Manager. We have used value 1 for SHO allocfactor for each road type which means that all of the VMT input by the users is ass igned to custom domain.

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

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

zoneID roadTypeID SHOAllocFactor

992100 1 1
992100 2 1
992100 3 1
992100 4 1
992100 5 1

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

zoneID roadTypeID SHOAllocFactor

993900 1 1
993900 2 1
993900 3 1
993900 4 1
993900 5 1



## 3. Ramp Inventory Runs

As discussed earlier, current version of the MOVES model cannot calculate Emission Rates for Ramps. To deal with this problem, FHWA has suggeste d an approach. The steps involved in this method are: (a) Calculating Emission Inventory for Urban Restricted and Rural Restricted road types keeping Ramp fraction as 1 (b) Finding out to tal VMT of Urban Restricted and Rural Restricted road types using MOVESactivityoutput opt ion (c) Calculation Emission Rates for Ramps through dividing Emission Inventory with VMT ( d) Finally, using the Emission Rates in post processing for calculating regional Emission I nventory.

Table 23 :Ramp fraction Input

roadTypeID rampFraction

2 1 4 1

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



## 4. Post-Processing of MOVES Output

4.1 Linking SQL tables to Microsoft Access Microsoft Access 2007 was used for the post-process ing. An ODBC connection with the MOVES output directory was established. Information on h ow to link or import SQL tables to Access can be found in the MOVES Users Guide.


### 4.2 Creating Emission Rate Lookup Tables The ratepervehicle and rateperdistance SQL tables, one set for each state (Kentucky and Ohio) and analysis year, were imported into Access. Ohio emission rates are used for the nonattainment portion of Dearborn County Indiana. Rateperprofile output was not generated by MOVES because evaporative output was not selecte d (i.e. VOC). Tables were renamed with state and analysis year in the format OH_20xxratepe rdistance. All rateperdistance tables were merged with a Union query. The SQL commands are sh own in Figure 3.1. ratepervehicle tables were merged in the same manner.

Table 24 :Rateperdistance Union Query

SELECT * FROM OH_2008rateperdistance WHERE MOVESRunID = (select max (MOVESRunID) from OH _2008rateperdistance) AND pollutantID = 3 Or MOVESRunID = (select max (MOVES RunID) from OH_2008rateperdistance) AND pollutantID=110 Or MOVE SRunID = (select max (MOVESRunID) from OH_2008rateperdistance) AND pollu tantID=116 Or MOVESRunID = (select max (MOVESRunID) from OH_2008rateperdistanc e) AND pollutantID=117 Or MOVESRunID = (select max (MOVESRunID) from OH_2008r ateperdistance) AND pollutantID = 31 UNION ALL select * FROM OH_2011rateperdistance WHERE ..... (repeated for each file)



### “Rateperdistance_state” and “Ratepervehicle_state” tables were created from the union query output using a Make Table query. Emission rates fo r each process were summed by pollutant and a stateID field is created. The SQL commands f or creating the “Rateperdistance_state” table are shown in Table 25. Unique index fields w ere identified for each of the two tables. Indexes facilitate more efficient data processing.

Table 25: Rateperdistance_State Query

SELECT Val(Mid([LinkID],3,2)) AS StateID, Union_rat eperdistance_state.yearID, Union_rateperdistance_state.monthID, Union_rateperd istance_state.linkID, Union_rateperdistance_state.hourID, Union_rateperdi stance_state.sourceTypeID, Union_rateperdistance_state.roadTypeID, Union_rateperdistance_state.avgSpeedBinID, Union_rateperdistance_state.pollutantID, Sum(Union_rateperdistance_state.ratePerDistance) AS SumOfratePerDistance INTO rateperdistance_state

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

FROM Union_rateperdistance_state GROUP BY Val(Mid([LinkID],3,2)), Union_rateperdista nce_state.yearID, Union_rateperdistance_state.monthID, Union_rateperd istance_state.linkID, Union_rateperdistance_state.hourID, Union_rateperdi stance_state.sourceTypeID, Union_rateperdistance_state.roadTypeID, Union_rateperdistance_state.avgSpeedBinID, Union_rateperdistance_state.pollutantID ORDER BY Union_rateperdistance_state.linkID, Union_rateperdistance_state.hourID, Union_rateperdi stance_state.pollutantID;


### 4.1 Creating a VMT Table by County

The VMT table includes Daily VMT by county by analy sis year from the OKI Travel Demand Model (TDM). Summer factors and applied by functio nal class to create Summer VMT. Seasonal factors by functional class are contained in the report, “OKI Travel Demand Forecasting Model, Update of Hourly and Seasonal Fa ctors as Used in Air Quality Impact Calculations”, September 2001. Annual VMT is calcu lated by using EPA’s VMT converter to grow daily VMT to annual VMT. In order to accommod ate an error in MOVES 2010, all VMT values are exclusive of ramp VMT. Ramp VMT and emi ssion are added in later in the process. In order to apply the emission rates, it is necessa ry to factor the county VMT by source type, hour, road type and speed bin.

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

County Daily VMT Summer
VMT Annual VMT yearID SourceType Population stateID

Boone 3924117 4186006 1273226984 2005 129823 21
Boone 4076584 4355527 1350001557 2008 134028 21
Boone 4383716 4681593 1448879510 2011 136181 21
Boone 4950741 5276742 1628041303 2015 140590 21
Boone 5260102 5597287 1729595179 2018 143991 21
Boone 5478224 5826768 1800571708 2021 147476 21 Campbell 2286217 2437698 741790605 2005 86065 21 Campbell 2339542 2495174 774762729 2008 88853 21 Campbell 2421600 2582758 800372702 2011 90279 21 Campbell 2663159 2844504 875774499 2015 93204 21 Campbell 2771476 2958827 911300109 2018 95458 21 Campbell 2849127 3041704 936445364 2021 97768 21
Kenton 3927743 4182042 1274091658 2005 148193 21
Kenton 3927332 4185652 1300575265 2008 152992 21
Kenton 4049886 4327836 1338544021 2011 155451 21
Kenton 4341124 4614242 1427569992 2015 160484 21
Kenton 4629694 4880614 1522308203 2018 164368 21
Kenton 4715306 5006383 1549817345 2021 168343 21
Butler 578641 7804476 196737836 2005 24915 39

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

Butler 587582 8133554 199777880 2008 25722 39
Butler 605620 8454053 205910800 2011 26135 39
Butler 657778 8768598 223644400 2015 26982 39
Butler 684361 9232457 232682740 2018 27635 39
Butler 706828 9592567 240321520 2021 28303 39
Clermont 7452286 5391578 2469166037 2005 401759 39
Clermont 7745685 5599530 2598059212 2008 414771 39
Clermont 8050701 5841102 2693716250 2011 421434 39
Clermont 8361487 6035155 2792188144 2015 435082 39
Clermont 8806042 6314640 2940849935 2018 445608 39
Clermont 9150031 6562428 2966037449 2021 456389 39
Dearborn NA 5083331 599761 1684259908 2005 232380 39
Dearborn NA 5262489 613027 1765145113 2008 239906 39
Dearborn NA 5489545 631914 1836768820 2011 243760 39
Dearborn NA 5687698 685272 1899318043 2015 251654 39
Dearborn NA 5952603 712461 1987920583 2018 257742 39
Dearborn NA 6186441 735862 2005371969 2021 263978 39
Hamilton 21859452 23170766 7241529618 2005 862422 39
Hamilton 22124503 23447460 7421005221 2008 890352 39
Hamilton 22426021 23803187 7503612070 2011 904655 39
Hamilton 22849494 24259554 7630232069 2015 933953 39
Hamilton 23630554 25096560 7891617279 2018 956548 39
Hamilton 24098698 25596996 7811737549 2021 979689 39
Warren 5884216 6263010 1949617151 2005 233106 39
Warren 6057338 6464217 2031753523 2008 240655 39
Warren 6406284 6835660 2143504189 2011 244521 39
Warren 6842828 7279441 2285055662 2015 252440 39
Warren 7368035 7836746 2460615706 2018 258547 39
Warren 7707500 8194596 2498432370 2021 264802 39



### 4.2 Source type population and source type VMT distr ibution
A combination of local and MOVES default data were used for the source type populations. The source type VMT fractions are based on the ratio of MOVES default source type population and MOVES default source type VMT. It is assumed that the growth rate of source type populations is equal to the regional annual household growth ra te of 0.8%. Source type VMT fractions are the same for all analysis years.

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

Table 27: Base Year Source Type Population and VMT Fraction

stateID sourceTypeID sourceType Population sourceTypeFraction sourceTypeVMTFraction

39 11 69121 0.038559 0.005026
39 21 1200827 0.669872 0.555019
39 31 486373 0.271319 0.277725
39 32 15947 0.008896 0.092783
39 41 458 0.000255 0.000754
39 42 82 0.000046 0.000225
39 43 3681 0.002053 0.000858
39 51 0 0.000000 0.000644
39 52 369 0.000206 0.020527
39 53 364 0.000203 0.002663
39 54 4928 0.002749 0.001224
39 61 4879 0.002722 0.017977
39 62 5593 0.003120 0.024576
21 11 8040 0.021370 0.005063
21 21 198623 0.527931 0.551736
21 31 121506 0.322958 0.275546
21 32 40593 0.107894 0.092055
21 41 128 0.000340 0.000745
21 42 21 0.000056 0.000222
21 43 985 0.002618 0.000847
21 51 0 0.000000 0.000641
21 52 767 0.002039 0.020433
21 53 757 0.002012 0.002650
21 54 1390 0.003695 0.001218
21 61 1593 0.004234 0.020634
21 62 1826 0.004853 0.028210



### 4.3 Hourly distribution MOVES default hourly distribution by source type wa s used during the post-processing.


### 4.4 Road type distribution Road type VMT fractions by source type are default values, except for passenger cars (source type 21) and passenger trucks (source type 31). VM T fractions from the OKI TDM are used for passenger cars and passenger trucks.

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

Table 28: Base Year Source Type Population and VMT Fraction

sourceTypeID roadTypeID roadTypeVMTFraction stateID

21 1 0 21
21 2 0.0952 21
21 3 0.0818 21
21 4 0.4741 21
21 5 0.3489 21
31 1 0 21
31 2 0.0952 21
31 3 0.0818 21
31 4 0.4741 21
31 5 0.3489 21
21 1 0 39
21 2 0.0436 39
21 3 0.1256 39
21 4 0.4143 39
21 5 0.4165 39
31 1 0 39
31 2 0.0436 39
31 3 0.1256 39
31 4 0.4143 39
31 5 0.4165 39



### 4.5 Average speed distribution Average speed fractions for each of the 16 speed bi ns are provided by the OKI TDM. The average speed fractions vary by state, year, road t ype and hour.

