Transportation Conformity Guidance
for Quantitative Hot-spot Analyses in
PM9 and PMtn Nonattainment and
^•.D -L \)
Maintenance Areas
Public Draft
&EPA
United States
Environmental Protection
Agency
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Transportation Conformity Guidance
for Quantitative Hot-spot Analyses in
PM9 and PMtn Nonattainment and
^•.D -L \)
Maintenance Areas
Public Draft
Transportation and Regional Programs Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
&EPA
United States EPA-420-P-10-001
Environmental Protection .. oriin
Agency May 2010
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PUBLIC DRAFT-MAY 2010
Table of Contents
LIST OF EXHIBITS 6
LIST OF APPENDICES 7
SECTION 1: INTRODUCTION 9
1.1 PURPOSE OF THIS GUIDANCE 9
1.2 TIMING OF QUANTITATIVE PM HOT-SPOT ANALYSES 9
1.3 DEFINITION OF A HOT-SPOT ANALYSIS 10
1.4 PROJECTS REQUIRING A PM HOT-SPOT ANALYSIS 10
1.5 OTHER PURPOSES FOR THIS GUIDANCE 11
1.6 ORGANIZATION OF THIS GUIDANCE 11
1.7 ADDITIONAL INFORMATION 12
1.8 GUIDANCE AND EXISTING REQUIREMENTS 12
SECTION 2: TRANSPORTATION CONFORMITY REQUIREMENTS 15
2.1 INTRODUCTION 15
2.2 OVERVIEW OF STATUTORY AND REGULATORY REQUIREMENTS 15
2.3 INTERAGENCY CONSULTATION AND PUBLIC PARTICIPATION REQUIREMENTS 16
2.4 HOT-SPOT ANALYSES ARE BUILD/NO-BUILD ANALYSES 17
2.5 EMISSIONS CONSIDERED IN PM HOT-SPOT ANALYSES 19
2.5.1 General requirements 19
2.5.2 PM emissions from motor vehicle exhaust, brake wear, and tire wear 19
2.5.3 PM2.i emissions from re-entrained road dust. 19
2.5.4 PM10 emissions from re-entrained road dust 20
2.5.5 PM emissions from construction-related activities 20
2.6 NAAQS CONSIDERED IN PM HOT-SPOT ANALYSES 20
2.7 BACKGROUND CONCENTRATIONS 21
2.8 APPROPRIATE TIME FRAME AND ANALYSIS YEARS 21
2.9 AGENCY ROLES AND RESPONSIBILITIES 22
2.9.1 Project sponsor 22
2.9.2 DOT 22
2.9.3 EPA 22
2.9.4 State and local transportation and air agencies 22
SECTION 3: OVERVIEW OF A QUANTITATIVE PM HOT-SPOT ANALYSIS 25
3.1 INTRODUCTION 25
3.2 DETERMINE NEED FOR A PM HOT-SPOT ANALYSIS (STEP 1) 25
3.3 DETERMINE APPROACH, MODELS, AND DATA (STEP 2) 27
3.3.1 General 27
3.3.2 Determining the geographic area and emission sources to be covered by the analysis 27
3.3.3 Deciding the general analysis approach and analysis year(s) 28
3.3.4 Determining which PM NAAQS to be evaluated 28
3.3.5 Deciding on the type of PM emissions to be modeled. 29
3.3.6 Determining the models and methods to be used. 29
3.3.7 Obtaining the project-specific data to be used 29
3.4 ESTIMATE ON-ROAD MOTOR VEHICLE EMISSIONS (STEPS) 30
3.5 ESTIMATE DUST AND OTHER EMISSIONS (STEP 4) 30
3.6 SELECT AN AIR QUALITY MODEL, DATA INPUTS AND RECEPTORS (STEP 5) 30
3.7 DETERMINE BACKGROUND CONCENTRATIONS (STEP 6) 31
3.8 CALCULATE DESIGN VALUES AND COMPARE BUILD AND NO-BUILD SCENARIO RESULTS
(STEP 7) 31
3.9 CONSIDER MITIGATION OR CONTROL MEASURES (STEP 8) 31
3.10 DOCUMENT THE PM HOT-SPOT ANALYSIS (STEP 9) 31
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SECTION 4: ESTIMATING PROJECT-LEVEL PM EMISSIONS USING MOVES 33
4.1 INTRODUCTION 33
4.2 CHARACTERIZING A PROJECT IN TERMS OF LINKS 35
4.2.1 Highway and inter section projects 35
4.2.2 Transit and other terminal projects 37
4.3 DETERMINING THE NUMBER OF MOVES RUNS 38
4.3.1 General 38
4.3.2 For projects with typical travel activity data 39
4.3.3 For projects with additional travel activity data 40
4.4 DEVELOPING BASIC RUN SPECIFICATION INPUTS 41
4.4.1 Description 41
4.4.2 Scale 41
4.4.3 Time Spans 42
4.4.4 Geographic Bounds 42
4.4.5 Vehicles/Equipment 43
4.4.6 Road Type 43
4.4.7 Pollutants and Processes 44
4.4.8 Manage Input Data Sets 45
4.4.9 Strategies 45
4.4.10 Output 46
4.4.11 Advanced Performance Features 46
4.5 ENTERING PROJECT DETAILS USING THE PROJECT DATA MANAGER 47
4.5.1 Meteorology 48
4.5.2 Age Distribution 49
4.5.3 Fuel Supply and Fuel Formulation 50
4.5.4 Inspection and Maintenance (I/M) 50
4.5.5 Link Source Type 50
4.5.6 Links 51
4.5.7 Describing Vehicle Activity 51
4.5.8 Deciding on an approach for activity 53
4.5.9 Off-Network 53
4.6 GENERATING EMISSION FACTORS FOR USE IN AIR QUALITY MODELING 54
4.6.1 Highway and intersection links 54
4.6.2 Transit and other terminal links 55
SECTION 5: ESTIMATING PROJECT-LEVEL PM EMISSIONS USING EMFAC (IN
CALIFORNIA) 57
5.1 INTRODUCTION 57
5.2 CHARACTERIZING A PROJECT IN TERMS OF LINKS 59
5.2.7 Highway and inter section projects 59
5.2.2 Transit and other terminal projects 60
5.3 DETERMINING THE NUMBER OF EMFAC RUNS 61
5.4 DEVELOPING BASIC SCENARIO INPUTS 63
5.4.1 Geographic area and calculation method 63
5.4.2 Calendar year 64
5.4.3 Season or month 64
5.4.4 Scenario title 64
5.4.5 Model years 64
5.4.6 Vehicle classes 65
5.4.7 I/M program schedule and other state control measures 65
5.5 CONFIGURING EMISSION FACTOR OUTPUTS 66
5.5.7 Temperature and relative humidity. 66
5.5.2 Speed. 67
5.5.3 Output rate file 68
5.5.4 Output particulate 68
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5.6 EDITING PROGRAM CONSTANTS 69
5.6.1 Overview 69
5.6.2 Default data in the Emfac mode 69
5.6.3 Comparing project data and EMFA C defaults to determine adjustments 70
5.6.4 Adjustment of default activity distributions to reflect project data 70
5.7 GENERATING EMISSION FACTORS FOR USE IN AIR QUALITY MODELING 73
5.7.7 Highway and intersection links 73
5.7.2 Transit and other terminal links 74
SECTION 6: ESTIMATING EMISSIONS FROM ROAD DUST, CONSTRUCTION, AND OTHER
EMISSION SOURCES 77
6.1 INTRODUCTION 77
6.2 OVERVIEW OF DUST METHODS AND REQUIREMENTS 77
6.3 ESTIMATING RE-ENTRAINED ROAD DUST 78
6.3.1 PM2.s nonattainment and maintenance areas 78
6.3.2 PM 10 nonattainment and maintenance areas 78
6.3.3 Using AP-42 to estimate emissions of re-entrained road dust on paved roads 78
6.3.4 Estimating emissions of re-entrained road dust on unpaved roads 79
6.3.5 Using alternative local approaches for estimating re-entrained road dust 79
6.4 ESTIMATING TRANSPORTATION-RELATED CONSTRUCTION DUST 80
6.4.1 Determining whether construction dust must be considered 80
6.4.2 Using AP-42 to estimate emissions of construction dust 80
6.5 ADDING DUST EMISSIONS TO MOVES/EMFAC MODELING RESULTS 81
6.6 ESTIMATING OTHER SOURCES OF EMISSIONS IN THE PROJECT AREA 81
6.6.1 Construction-related vehicles and equipment 81
6.6.2 Locomotives 81
6.6.3 Other emission sources 81
SECTION 7: SELECTING AN AIR QUALITY MODEL, DATA INPUTS, AND RECEPTORS.... 83
7.1 INTRODUCTION 83
7.2 GENERAL OVERVIEW OF AIR QUALITY MODELING 83
7.3 SELECTING AN APPROPRIATE AIR QUALITY MODEL 85
7.3.1 Recommended air quality models 85
7.3.2 How emissions are represented in CAL3QHCR andAERMOD 87
7.3.3 Alternate models 88
7.4 CHARACTERIZING EMISSION SOURCES 88
7.4.1 Physical characteristics and location 88
7.4.2 Emission rates/emission factors 89
7.4.3 Timing of emissions 89
7.5 INCORPORATING METEOROLOGICAL DATA 89
7.5.7 Finding representative meteorological data 89
7.5.2 Surface and upper air data 91
7.5.3 Time duration of meteorological data record 92
7.5.4 Considering surface characteristics 93
7.5.5 Specifying urban or rural sources 94
7.6 PLACING RECEPTORS 95
7.6.1 Overview 95
7.6.2 General guidance for receptors for all PMNAAQS 96
7.6.3 Additional guidance for receptors for the PM2.s NAAQS 97
7.6.4 Summary 98
7.7 RUNNING THE MODEL AND OBTAINING RESULTS 99
SECTION 8: DETERMINING BACKGROUND CONCENTRATIONS FROM NEARBY AND
OTHER EMISSION SOURCES 101
8.1 INTRODUCTION 101
8.2 BACKGROUND CONCENTRATIONS FROM NEARBY SOURCES 102
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8.3 OPTIONS FOR BACKGROUND CONCENTRATIONS FROM OTHER SOURCES 103
8.3.1 Using ambient monitoring data to estimate background concentrations 104
8.3.2 Adjusting air quality monitoring data to account for future changes in air quality 106
8.3.3 Other methods of combining ambient monitoring data and modeling results 108
SECTION 9: CALCULATING PM DESIGN VALUES AND DETERMINING CONFORMITY.. 109
9.1 INTRODUCTION 109
9.2 USING DESIGN VALUES IN BUILD/NO-BUILD ANALYSES 110
9.3 CALCULATING DESIGN VALUES AND DETERMINING CONFORMITY FOR PM HOT-SPOT
ANALYSES 112
9.3.1 General 112
9.3.2 Annual PM2i NAAQS. 113
9.3.3 24-hour PM2.5NAAQS 116
9.3.4 24-hour PM10NAAQS. 123
9.4 DETERMINING APPROPRIATE RECEPTORS FOR COMPARISON TO THE ANNUAL PM2.5NAAQS.. 127
9.4.1 General 727
9.4.2 Factors for appropriate receptors for comparison to the annual PM2.5 NAAQS. 128
9.4.3 Overview of PM2.s monitoring regulations 128
9.4.4 Conformity guidance for all projects in annual PM2.5 NAAQS areas 130
9.4.5 Additional conformity guidance for the annual PM2.5 NAAQS and highway and intersection
projects 132
9.5 DOCUMENTING CONFORMITY DETERMINATION RESULTS 134
SECTION 10: MITIGATION AND CONTROL MEASURES 135
10.1 INTRODUCTION 135
10.2 MITIGATION AND CONTROL MEASURES BY CATEGORY 135
10.2.1 Retrofitting, replacing vehicles/engines, and using cleaner fuels 135
10.2.2 Reduced idling programs 136
10.2.3 Transportation project design revisions 137
10.2.4 Fugitive dust control programs 137
10.2.5 Addressing other source emissions 138
List of Exhibits
EXHIBIT 3-1. OVERVIEW OF THE QUANTITATIVE HOT-SPOT ANALYSIS PROCESS 26
EXHIBIT 4-1. STEPS FOR USING MOVES IN A QUANTITATIVE PM HOT-SPOT ANALYSIS 34
EXHIBIT 4-2. TYPICAL NUMBER OF MOVES RUNS FOR AN ANALYSIS YEAR 39
EXHIBIT 5-1. STEPS FOR USING EMFAC IN A QUANTITATIVE PM HOT-SPOT ANALYSIS 58
EXHIBIT 5-2. SUMMARY OF EMFAC INPUTS NEEDED TO EVALUATE A PROJECT SCENARIO 63
EXHIBIT 5-3. CHANGING EMFAC DEFAULT SETTINGS FOR TEMPERATURE AND RELATIVE HUMIDITY 67
EXHIBIT 5-4. SELECTING POLLUTANT TYPES IN EMFAC FOR PMIO AND PM2.5 68
EXHIBIT 5-5. EMFAC PROGRAM CONSTANTS AND MODIFICATION NEEDS FOR PM HOT-SPOT ANALYSES 69
EXHIBIT 5-6. MAPPING EMFAC VEHICLE CLASSES TO PROJECT-SPECIFIC ACTIVITY INFORMATION 70
EXHIBIT 5-7. EXAMPLE DEFAULT EMFAC VMT BY VEHICLE CLASS DISTRIBUTION 71
EXHIBIT 5-8. EXAMPLE ADJUSTED EMFAC VMT BY VEHICLE CLASS DISTRIBUTION 72
EXHIBIT 5-9. EXAMPLE EMFAC RUNNING EXHAUST, TIRE WEAR, AND BRAKE WEAR EMISSION FACTORS IN
THE SUMMARYRATES (RTS) OUTPUT FILE 74
EXHIBIT 5-10. EXAMPLE SOAK TIMES FOR SEVERAL PROJECT SCENARIOS 75
EXHIBIT 7-1. OVERVIEW AND DATA FLOW FOR AIR QUALITY MODELING 84
EXHIBIT 7-2. SUMMARY OF RECOMMENDED AIR QUALITY MODELS 85
EXHIBIT 7-3. AIR QUALITY MODEL CAPABILITIES FOR METEOROLOGICAL DATA 93
EXHIBIT 7-4. GUIDANCE FOR RECEPTORS IN PM HOT-SPOT ANALYSES 98
EXHIBIT 9-1. GENERAL PROCESS FOR CALCULATING DESIGN VALUES FOR PM HOT-SPOT ANALYSES 109
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EXHIBIT 9-2. GENERAL PROCESS FOR USING CESIGN VALUES IN BUILD/NO-BUILD ANALYSES Ill
EXHIBIT 9-3. DETERMINING CONFORMITY TO THE ANNUAL PM2.5NAAQS 115
EXHIBIT 9-4. DETERMINING CONFORMITY TO THE 24-nouR PM2.5NAAQS USING FIRST TIER ANALYSIS 118
EXHIBIT 9-5. RANKING OF 98™ PERCENTILE BACKGROUND CONCENTRATION VALUES 119
EXHIBIT 9-6. DETERMINING CONFORMITY TO THE 24-nouR PM2.5NAAQS USING SECOND TIER ANALYSIS ... 121
EXHIBIT 9-7. RANKING OF 98™ PERCENTILE BACKGROUND CONCENTRATION VALUES 123
EXHIBIT 9-8. DETERMINING CONFORMITY TO THE 24-HOURPM10NAAQS 125
EXHIBIT 9-9. DETERMINING SCALE OF RECEPTOR LOCATIONS FOR THE ANNUAL PM2.5 NAAQS 133
List of Appendices
APPENDIX A: CLEARINGHOUSE OF WEBSITES, GUIDANCE, AND OTHER TECHNICAL RESOURCES FOR PM
HOT-SPOT ANALYSES
APPENDIX B: EXAMPLES OF PROJECTS OF LOCAL AIR QUALITY CONCERN
APPENDIX c: HOT-SPOT REQUIREMENTS FOR PMIO AREAS WITH APPROVED CONFORMITY SIPS
APPENDIX D: CHARACTERIZING INTERSECTION PROJECTS FOR MOVES
APPENDIX E: EXAMPLE QUANTITATIVE PM HOT-SPOT ANALYSIS OF A HIGHWAY PROJECT USING MOVES
AND CAL3QHCR
APPENDIX F : EXAMPLE QUANTITATIVE PM HOT-SPOT ANALYSIS OF A TRANSIT PROJECT USING MOVES AND
AERMOD
APPENDIX G: EXAMPLE OF USING EMFAC FOR A HIGHWAY PROJECT
APPENDIX H: EXAMPLE OF USING EMFAC TO DEVELOP EMISSION FACTORS FOR A TRANSIT PROJECT
APPENDIX i: ESTIMATING LOCOMOTIVE EMISSIONS
APPENDIX j: ADDITIONAL REFERENCE INFORMATION ON AIR QUALITY MODELS AND DATA INPUTS
APPENDIX K: EXAMPLES OF DESIGN VALUE CALCULATIONS FOR PM HOT-SPOT ANALYSES
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Section 1: Introduction
1.1 PURPOSE OF THIS GUIDANCE
This guidance describes how to complete quantitative hot-spot analyses for certain
highway and transit projects in PM2.5 and PMio nonattainment and maintenance areas.
This guidance describes conformity requirements for hot-spot analyses, and provides
technical guidance on estimating project emissions with the Environmental Protection
Agency's (EPA's) MOVES2010 model, California's EMFAC2007 model, and other
methods. It also outlines how to apply air quality models for PM hot-spot analyses and
includes additional references and examples. However, the guidance does not change the
specific transportation conformity rule requirements for quantitative PM hot-spot
analyses, such as what projects require these analyses. EPA has coordinated with the
Department of Transportation (DOT) in developing this guidance.
Transportation conformity is required under Clean Air Act section 176(c) (42 U.S.C.
7506(c)) to ensure that federally supported highway and transit project activities are
consistent with ("conform to") the purpose of a state air quality implementation plan
(SIP). Conformity to the purpose of the SIP means that transportation activities will not
cause new air quality violations, worsen existing violations, or delay timely attainment of
the relevant national ambient air quality standards (NAAQS) and interim milestones.
EPA's transportation conformity rule (40 CFR 51.390 and Part 93) establishes the criteria
and procedures for determining whether transportation activities conform to the SIP.
Conformity applies to transportation activities in nonattainment and maintenance areas
for transportation-related pollutants, including PM2.5 and
1.2 TIMING OF QUANTITATIVE PM HOT-SPOT ANALYSES
On March 10, 2006, EPA published a final rule establishing transportation conformity
requirements for analyzing the local PM air quality impacts of transportation projects (71
FR 12468). The conformity rule requires a qualitative PM hot-spot analysis to be
performed until EPA releases guidance on how to conduct quantitative PM hot-spot
analyses and announces in the Federal Register that such requirements are in effect (40
CFR 93.123(b)).l EPA also stated in the March 2006 final rule that quantitative PM hot-
spot analyses would not be required until EPA released an appropriate motor vehicle
emissions model for these project-level analyses.2
1 For more information on qualitative PM hot-spot analyses, see "Transportation Conformity Guidance for
Qualitative Hot-spot Analyses in PM2 5 and PM10 Nonattainment and Maintenance Areas," EPA420-B-06-
902 (March 2006); available online at: www.epa.gov/otaq/stateresources/transconf/policv/420b06902.pdf.
The qualitative PM hot-spot requirements under 40 CFR 93.123(b)(2) will no longer apply in any PM25
and PM10 nonattainment and maintenance areas once quantitative requirements are in effect. At that time,
the 2006 EPA/FHWA qualitative PM hot-spot guidance will be superseded by EPA's quantitative PM hot-
spot guidance.
2 See EPA's March 2006 final rule for further information (71 FR 12498-12502).
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Quantitative PM hot-spot analyses will be required after the end of the conformity grace
period for applying motor vehicle emissions models for such analyses. To that end, EPA
will soon approve its new motor vehicle emissions model (MOVES2010) for use in
project-level transportation conformity determinations, including PM and carbon
monoxide (CO) hot-spot analyses.3 EPA plans to establish a two-year grace period
before MOVES is required in quantitative PM and CO hot-spot analyses. EPA will
publish a Federal Register notice of availability to approve MOVES2010 (and
EMFAC2007 in California) for PM hot-spot analyses, and the effective date of that notice
will constitute the start of the two-year conformity grace period. EPA has issued policy
guidance on when these models will be required for PM hot-spot analyses and other
purposes.4
1.3 DEFINITION OF A HOT-SPOT ANALYSIS
A hot-spot analysis is defined in 40 CFR 93.101 as an estimation of likely future
localized pollutant concentrations and a comparison of those concentrations to the
relevant NAAQS. A hot-spot analysis assesses the air quality impacts on a scale smaller
than an entire nonattainment or maintenance area, including, for example, congested
highways or transit terminals. Such an analysis of the area substantially affected by the
project is a means of demonstrating that Clean Air Act conformity requirements are met
for the relevant NAAQS in the "project area." When a hot-spot analysis is required, it is
included within a project-level conformity determination.
1.4 PROJECTS REQUIRING A PM HOT-SPOT ANALYSIS
PM hot-spot analyses are required for projects of local air quality concern, which include
certain highway and transit projects that involve significant levels of diesel vehicle traffic
or any other project identified in the PM2.s or PMi0 SIP as a localized air quality
concern.5 See Section 2.2 of the guidance for further information on the specific types of
projects that require PM hot-spot analyses. A PM hot-spot analysis is not required for
projects that are not of local air quality concern. For these projects, state and local
project sponsors should document in their project-level conformity determinations that
the requirements of the Clean Air Act and 40 CFR 93.116 are met without a hot-spot
analysis, since such projects have been found not to be of local air quality concern under
3 EPA plans to issue a separate guidance document on how to use MOVES for CO project-level analyses
(including CO hot-spot analyses for conformity purposes), consistent with EPA's "Guideline for Modeling
Carbon Monoxide from Roadway Intersections," November 1992 (EPA-454/R-92-005). This guidance
will be available when MOVES is approved for project-level conformity analyses at the following website:
www.epa.gov/otaq/sMeresources/transconf/policv.htnrfmodels.
4 "Policy Guidance on the Use of MOVES2010 for State Implementation Plan Development,
Transportation Conformity, and Other Purposes," EPA-420-B-09-046 (December 2009); available online
at: www.epa.gov/otaq/sMeresources/transconf/policy.htnrfmodels.
5 See the preamble of the March 2006 final rule for further information regarding how and why EPA
defined projects of local air quality concern (71 FR 12491-12493).
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40 CFR 93.123(b)(l). See Appendix B of this guidance for examples of projects that are
most likely to be of local air quality concern, as well as examples of projects that are not
(and do not require a PM hot-spot analysis). This guidance does not alter the types of
projects that require a PM hot-spot analysis.
Note that additional projects may need hot-spot analyses in some PMi0 nonattainment
and maintenance areas with approved conformity SIPs which are based on the federal
PMio hot-spot requirements that existed before the amendments contained in the March
2006 final rule.6 EPA strongly encourages states with these types of approved
conformity SIPs to revise their conformity SIPs to take advantage of the streamlining
flexibilities provided by the current Clean Air Act.7 See Appendix C for further details
on how these types of approved conformity SIPs can affect what projects are required to
have PM hot-spot analyses. Project sponsors should use the interagency consultation
process to verify the requirements before beginning a quantitative PMio hot-spot analysis.
1.5 OTHER PURPOSES FOR THIS GUIDANCE
This guidance addresses how to complete a quantitative PM hot-spot analysis for
transportation conformity purposes. However, certain sections of this guidance, such as
Sections 4 or 5 for estimating project-level emissions using MOVES or EMFAC, may
also be consulted when completing air quality analyses for transportation projects for
other purposes.
1.6 ORGANIZATION OF THIS GUIDANCE
The remainder of this guidance is organized as follows:
• Section 2 provides an overview of transportation conformity requirements for PM
hot-spot analyses.
• Section 3 describes the general process for conducting PM hot-spot analyses.
• Sections 4 and 5 describe how to estimate vehicle emissions from a project using
the latest approved emissions model, either MOVES (for all states other than
California) or EMFAC (for California).
• Section 6 discusses how to estimate emissions from road dust, construction dust,
and from other sources, if necessary.
• Section 7 describes how to determine the appropriate air quality dispersion model
and select model inputs.
• Section 8 covers how to determine background concentrations, including nearby
source emissions in the project area.
6 A "conformity SIP" includes a state's specific criteria and procedures for certain aspects of the
transportation conformity process (40 CFR 51.390).
7 For more information about conformity SIPs, see EPA's "Guidance for Developing Transportation
Conformity State Implementation Plans (SIPs)," EPA-420-B-09-001 (January 2009); available online at:
www.epa.gov/otaq/stateresources/transconf/policv/420b09001 .pdf.
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• Section 9 describes how to calculate the appropriate design values and determine
whether or not the project conforms.
• Section 10 describes some mitigation and control measures that could be
considered, if necessary.
The following appendices for this guidance may also help state and local agencies
conduct PM hot-spot analyses:
• Appendix A is a clearinghouse of information and resources external to this
guidance which may be useful when completing PM hot-spot analyses.
• Appendix B gives examples of projects of local air quality concern.
• Appendix C discusses what projects need a hot-spot analysis if a state's approved
conformity SIP is based on pre-2006 requirements.
• Appendix D demonstrates how to characterize links in an intersection when
running MOVES.
• Appendices E and F are abbreviated PM hot-spot analysis examples (using
MOVES) for a highway and transit project, respectively.
• Appendices G and H are examples on how to configure and run EMFAC for a
highway and transit project, respectively.
• Appendix I describes guidance on estimating locomotive emissions in the project
area.
• Appendix J includes details on how to input data and run air quality models for a
PM hot-spot analysis as well as prepare outputs for design value calculations.
• Appendix K has examples of how to calculate design values and determine
transportation conformity.
Except where indicated, this guidance applies equally for the annual PM2.5 NAAQS, the
24-hour PM2.5 NAAQS, and the 24-hour PMio NAAQS.
1.7 ADDITIONAL INFORMATION
For specific questions concerning a particular nonattainment or maintenance area, please
contact the transportation conformity staff person responsible for your state at the
appropriate EPA Regional Office. Contact information for EPA Regional Offices can be
found at: www.epa.gov/otaq/stateresources/transconf/contacts.htm.
General questions about this draft guidance can be directed to Meg Patulski at EPA's
Office of Transportation and Air Quality, patulski.meg@epa.gov, (734) 214-4842.
1.8 GUIDANCE AND EXISTING REQUIREMENTS
This guidance does not create any new requirements. The Clean Air Act and the
regulations described in this document contain legally binding requirements. This
guidance is not a substitute for those provisions or regulations, nor is it a regulation in
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itself. Thus, it does not impose legally binding requirements on EPA, DOT, states, or the
regulated community, and may not apply to a particular situation based upon the
circumstances. EPA retains the discretion to adopt approaches on a case-by-case basis
that may differ from this guidance but still comply with the statute and applicable
regulations. This guidance may be revised periodically without public notice. As noted
above, EPA plans to describe in its upcoming Federal Register notice the two-year
conformity grace period for MOVES2010 and EMFAC2007 for PM hot-spot analyses,
and when the requirements for quantitative PM hot-spot analyses in 40 CFR 93.123(b)
will take effect.
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Section 2: Transportation Conformity Requirements
2.1 INTRODUCTION
This section outlines the transportation conformity requirements for quantitative PM hot-
spot analyses. This section describes general statutory and regulatory requirements,
specific analytical requirements, and the different types of agencies that are involved in
developing hot-spot analyses.
2.2 OVERVIEW OF STATUTORY AND REGULATORY REQUIREMENTS
Clean Air Act section 176(c)(l) is the statutory requirement that must be met by all
projects in nonattainment and maintenance areas that are subject to transportation
conformity. Section 176(c)(l)(B) states that federally-supported transportation projects
must not "cause or contribute to any new violation of any standard in any area; increase
the frequency or severity of any existing violation of any standard in any area; or delay
timely attainment of any standard or any required interim emission reductions or other
milestones in any area."
Section 93.109(b) of the conformity rule outlines the requirements for project-level
conformity determinations.8 For example, PM hot-spot analyses must be based on the
latest planning assumptions available at the time the analysis begins (40 CFR 93.110).
Also, the design concept and scope of the project must be consistent with that included in
the conforming transportation plan and transportation improvement program (TIP) or
regional emissions analysis (40 CFR 93.114).
Section 93.123(b)(l) of the conformity rule defines the projects that require a PM2.5 or
hot-spot analysis as:
"(i) New highway projects that have a significant number of diesel vehicles, and
expanded highway projects that have a significant increase in the number of diesel
vehicles;
(ii) Projects affecting intersections that are at Level-of-Service D, E, or F with a
significant number of diesel vehicles, or those that will change to Level-of-
Service D, E, or F because of increased traffic volumes from a significant number
of diesel vehicles related to the project;
In general, when a hot-spot analysis is required, it is done when a project-level conformity determination
is completed. Conformity determinations are typically developed during the National Environmental
Policy Act (NEPA) process, although conformity requirements are separate from NEPA-related
requirements. There can also be limited cases when conformity requirements apply after the initial NEPA
process has been completed.
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(iii) New bus and rail terminals and transfer points that have a significant number
of diesel vehicles congregating at a single location;
(iv) Expanded bus and rail terminals and transfer points that significantly increase
the number of diesel vehicles congregating at a single location; and
(v) Projects in or affecting locations, areas, or categories of sites which are
identified in the PM2.5 or PMi0 applicable implementation plan or implementation
plan submission, as appropriate, as sites of violation or possible violation."
A PM hot-spot analysis is not required for projects that are not of local air quality
concern. See Section 1.4 for more background on projects that require PM hot-spot
analyses.
Section 93.123(c) of the conformity rule includes the general requirements for all PM
hot-spot analyses. A PM hot-spot analysis must:
• Estimate the total emissions burden of direct PM2.5 or PMio emissions that may
result from the implementation of the project(s), summed together with future
background concentrations;
• Include the entire transportation project, after identifying the major design
features that will significantly impact local concentrations;
• Use assumptions that are consistent with those used in regional emissions
analyses for inputs that are required for both analyses (e.g., temperature,
humidity);
• Assume the implementation of mitigation or control measures only where written
commitments for such measures have been obtained; and
• Consider emissions increases from construction-related activities if they occur
only during the construction phase and last more than five years at any individual
site.
Finally, the interagency consultation process must be used to develop project-level
conformity determinations to meet all applicable conformity requirements for a given
project.
2.3 INTERAGENCY CONSULTATION AND PUBLIC PARTICIPATION
REQUIREMENTS
The interagency consultation process is an important tool for completing project-level
conformity determinations and hot-spot analyses. Interagency consultation must also be
used to develop a process to evaluate and choose associated methods and assumptions to
be used in PM hot-spot analyses (40 CFR 93.105(c)(l)(i)). The agencies that may be
involved in the interagency consultation process include the project sponsor, state and
local transportation and air quality agencies, EPA, and DOT. The roles and
responsibilities of various agencies for meeting the transportation conformity
requirements are addressed in 40 CFR 93.105 or in a state's approved conformity SIP.
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See Section 2.9 for further information on the agencies involved in interagency
consultation.
The conformity rule requires agencies completing project-level conformity
determinations to establish a proactive public involvement process that provides
opportunity for public review and comment (40 CFR 93.105(e)). The NEPA public
involvement process can be used to satisfy this public participation requirement. If a
project-level conformity determination that includes a PM hot-spot analysis is performed
after NEPA is completed, a public comment period must still be provided to support that
determination.
2.4 HOT-SPOT ANALYSES ARE BUILD/NO-BUILD ANALYSES
The conformity rule requires that the emissions from the proposed project, when
considered with background concentrations, will not produce a new violation of the
NAAQS, increase the frequency or severity of existing violations, or delay timely
attainment of the NAAQS or any required interim reductions or milestones.9 As
described in Section 1.4, the hot-spot analysis examines the area substantially affected by
the project (i.e., the "project area").
In general, a hot-spot analysis compares the air quality concentrations with the proposed
project (the build scenario) to the air quality concentrations without the project (the no-
build scenario).10 A build/no-build analysis is necessary for each analysis year(s) chosen
(see Section 2.8). It is always necessary to complete emissions and air quality modeling
on the build scenario and compare these results to the relevant PM NAAQS. However, it
will not always be necessary to conduct emissions and air quality modeling for the no-
build scenario, as described further below.
In order to properly scope the level of analysis and prevent unnecessary work, EPA
suggests the following approach when completing a PM hot-spot analysis:
• First, model the build scenario and account for background concentrations in
accordance with this guidance. If the design values for the build scenario are less
than or equal to the relevant NAAQS, the project is considered to conform and no
further modeling is required (i.e., there is no need to model the no-build scenario).
• If the build scenario results in design values greater than the NAAQS, then the
no-build scenario will also need to be modeled. The no-build scenario will model
the air quality impacts of sources without the proposed project. The modeling
results of the build and no-build scenarios should be combined with background
concentrations as appropriate. If the design values for the build scenario are less
9See 40 CFR 93.116(a). See also November 24, 1993 conformity rule for background on EPA's intentions
for hot-spot analyses (58 FR 62212-62213).
10 Please note that a build/no-build analysis for project-level conformity determinations is different than the
build/no-build interim emissions test for regional emissions analyses in 40 CFR 93.119.
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than or equal to the design values for the no-build scenario, then the project meets
the conformity rule's hot-spot requirements. If not, then the project does not meet
conformity requirements without further mitigation or control measures. If such
measures are considered, additional modeling will need to be completed and new
design values calculated to ensure that the build is less than or equal to the no-
build scenario.
The project sponsor can decide to use the suggested approach above or a different
approach (e.g., conduct the no-build analysis first, calculate design values at all build and
no-build scenario receptors). This guidance can accommodate whatever approach is used
for a given PM hot-spot analysis. In general, assumptions should be consistent between
the build and no-build scenarios for a given analysis year, except for traffic volumes and
other project activity changes or changes in nearby sources that are expected to occur due
to the project (e.g., increased activity at a nearby marine port or intermodal terminal due
to a new freight corridor highway). Project sponsors should document the build/no-build
analysis in the project-level conformity determination, including the assumptions,
methods, and models used for each analysis year(s).
The interagency consultation process should be used to determine if new NAAQS
violations or increases in the frequency or severity of existing violations are anticipated
based on the hot-spot analysis. 40 CFR 93.101 already defines when a new or worsened
air quality violation is determined to occur:
"Cause or contribute to a new violation for a project means:
(1) To cause or contribute to a new violation of a standard in the area
substantially affected by the project or over a region which would
otherwise not be in violation of the standard during the future period in
question, if the project were not implemented; or
(2) To contribute to a new violation in a manner that would increase the
frequency or severity of a new violation of a standard in such area."
"Increase the frequency or severity means to cause a location or region to exceed
a standard more often or to cause a violation at a greater concentration than
previously existed and/or would otherwise exist during the future period in
question, if the project were not implemented."
