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Regional Air Quality Analyses for Ozone, PM2.5, and Regional Haze: 

Base C Emissions Inventory (September 12, 2011)

The Lake Michigan Air Directors Consortium, in conjunction with its member states, are 

conducting air quality modeling for ozone and fine particles (PM2.5) in preparation for future 

State Implementation Plan (SIP) work. The modeling analyses are focused on examining model 

performance (by comparing base year modeled and monitored concentrations) and providing

strategy-relevant information (through a series of model sensitivity runs). This document 

describes the development of the latest base year emissions inventory (Base Cv7). 

Improvements to the base year inventory are summarized in the table below.

Table 1. Summary of Base C Inventory Improvements

Sector Activity

Point Updated State data (Indiana, Iowa, Minnesota, Ohio, Wisconsin)

Updated EGU-nonEGU splitting

Area Updated State data (Indiana, Iowa, Minnesota, Ohio, Wisconsin)

Improved PM speciation profiles

Revised woodburning emissions

Nonroad Incorporated ERTAC rail data

Other Reviewed and identified appropriate speciation profiles

Overview

The primary base year selected for initial modeling is 2007.1 Meteorology was developed for 

using MM5 and WRF. (Note: WRF meteorology is being used in the latest base year modeling.) 

The base year inventory was developed in phases, as summarized in Table 2. 

Outstanding emissions inventory issues include: 

 Reliability of the MOVES-based on-road emission estimates. (Note, a separate effort 

is underway to do a top-down evaluation of these emissions using ambient 

monitoring data.)

 Reliability of new MEGAN biogenic emission estimates

 Development of improved agricultural ammonia emission estimates

 Use of improved PM and VOC speciation profiles

 Appropriateness of assumed transportable dust fraction (i.e., 90%)

 Inclusion of both PM-filterable and PM-condensible emissions

 Inclusion of fire emissions

 

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Limited modeling was also performed for a 2008 base year, but a decision was made to focus on 2007 

because of generally higher air quality concentrations in that year. In addition, the LADCO States 

recently decided to update the modeling base year to 2011. Work is underway to prepare emissions and 

meteorology for this year. Until the new inventory and meteorology are ready, LADCO will continue to 

use the 2007 base year data in its regional modeling analyses.
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Table 2. Contents of Basecase Inventory

Sector Base B Base Cv2 Base Cv4 Base Cv7

EGU Point 2007 CEM data (annualized w/ 

2005-2007 temporal)

2007 CEM data (annualized w/ 

updated temporal)

(updated EGU-nonEGU splitting) 

Improved Translation from

Staging tables to NIF(IL,OH)

Non-EGU Point 2008 State data 

2008 State data

(updated EGU-nonEGU splitting and 

PM-condensibles) 

Updated State data 

Improved Translation from

Staging tables to NIF(IL,OH)

Area 2008 State data Improved PM speciation profiles Updated State data

Modified woodburning emissions

Nonroad 2007 NONROAD2008 

MAR Base M 

Rail – ERTAC

Marine – Base M (w/ 2005 Sip

inventory growth)

Aircraft – 2008 State date 

Agricultural Ammonia Base M 

Biogenics MEGAN 

On-road Base M (new grid)

B+: initial MOVES emissions 

MOVES w/ nat’l ldefaults 

(MW states only) 

MOVES w/ nat’l defaults (all 

states) 

Fires (not included) (not included) (not included) 

Non-LADCO Region Base M 

Note: The other versions of Base C (Cv3, Cv5, Cv6) reflected minor corrections to individual sectors.
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Summary of Base Year Emissions 

The emissions base year for the current regional modeling is 2008. 2008 emissions were

provided for EGU point, nonroad, on-road, and biogenics, and 2008 emissions (only) were

provided for the other sectors (non-EGU point and area). Based on consultation with the States

and examination of various emission surrogates (e.g., population, employment, and VMT), 2008 

emissions for these sectors were used “as is” to represent 2007 emissions. 