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

roadT ypeID hourID avgSpeedBinID avgSpeedFraction YearID stateID

2 1 1 0.00000000 2011 39

2 1 2 0.00000000 2011 39

2 1 3 0.00000000 2011 39

2 1 4 0.00000000 2011 39

2 1 5 0.00000000 2011 39

2 1 6 0.00000000 2011 39

2 1 7 0.00000000 2011 39

2 1 8 0.00000000 2011 39

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

2 1 9 0.00000000 2011 39

2 1 10 0.11922233 2011 39

2 1 11 0.12547629 2011 39

2 1 12 0.19752816 2011 39

2 1 13 0.00589550 2011 39

2 1 14 0.00000000 2011 39

2 1 15 0.55187773 2011 39

2 1 16 0.00000000 2011 39

2 2 1 0.00000000 2011 39

2 2 2 0.00000000 2011 39

2 2 3 0.00000000 2011 39

2 2 4 0.00000000 2011 39

2 2 5 0.00000000 2011 39

2 2 6 0.00000000 2011 39

2 2 7 0.00000000 2011 39

2 2 8 0.00000000 2011 39

2 2 9 0.00000000 2011 39

2 2 10 0.11922233 2011 39

2 2 11 0.12547629 2011 39

2 2 12 0.19752816 2011 39

2 2 13 0.00589550 2011 39

2 2 14 0.00000000 2011 39

2 2 15 0.55187773 2011 39

2 2 16 0.00000000 2011 39

2 3 1 0.00000000 2011 39

2 3 2 0.00000000 2011 39

2 3 3 0.00000000 2011 39

2 3 4 0.00000000 2011 39

2 3 5 0.00000000 2011 39

2 3 6 0.00000000 2011 39

2 3 7 0.00000000 2011 39

2 3 8 0.00000000 2011 39

2 3 9 0.00000000 2011 39

2 3 10 0.11922233 2011 39

2 3 11 0.12547629 2011 39

2 3 12 0.19752816 2011 39

2 3 13 0.00589550 2011 39

2 3 14 0.06436270 2011 39

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

2 3 15 0.48751503 2011 39

2 3 16 0.00000000 2011 39

2 4 1 0.00000000 2011 39

2 4 2 0.00000000 2011 39

2 4 3 0.00000000 2011 39

2 4 4 0.00000000 2011 39

2 4 5 0.00000000 2011 39

2 4 6 0.00000000 2011 39

2 4 7 0.00000000 2011 39

2 4 8 0.00000000 2011 39

2 4 9 0.00000000 2011 39

2 4 10 0.11922233 2011 39

2 4 11 0.12547629 2011 39

2 4 12 0.19752816 2011 39

2 4 13 0.00589550 2011 39

2 4 14 0.12369152 2011 39

2 4 15 0.42818621 2011 39

2 4 16 0.00000000 2011 39

2 5 1 0.00000000 2011 39

2 5 2 0.00000000 2011 39

2 5 3 0.00000000 2011 39

2 5 4 0.00000000 2011 39

2 5 5 0.00000000 2011 39

2 5 6 0.00000000 2011 39

2 5 7 0.00000000 2011 39

2 5 8 0.00000000 2011 39

2 5 9 0.00000000 2011 39

2 5 10 0.11922233 2011 39

2 5 11 0.12547629 2011 39

2 5 12 0.26189086 2011 39

2 5 13 0.09782827 2011 39

2 5 14 0.06250896 2011 39

2 5 15 0.33307330 2011 39

2 5 16 0.00000000 2011 39

2 6 1 0.00000000 2011 39

2 6 2 0.12369152 2011 39

2 6 3 0.03260396 2011 39

2 6 4 0.03085494 2011 39

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

2 6 5 0.01601927 2011 39

2 6 6 0.01563475 2011 39

2 6 7 0.00000000 2011 39

2 6 8 0.00000000 2011 39

2 6 9 0.00000000 2011 39

2 6 10 0.11922233 2011 39

2 6 11 0.12547629 2011 39

2 6 12 0.19752816 2011 39

2 6 13 0.00589550 2011 39

2 6 14 0.00278921 2011 39

2 6 15 0.33028409 2011 39

2 6 16 0.00000000 2011 39

2 7 1 0.21880443 2011 39

2 7 2 0.00000000 2011 39

2 7 3 0.00278921 2011 39

2 7 4 0.00000000 2011 39

2 7 5 0.01131028 2011 39

2 7 6 0.03548550 2011 39

2 7 7 0.03519436 2011 39

2 7 8 0.03514937 2011 39

2 7 9 0.00617093 2011 39

2 7 10 0.08564905 2011 39

2 7 11 0.14691446 2011 39

2 7 12 0.13064235 2011 39

2 7 13 0.00589550 2011 39

2 7 14 0.14352342 2011 39

2 7 15 0.14247115 2011 39

2 7 16 0.00000000 2011 39

2 8 1 0.21880443 2011 39

2 8 2 0.00278921 2011 39

2 8 3 0.01131028 2011 39

2 8 4 0.01356074 2011 39

2 8 5 0.05711912 2011 39

2 8 6 0.03514937 2011 39

2 8 7 0.02902228 2011 39

2 8 8 0.01193860 2011 39

2 8 9 0.07229727 2011 39

2 8 10 0.05473480 2011 39

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

2 8 11 0.08493157 2011 39

2 8 12 0.11645227 2011 39

2 8 13 0.04360641 2011 39

2 8 14 0.20648097 2011 39

2 8 15 0.04180267 2011 39

2 8 16 0.00000000 2011 39

2 9 1 0.21880443 2011 39

2 9 2 0.00000000 2011 39

2 9 3 0.00000000 2011 39

2 9 4 0.00000000 2011 39

2 9 5 0.00000000 2011 39

2 9 6 0.00278921 2011 39

2 9 7 0.00000000 2011 39

2 9 8 0.04679577 2011 39

2 9 9 0.03519436 2011 39

2 9 10 0.08369185 2011 39

2 9 11 0.14590244 2011 39

2 9 12 0.15349370 2011 39

2 9 13 0.02733367 2011 39

2 9 14 0.03771092 2011 39

2 9 15 0.24828365 2011 39

2 9 16 0.00000000 2011 39

2 10 1 0.15629548 2011 39

2 10 2 0.04687421 2011 39

2 10 3 0.01563475 2011 39

2 10 4 0.00000000 2011 39

2 10 5 0.00000000 2011 39

2 10 6 0.00000000 2011 39

2 10 7 0.00000000 2011 39

2 10 8 0.00000000 2011 39

2 10 9 0.00000000 2011 39

2 10 10 0.11922233 2011 39

2 10 11 0.13957578 2011 39

2 10 12 0.18621788 2011 39

2 10 13 0.00589550 2011 39

2 10 14 0.04428952 2011 39

2 10 15 0.28599457 2011 39

2 10 16 0.00000000 2011 39

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

2 11 1 0.06436270 2011 39

2 11 2 0.12278771 2011 39

2 11 3 0.01601927 2011 39

2 11 4 0.01563475 2011 39

2 11 5 0.00000000 2011 39

2 11 6 0.00000000 2011 39

2 11 7 0.00000000 2011 39

2 11 8 0.00000000 2011 39

2 11 9 0.00000000 2011 39

2 11 10 0.11922233 2011 39

2 11 11 0.13678656 2011 39

2 11 12 0.18900709 2011 39

2 11 13 0.00589550 2011 39

2 11 14 0.02285135 2011 39

2 11 15 0.30743274 2011 39

2 11 16 0.00000000 2011 39

2 12 1 0.06436270 2011 39

2 12 2 0.09193278 2011 39

2 12 3 0.04687421 2011 39

2 12 4 0.01563475 2011 39

2 12 5 0.00000000 2011 39

2 12 6 0.00000000 2011 39

2 12 7 0.00000000 2011 39

2 12 8 0.00000000 2011 39

2 12 9 0.00000000 2011 39

2 12 10 0.11922233 2011 39

2 12 11 0.12547629 2011 39

2 12 12 0.20031737 2011 39

2 12 13 0.00589550 2011 39

2 12 14 0.02285135 2011 39

2 12 15 0.30743274 2011 39

2 12 16 0.00000000 2011 39

2 13 1 0.06436270 2011 39

2 13 2 0.09193278 2011 39

2 13 3 0.04687421 2011 39

2 13 4 0.01563475 2011 39

2 13 5 0.00000000 2011 39

2 13 6 0.00000000 2011 39

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

2 13 7 0.00000000 2011 39

2 13 8 0.00000000 2011 39

2 13 9 0.00000000 2011 39

2 13 10 0.11922233 2011 39

2 13 11 0.12547629 2011 39

2 13 12 0.19752816 2011 39

2 13 13 0.00868471 2011 39

2 13 14 0.00000000 2011 39

2 13 15 0.33028409 2011 39

2 13 16 0.00000000 2011 39

2 14 1 0.06436270 2011 39

2 14 2 0.09193278 2011 39

2 14 3 0.03085494 2011 39

2 14 4 0.03165402 2011 39

2 14 5 0.00000000 2011 39

2 14 6 0.00000000 2011 39

2 14 7 0.00000000 2011 39

2 14 8 0.00000000 2011 39

2 14 9 0.00000000 2011 39

2 14 10 0.11922233 2011 39

2 14 11 0.12547629 2011 39

2 14 12 0.19752816 2011 39

2 14 13 0.00868471 2011 39

2 14 14 0.00000000 2011 39

2 14 15 0.33028409 2011 39

2 14 16 0.00000000 2011 39

2 15 1 0.06436270 2011 39

2 15 2 0.09193278 2011 39

2 15 3 0.04687421 2011 39

2 15 4 0.01563475 2011 39

2 15 5 0.00000000 2011 39

2 15 6 0.00000000 2011 39

2 15 7 0.00000000 2011 39

2 15 8 0.00000000 2011 39

2 15 9 0.00000000 2011 39

2 15 10 0.11922233 2011 39

2 15 11 0.12547629 2011 39

2 15 12 0.19752816 2011 39

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

2 15 13 0.00868471 2011 39

2 15 14 0.02285135 2011 39

2 15 15 0.30743274 2011 39

2 15 16 0.00000000 2011 39

2 16 1 0.12369152 2011 39

2 16 2 0.07947816 2011 39

2 16 3 0.01563475 2011 39

2 16 4 0.00000000 2011 39

2 16 5 0.00000000 2011 39

2 16 6 0.00000000 2011 39

2 16 7 0.00000000 2011 39

2 16 8 0.00000000 2011 39

2 16 9 0.00000000 2011 39

2 16 10 0.11922233 2011 39

2 16 11 0.13957578 2011 39

2 16 12 0.18621788 2011 39

2 16 13 0.00589550 2011 39

2 16 14 0.04428952 2011 39

2 16 15 0.28599457 2011 39

2 16 16 0.00000000 2011 39

2 17 1 0.20316968 2011 39

2 17 2 0.01563475 2011 39

2 17 3 0.00000000 2011 39

2 17 4 0.00000000 2011 39

2 17 5 0.00000000 2011 39

2 17 6 0.00000000 2011 39

2 17 7 0.00278921 2011 39

2 17 8 0.02487101 2011 39

2 17 9 0.05711912 2011 39

2 17 10 0.05722137 2011 39

2 17 11 0.15811770 2011 39

2 17 12 0.14489757 2011 39

2 17 13 0.05018502 2011 39

2 17 14 0.00000000 2011 39

2 17 15 0.28599457 2011 39

2 17 16 0.00000000 2011 39

2 18 1 0.15629548 2011 39

2 18 2 0.04687421 2011 39

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

2 18 3 0.01563475 2011 39

2 18 4 0.00000000 2011 39

2 18 5 0.00000000 2011 39

2 18 6 0.00000000 2011 39

2 18 7 0.00000000 2011 39

2 18 8 0.00000000 2011 39

2 18 9 0.00000000 2011 39

2 18 10 0.11922233 2011 39

2 18 11 0.13957578 2011 39

2 18 12 0.18621788 2011 39

2 18 13 0.00589550 2011 39

2 18 14 0.04428952 2011 39

2 18 15 0.28599457 2011 39

2 18 16 0.00000000 2011 39

2 19 1 0.00000000 2011 39

2 19 2 0.00000000 2011 39

2 19 3 0.00000000 2011 39

2 19 4 0.00000000 2011 39

2 19 5 0.06436270 2011 39

2 19 6 0.00000000 2011 39

2 19 7 0.05932882 2011 39

2 19 8 0.03260396 2011 39

2 19 9 0.00000000 2011 39

2 19 10 0.15007727 2011 39

2 19 11 0.14149556 2011 39

2 19 12 0.21316291 2011 39

2 19 13 0.00589550 2011 39

2 19 14 0.00000000 2011 39

2 19 15 0.33307330 2011 39

2 19 16 0.00000000 2011 39

2 20 1 0.00000000 2011 39

2 20 2 0.00000000 2011 39

2 20 3 0.00000000 2011 39

2 20 4 0.00000000 2011 39

2 20 5 0.00000000 2011 39

2 20 6 0.00000000 2011 39

2 20 7 0.00000000 2011 39

2 20 8 0.00000000 2011 39

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

2 20 9 0.00000000 2011 39

2 20 10 0.11922233 2011 39

2 20 11 0.12547629 2011 39

2 20 12 0.19752816 2011 39

2 20 13 0.00589550 2011 39

2 20 14 0.15629548 2011 39

2 20 15 0.39558226 2011 39

2 20 16 0.00000000 2011 39

2 21 1 0.00000000 2011 39

2 21 2 0.00000000 2011 39

2 21 3 0.00000000 2011 39

2 21 4 0.00000000 2011 39

2 21 5 0.00000000 2011 39

2 21 6 0.00000000 2011 39

2 21 7 0.00000000 2011 39

2 21 8 0.00000000 2011 39

2 21 9 0.00000000 2011 39

2 21 10 0.11922233 2011 39

2 21 11 0.12547629 2011 39

2 21 12 0.19752816 2011 39

2 21 13 0.00589550 2011 39

2 21 14 0.00000000 2011 39

2 21 15 0.55187773 2011 39

2 21 16 0.00000000 2011 39

2 22 1 0.00000000 2011 39

2 22 2 0.00000000 2011 39

2 22 3 0.00000000 2011 39

2 22 4 0.00000000 2011 39

2 22 5 0.00000000 2011 39

2 22 6 0.00000000 2011 39

2 22 7 0.00000000 2011 39

2 22 8 0.00000000 2011 39

2 22 9 0.00000000 2011 39

2 22 10 0.11922233 2011 39

2 22 11 0.12547629 2011 39

2 22 12 0.19752816 2011 39

2 22 13 0.00589550 2011 39

2 22 14 0.00000000 2011 39

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

2 22 15 0.55187773 2011 39

2 22 16 0.00000000 2011 39

2 23 1 0.00000000 2011 39

2 23 2 0.00000000 2011 39

2 23 3 0.00000000 2011 39

2 23 4 0.00000000 2011 39

2 23 5 0.00000000 2011 39

2 23 6 0.00000000 2011 39

2 23 7 0.00000000 2011 39

2 23 8 0.00000000 2011 39

2 23 9 0.00000000 2011 39

2 23 10 0.11922233 2011 39

2 23 11 0.12547629 2011 39

2 23 12 0.19752816 2011 39

2 23 13 0.00589550 2011 39

2 23 14 0.00000000 2011 39

2 23 15 0.55187773 2011 39

2 23 16 0.00000000 2011 39

2 24 1 0.00000000 2011 39

2 24 2 0.00000000 2011 39

2 24 3 0.00000000 2011 39

2 24 4 0.00000000 2011 39

2 24 5 0.00000000 2011 39

2 24 6 0.00000000 2011 39

2 24 7 0.00000000 2011 39

2 24 8 0.00000000 2011 39

2 24 9 0.00000000 2011 39

2 24 10 0.11922233 2011 39

2 24 11 0.12547629 2011 39

2 24 12 0.19752816 2011 39

2 24 13 0.00589550 2011 39

2 24 14 0.00000000 2011 39

2 24 15 0.55187773 2011 39

2 24 16 0.00000000 2011 39

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



### 4.6 Creating a VMT Table by County, Year, Source Type, Hour, Road Type, and Average Speed Bin

The ‘CountyVMT’ query creates a County VMT Table by source type, hour, road type and average speed utilizing the VMT distribution factor s described in 4.2, 4.3, 4.4 and 4.5. The SQL commands for this query are shown in Table 30.

Table 30 :County VMT Table Query

SELECT VMT.yearID, VMT.State, roadtypedistribution1 .stateID, VMT.County, roadtypedistribution1.sourceTypeID, hourvmtfraction .hourID, roadtypedistribution1.roadTypeID, avgSpeedDistribut ion.avgSpeedBinID, sourecetypepopulation.sourceTypeFraction, hourvmtfr action.hourVMTFraction, roadtypedistribution1.roadTypeVMTFraction, avgSpeedDistribution.avgSpeedFraction, First(VMT.[A nnual VMT]) AS [FirstOfAnnual VMT], First(VMT.[Summer VMT]) AS [Fi rstOfSummer VMT], [FirstOfSummer VMT]*[hourVMTFraction]*[sourceTypeFraction]*[roadTy peVMTFraction]*[avgSpeedFr action] AS DailyVMT, [FirstOfAnnual VMT]*[sourceTypeFraction]*[hourVMTFraction]*[roadTy peVMTFraction]*[avgSpeedFr action] AS AnnualizedVMT INTO CountyVMT_Table FROM (((avgSpeedDistribution INNER JOIN hourvmtfrac tion ON (avgSpeedDistribution.hourDayID = hourvmtfraction.h ourID) AND (avgSpeedDistribution.roadTypeID = hourvmtfraction. roadTypeID) AND (avgSpeedDistribution.sourceTypeID = hourvmtfractio n.sourceTypeID)) INNER JOIN roadtypedistribution1 ON (avgSpeedDistribution .stateID = roadtypedistribution1.stateID) AND (avgSpeedDistrib ution.roadTypeID = roadtypedistribution1.roadTypeID) AND (avgSpeedDist ribution.sourceTypeID = roadtypedistribution1.sourceTypeID)) INNER JOIN sou recetypepopulation ON (avgSpeedDistribution.sourceTypeID = sourecetypepop ulation.sourceTypeID) AND (avgSpeedDistribution.stateID = sourecetypepopulati on.stateID)) INNER JOIN VMT ON (avgSpeedDistribution.YearID = VMT.yearID) A ND (avgSpeedDistribution.stateID = VMT.stateID) GROUP BY VMT.yearID, VMT.State, roadtypedistributio n1.stateID, VMT.County, roadtypedistribution1.sourceTypeID, hourvmtfraction .hourID, roadtypedistribution1.roadTypeID, avgSpeedDistribut ion.avgSpeedBinID, sourecetypepopulation.sourceTypeFraction, hourvmtfr action.hourVMTFraction, roadtypedistribution1.roadTypeVMTFraction, avgSpeedDistribution.avgSpeedFraction HAVING (((avgSpeedDistribution.avgSpeedFraction)>0) ) ORDER BY VMT.yearID, roadtypedistribution1.stateID, VMT.County, hourvmtfraction.hourID;



## 5. Combining VMT and Emission Rates; Calculating To tal Emissions

5.1 Summarizing Distance-based Emissions by Source T ype The daily VMT and annual VMT in each county, year, hour, source type, road type, and speed bin is multiplied by the appropriate rate per dista nce for each pollutant. This query is shown in Table 31.

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

Table 31 :Emissions distance Query

SELECT CountyVMT_Table.stateID, CountyVMT_Table.Sta te, CountyVMT_Table.County, CountyVMT_Table.yearID, rateperdistance_state.monthID, CountyVMT_Table.hour ID, rateperdistance_state.sourceTypeID, CountyVMT_Table .roadTypeID, CountyVMT_Table.avgSpeedBinID, rateperdistance_stat e.pollutantID, CountyVMT_Table.DailyVMT, CountyVMT_Table.Annualize dVMT, rateperdistance_state.SumOfratePerDistance, [DailyV MT]*[SumOfratePerDistance] AS EmissionsDist, [AnnualizedVMT]*[SumOfratePerDist ance] AS AnnualEmissionsDist FROM rateperdistance_state INNER JOIN CountyVMT_Tab le ON (rateperdistance_state.avgSpeedBinID = CountyVMT_Ta ble.avgSpeedBinID) AND (rateperdistance_state.roadTypeID = CountyVMT_Table .roadTypeID) AND (rateperdistance_state.sourceTypeID = CountyVMT_Tab le.sourceTypeID) AND (rateperdistance_state.hourID = CountyVMT_Table.hou rID) AND (rateperdistance_state.StateID = CountyVMT_Table.st ateID) AND (rateperdistance_state.yearID = CountyVMT_Table.yea rID) GROUP BY CountyVMT_Table.stateID, CountyVMT_Table.S tate, CountyVMT_Table.County, CountyVMT_Table.yearID, rateperdistance_state.monthID, CountyVMT_Table.hour ID, rateperdistance_state.sourceTypeID, CountyVMT_Table .roadTypeID, CountyVMT_Table.avgSpeedBinID, rateperdistance_stat e.pollutantID, CountyVMT_Table.DailyVMT, CountyVMT_Table.Annualize dVMT, rateperdistance_state.SumOfratePerDistance;



### A second query further summarizes the emissions by source type. This is necessary in order to combine with vehicle-based emissions that are indep endent of road type and speed.



### 5.2 Summarizing Vehicle-based Emissions by Source ty pe The source population for each county, year, hour, and source type is multiplied by the rate per vehicle for each pollutant. This query is shown in Table 32.

Table 32: Emissions Vehicle Query

SELECT VMT.stateID, VMT.County, ratepervehicle_stat e.yearID, ratepervehicle_state.monthID, ratepervehicle_state. hourID, ratepervehicle_state.sourceTypeID, sourecetypepopul ation.sourceTypeFraction, VMT.SourceTypePopulation, ratepervehicle_state.poll utantID, ratepervehicle_state.SumOfratePerVehicle, First(VMT .BudgetAreaPop) AS FirstOfBudgetAreaPop, ((Nz([VMT]![sourceTypePopulation]*[sourceTypeFracti on],0)/24)) AS STPop, Nz([VMT]![sourceTypePopulation]*[sourceTypeFraction ]*[SumOfratePerVehicle],0) AS emissionsVehicle, Nz(([VMT]![sourceTypePopulation]*[sourceTypeFractio n]*[SumOfratePerVehicle])* 365,0) AS AnnualemissionsVehicle FROM sourecetypepopulation INNER JOIN (ratepervehic le_state INNER JOIN VMT ON (ratepervehicle_state.yearID = VMT.yearID) AND (rat epervehicle_state.StateID = VMT.stateID)) ON (sourecetypepopulation.sourceTyp eID = ratepervehicle_state.sourceTypeID) AND (sourecetype population.stateID = ratepervehicle_state.StateID) GROUP BY VMT.stateID, VMT.County, ratepervehicle_st ate.yearID, ratepervehicle_state.monthID, ratepervehicle_state. hourID, ratepervehicle_state.sourceTypeID, sourecetypepopul ation.sourceTypeFraction, VMT.SourceTypePopulation, ratepervehicle_state.poll utantID, ratepervehicle_state.SumOfratePerVehicle;

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



### 5.3 Ramp Emissions Ramp emission rates, calculated as discussed in Sec tion 3, are multiplied by ramp VMT in each county, year and source type. This query is shown in Table 33.