A build/no-build analysis is typically based on design value comparisons done on a
receptor-by-receptor basis. However, there may be certain cases where a "new" violation
at one receptor (in the build scenario) is relocated from a different receptor (in the no-
build scenario). As discussed in the preamble to the November 24, 1993 transportation
conformity rule, EPA believes that "a seemingly new violation may be considered to be a
relocation and reduction of an existing violation only if it were in the area substantially
affected by the project and if the predicted [future] design value for the "new" site would
be less than the design value at the "old" site without the project - that is, if there would
be a net air quality benefit" (58 FR 62213). Since 1993, EPA has made this interpretation
only in limited cases with CO hot-spot analyses where there is a clear relationship
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between a proposed project and a possible relocated violation (e.g., a reduced CO
NAAQS violation is relocated from one corner of an intersection to another due to traffic-
related changes from an expanded intersection). The interagency consultation process
should be used to discuss any potential relocated violations in PM hot-spot analyses. See
Section 9 for further information regarding how conformity would be determined in such
a case.
2.5 EMISSIONS CONSIDERED IN PM HOT-SPOT ANALYSES
2. 5. 1 General requirements
PM hot-spot analyses include only directly emitted PM2.5 or PMio emissions. PM2.5 and
PMio precursors are not considered in PM hot-spot analyses. n
2.5.2 PM emissions from motor vehicle exhaust, brake wear, and tire wear
Exhaust, brake wear, and tire wear emissions from on-road vehicles must always be
included in a project's PM2.5 or PMio hot-spot analysis. See Sections 4 and 5 for how to
quantify these emissions using MOVES (outside California) or EMFAC (within
California).
2. 5. 3 PM2.5 emissions from re-entrained road dust
Re-entrained road dust must be considered in PM2.5 hot-spot analyses only if EPA or the
state air agency has made a finding that such emissions are a significant contributor to the
PM2.s air quality problem in a given nonattainment or maintenance area (40 CFR
93.102(b)(3) and93.119(f)(8)).12
• If a PM2 5 area has no adequate or approved SIP budgets for the PM2 5 NAAQS,
re-entrained road dust is not included in a hot-spot analysis unless the EPA
Regional Administrator or state air quality agency determines that re-entrained
road dust is a significant contributor to the PM2.s nonattainment problem and has
so notified the metropolitan planning organization (MPO) and DOT.
• If a PMg^ area has adequate or approved SIP budgets, re-entrained road dust
would have to be included in a hot-spot analysis only if such budgets include re-
entrained road dust.
Please refer to your EPA Regional Office for information on whether a finding of
significance for re-entrained road dust has been made for a given PM2.s area. See Section
11 See 40 CFR 93.102(b) for the general requirements for applicable pollutants and precursors in
conformity determinations. Section 93.123(c) provides additional information regarding certain PM
emissions for hot-spot analyses. See EPA's March 2006 final rule preamble for additional background (71
FR 12496-8).
12 See the July 1, 2004 final conformity rule for further information (69 FR 40004).
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6 for further information regarding how to estimate re-entrained road dust for PM2.5 hot-
spot analyses, if necessary.
2.5.4 PMi o emissions from re-entrained road dust
Re-entrained road dust must be included in all PMi0 hot-spot analyses. Because road
dust dominates PMio inventories, EPA has historically required road dust emissions to be
included in all conformity analyses of direct PMio emissions - including hot-spot
analyses.13 See Section 6 for further information regarding how to estimate re-entrained
road dust for PMio hot-spot analyses.
2.5.5 PMemissions from construction-related activities
Emissions from construction-related activities are not required to be included in PM hot-
spot analyses if such emissions are considered temporary as defined in 40 CFR
93.123(c)(5) (i.e., emissions which occur only during the construction phase and last five
years or less at any individual site). Construction emissions would include any direct PM
emissions from construction-related dust and exhaust emissions from construction
vehicles and equipment.
For most projects, construction emissions would not be included in PM2.5 or PMio hot-
spot analyses (because in most cases, the construction phase is less than five years at any
one site). However, there may be limited cases where a large project is constructed over
a longer time period, and non-temporary construction emissions must be included when
an analysis year is chosen during project construction. See Section 6 for further
information regarding how to estimate transportation-related construction emissions for
PM hot-spot analyses, if necessary.
2.6 NAAQS CONSIDERED IN PM HOT-SPOT ANALYSES
The Clean Air Act and transportation conformity regulations require that conformity be
met for all transportation-related NAAQS for which an area has been designated
nonattainment or maintenance. Therefore, a project-level conformity determination must
address all applicable NAAQS for a given pollutant.14
Accordingly, results from a quantitative hot-spot analysis will need to be compared to all
relevant PM2.5 and PMio NAAQS in effect for the area undertaking the analysis.15 For
example, in an area designated nonattainment or maintenance for only the 1997 annual
PM2.5 NAAQS or only the 2006 24-hour PM2.5 NAAQS, the hot-spot analysis would have
to address only that respective PM2.5 NAAQS. If an area is designated nonattainment or
13 See the March 2006 final rule for further background (71 FR 12496-98).
14 See EPA's March 2006 final rule (71 FR 12468-12511).
15 This guidance is written for the PM2 5 and PM10 NAAQS in effect at the time of writing (see the EPA
Green Book, available online at www.epa.gov/oar/oaqps/greenbk/index.html). However, the guidance may
also accommodate future PM NAAQS that can be implemented in a similar manner.
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maintenance for the 1997 annual PM2.5NAAQS and the 2006 24-hour PM2.5NAAQS, the
hot-spot analysis would have to address both NAAQS.
2.7 BACKGROUND CONCENTRATIONS
As required by 40 CFR 93.123(c)(l) and discussed in Section 2.2, a PM hot-spot analysis
must analyze the total emissions burden which results from the implementation of a
project, summed with future background concentrations. By definition, background
concentrations do not include emissions from the project itself. Background
concentrations include the emission impacts of all other sources in the project area,
including any nearby sources (e.g., locomotives at an intermodal terminal). Section 8
provides further information on how background concentrations can be determined.
2.8 APPROPRIATE TIME FRAME AND ANALYSIS YEARS
Section 93.116(a) of the conformity rule requires that PM hot-spot analyses must
consider either the full time frame of an area's transportation plan or, in an isolated rural
nonattainment or maintenance area, the 20-year regional emissions analysis.16
Conformity requirements are met if areas demonstrate that no new or worsened violations
occur in the year(s) of highest expected emissions - which includes the project's
emissions in addition to background concentrations.17 Areas should analyze the year(s)
within the transportation plan or regional emissions analysis, as appropriate, during
which:
• Peak emissions from the project are expected; and
• A new NAAQS violation or worsening of an existing violation would most likely
occur due to the cumulative impacts of the project and background concentrations
in the project area.18
In some cases, modeling the last year of the transportation plan or the year of project
completion may not be sufficient to satisfy this requirement. For example, if a project is
opened in two stages and the entire two-stage project is being approved, the interagency
consultation process may result in a decision to analyze two years: one to examine the
impacts of the first stage of the project and another to examine the impacts of the
completed project. The interagency consultation process should be used to select an
appropriate analysis year or years to demonstrate the project conforms over the entire
16 Although Clean Air Act section 176(c)(7) and 40 CFR 93.106(d) allow the election of changes to the
time horizons for transportation plan and TIP conformity determinations, these changes to do not affect the
time frame and analysis requirements for hot-spot analyses.
17 If such a demonstration can be made, then EPA believes it is reasonable to assume that no adverse
impacts would occur in any other years within the time frame of the transportation plan or regional
emissions analysis.
18 See EPA's July 1, 2004 final conformity rule (69 FR 40056-40058).
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time frame of the transportation plan and regional emissions analysis, per 40 CFR
93.105(c)(l)(i)and93.116.
2.9 AGENCY ROLES AND RESPONSIBILITIES
The typical roles and responsibilities of agencies implementing the PM hot-spot analysis
requirements are described below. Further details are provided throughout later sections
of this guidance.
2.9.1 Project sponsor
The project sponsor is typically the agency responsible for implementing the project (e.g.,
a state department of transportation, regional or local transit operator, or local
government). The project sponsor is the lead agency for developing the PM hot-spot
analysis, meeting interagency consultation and public participation requirements, and
documenting the final hot-spot analysis in the project-level conformity determination.
2.9.2 DOT
DOT is responsible for making project-level conformity determinations. PM hot-spot
analyses and conformity determinations would generally be included in documents
prepared to meet NEPA requirements.19 It is possible for DOT to make a project-level
conformity determination outside of the NEPA process (for example, if conformity
requirements apply after NEPA has been completed but additional federal action on the
project is required). DOT is also an active member of the interagency consultation
process for conformity determinations.
2.9.3 EPA
EPA is responsible for promulgating transportation conformity regulations and provides
policy and technical assistance to federal, state, and local conformity implementers. EPA
is an active member of the interagency consultation process for conformity
determinations. In addition, EPA reviews submitted SIPs, and provides policy and
technical support for air quality modeling, monitoring, and other issues.
2.9.4 State and local transportation and air agencies
State and local transportation and air quality agencies are part of the interagency
consultation process and assist in modeling of transportation activities, emissions, and air
quality. These agencies are likely to provide data required to perform a PM hot-spot
analysis, although the conformity rule does not specifically define the involvement of
19 As noted above, transportation conformity requirements are separate from NEPA-related requirements,
although conformity determinations are typically developed during the NEPA process and reviewed in
parallel.
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these agencies in project-level conformity determinations. For example, the state or local
air quality agency operates the air quality monitoring network, processes meteorological
data, uses air quality models for air quality planning purposes (such as SIP development
and modeling applications for other purposes). MPOs often conduct emissions modeling,
maintain regional population forecasts, and project future traffic conditions relevant for
project planning. The interagency consultation process can be used to discuss the role of
the state or local air agency, the MPO, and other agencies in project-level conformity
determinations, if such roles are not already defined in the state's conformity SIP.
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Section 3: Overview of a Quantitative PM Hot-Spot Analysis
3.1 INTRODUCTION
This section provides an overview of the process for conducting a quantitative PM hot-
spot analysis. This section may be particularly helpful to those who are looking for a
general understanding of this process. All individual elements or steps presented here are
covered in more depth and with more technical information throughout the remainder of
the guidance. The general steps required to complete a quantitative PM hot-spot analysis
are depicted in Exhibit 3-1 (following page) and summarized in this section.
Note that the interagency consultation process is an essential part of developing PM hot-
spot analyses. As a number of fundamental aspects of the analysis need to be determined
through consultation, it is recommended that these discussions take place at the earliest
opportunity and well in advance of beginning any modeling. In addition, early
consultation allows potential data sources for the analysis to be more easily identified.
3.2 DETERMINE NEED FOR A PM HOT-SPOT ANALYSIS (STEP i)
The conformity rule requires a PM hot-spot analysis only for projects of local air quality
concern. See Section 1.4 and Appendix B regarding how to determine if the project is of
local air quality concern according to the conformity rule and through the interagency
consultation process.
As stated earlier, if the project is not of local air quality concern, then the project meets
40 CFR 93.116 requirements for PM without a hot-spot analysis. For this type of project,
project sponsors should briefly document in the project-level conformity determination
that the requirements of the Clean Air Act and 40 CFR 93.116 are met without a hot-spot
analysis, since such projects have been found not to be of local air quality concern under
40 CFR 93.123(b). Note that all other project-level conformity requirements must
continue to be met.
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Exhibit 3-1. Overview of the Quantitative Hot-spot Analysis Process
Step 1: Determine Need
for Analysis
Is this a project of
local air quality
1
No
PM hot-spot analysis
not required
Step 2:
Determine Approach, Models, and Data
Step 3: Estimate On-Road Motor
Vehicle Emissions
/ \ Yes
_^/ Is project located \
^^^ in California? /
i
No
r
Estimate using
MOVES
1
Estimate using
EMFAC
Step 4:
Estimate Dust and Other Emissions
Does road or
construction
dust need to
be considered'^
Step 5: Select Air
Quality Model, Data
Inputs, and Receptors
Obtain and input
required site data (e.g.,
meteorological)
Input MOVES/
EMFAC, dust, and
nearby source outputs
Run air quality model
and obtain results
Step 6:
Determine Background
C oncentrations
Step 7: Calculate Design
Values and Compare
Build/No-Build Results
Add Step 5 results to
background concentrations
to obtain design values for
build/no-build scenarios
Do the design
values allow the
project to
conform?
Yes
Step 8:
Consider Mitigation or
Control Measures
Consider measures to
reduce emissions and redo
analysis
Y / Do the design
values allow the
project to
conform?
Step 9:
Document Analysis
No
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3.3 DETERMINE APPROACH, MODELS, AND DATA (STEP 2)
3.3.1 General
There are several decisions that need to be made before beginning a PM hot-spot
analysis, including:
• The geographic area to be covered by the analysis (the "project area") and
emission sources to be modeled;
• The general approach and analysis year(s) for emissions and air quality modeling;
• The applicable PM NAAQS to be evaluated;
• The type of PM emissions to be modeled for different sources;
• The emissions and air quality models and methods to be used;
• The project-specific data to be used; and
• The schedule for conducting the analysis and points of consultation.
Further details on these decisions are provided below.
3.3.2 Determining the geographic area and emission sources to be covered by the
analysis
The geographic area to be covered by a PM hot-spot analysis (the "project area") is to be
determined on a case-by-case basis through the interagency consultation process. PM
hot-spot analyses must examine the air quality impacts of the relevant PM NAAQS in the
area substantially affected by the project (40 CFR 93.123(c)(l)). To meet this and other
conformity requirements, it is necessary to define the project, determine where it is to be
located, and determine whether any other emission sources are also located in the project
area.20 In addition to emissions from the proposed highway or transit project,21 there
may be other nearby sources of emissions (e.g., a freight rail terminal) that need to be
estimated and considered along with other background concentrations. There may be
other sources in the project area that are determined through the interagency consultation
process to be insignificant to project emissions (e.g., a service drive or small employee
parking lot). See Sections 4 through 6 for how to estimate emissions from the proposed
project, and Sections 6 through 8 for when and how to include nearby source emissions
as well as other background concentrations.
Hot-spot analyses must include the entire project (40 CFR 93.123(c)(2)). However, it
may be appropriate in some cases to focus the PM hot-spot analysis only on the locations
of highest air quality concentrations. For example, for large projects, it may be necessary
to analyze multiple locations that are expected to have the highest air quality
concentrations, and consequently, the most likely new or worsened PM NAAQS
violations.
20 See more in the March 24, 2010 final conformity rule entitled "Transportation Conformity Rule PM2s
and PM10 amendments," 75 FR 14281; found online at: www.epa.gov/otaq/stateresources/transconf/conf-
regs.htm.
21 40 CFR 93.101 defines "highway project" and "transit project" for transportation conformity purposes.
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3.3.3 Deciding the general analysis approach and analysis year(s)
As stated in Section 2.4, there are several approaches for completing a build/no-build
analysis for a given project. For example, a project sponsor may want to start by
completing the build scenario first to see if a new or worsened PM NAAQS violation is
predicted (and if not, then modeling the no-build scenario would be unnecessary). In
contrast, a project sponsor could start with the no-build scenario first if a future PM
NAAQS violation is anticipated in both the build and no-build scenarios.
It is also necessary to select one or more analysis years within the time frame of the
transportation plan or regional emissions analysis when emissions from the project, any
nearby sources, and background are expected to be highest. Analysis year(s) should be
determined through the interagency consultation process. See Section 2.8 for more
information on selecting analysis year(s).
3.3.4 Determining which PM NAAQS to be evaluated
As stated in Section 2.6, PM hot-spot analyses need to be evaluated only for the NAAQS
for which an area has been designated nonattainment or maintenance. In addition, there
are aspects of modeling that can be affected by whether a NAAQS is an annual or a 24-
hour PM NAAQS. For example, a hot-spot analysis for the annual PM2 5 NAAQS would
involve data and modeling throughout a given analysis year (i.e., all four quarters of the
analysis year).22
A hot-spot analysis for the 24-hour PM2 5 or PMi0 NAAQS would also involve data and
modeling throughout an analysis year, except when future NAAQS violations and peak
emissions in the project area are expected to occur in only one quarter of the future
analysis year(s). In such cases, a project sponsor could choose to complete emissions and
air quality modeling for only that quarter if agreed to through the interagency
consultation process. For example, a PMio nonattainment or maintenance area may only
have PMio NAAQS violations during the first quarter of the year (January-March), when
PM emissions from other sources, such as wood smoke, are also highest. In such an area,
if the highest emissions from the project area are also expected to occur in this same
quarter, then the project sponsor could complete the PM hot-spot analysis for only that
quarter (if agreed to through interagency consultation).
Note: It may be difficult to determine whether 24-hour PM2.s NAAQS violations will
occur in only one quarter, due to the number ofPM2.s emission sources in a given project
area that can occur throughout the year. In such cases, it is important to analyze all
quarters to ensure that any new or worsened PM NAAQS violation can be identified
through modeling.
22 Calendar quarters in this guidance are defined in the following manner: Ql (January-March), Q2 (April-
June), Q3 (July-September), and Q4 (October-December).
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3.3.5 Deciding on the type of PM emissions to be modeled
The interagency consultation process should be used to determine what types of directly
emitted PM2.5 or PMio are relevant for estimating the emissions in the project area. See
Section 2.5 for further information on what types of directly emitted PM must be
included in hot-spot analyses and Sections 4 through 6 and Section 8 on when and how to
quantify PM emissions.
3.3.6 Determining the models and methods to be used
The interagency consultation process must be used to determine the emissions and air
quality models and methods used in the PM hot-spot analysis (40 CFR 93.105(c)(l)(i)).
The latest approved emissions models must be used in PM hot-spot analyses (40 CFR
93.111). See Sections 3.4 through 3.6 as well as the subsequent sections of the guidance
they refer to for specific information about models and methods that apply.
Note: It is important to select an air quality model to be used in the PM hot-spot analysis
early in the process, since this information is necessary to prepare emissions model
outputs for air quality modeling purposes. See Section 7 for further information on when
AERMOD or CAL3QHCR are recommended air quality models for PM hot-spot
analyses.
3.3.7 Obtaining the project-specific data to be used
The transportation conformity rule requires that the latest planning assumptions available
at the time that the analysis begins be used in conformity determinations (40 CFR
93.110). In addition, the regulation states that hot-spot analysis assumptions must be
consistent with those assumptions used in the regional emissions analysis for any inputs
which are required for both analyses (40 CFR 93.123(c)(3)).
The project sponsor should use project-specific data for both emissions and air quality
modeling, whenever possible, though default inputs may be appropriate in some cases.
The use of project-specific versus default data is discussed further in Sections 4 through
The following are examples of data needed to run MOVES or EMF AC, as described in
Sections 4 and 5:
• Traffic data sufficient to characterize each link in the project area;
• Starts per hour and number of vehicles idling during each hour for off-network
links/sources;
• Vehicle types and age distribution expected in the project area; and
• Temperature and humidity data for each month and hour included in the analysis.
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Depending on the air quality model to be used, the following are examples of data that
will likely be needed, as described in Sections 7 through 9:
• Surface meteorological data from monitors that measure the atmosphere near the
ground;
• Upper air data describing the vertical temperature profile of the atmosphere;
• Data describing surface characteristics near the surface meteorological monitors;
• Nearby population data; and
• Information necessary for determining locations of air quality modeling receptors.
To complete the PM hot-spot analysis, areas will also need data on background
concentrations from nearby or other emission sources in the project area, as described in
Section 8.
3.4 ESTIMATE ON-ROAD MOTOR VEHICLE EMISSIONS (STEP 3)
There are two approved motor vehicle emissions models available for estimating the
project's exhaust, brake wear, and tire wear emissions. See Section 4 for more on
estimating these PM emissions with EPA's MOVES model. Section 5 describes how to
apply EMFAC for estimating these emissions for projects in California.
3.5 ESTIMATE DUST AND OTHER EMISSIONS (STEP 4)
Section 2.5 provides more information about when re-entrained road dust and/or
construction emissions are included in PM2.s and PMi0 hot-spot analyses. Section 6
describes methods for estimating these emissions.
There may be other sources of emissions that also need to be estimated, and included in
air quality modeling. Section 8 provides further information regarding how to account
for these emissions in a PM hot-spot analysis. Appendix I provides further information
for estimating locomotive emissions.
3.6 SELECT AN AIR QUALITY MODEL, DATA INPUTS AND RECEPTORS
(STEP 5)
An air quality model estimates PM concentrations at specific points in the project area
known as "receptors." Emissions that result from the project (including those from
vehicles, dust, and construction from Steps 3 and 4) as well as any other nearby emission
sources (e.g., locomotives) must be input into the selected air quality model, which
predicts how emissions are dispersed based on meteorological and other input data.
There are two air quality models (AERMOD and CAL3QHCR) recommended for use in
PM hot-spot analyses. Basic information about these models, including how to select a
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model for a particular project and the data needed to run them, is found in Section 7 and
Appendix J.
3.7 DETERMINE BACKGROUND CONCENTRATIONS (STEP 6)
The PM hot-spot analysis must also account for background PM concentrations in the
project area to account for emissions that are not related to the project or nearby sources.
Section 8 provides further information on selecting representative background
concentrations.
3.8 CALCULATE DESIGN VALUES AND COMPARE BUILD AND NO-BUILD
SCENARIO RESULTS (STEP 7)
In general, the PM concentrations estimated from air quality modeling (in Step 5) are
then combined with background concentrations (in Step 6) at the receptor locations for
both the build and no-build scenarios. The resulting statistic is referred to as a design
value; how it is specifically calculated depends on the form of the NAAQS. If the design
value in the build scenario is less than or equal to the relevant PM NAAQS at appropriate
receptors, then the project meets conformity requirements. In the case where the design
value is greater than the NAAQS in the build scenario, a project could still meet
conformity requirements if the design values in the build scenario were less than or equal
to the design values in the no-build scenario at appropriate receptors. See Section 2.4 and
Section 9 for further details on build/no-build approaches and implementation.
3.9 CONSIDER MITIGATION OR CONTROL MEASURES (STEP 8)
Where a project does not meet conformity requirements, a project sponsor may consider
mitigation or control measures to reduce emissions in the project area. If mitigation or
control measures are considered, additional modeling will need to be completed and new
design values calculated to ensure that conformity requirements are met. See Section 10
for more information on possible measures for consideration.
3.10 DOCUMENT THE PM HOT-SPOT ANALYSIS (STEP 9)
The PM hot-spot analysis should include sufficient documentation to justify the
conclusion that a proposed project meets conformity rule requirements per 40 CFR
93.116 and 93.123.
Hot-spot analysis documentation should include, at a minimum:
• A description of the proposed project, including where the project is located, the
project's scope (e.g., adding an interchange, widening a highway, expanding a
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major bus terminal), when the project is expected to be open to traffic, travel
activity projected for the analysis year(s), and what part of 40 CFR 93.123(b)(l)
is applicable;23
• A description of the analysis year(s) examined;
• Emissions modeling, including the emissions model used (e.g., MOVES),
modeling inputs and results, and how the project was characterized in terms of
links;
• Modeling inputs and results for estimating re-entrained road dust, construction
emissions, and other nearby source emissions, as applicable to a particular PM
hot-spot analysis;
• Air quality modeling data, including the air quality model used, modeling inputs
and results, and description of the receptors employed in the analysis;
• A description of the assumptions used to determine background concentrations;
• A discussion of any mitigation or control measures that will be implemented, the
methods and assumptions used to quantify their expected effects, and associated
written commitments; and
• A conclusion for how the proposed project meets 40 CFR 93.116 and 93.123
conformity requirements for the PM2 5 and/or PMioNAAQS.
Documentation should consistently describe the sources of data used in preparing
emissions and air quality modeling inputs. This documentation should also describe any
other critical assumptions that have the potential to affect predicted concentrations.
Documentation of PM hot-spot analyses would be included in the project-level
conformity determination.
23 This information could reference the appropriate sections of any NEPA document prepared for the
project.
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Section 4: Estimating Project-level PM Emissions Using
MOVES
4.1 INTRODUCTION
This section of the guidance describes how to use MOVES to estimate PM exhaust, brake
wear, and tire wear emissions for PM hot-spot analyses outside of California. This
section focuses on determining what the appropriate project-level inputs are and how
MOVES should be run to provide the necessary information to complete air quality
modeling.24
MOVES2010 is a computer model designed by EPA to estimate emissions from cars,
trucks, buses and motorcycles. MOVES2010 replaces MOBILE6.2, EPA's previous
emissions model.25 MOVES is based on an extensive review of in-use vehicle data
collected and analyzed since the release of MOBILE6.2. MOVES estimates PM
emissions to account for speed and temperature variations and models emissions at a high
resolution. As a result, users can now incorporate a much wider array of vehicle activity
data for each roadway link, as well as start and idle activity in transit or other terminal
projects.
Exhibit 4-1 (following page) shows the necessary steps for applying the MOVES model
for project-level PM hot-spot analyses.
This section presumes users already have a basic understanding of how to run MOVES,
either by attending MOVES training or reviewing the MOVES User Guide.26 MOVES
includes a default database of meteorology, fleet, activity, fuel, and control program data
for the entire United States. The data included in this database come from a variety of
sources and are not necessarily the most accurate or up-to-date information available at
the local level for a particular project. This section describes when the use of that default
database is appropriate for PM hot-spot analysis, as well as when available local data
must be used (40 CFR 93.110 and 93.123(c)).
24 Technical guidance on using MOVES for regional emissions inventories can be found in "Technical
Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans
and Transportation Conformity," EPA-420-B-10-023 (April 2010); available online at:
www.epa.gov/otaq/stateresources/transconf/policv.htm.
25 EPA stated in the preamble to the March 2006 final rule that finalizing the MOVES emissions model was
critical before quantitative PM hot-spot analyses are required, due to the limitations of applying
MOBILE6.2 for PM at the project level. See EPA's March 2006 final rule for further information (71 FR
12498-12502).
26 The MOVES model, User Guide, and supporting documentation are available online at:
www.epa.gov/otaq/models/moves/index.htm.
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Exhibit 4-1. Steps for Using MOVES in a Quantitative PM Hot-spot Analysis
Divide the project into
links
(Section 4.2)
I
Determine the number of
MOVES runs
(Section 4.3)
Generate Run Specification ("RunSpec")
Enter time period
(Section 4.4.3)
Specify county
(Section 4.4.4)
1
Select
fuel/vehicle
combination
(Section 4.4.5)
1 Select road type
(Section 4.4. 6)
I
/ Does pn
/ an "off-
( compor
\ signifies
\ starts o
yect have \
network" \ Yes
mt engine /
r idling? /
I No
Select PM
pollutants & ^
processes
(Section 4.4. 7)
•->
Enter meteorology
data ft
(Section 4.5.1)
I
Build age
distribution table —
(Section 4.5.2)
Enter Data into P
Define
fuels/fuel mix
(Section 4.5. 3)
reject Data Manager
Po
net
(Se<
1
Populate link
source type
(Section 4.5.5)
Describe link
^ activity
(Sections 4.5.6 -
4.5.8)
Include "off-
type
pulate off-
work table 4 •" "•
;tion 4.5.9)
Run MOVES &
^ generate emission
factors
(Section 4.6)
1
Output emission
factor database
Note: The steps in this exhibit and in the accompanying text describe how to use MOVES at the
project-level for a PM hot-spot analysis.
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As discussed in Section 2.4, project sponsors should conduct emissions and air quality
modeling for the project build scenario first. If this scenario does not exceed the
NAAQS, then it is not necessary to model the no-build scenario. Following this approach
will allow users to avoid unnecessary emissions and air quality modeling. Finally,
Section 4 describes how to use MOVES to estimate emissions from a highway or transit
project that requires a PM hot-spot analysis ("the project"); this section could also be
used to estimate emissions for any other highway and transit facilities in the project area,
when necessary.
4.2 CHARACTERIZING A PROJECT IN TERMS OF LINKS
Prior to entering data into MOVES, users need to first identify the project type and the
associated emission processes (running, start, and idle exhaust) to be modeled. This
guidance distinguishes between two types of transportation projects: (1) highway and
intersection projects, and (2) transit or other terminal projects:
• For highway and intersection projects, running exhaust, crankcase, brake wear,
and tire wear emissions are the main focus.
• For transit and other terminal projects, start, crankcase, and extended idle
emissions are typically needed, and in some cases these projects will also need to
address cruise, approach and departure running exhaust emissions on affected
links.
The goal of defining a project's links is to accurately capture emissions where they occur.
Within MOVES, a link represents a segment of road or an "off-network" location where
a certain type of vehicle activity occurs.27 Generally, the links specified for a project
should include segments with similar traffic/activity conditions and characteristics. From
the link-specific activity and other inputs, MOVES calculates emissions from every link
of a project for a given time period (or run). In MOVES, running emissions, including
periods of idling at traffic signals, are defined in the Links Importer (see Section 4.5.6),
while starts and extended periods of idling (e.g., truck idling at a freight terminal) are
defined in the Off-Network Importer (see Section 4.5.9).
4.2.1 Highway and intersection projects
General
A PM hot-spot analysis fundamentally depends on the availability of accurate data on
roadway link speed and traffic volumes for build and no-build scenarios.28 Thus, local
27 "Off-network" in the context of MOVES refers to an area of activity not occurring on a roadway.
Examples of a MOVES off-network link include parking lots and freight or bus terminals.
28 Project sponsors should document available traffic data sets, their sources, key assumptions, and the
methods used to develop build and no-build scenario inputs for MOVES. Documentation should include
differences between how build and no-build traffic projections are obtained. For projects of local air
quality concern, there will always be differences in traffic volumes and other activity changes between the
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traffic data should be used to characterize each link sufficiently. It is recommended that
the user divide a project into separate links to allow sufficient resolution at different
vehicle traffic and activity patterns; characterizing this variability in emissions within the
project area will assist in air quality modeling (see Section 7).
For analyses with MOVES, a minimum of both an average speed and traffic volume is
required for each link. If that is the only information available, MOVES uses default
assumptions of vehicle activity patterns (called drive cycles) for that average speed and
type of roadway to estimate emissions. Those default drive cycles use different
combinations of vehicle activity (acceleration, deceleration, cruise, and/or idle)
depending on the speed and road type. For example, if the link average speed is 30 mph
and it is an urban street, MOVES uses a default drive cycle that includes a high
proportion of acceleration, deceleration, and idle activity as would be expected on an
urban street with frequent stops. If the average speed is 60 mph and it is a rural freeway,
MOVES uses a default drive cycle that assumes a higher proportion of cruise activity,
smaller proportions of acceleration and deceleration activity, and little or no idle activity.
As described further in Section 4.5.7, users should take advantage of the full capabilities
of MOVES for estimating emissions on different highway and intersection project links.
Although average speeds and travel volumes are typically available for most
transportation projects and may need to be relied upon during the transition to using
MOVES, users can develop and use more precise data through the MOVES Operating
Mode Distribution Importer or Link Drive Schedule Importer, as described further below.
When more detailed data are available to describe the pattern of changes in vehicle
activity (proportion of time in acceleration, deceleration, cruise, or idle activity) over a
length of road, MOVES is capable of calculating these specific emission impacts. EPA
encourages users to consider these options for highway and intersection projects,
especially as MOVES is implemented further into the future, or for more advanced
MOVES applications.
Free-flow Highway Links
The links defined in MOVES should capture the expected physical layout of a project and
representative variations in vehicle activity. The simplest example is a single, one
directional, four-lane highway that could be characterized as just one link. More
sophisticated analyses may break up traffic flow on that single link into multiple links of
varying operating modes or drive cycles that may have different emission factors
depending on the relative acceleration, cruise, or deceleration activity on each segment of
that link. In general, the definition of link will depend on how much the type of vehicle
activity (acceleration, deceleration, cruise or idle) changes over a length of roadway, the
level of detail of available data, and the modeling approach used with MOVES. For a
highway lane where vehicle behavior is fairly constant, the length of the link could be
longer and the use of detailed activity data will have a smaller impact on results. In
build and no-build scenarios, and these differences must be accounted for in the data that is used in the PM
hot-spot analysis.
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MOVES, activity on free-flow highway links can be defined by an average speed, link
drive schedule, or operating mode ("Op-Mode") distribution (discussed in Section 4.5.7).
Intersection Links
If the project analysis involves intersections, the intersections need to be treated
separately from the free-flow links that connect to those intersections. Although road
segments between intersections may experience free-flow traffic operations, the
approaches and departures from the intersections will likely involve acceleration,
deceleration, and idling activity not present on the free-flow link. For intersection
modeling, the definition of link length will depend on the geometry of the intersection,
how that geometry affects vehicle activity, and the level of detail of available activity
information. Guidance for defining intersection links are given in Appendix D, but the
definition of links used for a particular project will depend of the specific details of that
project and the amount of available activity information.29
Note: For both free-flow highway and intersection links, users may directly enter output
from traffic simulation models in the form of second-by-second individual vehicle
trajectories. These vehicle trajectories for each road segment can be input into MOVES
using the Link Drive Schedule Importer and defined as unique LinklDs. There are no
limits in MOVES as to how many links that can be defined, however model run times
increase as the user defines more links. A representative sampling of vehicles can be
used to model higher volume segments by adjusting the resulting sum of emissions to
account for the higher traffic volume. For example, if a sampling of 5,000 vehicles
(5,000 links) was used to represent the driving patterns of 150,000 vehicles, then the sum
of emissions would be adjusted by a factor of 30 to account for the higher traffic volume
(i.e., 150,000 vehicles/5,000 vehicles). Since the vehicle trajectories include idling,
acceleration, deceleration, and cruise, separate roadway links do not have to be
explicitly defined to show changes in driving patterns (as described in Appendix D). The
sum of emissions from each vehicle trajectory (LinkID) represents the total emission
contribution of a given road segment.
4.2.2 Transit and other terminal projects
For off-network sources such as a bus terminal or intermodal freight terminal, the user
should have information on starts per hour and number of vehicles idling during each
hour. This activity will likely vary from hour to hour. It is recommended that the user
divide such a project into separate links to appropriately characterize variability in
emission density within the project area (as discussed in Section 7). In this case, each
"link" describes an area with a certain number of vehicle starts per hour, or a certain
number of vehicles idling during each hour.
29 As discussed in Section 7, the use of the CAL3QHCR queuing algorithm for intersection idle queues is
not recommended. Rather, idling vehicles should be represented in combination with decelerating,
accelerating, and free-flow traffic on an approach segment of an intersection.
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Some transit and other terminal projects may have significant running emissions similar
to free-flow highway projects (such as buses and trucks coming to and from an
intermodal terminal). These emissions can be calculated by defining one or more unique
running links as described in Section 4.2.1 and Appendix D (that is, in addition to any
other roadway links associated with the project). These running link emissions can then
be aggregated with the emissions from starts and idling from non-running activity on the
transit or other terminal link outside of the MOVES model to generate the necessary air
quality model inputs.
Long duration idling (classified in MOVES as Operating Mode ID "200") can only be
modeled in MOVES for long-haul combination trucks. Idling for other vehicles and
shorter periods of idling for long-haul combination trucks should be modeled as a project
link with an operating mode distribution that consists only of idle operation (Op-Mode 1).
This can be specified in the Links table by inputting the vehicle population and
specifying an average speed of "0" mph.
Note: The user may choose to exclude sources such as a separate service drive, separate
small employee parking lot, or other minor sources that are determined through
inter agency consultation to be insignificant to project emissions.