For on-road, off-road, ammonia, and biogenic sources, emissions were estimated using 

emission models. Three other off-road categories are not accounted for by the NONROAD 

emissions model: commercial marine, aircraft, and rail. Marine emissions will continue to be 

based on LADCO’s Base M (2005) inventory. Aircraft emissions for 2008 were provided by the 

States. Rail emissions were based on the new 2008 inventory prepared by ERTAC. For point 

and area sources, emissions were prepared using data supplied by the States. Further 

discussion is provided below for each source sector in the LADCO region. For the rest of the 

modeling domain, Base M inventories will be used until more recent inventories are available 

from other regional organizations.

A summary of annual emissions by pollutant and source sector is provided in Figure 1. A more 

complete emissions summary is provided in Attachment 1.

Figure 1. Annual emissions summary by pollutant and source sector

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

NOX ROG SO2 PM2.5

area

on-road

MAR

nonroad

nonEGU

EGU

0%

20%

40%

60%

80%

100%

NOX ROG SO2 PM2.5

area

on-road

MAR

nonroad

nonEGU

EGU
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On-Road Sources: Emission estimates were derived using EPA’s new MOVES2010a model for 

on-road motor vehicles. MOVES2010a was developed for state/local air quality and 

transportation agencies to use in preparing on-road inventories for SIPs and transportation 

conformity. MOVES2010 replaced the previous model for estimating on-road mobile source 

emissions, MOBILE6.2. EPA guidance is for states to use MOVES2010a “in ozone, CO, PM, 

and NO2 SIP development as expeditiously as practicable.” 

MOVES2010a was developed principally to be run in an “inventory” mode (i.e., monthly 

average, county-level emission estimates), which is what state/local agencies need for their SIP 

inventories and conformity demonstrations. For photochemical modeling, however, more 

refined temporal and spatial information is desired. Two options exist for providing this more 

refined information: (1) running MOVES2010a for each hour (using concurrent meteorological 

conditions) to provide the appropriate emission factor, or (2) developing MOVES2010a “look-up” 

tables which cover the expected ranges of key meteorological variables and then using the 

tables to provide the emission factor for each hour based on the concurrent meteorological 

conditions. Initial PC-based modeling showed that the first option was prohibitive. Subsequent 

Linux-based modeling produced a factor of 2 decrease in model run times, but still suggested 

that the look-up table option was preferable.

Using a Linux-based version of MOVES2010a created by Wisconsin DNR, on-road motor 

vehicle inventories were prepared based on a multi-step process:

Step 1: A state-level run was conducted to produce temporal profiles for each state for 

weekday and weekend emissions as a function of pollutant, emission type (exhaust and 

evaporative), and vehicle type (gasoline and diesel).

Step 2: A county-level run was conducted for each representative county for January and 

July to produce a weekday and weekend emission estimate for these two months. 

Step 3: The monthly profiles were applied to the representative county results to generate a 

weekday and weekend emission estimate for all months.

For Base Cv4, national defaults were used in the MOVES runs. State-specific inputs were 

supplied by most LADCO States, but were not used to produce a modeling inventory.2 The 

resulting county-level weekday and weekend emission estimates for each month were then 

modeled as an area source in EMS. Emission processing assumptions include:

Temporal: Weekday emissions assigned to Monday-Friday, and weekend emissions 

assigned to Saturday-Sunday. It should be noted that the emission files already reflect 

hourly values.

Spatial: Applied 2002 surrogates based on roadway type.

Speciation: Exhaust and evaporative emissions by vehicle type (gasoline and diesel) were 

speciated separately.