Table 33: Ramp Emissions Query

SELECT VMT.stateID, VMT.County, VMT.yearID, hourvmt fraction.hourID, hourvmtfraction.sourceTypeID, hourvmtfraction.hourVMTFraction, ramp_rate.pollutan tID, VMT.[Ramp VMT], ([Ramp VMT]*[hourVMTFraction])/13 AS HourlyRampVMT, ramp_r ate.ramprate, [HourlyRampVMT]*[ramprate] AS RampEmissions, ([HourlyRampVMT]*[ramprate])*340 AS RampEmissionsAnnual FROM hourvmtfraction INNER JOIN (VMT INNER JOIN ram p_rate ON (VMT.stateID = ramp_rate.StateID) AND (VMT.yearID = ramp_rate.yearID)) ON hourvmtfrac tion.hourID = ramp_rate.hourID WHERE (((hourvmtfraction.roadTypeID)=4)) ORDER BY VMT.stateID, VMT.County, VMT.yearID, hourv mtfraction.hourID, hourvmtfraction.sourceTypeID, ramp_rate.pollutantID ;



### 5.4 Summarizing Results Distance-based emissions by source type, vehicle-ba sed emissions by source type, and ramp emissions by source type are summed by county, year and pollu tant. This query is shown below. This is also where criteria may be set for limiting the results by state, county, year or pollutant. A sum of VMT and source type population is also useful as a verifica tion that all steps were run properly. The appropr iate monthID criteria should be set here. The annual av erage temperature profile is contained in April (monthID=4). July (monthID=7) should be used for summer weekday emissions.

Table 34:Results Query

SELECT EmissionsDistance_bySourceType.State, Emissi onsDistance_bySourceType.County, EmissionsDistance_bySourceType.yearID, EmissionsDis tance_bySourceType.pollutantName, Sum(EmissionsDistance_bySourceType.SumOfDailyVMT) A S SumOfSumOfDailyVMT, Sum(RampEmissions_Query.HourlyRampVMT) AS SumOfHour lyRampVMT, Sum(EmissionsDistance_bySourceType.SumOfAnnualizedV MT) AS SumOfSumOfAnnualizedVMT, First(EmissionsVehicle_Query.SourceTypePopulation) AS FirstOfSourceTypePopulation, Sum(EmissionsDistance_bySourceType.SumOfEmissionsDi st) AS SumOfSumOfEmissionsDist, Sum(Nz([emissionsVehicle],0)) AS EmissionsVeh, Sum( RampEmissions_Query.RampEmissions) AS SumOfRampEmissions, Sum(EmissionsDistance_bySourceT ype.SumOfAnnualEmissionsDist) AS SumOfSumOfAnnualEmissionsDist, Sum(Nz([Annualemissi onsVehicle],0)) AS AnnualEmissionsVeh, Sum(RampEmissions_Query.RampEmissionsAnnual) AS Sum OfRampEmissionsAnnual, (([SumOfSumOfEmissionsDist]+[EmissionsVeh]+[SumOfHo urlyRampVMT])/1000)*0.001102 AS DailyEmissionsTONS, (([SumOfSumOfAnnualEmissionsDist]+[AnnualEmissionsV eh]+[SumOfRampEmissionsAnnual])/1000)*0.0 01102 AS AnnualEmissionsTONS, Sum([RampEmissions_Query]![HourlyRampVMT]+[Emission sDistance_bySourceType]![SumOfDailyVMT] ) AS AllVMT

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

FROM (EmissionsDistance_bySourceType LEFT JOIN Emis sionsVehicle_Query ON (EmissionsDistance_bySourceType.pollutantID = Emiss ionsVehicle_Query.pollutantID) AND (EmissionsDistance_bySourceType.sourceTypeID = Emis sionsVehicle_Query.sourceTypeID) AND (EmissionsDistance_bySourceType.hourID = EmissionsV ehicle_Query.hourID) AND (EmissionsDistance_bySourceType.monthID = Emissions Vehicle_Query.monthID) AND (EmissionsDistance_bySourceType.yearID = EmissionsV ehicle_Query.yearID) AND (EmissionsDistance_bySourceType.County = EmissionsV ehicle_Query.County)) INNER JOIN RampEmissions_Query ON (EmissionsDistance_bySourceT ype.County = RampEmissions_Query.County) AND (EmissionsDistance_bySourceType.yearID = RampEm issions_Query.yearID) AND (EmissionsDistance_bySourceType.hourID = RampEmissi ons_Query.hourID) AND (EmissionsDistance_bySourceType.sourceTypeID = Ramp Emissions_Query.sourceTypeID) AND (EmissionsDistance_bySourceType.pollutantID = RampE missions_Query.pollutantID) GROUP BY EmissionsDistance_bySourceType.State, Emis sionsDistance_bySourceType.County, EmissionsDistance_bySourceType.yearID, EmissionsDis tance_bySourceType.pollutantName, EmissionsDistance_bySourceType.monthID, EmissionsDi stance_bySourceType.pollutantID HAVING (((EmissionsDistance_bySourceType.monthID)=4 ));

# Regional Air Quality Analyses for

Ozone, PM 2. 5 

# , and Regional Haze:

F inal Technical Support Document

April 25 , 2008

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

# Table of Contents

Section

- Title Pa ge

Executive Summary i i i

1

. 0 Introduction 1 1

. 1 SIP Requirements 1 1

. 2 Organization 2 1

. 3 Technical Work: Overview 3

2

. 0 Ambient Data Analyses 4 2

. 1 Ozone 4 2

. 2 PM 2. 5 2 1 2

. 3 Regional Haze 35

3

. 0 Air Quality Model ing 4 6 3

. 1 Selection of Base Year 46 3

. 2 Future Years of Interest 46 3

. 3 Modeling System 4 7 3

. 4 Domain/Grid Resolution 47 3

. 5 Model Inputs: Meteorology 48 3

. 6 Model Inputs: Emissions 51 3

. 7 Base Year Modeling Results 60

4

. 0 Attainment De monstration for Ozone and PM 2.5 7 1 4

. 1 Future Year Modeling Results 71 4

. 2 Supplemental Analyses 82 4

. 3 Weight of Evidence Determination for Ozone 82 4

. 4 Weight of Evidence Determination for PM 2. 5 90

5

. 0 Reasonable Progress Assessment for Regional Haze 9 3 5

. 1 Class I Areas Impacted 93 5

. 2 Future Year Modeling Results 96 5

. 3 Weight of Evidence Determination for Regional Haze 1 04

6

. 0 Summary 1 11

7

. 0 References 1 1 4

Appendix I Ozone and PM 2.5 Modeling Results Appendix II Ozone Source Apportio nment Modeling Results Appendix III PM 2.5 Source Apportionment Modeling Results Appendix IV Haze Source Apportionment Modeling Resultsiii EXECUTIVE SUMMARY

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

- hour ozone standard and 60 for the f ine particle ( PM 2.5 ) standard (1997 version s ). A map of these nonattainment areas is provided in the figure below. In addition, visibility impairment due to regional haze is a problem in the larger national parks and wilderness areas (i.e., Class I areas). There are 156 Class I areas in the U.S., including two in northern Michigan

.

Figure i

. Current nonattainment counties for ozone (left) and PM 2. 5 (right )

T o support the development of State Implementation Plans (SIPs) for ozone, PM 2.5 , and regional haze in the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin, technical analyses were conducted by the Lake Michigan Air Directors Consortium (LADCO ), its member states, and various contractors

. The analyses include preparation of regional emissions inventories and meteorological data, evaluation and application of regional chemical transport models, and collection and analysis of ambient monitoring data.

M onitoring data were analyzed to produce a conceptual understanding of the air quality problems. Key findings of the analyses include:

Ozone

• Current monitoring data (2005

- 2007) show about 20 sites in violation of the 8

- hour ozone standard of 85 parts per billion (ppb) . Historical ozone data show a steady downward trend over the past 15 years, especially since 2001

- 2003, due likely t o federal and state emission control programs.

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

• Inter

- and intra

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

.

PM 2.5

• Current monitoring data (200 5

- 200 7 ) show 3 0 sites in violation of the annual PM 2.5 standard of 15 ug/m 3

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

– 14 ug/m 3 ) and a significant local (urban) increment ( about 2

– 3 ug/m 3 ). Historical PM 2.5 data show a slight downward trend since deployment of the PM 2.5 monitoring network in 1999.

• PM 2.5 concentrations are also influenced by meteorology , but the re lationship is more complex and less well understood compared to ozone.

• O n an annual average basis, PM 2.5 chemical composition consists mostly of sulfate, nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data (2000

- 2004) show visibility levels in the Class I areas in northern Michigan are on the order of 2 2

– 2 4 deciviews . The goal of EPA’s visibility program is to achieve natural conditions, which is about 12 deciviews for these Class I areas, by the year 2064.

• Visibility im pairment is dominated by sulfate and nitrate.

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

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

Future year strategy modeling was conducted to determine whether existing (“on the books”) controls would be sufficient to provide for attainment of the standards for ozone and PM 2.5 and if not, then what additional emission reductions would be necessary f or attainment. Based on th e modeling and other supplemental analyses , the following general conclusions can be made:

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

### PM 2.5 concentrations and visibility levels

.

• The choice of

the base year affects the future year model projections. A key difference between the base years of 2002 and 2005 is meteorology. 2002 was more ozone conducive than 2005. The choice of which base year to use as the basis for the SIP is a policy decisio n (i.e., how much safeguard to incorporate).

• M odeling suggests that mo st sites are expected to meet the current 8

- hour ozone standard by the applicable attainment date, except for sites in western Michigan and, possibly, in eastern Wisconsin a nd northeast ern Ohio.v

• Modeling suggests that m ost sites a re expected to meet the current PM 2.5 standard by the applicable attainment date, except for sites in Detroit, Cleveland, and Granite City

.

T he regiona l modeling for PM 2.5 does not include air quality benefit s expected from local controls. States are conducting local

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

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

.

• These findings of residual nonattainment for ozon e and PM 2.5 are supported by current (2005

– 2007) monitoring data which show significant nonattainment in the region (e.g., peak ozone design values on the order of 90

– 93 ppb, and peak PM 2.5 d esign values on the order of 16

- 17 ug/m 3 ). It is unlikely that sufficient emission reductions will occur in the next couple of years to provide for attainment at all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual future year meteorology (e.g., if the weather conditions a re consistent with [or less severe than] 2005, then attainment is likely) and actual future year emissions (e.g., if the emission reductions associated with the existing controls are achieved, then attainment is likely). I f either of these conditions is n ot met, then attainment may be less likely.

• M ode ling s uggests that the new PM 2.5 24

- hour standard and the new lower ozone standard will not be met at several sites , even by 2018, with existing controls.

• Visibility levels in a few Class I areas in the eas tern U.S. are expected to be greater than

(less improved than) the uniform rate of visibility improvement value s in 2018 based on existing controls , including those in northern Michigan and s ome in the northeastern U.S. Visibility levels in m any other Cla ss I areas in the eastern U.S. are expected to be less than (more improved than) the uniform rate of visibility improvement value s in 2018. These results, along with information on the costs of compliance, time necessary for compliance, energy and non air

quality environmental impacts of compliance, and remaining useful life of existing sources, should be considered by the states in setting reasonable progress goals for regional haze.1 Section 1. 0 Introduction

This Technical Support Document summarizes the final air quality analyses conducted by the Lake Michigan Directors Consortium (LADCO) 1 and its contractors to support the development of State Implementation Plans (SIPs) for ozone, fine particles ( PM 2.5 ), and regional haze in the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin

. The analyses include preparation of regional emissions inventories and meteorological modeling data for two base years (2002 and 2005), evaluation and application of regional chemical transport models, and analysis of ambient monitoring data.

Two aspects of the analyses should be emphasize d. First, a regional , multi

- pollutant approach was taken in addressing ozone, PM 2.5 , and haze f or technical reasons (e.g., common ality in precursors, emission sources, atmosphe ric processes, transport influences, and geographic areas of concern), and practical reasons (e.g., more efficient use of program resources). Furthermore, EPA has consistently encouraged multi

- pollutant planning in its rule for the haze program (64 FR 357 19), and its implementation guidance for ozone (70 FR 71663) and PM 2.5 (72 FR 20609). Second, a weight

- of

- evidence approach was taken in considering the results of the various analyses (i.e., two sets of modeling results

-- one for a 2002 base year and on e for a 2005 base year

-- and ambient data analyses) in order to provide a more robust assessment of expected future year air quality.

The report is orga nized in the following sections. This I ntroduction p rovides an overview of regulatory requirements and background information on regional planning . Section 2 r eviews the ambient monitoring data and presents a conceptual model of ozone, PM 2.5 , and haze for the region . Section 3 d iscusses the air quality modeling analyses, including development of the k ey model inputs (emissions inventory and meteorological data), and basecase model performance evaluation . A modeled a ttainment d emonstration for o zone and PM 2.5 is presented in Section 4, along with relevant data analyses considered as part of the weight

- of

- evidence de termination

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

- of

- evidence determination . Finally, ke y study findings are reviewed and summarized in Section 6.

1.1 SIP Requirements For ozone, EPA promulgated designations on April 15, 2004 (69 FR 23858, April 30, 2004). In the 5

- state region, m ore than 100 counties were designated as nonattainment

. 2 The designations became effective on June 15, 2004. SIPs for ozone we re due no later than three years from the effective date of the nonattainment designations (i.e., by June 2007). The attainment date for ozone varies as a function of nonattainment classification. For the region, the attainment dates a re either June 2007 (marginal nonattainment areas), June 2009 (basic nonattainment areas), or June 2010 (moderate nonattainment areas).

1 A sub

- entity of LADCO, known as the Midwest Regional Planning Organization (MRPO), is responsible for the regional haze activities of the multi

- state organization.

2 Based on more recent air quality data, many counties in Indiana, Michigan, and Ohio were subsequently redesignated as attainment. As of December 31 , 2007, there are 53 counties designated

as n onattainment in the region.2

For PM 2.5 , EPA promulgated designations on December 17, 2004 (70 FR 944, January 5, 2005). In the 5

- state region, 70 c ounties were designated as nonattainment. 3 The designations bec a me effective on April 5, 2005. SIPs for PM 2.5 are due no later than three years from the effective date of the nonattainment designations (per section 172(b) of the Clean Air Act) (i.e., by A pril 2008) and for haze no later than three years after the date on which the Administrator promulgate d the PM 2.5 designations (per the Omnibus Appropriations Act of 2004) (i.e., by December 2007). The applicable attainment date for PM 2.5 nonattainment ar eas is five years from the date of the nonattainment designation (i.e., by April 2010).

For haze, the Clean Air Act sets “as a national goal the prevention of any future, and the remedying of any existing, impairment of visibility in Class I ar eas which impairment results from manmade air pollution.” T here are 156 Class I areas, including two in northern Michigan: Isle Royale National Park and Seney National Wildlife Refuge 4

. EPA’s visibility rule (64 FR 35714, July 1, 1999) require s reasonabl e progress in achieving “natural conditions” by the year 2064. As noted above, the first regional haze SIP wa s due in December 2007 and must address the initial 10

- year implementation period (i.e., reasonable progress by the year 2018). SIP requirements (pursuant to 40 CFR 51.308(d)) include setting reasonable progress goals, determining baseline conditions, determining natural conditions, providing a long

- term control strategy, providing a monitoring strategy (air quality and emissions), and establishing BART emissions limitations and associated compliance schedule.

1.2 Organization LADCO wa s established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. T he four states and EPA signed a Memorandum of Agreement (MOA) that initiated t he Lake Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee th e study. Additional MOAs were signed by the States in 1991 (to establish the Lake Michigan Ozone Control Program)

, January 2000 (to broaden LADCO’s responsibilities) , and June 2004 (to update LADCO’s mission and reaffirm the commitment to regional planning). In March 2004, Ohio joined LADCO. LADCO consists of a Board of Directors (i.e., the State Air Directors), a technical staff, and various workgroups. The main p urpose s of LADCO are to provide technical assessments for and assistance to its member states

, and to provide a forum for its member states to discuss regional air quality issues.

MRPO is a similar entity led by the five LADCO States and involves the fe derally recognized tribes in Michigan and Wisconsin

, EPA

, and Federal Land Managers (i.e., National Park Service, U.S. Fish & Wildlife Agency, and U.S. Forest Service )

. In October 2000, the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin signed a n MOA that established the MRPO. An

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

. M RPO has a similar
purpose as LADCO , but is focus ed on visibility impairment due to regional haze in the Federal Class I areas located inside the borders of the five states, and the impact of emissions from the five states on visibility impairment due to regional haze in the Federal Class I areas located outside the borders of the five states. MRPO works cooperatively with the R egional Planning Organizations (R POs ) representing other parts of the country. The RPOs sponsored several

3 USEPA subsequently adjusted the final designations, which resulted in 63 counties in the region being designate d as nonattainment (70 FR 19844, April 15, 2005)

.