4.3 DETERMINING THE NUMBER OF MOVES RUNS
4.3.1 General
Before running MOVES to calculate emission factors, users should first determine the
number of unique scenarios that can sufficiently describe activity variation in a project.
In most projects traffic volume, average speed, idling, fleet mix, and the corresponding
emission factors will likely vary from hour to hour, day to day, and month to month.
However, it is unlikely that data are readily available to capture such finite changes.
Project sponsors may have activity data collected at a range of possible temporal
resolutions. The conformity rule requires the latest activity data available at the time of
the analysis to be used in a quantitative hot-spot analysis (40 CFR 93.110).30 Depending
on the sophistication of the activity data analysis for a given project, these data may range
from a daily average-hour and peak-hour value to hourly estimates for all days of the
year. EPA encourages the development of sufficient travel activity data to capture the
expected ranges of traffic conditions for the build and no-build scenarios. The number of
MOVES runs should be based on the best available activity data and the PM NAAQS
involved.31 Exhibit 4-2 includes EPA's recommendations for PM hot-spot analyses:
30 See "EPA and DOT Joint Guidance for the Use of Latest Planning Assumptions in Transportation
Conformity Determinations," EPA420-B-08-901 (December 2008); available online at:
www.epa.gov/otaq/stateresources/transconf/policy/420b08901.pdf.
31 The conformity rule requires the latest activity data available at the time of the analysis to be used (40
CFR 93.110).
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Exhibit 4-2. Typical Number of MOVES Runs for an Analysis Year
Applicable NAAQS
Annual PM2.5 NAAQS only
24-hour PM2.5 NAAQS only
24-hour PMio NAAQS only
Annual and 24-hour PM
NAAQS33
Build Scenario
16
16 (4 in certain cases)
16 (4 in certain cases)
16
No-build Scenario32
16
16 (4 in certain cases)
16 (4 in certain cases)
16
Hot-spot analyses for the annual PM2.5 NAAQS should include 16 unique MOVES runs
(i.e., four runs for different time periods for each of four calendar quarters). Therefore,
for a typical build/no-build analysis, a total of 32 runs would be needed (16 for each
scenario). Hot-spot analyses for only the 24-hour PM2.5 or PMio NAAQS should also be
completed with the 16 MOVES runs, except in cases where potential PM NAAQS
violations are expected to occur in only one quarter of the calendar year. In such cases,
the user may choose to model only that quarter with four MOVES runs for each scenario.
See Section 3.3 for further details for when fewer MOVES runs is appropriate for the 24-
hour PM NAAQS; this decision should be determined through interagency consultation.
The product of the MOVES analysis is a year's (or quarter's) worth of hour-specific
emission factors for each project link that will be applied to the appropriate air quality
model (discussed in Section 7) and compared to the relevant PM NAAQS (discussed in
Section 9). The following subsections provide further information for determining
MOVES runs for all PM NAAQS, based on the level of available travel activity data.
4.3.2 For projects w ith typical travel activity data
Traffic forecasts for highway and intersection projects are often completed for annual
average daily traffic volumes, with an allocation factor for a daily peak-hour volume.
This data can be used to conduct an analysis with MOVES that is representative for all
hours of the year. To complete 16 MOVES runs as outlined above, the user should run
MOVES for four months: January, April, July, and October; and four weekday time
periods: morning peak (AM), midday (MD), evening peak (PM), and overnight (ON).34
The AM and PM peak periods should be run with peak-hour traffic activity; MD and ON
periods should be run with average-hour activity. The most reasonable methods in
accordance with good practice should be used to obtain the allocation factors and diurnal
There are some cases where the no-build scenario and associated emissions and air quality modeling is
not necessary. See Section 2.4 for further information.
33 Such a situation would include cases where a project is located in a nonattainment/maintenance area for
both the annual PM2 5 NAAQS and either a 24-hour PM2 5 NAAQS or the 24-hour PM10 NAAQS.
34 If it is determined through interagency consultation that only four MOVES runs are required for a PM
hot-spot analysis for a 24-hour PM NAAQS, four runs would be done for the same weekday time periods,
except only for one quarter (i.e., January, April, July, or October).
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distribution of traffic and the methods must be decided in accordance with interagency
consultation procedures (40 CFR 93.105(c)(l)(i)).
The results for each of the four hours can then be extrapolated to cover the entire day.
For example, the peak-hour volume can be used to represent activity conditions over a
three-hour morning (AM) and three-hour evening (PM) period. The remaining 18 hours
of the day can be represented by the average-hour activity. These 18 hours would be
divided into a midday (MD) and overnight (ON) scenario.
The following is one suggested approach for an analysis employing the average-
hour/peak-hour traffic scenario based on an examination of national-scale data:
• Morning peak (AM) emissions based on traffic data and meteorology occurring
between 6 a.m. and 9 a.m.;
• Midday (MD) emissions based on data from 9 a.m. to 4 p.m.;
• Evening peak (PM) emissions based on data from 4 p.m. to 7 p.m.; and
• Overnight (ON) emissions based on data from 7 p.m. to 6 a.m.
If there are local or project-specific data to suggest that the AM or PM peak traffic
periods will occur in different hours than the default values suggested here, or over a
longer or shorter period of time, that information should be documented and the hours
representing each time period adjusted accordingly. Additionally, users should
independently determine peak periods for the build and no-build scenarios, and should
not assume that each scenario is identical, as determined through interagency
consultation.
The emission factors for each month's runs should be used for the other months within
the quarter. The months suggested for the minimum number of MOVES runs correspond
to the first month of each quarter. For instance, January emissions should be assumed to
represent February and March emissions, April should be used to represent May and June
emissions, and so forth.35
4.3.3 For projects with additional travel activity data
Some project sponsors may have developed traffic or other activity data to show
variations in volume and speed across hours, days, or months. Additionally, if users are
modeling a transit or other terminal project, traffic volumes, starts, and idling estimates
are likely to be readily available for each hour of the day. Under either of these
circumstances, users have the option of applying the methodology described above (using
average-hour and peak-hour as representative for all hours of the year) if it is determined
through the interagency consultation process that using the additional data would not
significantly impact the emissions modeling results. Alternatively, additional MOVES
runs could be generated to produce a unique emission factor for additional activity data
(i.e., each period of time for which specific activity data are available).
35 Rather than use the middle month of the first quarter (February), January is used because it is typically
the coldest month of the year and therefore has the highest PM emission rates.
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4.4 DEVELOPING BASIC RUN SPECIFICATION INPUTS
Once the user has defined the project conceptually in terms of links and determined the
number of MOVES runs, the next step in using MOVES for project-level analyses is to
develop a run specification ("RunSpec"). The RunSpec is a computer file in XML
format that can be edited and executed directly or with the MOVES Graphical User
Interface (GUI). MOVES requires the user to set up a RunSpec to define the place and
time of the analysis as well as the vehicle types, road types, fuel types, and the emission-
producing processes and pollutants that will be included in the analysis. The headings in
this subsection describe each set of input options needed to create the RunSpec as defined
in the navigation panel of the MOVES GUI. In order to create a project-level RunSpec,
the user must go down the navigation panel filling in the appropriate data for each of the
menu items listed in the panel. Those menu items are:
• Description
• Scale
• Time Spans
• Geographic Bounds
• Vehicles/Equipment
• Road Type
• Pollutants and Processes
• Manage Input Data Sets
• Strategies
• Output
• Advanced Performance Features
Additional information on each menu item can be found in the MOVES User Guide
available on EPA's website (www.epa.gov/otaq/models/moves/index.htm). The
appropriate sections of the user guide are referenced when describing the RunSpec
creation process below.
4.4.1 Description
(MOVES User Guide Section 2.2.1)
This menu item allows the user to enter a description of the RunSpec using up to 5,000
characters of text. Entering a complete description of the RunSpec is important to help
keep track of multiple MOVES runs that may be needed for a PM hot-spot analysis and
to provide supporting documentation for the regulatory submission.
4.4.2 Scale
(MOVES User Guide Section 2.2.2)
The Scale menu item in MOVES allows the user to select different scales or domains for
the MOVES analysis. All MOVES runs for project-level analysis must be done using the
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"Project" domain in the "Scale" panel. Selecting the "Project" domain is necessary to
allow MOVES to accept detailed activity input at the link level.36
Users should select either "Inventory" or "Emission Rates" as output depending on the
air quality model being used:
• When using AERMOD, a grams/hour emission factor is needed. Users should
select "Inventory", which produces results for total emissions on each link; this is
equivalent to a grams/hour/link emission factor.
• When using CAL3QHCR, the "Emission Rates" option should be selected to
produce link specific grams/vehicle-mile emission factors.
This guidance explains the steps of post-processing both "Inventory" and "Emission
Rate" results to produce the desired emission factors in Section 4.6.
4.4.3 Time Spans
(MOVES User Guide Section 2.2.3)
The Time Spans menu item is used to define the specific time period covered in the
MOVES run. The Time Spans panel is divided into five sections, which allow the user to
select the time aggregation level, year, month, day, and hour included in the run.
For the project domain, the MOVES model processes one hour, of one day, of one
month, of one year for each run; that is, each MOVES run represents one specific hour.
The user should enter the desired time period in the MOVES Time Span panel for
estimating PM2.5 and/or PMio emissions for the relevant NAAQS in a given
nonattainment or maintenance area. Time aggregation should be set to "hour" which
indicates no pre-aggregation. The "day" selection should be set to "weekday" or
"weekend," but not both. Most users will be defining activity for a typical weekday. The
year, month, and hour should be set to specifically describe each MOVES run. For
instance, one run might be: 2015, January, 8:00 to 8:59 a.m. (the start and end hours set
to 8:00 to 8:59 a.m., respectively). The user may choose to build a batch file to automate
the process of running multiple scenarios.
4.4.4 Geographic Bounds
(MOVES User Guide Section 2.2.4)
The Geographic Bounds menu item allows the user to define the specific county that will
be modeled. The MOVES database includes county codes and descriptive information
for all 3,222 counties in the United States. Specifying a county in MOVES determines
certain default information for the analysis. Users should select the specific county
where the project is located. Only a single county (or single custom domain) can be
included in a MOVES run at the project level. If a project spans multiple counties, users
have three options:
36 Running MOVES using the "County" or "National" domains would not allow for detailed link level
input or output that is needed for PM hot-spot analyses.
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• If the county-specific local data is the same for all the counties, select the county
in which the majority of the project area is located;
• If not, separate the project into multiple parts, each of which is in a separate
county, and do a separate MOVES run for each part; or
• Use the custom domain option to model one unique area that represents all the
project counties.
4.4.5 Vehicles/Equipment
(MOVES User Guide Section 2.2.5)
The Vehicles/Equipment menu item and panel is used to specify the vehicle types that are
included in the MOVES run. MOVES allows the user to select from among 13 "source
use types" (the terminology that MOVES uses to describe vehicle types) and four
different fuels. Some fuel/source type combinations do not exist (e.g., diesel
motorcycles), and therefore, are not included in the MOVES database. PM hot-spot
analyses must include all vehicle types that are expected to operate in the project area.
Users should select the appropriate fuel and vehicle type combinations in the On Road
Vehicle Equipment panel to reflect the full range of vehicles that will operate in the
project area. In general, users should simply select all vehicle and fuel types, unless data
are available showing that some vehicles or fuels are not used in the project area.
4.4.6 Road Type
(MOVES User Guide Section 2.2.6)
The Road Type panel is used to define the types of roads that are included in the project.
MOVES defines five different road types:
• Rural Restricted Access - a rural highway that can be accessed only by an on-
ramp;
• Rural Unrestricted Access - all other rural roads (arterials, connectors, and local
streets);
• Urban Restricted Access - an urban highway that can be accessed only by an on-
ramp;
• Urban Unrestricted Access - all other urban roads (arterials, connectors, and local
streets); and
• Off-Network - any location where the predominant activity is vehicle starts and
idling (parking lots, truck stops, rest areas, freight or bus terminals).
MOVES uses these road types to determine the default drive cycle on a particular link.
For example, MOVES uses drive cycles for unrestricted access road types that assume
stop-and-go driving, including multiple accelerations, decelerations, and short periods of
idling. For restricted access road types, MOVES uses drive cycles that include a higher
fraction of cruise activity with much less time spent accelerating or idling.
For project-level analyses, the extent upon which MOVES uses these default drive cycles
will depend on how much additional information the user can supply for the link. The
process of choosing default or local drive cycles is described in Sections 4.2 and 4.5.7.
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However, even if the user will be supplying detailed, link-specific drive cycle
information or an Op-Mode distribution, road type is a necessary input in the RunSpec
and users should select one or more of the five road types that correspond to the road
types of the links that will be included in the project area. The determination of rural or
urban road types should be based on the Highway Performance Monitoring System
(HPMS) functional classification of the road type.
Additionally, any project that includes significant numbers of engine starts or significant
amounts of extended idling for heavy-duty vehicles needs to include the "Off-Network"
road type to properly account for emissions from that activity. More details on
describing inputs to describe engine start and idling activity are given in Section 4.5.9.
4.4.7 Pollutants and Processes
(MOVES User Guide Section 2.2.7)
The Pollutant and Processes panel is used to select both the types of pollutants and the
emission processes that produce them. For PM2.5 or PMio emissions, MOVES calculates
emissions for several pollutant species:
• Organic Carbon (OC)
• Elemental Carbon (EC)
• Sulfate Particulate
• Brake Wear Particulate
• Tire Wear Particulate
In addition, MOVES divides emissions by pollutant process. For a PM hot-spot analysis,
the categories are:
• Running Exhaust
• Start Exhaust
• Extended Idle Exhaust
• Crankcase Running Exhaust
• Crankcase Start Exhaust
• Crankcase Extended Idle Exhaust
• Brake Wear
• Tire Wear
For a PM2.5 hot-spot analysis, the user should select "Primary Exhaust PM2.5 - Total" (or
"Primary Exhaust PMio - Total" if it is a PMio hot-spot analysis), which is an aggregate
of each of the pollutant species (OC, EC, and sulfate) for each process. For MOVES to
run, the user must also select each individual PM species (i.e., "Primary PM2 5 - Organic
Carbon," "Primary PM2.5 - Elemental Carbon," "Primary PM2.5 - Sulfate Particulate," or
the PMio equivalents). In addition, if the analysis has road links with running emissions,
users must also select "Primary PM2 5 - Brake Wear Particulate" and "Primary PM2 5 -
Tire Wear Particulate" (or their PMio equivalents) as brake wear and tire wear are not
included in the exhaust totals.
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The user should calculate total PM from the MOVES output table results for each link
using the formulas described below:
For highway links (roads, intersections, ramps, etc.) where output was specified as a
grams/vehicle-mile emission factor ("Emission Rates" output), the aggregate total PM
emission factor (i.e., the sum of all PM emission factors for a link) needs to be calculated
using the formula:
PMaggregate total = (PMtotal running) + (PMtotal crankcase running) + (brake Wear) + (tire Wear)
For transit and other terminal project activity (starts and extended idle) where output was
selected as grams/hour ("Inventory" output), the aggregate total PM emission factor (i.e.,
the sum of all PM emission factors for a link) needs to be calculated using the formula:
= (PMtotal starts) + (PMtotal crankcase starts) + (PMtotal ext. idle) +
total crankcase ext. idle)
For transit and other terminal project links that contain starts and extended idling as well
as running emissions, and output was selected as "Inventory" output (grams/hour/link),
the aggregate total PM emission factor for each link needs to be calculated using the
formula:
PMaggregate total = (PMtotal running) + (PMtotal crankcase running) + (PMtotal starts) +
(PMtotal crankcase starts) + (PMtotal ext. idle) + (PMtotal crankcase ext. idle) +
(brake wear) + (tire wear)
4. 4. 8 Manage Input Data Sets
(MOVES User Guide Section 2.2.8)
Most analyses will not use the Manage Input Data Sets panel. One possible application is
to specify user-supplied databases to be read by the model during execution of a run.
However, for project-level analysis in MOVES, the Project Data Manager, described
below, serves this same function while providing for the creation of data table templates
and for the review of default data. EPA specifically developed the Project Data Manager
for project analyses and recommends using it to create and specify user supplied database
tables, instead of the Manage Input Databases panel.
4.4.9 Strategies
(MOVES User Guide Section 2.2.9)
In MOVES, the Strategies panel can be used to model alternative control strategies that
affect the composition of the vehicle fleet. The MOVES model has two alternative
control strategies built into the Strategies panel:
• The Alternative Vehicle Fuels and Technologies (AVFT) strategy allows users to
modify the fraction of alternative fueled vehicles and advanced technology
vehicles in each model year.
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• The On-Road Retrofit strategy allows the user to enter information about diesel
trucks and buses that have been retrofitted with emission control equipment.
In general, most PM hot-spot analyses would not include any inputs to the Strategies
panel. However, there are some exceptions. For example, a bus terminal project might
include plans to mitigate emissions by retrofitting the bus fleet that will operate at that
terminal with control equipment that reduces PM emissions. In that case, the user would
specify the details of the retrofit project using the On-Road Retrofit strategy panel. The
latest guidance on retrofit programs can be located at the EPA's conformity website:
www.epa.gov/otaq/stateresources/transconf/policy.htm. Strategies that affect vehicle
activity, such as implementing a truck idle reduction plan, should be handled in the Off-
Network Importer and Links Importer.
See Section 10 for further information regarding the inclusion of mitigation and/or
control measures in PM hot-spot analyses.
4.4.10 Output
(MOVES User Guide Section 2.2.10)
Selecting Output in the Navigation panel provides access to two additional panels:
General Output and Output Emissions Detail. Each of these allows the user to specify
aspects of the output data.
Under General Output, users should make sure to choose "grams" and "miles" for the
output units in order to provide results for air quality modeling. Also, "Distance
Travelled" and "Population" should be selected under the "Activity" heading to obtain
vehicle volume information for each link in the output.
Output Emissions Detail is used to specify the level of detail desired in the output data.
Emissions by hour and link are the default selections and should not be changed. Road
type will also be checked if output by Emission Rate was selected. EPA recommends
that users check the box labeled "Emission Process." No other boxes should be selected
in order to produce fleet aggregate emission factors for each link. Emission rates for each
process can be appropriately summed to calculate aggregate PM emission factors for each
link (as described in Section 4.4.7).
4.4.11 Advanced Performance Features
(MOVES User Guide Section 2.2.11)
This menu item is used to invoke features of MOVES that improve run time for complex
model runs by saving and reusing intermediate results. For specific applications, the user
may want to "save data" for deriving the intermediate MOVES calculation of an Op-
Mode Distribution from an average speed or link drive schedule. This is discussed
further in the MOVES User Guide, as well as demonstrated in the quantitative PM hot-
spot analysis example of a transit project in Appendix F.
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4.5 ENTERING PROJECT DETAILS USING THE PROJECT DATA MANAGER
After completion of all the necessary panels to create the RunSpec, the user must then
create the appropriate input database tables that describe the project in detail. As
described in Section 4.3, a typical PM hot-spot analysis will involve 32 MOVES runs
(build/no-build), each needing individual sets of input database tables to be created (four
sets of database tables for a build scenario of a single quarter). This is done using the
Project Data Manager, which can be accessed from the Pre-Processing menu item at the
top of the MOVES GUI or by selecting Enter/Edit Data in the Domain Input Database
section of the Geographic Bounds panel.
Since modeling a project involves many MOVES runs, good data management practices
are essential to prevent confusion and errors. For example, the name of the project input
database for each run should reflect the purpose of that run (e.g.,
"NoBuildSpringAMPeak_in"). A similar naming protocol should be used for the
RunSpec for each run. Also, each tab of the Project Data Manager includes a box for
entering a "Description of Imported Data." Modelers should make liberal use of these
descriptions to (1) indicate whether default or local data were used, and (2) indicate the
source and date of any local data, along with the filename of imported spreadsheets.
These descriptions are preserved with the input database so reviewers (or future users of
the same runs) will have the documentation for the inputs readily at hand.
The Project Data Manager includes multiple tabs that open importers, which are used to
enter project-specific data. These tabs and importers are:
• Meteorology
• Age Distribution
• Fuel Supply
• Fuel Formulation
• Inspection and Maintenance
• Link Source Type
• Links
• Link Drive Schedule
• Operating Mode Distribution
• Off-Network
Each of the importers allows the user to create a template file with required data field
names and with some key fields populated. The user then edits this template to add
project-specific local data with a spreadsheet application or other tool and imports the
data files into MOVES. In some importers, there is also the option to export default data
from the MOVES database in order to review it. Once the user determines that the
default data are accurate and applicable to the particular project, or determines that the
default data need to be changed and makes those changes, the user then imports that data
into MOVES. Details of the mechanics of using the data importers are provided in the
MOVES User Guide. Guidance for the use of these importers in PM hot-spot analyses is
described below.
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4.5.1 Meteorology
(MOVES User Guide Section 2.3.3.4.1)
The Meteorology Data Importer is used to import temperature and humidity data for the
month and hour that are defined in the MOVES run specification. Although temperature
and humidity data can be entered for all hours, only the one hour selected in the run
specification will be used for PM hot-spot analyses. In order to populate emission factor
inputs for air quality models, multiple hours of the day should be run based on the
guidance outlined in Section 4.3. Meteorology inputs for MOVES should be the same for
build and no-build scenarios.
Users should enter data specific to the project's location and time period modeled, as PM
emissions are found to vary significantly depending on temperature. The accuracy of
emission estimates at the project level improves when meteorological data gathered
specific to the modeled location is included. Default temperature and humidity values are
available in MOVES, but are not recommended for use in a PM hot-spot analysis.
Temperatures must be consistent with those used for the project's county in the regional
emissions analysis (40 CFR 93.123(c)(3)) as well as the air quality modeling inputs used
in the hot-spot analysis. Meteorological data may be obtained either from the National
Weather Service (NWS) or as part of a site-specific measurement program. Local
universities, the Federal Aviation Administration (FAA), military stations, and state and
local air agencies may also be sources of such data. The National Oceanic and
Atmospheric Administration's National Climatic Data Center (NCDC; online at
www.ncdc.noaa.gov/oa/ncdc.html) is the world's largest active archive of weather data
through which years of archived data can be obtained. A data source should be selected
that is representative of local meteorological conditions. Meteorological site selection is
discussed further in Section 7.5.
As discussed in Section 4.3, MOVES will typically be run for multiple time periods and
specific meteorology data that accurately represents these runs is needed to produce
emission estimates for comparison with both the 24-hour and annual PM NAAQS. The
user should employ a minimum of four hours (corresponding to AM peak traffic/PM
peak traffic/MD traffic/ON traffic), one day (weekday), for January, April, July, and
October. Within each period of day in each quarter, temperatures should be used that
represent the average temperature within that time period. For example, for January AM
peak periods corresponding to 6 a.m. to 9 a.m., the average January temperature based on
the meteorological record for those hours should be used in estimating the average
January AM peak period temperature for MOVES runs. The user may choose to run
additional hours and temperatures beyond the number of traffic periods for which data
exist. For example, within an 11-hour overnight (ON) modeling period, temperature data
could be used to differentiate hours with significantly different temperatures, despite
having assumed identical traffic estimates. Humidity estimates should be based on the
same hours and data source as the temperature estimates. See Section 4.3 for further
information on the number of MOVES runs recommended for different project analyses.
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4.5.2 Age Distribution
(MOVES User Guide Section 2.3.3.4.3)
The Age Distribution Importer is used to enter data that provides distribution of vehicle
fractions by age for each calendar year (yearlD) and vehicle type (sourceTypelD). These
data are required for running MOVES at the project level. The distribution of agelD (the
variable for age) fractions must sum to one for each vehicle type and year. These inputs
should generally be the same for build and no-build scenarios, unless something about the
project would change them (e.g., a bus terminal project that includes the purchase of new
buses in the build scenario).
To build a MOVES-compatible age distribution table, there are three possible options.
1. If available, users should use the latest state or local available age distribution
assumptions from their SIP or transportation conformity regional emissions
analysis. For the initial transition from MOBILE6.2 to MOVES, EPA has
provided a registration distribution converter.37 The tool allows users to input a
MOBILE6.2 registration distribution table (10, 10, 5 format) and obtain a
MOVES age distribution table. Over time, users should develop age distribution
data consistent with the requirements of MOVES.
Some users may have local registration distribution tables for all vehicle classes.
However, there may be cases where the user has registration distributions only for
one or more vehicle classes (e.g., LDVs) and therefore relies on MOBILE6.2
defaults for the remaining vehicle classes. In these cases, the user may use
MOVES default distributions available on the EPA's website.
2. If the project is designed to serve a fleet that operates only locally, such as a
drayage yard or bus terminal, the user should provide project-specific fleet age
distribution data. For most captive fleets, an exact age distribution should be
readily available or obtainable. The data should be in a format compatible with
MOVES. This format includes age fractions in 30-year bins rather than the 25
used in MOBILE6.2. Additionally, vehicle categories need to be in terms of the
13 MOVES source types.
3. Default distributions are available on the EPA website at:
www.epa.gov/otaq/models/moves/tools.htm. The user can select the analysis
year(s) and find the corresponding age distribution. These fractions are national
defaults and could be significantly different than the local project age distribution.
Age distribution can have a considerable impact on emission estimates, so the
default data should be used only if an alternative local dataset cannot be obtained
and the regional conformity analysis relies on national defaults.
37 This converter can be found online at: www.epa.gov/otaq/models/moves/tools.htm.
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If the user has relied in the past on the MOBILE6.2 default registration
distribution, they should now use the MOVES default age distribution if no other
state or local age distribution is available. This can be obtained from the tables
available on the EPA website given above.
4.5.3 Fuel Supply and Fuel Formulation
(MOVES User Guide Section 2.3.3.4.8 and 2.3.3.4.9)
The user must define in MOVES what fuel(s) and fuel mix will be used in the project
area. The Fuel Supply Importer and Fuel Formulation Importer are used to enter the
necessary information describing fuel type and fuel mix for each respective MOVES run.
These inputs should generally be the same for build and no-build scenarios, unless
something about the project would change them (e.g., a project that includes alternative
fuel vehicles and infrastructure in the build scenario).
In general, users should first review the default fuel formulation and fuel supply data in
MOVES, and then make changes only where local volumetric fuel property information
is available. The lone exception to this convention is in the case of Reid Vapor Pressure
(RVP) where a user should potentially change the value to reflect the differences between
ethanol and non-ethanol blended gasoline.
For additional guidance on defining fuel supply and formulation information, consult the
EPA document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory
Preparation in State Implementation Plans and Transportation Conformity" located at:
www.epa.gov/otaq/stateresources/transconf/policy.htm.
4.5.4 Inspection and Maintenance (I/M)
(MOVES User Guide Section 2.3.3.4.10)
MOVES does not provide a PM emission benefit from an I/M program. If the user
includes an I/M program in the run specification, the selection will have no impact on PM
emissions.
¥.5.5 Link Source Type
(MOVES User Guide Section 2.3.3.4.13)
The Link Source Type Importer allows the user to enter the fraction of the link traffic
volume which is represented by each vehicle type (source type). It is not required if the
project contains only a transit or other terminal (off-network) link. For each LinkID, the
SourceTypeHourFractions must sum to one across all source types.
Additionally, the user must ensure that the source types selected in the MOVES
Vehicles/Equipment panel match the source types defined in the Link Source Type
Importer.
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There are no defaults that can be exported from the Link Source Type Importer. For any
analysis at the project level, the user must provide source type fractions for all vehicles
being modeled and for each MOVES run (as vehicle mixes may change from hour to
hour and month to month). There are two options available to populate the Link Source
Type input:
1. For projects that will have an entirely different source type distribution than that
of the regional fleet, the preferred option is for the user to collect project-specific
data. For projects such as bus or freight terminals or maintenance facilities that
contain links that are primarily used by a specific subset of the regional fleet,
users must develop the fractions of link traffic volume by vehicle type data
specific to the type of project. This could be based on analysis of similar existing
projects through the interagency consultation process.
2. If the project traffic data suggests that the source type distribution for the project
can be represented by the distribution of the regional fleet for a given road type,
the user can provide a source type distribution consistent with the road type used
in the latest regional emissions analysis. For example, highways tend to have a
higher fraction of truck traffic than arterial roads. Therefore, the highway source
type distribution used in the regional emissions analysis may be appropriate to use
for a highway project.
4.5.6 Links
(MOVES User Guide Section 2.3.3.4.12)
The Links Importer is used to define the individual roadway links. All links being
modeled should have unique IDs. The Links Importer requires information on each
link's length (in miles), traffic volume (units of vehicles per hour), average speed, and
road grade (percent). Users should follow guidance given above in Section 4.2 when
determining the number of links and the length of specific links. Consult Section 7 for
information on how these links should be formatted for inputs into an air quality model.
4.5.7 Describing Vehicle Activity
(MOVES User Guide Section 2.3.3.4.14 through Section 2.3.3.4.16)
MOVES determines vehicle emissions based on operating modes, which are different
types of vehicle activity such as acceleration (at different rates), deceleration, idle, and
cruise that have distinct emission rates. MOVES handles these data in the form of a
distribution of the time vehicles spend in different operating modes. This capability is
central to the use of MOVES for PM hot-spot analyses because it allows for the analysis
of fine distinctions between vehicle behavior and emissions before and after construction
of the project. For example, the full emission benefits of a project designed to smooth
traffic flow can best be realized by taking into account the changes in acceleration,
deceleration, and idle activity that result from the project. This guidance suggests several
methods that users may employ to calculate an Op-Mode distribution based on the project
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design and available traffic information. MOVES currently offers three options that the
user can employ to add link activity data, depending on data availability. These are:
1. Provide average speed and road type through the Links input:
Using this approach, MOVES will generate an operating mode distribution and
calculate emissions based on a default drive cycle for a given speed, grade, and
road type. Input of link drive schedules or operating mode distributions is not
needed. For users modeling a free-flow link with only basic information on
average speed and volume on a link, this option may be appropriate. This
approach does account for some differences in emissions due to changes in
operating modes associated with different average speeds on a specific road type.
However, this approach provides the least resolution when analyzing the emission
impact of a project because the default drive cycles used by the model may not
accurately reflect the specific project. For instance, due to the range of operating
modes associated with intersection projects, a single average speed would not
spatially capture localized idling and acceleration emissions.
2. Provide a link drive schedule using the Link Drive Schedule Importer:
The Link Drive Schedule Importer allows the user to define the precise speed and
grade as a function of time (seconds) on a particular roadway link. The time
domain is entered in units of seconds, the speed variable is miles-per-hour and the
grade variable in percent grade (vertical distance/lateral distance, 100% grade
equals a 45-degree slope). MOVES builds an Operating Mode Distribution from
the Link Drive Schedule and uses it to calculate link running emissions.
Individual Link Drive Schedules cannot be entered for separate source types. The
Link Drive Schedule therefore represents the "tracer" path of an average vehicle
on each link. Link drive schedules could be based on observations using methods
such as chase (floating) cars on similar types of links, or for some links, on
expected vehicle activity based an analysis of link geometry. Link drive
schedules will only represent average vehicle activity, not the full range of
activity that will occur on the link. As described in Section 4.2, users can
overcome this limitation by defining multiple links (links that "overlap") with
separate source distributions and drive schedules to model individual vehicles.
3. Provide a detailed operating mode distribution for the link:
The Operating Mode Distribution Importer allows the user to directly import
operating mode fraction data for source types, hour/day combinations, roadway
links, and pollutant/process combinations that are included in the run
specification. Operating mode distributions may be obtained from:
• Op-Mode distribution data from other locations with similar geometric
and operational (traffic) characteristics;38 or
38 For example, chase (or floating) cars, traffic cameras, and radar guns have been used previously to
collect some traffic data for use in intelligent transportation systems and other applications. EPA
encourages the development of validated methods for collecting verifiable vehicle operating mode
distribution data at specific locations representative of different projects covered by this guidance.
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• Output from traffic simulation models.39
4.5.8 Deciding on an approach for activity
Users should consider the discussion in Section 4.2 when deciding on the appropriate
activity input. The MOVES model is capable of using very complex and highly resolved
activity datasets to calculate link level emissions. EPA encourages the development of
validated methods for collecting verifiable vehicle Op-Mode distribution data at locations
and in traffic conditions representative of different projects covered by this guidance.
However, the user should determine the most robust activity dataset that can be
reasonably collected while still achieving the goal of determining an accurate assessment
of the PM air quality impacts from a given project. The decision to populate the Links
table, Link Drive Schedule, or Op-Mode Distribution should be based on the data
available to the user and should reflect the vehicle activity and behavior on each link.
4.5.9 Off-Network
(MOVES User Guide Section 2.3.3.4.16)
The Off-Network Importer is where the user can provide information about vehicles not
driving on the project links, but still contributing to the project's emissions. Currently,
only one Off-Network link may be described per run. If more than one off-network link
is associated with the project, another set of 16 (or 32) MOVES runs would be required
to characterize each additional off-network location. The Off-Network Importer is
required if the project includes an area where highway vehicles are parked, starting their
engines, or in extended idling mode (such as at a truck stop, parking lot, or passenger or
freight intermodal terminal). All such areas within the project area should be modeled,
regardless of whether they are part of the project.
The Off-Network table must be populated by the user with information describing vehicle
activity in the off-network area being modeled. The required fields are vehicle
population, start fraction, and extended idle fraction. The population should reflect the
total number of vehicles parked, idling, entering, and exiting the off-network area over
the course of the given hour. The start fraction is the fraction of the total vehicle
population that starts during the hour.
The extended idle fraction specifies the fraction of time that the vehicle population
spends in extended idle operation in the given hour. Extended idle operation applies only
to long-haul combination trucks and is defined as any idling that lasts longer than 15
minutes. As discussed in Section 4.2.2, shorter periods of idling for long-haul
combination trucks and all idling for other vehicles should be modeled as a project link
39 A traffic micro-simulation model to construct link drive schedules or operating mode distributions can be
used if prior validation of the model's predictions of speed and acceleration patterns for roadway links
similar to those in the project was conducted. If a user has a micro-simulation model that has been
previously demonstrated to adequately predict speed/acceleration patterns for relevant vehicle classes (e.g.,
heavy-duty), and has a procedure for importing data into MOVES, it may be appropriate to use the micro-
simulation model, subject to interagency consultation.
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with an Op-Mode distribution that consists only of idle operation (Op-Mode 1). This can
be specified in the Links table by inputting the vehicle population and specifying an
average speed of "0" mph.
There are no default values available for any of the Off-Network inputs, so users will
need to input the data as described above. For a transit or other terminal project, the user
will need to estimate vehicle population, starts, and idle operation of the facility. For
example, in a bus terminal project, the user would need to estimate the bus population,
starts, and idling based on expected passenger ridership and proposed operating schedules
for the buses using the terminal.
If an Off-Network link is defined, users must also define an Op-Mode distribution that
describes the soak-time distribution of vehicles on the link; this will affect the start
emissions. Additionally, any extended idle operation on an Off-Network link must be
described by the Op-Mode distribution with a fraction of 1.0 for Op-Mode 200 (Extended
Idle Mode). Since there is only one possible extended idle mode in MOVES, this
fraction should always be 1.0.