 

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Preliminary MOVES runs for a few cities (Cleveland, Milwaukee, and E.St.Louis) indicate that the 

emission estimates are about 8-10% higher using state-specific inputs.
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Off-Road Sources: NMIM2008 was run (using default monthly temperatures) by Wisconsin 

DNR for the 2007 and 2008 for the five LADCO States plus MN, IA, and MO. Local data for 

agricultural equipment, construction equipment, commercial marine, recreational marine, and 

railroads were prepared previously by contractors (Environ, 2004, E.H. Pechan, 2004, and 

Environ, 2007a, 2007b). 

Three other off-road categories are not accounted for by the NMIM emissions model: 

commercial marine, aircraft, and rail. Marine emissions will continue to be based on LADCO’s 

Base M (2005) inventory (Environ, 2007a). A summary of the annual emissions by vessel type 

for the 5-state LADCO region are provided in Table 3.

Table 3. Annual Commercial Marine Emissions for 2005 (LADCO Region)

Aircraft emissions for 2008 were provided by the States. 

Rail emissions were based on the new 2008 inventory prepared by ERTAC. Emissions data 

were developed for rail yards and line haul operations. Documentation for the rail emissions is 

available at http://www.ertac.us/rail/index.html

Area and Point Sources: EMS was run by LADCO using data supplied by the LADCO States

and Minnesota to produce weekday, Saturday, and Sunday emissions for each month. The 

ERTAC recommendations on area sources were followed in preparing these data. 

Two significant area source improvements were made in developing Base Cv4: application of 

appropriate PM speciation profiles, and adjustment of residential wood combustion (RWC)

emissions. First, several PM area source categories, which were using the default speciation 

profile, were assigned SCC-specific profiles based on several recent references (CMU, 2004, 

EC/R, 2005, and Turner, 2010). This resulted in significantly more primary OC and EC 

emissions.

Second, Michigan DEQ adjusted EPA’s RWC tool to reallocate the indoor furnaces and outdoor 

hydronic heaters (*8510 and *8610, respectively) to nonMSA counties. Counties that are part of 

an MSA, were assigned zero appliances for both categories. Additionally, an issue with double 

counting some county activity data in WI and MI was addressed. Table 4 shows the statewide 

changes in emissions from the standard tool to the revised tool. These changes resulted in a 

shift in the spatial distribution of emissions, and (due to the double counting) a reduction in 

statewide emissions in some states.
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Table 4. Standard and Adjusted Residential Wood Combustion Emissions

Standard 

RWC Tool

Adjusted 

RWC Tool

Adjusted 

RWC Tool

Adjusted 

RWC Tool

Adjusted 

RWC Tool

Adjusted 

RWC Tool

State Pollutant Emissions Emissions 2104008400 2104008510 2104008610 sum

17 Primary PM10 13,880 14,012 51 4,570 2,454 7,075

18 Primary PM10 11,729 11,724 37 3,762 3,274 7,073

26 Primary PM10 35,143 27,255 91 4,537 6,630 11,257

27 Primary PM10 17,741 17,741 45 879 3,860 4,784

39 Primary PM10 25,703 23,332 80 7,919 5,890 13,889

55 Primary PM10 23,407 20,311 53 2,887 6,156 9,096

For EGU point sources, the annual and summer season emissions were temporalized for 

modeling purposes using profiles prepared by Scott Edick (Michigan DEQ) based on CEM data. 

Figure 3 shows the location (and magnitude) of non-EGU and EGU point sources.

Figure 3. Spatial plot of non-EGU (left) and EGU (right) sources
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Biogenics: Environ, under contract to the Midwest Ozone Group, produced day-specific 

biogenic emissions for the full modeling domain using the CONCEPT/MEGAN biogenics model 

for 2007 and 2008 (Guenther, et al, 2006, and Sakulyanontvittaya, et al, 2008). MEGAN2.03a 

was used, which is a modified version of MEGANv2.03 available from 

http://acd.ucar.edu/~guenther/MEGAN/MEGAN.htm. The improvements for the modified 

version include:

 Enabling input of temperature and solar radiation data from various sources.