4 Although Rainbow Lake in northern Wisconsin is also a Class I area, the visibility rule does not apply because the Federal Land Manager determined that visibility is not an air quality related value there

.3

joint projects and, with assistance by EPA, maintain regular contact on technical and policy matters.

1.3 Technical Work: Overview To ensure the reliability and effectiveness of its planning process, LADCO has made data collec tion and analysis a priority. M ore than $ 7 M in RPO grant funds were used for special purpose monitor ing, preparing and improving emissions inventories, and conducting air quality analyses 5 . An overview of the technical work is provided below.

Monitoring: Numerous monitoring projects were conducted to supplement on

- going state and local air pollution mo nitoring. These projects include rural monitoring (e.g., comprehensive sampling in the Seney National Wildlife Refuge and in Bondville, IL ) ; urban monitoring (e.g., continuation of the St. Louis Supersite); aloft (aircraft) measurements; regional ammonia monitoring; and organic speciation sampling in Seney, Bondville, and five urban areas.

Emissions: B aseyear emissions inventor ies were prepared for 2002 and 2005 . States provided point source and area source emissions data , and MOBILE6 input files and mob ile source activity data. LADCO and its contractors developed the emissions data for other source categories (e.g., select nonroad sources, ammonia, fires, and biogenics) and processed the data for input into an air quality model. To support control stra tegy modeling, future year inventories were prepared. The future years of interest include 2008 (planning year to address the 2009 attainment year for basic ozone nonattainment ares), 2009 ( planning year to address the 2010 attainment year for P M 2.5 and m oderate ozone nonattainment areas ), 2012 ( planning to address a 201 3 alternative attainment date), and 2018 (first milestone year for regional haze).

Air Quality Analyses: The weight

- of

- evidence approach relies on data analysis and modeling. Air quality data analyses were used to provide both a conceptual model (i.e., a qualitative description of the o zone , PM 2.5 , and regional haze problems) and supplemental information for the attainment demonstration . Given uncertainties in emissions inventories and mo deling

, especially for PM 2. 5 , these data analyses are a necessary part of the overall technical support.

Modeling includes baseyear analyses for 2002 and 2005 to evaluate model performance and future year strategy analyses to assess candidate control stra tegies. The analyses wer e conducted in accordance with EPA’s modeling guidelines ( EPA, 2007a)

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

.

5 Since 1999, MRPO has received almost $10M in RPO grant funds from USEPA.4 Section 2.0 Ambient Data Analyses

An extensive network of air quality monitors in the 5

- state region provides data for ozone (and its precurs ors)

, PM 2. 5 ( both total mass and individual chemical species), and visibility. These data are used to determine attainment/nonattainment designations, support SIP development, and provide air quality information to public (see, for example, www.airnow.gov ).

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

summar y of the physical, chemical

, and m eteorological processes that control the formation and distribution of pollutants i n a given region. This section reviews the relevant data analyses and describes our understanding of ozone, PM 2.5, and r egional haze with respect to c urrent

co nd i tions

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

, i nfluence of meteorology

( includin g t ransport patterns )

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

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

- hour period. This standard i s attained when the number of days per calendar year with maximum hourly av erage concentrations above 0.12 ppm is equal to or less than 1.0, averaged over a 3

- year period

, which generally reflects a design value (i.e., the 4 th highest daily 1

- hour value over a 3

- year period) less than 0.12 ppm.

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

- hour period 6

. T he standard is attain ed if

the 3

- year average of the 4th

- highest daily maximum 8

- hour average ozone concentrations ( i.e., the design value) measured at each monitor within an area is less than 0.08 pp m (or 85 ppb)

.

Current C onditions : A map of the 8

- hour ozone design values at each monitoring site in the region for the 3

- year period 2005

- 2007 is shown in Figure 1. The “hotter” colors represent higher concentrations , where yellow and orange dots re present sites w ith design values above the standard

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

- hour oz one NAAQS in the 5

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

Table 1 provides the 4 th

- highest daily 8

- hour ozone values and the associated design values since 2001 for several high monitoring sites throughout the region.

6 On March 12, 2008 , USEPA further t ighten ed the 8

- hour ozone standard to increase public health protection and prevent environmental damage from ground

- level ozone. USEPA set the primary (health) standard and secondary (welfare) standard at the same level : 0.075 ppm (75 ppb), averaged ove r an 8

- hour period.5 Figure 1

. 8

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

- 2007 )Key Sites '01 '02 '03 '04 '05 '06 '07 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

Lake Michigan Area Chiwaukee 99 116 88 78 93 79 85 101 94 86 83 85 Racine 92 111 82 69 95 71 77 95 87 82 78 81 Milwaukee-Bayside 93 99 92 73 93 73 83 94 88 86 79 83 Harrington Beach 102 93 99 72 94 72 84 98 88 88 79 83 Manitowoc 97 83 92 74 95 78 85 90 83 87 82 86 Sheboygan 102 105 93 78 97 83 88 100 92 89 86 89 Kewaunee 90 92 97 73 88 76 85 93 87 86 79 83 Door County 95 95 93 78 101 79 92 94 88 90 86 90 Hammond 90 101 81 67 87 75 77 90 83 78 76 79 Whiting 64 88 81 88 77 85 Michigan City 90 107 82 70 84 75 73 93 86 78 76 77 Ogden Dunes 85 101 77 69 90 70 84 87 82 78 76 81 Holland 92 105 96 79 94 91 94 97 93 89 88 93 Jenison 86 93 91 69 86 83 88 90 84 82 79 85 Muskegon 95 96 94 70 90 90 86 95 86 84 83 88

Indianapolis Area Noblesville 88 101 101 75 87 77 84 96 92 87 79 82 Fortville 89 101 92 72 80 75 81 94 88 81 75 78 Fort B. Harrison 87 100 91 73 80 76 83 92 88 81 76 79

Detroit Area New Haven 95 95 102 81 88 78 93 97 92 90 82 86 Warren 94 92 101 71 89 78 91 95 88 87 79 86 Port Huron 84 100 87 74 88 78 89 90 87 83 80 85

Cleveland Area Ashtabula (Conneaut) 97 103 99 81 93 86 92 99 94 91 86 90 Notre Dame (Geauga) 99 115 97 75 88 70 68 103 95 86 77 75 Eastlake (Lake) 89 104 92 79 97 83 74 95 91 89 86 84 Akron (Summit) 98 103 89 77 89 77 91 96 89 85 81 85

Cincinnati Area Wilmington (Clinton) 93 99 96 78 83 81 82 96 91 85 80 82 Sycamore (Hamilton) 88 100 93 76 89 81 90 93 89 86 82 86 Hamilton (Butler) 83 100 94 75 86 79 91 92 89 85 80 85 Middleton (Butler) 87 98 83 76 88 76 91 89 85 82 80 85 Lebanon ( Warren) 85 98 95 81 92 86 88 92 91 89 86 88

Columbus Area London (Madison) 84 97 90 75 81 76 83 90 87 82 77 80 New Albany (Franklin) 90 103 94 78 92 82 87 95 91 88 84 87 Franklin (Franklin) 83 99 84 73 86 79 79 88 85 81 79 81

Ohio Other Areas Marietta ( Washington) 85 95 80 77 88 81 86 86 84 81 82 85

St. Louis Area W. Alton (MO) 85 99 91 77 89 91 89 91 89 85 85 89 Orchard (MO) 88 98 90 76 92 92 83 92 88 86 86 89 Sunset Hills (MO) 88 98 88 70 89 80 89 91 85 82 79 86 Arnold (MO) 86 93 82 70 92 79 87 87 81 81 80 86 Margaretta (MO) 80 98 90 72 91 76 91 89 86 84 79 86 Maryland Heights (MO) 88 84 94 88 4th High 8-hour Value Design Values Table 1. Ozone Data for Select Sites in 5-State Region7

Meteorology

and Transport

: Most pollutants exhibit some dependence on meteorological factors, especially wind direction, because that governs which sources are upwind and thus most influential on a given sample. Ozone is even more dependent, since its production is driven by high temperatures and sunligh t

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

.

Figure 2. Number of h ot d ays and 8

- h our “ e xceedance” d ays in 5

- state region

Qualitatively, ozone episodes in the region are associated with hot weather, clear skies (sometimes hazy ), low wind speeds, high solar radiation, and southerly to southwesterly winds. These conditions are often a result of a s low

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

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

- day period, somewhat stagnant summertime conditions can lead to the build

- up in ozone and ozone precursor concentrations over a large spatial area

. Th is pollutant air mass can be advected long distances, resulting in elevated ozone levels in locations far downwind. An example of such an episode is shown in Figure 3.

Figure 3. Example of e levated r egional o zone c oncentrations (June 23

– 25, 2005)

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

- hour standard8

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

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

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

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

- hour standard

Aloft (aircraft) measurements in the Lake Michigan area also provide evidence of elevated regional backgro und concentrations and “plumes” from urban areas. For one example summer day (August 20, 2003

– see Figure 5), the incoming background ozone levels were on the order of 80

– 100 ppb and the downwind ozone levels over Lake Michigan were on the order of 100

- 150 ppb (STI, 2004)

.

Figure 5. Aircraft o zone m easurements over Lake Michigan (left) and a long u pwind b oundary (right)

– August 20, 2003 (Note: aircraft measurements reflect instantaneous values)9

As discussed in Section 4, residual nonattainment is projected in at least one area in the 5

- state region

– i.e., western Michigan. To understand the source regions likely impacting high ozone concentrations in western Michigan and e stimate the impact of these source regions , two s imple transport

- related analyses were performed

. First, back trajectories were constructed using the HYSPLIT model for high ozone days (8

- hour peak > 80 ppb) during the period 2002

- 2006 in western Michigan to characterize general transport patterns. Comp osite trajectory plots for all high ozone days based on data from three sites (Cass County, Holland, and Muskegon) are provided in Figure 6. The plots point back to areas located to the south

- southwest (especially, northeastern Illinois and northwestern I ndiana) as being upwind on these high ozone days.

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

Second, to assess the impact from Chicago /NW Indiana , Blanchard (2005 a ) compar ed ozone concentrat ions upwind (Braidwood, IL), within Chicago (ten sites in the City), and downwind (Holland and Muskegon) for days in 1999

– 2002 with southwesterly winds

- i.e., transport towards western Michigan. Figure 7 shows the distribution of daily peak 8

- hour ozon e concentrations by day

- of

- week, with a line connecting the mean values. The difference between day

- of

- week mean values at downwind and upwind sites indicates that Chicago /NW Indiana contribute s about 10

- 15 ppb to downwind ozone levels.10

Figure 7 . Mean day

- of

- week peak 8

- hour ozone concentrations at sites upwind, within, and downwind of Chicago, 1999

– 2002 (southwesterly wind days)

Based on this information, the following key findings related to transport can be made:

• Ozone transport is a problem af fecting many portions of the eastern U.S. The Lake Michigan area (and other areas in the LADCO region ) both receive high levels of incoming (transported) ozone and ozone precursors from upwind source areas on many hot summer days, and contribute to the hi gh levels of ozone and ozone precursors affecting downwind receptor areas.

• The presence of a large body of water (i.e., Lake Michigan) influences for the formation and transport of ozone in the Lake Michigan area . Depending on large

- scale synoptic winds and local

- scale lake breezes, different parts of the area experience high ozone concentrations. For example, under southerly flow, high ozone can occur in eastern Wisconsin, and under southwesterly flow, high ozone can occur in western Michigan.

• Downwi nd shoreline areas around Lake Michigan are affected by both regional transport of ozone and subregional transport from major cities in the Lake Michigan area

. Counties along the western shore of Michigan (from Benton Harbor to Traverse City, and

even as far north as the Upper Peninsula) are impacted by high levels of incoming (transported) ozone.11

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

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

- hour a nd 8

- hour design values for the 5

- state LADCO region over the last 25 years.

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

- State r egion

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

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

- 2003 period , due, in part, to the very low 4 th high values in 2004.

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

The imp rovement in ozone concentrations is also seen in the decrease in the number of sites measuring nonattainment over the past 15 years in the Lake Michigan area (see Figure 10). 75 85 95 105

'95-'97 '96-'98 '97-'99 '98-'00 '99-'01 '00-'02 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

65 75 85 95 105 115

'95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 

### Design Values

4 th High Values12 Figure 10 . Ozone d esign v alue m aps for 1995

- 1 997, 2000

- 2002, and 2 00 5

- 200 713

Given the effect of meteorology on ambient ozone levels, year

- to

- year variations in meteorology can make it difficult to assess trends in ozone air quality. Two approaches were considered to adjust ozone trends for meteorological influences : an air quality

- meteorology statistical m odel developed by EPA (i.e., Cox method), and statistical grouping of meteorological variables performed by LADCO (i.e., Classification and Regression Trees, or CART).

Cox M ethod

: This method uses a statistical m odel to ‘remove’ the annual effect of meteorology on ozone (Cox and Chu, 1993 ). A regression model was fit to the 1997

- 2007 data to relate daily peak 8

- hour ozone concentrations to six daily meteorological variables plus seasonal and annual factors (Kensk i, 2008a) . Meteorological variables included were daily maximum temperature, mid

- day average relative humidity, morning and afternoon wind speed and wind direction. The model is then used to predict 4 th high ozone values. By holding the meteorological e ffects constant, the long term trend can be examined independently of meteorology. Presumably

, any trend reflects changes in emissions of ozone precursors.

Figure 11a shows the meteorologically

- adjusted 4 th high ozone concentrations for several monitor s near major urban areas in the region . The plots indicate a general downward trend since the late 1990s for most cities, indicating that recent emission reductions have had a positive effect in improving ozone air quality.

A similar model was run to e xamine meteorologically adjusted trends in seasonal average ozone. This model incorporates more meteorological variables, including rain and long

- distance transport (direction and distance). Model development was docume nted in Camalier et al., 2007. The seasonal average trend s are shown in Figure 11b. Trends determined by seasonal model for the same set of sites examined above are consistent with those developed by the 4 th high model

.14 Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 11 a

. Trends in meteorologically adjusted 4 th high 8

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

– 200 7 )15 Chiwaukee, WI Sheboygan, WI

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

Adjusted for Weather

Unadjusted for Weather

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

Adjusted for Weather

Unadjusted for Weather

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

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

Adjusted for Weather

Unadjusted for Weather

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

Adjusted for Weather

Unadjusted for Weather

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

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

Adjusted for Weather

Unadjusted for Weather

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

Adjusted for Weather

Unadjusted for Weather

Indianapolis, IN

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

Adjusted for Weather

Unadjusted for Weather Figure 11b

. Trends in seasonal 8

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

– 200 7 )16

CART : Classification and Regression Tree (CART) analysis is another statistical technique which partitions data sets into similar groups (Breiman et al., 1984). CART analysis was performed using data for the period 1995

- 2007 for 22 selected ozone monitors w ith current 8

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

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

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

September) by its meteorological conditions. Similar d ays are assigned to nodes, which are equivalent to branches of the regression tree. Ozone time series for the higher concentration nodes are plotted for select sites in Fig ure 12 . By grouping days with similar meteorology, the influence of meteorological variability on the trend in ozone concentrations is partially removed; the remaining trend is presumed to be due to trends in precursor emissions or other non

meteorological influences. Trends over the 13

- year period at most sites were found to be declin ing, with the exception of Detroit which showed fairly flat trends. C omparison of the average of the high concentration node values for 2001

- 2003 v. 2005

- 2007 showed an improvement of about 5 ppb across all sites (even Detroit)

. The effect of meteorology was further examined by using an ozone conduciveness index (Kenski, 2008 b ) . This metric reflects the variability from the 1 3

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

. Examination of these plots indicates :

• 2002 and 2005 w

ere both above normal

, with 2002 tending to be more severe ; and

• 2001

- 2003 and 2005

- 2007 were both above normal, with no clear pattern in which period was more severe (i.e., ozone conduciveness values were similar at most sites, 2001

- 2003 values were higher at a few sites, and 2005

- 2007 values were higher at a few sites)

. Given the similarity in ozone conduciveness between 2001

- 2003 and 2005

- 2007, the improvement in ozone levels noted above is presumed to be due to non

- meteorological fact ors (i.e., emission reductions).