4.6 GENERATING EMISSION FACTORS FOR USE IN AIR QUALITY
MODELING
The MOVES model outputs emissions as either an emission total (if "Inventory" output
is selected) or an emission factor (if "Emission Rates" output is selected). The emission
results are output for each pollutant and process and are calculated in terms of grams per
link or grams/vehicle-mile per link. Using the equations given in Section 4.4.7, the user
will need to sum the appropriate pollutants and processes to derive a link total
grams/vehicle-mile or grams/hour emission factor. These totals will be needed as inputs
into the appropriate air quality model. Instructions on running AERMOD and
CAL3QHCR for quantitative PM hot-spot analyses are given in Section 7.
Note: If MOVES is being run in batch-mode, or if multiple runs are being saved to the
same output database, the user should make sure to separate link emissions in the result
database by "runID " or "monthID, daylD, hourlD. " Aggregating separate runs will
result in incorrect emission rates.
4.6.1 Highway and intersection links
For links characterized as "highway" or "running" segments of a project, a
grams/vehicle-mile emission rate is needed for CAL3QHCR; if AERMOD is being used,
a grams/hour emission factor for each roadway link is needed.
• CAL3QHCR uses grams/vehicle-mile emission factors and calculates air quality
estimates based on the volume of traffic and length of a given link. All of the
information necessary to generate the necessary inputs is available in the MOVES
MySQL output database. After running MOVES for a particular hour/day/month
scenario, emission results can be located in the user defined MOVES output
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database in the table "rateperdistance." All links defined in the Project Level
Importer will have results in the column "rateperdistance." The units should have
been defined as grams and miles in the MOVES RunSpec (see Section 4.4.10).
As shown in the equations in Section 4.4.7, all relevant pollutants and processes
should be summed together to get a single "rateperdistance" value. This value
can then be paired with link volume and link length for use in CAL3QHCR for
each link.
• AERMOD requires a grams/hour emission factor for each hour of the day (which
should be mapped based on the time periods analyzed with MOVES). If
"Inventory" is selected in the Scale panel, MOVES will produce output in terms
of grams/hour/link. The user should then calculate aggregate PM grams/hour
emission factors by summing the appropriate pollutants and processes as
described in Section 4.4.7. Since AERMOD processes emission factors in terms
of grams/hour (or second), no further calculation is necessary. Section 7
discusses input formats for different AERMOD source configurations.
4.6.2 Transit and other terminal links
For transit and other terminal projects, or a combination of highway and transit or other
terminal components, AERMOD is recommended (see Section 7). AERMOD requires a
grams/hour emission factor for each hour of the day (which should be mapped based on
the time periods analyzed with MOVES). If "Inventory" is selected in the Scale panel,
MOVES will produce output in terms of grams/hour/link. The user should then calculate
aggregate PM grams/hour emission factors by summing the appropriate pollutants and
processes as described in Section 4.4.7. Since AERMOD processes emission factors in
terms of grams/hour (or second), no further calculation is necessary. Section 7 discusses
input formats for different AERMOD source configurations.
Note: If a link is defined with an average speed of 0, or all activity in idle mode (Op-
ModelD 1), MOVES will output emissions for running processes as well as brake wear
and tire wear. In this case, since idling vehicles do not produce any brake wear and tire
wear emissions, only running emissions should be considered and the user should
disregard the brake wear and tire wear emissions.
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Section 5: Estimating Project-Level PM Emissions Using
EMFAC (in California)
5.1 INTRODUCTION
This section of the guidance addresses the necessary steps to run EMFAC to estimate a
project's exhaust, brake wear, and tire wear emissions for PM hot-spot analyses in
California. The California Air Resources Board (ARB) maintains the EMission FACtors
(EMFAC) model which is approved by EPA for developing on-road motor vehicle
emission inventories and conformity analyses in California.40 EMFAC models on-road
mobile source emissions under multiple temporal and spatial scales; it produces
composite emission factors for an average day of a month (January to December), a
season (summer and winter), or an annual average, for specific California geographic
areas by air basin, district, and county as well as the statewide level. EMFAC produces
PM2.5 and PMio emission rates for three exhaust emission processes (running, starting,
and idle), tire wear, and brake wear.
To complete an EMFAC-based PM hot-spot analysis, users need to determine the scope
and resolution of traffic activity data, specify basic scenario data inputs, choose the
desired outputs of the EMFAC model, gather project-specific traffic data and fleet data,
and run EMFAC through the "EMFAC Area Fleet Average Emissions Output Mode"
(Emfac mode) to produce a look-up table of average emission factors for the planning
area and/or county where the project is located. Outside of the model, the relevant
emission factors can be combined with project-specific activity data to calculate total link
level emission factors. The emission factors can then be used in air quality modeling as
discussed in Section 7 of the guidance. The steps to using EMFAC are illustrated in
Exhibit 5-1 (following page).
As discussed in Section 2.4, project sponsors should conduct emissions and air quality
modeling for the project build scenario first. If this scenario does not exceed the
NAAQS, then it is unnecessary to model the no-build scenario. Following this approach
will allow users to avoid additional emissions and air quality modeling. Finally, Section
5 describes how to use EMFAC to estimate emissions from a highway and transit project
that requires a PM hot-spot analysis ("the project"); this section could also be used to
estimate emissions for any other highway and transit facilities in the project area, when
necessary.
40 The current version of the EMFAC model (EMFAC2007), future model versions, and supporting
documentation can be downloaded from the ARB website at:
www.arb.ca.gov/msei/onroad/latest versioahtm.
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Exhibit 5-1. Steps for Using EMFAC in a Quantitative PM Hot-spot Analysis
Divide the project into
links
(Section 5.2)
i
r
Determine the number of
EMFAC runs
(Section 5.3)
i
Spe
r
Select
geographic area
(Section 5.4.1)
i
Select c
ye
(Sectio
r
alendar
15.4.2)
Configure
Emission Factor C
(Section 5.5)
Select "Emfac" Select
cify Basic Scenario Inputs (Section 5.4^
/ Does fleet \
±/ activity vary \ ^
\ by /
\ season/month? /
Yes
Build EMFAC
scenario for each ^
month/season
(Section 5.4.3)
Use annual
average
(Section 5.4.3)
i
r
Enter scenario
title
(Section 5.4.4)
i
Modify vehicle NO
(Sections 5.4.5-6)
r
/ Does project \
' include all \
\ vehicle /
\ classes? /
,r
)utputs Edit Program
Constants (Section 5.6)
Change distributions of
Output VMT, trips, and/or
Yes
Generate Emission
Factors (Section 5.7)
Save scenario
Type
(Section 5.5.4)
vehicle population to
reflect project fleet mix
mode
Configure temp.,
relative humidity,
& speed
(Section 5.5.1-2)
Select Output
Summary Rate
File (RTS File)
(Section 5.5.3)
Output emission
factor look-up
table
Note: The steps in this exhibit and in the accompanying text describe how to use EMFAC to
complete a scenario run using the model's "Emfac " mode for a PM hot-spot analysis.
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This section presumes users already have a basic understanding of how to run EMFAC.
Please note that there are some aspects of Section 5 that differ from the MOVES
guidance discussed in Section 4, due to the inherent differences between MOVES and
EMFAC. For example, unlike MOVES, EMFAC emission rates do not vary by
temperature. EMFAC users do not need to account for variations in temperature over the
course of the day or year, and therefore will complete fewer model runs. Additionally,
EMFAC generates an emission factor look-up table for a range of average speeds.
MOVES calculates emission factors based on a distribution of operating modes, which
allows the option of more advanced methods of defining link-level activity.41
5.2 CHARACTERIZING A PROJECT IN TERMS OF LINKS
Prior to using EMFAC, users need to first identify the project type and the associated
emission processes (running, start, and idle exhaust) to be modeled. This guidance
distinguishes between two types of transportation projects: (1) highway and intersection
projects, and (2) transit or other terminal projects:
• For highway and intersection projects, running exhaust, brake wear, and tire wear
emissions are the main focus.
• For transit and other terminal projects, modeling start and idle emissions is also
typically needed, and in some cases these projects will also need to address cruise,
approach and departure running exhaust emissions on affected links.
The goal of defining a project's links is to best capture emissions where they occur.
From link-specific activity and other inputs, EMFAC calculates emissions from each
link.
5.2.1 Highway and intersection projects
General
A PM hot-spot analysis fundamentally depends on the availability of accurate data on
roadway link speed and traffic volumes for build and no-build scenarios.42 Thus, local
traffic data should be used to characterize each link sufficiently. Generally, the links
specified for a highway project should include road segments with similar traffic
conditions and characteristics. It is recommended that the user divide a project into
separate links to allow sufficient resolution at different vehicle traffic and activity
41 If future versions of EMFAC include PM emission rates that differ by temperature, EPA would work
with ARB to develop additional EMFAC guidance as needed so that users could adequately capture hourly
and seasonal temperature variability in PM hot-spot analyses.
42 Project sponsors should document available traffic data sets, their sources, key assumptions, and the
methods used to develop build and no-build scenario inputs for EMFAC. Documentation should include
differences between how build and no-build traffic projections are obtained. For projects of local air
quality concern, there will always be differences in traffic volumes and other activity changes between the
build and no-build scenarios, and these differences must be accounted for in the data that is used in the PM
hot-spot analysis.
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patterns; characterizing this variability in emissions within the project area will assist in
air quality modeling (see Section 7).
For analyses with EMFAC, an average speed and traffic volume is required for each link.
Unlike MOVES, the current version of EMFAC does not allow a user to account for
more detailed data to describe the pattern of changes in vehicle activity (proportion of
time in acceleration, deceleration, cruise, and idle activity) over the length of a road. The
simplest example is a single, one directional, four-lane highway that could be
characterized as one link with one average speed. If the project analysis involves
intersections, the intersections need to be treated separately from the free-flow links that
connect to those intersections. Although road segments between intersections may
experience free-flow traffic operations, the approaches and departures from the
intersections will involve acceleration, deceleration, and idling activity not present on the
free-flow link. For intersection modeling, the definition of link length will depend on the
geometry of the intersection, how that geometry affects vehicle activity, and the level of
detail of available activity information.
When using EMFAC, project sponsors can use average speeds for highway and
intersection links based on travel time and distance. Travel time should account for the
total delay attributable to traffic signal operation, including the portion of travel when the
light is green and the portion of travel when the light is red. The effect of a red signal
cycle on travel time includes deceleration delay, move-up time in a queue, stopped delay,
and acceleration delay. Each approach link would be modeled as one link to reflect the
higher emissions associated with vehicle idling through lower speeds affected by stopped
delay; each departure link would be modeled as another link to reflect the higher
emissions associated with vehicle acceleration through lower speeds affected by
acceleration delay. A variety of methods are available to estimate average speed. Project
sponsors should determine congested speeds by using appropriate methods based on best
practices used for highway analysis.43 Some resources are available through FHWA's
Travel Model Improvement Program (TMIP).44 Methodologies for computing
intersection control delay are provided in the "Highway Capacity Manual 2000."45
5.2.2 Transit and other terminal projects
For transit and other terminal projects such as a bus terminal or intermodal freight
terminal, the user should have information on starts per hour and number of vehicles
idling during each hour. This activity will likely vary from hour to hour. It is
recommended that the user divide such a project into separate links to appropriately
characterize variability in emission density within the project area (as discussed in
43 As discussed in Section 7, the use of the CAL3QHCR queuing algorithm for intersection idle queues is
not recommended. Rather, idling vehicles should be represented in combination with decelerating,
accelerating, and free-flow traffic on an approach segment of an intersection.
44 See FHWA's Travel Model Improvement Program website: http://tmip.fhwa.dot.gov/.
45 Users should consult the most recent version of the Highway Capacity Manual. As of the release of this
guidance, the latest version is the "Highway Capacity Manual 2000," which can be obtained from the
Transportation Research Board (see http://144.171.ll.107/Main/Public/Blurbs/152169.aspx for details).
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Section 7). In this case, each "link" describes an area with a certain number of vehicle
starts per hour, or a certain number of vehicles idling during each hour.
Generally, users need to account for the number of vehicle starts and the amount (in
hours) of idle activity. Grams/trip rates can be calculated for start exhaust emissions.
Additionally, grams/idle-hour (grams/hour) emission rates can be calculated for both
regular idle and extended idle exhaust emissions, but only for heavy-duty vehicles. Users
need to have data on the number of vehicle starts per hour and number of heavy-duty
diesel vehicles idling during each hour to get the total project or project area emission
factor.
In addition, some transit and other terminal projects may have significant running
emissions similar to free-flow highway projects (such as buses and trucks coming to and
from an intermodal terminal). These emissions can be calculated by defining one or
more unique running links as described in Section 5.2.1 (that is, in addition to any other
roadway links associated with the project). These running link emissions can then be
aggregated with the emissions from starts and idling from non-running activity on the
transit or other terminal link to generate the necessary air quality model inputs.
5.3 DETERMINING THE NUMBER OF EMFAC RUNS
5.3.1 General
Before running EMFAC to calculate emission factors, users should first determine the
number of unique scenarios that can sufficiently describe activity variation in a project.
In most projects, traffic volume, average speed, idling, fleet mix, and the corresponding
emission factors will likely vary from hour to hour, day to day, and month to month.
However, it is unlikely that data are readily available to capture such finite changes.
Project sponsors may have activity data collected at a range of possible temporal
resolutions. The conformity rule requires the latest activity data available at the time of
the analysis to be used in a quantitative hot-spot analysis (40 CFR 93.110).46 Depending
on the sophistication of the activity data analysis for a given project, these data may range
from a daily average-hour and peak-hour value to hourly estimates for all days of the
year. EPA encourages the development of sufficient travel activity data to capture the
expected ranges of traffic conditions for the build and no-build scenarios.
5.3.2 For projects w ith typical travel activity data
Traffic forecasts for highway and intersection projects are often completed for annual
average daily traffic volumes, with an allocation factor for a daily peak-hour volume.
This data can be used to conduct an analysis with EMFAC that is representative for all
hours of the year. The most reasonable methods in accordance with good practice should
46 See "EPA and DOT Joint Guidance for the Use of Latest Planning Assumptions in Transportation
Conformity Determinations," EPA420-B-08-901 (December 2008); available online at:
www.epa.gov/otaq/stateresources/transconf/policv/420b08901 .pdf.
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be used to obtain the allocation factors and diurnal distribution of traffic and the methods
must be decided in accordance with interagency consultation procedures (40 CFR
One option is to use average-hour and peak-hour traffic volumes to represent traffic over
four time periods: morning peak (AM), midday (MD), evening peak (PM), and overnight
(ON). The peak-hour volume can be used to represent activity conditions over a three-
hour morning (AM) and three-hour evening period (PM). The remaining 18 hours of the
day can be represented by the average-hour volume. These 18 hours would be divided
into a midday and overnight scenario.
The following is one suggested approach for an analysis employing the average-
hour/peak-hour traffic scenario based on an examination of national-scale data:
• Morning peak (AM) emissions based on traffic data occurring between 6 a.m. and
9 a.m.;
• Midday (MD) emissions based on data from 9 a.m. to 4 p.m.;
• Evening peak (PM) emissions based on data from 4 p.m. to 7 p.m.; and
• Overnight (ON) emissions based on data from 7 p.m. to 6 a.m.
If there are local or project-specific data to suggest that the AM or PM peak traffic
periods will occur in different hours than the default values suggested here, or over a
longer or shorter period of time, that information should be documented and the hours
representing each time period adjusted accordingly. Additionally, users should
independently determine peak periods for the build and no-build scenarios, and should
not assume that each scenario is identical, as determined through the interagency
consultation process.
If the fleet mix does not vary between the peak-hour and average-hour, then only one
EMFAC run is necessary. If there is a difference in fleet mix, two separate runs are
necessary.
5. 3. 3 For projects with additional travel activity data
Some project sponsors may have developed traffic or other activity data to show
variations in volume and speed across hours, days, or months. Additionally, if users are
modeling a transit or other terminal project, traffic volumes, starts, and idling estimates
are likely to be readily available for each hour of the day. Under either of these
circumstances, users have the option of applying the methodology described above (using
average-hour and peak-hour as representative for all hours of the year) if it is determined
through the interagency consultation process that using the additional data would not
significantly impact the emissions modeling results. Alternatively, additional EMFAC
scenarios could be generated to produce a unique emission factor for each activity
scenario (i.e., each period of time for which specific activity data are available).
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5.4 DEVELOPING BASIC SCENARIO INPUTS
To generate emission factors in EMFAC for PM hot-spot analyses, users need to first
enter a series of basic inputs to the user interface of the EMFAC model. Exhibit 5-2
presents a summary of all basic inputs needed to complete an EMFAC scenario run
("scenario"). The EMFAC defaults can be used directly for most basic input categories;
however, some inputs need to be modified to reflect project-specific information.
5.4.1 Geographic area and calculation method
Users should enter into EMFAC the geographic area where the project is located.
EMFAC offers four geographic scales and each corresponds to specific defaults for fleet
characteristics. The "Area Type" category includes State, Air Basin, District, and
County. For PM hot-spot analyses, users will typically select the County area type.
When "County" is selected, a list of all the counties in California will be available. Users
should select the county where the project is located.
If the selected county is part of only one air basin, users can continue to the next step to
specify calendar years. However, if the selected county is within multiple air basins,
EMFAC will show two options, "By Sub-Area" and "Use Average," as calculation
methods. Users should select "By Sub-Area" to generate EMFAC emission factors in
look-up tables for all sub-areas within the selected county.
Exhibit 5-2. Summary of EMFAC Inputs Needed to Evaluate a Project Scenario
Step
1
2
o
6
4
5
6
7
EMFAC Basic Input Category
Geographic Area
Calculation Method
Calendar Year
Season or Month
Scenario Title
Model Years
Vehicle Classes
I/M Program Schedule and
Other State Control Measures
EMFAC Basic Input Data
State
Air Basin
District
County
By Sub-Area
Use Average
Calendar Year
Month
Season
Annual
Default
Modify
All
Modify
All
Modify
Default
Modify
Modification Needed?
Yes
Yes
Yes
Yes
Optional
No
Optional*
No (for I/M); Varies for
Other Measures
* If a project uses a subset of the default fleet, users should delete unwanted vehicle classes through
the "Vehicle Classes" user interface.
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For instance, Los Angeles County is located in both the Mojave Desert Air Basin and the
South Coast Air Basin. If the project is located only in the Port of Los Angeles and "Los
Angeles County" with "By Sub-Area" is selected in EMFAC runs, EMFAC will provide
emission data for both the Mojave Desert Air Basin and the South Coast Air Basin. Only
the look-up tables for the South Coast Air Basin would be used because this is where the
port is located; the Mojave Desert Air Basin data would be ignored.
5.4.2 Calendar year
EMFAC is able to analyze calendar years from 1970 to 2040 and allows emission
calculations for multiple calendar years in a single run. Users should select one or more
calendar years in EMFAC based on the project scenarios to be analyzed. If an analysis
year beyond 2040 is needed, select 2040 to represent that year.
5.4.3 Season or month
EMFAC can estimate emission factors for each month, two seasons (winter and summer),
or an annual average. Although vehicle miles traveled (VMT) and speed is handled
external to the model, the vehicle mix may vary by hour and season and these scenarios
should be modeled explicitly. As discussed in Section 5.3, users should run EMFAC for
the appropriate number of scenarios based on the availability of travel activity data.
Users with typical travel activity data may run one or two scenarios (depending whether
vehicle mix varies between the peak-hour and average-hour) and will select "annual
average" in the "Season or Month" selection panel. Users with additional data that shows
variation in fleet mix across seasons or months should select the appropriate month or
season for each run.
5.4.4 Scenario title
EMFAC generates a default scenario title that includes the name of the county,
calculation method, season or month, and calendar year. A replacement scenario title can
be specified, if desired.
5.4.5 Model years
EMFAC includes vehicle model years from 1965 to 2040 and default assumptions about
mileage accumulation that vary by model year. EMFAC will generate emission factors
for 45 model years (ages 1 through 45) for the build and no-build scenarios for each
analysis year. Users can change the range of model years to be included in an EMFAC
run through the model interface. If a project involves a specialized and simple fleet (e.g.,
buses operating in a bus terminal) for which the range of model years is well known or
reliably estimated, users may consider including only those model years and exclude
unrelated vehicle types in an EMFAC run.
However, under most circumstances, projects that involve multiple vehicle types and
model years will require EMFAC defaults to be used for PM hot-spot analyses. The two
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reasons for this recommendation are: (1) most projects will not affect the age distribution
of the vehicles operating at the project site, and (2) changing EMFAC defaults to reflect
specific fleet age distributions is complicated for projects that involve multiple vehicle
types and model years. These changes require a level of familiarity with EMFAC that
many users may not have or need for most hot-spot analyses. Therefore, if users
anticipate that it will be necessary to adjust the age distribution of their vehicle fleet, they
should consult with ARB for further guidance.
5.4.6 Vehicle classes
All 13 default vehicle classes should be selected for most projects. The exception would
be a project or link that involves a specialized fleet of limited vehicle types (e.g., a bus
terminal). The EMFAC model assumes vehicle population and travel activity
distributions by vehicle class, depending on the geographic area and analysis year
selected. Editing the default distribution of vehicle classes will be discussed in Section
5.5. If only one vehicle type is selected (e.g., HUDTs), all emission information in the
EMFAC output will be calculated for that one vehicle type.
5.4.7 I/Mprogram schedule and other state control measures
When a particular county from the Geographic Area panel is selected in EMFAC, the
model assumes a default I/M program. Although EMFAC allows edits for each I/M
program, users should not alter the default settings and parameters associated with I/M
programs and their coverage. If I/M program modifications are considered, users should
consult with the local air district or ARB for specific guidance. Currently, no PM
emission benefit for I/M programs exists in EMFAC2007.
The PM emission reductions from any additional state PM emission control measure
should be applied outside of the EMFAC model and be consistent with current
implementation of measures and how reductions are calculated for SIP and other air
quality planning purposes. For instance, EMFAC2007 currently does not have the
capability of modeling diesel engine retrofits. It is recommended that manufacturer
specification data be used for calculating emission factors from engines equipped with
such devices, consistent with EPA's and ARB's retrofit guidance and methods used to
calculate reductions for the SIP. The interagency consultation process should be used to
discuss any issues regarding the inclusion of state control measures in PM hot-spot
analyses.47
47 For information about quantifying the benefits of retrofitting diesel vehicles and engines to conformity
determinations, see EPA's website for the most recent guidance on this topic:
www.epa.gov/otaq/stateresources/transconf/policy.htm. Also, see ARB's website at:
www.arb.ca.gov/msprog/onrdiesel/calculators.htm.
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5.5 CONFIGURING EMISSION FACTOR OUTPUTS
Users must configure how the model will output emission factor information based on the
inputs provided in the previous steps. The discussion that follows walks users through
these configuration steps in the same order in which users will encounter these options
when running EMFAC.
EMFAC includes three scenario types or modeling modes: Burden, Emfac, and
Calimfac. For PM hot-spot analyses, users should select the "Emfac" mode, which
generates area-specific fleet average emission factors for running exhaust, brake wear,
tire wear, starting, and idling emissions.
5.5.1 Temperature and relative humidity
The default settings in the Emfac mode include 15 temperature bins (-20F to 120F) and
11 relative humidity bins (0% to 100% RH) to generate average emission factors.
However, because EMFAC PM emission rates are insensitive to changes in temperature
and humidity, generating emission factors for all default temperature/relative humidity
combinations throughout an analysis year is not necessary. As shown in Exhibit 5-3
(following page), users need to remove the default temperature/relative humidity settings
and input only one value (e.g., 60F, 70% RH) for temperature and relative humidity,
respectively, to perform an Emfac mode run. Selecting one combination of
temperature/relative humidity will reduce computer run time and produce PM emission
factor look-up tables that can be easily used. Temperatures must be consistent with those
used for the project county's regional emissions analysis (40 CFR 93.123(c)(3)) as well
as the air quality modeling inputs used in the hot-spot analysis. See Section 7.5 for more
information on selecting representative meteorology data.
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Exhibit 5-3. Changing EMFAC Default Settings for Temperature and Relative
Humidity
IE rtfgf date to tempeiatue
i* Dele's tempeeaHae 1
1 Delete temper atme 2
'"* Delete tempetaUBe 3
1 " Detete hsmpefaKae 4
'"" Delete SemperatiBe 5
'" Dele's lemp-eEitUtfe 8
f" Delete temperature 7
f D ©lets temper aftiae 8
f Debte temper ahjie 9
''" Detetetemperatiae 10
r Delete tempefaiise 11
Del- butson lo enable new yaks
(^ Defeae (en^aatLte 1 ^
1 rj ' ^ Oelele ten^^atwe 1 4
Q " Delete tempenatLHe 15
1 Q Errfra1 'emperaiwe 1 6
20
30
40
50
60
70 ^
""so ^
100
110
120
„
- -
! Detete temperature 12 gg
F7 Scat the m-^y (done aftei exftj j QK j C*w«f 1
fetretfjEdii, temperature for Enjfac cateuiaticns
Enfer cfefa (or tempefaH*e Click button to enable tiew value
** Delete (emperaftufe 1 'Iff
f Emer impaahm 2
Sat the atfay [done afta e
Entej data EOT rel hum C ltd
•"• Delete rel hum 1
1 Delete rel hum 2
•' Delete [el hum 3
i" Delete rel hum 4
' Deists rd hum 5
'" Delete rel hum 6
f" Delete rel hum 7
1" Delete rel hum 8
r DeteterelhumS
<~ Delete rel hum 10
<~ Delete rel hum 11
r Enter lei hum 12
W Sort tile atjay (done afb
, button to enable new value
1 " I
10 ' 1
30 '
40
50
60 '
70
80 '' 1
SO ' j
100 '' I
Meal) [^^OK^[ Cancel j
Select/Edit relhufn for Emfaccaleulalious
Enter data (or rei Nim tick button to enable reew v
* Detete lei hum 1 ~~ffi
•=>
Soil! the affaji (ifeMie aftej e
5.5.2
The Emfac mode allows users to input up to 24 speed values to populate average
emission factors. The default setting specifies speed bins for 0 mph through 65 mph in 5
mph increments. Emission factors associated with the 0 mph speed bin can be applied for
idle emissions (essentially for heavy-duty trucks only; EMFAC idle emission factors are
unavailable for most other vehicle classes).48 Emission factors for intermediate speeds
can also be generated if specific speed values are input into the EMFAC model.
Users have several options to calculate appropriate speed-dependent emission factors for
a project. For instance, if a highway link in a build scenario is known to have an average
speed of 32 mph, it can be directly input into the speed list of EMFAC to produce the
associated PM emission factors. Alternatively, if the EMFAC default settings are used to
generate a look-up table for different speed bins, users can either select the emission
48 Among the 13 vehicle classes in EMFAC, idle emission factors are available only for LHDT1 and
LHDT2 (included in the MDT vehicle group in the output .rts file) and MHDT, HHDT, School Buses, and
Other Buses (included in the HDT vehicle group in the output .rts file); see Exhibit 5-6 for further
information.
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factors associated with the closest speed bin (30 mph bin, representing speeds of
27.5 mph to 32.5 mph), or interpolate between the emission factors for speed bins of
30 mph and 35 mph.
Users should include 0 mph in an EMFAC run unless the project to be evaluated does not
involve idle emissions. For specific cases for which the average link speed is less than
5 mph, users can either select the emission factors from the 5 mph speed bin, or
extrapolate down to the desired speed by using the emission factors from the speed bins
for 5 mph and 10 mph to create a trend line to lower speeds.
5. 5. 3 Output rate file
The Emfac mode can provide emission information in four output formats with different
levels of detail. Users should select "Summary Rates (RTS)." The Summary Rates
format generates average emission factors by speed for six vehicle groups (aggregated
from the 13 vehicle classes modeled in EMFAC) and an overall average emission factor
for the entire vehicle fleet. The overall average emission factors are appropriate for use
in air quality dispersion modeling.
5. 5. 4 Output particulate
As shown in Exhibit 5-4, users have to select either PMi0 or PM2 5 in an Emfac mode run
to obtain particulate emission factors. EMFAC must be run twice to obtain both
and PM2.s data for those projects that are located in both PMi0 and PM2.s
nonattainment/maintenance areas.
Exhibit 5-4. Selecting Pollutant Types in EMFAC for PM10 and PM2.5
J
'•~-£
S BOzlfLi
Input 1 )npa2 Mo* and Output TecMM ! CYi Basis I
- Aie-a planning inventory
Emfac -Area fleet average emissions Calirnfac - Detailed vehicle data
Scenaro Type: EMFAC - Area-specific fleet average emissions (g/hrj for selected temperatures, relative hurnidites.
Configure EMpHt, Outputs
Tempeiatures
Relativ
Speed
.-_r_
Emfac Rate Files
Binary Impacts (BIN)
ASDI Impacts (ERP)
I Summary Rates (RTS)
Detailed Impact Rates (RTL] I
< Back
Edit Program
Constants
Output Particulate As...
C Total PM
r PM2.5
Output Hydrocarbons As...
<* Toa r THC
r ROB r CH4
Finish j^=^=
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5.6 EDITING PROGRAM CONSTANTS
5.6.1 Overview
Typically, users will start the analysis process with only a broad understanding of the
project-specific vehicle fleet - specifically, the percentage of vehicles that are considered
"trucks," vs. those that are "non-trucks." In all cases, projects that require a quantitative
PM hot-spot analysis will have a different fleet distribution than the EMFAC regional
default mix. Users will therefore need to adjust the project fleet and fleet activity (VMT,
trips) to reflect the expected project fleet mix for each EMFAC scenario. Depending on
the project, users should modify some combination of VMT (which affects running
exhaust emission factors), vehicle trips (which affects starting emission factors), and/or
vehicle population (which affects idling emission factors). In the following discussion,
overall guidance is provided on how to make these adjustments. Appendices G and H
provide more specific illustrations of the step-by-step procedures involved.
5.6.2 Default data in the Emfac mode
The Emfac mode is associated with a range of pre-populated program constants linked to
specific time periods and California geographic areas. Exhibit 5-5 lists the default data
available in the Emfac mode that can be accessed through the "Edit Program Constants"
in the user interface. For a PM hot-spot analysis, many of the defaults do not need to be
modified. However, users do need to determine which adjustments are needed for the
default distributions of VMT, trips, and vehicle population by vehicle class. The
EMFAC interface has "Copy with Headers" and "Paste Data Only" tabs that are helpful
for users to easily export the default data and import the adjusted data.
Exhibit 5-5. EMFAC Program Constants and Modification Needs for PM Hot-spot
Analyses
EMFAC Program
Constants
Exh Tech Fractions
Evap Tech Fractions
Interim I/M
Population
Accrual
Trips
VMT
Speed Fractions
Idle Time
Description
Exhaust control technology fractions
Evaporative control technology factions
Enhanced interim I/M program
Vehicle population by class, fuel type, and age
Odometer accrual rate by class, fuel type, and age
Vehicle trips/starts per day by class, fuel type, and age
Vehicle miles traveled per day by class, fuel type, and
age
VMT by speed bin distribution for each vehicle class
Idle times by vehicle class, fuel, and hour of day
Modification
Needed for PM
Analyses?
No
No
No
Yes*
No
Yes*
Yes*
No
No
* Different distributions in VMT, trips, or vehicle population than those reflected by the EMFAC
defaults should be updated through the user interface to incorporate project-specific vehicle activity
information.
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PUBLIC DRAFT-MAY 2010
5.6.3 Comparing project data andEMFAC defaults to determine adjustments
Individual projects will have a mix of vehicle types that varies from the regional average
fleet mix. Because PM hot-spot analyses can be especially sensitive to diesel-powered
truck activity, it is important to properly characterize the relative fraction of the fleet that
is comprised of trucks compared to light-duty vehicles. Users should determine the base
(default) case and forecasted vehicle mix (trucks versus non-trucks) applicable to their
project's build and no-build scenarios and use that information to adjust EMFAC
defaults.
Users should first collapse VMT, vehicle trip and vehicle population data for EMFAC's
13 vehicle classes to two general data categories: "truck" and "non-truck." The common
practice in California is to define, for emission purposes, "truck" activity as being
comprised of all activity associated with what EMFAC identifies as medium-duty and
above heavier vehicles. In addition, travel activity data typically identify "trucks" in a
general sense, without regard to their fuel type. Exhibit 5-6, therefore, shows the
suggested vehicle class mapping given the likely data available at the project level.
Exhibit 5-6. Mapping EMFAC Vehicle Classes to Project-specific Activity
Information
Typical Projects
(2 Categories)
Non-truck
Truck
EMFAC
Default
(13 Classes)
LDA
LDT1
LDT2
MCY
MDV
LHDT1
LHDT2
MHDT
HHDT
MH
OBUS
SBUS
UBUS
Description
Passenger cars
Light-duty trucks 1
Light-duty trucks 2
Motorcycles
Medium-duty trucks
Light-heavy-duty trucks 1
Light-heavy-duty trucks 2
Medium-heavy-duty trucks
Heavy-heavy-duty trucks
Motor homes
Other buses
School buses
Urban buses
EMFAC Output
Summary Rates (RTS)
File
(6 Groups)
LDA
LOT
MCY
MDT
HOT
UBUS
5.6.4
Adjustment of default activity distributions to reflect project data
After the vehicle mapping is complete, users will need to compare the project-specific
distributions to the default data included in EMFAC for trucks and non-trucks. For
example, assume 2009 is used as the analysis year for a hypothetical highway project in
Sacramento County with 25% of total annual average daily VMT apportioned to trucks.
After entering all the basic inputs in the EMFAC modeling software, pre-populated
(default) county VMT for the truck portion of the fleet is equal to 6,269,545 (when all
70
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PUBLIC DRAFT-MAY 2010
appropriate vehicle classes are summed up), and the model default activity shows that
truck VMT represents 19% of total VMT in Sacramento County (see Exhibit 5-7).
The VMT should then be re-allocated to the correct percentage. EMFAC allows users to
adjust the calculated fleet-average emission factors by varying the relative weightings of
the 13 vehicle classes. This adjustment is done by replacing the default numbers for each
vehicle class in the EMFAC user interface, using the "VMT" option for a highway
project, or the "Trips" or "Population" option if analyzing a transit or other terminal
project, under the "Edit Program Constants" function available via the Emfac mode
screen.
Note: EMFAC also allows users to modify the fuel characteristics (gas/diesel/electric) for
each of the 13 vehicle classes. For most PMhot-spot analyses for highway projects with
non-captive fleets, users will not need to modify the fuel assumed for the fleet vehicles.
For projects involving captive fleets with known fuel use distributions, the default
fractions should be modified.
Exhibit 5-7. Example Default EMFAC VMT by Vehicle Class Distribution
Editing VMT data for scenario 1: Sacramento County Subarea Annual CYr 2QQ9 Default.
%MiKy!te&4a*JW«i*v*ate«!ta " *" ' ' ' " " " ' "
Total VMT for area
Saciamerto Cmrty
I
EdingMode EditingVMT (vehicle miles tewted per weekday)
Total VMT By Vehicle Claw I By Vehicle and Fuel By Vehicle/Fuel/Hour ^
01 -Light-Duty Auto* (PC)
EG • Light-Duty Trucks (T1)
03 -Ught-Dutj* Trucks (T2)
04 - Medium-Duty Trucks (T3)
05 -Light HO Trucks (T 4]
06- Light HD Trucks (T5)
07 -Mtdiuffl HO Trucks (T6)
Oft • Heavy HD Truck* (T 7]
09 -Other Buses
10 -Urban Buses
11 -Motorcycles
12 -School Buses
13- Motor Homes
[15271757.