Previous versions of MEGAN obtained these data only from MCIP model outputs. 

Environ developed code to allow input of solar radiation from satellite data and 

temperature from MM5 and MM5CAMX models.

 Pre-processors were developed to allow the use of alternative modeling grids 

developed in ArcGIS.

 The MEGAN code is currently capable of outputting model-ready data for CMAQ.

The MEGAN code was modified to include outputting of CAMx model-ready data. 

 Modification of the MEGAN FORTRAN code that runs on Linux to accept observed 

temperature data and satellite radiation data and generate emissions for griddefinitions that are developed using ArcGIS.

Figure 4 compares the biogenic isoprene and NO emission estimates for 2007 with those for 

2005 (Base M).

Figure 4. Biogenic emissions: isoprene (left column) and NO (right column) for 2005 (top) and 

2007 (bottom)
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Ammonia: The agricultural ammonia emissions were based on Carnegie Mellon University’s

Ammonia Emission Inventory for the Continental United States (http://www.cmu.edu/ammonia). 

Specifically, the CMU annual emissions for 2002 were first grown to reflect 2007 conditions 

based on the methodology use to create the 2005 LADCO SIP inventories. A new processbased ammonia emissions model developed for LADCO (see Zhang, et al, 2005, and Mansell, 

et al, 2005) was then used to develop temporal factors – i.e., month of year, and hour of day to

reflect the impact of average meteorology on livestock emissions.

Emissions Tile plot for Livestock Ammonia in the Upper Midwest and Mid Atlantic States.

Canadian Emissions: Scott Edick (Michigan DEQ) processed the 2005 Canadian National 

Pollutant Release Inventory, Version 1.0 (NPRI). A subset of the NPRI data (emissions and 

stack parameters) relevant to the air quality modeling were reformatted and used in the regional 

modeling.

References

Carnegie Mellon University, Spreadsheet: emission.profiles,v2.xls, February 17, 2004

EC/R, Incorporated, 2005, “Improving Modeling Inventory Data: Speciation Profiles”, Draft 

Technical Report, February 17, 2005
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E.H. Pechan, 2004, “LADCO Nonroad Emissions Inventory Project – Development of Local 

Data for Construction and Agricultural Equipment”, Final Report, September 10, 2004

Environ, 2004, “LADCO Nonroad Emissions Inventory Project for Locomotive, Commercial 

Marine, and Recreational Marine Emission Sources, Final Report, December 2004.

Environ, 2007a, “LADCO 2005 Locomotive Emissions”, Draft, February 2007.

Environ, 2007b, “LADCO 2005 Commercial Marine Emissions”, Draft, March 2, 2007.

Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. Palmer, and C. Geron, Estimates of global 

terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from 

Nature), Atmos. Chem Phys., 6, 3181-3210, 2006.

Mansell, G, Z. Wang, R. Zhang, J. Fadel, T. Ramsey, H. Xin, Y. Liang, and J. Arogo, 2005, “An 

Improved Process-Based Ammonia Emissions Model for Agricultural Sources – Emission 

Estimates”

Sakulyanontvittaya, T., Duhl, T., Wiedinmyer, C., Helmig, D., Matsunaga, S., Potosnak, M., 

Milford, J., and Guenther, A. Monoterpene and Sesquiterpene Emission Estimates for the 

United States, Environ. Sci. Technol., 2008, 42, p. 1623-1629.

Turner, Jay, Spreadsheet: WUSTL-LADCO_SourceProfiles_v3.xlsx, and Metadata for Source 

Profiles for CMB Modeling of Midwest U.S. Urban Areas Version 3 (August 24, 2010), 

Washington University in St. Louis, jrturner@wustl.edu

Zhang, R, T. Ramsey, J. Fadel, J. Arogo, Z. Wang, G. Mansell, and H. Xin, 2005, “An Improved 

Process-Based Ammonia Emissions Model for Agricultural Sources – Model Development”