In conclusion, a ll three statistical approaches ( CART and the two nonlinear regression models) show a similar result ; ozone in the urban areas of the LADCO region has declined during the 1997

- 2007 period, even when meteoro logical variability is accounted for. The decreases are present whether seasonal average ozone, peak values (annual 4 th highs), or a subset of high days with similar meteorology are considered . The consistency in results across models is a good indicatio n that these trends reflect impacts of emission control programs

.17 Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 12

. Trends for higher o zone C ART groups (average ozone > 6 5 ppb) for seven Midwestern sites (199 5

– 20 0 7 )

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

– 200 7 )19

Pr ecursor S ensitivity : O zone i s formed from the reactions of hydrocarbons and nitrogen oxides under meteorological conditions that are conducive to such reactions (i.e., warm temperatures and strong sunlight )

. In areas with high VOC/NO x ratios, typical of r ural environments ( with low NO x), ozone tends to be more responsive to reductions in NO x . Conversely, in areas with low VOC/NO x

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

An analysis of V OC and NO x - limitation was conducted with the ozone MAPPER program, which is based on the Smog Production (SP) algorithm (Blanchard

, et al., 200 3 ) . The “Extent of Reaction” parameter in the SP algorithm provides an indication of VOC and NOx sensitivity:

Extent Range Precursor Sensitivity

< 0 .6 VOC

- sensitive 0. 6

– 0.8 Transitional > 0.8 NOx

- sensitive

A map of the Extent of Reaction values for high ozone days is provided in Figure 1 4 . As can be
seen, ozone is usually VOC

- l imited in cities and NOx

- limited in rural areas. ( Data from aircraft measurements suggest that ozone is usually NO x - limited over Lake Michigan and away from urban centers on days when ozone in the urban centers is VOC

- limited. ) T he highest ozone days we re found t o be NO x - limite d. This analysis suggests that a NOx reduction strategy would be effective in reducing ozone levels. Examination of day

- of

- week concentrations, however, raises some question about the effectiveness of NOx reductions.

Figure 1 4 . Mean afternoon extent of reaction (1998

– 2002)20

Blanchard (2004 and 2005 a ) examin ed weekend

- weekday differences in ozone and NO x in the Midwest. All urban areas in th e s e two stud ies exhibited substantially lower (40

- 60%) weekend concentrations of NO x compared to weekday concentrations. Despite lower weekend NO x concentrations, weekend ozone concentrations were not lower; in fact, most urban sites had higher concentrations of ozone

, although the increase was generally not statistically significant ( see Figure 1 5 ). This small but counterproductive change in local ozone concentrations suggests that local urban

- scale NO x reductions alone may not be very effective.

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

- h our o zone
– number of sites with weekend inc rease (positive values) v. number of sites with weekend decreases (negative values)

Two additional analyses, however, demonstrate the positive effect of NOx emission reductions on downwind ozone concentrations. First, Blanchard (2005 a ) looked at the effe ct of changes in precursor emissions in Chicago on downwind ozone levels in western Michigan. For the transport days of interest (i.e., southwesterly flow during the summers of 1999

– 2002), mean NOx concentrations in Chicago are about 50% lower and mean ozone concentrations at the (downwind) western Michigan sites are about 1.5

– 5.2 ppb (3

– 8 %) lower on Sunday compared to Wednesday. This degree of change in downwind ozone levels suggests a positive, albeit non

- linear response to urban area emission re ductions.

Second, Environ (2007a) examined the effect of differences in day

- of

- week emissions in southeastern Michigan on downwind ozone levels. This modeling study found that w eekend changes in ozone precursor emissions cause both increases and decrease s in Southeast Michigan ozone, depending upon location and time :21

• Weekend increases in 8

- hour maximum ozone occur in and immediately downwind of the Detroit urban area (i.e., in VOC

- sensitive areas).

• Weekend decreases in 8

- hour maximum ozone occur outside and downwind of the Detroit urban area (i.e., in NOx

- sen sitive areas).

• At the location of the peak 8

- hour ozone downwind of Detroit, ozone was lower on weekends than weekdays.

• Ozone benefits (reductions) due to weekend emission changes in Southeast Michiga n can be transported downwind for hundreds of miles.

• Southeast Michigan benefits from lower ozone transported into the region on Saturday through Monday because of weekend emission changes in upwind areas.

In summary, these analyses suggest that urban VOC reductions and regional (urban and rural) NOx reductions will be effective in lowering ozone concentrations. Local NOx reductions can lead to local ozone increases (i.e., NOx disbenefits), but this effect does not appear to pose a problem with respect to attainment of the standard. It should also be noted that urban VOC and regional NOx reductions are likely to have multi

- pollutant benefits (e.g., both lower ozone and P M 2. 5 impacts).

2.2 P M 2.5 In 1997, EPA adopted the PM 2. 5 standard s of 15 ug/ m 3 (annu al average) and 65 ug/m3 (24

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

- year average of the annual average PM 2. 5 concentration is less than or equal to the level of the standard. The daily standard is attained if the 98th percentile of 24

- h our PM 2. 5 concentrations in a year, averaged over three years, is less than or equal to the level of the standard

.
In 2006, EPA

revised the PM 2. 5 standard s to 15 ug/m 3 (annual average) and 35 ug/m 3 (24

- hour average).

Current C onditions : Maps of annual and 24

- hour PM 2. 5 design values for the 3

- year period 200 5

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

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

Table 2 provides the annual PM 2. 5 concentrations and associated design values since 200 3 for several high monitoring sites throughout the region.22

Figure 1 6

. PM 2.5 d esign v alues

- a nnual a verage (top) and 24

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

- 2007)2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07 Average w/ 2007 Average

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6
Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 Indy - Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 Indy - W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 Indy - Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5
Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.0 15.3 17.0 16.2 16.2 16.5 16.7 Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 Columbus - Ann Street Franklin 390490025 15.3 14.6 16.4 13.6 14.7 15.4 14.9 14.9 15.1 16.0 Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 15.9 16.2 16.3 St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5
Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.4 15.2 15.7 Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9
New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7
Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 Annual Average Conc. Design Values Table 2. PM2.5 Data for Select Sites in 5-State Region24

When EPA initially set the 2 4

- hour s tandard at 65 μg/m 3

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

Good < 1 5 ug/m 3
Moderate 15

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

- 65 μg/m 3 Unhealthy 65

- 150 μg/m 3

Fig ure 1 7 shows the frequency of these AQI categories for major metropolitan areas in the region. Daily average concentrations are often in the moderate range and occasionally in the USG range. Moder ate and USG levels can occur any time of the year.

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

- 2004)

Data Variability : PM 2. 5 concentrations vary spatially, temporally, and chemically in the region. This variability is discussed furt her below.

On an annual basis, PM 2.5 exhibits a distinct and consistent spatial pattern. As seen in Figure 1 6 , a cross the Midwest, annual concentrations follow a gradient from low values (5

- 6 μg/m 3 ) in

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

- 18 μg/m 3 ) in Ohio and along the Ohio River. In addition, c oncentrations in urban areas are higher than in upwind rural areas, indicating that local urban sources add a significant increment of 2

- 3 μg/m 3
to the regional backgro und of 12

- 14 μg/m 3 (see Figure 1 8 ).

Figure 1 8

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

Because monitoring for PM 2. 5 only began in earnest in 1999, after promulgation of the PM 2. 5 standar d

, limited data are available to assess trends

. T ime series based on federal reference method (FRM) PM 2. 5 - mass data show a downward trend in each state (see Figure 19 ) 7

.

Figure 19

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

7 Despite the general downward trend since 1999, all states experienced an increase during 2005. Further analyses are underway to understand this increase (e.g., examination of meteorological and emissions effects).26

A statistical analysis of PM 2. 5 trends was performed using the n onparametric Theil test for slope (Hollander and Wolfe, 1973). Trends were generally consistent around the region, for both PM

mass a nd for the individual components of mass. Figure 2 0 shows trends for PM 2. 5 based on FRM data at sites with six or more years of data since 1999. The size and direction of each arrow shows the size and direction of the trend for each site; solid arrows sh ow statistically significant trends and open arrows show trends that are not significant. Region

- wide decreases are widespread and consistent; all sites had decreasing concentration trends ( 13 of the 38 were statistically significant ) . The average decrea se for this set of sites is

- 0.24 ug/m 3 /year.

Figure 2 0

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

– 2006 )

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

- 0.15 to

- 0.56 ug/m 3 /year. The strongest and most significant decreases took place during the winter quarter (Jan uary

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

– 2006 )

PM 2.5 shows a slight variation from weekday to weekend , as seen in Figure 2 2

. Although m ost cities have slightly lower concentrations on the weekend , t he difference is usually less than 1 μg/m 3 . The re is a more pronounced w eekday/weekend difference at monitoring sites that are strongly source

- influenced. Rural monitors tend to show less of a weekday/weekend pattern than urban monitors.

Figure 2 2 Day

- of

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

- 2004)28

In the Midwest, PM 2.5 is made u p of mostly ammonium sulfate, ammonium nitrate , and organic carbon in approximately equal proportions on an annual average basis. Elemental carbon and crustal matter (also referred to as soil) contribute less than 5% each.

Figure 2 3 . Spatial map of P M 2. 5 chemical composition in the Midwest (2002

- 2003)

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

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

Ammonium sulfate peaks in the summer and is highest in the southern and eastern parts of the Midwest, closest to the Ohio River Valley. Sulfate is primarily a regional pollutant; concentrations are similar in rural and urban areas and highly correlated over large distances. It is for med when sulfuric acid (an oxidation product of sulfur dioxide) and ammonia react in the atmosphere, especially in cloud droplets. Coal combustion is the primary source of sulfur dioxide; ammonia is emitted primarily from animal husbandry operations and f ertilizer use.

Ammonium nitrate has almost the opposite spatial and seasonal pattern, with the highest concentrations occurring in the winter and in the northern parts of the region. Nitrate seems to have both regional and local sources, because urban co ncentrations are higher than rural upwind concentrations. Ammonium nitrate forms when nitric acid reacts with ammonia, a process that is enhanced when temperatures are low and humidity is high. Nitric acid is a product of the oxidation of nitric oxide, a pollutant that is emitted by combustion processes.

Organic carbon is more consistent from season to season and city to city , although concentrations are generally slightly higher in the summe r . Like nitrate, organic carbon has both regional and local co mponents. Particulate organic carbon can be emitted directly from cars and other fuel combustion sources or formed in a secondary process as volatile organic gases react and condense. In rural areas, summer organic carbon has significant contributions fr om biogenic sources.

Precursor S ensitivity:

Data from the Midwest ammonia monitoring network were analyzed with thermodynamic equilibrium models to assess the effect of changes in precursor gas concentrations on PM 2.5 concentrations (Blanchard, 200 5 b ). These analyses indicate that particle formation responds in varying degrees to reductions in sulfate, nitric acid, and ammonia

. Based on Figure 2 6 , which shows PM 2.5 concentrations as a function of sulfate, nitric acid (HNO 3), and ammonia (NH3), several k ey findings should be noted:

• P M 2. 5 mass is sensitive to reductions in sulfate at all times of the year and all parts of the region . Even though sulfate reductions cause more ammonia to be available to form ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase is generally offset by the sulfate reductions, such that PM 2.5 mass decreases.

• PM 2. 5 mass is also sensitive to reductions in nitric acid and ammonia . The greatest PM 2. 5 decrease in response to nitric acid reductio ns occurs during the winter , when nitrate is a significant fraction of PM 2. 5

.

• Under conditions with lower sulfate levels (i.e., prox y of future year conditions), P M 2. 5 is more sensitive to reductions in nitric acid compared to reductions in ammonia.

• Ammo nia becomes more limiting as one moves from west to east across the region.

Examination of weekend/weekday difference in PM

- nitrate and NOx concentrations in the Midwest demonstrate that reductions in local (urban) NO x lead to reductions , albeit non

propo rtional reductions, in PM

- nitrate (Blanchard, 200 4 )

. This result is consistent with analyses of continuous PM

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

Figure 2 6

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

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

Meteorology : PM 2 .5 concentrations are not as strongly influenced by meteorolog y as ozone, but the two pollutants share some similar meteorological dependencies. In the summer, conditions that are conducive to ozone (hot temperatures, stagnant air masses

, and low wind speeds due to stationary high pressure systems) also frequently give rise to high PM 2.5 . In the case of PM

, the reason is two

- fold: (1) stagnation and limited mixing under these conditions cause PM 2. 5 to build up, usually over several days, and (2) these conditions generally promote higher conversion of important precursors (SO 2 to SO 4 ) and higher emissions of some precursors, especially biogenic ca rbon. Wind direction is another strong determinant of PM 2.5 ; air transported from polluted source regions has higher concentrations.

Unlike ozone, PM 2.5 has occasional winter episodes. Conditions are similar to those for summer episodes, in that station ary high pressure and ( seasonally ) warm temperatures are usually factors. Winter episodes are also fueled by high humidity and low mixing heights.

PM 2. 5 chemical species show noticeable transport influences. T rajectory analyses have demonstrated that high PM

- sulfate is associated with air masses that traveled through the sulfate

- rich Ohio River Valley ( Poirot, et al, 2002 and Kenski

, 2004). Likewise, high PM

- nitrate is associated with air masses that traveled through the ammonia

- rich Midwest. Figure 2 7 shows results from an ensemble trajectory analysis of 17 rural eastern IMPROVE sites.

Figure 2 7

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

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

, one possible conclusion is that ammonia control in the Midwest could be effective at reducing nitrate concentrations. The thermodynamic equilibrium modeling shows that ammonia reductions would reduce PM concentrations in the Mid west, but that nitric acid reductions are more effective when the probable reductions in future sulfate levels are considered.33

Source Culpability :

Three source apportionment studies were performed using speciated PM 2. 5 monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 200 8 ). Figure 2 8 summarizes the source contributions from these studies. The studies show that a large portion of PM 2. 5 mass consists of secondary, regional impacts, which cannot be attributed to indivi dual facilities or sources (e.g., secondary sulfate, secondary nitrate, and secondary organic aerosols). Nevertheless, wind analyses (e.g., Figure 2 7 ) provide information on likely source regions. Regional

- or national

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

The stu dies also show that a smaller, yet significant portion of PM 2. 5 mass is due to emissions from nearby (local) sources. Local (urban) excesses occur in many urban areas for organic and elemental carbon, crustal matter, and, in some cases, sulfate. The stat istical analysis methods h elp to identify local sources and quantify their impact. This information is valuable to s tates wishing to develop control programs to address local impacts. A combination of national/regional

- scale and local

- scale emission redu ctions may be necessary to provide for attainment.

The carbon sources are not easily identified in complex urban environments. LADCO’s Urban Organics Study (STI, 2006) identified four major sources of organic carbon: mobile sources, burning, industrial s ources, and secondary organic aerosols. Additional sampling and analysis is underway in Cleveland and Detroit to provide further information on sources of organic carbon.34 0 1 2 3 4 5 6 7CoalCombustionSecondaryNitrateMobileIndustrialSoilBurningug/m3 Northbrook (Jan 03-Mar 05) Cincinnati (Dec 03-Mar 05) Indianapolis (Jan 02-Mar 05) Allen Park (2002-2004) Dearborn (Jun 02-May 05) Bondville (2002-2004) St. Louis (2004) Granite City (2002-2004) Tikhon (Jan 03-Jun05) Orange (Apr 01-Mar 05) Lorain (Feb 02-Dec04)

0 2 4 6 8 10

sulfate nitrate OM diesel soil steel ind. zinc ind. copper mixed ind. Allen Park

Dearborn

La w ndale Springfield

Tikhon Craig

Taft

Figure 2 8

. Major Source Contributions in the Midwest based on Hopke , 2005 (upper left), STI, 2006 (upper right), and STI, 200 8 (lower left) (Note: the labeling of similar source types varies between studies

– e.g., organic carbon/mobile sources are named gasoline and diesel by Hopke , mobile b y STI 2006, and OM and diesel by STI 2008 )35

2.3 Haze Section 169A of the Clean Air Act sets as a national goal “the prevention of any future

, and the remedying of any existing, impairment of visibility in mandatory Class I Federal areas which imp airment re sults from manmade air pollution” . To impl ement this provision, in 1999, EPA adopted regulations to address regional haze visibility impairment (USEPA, 1999)

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

establish reasonable progress goals , which provide for improved visibility on the most impaired (20% worst) days sufficient to achieve natu ral conditions by the year 2064, and for no degradation on the least impaired (20% best) days

.
The primary cause of impaired visibility in the Class I areas is pollution by fine particles that scatter light. The degree of impairment, w hich is expressed in terms of visual range, light extinction (1/Mm)

, or deciviews (dv), depends not just on the t otal PM 2. 5 mass concentration, but also on the chemical composition of the particles and meteorological conditions.

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

- year baseline period ( 2000

- 2004 ) is shown in Figure 2 9

.