3340492
7266306
3535454 ^
816278
302809"
698543"
7041%"
49590"
401 98 >
25636?'
>• ^
31 176. "I
91 341. J
Total "truck"
VMT = 6,269,545,
accounting for
19% of total VMT
Done
Continuing with the Sacramento County illustration from the previous step, users would
need to scale the EMFAC defaults to reflect the truck/non-truck VMT fractions
appropriate to the project (i.e., truck VMT needs to be adjusted from 19% to 25% of the
total). The fractional differences for trucks and non-trucks are then applied to the default
VMT for each corresponding vehicle class in the EMFAC user interface. As illustrated
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PUBLIC DRAFT-MAY 2010
in Exhibit 5-8, when the VMT values for the truck classes are adjusted, their sum is equal
to 8,101,117 (25% of total county VMT). Adjusted non-truck VMT is now 24,303,350
(75% of total VMT). The details of this example are presented in Appendix G. When
updating the EMFAC default VMT by vehicle class, the total VMT (for all 13 vehicle
classes) must remain unchanged.
Exhibit 5-8. Example Adjusted EMFAC VMT by Vehicle Class Distribution
Editing VMT data for scenario 1: Sacramento County Subarea Annual CYr 2009 Default Title
Total VMT for area
Sacramento County
Copy with Headings] Paste Data Only
Editing Mode Editing VMT (vehicle miles traveled per weekday)
Total VMT By Vehicle Class By Vehicle and Fuel ] By Vehicle/Fuel/Hour
01 -Light-Duty Autos (PC)
02-Light-DutyT rucks (T1)
03-Light-DutyTrucks(T2)
04 - Medium-Duty Trucks [T3]
05 - Light HD Trucks (T4)
06 - Light HD Trucks (T5)
07 - Medium HD Trucks (T6)
08 - Heavy HD Trucks (T7)
09 - Other Buses
10-Urban Buses
11 -Motorcycles
12-School Buses
13-Motor Homes
14201491.
3106386.
6757073.
4569294.
1054743.
391271.
902614.
909867.
64077.
51841.
238400.
40284.
118025.
Done
Note that, in special cases, if one or more of the default vehicle classes are not present in
the project area, users should set VMT (to address running exhaust emissions), number of
trips (to address starting emissions) and population (to address idling emissions) for that
class to "1" in the EMFAC interface. In other words, users should functionally zero-out
the appropriate vehicle class by inputting a value of "1" because EMFAC does not allow
an input of zero in the interface for VMT, trip, and vehicle population distributions. A
complete example illustrating how to change EMFAC default distributions to exclude
some vehicle classes for a transit project is presented in Appendix H. An alternate way is
to delete unwanted vehicle classes in the basic scenario data input to the model.
Appendices G and H provide more detailed examples of these steps; these modifications
will typically only be necessary for projects involving unique conditions such as truck-
only activity.
Note: The average emission factors provided by EMFAC in the "Emfac mode " are VMT-
weighted (for running emissions), vehicle trip-weighted (for start emissions), or vehicle
population-weighted (for idle emissions) across different vehicle classes. If a user runs
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PUBLIC DRAFT-MAY 2010
the model for a county, the weighting reflects county-level VMT, trips (starts), or vehicle
fleet and their absolute values are not relevant at the project level.
For most transit and other terminal projects, users may have very detailed information on
not only vehicle mix, but also fuel mix (diesel/gas/electric) and age distribution (model
year distribution). Users should adjust the fuel mix (changed through the "By Vehicle
and Fuel" tabs of the VMT, Population, and Trips panels) to reflect the known or
expected fuel use (if, for instance, a bus fleet is expected to use entirely diesel fuel).
Similarly, if the age distribution (model year distribution) is known for a particular fleet,
this should be entered in place of the EMFAC default values (found in the "By
Vehicle/Fuel/Age" tab of the Edit Population panel). Note that EMFAC's ability to
model alternate fuel options is not uniform among vehicle classes. If users determine that
modification of the fleet in terms of fuel or age distribution is needed, they should contact
ARB for further guidance. However, for most highway and intersection projects with a
non-captive fleet, the EMFAC default fuel mix and age distribution should be used.
5.7 GENERATING EMISSION FACTORS FOR USE IN AIR QUALITY
MODELING
For each EMFAC run, emission factors will be generated in the "Summary Rates (RTS)"
file (.its file) in the form of look-up tables. These tables are organized and numbered by
different emission processes and pollutant types. PM emission factors for running
exhaust, idle exhaust, tire wear, and brake wear are included in Table 1 of the .its file;
PM start emission factors are included in Table 2 of the .its file. Exhibit 5-9 (following
page) includes example screenshots of EMFAC .its file output.
5.7.1 Highway and intersection links
For each speed value (greater than 0 mph), EMFAC outputs running exhaust, tire wear,
and brake wear emission factors in grams/vehicle-mile, for six vehicle groups plus an
aggregate emission factor named as "All" (see Exhibit 5-6). Note that the .its output file
includes only six vehicle groups - an aggregation of the 13 vehicle classes manipulated
during the input process. In general, assuming users have run the model with VMT-
weighted distributions appropriate for the project's fleet activity (see Section 5.6), only
the emission factors from the "All" column will be needed. The "All" column includes a
grams/vehicle-mile value that is a VMT-weighted average based on the user-provided
vehicle activity mix. The sum of running exhaust, tire wear, and brake wear grams/mile
PM emission factors for a given speed is the total fleet-average grams/vehicle-mile
emission factor appropriate for modeling highway project links:
Total Link Emission Factor = (EFranning) + (EFtire wear) + (£Fbrake wear)
The total link emission factor (grams/vehicle-mile) can be used in combination with the
link volume and link length as input into CAL3QHCR. If using AERMOD, an emission
rate (in grams/hour) should be calculated for each link. This can be done by multiplying
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PUBLIC DRAFT-MAY 2010
the total link emission factor (calculated above) by the link hourly volume and link
length.
Exhibit 5-9. Example EMFAC Running Exhaust, Tire Wear, and Brake Wear
Emission Factors in the Summary Rates (rts) Output File
"•W"^^^^^^^^^^^^^^^^^^^^^— ^^^^^^^^^^^^^^^^^^^^^~
S default, rts WordPad
File Edit View Insert
D B>Q
Pollutant
Speed
HPH
0
5
10
15
20
25
30
35
40
45
50
55
60
65
Pollutant
Speed
HPH
0
5
10
IS
20
25
30
35
40
45
50
55
60
65
Pollutant
Speed
HPH
0
5
10
15
20
25
30
<
For Helpj press Fl
Format
rt
Name :
LDA
0.000
0.050
0.033
0.022
0.016
0.013
0.010
0.009
0.008
0.007
0.007
0.007
0.008
0.009
Name :
LDA
0.000
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
Name :
LDA
0.000
0.013
0.013
0.013
0.013
0.013
0.013
Help
PH10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PH10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PH10
0
0
0
0
0
0
0
@ «
LDT
.000
.095
.062
.043
.032
.024
.020
.017
.015
.014
.014
.014
.015
.018
-
LDT
.000
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
-
LDT
.000
.013
.013
.013
.013
.013
.013
, «
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Tire
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Brake
0
0
0
0
0
0
0
HDT
.057
.098
.065
.046
.034
.026
.021
.018
.016
.015
.014
.015
.016
.018
Bear
HDT
.000
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
Wear
HDT
.000
.013
.013
.013
.013
.013
.013
HDT
1.380
1.630
1.129
0.763
0.549
0.460
0.395
0.350
0.327
0.324
0.340
0.376
0.431
O.SOS
HDT
0.000
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
HDT
0.000
0.022
0.022
0.022
0.022
0.022
0.022
Temperature :
UBUS
0.000
0.888
0.643
0.483
0.376
0.303
0.252
0.218
0.195
0.181
0.173
0.172
0.177
0.189
Temperature :
UBUS
0.000
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
Temperature :
UBUS
0.000
0.013
0.013
0.013
0.013
0.013
0.013
60F
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HCY
.000
.051
.040
.033
.029
.026
.025
.024
.025
.027
.031
.037
.046
.060
60F
0
0
0
0
0
0
0
0
0
0
0
0
0
0
MCY
.000
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
60F
0
0
0
0
0
0
0
MCY
.000
.006
.006
.006
.006
.006
.006
1
Relative Humidity: 70% A
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ALL
.084
.163
.111
.076
.055
.045
.037
.033
.030
.029
.030
.032
.036
.042
Relative Humidity: 70%
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ALL
.000
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
.009
Relative Humidity: 70%
0
0
0
0
0
0
0
ALL
.000
.013
.013
.013
.013
.013
.013
V
>
MUM •
5.7.2 Transit and other terminal links
For transit and other terminal projects, such as bus terminals or intermodal freight terminals,
grams/trip (or grams/start) emission factors can be combined with project-specific estimates of
vehicle trips (or starts) per hour to calculate grams/hour emissions. Starting emission factors are
74
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PUBLIC DRAFT- MAY 2010
dependent on the vehicle soak time (the soak time is the time a vehicle is stationary with the
engine turned off, following the last time it was operated). The longer a vehicle is turned off, or
soaks, the higher the start emissions embedded in EMFAC. The output look-up table for start
emissions includes 18 time bins (5 minutes to 720 minutes); users need to choose an appropriate
time bin that is representative for the project activity. Selected examples of some potential
associated soak times are shown in Exhibit 5-10 for several possible scenarios.
Exhibit 5-10. Example Soak Times for Several Project Scenarios
Project Type
Bus Transit Facility
Truck Refueling Station
Intermodal Distribution Center
Truck Stop Parking Lot
Example Soak Time
(min)*
10
60
180
480
PMio Start Emission
Factors (g/trip)
0.002
0.008
0.013
0.016
* Example soak times and emission factors are for illustration purposes and are not to be used
as literal values. Users should select soak times and estimate emission factors appropriate to
the specific project and implementation dates to be evaluated. Emission factors will vary by
analysis year.
Idling emission factors are in grams/idle-hour and are available in Table 1 of the .its file
associated with a speed value of 0 mph (available only for MDT and HDT groups due to
EMFAC's data limitations). Note that, for transit and other terminal projects, idling and
starting emission factors from EMFAC should not be combined directly because they are
generated in different units. The project idling and starting emissions (in grams) need to
be calculated separately for a particular time period, based on project-specific idle hour
and trips/hour data. The total transit or other terminal project emissions for the time
period are the sum of the two values:
Total Project Emissions = (£Fidiing * idle hours) + (£Fstarting * trips)
The result of this calculation is a grams/hour emission rate that can be used for air quality
modeling.
In some cases, users may need to model running exhaust emissions from cruise,
approach, and departure link activity, as well as start and idle emissions at the project
site. For instance, to assess impacts from a proposed bus terminal, users may need to
evaluate start and idle emissions from buses at the terminal itself, and bus running
exhaust emissions along the links approaching and departing from the terminal. Given
that the link activity will involve a unique vehicle fleet (one with a disproportionate
amount of bus activity), users should modify the default travel activity in EMFAC to
reflect the bus activity (see the discussion above). EMFAC allows users to generate
emission factors for both the approaching/departing links and the bus terminal itself in a
single run. To obtain project-specific running exhaust emission factors, users can modify
the VMT associated with the buses at the approaching link by adjusting the values for
each of the 13 vehicle classes in the user interface with the method described in Section
5.6. In the same EMFAC run, users can enter project-specific vehicle population and trip
distributions to produce project-specific start and idle emission factors.
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PUBLIC DRAFT-MAY 2010
Another special case may involve modeling idling emissions for a specific fleet of heavy
heavy-duty diesel trucks (HHDT). Because EMFAC provides only an overall average
idle emission factor for heavy-duty trucks regardless of fuel type, ambient conditions,
accessory usage, and engine speed, ARB has created supplemental guidance that, off-
model, provides season-specific HHDT emission factors for activity that ARB has termed
"high idle" and "low idle."49 Low idle (sometimes also called "curb idle") involves
short-term idling with engine speeds of 800 rpm or less and no accessory loading. High
idle is idling over an extended period of time with engine speeds over 800 rpm, usually
involving the use of heaters, air conditioners, or other vehicle accessories. If the project
under evaluation involves HHDT and the user has detailed information about the fleet
(vehicle model years and the amount of time spent in low and high idle, in particular), the
information from this supplemental guidance may be used to obtain more specific idle
emission factors for HHDT than would otherwise be available by simply using EMFAC.
Other special projects may require additional data manipulation. Project sponsors should
contact ARB or the local air quality management district for further guidance.
Note: The product of any transit or other terminal project should be a grams/hour
emission factor for each defined project area. If approach/departure running emissions
are calculated, a grams/hour emission factor should be calculated from the grams/mile
EMFAC output as described in Section 5.7.1.
Alternative Method to Estimate Idle and Start Emission Factors for a Specific Vehicle
Class
A relatively simple method is available to obtain idle and start emission factors for those
cases in which users are interested in only one vehicle class (such as for heavy-duty
trucks). Note that this method is not recommended for situations involving multiple
vehicle classes (e.g., medium- and heavy-duty trucks). Because this is a methodology to
support development of idle and start emission factors, it is applicable only to those
vehicle classes for which EMFAC includes idle emissions (LHDT1, LHDT2, MHDT,
HHDT, School Buses, and Other Buses).
For example, suppose a project or link involves just HHDT: users could modify
EMFAC's basic input of Vehicle Classes in Section 3.6 in the user interface and select
"Heavy Heavy Duty Trucks." Editing EMFAC default population and trip distributions
is not needed because the output .its file will reflect emission factors that are associated
with the selected single vehicle class only.
49 See EMFAC Modeling Change Technical Memo, "Revision of Heavy Duty Diesel Track Emissions
Factors and Speed Correction Factors" (original and amendment), October 20, 2006; available through
ARB online at: www.arb.ca.gov/msei/supportdocs.htm#onroad.
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Section 6: Estimating Emissions from Road Dust,
Construction, and Other Emission Sources
6.1 INTRODUCTION
This section provides guidance on how to estimate re-entrained road dust and
transportation-related construction dust emissions. MOVES and EMFAC do not estimate
emissions of road or construction dust, so this section must be consulted if dust is
required to be included in the PM hot-spot analysis. See Section 2.5 for further
information regarding when dust emissions are required to be included in a PM hot-spot
analysis. This section also includes information on quantifying emissions from
construction vehicles and equipment, locomotives, and other sources of emissions in the
project area, when applicable.
6.2 OVERVIEW OF DUST METHODS AND REQUIREMENTS
AP-42 is EPA's compilation of data and methods for estimating average emission rates
from a variety of activities and sources from various sectors. Refer to EPA's website
www.epa.gov/ttn/chief/ap42/index.html to access the latest versions of AP-42 sections
and for more information about AP-42 in general. The sections of AP-42 that address
emissions of re-entrained road dust from paved and unpaved roads and emissions of
construction dust are found in AP-42, Chapter 13, "Miscellaneous Sources." The key
portions of the chapter include:
• Section 13.2: "Introduction to Fugitive Dust Sources,"
• Section 13.2.1: "Paved Roads"
• Section 13.2.2: "Unpaved Roads"
• Section 13.2.3: "Heavy Construction Operations" (includes road construction)
The discussion in this section is based on the November 1, 2006 update to AP-42. Users
should consult the above website to ensure they are using the latest final version, as the
methodology and procedures may change over time.
Although EPA has approved AP-42 as the official model for calculating re-entrained road
dust for regional conformity analyses, there is additional flexibility for what method can
be used for calculating road dust for PM hot-spot analyses.50 In addition to the latest
version of AP-42, alternative local methods can be used for estimating road or
50 See EPA's notice of availability published in the Federal Register on May 19, 2004 (69 FR 28830-
28832). Also see EPA's memoranda: "Policy Guidance on the Use of the November 1, 2006, Update to
AP-42 for Re-entrained Road Dust for SIP Development and Transportation Conformity," EPA420-B-07-
055 (August 2, 2007); and "Policy Guidance on the Use of MOBILE6.2 and the December 2003 AP-42
Method for Re-entrained Road Dust for SIP Development and Transportation Conformity," (February 24,
2004). These documents are available online at:
www. epa. gov/otaa/stateresources/transconf/policY. htm#models.
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PUBLIC DRAFT-MAY 2010
construction dust. The interagency consultation process must be used to discuss what
modeling methods and assumptions are appropriate for a given project's PM hot-spot
analysis for road dust and construction-related dust (40 CFR 93.105(c)(l)(i)).
This section presumes users already have a basic understanding of how to use AP-42 or
other dust methods.
6.3 ESTIMATING RE-ENTRAINED ROAD DUST
6.3.1 PM2.5 nonattainment and maintenance areas
The transportation conformity rule requires a hot-spot analysis in a PM2.5 nonattainment
and maintenance area to include emissions from re-entrained road dust only if emissions
from re-entrained road dust are determined to be a significant contributor to the PM2.5
nonattainment problem. See Section 2.5 for further information.
6.3.2 PMi o nonattainment and maintenance areas
Re-entrained road dust must be included in all PMi0 hot-spot analyses. EPA has
historically required road dust emissions to be included in all conformity analyses of
direct PMi0 emissions - including hot-spot analyses. See Section 2.5 for further
information.
6.3.3 Using AP-42 to estimate emissions of re-entrained road dust on paved roads
Section 13.2.1 of AP-42 provides a method for estimating emissions of re-entrained road
dust from paved roads for situations for which silt loading, mean vehicle weight, and
mean vehicle speeds on paved roads fall within ranges given in AP-42, Section 13.2.1.3
and with reasonably free-flowing traffic (if the project doesn't meet these conditions, see
Section 6.3.5, below). Section 13.2.1 of AP-42 contains predictive emission factor
equations that can be used to estimate an emission factor for road dust. This section can
be downloaded from EPA's website at: www.epa.gov/ttn/chief/ap42/chl3/index.html.
The following bullets describe the type of data needed when using Section 13.2.1 of AP-
42 and are based on the November 2006 version of Section 13.2.1 of AP-42.51
• Users will need to provide the average weight in tons of vehicles traveling the
road (Section 13.2.1 states that the average weight needs to be provided and that
the equations are not intended to be used to calculate a separate emission factor
for each vehicle weight class).
• Users should obtain and use site-specific silt loading data. The default, site-
specific silt loading data contained in Table 13.2.1-3 should not be used.
51 Please consult the latest version of AP-42, Section 13.2.1 on EPA's website for specific directions for
using these equations and to determine whether any updates have been made.
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• Users have the choice to include a precipitation correction term. Users could
either provide local information or rely on the national map showing mean
number of days with measurable precipitation (Figure 13.2.1-2) provided in
Section 13.2.1.
• If the project is located in an area where anti-skid abrasives for snow-ice removal
are utilized, users should include information about their use, including the
number of times such anti-skid abrasives are applied. Section 13.2.1 includes a
table of silt loading default values, which can be used when local data are not
available (Table 13.2.1-3).
6.3.4 Estimating emissions of re-entrained road dust on unpaved roads
Section 13.2.2 of AP-42 provides a method for estimating emissions of re-entrained road
dust from unpaved roads. Different equations are provided for vehicles traveling
unpaved surfaces at industrial sites (Equation la) and vehicles traveling on publicly
accessible roads (Equation Ib). Most PM hot-spot analyses will involve only vehicles
traveling on publicly accessible roads. When applying Equation Ib, the following data
requirements apply:
• Users will need to provide the mean vehicle speed for traffic using the road.
• The percentage of surface material moisture will also need to be obtained and
used in the equation. The default moisture content value should not be used.
As above, this discussion is based on the November 2006 version of Section 13.2.1 of
AP-42. Users should consult the latest version of AP-42, Section 13.2.1 on EPA's
website to determine whether any updates to the road dust methods have been made.
6.3.5 Using alternative local approaches for estimating re-entrained road dust
PM2.5 and PMio nonattainment and maintenance areas can use a locally-developed
method for estimating re-entrained road dust for hot-spot analyses. Some areas have
historically used alternative methods for estimating re-entrained road dust emissions that
may be more appropriate than the AP-42 methods given specific local conditions. Other
areas may develop alternatives in the future.
For example, an area may have a locally-developed method that has been
approved by EPA for estimating road dust for regional emissions analyses. Also,
an alternative method could be used if the equations in AP-42 do not apply to a
particular project, as they were developed using a particular range of source
conditions. Section 13.2.1 of AP-42 states that the equation provides a range of
silt loads, mean vehicle weights, and mean vehicle speeds, but it should not be
used outside the specified range. In these cases, users are encouraged to consider
alternative methods that can better reflect local conditions.
Therefore, if the project undergoing a PM hot-spot analysis does not fit within the
parameters described within AP-42, users should consider whether an alternative method
of estimating road dust is appropriate.
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As stated above, the interagency consultation process must be used to determine the
models and methods used in PM hot-spot analyses.
6.4 ESTIMATING TRANSPORTATION-RELATED CONSTRUCTION DUST
6.4.1 Determining whether construction dust must be considered
Construction-related PM2.s or PMi0 emissions associated with a particular project are
required to be included in hot-spot analyses only if such emissions are not considered
temporary as defined in 40 CFR 93.123(c)(5) (i.e., temporary emissions are those that
occur only during the construction phase and last five years or less at any individual site).
The following discussion includes guidance only for construction-related dust emissions;
any other construction emissions (e.g., exhaust emissions from construction equipment)
would need to be calculated separately, as discussed in Section 6.6.
6.4.2 Using AP-42 to estimate emissions of construction dust
Section 13.2.3 of AP-42 describes how to estimate emissions of dust from construction of
transportation projects. This section can be downloaded from EPA's website at:
www. epa. gov/ttn/chief/ap42/ch 13/index.html.
The following discussion is based on the latest version of Section 13.2.3 of AP-42,
released in 1995. Users should consult EPA's website for the most recent edition of AP-
42, Section 13.2.3. Some nonattainment or maintenance areas have historically used
alternative methods for estimating construction dust that may be more appropriate than
AP-42 given specific local conditions. Other areas may develop alternatives in the
future. The interagency consultation process must be used to determine model and
methods, as described above.
This section of AP-42 includes one equation for estimating dust where the user would
need to provide only the size of the construction site (in acres or hectares) and the number
of months of activity. However, Section 13.2.3 indicates there are limitations to this
equation's usefulness for specific construction sites and therefore strongly recommends
that, when emissions are to be estimated for a particular construction site, the
construction process be broken down into component operations (e.g., bulldozing,
demolition, or motor grading). Table 13.2.3-1 provides recommended emission factors
for the various component operations.
In addition, Section 13.2.3 indicates that another substantial source of emissions could be
from material that is tracked out from the site and deposited on adjacent paved streets.
Therefore, AP-42 states that persons developing construction site emission estimates
must consider the potential for increased adjacent emissions from off-site paved
roadways; users should refer to the discussion regarding paved roads in Section 6.3.3.
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6.5 ADDING DUST EMISSIONS TO MOVES/EMFAC MODELING RESULTS
Once any emissions from road and construction dust have been determined, these results
should be added to the emission factors generated by the motor vehicle emissions model
that was used for each link (MOVES, or EMFAC in California). Once this data is
available, the user can move on to Section 7 to develop input files for the appropriate air
quality model.
6.6 ESTIMATING OTHER SOURCES OF EMISSIONS IN THE PROJECT AREA
6.6.1 Construction-related vehicles and equipment
The interagency consultation process must be used to evaluate and choose the data,
models, and methods for quantifying emissions from construction vehicles and
equipment, when applicable (40 CFR 93.105(c)(l)(i)). In addition, state and local air
agencies may have quantified these types of emissions for the development of SIP non-
road mobile source inventories that should be considered for PM hot-spot analyses.
6.6.2 Locomotives
EPA has developed guidance to quantify locomotive emissions when they are a
component of a transit or freight terminal or otherwise a source in the project area being
modeled. See Appendix I for further general guidance, resources, and examples.
6.6.3 Other emission sources
When applicable, emissions from other sources affecting the project area must be
estimated and included in air quality modeling. See Section 8 for further information and
use of the interagency consultation process as appropriate.
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Section 7: Selecting an Air Quality Model, Data Inputs, and
Receptors
7.1 INTRODUCTION
This section describes the recommended air quality models, data inputs, and receptor
considerations for PM hot-spot analyses. This guidance is consistent with the conformity
rule and recommendations for air quality modeling in EPA's "Guideline on Air Quality
Models" (Appendix W to 40 CFR Part 51).
Regardless of the model used, the quality of a model's predictions depends on
appropriate input data, proper formatting, model setup, quality assurance, and other
assumptions. As noted in Section 2, air quality modeling for PM hot-spot analyses must
meet the conformity rule's general requirements for such analyses (40 CFR 93.123(c))
and rely on the latest planning assumptions available when the analysis begins (40 CFR
93.110).
This section presumes that users already have a basic understanding of air quality models
and their operation, through previous experience, attending training, and/or reviewing the
user guides for the appropriate models. EPA has also included additional details on air
quality modeling in Appendix J of this guidance. The models in this section, user guides,
and supporting documentation are available through EPA's Support Center for
Regulatory Air Models (SCRAM) website at: www.epa.gov/scramOO 1. Project sponsors
conducting PM hot-spot analyses will need to refer to the existing user guides and
available guidance for complete instructions.
7.2 GENERAL OVERVIEW OF AIR QUALITY MODELING
Air quality models and data inputs need to be determined on a case-by-case basis for each
PM hot-spot analysis through the interagency consultation process (40 CFR
93.105(c)(l)(i)). Exhibit 7-1 (following page) outlines the basic process for conducting
air quality modeling for a given project. This exhibit depicts the flow of information
developed for air quality modeling (as described in this section), the development of
background concentration estimates (see Section 8), and the calculation of design values
and comparison to the NAAQS (see Section 9).
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Exhibit 7-1. Overview and Data Flow for Air Quality Modeling
Reference Documents
Appendix W to
40CFRPart51
(throughout)
AERMOD
Implementation
Guide
MPRM User's
Guide (for
CAL3QHCR)
AERMET
User's Guide
(for AERMOD)
AERMOD
Implementation
Guide
AERMOD
User's Guide
CAL3QHCR
User's Guide
Running the Air Quality Model
(Section 7)
Select appropriate air
quality model
(Section 7.3)
Characterize sources
(location, timing, etc.)
(Section 7.4)
Obtain representative
meteorological data
(Section 7.5)
Run appropriate met pre-
processor
(Section 7.5)
Specify urban or rural
sources
(Section 7.5)
Specify receptors
(Section 7.6)
Run air quality model
(Section 7.7)
Calculate design
values and determine
conformity
(Section 9)
Determine
background
concentrations from
other sources
(Section 8)
Modeling sequence 1—
*. Q
Model inputs
_J Action
j Document
CD
( [
Results previously
calculated
External data
* If applicable
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7.3 SELECTING AN APPROPRIATE AIR QUALITY MODEL
7.3.1 Recommended air quality models
PM hot-spot analyses should be developed consistent with EPA's current recommended
models under Appendix W to 40 CFR Part 51. The purpose of recommending a
particular model is to ensure that the best-performing methods are used in assessing PM
impacts from a particular project and are employed in a consistent fashion.52 Exhibit 7-2
summarizes the recommended air quality models for PM hot-spot analyses for required
projects under 40 CFR 93.123(b)(l).
Exhibit 7-2. Summary of Recommended Air Quality Models
Type of Project
Highway and intersection projects
Transit, freight, and other terminal projects
Projects that involve both highway /intersections
and terminals, and/or nearby sources
Recommended Model
AERMOD, CAL3QHCR
AERMOD
AERMOD
As noted above, the selection of an air quality model must be made on a case-by-case
basis through the interagency consultation process.
The American Meteorological Society/EPA Regulatory Model (AERMOD) is EPA's
recommended near-field dispersion model for many regulatory applications. AERMOD
includes options for modeling emissions from volume, area, and point sources and can
therefore model the impacts of many different source types.53
CAL3QHCR is an extension of the CAL3QHC model, which is the model recommended
for use in analyzing CO impacts from intersections.54 It is appropriate to use
CAL3QHCR for PM hot-spot modeling for specified projects.
52 The best performing model is one that best predicts regulatory design values for a particular pollutant.
EPA's "Protocol for Determining the Best Performing Model" (EPA-454/R-92-025) defines operational
and statistical criteria for this evaluation. According to the document: "For a pollutant... for which short-
term ambient standards exist, the statistic of interest involves the network-wide highest concentration.. .the
precise time, location, and meteorological condition is of minor concern compared to the magnitude of the
highest concentration actually occurring."
53 EPA recommended AERMOD in a November 9, 2005 final rule that amended EPA's "Guideline on Air
Quality Models." The final rule can be found at: www.epa.gov/scram001/guidance/guide/appw 05.pdf.
Extensive documentation is available describing the various components of AERMOD, including user
guides, model formulation, and evaluation papers. See EPA's SCRAM website for AERMOD
documentation: www.epa.gov/scram001/dispersion_prefrec.htm#aermod.
54 CAL3QHC is a CALINE3-based model with a traffic model to calculate delays and queues at signalized
intersections; CAL3QHCR is a refined model based on CAL3QHC that requires local meteorological data.
CALSQHCR's user guide ("User's Guide to CAL3QHC Version 2.0: A Modeling Methodology for
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Both the AERMOD and CAL3QHCR models (and related documentation) can be
obtained through EPA's SCRAM website. EPA's Office of Air Quality Planning and
Standards (OAQPS) maintains the SCRAM website and maintains, codes, and supports
AERMOD on an ongoing basis. Modelers should regularly check this website to ensure
use of the latest regulatory version. CAL3QHCR is no longer updated and technical
support for the model is not available through OAQPS.
Appendix J includes important additional information about configuring AERMOD and
CAL3QHCR when using these models to complete PM hot-spot analyses.
Highway and Intersection Projects
Some projects may consist exclusively of highways and intersections, with little or no
emissions coming from long-term idling, non-road engine operations, or explicitly-
modeled nearby sources (see more below). Both AERMOD and CAL3QHCR are
recommended air quality models for these types of projects.55 When using CAL3QCHR
for highway and intersection projects, its queuing algorithm should not be used. As
discussed in Sections 4 and 5, idling vehicle emissions should instead be accounted for
by properly specifying links for emission analysis, and reflecting idling activity in the
activity patterns used for MOVES or EMFAC modeling.
Note: Users should be aware that to handle quarterly emissions and multiple years of
meteorological data, AERMOD and CAL3QHCR require different numbers of input files
and runs. AERMOD can handle quarterly variations in emissions and multiple years of
meteorological data using a single input file and run. In contrast, CAL3QHCR can
handle only one quarter's emissions and one year of meteorological data at a time. See
further information in Section 7.5.3.
Transit and Other Terminal Projects
Other projects may include only transit or freight terminals and transfer points where a
large share of total emissions arise from engine start and idling emissions or from non-
road engine activity. AERMOD is the recommended air quality model for these types of
projects.
Projects that Involve Both High way/Inter section and Terminal Projects, and/or Nearby
Sources
There may be some projects that are a combination of the "highway and intersection" and
"transit and freight terminal" project types. AERMOD is the recommended model for
Predicting Pollutant Concentrations Near Roadway Intersections") can be found at:
www.epa. gov/scramOO 1.
55 Appendix W to 40 CFR Part 51 describes both AERMOD and CAL3QHCR as being appropriate for
modeling line sources. For further background, see Sections 3.0, 4.0, 5.0, and 8.0 of Appendix W, as well
as Appendix A to Appendix W of 40 CFR Part 51.
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these projects. As a general recommendation, if AERMOD is used for modeling any
source associated with the project, it should be the only air quality model used for the PM
hot-spot analysis.56 There may be other cases where the project area also includes a
nearby source that must be explicitly modeled to account for background concentrations
around the project (e.g., locomotives at a nearby freight terminal or marine port). In
these cases, AERMOD should be used for the project and any such nearby sources. See
Section 8 for further information on nearby sources.
7.3.2 How emissions are represented in CAL3QHCR and AERMOD
Both CAL3QHCR and AERMOD simulate how pollutants disperse in the atmosphere.
To do so, the models classify emission sources within a project as line, volume, area, and
point sources:
• Line sources are generally linear emission sources, which can include highways,
intersections, and rail lines. They are directly-specified in the CAL3QHCR input
file using road link coordinates. AERMOD can simulate a highway "line source"
using a series of adjacent volume or area sources (see the AERMOD user guide
and the AERMOD Implementation Guide for suggestions).
• Volume sources (used in AERMOD only) are three-dimensional spaces from
which emissions originate. Examples of sources that could be modeled as volume
sources include areas designated for truck or bus queuing or idling (e.g., off-
network links in MOVES), driveways and pass-throughs in bus terminals, and
locomotive activity at commuter rail or freight rail terminals.57
• Area sources (used in AERMOD only) are flat, two-dimensional surfaces from
which emissions arise (e.g., parking lots).
• Point source emissions (used in AERMOD only) emanate from a discrete location
in space, such as a bus garage or transit terminal exhaust stack.
Each of these source types may be appropriate for representing different sources in a PM
hot-spot analysis. For example, highways may be modeled as line sources in
CAL3QHCR, but they may also be modeled as a series of adjoining volume sources in
AERMOD, as described below. Using another example, an exhaust vent from a bus
garage might be best represented as a point source, area source, or volume source,
depending on its physical characteristics. Project sponsors should consult with the most
recent user guides for air quality models to determine the most appropriate way to
represent a particular source within a model.
56 There are several reasons for this recommendation. First, AERMOD is flexible in how different sources
are represented, while CAL3QHCR must represent all sources as "line sources" (see Section 7.3.2).
Second, AERMOD allows a much wider number of receptors and sources to be modeled simultaneously,
which is useful for large projects with different source configurations. Third, AERMOD's treatment of
dispersion in the lower atmosphere is based on more current atmospheric science than CAL3QHCR.
Furthermore, the use of a single model, rather than multiple models, is recommended to avoid the need to
run the same meteorological data through different pre-processors (AERMET, MPRM), avoid different
receptor networks for different sources, reduce the number of atmospheric modeling runs required to
analyze a project, avoid the use of different modeling algorithms that perform the same task, and reduce
double-counting or other errors.
57 See Section 6 and Appendix I for information on estimating locomotive emissions.
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7.3.3 A Iternate models
In some limited cases, an alternate model for use in a PM hot-spot analysis may be
considered. As stated in Section 3.2 of Appendix W, "Selection of the best techniques
for each individual air quality analysis is always encouraged, but the selection should be
done in a consistent manner." This section of Appendix W sets out objective criteria by
which alternate models may be considered.
Analyses of individual projects are not expected to involve the development of new air
quality models. However, should a project sponsor seek to employ a new or alternate
model for a particular transit or highway project, that model must address the criteria set
forth in Section 3.2 of Appendix W. Determining model acceptability in a particular
application is an EPA Regional Office responsibility involving consultation with EPA
Headquarters, when appropriate.