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

– 2004) , units: Mm - 136

Initially, the baseline (2000

– 2004) visibility condition values were derived using the average for the 20% worst and 20% best days for each year, as reported on the VIEWS website: http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx . These values were calculated using the original IMPROVE equation for reconstructed light extinction

. Three changes were made to the baseline calculations to produce a new set of values. First, the reconstructed light extinction equation was revised by the IMPROVE Steering Committee in 2005

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

Second, d ue to sampler problems, the 2002

- 2004 data for Boundary Waters were invalid for certain chemical species. (Note, sulfate and nitrate data were valid.) A “substituted” dat a set was developed by using values from Voyageurs for the invalid species



### .
Third, LADCO identified a number of days during 2000

- 2004 where data capture at the Class I monitors was incomplete ( Kenski, 2007b ). The missing data cause the se days to be exclu ded from the baseline calculations. However, the light extinction due to the remaining measured species is significant ( i.e., above the 80 th percentile). It makes sense to include these days in the baseline calculations, because they are largely dominate d by anthropogenic sources. ( Only one of these days is driven by high organic carbon, which might indicate non

- anthropogenic aerosol from wildfires. ) As seen in Table 3 , inclusion of these days i n the baseline calculation results in a s mall

, but measurab le

, effect on the baseline values (i.e., v alues increase from 0.2 to 0.8 dv )

.

Table 3 . Average of 20% worst days, with and without missing data days

Average Worst Day DV, per RHR Average Worst Day DV,

with Missing Data Days Difference

BOWA 19.59 19.8 6 0.27
ISLE 20.74 21.59 0.85
SENE 24.16 24.38 0.22
VOYA 19.27 19.48 0.21

A summary of the initial and updated baseline values for the Class I areas in northern Michigan and northern Minnesota are presented in Table 4

. The updated baseline values reflect the most current, complete understanding of visibility impairing effects and, as such, will be used for SIP planning purposes.37 Table 4 . Summary of v isibility m etrics (deciviews) for n orthern Class I a reas

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

2000 2001 2002 2003 2004 Baseline Value 2018

UR I Value Natural

Conditions Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.0 9
BWCA 19.85 19.99 19.68 19.73 17.65 19.38 17.47 11.21 Isle Royale 20.00 22.00 20.80 19.50 19.10 20.28 18.17 11.22 Seney 22.60 24.90 24.00 23.80 22.60 23.58 20.73 11.37

20% Best Days

2000 2001 2002 2003 2004 Baseline Value Natur al Conditions Voyageurs 6.30 6.20 6.70 7.00 5.40 6.32 3.41
BWCA 5.90 6.52 6.93 6.67 5.61 6.33 3.53 Isle Royale 5.70 6.40 6.40 6.30 5.30 6.02 3.54 Seney 5.80 6.10 7.30 7.50 5.80 6.50 3.69

New IMPROVE Equation (Cite: VIEWS, March 2006) 20% Worst Days

2000 2001 2002 2003 2004 Baseline Value 2018

UR I Value Natural

Conditions Voyageurs 19.55 18.57 20.14 20.25 18.87 19.48 17.74 12.05
BWCA 20.20 20.04 20.76 20.13 18.18 19.86 17.94 11.61 Isle Royale 20.53 23.07 2 1.97 22.35 20.02 21.59 19.43 12.36 Seney 22.94 25.91 25.38 24.48 23.15 24.37 21.64 12.65

20% Best Days

2000 2001 2002 2003 2004 Baseline Value Natural Conditions Voyageurs 7.01 7.12 7.53 7.68 6.37 7.14 4.26
BWCA 6.00 6.92 7.00 6 .45 5.77 6.43 3.42 Isle Royale 6.49 7.16 7.07 6.99 6.12 6.77 3.72 Seney 6.50 6.78 7.82 8.01 6.58 7.14 3.73

Notes: (1) BWCA values for 2002

- 2004 reflect "substituted" data. (2) New IMPROVE equation values include Ken s ki, 200 7 adjustment for missing days

UR I

= uniform rate of improvement38

As noted above, the goal of the visibility program is to achieve natural conditions. Initially, the natural conditions values for each Class I area were taken directly from EP A guidance (EPA, 2003). These values were calculated using the original IMPROVE equation. This equation was revised by the IMPROVE Steering Committee in 2005, and the new IMPROVE equation was used to calculate updated natural conditions values. The upda ted values are reported on the VIEWS website.

A summary of the initial and updated natural conditions values are presented in Table 4 . The updated natural conditions values (based on the new IMPROVE equation) will be used for SIP planning purposes.

Data Variability : For the four northern Class I areas, the most important PM 2. 5 chemical species are ammonium sulfate, ammonium nitrate, and organic carbon. The contribution of these species on the 20% best and 20% worst visibility days (based on 2000

– 2004 data) is provided in Figure 30 . For the 20% worst visibility days, the contributions are: sulfate = 35

- 55%, nitrate = 25

- 30%, and organic carbon = 12

- 22%. Although the chemical c omposition is similar , sulfate increases in importance from west to east and concentrations are highest at Seney ( the easternmost site )

. It should also be noted that sulfate and nitrate contribute more to light extinction than to PM 2. 5 mass because of their hygroscopic properties.

20% Best Days 20% Worst Days

0 20 40 60 80 100 120

VOYA BOWA ISRO Seney VOYA BOWA ISRO Seney sea salt

soil

elem. carbon

coarse mass organic carbon

nitrate

sulfate

Figure 30 . Chemical composition of light extinction for 20% best visibility days (left) and 20% worst visibility days (right) in terms of Mm - 1

Analysis of PM 2. 5 mass and chemical species for rural IMPROVE (and IMPROVE

- protocol) sites in the ea stern U.S. showed a high degree of correlation between PM 2. 5 - mass , sulfate, and nitrate levels (see Figure 3 1 ). The Class I sites in northern Michigan and northern Minnesota , in particular, are highly correlated for PM 2. 5 mass, sulfates, and org anic carbo n mass (AER, 2004)

.39

Figure 3 1

. Correlations a mong IMPROVE (and IMPROVE

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

Lo ng

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

- 0.005 ug/year) and SO4 (

- 0.04 ug/year) and an increase in NO3 (+0.01 ug/year). These PM 2. 5 and SO4 trends are generally consistent with long

- term trends at other IMPROVE sites in the eastern U.S. , which have shown widespre ad decreases in SO4 and PM 2. 5 (DeBell, et al, 2006 ). Detecting changes in nitrate has been hampered by uncertainties in the IMPROVE data for particular years and

, thus

, this estimate should be considered tentative.

Haze in the Midwest Class I areas has no strong seasonal pattern. P oor visibility days occur throughout the year, as indicated in Fig ure 3 2

. (Note, in contrast, other parts of the country

, such as Shenandoah National Park in Virginia, show a strong tendency for the worst air quality days to occur in the summer months. ) This figure and Figure 3 3 (which presents the monthly average light extinction values based on all sampling days) also show that sulfate and organic carbon concentrations are higher in the summer, and nitrate concentrations are higher in the winter, sugge sting the importance of different sources and meteorological conditions at different times of the year.40

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

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

Precursor S ensitivity: Results from two analyses using t hermodynamic equilibrium model s provide information on the effect of changes in precursor concentrations on PM 2.5 concentrations (and, in turn , visibility levels) in the northern Class I areas . First, a preliminary analysis using data collected at Seney indicate d that PM 2.5 there is most sensitive to reductions in sulfate, but is also sensitive to reductions in nitric acid (Blanchard

, 2004).

Second, an analysis was performed using data from the Midwest ammonia monitoring network for a site in Minnesota

-- Great River Bluffs, which is the closest ammonia monitoring site to the northern Class I areas (Blanchard, 2005 b )

. F igure 34 shows PM 2.5 co ncentrations as a function of sulfate, nitric acid ( HNO3 ) , and ammonia ( NH3 ) . Reductions in sulfate (i.e., movement to the left of baseline value [represented by the red star]), as well as reductions in nitric acid (i.e., movement downward) and NH3 (i.e., movement to the left), result in lower PM 2.5 concentrations. Thus, reductions in sulfate, nitric acid, and ammonia will lower PM 2.5 concentrations and improve visibility in the northern Class I areas.

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

Meteorology and T

ransport : The role of meteorology in haze is complex. Wind speed and wind direction govern the movement of air masses from polluted areas to the cl eaner wilderness areas. As noted above, increasing humidity increases the efficiency with which sulfate and nitrate aerosols scatter light. Temperature and humidity together govern whether ammonium nitrate can form from its precursor gases, nitric acid a nd ammonia. Temperature and sunlight also play an indirect role in emissions of biogenic organic species that condense to form particulate organic matter; emissions increase in the summer daylight hours.

Trajectory analyses were performed to understan d transport patterns for the 20% worst and 20% best visibility days. The composite results for the four northern Class I areas are provided in Figure 3 5

. The o range areas are where the air is most likely to come from, and the green
areas are where the ai r is least likely to come from . As can be seen, bad air days are generally associated with transport from regions located to the south, and good air days with transport from Canada.43

Figure 3 5 . Composite back trajectories f or light extinction

- 20% best visibility days (left) and 20% worst visibility days (right) ( 2000

– 2005 )

Source Culpability : Air quality data analyses (including the trajectory analyses above) and dispersion modeling were used to provide information on source region and source sector contributions to regional haze in the northern Class I areas (see MRPO, 200 8 ). Based on this information, the most important contributing states are Michigan, Minnesota, and Wisconsin, as well as Missouri, North Dakota, Iow a, Indiana and Illinois (see, for example, Figure 3 5 above). The most important contributing pollutants and source sectors are SO2 emissions from electrical generating units (EGUs) and certain non

- EGUs, which lead to sulfate formation, and NOx emissions f rom a variety of source types (e.g., motor vehicles), which lead to nitrate formation. Ammonia emissions from livestock waste and fertilizer applications are also important, especially for nitrate formation.

A source apportionment study was performed usi ng monitoring data from Boundary Waters and statistical analysis methods (DRI, 2005). The study shows that a large portion of PM 2.5 mass consists of secondary, regional impacts, which cannot be attributed to individual facilities or sources (e.g., seconda ry sulfate, secondary nitrate, and secondary organic aerosols). Industrial

sources contribute about 3

- 4% and mobile sources about 4

- 7 % to PM 2.5 mass.

A special study was performed in Seney to identify sources of organic carbon ( Sheesley, et al, 2004 ). As seen in Figure 3 6 , t he highest PM 2.5 concentrations occurred during the summer, with organic carbon being the dominant species. The higher summer organic carbon concentrations were attributed mostly to secondary organic aerosols of biogenic origin b ec ause of the lack of primary emission markers, and concentrations of know biogenic

- related species (e.g., pinonic acid

– see Figure 36) were also high during the summer.44

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

- related organic carbon species in Seney (bottom )45

Although the Seney study showed that biomass burning was a relatively small contributor to organic carbon on an annual average basis, episodic impacts are apparent (see, for example, high organic carbon days in Figure 32). To assess further whether burning is a significant contributor to visibility impairment in the northern Class I areas, the PM 2.5 chemical speciation data were examined for days with high organic carbon and elemental carbon conc entrations, which are indicative of biomass burning impacts. Only a handful of such days were identified:

Table 5 . Days with high OC and EC concentrations in northern Class I areas

Site 2000 2001 2002 2003 2004 Voyageurs

--- --- Jun 1 Aug 25 Jul 17
Jun 28
Jul 19 Boundary Waters

--- --- Jun 28 Aug 25 Jul 17
Jul 19 Isle Royale

--- --- Jun 1 Aug 25

--- Jun 28 Seney

--- --- Jun 28

--- ---

Back trajectories on these days point mostly to wildfi res in Canada. Elimination of these high organic carbon concentration days has a small effect in lowering the baseline visibility levels in the northern Class I areas (i.e., Minnesota Class I areas change by about 0.3 deciviews and Michigan Class I areas change by less than 0.2 deciviews). This suggests that fire activity, although significant on a few days, is on average a relatively small contributor to visibility impairment in the northern Class I areas.

In summary, these analyses show that organic ca rbon in the northern Class I is largely uncontrollable.46 Section 3.0 Air Quality Modeling

Air quality models are relied on by federal and state regulatory agencies to support their planning efforts

. Used properly, models can assist policy makers in dec iding which control programs are most effective in improving air quality, and meeting specific goals and objectives. For example, models can be used to conduct “what if” analyses, which provide information for policy makers on the effectiveness of candida te control programs.

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

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

pollutant analyses. Application of the modeling system (i.e., attainment demonstration for ozone and PM 2. 5 , and reasonable progress assessment for haze) is covered in the following sections.

3.1 Selection of Base Year Two base year s were used in the modeling analyses: 2002 and 2005. EPA’s modeling guidance recommends using 2002 as the baseline inventory year, but also allows for use of an alternative baseline inventory year, especially a more recent ye ar. Initially , LADCO conducted modeling with a 2002 base year (i.e., Base K/Round 4 modeling, which was completed in 2006)

. A decision was subsequently made to conduct modeling with a 2005 base year (i.e., Base M/Round 5, which was completed in 2007). As discussed in the previous section, 2002 and 2005 both had above normal ozone conducive conditions, although 200 2 was more severe compared to 200 5 . Examination of multiple base years provides for a more complete technical assessment. Both sets of model runs are discussed in this document.

3.2 Future Years of Interest To address the multiple attainment requirements f or ozone and PM 2.5 , and reasonable progress goals for regional haze, several future years are of interest:

2008 Planning year for oz one basic nonattainment areas (attainment date 2009) 8 2009 Planning year for ozone moderate nonattainment areas and PM 2.5 nonattainment areas (attainment date 2010) 2012 Planning year for ozone moderate nonattainment areas and PM 2.5 nonattainment

areas , with 3

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

8 According to USE PA’ s ozone implementation rule (USEPA, 2005), emission reductions needed for attainment must be implemented by the beginning of the ozone season immediately preceding the area’s attainment date. The PM2.5 implementation rule contains similar provisions

– i.e., emission reductions should be in place by the beginning of the year preceding the attainment date (USEPA, 2007 c ). The logic for requiring emissions reductions by the year (or season) immediately preceding the attainment year follows from language in the Clean Air Act , and the ability for an area to receive up to two 1

- year extensions. Therefore, emissions in the year preceding the attainment year should be at a level that is consistent with attainment. It also follows that the year preceding the att ainment year should be modeled for attainment planning purposes.47

Detailed emissions inventories were developed for 2009 and 2018. To support modeling for other future years, less rigorous emissions processing w as conducted (e.g., 20 12 emissions

were estimated for several source sectors by interpolating between 2009 and 2018 emissions).

3.3 Modeling System The air quality analyses were conducted with the CAMx model, with emissions and meteorology generated using EMS (and CONCEPT ) and MM5, respectively. The selection of CAMx as the primary model is based on several factors: performance, operator considerations (e.g., ease of application and resource requirements), technical support and documentation, model extensions (e.g., 2

- way nest ed grids, process analysis, source apportionment, and plume

- in

grid), and model science

. CAMx model set

- up for Base M and Base K is summarized below:

Base M (2005) Base K (2002) • CAMx v4.50 * CAMx 4.30 • CB05 gas phase chemistry * CB

- IV wi th updated gas

- phase chemistry • SOA chemistry updates * No SOA chemistry updates • AERMOD dry deposition scheme * Wesley

- based dry deposition • ISORROPIA inorganic chemistry • ISORROPIA inorganic chemistry • SOAP organic chemistry • SOAP organic chemistry • RADM aqueous phase chemistry • RADM aqueous phase chemistry • PPM horizontal transport • PPM horizontal transport

3. 4 Domain/Grid Resolution The National RPO grid projection w as used for this modeling. A subset of the RPO domain was

us ed for the LADCO modeling. For PM 2. 5 and haze , the large eastern U.S. grid at 36 km (see box on right side of Figure 3 6 ) was used . A PM 2. 5 sensitivity run was also performed for this domain at 12 km. For ozone , the smaller grid at 12 km (see shaded port ion of the box on the right side of Figure 3 7 ) w as used for most model runs . An ozone sensitivity run was also performed with a 4km sub

- grid over the Lake Michigan a rea and Detroit/Cleveland.

The vertical resolution in the air quality model consists of 16 layers extending up to 15 km , with higher resolution in the boundary layer.

Figure 3 7

. Modeling grids

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

## 12 km

36 km48

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

- Generation NCAR/Penn State Meteorological Model (MM5)

– version 3.6.3 for the year s 2001

– 2003, and version 3.7 for the year 2 005

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

.

Figure 3 8 . MM5 m odeling domain for 2001

- 2003 (left) and 2005 (right)

The annual 2002 36 km MM5 simulation was completed by Iowa DNR. The 36/12 km 2

- way nested simulation for the summers of 2001, 2002, and 2003 were conducted jointly by Illinois EPA and LADCO. The 36 km n on

- summer portion of the annual 2003 simulation was conducted by Wisconsin DNR. The annual 2005 36/12 km (and summer season 4 km) MM5 modeling was completed by Alpine Geophysics

. Wisconsin DNR also completed 36/12 km MM5 runs for the summer season of 200 5.

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

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

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

Figure 3 9 . Sub

- domains used for m odel performance for 2001

- 2003 (left) and 2005 (right)

A summary of the performance evaluation results for the meteorological model ing is provided below. Further details are provided in two summary reports (LADCO, 2005 and LADCO, 2007 c ).49

Temperature : The biggest issue with the performance in the upper Midwest is the existence of a cool diurnal temperature bias in the winter and warm temperature bias over night during the

summer (see Figure 40 ). These features are common to other annual MM5 simulations for the central United States and do not appear to adversely affect mod el performance

.

Figure 40

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

Note: months are represented from left to right (January to December) and days are represented from top to bottom (1 to 30(31)

– i.e., upper left hand corner is January 1 and lower right hand corner is December 31

Wind Field

s : The win d fields are generally good . Wind speed bias is less than 0.5 m/sec and wind speed error is consistently between 1.0 and 1.5 m/sec. Wind direction error is generally within 15

- 30 degrees.

Mixing Ratio: The mixing ratio (a measure of humidity) is over

- pr edicted in the late spring and summer months, and mixing ratio error is highest during this period. There is little bias and error during the cooler months when there is less moisture in the air.

Rainfall: The modeled and observed rainfall totals show go od agreement spatially and in terms of magnitude in the winter, fall, and early spring months. There are , however, large over

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

. These over

predictions are seen spatially and in ma gnitude over the entire domain, particularly in the Southeast United States , and are likely due to excessive convective rainfall being predicted in MM5. This over

- prediction of rainfall in MM5 does not necessarily translate into over

- prediction of wet dep

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

non

- convective rainfall output by MM5, but estimates wet scavenging by hydromete o rs using cloud, ice, snow, and rain water mixing ratios output by MM5. Nevertheless, this could have an effect on model performance for PM 2. 5 , as discussed in Section 3.7, and may warrant further attention.50

Figure 4 1

. Comparison of observed (left column) and modeled (right column) monthly rainfall for July 2002 (top) and July 2005 (bottom)51

3.6 Model Inputs: Emissions E mission inventories were prepared for two base years: 2002 (Base K) and 2005 (Base M), and several future years: 2008, 2009, 2012, and 2018. Further details of the emission inventories are provide d in two summary reports (LADCO, 2006 a and LADCO, 200 8 a ) and the following pages of the LADCO web site:

http://www.ladco.org/tech/emis/basek/BaseK_Reports.htm http://www.ladco.org/tech/emis/r5/round5_reports.htm

For on

- road, nonroad, ammonia, and biogenic sources, emissions were estimated by models. For the other sectors (point sources, area sources, and MAR [comme rcial marine, air craft, and railroads]), emissions were prepared using data supplied by the LADCO States and other RPOs

.

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

. A ddit ional detail is provided in Table s 6 a (all sectors

– tons per day) and 6b (EGUs

– tons per year)

.

VOC NOx SO2

0 2000 4000 6000 8000 10000 12000

2002

(K) 2005 (M) 2002

(K) 2005 (M) 2002

(K) 2005 (M) WI

OH

MI

IN

IL

0 2000 4000 6000 8000 10000 12000

2002

(K) 2005 (M) 2002

(K) 2005 (M) 2002

(K) 2005 (M) Area

Nonroad+MAR

Onroad

Non-EGU

EGU

Figure 4 2 . Base K and Base M e missions for 5

- s tate LADCO r egion by state (top) and source sector (bottom) , u nits: tons per summer weekday

A summary of the base year emissions by sector for the LADCO States is provided below.VOC Base M BaseK Base M BaseK BaseK Base M NOx Base M BaseK Base M BaseK BaseK Base M SOX Base M BaseK Base M BaseK BaseK Base M PM2.5 Base M BaseK Base M BaseK BaseK Base M

July 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018

Nonroad

IL 224 321 164 257 149 130 213 324 333 263 275 224 154 155 31 33 5 5 0.6 0.4 0.4 30 24 14

IN 125 195 94 160 95 95 128 178 191 142 158 141 141 89 17 19 3 3 3 0.3 0.2 17 13 7

MI 348 414 307 350 276 222 271 205 239 159 197 133 93 112 19 22 3 3 0.5 0.3 0.3 22 18 11
OH 222 356 161 294 145 126 238 253 304 195 246 162 109 135 23 29 4 5 0.5 0.3 0.4 27 22 13

WI 214 238 194 203 175 140 157 145 157 114 129 97 69 77 13 15 2 2 0.3 0.2 0.2 14 12 7
5-State Total 1133 1524 920 1264 840 713 1007 1105 1224 873 1005 757 566 568 103 118 17 18 4.9 1.5 1.5 110 89 52
U.S. Total 8463 9815 5442 8448 5244 6581 6041 9060 6057 8120 5832 5100 505 654 117 153 104 13 573 750 475

MAR

IL 10 11 10 10 10 10 6 277 246 201 228 195 186 165 0 22 0 19 0 0 17 7 6 4

IN 5 5 5 5 5 5 3 123 93 89 87 87 84 65 0.2 8 0.2 7 0.2 0.2 6 2 2 2

MI 7 7 7 7 7 8 7 114 87 112 82 111 110 65 0.6 21 0.7 14 0.7 0.8 8 3 3 2
OH 8 7 8 7 8 8 5 177 134 128 126 126 122 94 0.4 14 0.3 12 0.3 0.3 10 4 4 2

WI 4 4 4 4 4 4 3 79 58 59 54 59 57 41 12.7 8 9.5 6 9.5 8.7 5 2 2 1
5-State Total 34 34 34 33 34 35 24 770 618 589 577 578 559 430 13.9 73 10.7 58 10.7 10 46 18 17 11
U.S. Total 307 317 321 157 329 346 334 4968 4515 4002 1813 3964 3919 3812 620 512 509 122 509 503 290 147 57 165

OtherArea

IL 679 675 688 594 700 738 582 62 48 68 48 70 73 49 11 11 12 16 12 13 16 40 64 69

IN 354 391 365 358 373 398 384 62 56 65 58 67 69 59 158 32 150 32 151 153 32 2 2 2

MI 518 652 516 562 520 541 549 49 49 52 50 53 54 51 71 29 68 29 68 68 28 111 114 120
OH 546 604 550 506 558 593 487 50 93 59 108 60 62 108 22 6 34 15 35 35 14 19 35 34

WI 458 315 467 290 474 506 293 32 37 34 37 34 35 37 9 17 9 13 10 10 13 11 12 12
5-State Total 2555 2637 2586 2310 2625 2776 2295 255 283 278 301 284 293 304 271 95 273 105 276 279 103 183 227 237
U.S. Total 17876 21093 18638 18683 20512 24300 3856 4899 4100 4220 4418 5357 2075 2947 2062 2559 2189 2709 2735 2621 2570

On-Road

IL 446 341 314 268 260 197 151 890 748 578 528 474 300 201 9 4 3 13 10 6

IN 405 282 237 235 193 150 138 703 541 425 402 313 187 173 11 3 2 9 7 2

MI 522 351 335 269 303 217 163 926 722 680 501 619 385 204 14 4 3 12 9 3
OH 574 680 365 424 340 238 242 1035 934 609 693 512 270 274 18 4 4 16 12 4

WI 238 175 144 119 117 88 68 481 457 303 322 226 118 138 9 2 2 8 6 2
5-State Total 2185 1829 1395 1315 1213 890 762 4035 3402 2595 2446 2144 1260 990 61 17 14 58 44 17
U.S. Total 14263 7825 23499 13170

EGU

IL 9 7 8 6 8 9 7 712 305 227 275 244 231 224 1310 1158 944 958 789 810 869 13 34 77

IN 6 6 6 6 7 6 6 830 393 406 370 424 283 255 2499 2614 1267 1033 1263 1048 1036 16 73 74

MI 12 6 11 4 11 12 4 448 393 218 242 219 247 243 1103 1251 1022 667 1031 1058 725 15 25 29
OH 5 4 6 5 7 7 6 1139 408 330 280 322 271 285 3131 3405 1463 1326 994 701 983 28 94 80

WI 3 5 3 2 4 4 3 293 213 146 165 139 147 177 602 545 512 460 492 500 435 0 22 25
5-State Total 35 28 34 23 37 38 26 3422 1712 1327 1332 1348 1179 1184 8645 8973 5208 4444 4569 4117 4048 72 248 285
U.S. Total 214 140 195 124 197 215 138 14371 10316 7746 7274 7721 7007 6095 31839 34545 20163 16903 17629 14727 14133 685 1131 1571

Non-EGU

IL 313 221 286 218 305 350 258 356 330 334 218 338 343 235 373 423 251 335 257 249 346 16 17 19

IN 150 130 160 137 170 199 167 238 179 212 175 216 225 178 292 218 270 216 274 290 180 35 36 44

MI 123 116 115 119 122 139 140 216 240 208 242 214 229 271 162 158 166 148 171 185 163 20 21 25
OH 77 84 75 87 79 90 104 177 175 157 166 160 167 178 240 289 231 288 210 216 293 27 28 33

WI 88 84 97 87 104 120 106 98 97 91 93 92 94 81 163 156 154 152 155 156 85 0 0.1 0.1
5-State Total 751 635 733 648 780 898 775 1085 1021 1002 894 1020 1058 943 1230 1244 1072 1139 1067 1096 1067 98 102 121
U.S. Total 4087 3877 4409 4700 5378 6446 6730 6129 6435 6952 5759 5630 6093 6340 6970 1444 1777

IL 1681 1576 1470 1353 1432 1434 1217 2621 2010 1671 1572 1545 1287 1029 1725 1656 1212 1337 1059 1072 1251 119 155 189

IN 1045 1009 867 901 843 853 826 2134 1453 1339 1250 1248 989 819 2966 2902 1690 1294 1691 1492 1256 81 133 131

MI 1530 1546 1291 1311 1239 1139 1134 1958 1730 1429 1314 1349 1118 946 1356 1495 1260 865 1271 1312 927 183 190 190
OH 1432 1735 1165 1323 1137 1062 1082 2831 2048 1478 1619 1342 1001 1074 3416 3761 1732 1650 1240 953 1304 121 195 166

WI 1005 821 909 705 878 862 630 1128 1019 747 800 647 520 551 800 750 687 635 667 675 540 35 54 47
5-State Total 6693 6687 5702 5593 5529 5350 4889 10672 8260 6664 6555 6131 4915 4419 10263 10564 6581 5781 5928 5504 5280 539 727 723Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35
IPM 2.1.9 241,000 73,000 1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107
IPM3.0 - will do 140,296 0.214 62,990 0.096 IPM3.0 - may do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45
IPM 2.1.9 377,000 95,000 1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120
IPM3.0 - will do 417,000 0.552 94,000 0.125 IPM3.0 - may do 417,000 0.552 94,000 0.125

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35
IPM 2.1.9 399,000 100,000 1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158
IPM3.0 - will do 244,151 0.484 79,962 0.158 IPM3.0 - may do 244,151 0.484 79,962 0.158

OH 1,306,296,589 2001 - 2003 (average) 1,144,484 1.75 353,255 0.54
IPM 2.1.9 216,000 84,000 1,628,081,545 IPM3.0 (base) 316,883 0.389 96,103 0.118
IPM3.0 - will do 348,000 101,000 IPM3.0 - may do 348,000 101,000

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36
IPM 2.1.9 155,000 46,000 675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167
IPM3.0 - will do 150,340 0.445 55,019 0.163 IPM3.0 - may do 62,439 0.185 46,154 0.137

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40
IPM 2.1.9 147,000 51,000 534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224
IPM3.0 - will do 115,938 0.434 59,994 0.224 IPM3.0 - may do 100,762 0.377 58,748 0.220

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42
IPM 2.1.9 86,000 42,000 447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186
IPM3.0 - will do 54,315 0.243 49,488 0.221 IPM3.0 - may do 51,290 0.229 39,085 0.175

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37
IPM 2.1.9 281,000 78,000 893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163
IPM3.0 - will do 237,600 0.532 72,950 0.163 IPM3.0 - may do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45
IPM 2.1.9 109,000 72,000 342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258
IPM3.0 - will do 56,175 0.328 58,850 0.343 IPM3.0 - may do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80
IPM 2.1.9 12,000 15,000 44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114
IPM3.0 - will do 4,464 0.199 2,548 0.114 IPM3.0 - may do 4,464 0.199 2,548 0.114 Table 6b. EGU Emissions for Midwest States (2018)54

On

- road Sources : For 2002, EMS was run by LADCO using VMT and MOBILE6 inputs supplied by the LADCO States. EMS was run to generate 36 days (weekda y, Saturday, Sunday for each month) at 36 km, and 9 days (weekday, Saturday, Sunday for June

– August) at 12 km. For 2005

, CONCEPT was run by a contractor (Environ) using transportation data (e.g., VMT and vehicle speeds) supplied by the state and local p lanning agencies in the LADCO States and Minnesota for 24 networks. These data were first processed with T3 (Travel Demand Modeling [TDM] Transformation Tool) to provide input files for CONCEPT to calculate link

- specific, hourly emission estimates (Envir o n, 2008 ) . CONCEPT was run with meteorological data for a July and January weekday, Saturday, and Sunday (July 15

– 17 and January 16

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

.

VOC Emissions NOx Emissions

Figure 4 3

. M otor v ehicle emissions for VOC (left) and NOx (right) for a July weekday (2005)

Off

- road Sources : For 2002 and 2005, NMIM and NMIM2005, respectively, were run by Wisconsin DNR. Additional off

- road sectors (i.e., commercial marine, aircraft, and railroads [MAR] ) were handled separately. Local data for agricultural equipment, construction equipment, commercial marine, recreational marine, and railroads were prepared by contractors (Environ

, 2004, and E.H. Pechan , 2004 ). For Base M, u pdated local data for railro ads and commercial marine w ere prepared by a contractor (Environ , 2007 b, 2007 c )

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

Table 7 . Locomotive and c ommercial m arine e missions for the five LADCO States ( 2002 v. 2005)

Railroads (TPY) Commercial Marine (TPY)

2002 2005 2002 2005

VOC 7,890 7,625 1,562 828

CO 20,121 20,017 8,823 6,727

NOx 182,226 145, 132 64,441 42,336

PM 5,049 4,845 3,113 1,413

SO2 12,274 12,173 25,929 8,637

NH3 86 85

---- ----55

Area Sources : For 2002 and 2005, E MS was run by LADCO using data supplied by the LADCO States to produce weekday, Saturday, and Sunday emissions for e ach month. For 2005,

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

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

.

Point Sourc es : For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO States to produce weekday, Saturday, and Sunday emissions for each month. For EGUs, t he annual and summer season emissions were temporalized for modeling purposes using profiles prepared by Scott Edick (Michigan DEQ) based on CEM data

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

cursors of secondary PM 2. 5 organ ic carbon mass

.

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

Ammonia : For Base M, t he CMU

- based 2002 (Base K) ammonia emissions were projected to 2005 using growth factors from the Round 4 emissions modeling. These emissions were then adjusted by applying temporal factors by month based on the process

- based ammonia emissions model

(Zhang, et al, 2005, and Mansell, et al, 2005) . A plot of average daily emissions by state and month is provided in Figure 4 5

. A spatial plot of emissi ons is provided in Figure 4 6 , which shows high emissions densities in the central U.S.

0 300 600 900 1200 1500 1800

Jan Fed March Apr May Jun Jul Aug Sept Oct Nov Dec

Illinois Indiana Iowa Michigan

Minnesota Ohio Wisconsin Figure 4 5 . Average daily ammonia emissions for Midwest States by month (2005)

- (units: average daily emissions

– tons per day)56 Figure 4 6

. Ammonia emissions for a July weekday (2005)

– 12 km modeling domain

Canadian Emissions: For Base M, Scott Edick (Michigan DEQ) processed the 2005 Canadian National Pollutant Release Inventory , Version 1.0 (NPRI). Specifically, a subset of the NPRI data (emissions and stack par ameters) relevant to the air quality modeling were reformatted

. The resulting emissions represent a significant improvement in the base year emissions.

A spatial plot of point source SO2 and NOx emissions is provided in Figure 4 7

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

Figure 4 7

. Canadian point source emissions for SO2 (left) and NOx (rig ht)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 agricultu ral burns. Projected emissions were also developed for 2010 and 2018 assuming “no smoke management” and “optimal smoke management” scenarios. A n early mode l sensitivity run showed very little difference in modeled PM 2. 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 e mission 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.)