7.4 CHARACTERIZING EMISSION SOURCES
Characterizing sources is the way in which the transportation project's features and
emissions are represented within an air quality model. In order to determine the
concentrations downwind of a particular emission source, an air quality model must have
a description of the sources, including:
• Physical characteristics and location;
• Emission rates/emission factors; and
• Timing of emissions.
Within any particular PM hot-spot analysis, there may be several different emission
sources within the project area. Sections 4 and 5 describe how a project can be
characterized into different links, which will each have separate emission rates to be used
in air quality modeling. Sections 6 and 8.2 outline how nearby source emissions, when
present, can be characterized to account for emissions throughout the project area.
Properly characterizing all of these distinct sources within the PM hot-spot analysis will
help ensure that the locations with the greatest impacts on PM air quality concentrations
are identified.
This section describes the major elements needed to characterize a source properly for
use in an air quality model.
7.4.1 Physical characteristics and location
When modeling an emission source, its physical characteristics and location must be
described using the relevant model's input format, as described in the appropriate user
guides. For the same emission rate, sources with different physical characteristics may
have different impacts on predicted concentrations.
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Refer to Appendix J of this guidance and to the user guides for CAL3QCHR and
AERMOD for specific information about how physical characteristics and location of
sources are included in these models.
7.4.2 Emission rates/emission factors
The magnitude of emissions within a given time period or location is a necessary
component of dispersion modeling. For motor vehicles, MOVES-based emission rates
are required in all areas other than the state of California, where EMF AC-based emission
rates are required, as described in Sections 4 and 5, respectively. For road and
construction dust, emission factors from AP-42 or a local method are required, as
described in Section 6. For other types of sources, the appropriate emission rates should
also be estimated, as described in Section 6.
CAL3QHCR and AERMOD accept emission rates in different formats. For highways
and intersections, CAL3QHCR requires emissions to be specified in grams/vehicle-mile
traveled (grams/mile).58 AERMOD needs emission rates in grams/hour (or
grams/second).
7.4.3 Timing of emissions
The proper description of emissions across time of year, day of week, and hour of day is
critical to the utility of air quality modeling.59 Sections 4 and 5 describe how to account
for different periods of the day in emissions modeling with MOVES and EMF AC. This
approach is then applied to air quality modeling to estimate air quality concentrations
throughout a day and year. As described in Section 3.3.4, air quality modeling for most
PM hot-spot analyses would involve data and modeling for all four quarters of the
analysis year, except in limited cases.
Sections 4 and 5 and Appendix J describes how results from MOVES and EMF AC
should be prepared for use as inputs in both AERMOD and CAL3QCFIR.
7.5 INCORPORATING METEOROLOGICAL DATA
7.5.1 Finding representative meteorological data
One of the key factors in producing credible results in a PM hot-spot analysis is the use
of meteorological data that is as representative as possible of the project area.
Meteorological data are necessary for running either AERMOD or CAL3QCFIR because
meteorology affects how pollutants will be dispersed in the lower atmosphere. The
58 CAL3QHCR uses the hourly volume of vehicles on each road link and the emission factor (in
grams/mile) for the vehicles on each link to calculate time-specific emission rates for use in air quality
modeling. As described in Sections 4 and 5, the idle emission factor inputs in CAL3QHCR should not be
used in a PM hot-spot analysis.
59 The timing of emissions in AERMOD is described in Section 3.3.5 of the AERMOD user guide.
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following paragraphs provide an overview of the meteorological data needed and sources
of this data. More detailed information can be found in Appendix J and in model user
and implementation guides.
Meteorological data is used by air quality dispersion models to characterize the extent of
wind-driven (mechanical) and temperature-driven (convective) mixing in the lower
atmosphere throughout the day.60 For emissions near the ground, as is common in
transportation projects, dispersion is driven more by mechanical mixing, but temperature-
driven mixing can still have a significant impact on nearby air quality. As a source's
plume moves further downwind, temperature-driven mixing becomes increasingly
important in determining concentrations.
Depending on the air quality model to be used, the following types of information are
needed to characterize mechanical and convective mixing:
• Surface meteorological data, from surface meteorological monitors that measure
the atmosphere near the ground (typically at a height of 10 meters—see Section
7.5.2);
• Upper air data on the vertical temperature profile of the atmosphere (see Section
7.5.2);
• Data describing surface characteristics, including the surface roughness, albedo,
and Bowen ratio (see Section 7.5.4); and
• Population data to account for the "urban heat island effect" (see Section 7.5.5).
Project sponsors should first consult with their respective state and local air quality
agencies for any representative meteorological data for the project area. In addition,
some state and local air agencies may maintain pre-processed meteorological data
suitable for use in PM hot-spot analyses. Interagency consultation should be used to
determine whether pre-processed meteorological data are available.
To format meteorological data appropriately and prepare them for use in air quality
models, EPA maintains meteorological processing software on the SCRAM website.61
These programs produce input data files that the air quality models read to produce
calculations of atmospheric dispersion. AERMOD and CAL3QHCR employ different
meteorological pre-processing programs. AERMET is the meteorological pre-processor
for AERMOD. The Meteorological Processor for Regulatory Models (MPRM) program
is the meteorological pre-processor for CAL3QHCR. User guides for both AERMET
and MPRM should be consulted for specific instructions.
The meteorological data used as input to an air quality model should be selected on the
basis of geographic and climatologic representativeness and how well measurements at
60 Mechanical turbulence arises when winds blow across rough surfaces. When wind blows across areas
with greater surface roughness (roughness length), more mechanical turbulence and mixing is produced.
Temperature-driven mixing is driven by convection (e.g., hot air rising).
61 These programs and their user guides may be downloaded from the SCRAM website at:
www.epa.gov/scramOO 1/metobsdata procaccprogs.htm.
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one site represent the likely transport and dispersion conditions in the area around the
project. The representativeness of the data depends on factors such as:
• The proximity of the project area to the meteorological monitoring site;
• The similarity of the project area to the meteorological monitoring site in surface
characteristics (particularly surface measurements);
• The time period of data collection;
• Topographic characteristics within and around the project area; and
• Year-to-year variations in weather conditions (hence, a sufficient length of
meteorological data should be employed, as discussed in Section 7.5.3 and
Appendix J).
The AERMOD Implementation Guide provides up-to-date information and
recommendations on how to judge the representativeness of meteorological data.62
Modelers should consult the most recent version of the AERMOD Implementation Guide
for assistance in obtaining and handling meteorological information. Although its
recommendations are intended for users of AERMOD, its recommendations for how to
assess the representativeness of meteorological data apply to analyses employing
CALSQHCRaswell.
7.5.2 Surface and upper air data
Surface Data
Air quality models require representative meteorological data from a near-ground surface
weather monitoring station ("surface data"). Models have minimum requirements for
what surface observations are needed. For example, when using National Weather
Service (NWS) data to produce meteorological input files for AERMOD, the following
surface data measurements are required:
• Wind vector (speed and direction);
• Ambient temperature; and
• Opaque sky cover (or, in the absence of opaque sky cover, total sky cover).
Station barometric pressure is recommended, but not required (AERMET includes a
default value in the absence of such data).
When processing data using MPRM for use in CAL3QHCR, information on stability
category is also required. MPRM estimates stability internally. Alternatively, when
using NWS data, the calculation requires:
• Wind speed and direction;
• Ceiling height; and
• Cloud cover (opaque or total).
For details, refer to the AERMET or MPRM user guides on the SCRAM website.
63
62 See www.epa.gov/scram001/dispersionjrefrec.htnrfaermod.
63 See www.epa.gov/scramOO 1/metobsdata procaccprogs.htm.
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Upper Air Data
Upper air soundings measure gradients of vertical temperature in the atmosphere. The
vertical temperature gradients of the lower atmosphere are used by air quality models to
calculate convective mixing heights. Models require upper air sounding data from a
representative measurement site. For AERMOD, consult the AERMOD Implementation
Guide for specific recommendations. For CAL3QHCR, consult the MPRM user guide.
Obtaining Surface and Upper Air Meteorological Data
Meteorological data that is most representative of the project area should always be
sought. Meteorological data that can be used for air quality modeling are routinely
collected by the NWS. Other organizations, such as the FAA, local universities, military
bases, industrial facilities, and state and local air agencies may also collect such data.
Project sponsors may also choose to collect on-site data for use in PM hot-spot analyses,
but it is not necessary to do so. If site-specific data are used, it should be obtained in a
manner consistent with EPA guidance on the topic.64
There are several locations where such data can be obtained. The National Oceanic and
Atmospheric Administration's National Climatic Data Center contains many years of
archived surface and upper air data (www.ncdc.noaa.gov) from NWS and other sources.
In addition, EPA's SCRAM web site contains archived surface and upper air data from
several sources, including NWS, as well as internet links to other data sources. In
addition, some states provide processed meteorological data for use in regulatory air
quality modeling applications. Other local agencies and institutions may also provide
meteorological data, as described above.
7.5.3 Time duration of meteorological data record
As discussed in Section 8.3.1 of Appendix W, when using meteorological data collected
off-site, five years of representative meteorological data need to be used when estimating
concentrations with an air quality model. Consecutive years are preferred. If
meteorological data are collected on the project area prior to analysis, at least one year of
site-specific data is required.64 Consult Section 8.3.1 of Appendix W for additional
explanation.
AERMOD and CAL3QHCR have different capabilities for modeling meteorological
data, as illustrated in Exhibit 7-3 (following page).
64 See Section 8.3.3 in Appendix W to 40 CFR Part 51 ("Site Specific Data") and the "Monitoring
Guidance for Regulatory Modeling Applications" (www.epa. gov/scramOO l/metguidance.htm). Other
meteorological guidance documents are also available through SCRAM, including procedures for
addressing missing data and for quality assuring meteorological measurements.
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Exhibit 7-3. Air Quality Model Capabilities for Meteorological Data
Type of Air
Quality Model
AERMOD
CAL3QHCR
Number of Runs Required
with 5 Years of Off-Site
Meteorological Data
1
20
Number of Runs Required
with 1 Year of On-Site
Meteorological Data
1
4
AERMOD can model either five years of off-site meteorological data or one year of on-
site data in a single run, since the model handles different emissions within a year and
multiple years of meteorological data with a single input file.
CAL3QHCR requires different input files for each quarter that is modeled using MOVES
or EMFAC, since CAL3QHCR does not distinguish between emission changes due to
seasonal differences. If off-site data is used, modeling five years of consecutive
meteorological data requires five runs of CAL3QHCR for each quarter. If on-site data is
collected, CAL3QHCR needs to be run only once for each quarter. As a result, for most
PM hot-spot analyses which will model four quarters for the analysis year(s),
CAL3QHCR should be run 20 times to represent different emissions by quarter using
five years of off-site meteorological data. Using one year of on-site meteorological data,
it should be run four times.
7.5.4 Considering surface characteristics
In addition to surface and upper air meteorological data, three surface characteristics for
the site of meteorological monitoring are needed for air quality modeling, depending on
the model used:
• The surface roughness length (z0), which indicates how much the surface features
at a given site (e.g., buildings, trees, grass) interrupt a smooth-flowing wind;
• Albedo (r), which is the amount of solar radiation absorbed by the ground; and
• Bowen ratio (B0), which indicates how much heat the ground imparts to the air.
AERMOD and AERMET make use of these parameters directly. CAL3QHCR and
MPRM do not require data on surrounding surfaces' albedo or Bowen ratio for modeling
ambient PM concentrations, but surface roughness is an input to CAL3QHCR.65 As
described above, surface characteristics are also used to assess a meteorological
monitor's representativeness.
The AERMOD Implementation Guide should be consulted for the latest information on
processing land surface data, when using either AERMOD or CAL3QHCR. Although its
recommendations are intended for AERMOD, they also apply to CAL3QHCR with
65 As described in Section 4.2 of its user guide, MPRM makes use of surface roughness in calculating
stability categories.
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meteorological data processed by MPRM.66 More detailed information about each of
these characteristics is found in Appendix J.
Sources of data that can be used to determine appropriate surface characteristics include
printed topographic and land use/land cover (LULC) maps available from the U. S.
Geological Survey (USGS), aerial photos from web-based services, site visits and/or site
photographs, and digitized databases of LULC data available from USGS. For specific
transportation projects, detailed nearby LULC data may be developed as part of project
design and engineering plans. Furthermore, some MPOs have adopted modeling
techniques that estimate the land use impacts resulting from individual highway and
transit projects.
LULC data may only be available for particular years in the past. As such, planning for
modeling should consider how representative these data are for the year when
meteorological data were collected, as well as the PM hot-spot analysis year(s).
The National Land Cover Database (NLCD) is a set of satellite-based land cover
measurements that are updated periodically.67 As of the writing of this guidance,
versions of the NLCD have been released representing calendar years 1992 and 2001,
with five areas/states (New England, Mississippi, South Dakota, Washington, and
Southern California) being updated to reflect 2006. The AERMOD Implementation
Guide currently recommends the use of 1992 NLCD data when processing
meteorological data. Consult that document for the most current recommendations with
regard to the use of NLCD data.68
7.5.5 Specifying urban or rural sources
In addition to surface characteristics, night-time dispersion in urban areas can be greater
than in surrounding rural areas with similar surface characteristics as a result of the
"urban heat island effect."69 After sunset, urban areas cool at slower rates than
surrounding rural areas, because buildings in urban areas slow the release of heat.
Furthermore, the urban surface cover has greater capacity for storing thermal energy due
to the presence of buildings and other urban structures. As a result, the vertical motion of
urban air is enhanced through convection, a phenomenon lacking (or reduced) in rural
areas. The magnitude of the urban heat island effect is driven by the urban-rural
temperature difference that develops at night.
66 The CAL3QHCR user guide does not address pre-processing meteorological data, which is necessary for
PM hot-spot analyses. In the absence of such information, project sponsors should rely on the AERMOD
Implementation Guide when using either dispersion model.
67 This database can be accessed at: www.mrlc.gov.
68 The AERSURFACE model, a non-regulatory component of AERMOD, may also be used to generate
information on surface roughness, albedo, and Bowen ratio. As of this writing, AERSURFACE is based on
the 1992 NLCD. The latest version of AERSURFACE may be accessed via SCRAM
(www.epa. gov/scramOO 1/X
69 The MPRM user guide refers to the "urban heat island effect" as "anthropogenic heat flux."
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The implications for highway and transit projects are that the same emissions in a rural
area will undergo less dispersion than the same source in an urban area, all other factors
(e.g., surface characteristics, meteorology) being equal. For the purposes of a hot-spot
analysis, then:
• In urban areas, sources should generally be treated as urban.
• In isolated rural nonattainment and maintenance areas (as defined by 40 CFR
93.101), sources should be modeled as rural.
• Near the edge of urban areas, additional considerations apply that should be
discussed through the interagency consultation process.70
Modeling sources as urban or rural can have a large impact on predicted concentrations.
Both AERMOD and CAL3QHCR can account for the urban/rural differences in
dispersion. When sources are modeled as urban in AERMOD, the urban area's
population is a required input.
For projects near or beyond the edge of an urbanized area, there may be situations where
the build and no-build scenarios result in different degrees of urbanization. In these
situations, sources in the build scenario might be treated as urban, while in the no-build
they are treated as rural. Local data on such cases may not be universally available,
although some planning agencies have adopted models that may allow the impacts of
projects on population growth to be described. Given the potentially large impact of
modeling sources as either urban or rural, all available information on population growth
in the greater area around the project should be used when modeling projects near or
beyond the edge of an urbanized area.
When using AERMOD, consult the latest version of the AERMOD Implementation
Guide for additional information, including instructions on what type of population data
should be used in making urban/rural determinations. When using CAL3QHCR, consult
Section 7.2.3 of Appendix W for guidance on determining urban sources. Refer to
Appendix J for additional information on how to handle this data for each model.
7.6 PLACING RECEPTORS
7.6.1 Overview
Receptors for conformity purposes are locations in the project area where an air quality
model estimates future PM concentrations. Section 93.123(c)(l) of the conformity rule
requires PM hot-spot analyses to estimate air quality concentrations at "appropriate
receptor locations in the area substantially affected by the project." An "appropriate
receptor location" is a location that is suitable for comparison to the relevant PM
NAAQS, consistent with how the PM NAAQS are established and monitored for air
70 Since the urban heat island is not a localized effect, but regional in character, Section 7.2.3 of Appendix
W recommends that all sources within an "urban complex" be modeled as urban.
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quality planning purposes.71 Section 7.2.2 of Appendix W to 40 CFR Part 51 provides
guidance on the selection of critical receptor sites for dispersion modeling applications,
and recommends that receptor sites be placed with sufficient detail to estimate the highest
concentrations. Placing receptors should take into account project emissions as well as
other modeled sources. Project sponsors should place receptors in the project area for the
relevant NAAQS consistent with applicable requirements. Data, models, and methods
used in placing receptors must be discussed through the interagency consultation process
(40 CFR 93.105(c)(l)(i)). Project sponsors are encouraged to consult with state and local
air quality agencies and EPA, since these agencies have significant expertise in air quality
modeling and monitors for the PM NAAQS.
The paragraphs below include general guidance for placing receptors for all PM NAAQS
as well as additional guidance for consideration in PM2.5 hot-spot analyses. A final
summary is also included to assist conformity implementers.
7.6.2 General guidance for receptors for all PM NAAQS
The following general guidance should be followed when placing receptors for air quality
modeling of all PM NAAQS. The selection of receptor sites should be determined on a
case-by-case basis taking into account factors on a project-specific basis that may
influence areas of expected high concentrations, such as prevailing wind directions and
topography. In designing a receptor network (e.g., the entire coverage of receptors for
the project area), the emphasis should be placed on resolution and location, not the total
number of receptors. Design of the receptor network should also consider whether any
locations within the project area should be excluded from the modeling based on a
location being restricted from public access, or based on a location where a member of
the public would normally be present only for a very short period of time. Examples
include locations within a fenced property of a business, a median strip of a highway, a
right-of-way on a limited access highway, or an approach to a tunnel.
As described in Appendix W, air quality dispersion models are more reliable for
estimating the magnitude of highest concentrations somewhere within a specified area
and span of time than in predicting concentrations at a specific place and time.
Therefore, receptors should be sited at all locations at which high concentrations may
occur, rather than simply focusing on the expected "worst case" location.
Receptor spacing in the vicinity of the source should be of sufficient resolution to capture
the concentration gradients around the locations of maximum modeled concentrations.
The majority of emissions from a highway or transit project will occur within several
meters of the ground, and concentrations are likely to be greatest in proximity of near-
ground sources. As such, receptors should be placed with finer spacing (e.g., 10-25
71 Clean Air Act section 176(c)(l)(B) requires that transportation activities do not cause new NAAQS
violations, worsen existing NAAQS violations, or delay timely attainment of the NAAQS or interim
milestones in the project area. EPA interprets "NAAQS" in this provision to mean the specific NAAQS
that has been established through rulemaking and monitored for designation purposes.
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meters) closer to a source, and with wider spacing (e.g., 50-100 meters) farther from a
source. While prevailing wind directions may influence where maximum impacts are
likely to occur, receptors should also be placed in all directions surrounding a project.
Receptors should be sited as near as 3 meters from a source (e.g., the edge of a traffic
lane or a source in a terminal),72 except possibly with projects involving urban street
canyons where receptors may be appropriate within 2-10 meters of a project.73 In
addition, if AERMOD is used to create a standardized receptor network (e.g., using
AERMOD's Cartesian or polar grid functions), receptors may inadvertently be placed
within 3 meters of a project, and subsequently modeled. Such receptors should not be
used when calculating design values in most cases.
Receptors should be extended out to a sufficient distance from sources to account for
emissions that affect concentrations throughout the project area, depending on the spatial
extent of the project and the impacts of other modeled sources.
When completing air quality modeling for build and no-build scenarios, receptors should
be placed in the same geographic locations in both scenarios so that direct comparisons
can be made between design values calculated at each receptor. Receptors are first
determined based on the build scenario, and then placed in the same locations in the no-
build scenario (when this scenario is modeled). See Section 9 for further information
regarding calculating design values in a build/no-build analysis and appropriate receptors.
7.6.3 Additional guidance for receptors for the PM2.s NAAQS
There are additional considerations when placing receptors for the PM2.5 NAAQS, due to
how this NAAQS was established. In the March 2006 final rule, EPA stated:
"Quantitative hot-spot analyses for conformity purposes would consider how
projects of air quality concern are predicted to impact air quality at existing and
potential PM2.5 monitor locations which are appropriate to allow the comparison
of predicted PM2.5 concentrations to the current PM2.5 standards, based on PM2.5
monitor siting requirements (40 CFR Part 58)." (71 FR 12471)
EPA included this language in the preamble to the March 2006 final rule so that PM2.5
hot-spot analyses would be consistent with how the PM2.5 NAAQS were developed,
monitored, and implemented. Receptors cannot be used for PM2.5 hot-spot analyses if
they are at locations that would be inappropriate for ambient air quality monitoring
purposes for the NAAQS.
72 This recommendation is to ensure that receptors are placed outside the immediate turbulent mixing zone
of traffic. This recommendation is consistent with EPA's 1992 "Guideline for Modeling Carbon Monoxide
from Roadway Intersections," EPA-454/R-92-005 (November 1992), available online at:
www.epa. gov/scramOO 1.
73 See 40 CFR Part 58, Appendix E, Sections 4.7. l(c)(l) and 6.3(b). The interagency consultation process
should be used to discuss when these provisions are relevant for a given analysis.
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In general, there are two factors in the PM2.5 monitoring regulations that need to be
considered in determining the appropriateness of receptors for use in PM2 5 hot-spot
analyses. First, a receptor must be "population-oriented" in order to be appropriate for
comparison to either the 24-hour or annual PM2 5 NAAQS. Section 58.1 of the PM2 5
monitoring regulations defines population-oriented sites as:
"...residential areas, commercial areas, recreational areas, industrial areas where
workers from more than one company are located, and other areas where a
substantial number of people may spend a significant fraction of their day."
Population-orientated receptors can be determined when receptors are placed for air
quality modeling. In general, most locations, especially in urban areas, are population-
oriented. Receptors placed near transportation projects, therefore, will most likely be
population-oriented. Also, consideration should be given to the presence of people at
locations around each receptor in determining whether the receptor is population-
oriented, because the concentration predicted for the receptor can represent
concentrations surrounding the receptor. Changes in the project area in the future
analysis year should also be considered when placing receptors. For example, if a
receptor is at a location that is currently not population-oriented, but a housing
development is planned for that location under the build and/or the no-build scenario, that
receptor may be appropriate for comparison to the PM2.5 NAAQS.
The second factor from the PM2 5 monitoring regulations is only relevant for the annual
PM2.5 NAAQS. The PM2.5 monitoring regulations require that receptors for the annual
PM2.5 NAAQS also represent "community-wide air quality." Although receptors can be
placed for the annual PM2 5 NAAQS prior to air quality modeling, further consideration is
needed after air quality modeling to determine whether any of the modeled receptors are
not appropriate for comparison to the annual PM2.5 NAAQS. See Section 9.4 of this
guidance for how to determine appropriate receptor locations for the annual PM2.5
NAAQS.
7.6.4 Summary
Exhibit 7-4 summarizes the applicable parts of this guidance that can be used for
receptors used in PM hot-spot analyses:
Exhibit 7-4. Guidance for Receptors in PM Hot-spot Analyses
NAAQS
24-hour PMio NAAQS
24-hour PM2.5 NAAQS
Annual PM2.5 NAAQS
24-hour and Annual PM2.5 NAAQS
Applicable Receptor Guidance
Section 7.6.2
Sections 7.6.2, 7.6.3
Sections 7.6.2, 7.6.3, and 9.4
Sections 7.6.2, 7.6.3, and 9.4
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As noted above, appropriate receptor locations for the 24-hour PM2.5 and 24-hour
NAAQS can be determined prior to air quality modeling. All receptor locations that are
consistent with the general guidance are considered appropriate for the current 24-hour
PMio NAAQS.74 For the 24-hour PM2.5 NAAQS, receptors need to be placed in
locations that are consistent with the general guidance as well as be population-oriented
locations. For PM hot-spot analyses involving the annual PM2 5 NAAQS, although
receptors are placed prior to air quality modeling, the additional guidance in Section 9.4
should be used for determining inappropriate receptor locations after modeling, when
needed.
7.7 RUNNING THE MODEL AND OBTAINING RESULTS
After characterizing emissions from the project and nearby sources, pre-processing
meteorological data, defining relevant surface characteristics, accounting for urban and
rural sources, specifying receptor locations, and any other necessary model inputs, the air
quality model should be run to predict concentrations. The model run should be checked
for errors and evaluated for data quality and reasonableness of results (e.g., ensuring that
concentrations fall with distance from sources).
Note that, before the results of either AERMOD or CAL3QHCR are ready for use in
calculating design values and determining conformity (as described in Section 9), the
data will have to undergo some post-processing, depending on how the data was run in
the models and the NAAQS being evaluated. See Appendix J for more details.
Following completion of air quality modeling, background concentrations must be
determined, as described in Section 8. Finally, the resulting concentrations at receptors
should be combined with background concentrations from other sources to calculate
design values, as described in Section 9.
74 The current 24-hour PM10 NAAQS was established to account for ambient air quality concentrations at
receptor locations that can be accessed by one or more members of the public around homes, hospitals,
schools, sidewalks, etc. Therefore, any receptor that follows the general guidance in Section 7.6.2 for
placing receptors should be appropriate for comparison to the 24-hour PM10 NAAQS. This conformity
guidance is consistent with how air quality planning and monitoring are done for this NAAQS.
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Section 8: Determining Background Concentrations from
Nearby and Other Emission Sources
8.1 INTRODUCTION
This section describes how to determine background concentrations for PM hot-spot
analyses. Section 93.123(c)(l) of the conformity rule states that "estimated pollutant
concentrations must be based on the total emissions burden which may result from the
implementation of the project, summed together with future background
concentrations...." For PM hot-spot analyses, background concentrations can include
"nearby sources" and "other sources" of emissions, as described further in this section.
By definition, background concentrations do not include the emissions from the project
itself.75
This section is consistent with EPA's "Guideline on Air Quality Models" (Appendix W
to 40 CFR Part 51), which provides the appropriate framework for defining the elements
of background concentrations. Section 8.2.1 of Appendix W states that: "Background
concentrations are an essential part of the total air quality concentration to be considered
in determining source impacts."76 Concentrations are expected to vary throughout a
nonattainment or maintenance area, resulting from differences in emission sources,
meteorology, terrain, and other factors. The interagency consultation process must be
used to determine appropriate background concentrations for each PM hot-spot analysis
(40 CFR 93.105(c)(l)(i)), including how nearby sources are characterized in the build
and no-build scenarios.
State and local air quality agencies will have the primary expertise on what emission
sources are expected to affect background concentrations, including any nearby sources.
The state or local air agency is likely to have an understanding of the project area and
knowledge about information needed to appropriately characterize background
concentrations, due to experience in developing air quality demonstrations, emission
inventories, and siting air quality monitors for a given NAAQS. The EPA Regional
Office is also a key resource for discussions regarding the air quality monitoring network,
SIP modeling, and other issues.
75 See Sections 4 through 6 for more information on how to estimate project emissions.
76 Section 8.2.1 also states, "Background air quality includes pollutant concentrations due to: (1) natural
sources; (2) nearby sources other than the one(s) currently under consideration; and (3) unidentified
sources." Section 8.2.3 recommends for "multi-source areas" that "two components of background should
be determined: contributions from nearby sources and contributions from other sources."
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8.2 BACKGROUND CONCENTRATIONS FROM NEARBY SOURCES
Some PM hot-spot analyses may include "nearby sources" that affect PM concentrations
in the project area (e.g., a freight terminal, port, stationary source, or adjacent
transportation facility).77 Project sponsors, the relevant state or local air agency, the EPA
Regional Office, and other members of the interagency consultation process should
discuss:
• Are there any nearby sources in the project area? If no, then the remainder of
Section 8.2 can be skipped. If yes, then:
o Which of those sources are expected to cause significant concentration
gradients in vicinity of the project or generally contribute to the air quality
concentrations in the project area?
o How much do any nearby sources emit?
o Are emissions from any nearby sources expected to differ between the
build and no-build scenarios?
• Are any of these nearby sources already captured in the background
concentrations from either ambient monitoring data or existing air quality
modeling (see Section 8.3)?
When nearby sources are identified, the interagency consultation process must be used
for determining how best to reflect these sources in background concentrations, and how
nearby source emissions will vary between the build and no-build scenarios for the
analysis year(s). In most cases, the emission impacts of nearby sources will need to be
explicitly modeled using the air quality models described in Section 7 of this guidance:
• There could be cases where the emissions from nearby sources change as a result
of the project. An example of a project that could affect nearby sources would be
a freight corridor highway project whose primary purpose is to accommodate
future growth in goods movement; such a project could affect emissions from
related activity at nearby marine ports, rail yards, or intermodal facilities.
• Other cases could involve nearby sources whose emissions are not expected to
change as a result of the project. In most cases, these emissions would be
explicitly modeled with the same results for both the build and no-build scenarios.
There may be limited cases where such nearby sources may be addressed by
finding suitable monitoring data that captures the impact of the source, rather than
modeling the source explicitly. However, most projects will probably not be near
monitors that capture the impacts of nearby sources; therefore, emissions from
77 Section 8.2.3 of Appendix W describes "nearby sources" by stating, "All sources expected to cause a
significant concentration gradient in the vicinity of the source or sources under consideration for emission
limit(s) should be explicitly modeled."
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nearby sources should be characterized for the time periods addressed in
emissions and air quality modeling for the PM hot-spot analysis.
As discussed in Section 7.3, EPA recommends that the AERMOD model be used for any
PM hot-spot analyses that involve nearby sources that need to be explicitly modeled (e.g.,
a highway expansion and new exit ramps to connect a highway or expressway to a major
freight or intermodal terminal). If emissions from nearby sources are expected to change
as a result of the project, the air quality modeling must include any reasonably expected
changes in operation of the nearby source between the build and no-build scenarios when
both are necessary to demonstrate conformity. Refer to Section 7 for more information
about using AERMOD, placing receptors, and other information for air quality modeling.
Specific information on emissions from nearby sources should be obtained. The state and
local air agency should be consulted on characterizing nearby sources. In addition,
emission rates and other parameters of nearby sources should be consistent with any
permits approved by the state or local air agency. For unpermitted sources, emission
information should be consistent with information used by air agencies for developing
emission inventories for regulatory purposes. Sections 8.1 and 8.2 of Appendix W
describe the information needed to characterize the emissions of nearby sources for air
quality models. For the 24-hour PM2.s and PMi0 NAAQS, it is also important to consider
Section 8.2.3 of Appendix W which states that it is appropriate to "model nearby sources
only during those times when they, by their nature, operate at the same time as the
primary source(s) being modeled." In nonattainment and maintenance areas, emission
inputs for nearby point sources should be consistent with Table 8-1 in Appendix W.
Finally, estimation of nearby source impacts may take into account the effectiveness of
anticipated control measures in the SIP if they are already enforceable in the SIP.
8.3 OPTIONS FOR BACKGROUND CONCENTRATIONS FROM OTHER
SOURCES
In addition to nearby sources, background concentrations from "other sources" must also
be estimated, and there are several ways to do so as described below.78 There are several
options provided below that meet the requirements of Section 93.123(c)(l) of the
conformity rule that involve using representative air quality monitoring data.
However, EPA has not included the option for calculating background concentrations
from section 93.123(c)(2) of the conformity rule. This provision states that ".. .The
future background concentration should be estimated by multiplying current background
by the ratio of future to current traffic and the ratio of future to current emission factors."
EPA has determined that this method is not a technically viable option for estimating
background concentrations in PM hot-spot analyses. This method has been a credible
option for CO hot-spot analyses, since on-road mobile sources dominate background
78 Section 8.2.3 of Appendix W defines "contributions from other sources" as "that portion of the
background attributable to all other sources (e.g., natural sources, minor sources and distant major
sources)
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concentrations and adjusting monitored concentrations according to traffic and emission
factor changes is appropriate. However, using the same ratios in PM analyses is not
supported and would not allow project sponsors to meet 40 CFR 93.123(c)(l) since there
are many other types of sources that contribute to PM background concentrations.
8.3.1 Using ambient monitoring data to estimate background concentrations
Ambient monitoring data for PMi0 and PM2 5 provide an important source of information
to characterize the contributions from "other sources" that are not captured by explicit
modeling of nearby sources. Nonattainment and maintenance areas, and areas that
surround them, have numerous sites for monitoring PM2.5 and PMio concentrations that
may be appropriate for estimating background concentrations.79 Project sponsors,
relevant state or local air agencies, and the EPA Regional Office should identify the
appropriate PMio and PM2.5 monitoring data, along with information on each monitor's
site location, purpose, geographic scale, nearby land uses, and sampling frequency. EPA
offers Air Explorer (based on Google Earth software) as a user-friendly way to identify
and visualize where monitoring sites are in operation and to obtain concentration data and
descriptions of the site (such as the reported scale of spatial representation).80
The evaluation and selection of monitoring data for use in a particular analysis should be
discussed through the interagency consultation process. These discussions as well as any
maps or statistical techniques used to analyze background data should be well-
documented and included in the project-level conformity determination.
Project sponsors should not use monitoring data for which EPA has granted data
exclusion under the Exceptional Events rule (see 40 CFR 50.14).
Using a Single Monitor
Background concentration data should be as representative as possible for the project area
examined by the PM hot-spot analysis.81 When considering monitors for use of their data
as representative background concentrations, several factors should be evaluated:
• First, how does the area around the monitor location compare with the project
area? Are there differences in land use or terrain between the two locations that
could influence air quality in different ways? Is the monitor probe located at a
79 Monitors in adjacent nonattainment, maintenance, and attainment areas should also be evaluated for use
in establishing background concentrations, which may be appropriate if the air quality situation at those
monitors can be determined to be reasonably similar to the situation in the project area.
80 Available online at: www.epa.gov/airexplorer/monitor_kml.htm.
81 In particular, there should be interagency consultation prior to using any ambient monitoring data set for
PM2.5 that does not meet EPA requirements in Appendix N to 40 CFR Part 50 regarding data completeness,
and any data set that reflects a sampling schedule that has been erratic or has resulted in more frequent
samples in some seasons of a year than others. The guidance in Section 9 of this document assumes that
the normal data completeness requirement (75% of scheduled samples in each calendar quarter of each
year) has been met, and that the monitoring data is evenly distributed across the year. Deviation from these
conditions may make the steps given in Section 9 inappropriate.
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similar height as the project? Is the mix of emission sources around the monitor
location similar to those around the project site? Does the monitor capture the
influence of nearby sources? What is the purpose of the monitor, and what
geographic scale of representation does the monitor have? Monitors should be
selected that are more representative of the project area whenever possible.
• Second, how far is the monitor from the project area? Monitors closer to the
project are more likely to have concentrations similar to the project area, but
consideration of distance alone may mask the influence of differences in the
characteristics of the project area and monitored location. In addition, monitors
close to a project may reflect the influence of nearby sources that are explicitly
modeled along with the project. In those cases, selection of the nearest monitor
may result in double-counting of emissions from nearby sources.
• Third, what are the prevailing wind patterns between the monitor(s) and the
project area? Monitors that are located in directions that are frequently upwind of
a project are more likely to represent a project area's background concentrations
than monitors that are infrequently upwind.82
The simplest approach to using ambient monitoring data for estimating background
concentrations in a project area is the use of data from a representative nearby monitor.
However, consideration of a nearby monitor as "representative" should also consider
whether it captures the influence of nearby sources. If no nearby sources are included in
the air quality model, monitors located in the project area or its immediate vicinity (e.g.,
less than 1 km) may be considered for selection of a representative site. If one or more
nearby sources are included in the air quality model, monitors outside the influence of
those sources should be considered to avoid double counting their impacts. The selection
of a monitor for representing background concentrations should be considered along with
which nearby sources it represents and which nearby sources are explicitly modeled as
part of the hot-spot analysis.
Interpolating Between Several Monitors
If, during interagency consultation, agencies conclude that no single ambient monitor is
sufficiently representative of the project area, interpolating the data of several monitors
surrounding the project area is also an option. The advantage of interpolation is that no
single monitor is used exclusively in representing air quality for a project area. There
may be projects sited in locations between large emission sources and areas several miles
away with relatively low emissions, suggesting a gradient in concentrations across the
nonattainment or maintenance area. If there are no nearby monitors, then background
concentrations from other sources may be difficult to estimate. Interpolation is an
approach that allows estimates of background concentrations for a project to take
Constructing a "wind rose" can be a useful tool in examining the frequency of wind blowing from
different directions. A wind rose is a graph that depicts the frequency of wind blowing from different
directions. EPA's SCRAM website contains two programs for calculating wind statistics and wind roses,
WINDROSE and WRPLOT.
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advantage of monitoring data from multiple monitoring sites. Any planned interpolation
methods must be discussed through the interagency consultation process.
There are several approaches to interpolation that can be used. One simple method is
weighted averaging, which places greater weight on nearby monitors and uses the inverse
distance between the project site and the monitor to weight each monitor. For example,
suppose monitors A, B, and C surround an unmonitored location, at distances 5, 10, and
15 miles from the site, respectively, the weighting of data from monitor A:
Weight(A) = - \ - + — + — I = 0.55
5/1,5 10 15 J
The weighting for monitor B:
Weight(B) = — /(- + — + —} = 0.27
10/ 15 10 15J
The weighting for monitor C:
Weight(C) = — \ - + — + — I = 0.18
15/ 1,5 10 15 J
If concentrations at A, B, and C are 10.0, 20.0, and 30.0 ng/m3, respectively, the
predicted concentration at the unmonitored site is 16.3 ng/m3. In most situations, the
inverse-distance weighted average will provide a reasonable approximation of
background concentrations due to other sources. Another interpolation approach is the
inverse-squared distance weighting that weights monitors based on how close they are to
the project (I/distance squared).
Other, more advanced statistical methods to interpolate monitoring data may also be
used, but these require significant geostatistical expertise.83
8.3.2 Adjusting air quality monitoring data to account for future changes in air quality
To account for future emission changes that are documented in a SIP, background
concentrations based on monitored PM concentrations may be adjusted with a chemical
transport model (CTM). These adjustments must be consistent with other regulatory
applications of CTMs for PM2.5 and PMi0. Specifically, when CTM adjustments are
used, agencies should refer to EPA's "Guidance on the Use of Models and Other
Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and
83 EPA's MATS (www.epa.gov/ttn/scram/modelingapps mats.htm) and BenMAP
(www.epa.gov/air/benmap') models incorporate an interpolation-based approach (Voronoi Neighbor
Averaging). Consult those models' documentation for further information.
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Regional Haze."84 CTMs are photochemistry models that are routinely used in
regulatory analyses, including attainment demonstrations for PM SIPs.85
Project sponsors are not expected to operate CTMs. Rather, the results of CTMs applied
by state and local air agencies should be considered to determine if relevant data are
available. The state or local air agency should be consulted to determine whether and
how the results of CTMs are appropriate for use in a PM hot-spot analysis. A CTM may
be used to adjust background concentrations based on monitored concentrations in a
current (base) year.
The absolute predictions of a CTM in a future analysis year should not be used to predict
future background concentrations directly. Instead, the results of a CTM for a current
(base) year and future year should be used to calculate a "relative response factor" (RRF)
that reflects the relative changes in concentrations between current and future years. An
RRF is calculated as:
„ „ „ Concentrations in future year, predicted by CTM
KKr =
Concentrations in base year, predicted by CTM
RRFs should be calculated with the same CTM using the same meteorological data for
base and future years, with different emissions for base and future years. RRFs should be
calculated in a manner consistent with EPA's "Guidance on the Use of Models and Other
Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2 5, and
Regional Haze," referenced above.
Background concentrations based on monitoring data may be adjusted to reflect
conditions in an analysis year based on the following equation:
Background Concentrationsfutureyear = Background Concentrationsbaseyearx RRF
To adjust background concentrations to reflect future-year conditions using a CTM,
several criteria should be met.
• The CTM should have demonstrated acceptable performance using standard
indicators of model performance.86
• There should be results of CTM runs that adequately represent both the years
from which monitoring data come and the future analysis year(s).
• Any future emission reductions for sources within the CTM modeling
demonstration should be based on enforceable commitments in the SIP or should
84 This document is available online at: www.epa.gov/scram001/guidance/guide/final-03-pm-rh-
guidance.pdf.
85 Examples of commonly employed photochemical models are shown on the SCRAM website at:
www.epa.gov/scram001/photochemicalindex.htm.
86 Examples of model evaluation statistics may be found in Appendix A of the document "Guidance on the
Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2 5,
and Regional Haze," referenced above.
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be consistent with the latest planning assumptions developed through interagency
consultation.
• Any future emission reductions for sources within the CTM modeling
demonstration should take effect prior to the year(s) for which the PM hot-spot
analysis is conducted.
Because the PM hot-spot analysis is based on a comparison of build and no-build
scenarios (see Section 2.4), how the modeled estimates of a project's impacts are
combined with CTM predictions for the grid cell should be approached with caution to
ensure no double counting of emissions from the project. CTM predictions for a future
year may already incorporate emissions that are projected as part of the no-build scenario,
including those from the project area and nearby sources. In those cases, the CTM results
may be considered representative of the no-build scenario. In those situations, to
evaluate predicted concentrations in the build scenario, at each receptor included in the
AERMOD or CAL3QHCR input file, the difference between concentrations at each
receptor in the build and no-build scenarios should be calculated as:
Difference receptori = Concentrationreceptori bmldscenano - Concentrationreceptori nobmldscenano
The result - the difference between the build and no-build scenarios at each receptor -
should be added to the CTM-adjusted background concentrations when calculating
design values. Using this approach, only the changes in receptor concentrations affected
by emission changes from the project or nearby sources whose emissions are changed by
the project are used in calculating design values.
8.3.3 Other methods of combining ambient monitoring data and modeling results
In addition to the methods described above, there may be other techniques for combining
information from monitors and air quality modeling that can be evaluated on a case-by-
case basis. Any technique considered for PM hot-spot analyses must be discussed
through interagency consultation (40 CFR 93.105(c)(l)(i)).
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Section 9: Calculating PM Design Values and Determining
Conformity
9.1 INTRODUCTION
This section describes how to combine all previous steps of a PM hot-spot analysis into a
design value so that a project sponsor can determine if conformity requirements are met.
For conformity purposes, a design value is a statistic that describes a future air quality
concentration in the project area that can be compared to a particular NAAQS.87 In
general, design values are calculated by combining two pieces of data:
• Modeled PM concentrations from the project and any nearby sources (Sections 7
and 8); and
• Monitored background PM concentrations from other sources (Section 8).88
Exhibit 9-1 illustrates the conceptual flow of information described in this section, which
is similar for all PM NAAQS.
Exhibit 9-1. General Process for Calculating Design Values for PM Hot-spot
Analyses
Data Inputs
(from Sections 7 and 8)
(Project and ^
nearby source /
from air quality \
model v_
/ Background /
I concentrations \
E
>etermining Conformity
(Section 9)
Combine to determine
total concentrations
i
Calculate design value(s)
i
Determine conformity
Design values based on monitoring data are used to determine the air quality status of a given
nonattainment or maintenance area (40 CFR Part 50). Design values are also used for SIP modeling and
other air quality planning purposes.
88 Section 9 provides specific guidance on calculating design values with background concentrations from a
single air quality monitor. Additional calculations and consultation would be necessary if background
concentrations resulted from interpolation between several monitors.
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This section describes how to calculate the specific statistical form of design values for
each PM NAAQS and how to apply design values in build/no-build analyses for
conformity purposes. This section also discusses how to determine which receptors for a
particular project may or may not be appropriate for comparison to the annual PM2.5
NAAQS.
This guidance is consistent with how design values are calculated for designations and
other air quality planning purposes for each PM NAAQS.89 EPA is considering whether
spreadsheet tools can be developed to assist state and local agencies in calculating design
values for PM hot-spot analyses.
The interagency consultation process must be used to determine the data, models, and
methods used for PM hot-spot analyses, including those used in calculating design values
and completing build/no-build analyses (40 CFR 93.105(c)(l)(i)). State and local air
quality agencies and EPA have significant expertise in air quality planning that may be
useful resources for the topics covered by this section. Project sponsors should document
the data and other details used for calculating design values for the build and no-build
scenarios for a project-level conformity determination as well as how appropriate
receptors were determined.
9.2 USING DESIGN VALUES IN BUILD/NO-BUILD ANALYSES
Design values are a fundamental component of PM hot-spot analyses, as they are the
values compared to the NAAQS and between build and no-build scenarios. In general, a
hot-spot analysis compares air quality concentrations with the proposed project (the build
scenario) to air quality concentrations without the project (the no-build scenario). The
conformity rule requires that the build scenario not produce any new violations of the
NAAQS, increase the frequency or severity of existing violations, or delay timely
attainment as compared to the no-build scenario (40 CFR 93.116(a) and 93.123(c)(l)).
Exhibit 9-2 (following page) illustrates the build/no-build analysis approach suggested in
Section 2.4.
89 Note that this section reflects the current PM2 5 and PM10 NAAQS; EPA will re-evaluate the applicability
of this guidance as needed, if different PM NAAQS are promulgated in the future.
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Exhibit 9-2. General Process for Using Design Values in Build/No-build Analyses
Identify the receptor
with the highest
concentration and
calculate its design
value
/ Is design value
less than or equal
toNAAQS? /
Yes
Calculate build
scenario design values
at all receptors
Calculate no-build
design values at all
receptors that
exceeded NAAQ Sin
build scenario
Are build design
values less than or
equal to no-build
design values?
\No
/ Are the receptors
where the build
exceeds the no-build
appropriate for
comparison to the
\ NAAQS?*
No
* Annual PM23 NAAQS only
In general, project sponsors could begin by determining the design value for only one
receptor in the build scenario: the receptor with the highest modeled air quality
concentration, as described in Section 9.3. If the design value for this receptor is less
than or equal to the relevant NAAQS, it can be assumed that conformity requirements are
met at all receptors in the project area, without further analysis. If this is not the case, the
project sponsor should calculate the design values at all receptors in the build scenario
and also model the no-build scenario. Design values should then be calculated for the no-
build scenario at all receptors with design values that exceeded the NAAQS in the build
scenario. Conformity requirements are met if the design value for every appropriate
receptor in the build scenario is less than or equal to the same receptor in the no-build
scenario.90 If not, then the project does not meet conformity requirements without further
mitigation or control measures to address air quality concentrations at such receptors,
except in certain cases described below.91
90 This would be the receptor at the same geographic location in the build and no-build scenarios.
91 When mitigation or control measures are considered, additional emissions and air quality modeling
would need to be completed and new design values calculated to ensure that conformity requirements are
met.
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A build/no-build analysis is typically based on design value comparisons done on a
receptor-by-receptor basis. However, there may also be cases where a possible "new"
violation at one receptor (in the build scenario) is relocated from a different receptor (in
the no-build scenario). It would be necessary to calculate the design values for all
receptors in the build and no-build scenarios to determine whether a "new" violation is
actually a relocated violation. EPA addressed this issue in the preamble to the
November 24, 1993 transportation conformity rule (58 FR 62213), where a "new"
violation within the same intersection could be considered a relocated violation. Since
1993, EPA has made this interpretation only in limited cases with CO hot-spot analyses
where there is a clear relationship between such changes (e.g., a reduced CO NAAQS
violation is relocated from one corner of an intersection to another due to traffic-related
changes from an expanded intersection). The interagency consultation process should be
used to discuss any potential relocated violations in PM hot-spot analyses.
When completing air quality modeling for build and no-build scenarios, receptors should
be placed in identical locations so that direct comparisons can be made between design
values calculated at receptors under each scenario. Also, design values are compared to
the relevant NAAQS and between build and no-build scenarios after rounding has been
QO
done, which occurs in the final steps of design value calculations. Further details on
rounding conventions for different PM NAAQS are included in Section 9.3 below.
Determining whether receptors are appropriate for the annual PM2.5 NAAQS would be
done after air quality modeling is completed and design values are calculated, as
described further in Section 9.4. Project sponsors should refer to Section 8.3.2 for
additional considerations for build/no-build analyses when chemical transportation model
(CTM) results are used to adjust background concentrations for other sources. In such
cases, it may be advisable to add only the difference between the build and no-build
modeled concentrations at each receptor to the CTM-adjusted future background
concentrations. This approach may be needed to avoid double-counting emissions.
9.3 CALCULATING DESIGN VALUES AND DETERMINING CONFORMITY FOR
PM HOT-SPOT ANALYSES
9.3.1 General
As noted above, this conformity guidance is generally consistent with how design values
are calculated for air quality monitoring and other EPA regulatory programs.93
92 For example, conformity requirements would be met at a receptor if the final build design value is no
greater than the final no-build design value, even if the pre-rounding build design value is greater than the
pre-rounding no-build design value.
93 EPA notes that design value calculations for PM hot-spot analyses involve using air quality modeling
results based on either one year of on-site measured meteorological data or five years of off-site measured
meteorological data, rather than three years.
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Further details are included below about how design values should be calculated at
receptors for build/no-build analyses, and examples of each design value calculation can
be found in Appendix K of this guidance. These details and examples are primarily
narrative in nature. EPA has also provided mathematical formulas of design values in
Appendix K, which may be helpful for certain users.
9.3.2 Annual PM2.5NAAQS
Design Value
The annual PM2.5 design value is defined as the average of three consecutive years'
annual averages, each estimated using equally-weighted quarterly averages.94 This
NAAQS is met when the three-year average concentration is less than or equal to the
annual PM2.5NAAQS (currently 15.0 ng/m3):
Annual PM2.5 design value = ([Yl] average + [Y2] average + [Y3] average) + 3
Where:
[Yl] = Average annual PM2.5 concentration for the first year of air quality
monitoring data
[Y2] = Average annual PM2.5 concentration for the second year of air quality
monitoring data
[Y3] = Average annual PM2.5 concentration for the third year of air quality
monitoring data
The annual PM2.5 NAAQS is rounded to the nearest tenth of a ng/m3. For example,
15.049 rounds to 15.0, and 15.050 rounds to 15.1.95 These rounding conventions should
be followed when calculating design values for this NAAQS.
Necessary Data
This design value calculation assumes the project sponsor already has the following data
in hand:
• Air quality modeling results: Average annual concentrations from the project and
any nearby sources should be calculated from the air quality model output files.96
The methodology for post-processing the air quality model output files will vary
depending on what air quality model is used. Refer to Appendix J for details on
preparing air quality model outputs for use in design value calculations.
94 The design value for the annual PM2 5 NAAQS is defined for air quality monitoring purposes in 40 CFR
Part 50.13.
95 A sufficient number of decimal places (3-4) should be retained during intermediate calculations for
design values, so that there is no possibility of intermediate rounding or truncation affecting the final result.
Rounding to the tenths place should only occur during final design value calculations, pursuant to
Appendix N to 40 CFR Part 50.
96 See Section 7.5.3 for further information on the number of years of meteorological data used in air
quality modeling. For most PM hot-spot analyses, five years of meteorological data will be used.
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• Air quality monitoring data: 12 quarters of background concentration
measurements (four quarters for each of three consecutive years). See Section 8
for more details on determining representative monitored background
concentrations that meet all applicable monitoring requirements (such as data
completeness).97
Calculating Design Values and Determining Conformity
Exhibit 9-3 (following page) illustrates how a design value is to be calculated and
conformity determined for the annual PM2.5 NAAQS. This exhibit assumes that the
project sponsor would first compare the receptor with the highest average annual
concentration in the build scenario to the NAAQS to determine conformity. If
conformity is not met at this receptor, design values would be calculated at all receptors
in the build scenario. For any receptors with design values above the NAAQS in the
build scenario, the project sponsor would then model the no-build scenario and calculate
design values to determine if conformity requirements are met.
An example of how to calculate design values for the annual PM2.5 NAAQS using this
procedure is included in Appendix K. The steps below can also be described
mathematically using the formulas found in Equation Set 1 in Appendix K.
The steps shown in Exhibit 9-3 are described below. The initial step is to compare the
build scenario to the NAAQS to see if the project conforms:
• Step 1. For each receptor, calculate the average annual concentrations with the air
quality modeling results for each quarter and year of meteorological data used. If
using AERMOD, the model does this step for you and provides the average
annual concentrations as output; proceed to Step 2. If using CAL3QHCR, for
each year of meteorological data, first determine the average concentration in
each quarter. Then, within each year of meteorological data, add the average
concentrations of all four quarters and divide by four to calculate the average
annual modeled concentration for each year of meteorological data. Sum the
modeled average annual concentrations from each year of meteorological data,
and divide by the number of years of meteorological data used.
• Step 2. Identify the receptor with the highest modeled average annual
concentration.
• Step 3. For each year of background data, first determine the average monitored
concentration in each quarter. Then, within each year of background data, add the
average concentrations of all four quarters and divide by four to calculate the
average annual background concentration for each year of monitoring data. Next,
add the average annual concentrations from each of the consecutive years of
monitoring data and divide by three. This value is the average annual background
concentration based on monitoring data.
97 This section does not address calculating design values with CTM-adjusted background concentrations.
The interagency consultation process should be used when situations require incorporation of any CTM
results into design value calculations.
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Exhibit 9-3. Determining Conformity to the Annual PM2.5 NAAQS
Build Scenario <= NAAQS
1. Calculate average
annual modeled
concentration at all
receptors (if using
AERMOD, skip to
Step 2)
2. Identify receptor
with the highest
average annual
concentration
3. Calculate average
annual background
concentration
4. Add values from
Steps 2 and 3
5. Round to nearest 0.1
ug/m3
I
Is design value
less than or equal
to NAAQS?
Build Scenario <=
>.
6. Repeat Step
1 for all
receptors
1
7. Add values from
Steps 6 and 3
1
8. Round to nearest 0. 1
ug/m3 and identify all
receptors mat exceed
NAAQS
/ Are build design \
Yes / values less than \
( or equal to no-
\ build design /
\ values?* /
1
No
Project does not
conform
Nc
3-build Scenario
9. Calculate annual
averages for the no-
build scenario
4
1 0. Add values from
Steps 9 and 3
i
1 1 . Round to nearest
0.1 ug/rn3
k
Consider
measures to
reduce emissions
and redo analysis
* May need to also determine appropriateness of receptors
• Step 4. Add the average annual background concentration (from Step 3) to the
average annual modeled concentration at the highest receptor (from Step 2) to
determine the total average annual background concentration at this receptor.
• Step 5. Round to the nearest 0.1 ug/m3. This result is the annual PM2 5 design
value at the highest receptor in the build scenario.
The project sponsor should then compare the design value from Step 5 to the annual
PM2.5 NAAQS (currently 15.0 ug/m3). If the value is less than or equal to the NAAQS,
the project conforms. If the design value is greater than the NAAQS, the project sponsor
should then continue to Step 6:
• Step 6. Repeat the calculations described in Step 1 to determine average annual
concentrations for all receptors in the build scenario.
• Step 7. Add the average annual modeled concentrations (from Step 6) to the
QO
average annual background concentrations (from Step 3). The result will be the
total average annual concentration at each receptor in the build scenario.
98As discussed in Section 8, the same air quality monitoring concentrations would not be expected to
change between the build and no-build scenarios. As a result, the same background concentrations would
be used for every receptor in the build and no-build scenario.
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• Step 8. Round to the nearest 0.1 ng/m3. At each receptor, this value is the annual
PM2.5 design value for the build scenario. Identify all receptors that exceed the
annual PM2.5NAAQS.
• Step 9. From the no-build air quality modeling results, calculate the average
annual concentrations at each receptor identified in Step 8.
• Step 10. For the no-build scenario, add the average annual modeled
concentrations for the no-build scenario (from Step 9) to the average annual
background concentrations (from Step 3). The result will be the total average
annual concentration for each receptor identified in Step 8 under the no-build
scenario.
• Step 11. Round to the nearest 0.1 ng/m3. This result is the annual PM2.5 design
value for each receptor identified in Step 8 under the no-build scenario.
For each receptor with a design value that exceeded the NAAQS in the build scenario,
compare the build design value (Step 8) to the no-build design value (Step 11). For the
project to conform, the build design value must be less than or equal to the no-build
design value at each receptor in the build scenario that exceeded the NAAQS (Step 8). If
this is not the case, the interagency consultation process would be used to determine if
any receptors are not appropriate for conformity purposes (see Section 9.4)." If a build
scenario design value is greater than the no-build design value at any appropriate
receptor, the sponsor should then consider additional mitigation and control measures,
and revise the PM hot-spot analysis accordingly. Refer to Section 10 for a discussion of
potential measures.
9.3.3 24-hour PM2.5 NAAQS
Design Value
The 24-hour PM2.5 design value is defined as the average of three consecutive years' 98th
percentile concentrations of 24-hour values for each of those years.100 The NAAQS is
met when that three-year average concentration is less than or equal to the currently
applicable 24-hour PM2.5 NAAQS for a given area's nonattainment designation (35
Hg/m3 for nonattainment areas for the 2006 PM2 5 NAAQS and 65 |J,g/m3 for
nonattainment areas for the 1997 PM2.5 NAAQS).101
The design value for comparison to any 24-hour PM2.5 NAAQS is rounded to the nearest
1 |J,g/m3 (decimals 0.5 and greater are rounded up to the nearest whole number; decimals
lower than 0.5 are rounded down to the nearest whole number). For example, 35.499
99 In certain cases, project sponsors can also decide to calculate the design values for all receptors in the
build and no-build scenarios and use the interagency consultation process to determine whether a "new"
violation has been relocated (see Section 9.2).
100 The design value for the 24-hour PM2 5 NAAQS is defined for air quality monitoring purposes in 40
CFRPart50.13.
101 There are only two areas where conformity currently applies for both the 1997 and 2006 24-hour PM25
NAAQS. While both 24-hour NAAQS must be considered in these areas, in practice if the more stringent
2006 24-hour PM2 5 NAAQS is met, then the 1997 24-hour PM2 5 NAAQS is met as well.
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rounds to 35 ng/m3, while 35.500 rounds to 36.102 These rounding conventions should be
followed when calculating design values for this NAAQS.
There are two analysis options, or tiers, that are available to project sponsors to estimate a
24-hour PM2.5 design value. Project sponsors can start with either the first or second tier
analysis, since either tier is a viable approach for meeting conformity requirements.
There may be cases where a project sponsor may decide to start with a first tier analysis,
which is a conservative but less data intensive approach.103 In other cases, project
sponsors may decide to go directly to a second tier analysis. For example, depending on
how the air quality model was run and its data post-processed, the actions required to
identify the highest modeled 24-hour concentration by quarter for a second tier analysis
may not involve much additional time or effort, in which case the second tier approach
may be preferred from that start. Under either tier, the contributions from the project, any
nearby sources, and background concentrations from other sources are combined for a
given analysis year, as described further below.
Examples of how to calculate design values for the 24-hour PM2.5 NAAQS using each
tier are included in Appendix K.
Necessary Data
This design value calculation assumes the project sponsor already has the following data
in hand:
• Air quality modeling results: The highest 24-hour average concentration from the
project and any nearby sources should be calculated based on the air quality
model output files, depending on what tier of analysis is used:
o In a first tier analysis, the highest 24-hour values from each year of
meteorological data should be averaged together.
o In a second tier analysis, the highest 24-hour values from each quarter and
year of meteorological data should be averaged together per quarter.
Post-processing the air quality model output files will vary depending on what air
quality model is used in the hot-spot analysis. Refer to Appendix J for a
discussion of air quality model output file formats.
• Air quality monitoring data: 12 quarters of background concentration
measurements (four quarters for each of three consecutive years). See Section 8
for more details on determining representative monitored background
102 A sufficient number of decimal places (3-4) should be retained during intermediate calculations for
design values, so that there is no possibility of intermediate rounding or truncation affecting the final result.
Rounding should only occur during final design value calculations, pursuant to Appendix N to 40 CFR Part
50.
103 While less data intensive and therefore possibly quicker to execute, the first tier approach is considered
more conservative as compared to the second tier analysis. The first tier approach assumes that the
estimated highest predicted concentration attributable to the project and nearby sources will occur in the
future on each of the days from which the three-year average 98th percentile background concentration is
derived (which may not occur).
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concentrations that meet all applicable monitoring requirements (such as data
104
completeness).
Calculating Design Values and Determining Conformity Using a First Tier Analysis
The first tier consists of directly adding the highest average modeled 24-hour
concentrations to the average 98th percentile 24-hour background concentrations.
Exhibit 9-4 illustrates how a design value would be calculated under a first tier analysis
for a given receptor. The steps shown in Exhibit 9-4 are described in detail below, and
are also described mathematically using the formulas found in Equation Set 2 in
Appendix K.
Exhibit 9-4. Determining Conformity to the 24-hour PM2.s NAAQS Using First Tier
Analysis
1. From build scenario
modeling results,
identify the receptor
with the highest
average 24-hour
concentration
2. Determine the 3-
year average of the
98th percentile 24-hour
background
concentrations
3. Add results of Steps
1 and 2 to obtain
design value
Project conforms
Yes / Is design value
4 ( less than or equal
\ to NAAQS?
Conduct no-build
analysis and/or
second tier
analysis
104 This section does not address calculating design values with CTM-adjusted background concentrations.
The interagency consultation process should be used when situations require incorporation of any CTM
results into design value calculations.
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The initial step in a first tier analysis is to compare the build scenario to the NAAQS to
see if the project conforms:
• Step 1. From the air quality modeling results from the build scenario, identify the
receptor with the highest average 24-hour concentration. This is done by first
separating the air quality model output into each year of meteorological data.
Second, for each receptor and year of meteorological data, identify the 24-hour
period (midnight-to-midnight) with the highest average concentration throughout
the entire year. Finally, at each receptor, calculate the average of the highest 24-
hour concentrations from each year of meteorological data, and average these
across all the years. The receptor with the highest value is used to calculate the
24-hour PM2.5 design value.
• Step 2. Calculate the average 98th percentile 24-hour background concentration
using the 98th percentile 24-hour concentrations of the three most recent years of
air quality monitoring data. To calculate the 98th percentile background
concentrations for each year of monitoring data, first count the number of 24-hour
background measurements in each year. Next, order the highest eight monitoring
values in each year from highest to lowest and rank each value from 1 (highest) to
8 (eighth highest). Consult Exhibit 9-5 to determine which of these eight values
is the 98th percentile value. Using the results from the three years of monitoring
data, calculate the three-year average of the 98th percentile concentrations.105
Exhibit 9-5. Ranking of 98th Percentile Background Concentration Values106
Number of
Background
Concentration
Values
1-50
51-100
101-150
151-200
201-250
251-300
301-350
351-366
Rank of Value
Corresponding to
98th Percentile
Concentration
1
2
3
4
5
6
7
8
Assuming a regular monitoring schedule and a resulting data set that meets the completeness
requirements of 40 CFR Part 50 Appendix N, the result of Step 2 will simply be the design value for the
monitoring site used to estimate the background concentrations. EPA calculates the design value for every
PM2.5 monitor each year, based on the most recent three-year period of data reported to EPA's Air Quality
System. Project sponsors may use the EPA-calculated design values directly instead of executing Step 2,
or may compare their result from Step 2 to the EPA-calculated design value. These design values appear in
the worksheet "Site Listing" of the latest PM25 design value spreadsheet posted at:
www.epa.gov/airtrends/values.html.
106 This exhibit is based on a table in Appendix N to 40 CFR Part 50, and ranks the 98th percentile of
background concentrations pursuant to the total number of air quality monitoring measurements.
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• Step 3. Add the highest average 24-hour modeled concentration (Step 1) to the
average 98th percentile 24-hour background concentration (Step 2) and round to
the nearest 1 ng/m3. The result is the 24-hour PM2.5 design value at the highest
receptor in the build scenario.
If the design value calculated in Step 3 is less than or equal to the relevant 24-hour PM2.5
NAAQS, then the project conforms. If it is greater than the 24-hour PM2.5 NAAQS,
conformity is not met, and the project sponsor has two options:
• Repeat the first tier analysis for the no-build scenario at all receptors that
exceeded the NAAQS in the build scenario. If the calculated design value for the
build scenario is less than or equal to the design value for the no-build scenario at
all of these receptors, then the project conforms;107 or
• Conduct a second tier analysis as described below.
Calculating Design Values and Determining Conformity Using a Second Tier Analysis
The second tier involves a greater degree of analysis, in that the highest modeled
concentrations and the 98th percentile background concentrations are not added together
for each receptor directly, as in a first tier analysis. Unlike a first tier analysis, which
uses the average of the highest modeled 24-hour concentration from each year of
meteorological data, a second tier analysis uses the average of the highest modeled 24-
hour concentration within each quarter of each year of meteorological data. In other
words, impacts from the project, nearby sources, and other background concentrations are
calculated on a quarterly basis before determining the 98* percentile concentration
resulting from these inputs.
Exhibit 9-6 (following page) and the following steps provide details for calculating a
design value for the 24-hour PM2.5 NAAQS under a second tier analysis. These steps can
also be described mathematically using the formulas found in Equation Set 3 in Appendix
K.
107 In certain cases, project sponsors can also decide to calculate the design values for all receptors in the
build and no-build scenarios and use the interagency consultation process to determine whether a "new"
violation has been relocated (see Section 9.2).
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Exhibit 9-6. Determining Conformity to the 24-hour PM2.s NAAQS Using Second
Tier Analysis
Build Scenario <= NAAQS
1. Count the number of
measurements for each
year of background
data
2. Determine the 8
highest 24-hour
background values for
each quarter
3. Identify the highest
concentration at each
receptor
4. At each receptor,
add values from Steps
2 and 3 for each
quarter
5. Rank values in Step
4 from highest to
lowest
6. Determine the value
in Step 5
corresponding to the
98th percentile
I
7. Repeat Steps 5 and
6 for each year of
background data
Average the three
98th percentile
concentrations
9. Round to nearest
1 ug/m3
Are all design
values less than
or equal to
NAAQS?
\No
Yes
Project
conforms
Build Scenario <= No-build
Scenario
10. Repeat Steps 3
through 9 using no-
build modeling results
Yes
Are build design
values less than
or equal to no-
build design
values?
No
Project does not
conform
Consider
measures to
reduce emissions
and redo analysis
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A project sponsor would initially complete these steps for the build scenario; then, if
necessary, repeat the steps for the no-build scenario. Steps 1 and 2 of a second tier
analysis are completed only once for all receptors, since the same background
concentrations would be used for every receptor in either the build or no-build scenario.
• Step 1. Count the number of measurements for each year of monitoring data used
for background concentrations for other sources.
• Step 2. For each year of monitoring data used, determine the eight highest 24-
hour background concentrations for each quarter modeled. For most hot-spot
analyses for the 24-hour PM2.5 NAAQS, modeling would be completed for all
four quarters of each analysis year. This would therefore result in 32 values
(eight concentrations for four quarters) for each year of monitoring data.108
The remaining steps are completed for calculating the 24-hour PM2.5 design value at each
receptor:
• Step 3. At each receptor, identify the highest modeled 24-hour concentration in
each quarter, averaged across each year of meteorological data used for air quality
modeling.
• Step 4. At each receptor, add the highest modeled concentration in each quarter
(from Step 3) to each of the eight highest 24-hour background concentrations for
the same quarter for each year of monitoring data (from Step 2). At each
receptor, this step will result in eight 24-hour concentrations in each of four
quarters for a total of 32 values for each year of monitoring data.
• Step 5. For each receptor and year of monitoring data, order the 32 values from
Step 4 from highest to lowest and rank each value from 1 (highest concentration)
to 32 (lowest concentration).
• Step 6. Based on the number of background concentration values you have (from
Step 1), use Exhibit 9-7 (following page) to determine which value in the column
(from Step 5) represents the 98th percentile concentration for each receptor. For
example, if you have 180 background concentration values in a year, Exhibit 9-7
shows that the 4th highest value would represent the 98th percentile. Take the
value at each receptor that has this rank.
• Step 7. Repeat Step 6 for each of the three years of background monitoring data.
The result will be three 24-hour 98th percentile concentrations at each receptor,
one for each year of monitoring data.
• Step 8. At each receptor, calculate the average of the three 24-hour 98th percentile
concentrations determined in Step 7.
• Step 9. Round the average concentrations from Step 8 to the nearest 1 ng/m3. At
each receptor, this value is the 24-hour PM2.5 design value for the build scenario.
1 °8 Section 3.3.4 describes how the number of quarters modeled should be determined. In most PM hot-
spot analyses for the 24-hour PM2 5 NAAQS, all four quarters of the analysis year will be modeled. There
are limited cases where modeling only one quarter would be appropriate.
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Exhibit 9-7. Ranking of 98th Percentile Background Concentration Values109
Number of
Background
Concentration
Values
1-50
51-100
101-150
151-200
201-250
251-300
301-350
351-366
Rank of Value
Corresponding to
98th Percentile
Concentration
1
2
3
4
5
6
7
8
Compare the design values to the relevant 24-hour PM2.5 NAAQS. If the design values at
all receptors are less than or equal to the NAAQS, then the project conforms. If this is
not the case, proceed to Step 10:
• Step 10. Using modeling results for the no-build scenario, repeat Steps 3 through
9 for all receptors with a design value that exceeded the PM2.5 NAAQS in the
build scenario. The result will be a 24-hour PM2.5 design value at such receptors
for the no-build scenario.
Compare the build design values (from Step 9) to the no-build design values (from Step
10), identifying which value is higher at each receptor. For the project to conform, the
build design values must be less than or equal to the no-build design value for all of the
receptors that exceeded the NAAQS in the build scenario.110 If the build scenario design
value is greater than the no-build design value at any appropriate receptor, the project
sponsor should then consider additional mitigation and control measures, and revise the
PM hot-spot analysis accordingly. Refer to Section 10 for a discussion of potential
measures.
9.3.4 24-hour PM10 NAAQS
Design Value
Compliance with the 24-hour PMio NAAQS is based on the expected number of 24-hour
in
exceedances of 150 ng/rn , averaged over three consecutive years. The NAAQS is met
when the expected number of exceedances is less than or equal to 1.0.
112
109 This exhibit is based on a table in Appendix N to 40 CFR Part 50, and ranks the 98th percentile of
background concentrations pursuant to the number of air quality monitoring measurements.