S ource se ctor emission summaries for the base years and future years are shown in Figure 4 8

. Additional detail is provided in Table s 6 a 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 4 8 . Base year and future year emissions for 5

- State LADCO Region (TPD, July wee kday)

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 C ONCEPT Modeling (July weekday

– ton s per day )

Year State Sum of CO Sum of TOG Sum of NO x 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 1,65 0,128,709.9

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 69 3.5 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 28 8,042,232.1

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 1,967,052,025.859

For EGUs

, future year emissions were based on IPM2.1.9 modeling completed by the RPOs in July 2005 Base K and IPM3.0 co mpleted by EPA in February 2007 for Base M. Several CAIR scenarios were assumed:

Base K 1a: IPM2.1.9, wi th 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 assum ed 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 IPM 3.0, 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 Sta tes, future year emission files were based on dat a 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 informa tion 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

• I nspection

- m aintenance programs, including IL’s vehicle emissions tests (NE IL), IN’s vehicle emissi ons testing program (NW IN), OH’s E

- check program (NE OH), and WI’ s vehicle inspection program (SE WI)

– note: a speci al emissions modeling run was done for the Cincinnati/Dayton area to reflect the removal of the state’s E

- check p rogram and inclusion of l ow RVP gasoline

• Refo rmulated 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 Spa rk 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

• Aeros ol coatings

• Portable fuel containers

Power Plants

• Title IV (Phases I and II)

• NOx SIP Call

• Clean Air Interstate Rule60 Other Point Sources

• VOC 2

- , 4

- , 7

- , and 10

- year MACT standards

• C ombustion turbine MACT

Other controls included in the modeling include: c onsent decrees (refineries, ethanol plants, and ALCOA) 9

, NOx RACT in Illinois and Ohio 10 , 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 ad ditional 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 Mo d el ing 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 m odel performance evaluation focus ed on the magnitude, spatial pattern, and temporal of modeled and measured concentrations. This exercise wa s intended to assess whether, and to w hat degree, confidence in the model is warranted (and to assess whether model improvements are necessary)

. Model performance w

as assess ed by comparing modeled and monitored concentrations. Graphical (e.g., side

- by

- side spatial plots, time series plots, a nd scatter plots) and statistical analyses were conducted

. No rigid acceptance/rejection criteria w ere used for this study. Instead, the statisti cal guidelines recommended by EPA and other modeling studies (e.g., modeling by the other RPOs) w ere used to assess the re asonableness of the results

. The model performance results presented here describe how well the model replicates observed ozone and PM 2. 5 concentrations after a series of iterative improvements to model inputs.

Ozone : Spatial plots are provi ded for high ozone period s in June 2002 and June 2005 (see Figure s 4 9a and 49b ). The plot s 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

- co unting 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 4 9a . Modeled (top) v. m onitored (bottom) 8

- h our o zone c oncentrations: June 20

– 25, 200262

Figure 4 9 b Modeled (top) v. m onitored (bottom) 8

- h our o zone c oncentrations: June 23

– 28 200563

Standard model performance statistics were g enerated 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 ge nerally within 30% (see Figure 50 ).

Fig ure 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 200564

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 5 2 ). 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 5 2

. 4 km grids for Lake Michigan region and Detroit

- Cleveland region

Hourly time series plots were prepared for several monitors (see Figure 5 3 ). The results are similar at 12 km and 4 km, with some site

- by

- site and day

- by

- day differences.

Figure 5 3 . Ozone time series plots for 12 km and 4 km modeling (June 17

- 29, 2002)65

An additional diag nostic analysis was performed to assess the response of the modeling system t o 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 95 th 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 t he modeled and monitored ozone concentration change s (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 str ategies.

Figure 5 4 . Comparison of change in predicted and observed ozone concentrations (2002 v. 2005)66

PM 2. 5 : Time series plots of the monthly average mean bias and annual fractional bias for Base M and Base K are shown in Figure 5 5 . 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 ca rbon 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 5 5 . PM 2. 5 Model p erformance

- m onthly a verage m ean b ias and a nnual f ractional b ias for Base M (left column) and Base K (right column) 

### Base K

Base M67

Two analyses were undertaken to understand sulfate model performance for 2005 :

• Assess Meteorological Influences: The MM5 model p erformance evaluation showed that rainfall is over

- predicted by MM5 over most of the domain during the summer months (LADCO, 2007 c ). Because CAMx does not explicitly use the rainfall output by MM5, this may or may not result in over

- predict ion sulfate wet deposition (and under

- predict ion 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 5 6) showed a noticeable difference from the ba secase (i.e., higher sulfate concentrations), and suggest

s 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 base case 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 p erformance (see purple line in Figure 56 ) showed lit tle difference from the basecase.

Figure 5 6 . Monthly sulfate bias for Base M (MRPO EGU) v. two sensitivity analyses (Note: positive values indicate over

- prediction, negative v alues 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 val ues were only slightly different from the current base strategy run . In general, t he future year values (without wet deposition)

were a little higher (+ 0.1 5 ug/m 3 or less ) in the Ohio Valley and a little lower (

- .1 0 ug/m 3 of less) in the Great Lakes regio n. 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 re sults are not expected to change very much

. Time series plots of daily sulfate, nitrate, elemental carbon, and organic carbon concentrations for three Mi dwest

ern locations are presented in Figure s 5 7 (2002) and 5 8 (2005) . These results are consistent wit h 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 Deposition68 

### SULFATE NITRATE ORGANIC CARBON Seney

Detroit

Chicago

Figure 5 7 . Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 200569 

### SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 5 8 . Time series of sulfate, nit rate, and organic carbon at three Midwest sit es for 200570

In summary, model performance for ozone and PM 2. 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 patte rns of modeled concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to reductions in precursor emissions.

PM 2. 5

• Good agreement in the magnitude of fine particle mass, but some spec ies 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: slig htly overestimated in the winter in the 2002 base year, but good a greement 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 ca rbon 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 thes e 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, consisten t 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 attainm ent modeling is supplemented by additional information to provide a weight of evidence determination.71 Section 4.0 Attainment Demonstration for Ozone and PM 2./ 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 PM 2.5 and if not, then what additional emission reductions would be necessary for attainment

. Traditionally, attainment demonstrations involved a “bright line” t est in which a single modeled value was compared to the ambient standard. To provide a more robust assessment of expecte d future year air quality, EPA’ s modeling guidelines call for consideration of supplemental information. This section summarizes the r esults of the primary (guideline) modeling analysis and a weight of evidence de termination 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 effectiven ess of existing and possible additional control programs. T he model was used in a relative sens e to project future year values, which are then compared to the standard to determine attainment / nonattainment. Specifically, t he modeling test consists of the following steps:

( 1 ) Calculate base year design values : F or ozone and PM 2.5 , 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

- 2007 11

( 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 valu es: For each grid cell (with a monitor), t he 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 2 002 and 2005 base year design values for ozone and PM 2. 5 is provided in Figure 5 9 . 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 5 9 . 2002 v. 2005 base year design values for ozone (top) and PM 2. 5 (bottom) 

### 2002 2005

Statis tical 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 4 1 Peak Value 19.3 ug/m 3 17.7 ug /m 3 Ave Exceedance Amount 1.2 ug/m 3 0.9 ug/m 373

O zone results are pr ovided for those grid cells with ozone monitors. The RRF calculation consider s 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.) PM 2.5 results are provid ed for those grid cells with FRM ( PM 2.5 - m ass) monitors. Spatial mapping w as performed to extrapolate PM 2.5 - speciation data from STN and IMPROVE sites to FRM sites. RRF values for PM 2.5 were derived as a function of quarter and chemical species.

Additional, hot

- spot modeling will be performed b y the states for certain PM 2.5 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 Ga ussian 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 th e 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 PM 2. 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 PM 2. 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 1 0 (PM 2. 5 ) , and s patial maps of the Base M future year concent r ations are provided in Figures 60

- 6 2

.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 2009 2012 2008 Table 9. Summary of Ozone Modeling ResultsCounty 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 Results76 2005 (observed) 2009

2012 2018

Figure 60 . Observed base year and projected future year design values for ozone

– Base M77 2005 (observed) 2009

2012 2018

Figure 61 . Observed base year and projected future year design values for PM 2. 5 (annual average)

– Base M78 2005 (observed) 2009

2012 2018

Figure 6 2 . Observed base year and projected future year design values for PM 2. 5 (24

- hr average)

- Base M79

The number of monitors with design va lues 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/m 3 )
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 2 6 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 Sect ion 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 PM 2. 5 concentrations (i.e., about 0.1 ug/ m 3 for PM 2. 5 and 0. 1

- 0.2 ppb for ozone). Second, EPA suggested adjust ments 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 PM 2. 5 concentrations (on the order o f 0.1

– 0.2 ug/m 3 ) , 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 show s the 2009 values with 12 km and 4 km grid
sp acing for the LADCO modeling and similar modeling conducted by a stakeholder group (M idwest 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 y ear (2009) v alues for Lake Michigan area (top) and D etroit

- Cleveland r egion (bottom)

T hese 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 resu lts for northwester n Indiana /northeastern Illinois should be viewed with caution (i.e., p robably 1

– 4 ppb too high).81

In summary, the ozone modeling provide s the following information for the nonattainment areas in the region (see Table 1 2 ):

Table 1 2 . Ozone Nonattain ment Ar eas in the LADCO Region (as of December 31 , 2007)

Area Name Category Number of C ounties Attainment

D eadline

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 p lanning year. Rather, 2005

– 2007 air quality data are available to determine attainment.

Basi c (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 Ind ianapolis

• 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 PM 2. 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 7 5 ppb. With respect to the new lower 24

- hour PM 2. 5 standard, the modeling shows 13 sites in 2012 and 10 in 2018 with design values greater t han 35 ug/m 3

.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 sup plemental

an alyses are recommend ed:

• additional modeling

• analyses of trends in ambient air quality and emissions, and

• observational models and diagnostic analyses

Furthermore, according to EPA’s guid elines , if the future year model ed values are “close” to the standar d (i.e., 82

– 87 ppb for ozone and 14.5

– 15.5 ug/m 3 for PM 2. 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 attai nment.

A WOE determination for ozone and PM 2. 5 is provided in the follow ing 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) :

Ozo ne 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 De termination for Ozone The WOE determination for ozone consists of the prim ary 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 i n Section 4.1. Key findings from th is modeling include:

• B ase M regional modeling shows attainment by 2008 and 2009 at all sites, except Holland (MI), and attainment at all sites by 2012.

• B ase K modeling results reflect generally higher future year values, and show more sites in nonattainm ent 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 mode ling also shows that the proposed lower 8

- hour standard wi ll not be met at many sites , even by 2018, with existing controls.

A dditional

Modeling : Four a dditional 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 o f 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 estim ates of future year ozone concentrations were calculated based on model

- based RRFs and appropriate monitor

- based concentrations for each year between 2001 and 2007. R RF values for 2001, 2003, 2004, 2006, and 2007 were derived based on the 2002 and 2005 mo deling results . M onitor

- based concentrations reflect 4 th high values, design values, or average of three design values centered on the year in question . The probability of attainment was de termined as the percentage of these seven estimates below the sta ndard. 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 re done with different types of assump tions for calculating base year design values (i.e., using the 3

- year period centered on base year , and using the h ighest 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 an d “acceptable” model performance )

. The results for several high concentration sites are presented in Table s 1 3 a and 13 b 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 asso ciated 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 monitori ng network just meets o r 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 predicte d to be greater than the NAAQS.

Based on exam ination of the spatial plots in Figures 4 9a and 49b , the most notable areas of high modeled ozone concentrations are over the Great Lakes. Over

- water monitoring, however, is not required by EPA 12

. A cursory analysis of unmonitored areas for ozone was perf ormed 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 o bserved values (in monitored grid cells). A spatial map of the estimated 2009 values is provided in Figure 6 4 . 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 o f high ozone throughout the region.

12 Air quality measurements over Lake Michigan were collected by LADCO previously to understand

oz one 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 1 3 a. Primary and Additional Ozone Modeling Results

– Lake Michigan and Cleveland Areas (2009)
2009 Modeling Results Lake Michigan Area C leveland 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 guidan ce - 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

- 2002bas eyear)
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 9 5.0

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 Pro

jected 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

- y ear 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 8 1.4 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 84.585

Table 1 3 b. Primary and Additional Ozone Modeling Results

– Cincinnati, Columbus, St. Louis, Indianapolis, and Detroit (2009)

2009 M odeling Results Cincinnati Area Columbus St. Louis Area Indianapolis Area Detroit Area Wilmington Lebanon Sycamore New Albany W. Alton OrchardFarm Noblesville Fortville New Haven
390271002 39165007 390610006 390490029 291831002 291831004 1 80571001 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) 0.8 85 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 thre e 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 8 6.3 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 Yea r 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 85.286 Fig ure 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, 2007 b ). 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 stan dard 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 T

rends : EPA’s modeling guid elines note that while air quality models are generally the m ost 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 tren ds should first be normalized to account for year

- to

- year variations in meteorological conditions ( EPA, 2002)

. Meterologically

- adjusted 4 th high 8

- hour ozone con centrations were derived using th e 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 trend s in precursor emissions. Both VOC and NOx em issions affect ozone concentrations. Given that observation

based 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 con sidered 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 va lues 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 Cle veland 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 6 5 . Estimates of Future Year Ozone Concentrations

– Lake Michigan Area (Sheboygan and Holland), Cincinnati (Sycamore ), and Cleveland (Ashtabula)

Note: Primary (guideline) modeling valu e s (Base K and Base M results) are represented by large red diamond s , additional modeling values by small black circles, and trends

- based value s by small pink squares88

Observation al Models and Diagnostic Analyses: The observation

- based modeling (i.e., MAPPE R) is presented in Section 3. The key findings from this modeling are that most urban

areas are VOC

- limited and rural areas are NOx

- li mited.

The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more quantitative informati on 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 6 6 ) and 6 source sectors (EGU point, non

- EGU point, on

- road, off

- road, area, and b iogenic sources) at o zone monitoring site s in the region.

Figure 6 6 . Source regions (left) and key monitoring sites (right) for ozone modeling analysis

Modeling results for 2009 (Base M ) and 20 12 ( 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 contribution s from NOx emissions are shown in blue, and from VOC emissions in green. )

The sector

- level results (see, for example, Figure 6 7 ) show that on

- road and nonroad NOx emissions generally have the largest contributio ns 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 6 7 . Source

- sector res ults for Holland (left) and Ashtabula (right) monitors

– 20 09 (Base M) Kenosha County

Cook County

Indianapolis

Holland

St. Louis Detroit

Cincinn ati Door County

Cleveland89

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 nonatt ainment 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 6 8 . Source

- region results for Holland (left) and Ashtabula (right) monitors

– 20 09 (Base M)

Summary : Air quality modeling and other supplemental analyses were performed to estimate future year ozone concentrations. Based on this information, the followi ng general conclusions can be made:

• E xisting (“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 o f 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 cu rrent 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 th e 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 we ather 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 han d, if either of these conditions is not met, then attainment may be less likely.90

4.3 Weight

- of

- Evidence Determination for PM 2.5 The WOE determination for PM 2. 5 consists of the primary modeling and other supplemental analyses. A summary of this informati on 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, a nd Granite City, and attainment at all sites by 2012, except for Detroit and Granite City.

T he regiona l modeling for PM 2. 5 does not reflect any air quality benefit expected from local controls. States are conducting local

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

- scale modeling, to support their attainment demonstrations for PM 2. 5

• Base K modeling results reflect generally higher future year values, and show more sites in nonattainment in 2009 and 2012 compared to the Bas e M modeling. The difference in the two modeling analyses is due mostly t o lower base year design values in Base M.



### • Base K and Base M modeling analyses are considered “SIP quality”, so the attainment demonstration for PM 2. 5 should reflect a weight

- of

- evi dence approach, with consideration of monitoring based information.

• Base M mode ling also shows that the new PM 2. 5 24

- hour standard wi ll not be met at many sites , even by 2018, with existing controls.

A dditional Modeling : EPA’s latest regional PM 2. 5 model ing was considered as corroborative information . This modeling was performed as part of the September 2006 revision to the PM 2. 5 standard ( USEPA, 2006)

. EPA applied the CMAQ model with 2001 meteorology to estimate PM 2. 5 levels in 20 15 and 2020 first with national rules in effect or proposed , and then with additional controls to attain the current standard (15 ug/m 3 annual/65 ug/m 3 daily). Additional analyses were performed to evaluate strategies for attaining more stringent standards in 2020 (15/35, and 14/35) . Baseline (20 15 ) PM 2. 5 levels were predicted to be above the current standard in four counties in the LADCO region : Madison County, IL at 15.2 ug/m 3 , Wayne County, MI at 17.4, Cuyahoga County, OH at 15.4 , and Scioto County, OH at 15.6. These resul ts are consistent with LADCO’s Base K modeling for 2012/ 2018, whic h is not surprising given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v. 2002).

Observation al Models and Diagnostic Analyses : The observation

- based modeli ng (i.e., application of thermodynamic equilibrium models) is presented in Section 3. The key findings from this modeling are that PM 2. 5 mass is sensitive to reductions in sulfate , nitric acid, and ammonia concentrations . Even though sulfate reductions c ause more ammonia to be available to form ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase is generally offset by the sulfate reductions, such that PM 2.5 mass decreases. Under conditions with lower sulfate levels (i. e., proxy of future year conditions), PM 2.5 is more sensitive to reductions in nitric acid compared to reductions in ammonia

.