110 In certain cases, project sponsors can also decide to calculate the design values for all receptors in the
build and no-build scenarios and use the interagency consultation process to determine whether a "new"
violation has been relocated (see Section 9.2).
111 The 24-hour PM10 NAAQS and supporting technical documentation can be found in 40 CFR Part 50.6.
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The 24-hour PMio NAAQS design value is rounded to the nearest 10 |J,g/m3. For
example, 155.511 rounds to 160, and 154.999 rounds to 150.113 These rounding
conventions should be followed when calculating design values for this NAAQS.
The contributions from the project, any nearby sources, and background concentrations
from other sources are combined for a given analysis year, as described further below.
Examples of how to calculate design values for the 24-hour PMio NAAQS are included in
Appendix K.
Necessary Data
This design value calculation assumes the project sponsor already has the following data
in hand:
• Air quality modeling results: In most PM hot-spot analyses, five years of
meteorological data will be used to complete air quality modeling for the project
and any nearby sources.114 In this case, the sixth-highest 24-hour modeled
concentration should be calculated for each receptor.115 Note that AERMOD can
be configured to give you these values directly. CAL3QHCR output must be
post-processed to obtain the sixth-highest value from five years of meteorological
data. See more details below and refer to Appendix J for a discussion of air
quality model output file formats.
• Air quality monitoring data: 12 quarters of background concentration
measurements (four quarters for each of three consecutive years). See Section 8
for more details on determining representative monitored background
concentrations that meet all applicable monitoring requirements (such as data
completeness).116
112 The term "expected" means that the actual number of observed exceedances is adjusted upwards when
observations are missing for some days, to reflect the air quality statistically expected for those days. The
design value for the 24-hour PM10 NAAQS is the next highest observed (monitored or modeled)
concentration after the concentrations that could be above 150 ug/m3 without causing the expected number
of exceedances to be greater than 1.0.
113 A sufficient number of decimal places (3-4) in modeling results should be retained during intermediate
calculations for design values, so that there is no possibility of intermediate rounding or truncation
affecting the final result. Rounding to the nearest 10 ug/m3 should only occur during final design value
calculations, pursuant to Appendix K to 40 CFR Part 50. Monitoring values typically are reported with
only one decimal place.
114 Section 7.5.3 of this guidance provides further information on the number of years of meteorological
data used in air quality modeling.
115 See description in Section 7.2.1.1 of Appendix W. Users with one year of on-site meteorological data
should select the 2nd highest 24-hour PM10 concentration. If using less than one year of meteorological data
(such as one quarter), users should select the highest 24-hour concentration.
116 This section does not address calculating design values with CTM-adjusted background concentrations.
The interagency consultation process should be used when situations require incorporation of any CTM
results into design value calculations.
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Calculating Design Values and Determining Conformity
The 24-hour PMio design value is calculated at each receptor by directly adding the sixth-
highest modeled 24-hour concentrations (if using five years of meteorological data) to the
highest 24-hour background concentration (from three years of monitoring data).
Exhibit 9-8 illustrates how a design value would be calculated. The steps shown in
Exhibit 9-8 are described in detail below and are also described mathematically using the
formulas found in Equation Set 4 in Appendix K.
Exhibit 9-8. Determining Conformity to the 24-hour PM10NAAQS
Build Scenario <= NAAQS
1. Identify the sixth
highest 24-hour
concentration at each
receptor
2. Identify the receptor
with the highest sixth-
highest concentration
3. Identify the highest
24-hour background
concentration
4. Add values from
Steps 2 and 3
5. Round to nearest 10
Is design value
less than or equal
to NAAQS?
Build Scenario <= No-build Scenario
6. Add values from
Step 1 and Step 3 at
each receptor
7. Round to nearest 10
jig/m3 and identify all
receptors that exceed
NAAQS
8. From no-build
modeling results,
identify sixth-highest
concentration for each
receptor identified in
Step?
9. Add values from
Steps 8 and 3
10. Round to nearest
10 ug/mj
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The initial step is to compare the build scenario to the NAAQS to see if the project
conforms:
• Step 1. From the air quality modeling results for the build scenario, identify the
sixth-highest 24-hour concentration for each receptor (across five years of
meteorological data, in most cases). When using AERMOD, the model can be
configured to produce these values.117 When using CAL3QHCR, output must be
post-processed to obtain the sixth-highest values from five years of
meteorological data.
• Step 2. Identify the receptor with the highest sixth-highest 24-hour concentration.
That is, compare the sixth-highest modeled concentrations (i.e., the concentrations
at Rank 6) across receptors and identify the receptor with the highest value at
Rank 6.
• Step 3. Identify the highest 24-hour background concentration from the three
most recent years of air quality monitoring data.
• Step 4. For the receptor identified in Step 2, add the sixth-highest 24-hour
modeled concentration to the highest 24-hour background concentration (from
Step 3).
10
3
• Step 5. Round to the nearest 10 |J,g/m . The result is the highest 24-hour PM
design value in the build scenario.
The project sponsor should then compare the design value from Step 5 to the 24-hour
PMio NAAQS (150 |j,g/m3). If the design value calculated in Step 5 is less than or equal
to the NAAQS, the project conforms. If the design value is greater than the NAAQS, the
project sponsor should then continue to Step 6:
• Step 6. For each receptor in the build scenario, add the sixth-highest 24-hour
modeled concentration (from Step 1) to the highest 24-hour background
concentration from the three most recent years of air quality monitoring data
(from Step 3).
• Step 7. Round to the nearest 10 |J,g/m3. At each receptor, this value is the 24-
hour PMio design value for the build scenario. Identify all receptors that exceed
the 24-hour PMio NAAQS.
• Step 8. From the no-build air quality modeling results, identify the sixth-highest
24-hour concentration for each receptor identified in Step 7.
• Step 9. Add the sixth-highest 24-hour modeled concentration in the no-build
scenario (from Step 8) to the highest 24-hour background concentration from the
three most recent years of air quality monitoring data (from Step 3).
• Step 10. Round to the nearest 10 ng/m3. The result is the 24-hour PMio design
value under the no-build scenario for each receptor identified in Step 7.
For each receptor with a design value that exceeded the NAAQS in the build scenario,
compare the build design value (from Step 7) to the no-build design value (from Step 10).
For the project to conform, the build design value must be less than or equal to the no-
build design value at each receptor in the build scenario that exceeded the NAAQS (Step
117 For example, users could employ the RECTABLE keyword in the AERMOD output pathway. See
Appendix J to this guidance for further information.
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7).118 If the build scenario design value is greater than the no-build design value at any
appropriate receptor, the project sponsor should then consider additional mitigation and
control measures, and revise the PM hot-spot analysis accordingly. Refer to Section 10
for a discussion of potential measures.
More advanced methods of calculating a PMio design value, such as combining modeled
and monitored concentrations on a quarterly basis, may be considered on a case-by-case
basis. The decision to pursue an alternative method should be decided through
interagency consultation.
9.4 DETERMINING APPROPRIATE RECEPTORS FOR COMPARISON TO THE
ANNUAL PM2.5NAAQS
9.4.1 General
When hot-spot analyses are done for the annual PM2.5 NAAQS, there is an additional step
that may be necessary to determine whether a receptor is appropriate to compare to this
NAAQS. In the March 2006 final rule, EPA stated:
"Quantitative hot-spot analyses for conformity purposes would consider how
projects of air quality concern are predicted to impact air quality at existing and
potential PM2.5 monitor locations which are appropriate to allow the comparison
of predicted PM2.5 concentrations to the current PM2.5 standards, based on PM2.5
monitor siting requirements (40 CFR Part 58)." (71 FR 12471)
EPA included this language in the preamble to the March 2006 final rule so that PM2.5
hot-spot analyses would be consistent with how the PM2.5 NAAQS are developed,
monitored, and implemented. Receptors cannot be used for PM2.5 hot-spot analyses if
they are at locations that would not be appropriate for air quality monitoring purposes for
the NAAQS. If conformity requirements are met at all receptors, it is unnecessary to
determine whether receptors are appropriate for comparison to the annual PM2.5
NAAQS; in such a case, project sponsors can conclude that conformity requirements are
met at all appropriate receptors.
An "appropriate receptor location" under Section 93.123(c)(l) of the conformity rule is a
location that is suitable for comparison to the relevant NAAQS, consistent with how the
PM NAAQS are established and monitored for air quality planning purposes.119
118 In certain cases, project sponsors can also decide to calculate the design values for all receptors in the
build and no-build scenarios and use the interagency consultation process to determine whether a "new"
violation has been relocated (see Section 9.2).
119See Clean Air Act section 176(c)(l)(B). EPA interprets "NAAQS" in this provision to mean the specific
NAAQS that has been established through rulemaking and monitored for designations purposes.
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9.4.2 Factors for appropriate receptors for comparison to the annual PM2.s NAAQS
As discussed in Section 7.6, receptors can be placed prior to air quality modeling for all
PM NAAQS. Furthermore, the appropriateness of receptor locations for the 24-hour
PM2.5 NAAQS (and the 24-hour PMi0 NAAQS) can be determined prior to air quality
modeling. However, for the annual PM2.5 NAAQS, appropriate receptors should be
determined after air quality modeling is completed. The paragraphs below provide
additional guidance when calculating design values and determining conformity for the
annual PM2.5 NAAQS, through the steps described in Section 9.3.2.
There are generally two factors in the PM2 5 monitoring regulations that need to be
considered in determining the appropriateness of receptors for use in PM2 5 hot-spot
analyses:
• Population-orientation: A receptor must be "population-oriented" in order to be
appropriate for comparison to either the 24-hour or annual PM2 5 NAAQS. 12°
This factor can be addressed when placing receptors prior to air quality modeling
(see Section 7.6).
• Community-wide air quality: A receptor for the annual PM2 5 NAAQS must also
represent "community-wide air quality;" this factor does not have to be satisfied
for the 24-hour PM2.5 NAAQS.
Section 9.3.2 includes an approach for conducting build/no-build analyses for the annual
PM2.5 NAAQS, in which the appropriateness of receptors is determined only in cases
where a design value in the build scenario is higher than the NAAQS and the design
value in the no-build scenario. As noted above, if conformity requirements are met at all
receptors, it is unnecessary to determine whether receptors are not appropriate for
comparison to the annual PM2.5 NAAQS; in such a case, project sponsors can conclude
that conformity requirements are met at all appropriate receptors.
The interagency consultation process must be used to discuss the data and methods in PM
hot-spot analyses (40 CFR 93.105(c)(l)(i)), including appropriate receptor locations for
the annual PM2.5 NAAQS. State and local air quality agencies and EPA have significant
expertise in air quality planning and monitoring purposes that may be useful resources in
determining appropriate receptor locations for the annual PM2 5 NAAQS. For example,
under the PM2.5 monitoring regulations, the EPA Regional Offices determine whether
micro or middle scale PM2.5 air quality monitors are eligible for comparison to the annual
PM2.s NAAQS, as discussed further below.
9.4.3 Overview ofPM2.5 monitoring regulations
The annual PM2.5 NAAQS was established to capture air quality concentrations over
larger areas that represent "community-wide air quality."121 Therefore, an appropriate
120 See 40 CFR 58.1.
121 The 1997 annual PM25 NAAQS was primarily based on health studies using neighborhood and larger
scale air quality monitoring data (62 FR 38651-38760).
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receptor for hot-spot analyses for this NAAQS must also represent community-wide air
quality. There are several parts of the PM2.5 monitoring regulations that describe how an
existing or potential monitor location can represent community-wide air quality, and EPA
will rely on this same information for determining appropriate receptor locations for
conformity purposes. Like ambient PM2.5 monitoring sites, not every receptor may be
appropriate for comparing a predicted design value with the annual PM2.5 NAAQS.
Air quality monitors that represent community-wide air quality and are compared to the
annual PM2.5 NAAQS typically are of neighborhood and larger scales, as defined by the
PM2 5 monitoring regulations. Section 4.7. l(b) of Appendix D to 40 CFR Part 58 states:
"The required monitoring stations or sites must be sited to represent community-
wide air quality... .These monitoring stations will typically be at neighborhood or
urban scale."
Therefore, conformity requirements must be met at any receptor that is at a location that
would also be appropriate for an existing or potential neighborhood or larger scale air
quality monitor for the annual PM2 5 NAAQS. In general, population-oriented receptors
that are farther away from the project would be similar to potential neighborhood or
larger scale monitoring sites, and would be representative of community-wide air quality
in all PM hot-spot analyses.
The PM2.s monitoring regulations also address when smaller scale locations are
considered to represent community-wide air quality and can be compared to the annual
PM2.5 NAAQS. Section 58.30(a) of the regulations states:
"(1) PM2.s data that are representative, not of areawide but rather, of relatively
unique population-oriented microscale, or localized hot-spot, or unique
population-oriented middle-scale impact sites are only eligible for comparison to
the 24-hour PM2.5 NAAQS; and
(2) There are cases where certain population-oriented micro scale or middle scale
PM2.5 monitoring sites are determined by the Regional Administrator to
collectively identify a larger region of localized high ambient PM2.5
concentrations. In those cases, data from these population-oriented sites would be
eligible for comparison to the annual PM2 5 NAAQS."
Other parts of the PM2.5 monitoring regulations also address middle and micro scale
locations. Section 4.7.1(b) of Appendix D to 40 CFR Part 58 states:
"... in certain instances where population-oriented micro- or middle-scale PM2.5
monitoring are determined by the Regional Administrator to represent many such
locations throughout a metropolitan area, these smaller scales can be considered
to represent community-wide air quality."
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Section 4.7.1(c)(l) and (2) note that sites very close to individual sources, such as traffic
corridors in urban areas, may be appropriate sites for locating PM2.5 monitors that
represent community-wide air quality:
"In some circumstances, the microscale is appropriate for particulate sites;
community-oriented... sites measured at the microscale level should, however, be
limited to urban sites that are representative of long-term human exposure and of
many such microenvironments in the area."
"In many situations, monitoring sites that are representative of microscale or
middle-scale impacts are not unique and are representative of many similar
situations. This can occur along traffic corridors or other locations in a residential
district. In this case, one location is representative of a number of small scale sites
and is appropriate for evaluation of long-term or chronic effects."
In general, receptors that are closer to a project would be similar to potential micro and
middle scale air quality monitoring sites, and would be appropriate for comparison to the
annual PM2.5 NAAQS if they represent community-wide air quality.
9.4.4 Conformity guidance for all projects in annual PM2.s NAAQS areas
Receptors at Neighborhood or Larger Scale Locations
As described above, all population-oriented receptors at locations where a neighborhood
or larger scale monitor could be located are appropriate for comparison to the annual
PM2.5 NAAQS in a PM2.5 hot-spot analysis. In general, receptors farther away from any
transportation project (e.g., 100 meters or more away from a larger highway project)
would represent neighborhood scale locations under the PM2.5 monitoring regulations.
The PM2.5 monitoring regulations do not provide further specific information for
determining neighborhood or larger scale locations for PM hot-spot analyses. However,
Figure E-l in Appendix E of 40 CFR Part 58 specifies distances from a roadside where
monitors of different scales may be located relative to a highway or intersection. See
Section 9.4.5 for further information on when a receptor represents neighborhood and
larger scale locations for these types of projects.
Receptors at Micro or Middle Scale Locations
As described above, population-oriented receptors that are at locations where a micro or
middle scale monitor could be located are appropriate for comparison to the annual PM2.5
NAAQS, if they represent community-wide air quality. In general, a receptor or
collection of receptors closer to any project (e.g., 100 meters or less from a larger
highway project) would represent community-wide air quality and be appropriate for the
annual PM2.5 NAAQS if such receptor(s) collectively identify a larger region of localized
high PM2 5 concentrations and are not within a unique location(s).
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The PM2.5 monitoring regulations do not provide further information for determining
when micro or middle scale locations are appropriate for PM hot-spot analyses.
However, the air quality modeling results for the PM hot-spot analysis will provide
critical information for determining whether there is a large region of high PM2.5
concentrations, especially if high concentrations are predicted in a large number of
adjacent receptors. In addition, a unique location may involve a portion of a project area
that involves concentrations, land uses, development, or a transportation project not like
other locations in the nonattainment or maintenance area. In addition, Figure E-l in
Appendix E of 40 CFR Part 58 specifies distances from a roadside where monitors of
different scales may be located relative to a highway or intersection. See Section 9.4.5
for further information on when a receptor represents a micro or middle scale location for
these types of projects.
The following are examples of micro and middle scale locations where receptors may
represent community-wide air quality and be compared to the annual PM2.5 NAAQS:
• Locations with characteristics (e.g., land use and development patterns, emission
sources, and/or populations) that are similar to locations where existing air quality
monitors are sited that are eligible for use in annual PM2.5 designations;
• Locations where similar high annual PM2.5 concentrations are modeled in the PM
hot-spot analysis at adjacent receptors that cover a sufficiently large populated
area; and
• Locations along urban highway corridors in residential areas that are not
considered unique and involve areas with large neighborhoods, schools, etc.
The following are examples of micro and middle scale locations where receptors may not
be appropriate to compare to the annual PM2.s NAAQS:
• Locations with characteristics (e.g., land use and development patterns, emission
sources, and/or populations) that are similar to locations where existing air quality
monitors are sited that are not eligible for use in annual PM2.s designations;
• Locations where uniquely high annual PM2.s concentrations at one or a few
adjacent receptors are modeled in the PM hot-spot analysis in small isolated
portions of the greater project area; and
• Locations closer to the project than neighborhood or larger scale that would be
considered unique under the PM2.5 monitoring regulations, such as locations
within 100 meters of a new or expanded transit terminal where no other such
terminals exist in the nonattainment or maintenance area.
The interagency consultation process would be used to determine when a receptor at a
micro or middle scale location is not appropriate for comparison to the annual PM2.s
NAAQS. The above examples are illustrative in nature, and may not reflect of a specific
PM hot-spot analysis. A case-by-case review of each situation is necessary to ensure that
PM hot-spot analyses for the annual PM2.5 NAAQS meet applicable requirements.
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Additional Considerations and Techniques
Decisions about whether receptors are appropriate for the annual PM2.5 NAAQS for
conformity purposes cannot be determined based on existing conditions in the project
area. Receptors will be at the same locations in the build and no-build scenarios, but the
decision on whether a receptor represents community-wide air quality should be based on
information for the build scenario. Any differences between the build and no-build
scenarios should be documented. For example, anticipated changes in the number of
populated areas within the project area such as zoned or platted housing or commercial
developments should be described.
To assist project sponsors, it is recommended that the locations of populations,
businesses, other institutions, any air quality monitors, and predicted receptor
concentrations and other relevant concentration data be displayed on a map along with
the project area, whenever possible. Such a map may help visualize locations where
receptors are population-oriented, and determine whether particular receptor
concentrations represent small, unique areas (and therefore are not appropriate for the
annual PM2.5 NAAQS), or represent "a larger region of localized high PM25
concentrations" (and therefore are appropriate for the annual PM2.5 NAAQS).
EPA notes that every air quality model produces estimates of concentrations at each
receptor. There are several common visualization techniques in the air quality modeling
and geography professions that are likely to be useful ways of displaying receptor
concentrations, such as contour plots, surface plots, and maps generated using geographic
information systems (GIS). Many computer programs can generate these types of
graphics.
9.4.5 Additional conformity guidance for the annual PM2.5 NAAQS and highway and
intersection projects
As noted above, Appendix E of the PM2.5 monitoring regulation provides further
information to determine whether a receptor represents a micro, middle, neighborhood, or
larger scale location for highway and intersection projects. Exhibit 9-9 (following page)
is a helpful guide in determining what receptor locations could be considered
neighborhood scale, and thus always appropriate for comparison to the annual PM2.5
NAAQS in PM hot-spot analyses.122 This exhibit could also help implementers identify
what receptor locations could be considered micro and middle scale.
Exhibit 9-9 categorizes population-oriented receptors into Portion A, Portion B, and
Portion C, expressed as annual average daily traffic (AADT) and the distance of receptors
from a proposed highway or intersection location.
22 Exhibit 9-9 is adapted from Figure E-1 in Appendix E of 40 CFR Part 51.
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Exhibit 9-9. Determining Scale of Receptor Locations for the Annual PM2.s NAAQS
200000
180000
160000
140000
^120000
a
s
Q
M100000
,
^ 80000
60000
40000
20000
PORTION B: Population-oriented receptors with this
spacing from the nearest traffic lane are "middle scale"
or "micro scale" in representation and initially
presumed to be comparable with the annual NAAQS,
but may not be.
Receptors in this region should be analyzed to
determine receptor eligibility.
PORTION A: Population-oriented
receptors with this spacing from the
nearest traffic lane are "neighborhood
scale" or "urban scale" in representation
and comparable with the annual
NAAQS.
10 20 30 40 50 60 70 80
Distance of receptor from project's nearest traffic lane (meters)
90
100
110
Note: Exhibit 9-9 does not apply to receptors near projects that consist of terminals,
garages, or other non-road emission sources, such as transit terminals, bus garages, and
intermodalfreight terminals. In addition, Exhibit 9-9 does not apply when evaluating
receptors that capture the impacts of nearby sources that do not involve highways and
intersections, since such projects do not involve AADT data. The interagency
consultation process should be used to discuss appropriate receptors for projects not
covered by the above exhibit.
Portion A
Receptors at these locations are considered appropriate for comparison to the annual
PM2.5 NAAQS because they represent locations that would be considered neighborhood
scale locations under the PM2.5 monitoring regulations. In addition, any receptor farther
than 100 meters from the nearest lane of traffic is comparable to the annual PM2.5
NAAQS, regardless of AADT. Neighborhood or urban scale monitoring sites are always
compared to the annual PM2.5 NAAQS.
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Receptors in Portion A are at least 10 meters away from the project's nearest lane of
traffic for every 10,000 AADT for a project. For example, if a highway has 80,000
AADT, any receptor presumed to be comparable to the annual PM2.5 NAAQS at
neighborhood and larger scales must be located at least 80 meters from the project's
nearest lane of traffic. Again, any receptor farther than 100 meters from the nearest lane
of traffic is comparable to the annual NAAQS, regardless of AADT.
Portion B
Receptors at these locations need further evaluation to determine if they are not
appropriate for comparison to the annual PM2.5 NAAQS because they represent micro
and middle scale locations under the PM2.5 monitoring regulations. Micro and middle
scale monitoring sites are compared to the annual PM2.5 NAAQS if they represent
community-wide air quality, as described above.
Receptors in Portion B of Exhibit 9-9 would initially be modeled with respect to the
annual PM2.5 NAAQS; subsequent analysis could then be used to determine whether
certain receptors or groups of receptors are appropriate for comparison to the annual
PM2.5 NAAQS (i.e., to determine whether such locations do or do not represent
community-wide air quality).
Portion C
Receptors within 3 meters of a highway or transit project are not considered appropriate
for comparison to any NAAQS, including the annual PM2.5 NAAQS, except possibly
with projects involving urban canyons where receptors may be appropriate for
comparison to both PM2.s NAAQS within 2-10 meters of a project.123
9.5 DOCUMENTING CONFORMITY DETERMINATION RESULTS
Once a PM hot-spot analysis is completed, details need to be documented in the
conformity determination. See Section 3.10 for more information on properly
documenting a PM hot-spot analysis, including modeling data, assumptions, and results.
Any questions about what information needs to be documented should be handled
through interagency consultation.
123 See 40 CFR Part 58, Appendix E, Section 6.3(b).
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Section 10: Mitigation and Control Measures
10.1 INTRODUCTION
This section describes mitigation and control measures that could be considered by
project sponsors to reduce emissions and any predicted new or worsened PM NAAQS
violations. These measures can be applied to the transportation project itself, or other PM
sources in the project area. Written commitments for mitigation or control measures
must be obtained from the project sponsor and/or operator, or other emission source's
owner and/or operator, as appropriate, prior to making a project-level conformity
determination (40 CFR 93.123(c)(4) and 93.125(a)). If measures are selected, additional
emissions and air quality modeling will need to be completed and new design values
calculated to ensure that conformity requirements are met.
The following information provides more details on potential measures for PM hot-spot
analyses; others may be possible. The interagency consultation process should be used to
discuss any measures that are relied upon in the PM hot-spot analysis. The models,
methods, and assumptions used to quantify reductions should be documented in the final
project-level conformity determination.
General categories of mitigation and control measures that could be considered include:
• Retrofitting, replacing vehicles/engines, and using cleaner fuels;
• Reducing idling;
• Redesigning the transportation project itself;
• Controlling fugitive dust; and
• Controlling other sources of emissions.
More information is provided for each of these categories below.
10.2 MITIGATION AND CONTROL MEASURES BY CATEGORY
10.2.1 Retrofitting, replacing vehicles/engines, and using cleaner fuels
• The installation of retrofit devices on older, higher emitting vehicles is one way to
reduce emissions. Retrofit devices such as Diesel Particulate Filters (DPFs) or
Diesel Oxidation Catalysts (DOCs) can be installed on diesel truck or bus fleets,
and off-road construction equipment when applicable to lower emissions cost-
effectively.124
• Replacing older engines with newer, cleaner engines, including engines powered
by compressed natural gas (CNG), liquefied natural gas (LNG), biodiesel, or
124 It would be appropriate to replace or retrofit construction equipment in those cases where construction
emissions are included in the analysis (i.e., when construction emissions are not considered temporary).
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electricity is another way to reduce emissions from existing diesel truck or bus
fleets. Many engines can also benefit from being rebuilt, repaired, upgraded to a
more recent standard, and properly maintained. The emission reduction
calculations should take into account whether retired vehicles or engines are
permanently scrapped.
• The accelerated retirement or replacement of older heavy-duty diesel vehicles
with cleaner vehicles is another way to reduce emissions. A replacement program
could apply to buses, trucks, or construction equipment.125 In some areas, local
regulations to ban older trucks at specific port facilities have encouraged early
replacement of vehicles. Such an option would need to be discussed through the
interagency consultation process and with the local government with
implementing authority.
o For additional information about quantifying the benefits of retrofitting
and replacing diesel vehicles and engines for conformity determinations,
see EPA's website for the most recent guidance on this topic:
www.epa.gov/otaq/stateresources/transconf/policy.htm.
o Also see EPA's National Clean Diesel Campaign website, which includes
information about retrofitting vehicles, including lists of EPA-verified
retrofit technologies and certified technologies; clean fuels; grants; case
studies; toolkits; and partnership programs: www.epa.gov/otaq/diesel/.
10.2.2 Reduced idling programs
• Anti-idling programs for diesel trucks or buses may be relevant for projects where
significant numbers of diesel vehicles are congregating for extended periods of
time (e.g., restrictions on long duration truck idling, truck stop electrification, or
time limits on bus idling at a terminal).
o For additional information about quantifying the benefits of anti-idling
programs for conformity determinations, see EPA's website for the most
recent guidance on this topic:
www.epa.gov/otaq/stateresources/transconf/policy.htm.
o A list of EPA-verified anti-idle technologies for trucks can be found at:
www. epa. gov/otaq/smartway/transport/what-smartway/verified-
technologies.htm.
125 The Federal Transit Administration (FTA) has minimum service life requirements for transit vehicles
purchased with FTA funds. If a transit agency disposes of a vehicle earlier than its full useful service life,
it will incur a payback penalty. Please refer to Chapter IV of FTA Circular 5010. ID for the establishment
and calculation of a vehicle's useful service life. In addition, Appendix D of the circular address the useful
life calculation and disposition of vehicles acquired with FTA funds:
www.fta.dot.gov/documents/C 5010 ID Finalpub.pdf.
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10.2.3 Transportation project design revisions
• For transit and other terminals, project sponsors could consider redesigning the
project to reduce the number of diesel vehicles congregating at any one location.
Terminal operators can also take steps to improve gate operations to reduce
vehicle idling inside and outside the facility. Fewer diesel vehicles congregating
could reduce localized PM2.5 or PMio emissions for transit and other terminal
projects.
o A list of strategies to reduce emissions from trucks operating at marine
and rail terminals is available at:
www. epa. gov/otaq/smartway/transport/partner-resources/resources-
publications.htm.
• It may be possible in some cases to route existing or projected traffic away from
populated areas to an industrial setting (e.g., truck only lanes). Project sponsors
should take into account any changes in travel activity, including additional VMT,
that would result from rerouting this traffic. Note that this option may also
change the air quality modeling receptors that are examined in the PM hot-spot
analysis.
• Finally, project sponsors could consider additional modes for travel and goods
movement. An example of such a mode would be transporting freight by cleaner
rail instead of by highway (e.g., putting port freight on electric trains instead of
transporting it by truck).
10.2.4 Fugitive dust control programs
Fugitive dust control programs will primarily be applicable in PMio hot-spot analyses,
since all PMio nonattainment and maintenance areas must include these emissions in such
analyses. However, there may be PM2.5 nonattainment and maintenance areas that also
could take advantage of these measures if re-entrained road dust or construction dust is
required for a PM2.5 hot-spot analysis. See Section 2.5 for further background.
• A project sponsor could commit to cover any open trucks used in construction of
the project if construction emissions are included in an analysis year. Some states
have laws requiring that open truck containers be covered to reduce dispersion of
material. Laws may differ in terms of requirements, e.g., some require covering
at all times, some require covering in limited circumstances, and some restrict
spillage.
• A project sponsor could employ or obtain a commitment from another local
agency to implement a street cleaning program. There is a variety of equipment
available for this purpose and such programs could include vacuuming or flushing
techniques. There have been circumstances where municipalities have
implemented street sweeping programs for air quality purposes.
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• Another option to reduce dust could be a site watering program, which may be
relevant during the construction phase of a project, if construction emissions are
included in the PM hot-spot analysis.
• Project sponsors may consider street and shoulder paving and runoff and erosion
control in the project area, which can reduce significant quantities of dust.
• It may also be possible to reduce the use of sand in snow and ice control
programs, apply additional chemical treatments, or use harder material (that is
less likely to grind into finer particles).
10.2.5 Addressing other source emissions
Note: Controlling emissions from other sources may sufficiently reduce background
concentrations in the PM hot-spot analysis.
• Reducing emissions from school buses may be relevant where such emissions are
part of background concentrations. Information about retrofitting, replacing, and
reducing idling of school buses can be found on EPA's website at:
www.epa.gov/otaq/schoolbus/index.htm.
• Reducing emissions from ships, cargo handling equipment and other vehicles at
ports may change the result of the PM hot-spot analysis. Options such as
retrofitting, repowering, or replacing engines or vehicles, use of cleaner fuels, or
"cold ironing" (that allows ships to plug in to shore-side power units) could be
relevant where these sources significantly influence background concentrations in
the project area. More information about reducing emissions at ports can be
found on EPA's website at: www.epa.gov/otaq/diesel/ports/index.htm and
www. epa. gov/otaq/smartway/transport/partner-resources/resources-
publications.htm.
• Adopting locomotive anti-idling policies or other measures. For additional
information, see the following EPA resources:
o "Guidance for Quantifying and Using Long Duration Switch Yard
Locomotive Idling Emission Reductions in State Implementation Plans,"
EPA420-B-04-09-037 (October 2009) available at:
www.epa.gov/otaq/diesel/documents/420b09037.pdf.
o EPA-verified anti-idle technologies for locomotives can be found at:
www. epa. gov/otaq/smartway/transport/what-smartway/verified-
technologies.htm.
• Remanufacturing existing locomotives to meet more stringent standards at a rate
faster than the historical average, or using only Tier 3 and/or Tier 4 locomotives
at a proposed terminal (once such locomotives become available).
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• Reducing emissions from a stationary source might also change the result of the
PM hot-spot analysis. Reductions could come from adding a control technology
to a stationary source or adopting policies to reduce peak emissions at such a
source. EPA and the state and/or local air quality agency could provide input on
the feasibility and implementation of such a measure, as well as any necessary
commitments to such measures from operators.
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^•.D -L \)
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Appendix A:
Clearinghouse of Websites, Guidance, and Other Technical
Resources for PM Hot-spot Analyses
A.1 INTRODUCTION
This appendix is a centralized compilation of documents and websites referenced in the
guidance, along with additional technical resources that may be of use when completing
quantitative PM hot-spot analyses. Refer to the appropriate sections of the guidance for
complete discussions on how to use these resources in the context of completing a
quantitative PM hot-spot analysis.
A.2 TRANSPORTATION CONFORMITY AND CONTROL MEASURE GUIDANCE
The EPA hosts an extensive library of transportation conformity guidance online at:
www.epa.gov/otaq/stateresources/transconf/policy.htm (unless otherwise noted). The
following specific guidance documents, in particular, may be useful references when
implementing PM hot-spot analyses:
• "Policy Guidance on the Use of MOVES2010 for SIP Development and
Transportation Conformity, and Other Purposes," EPA-420-B-09-046 (December
2009). This document describes how and when to use the MOVES2010
emissions model for SIP development, transportation conformity determinations,
and other purposes.
• "Technical Guidance on the Use of MOVES2010 for Emission Inventory Prepara-
tion in State Implementation Plans and Transportation Conformity." This
document provides guidance on appropriate input assumptions and sources of data
for the use of MOVES2010 in SIP submissions and regional emissions analyses
for transportation conformity purposes.
• EPA and FHWA, "Transportation Conformity Guidance for Qualitative Hot-spot
Analyses in PM2.5 and PMio Nonattainment and Maintenance Areas," EPA420-B-
06-902 (March 2006).
• EPA and FHWA, "Guidance for the Use of Latest Planning Assumptions in
Transportation Conformity Determinations," EPA420-B-08-901 (December
2008).
• "Guidance for Developing Transportation Conformity State Implementation
Plans," EPA-420-B-09-001 (January 2009).
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• The most recent guidance for quantifying and using long duration truck idling
emission reductions in transportation conformity can be found at:
www.epa.gov/otaq/stateresources/transconf/policy.htm.
• EPA-verified anti-idle technologies (including technologies that pertain to trucks)
can be found at: www.epa.gov/otaq/smartway/transport/what-smartway/verified-
technol ogi e s. htm#i dl e.
• For additional information about quantifying the benefits of retrofitting and
replacing diesel vehicles and engines for conformity determinations, see EPA's
website for the most recent guidance on this topic:
www.epa.gov/otaq/stateresources/transconf/policy.htm.
• For additional information about quantifying the benefits of anti-idling programs
for conformity determinations, see EPA's website for the most recent guidance on
this topic: www.epa.gov/otaq/stateresources/transconf/policy.htm.
FHWA's transportation conformity site has additional conformity information, including
examples of qualitative PM hot-spot analyses. Available at:
www.fhwa.dot.gov/environment/conformity/practices/index.cfm.
A.3 MOVES MODEL TECHNICAL INFORMATION AND USER GUIDES
Technical information on the MOVES model can be found at
www.epa.gov/otaq/models/moves/index.htm, including the following:
• "MOVES2010 User Guide." This guide provides detailed instructions for setting
up and running MOVES2010. Available at
www.epa.gov/otaq/models/moves/index.htm.
Guidance on using the MOVES model at the project level, as well as examples of using
MOVES for quantitative PM hot-spot analyses, can be found in Section 4 of the guidance
and in Appendices D, E and F.
A. 4 EMFAC2007 MODEL TECHNICAL INFORMATION, USER GUIDES, AND
OTHER GUIDANCE
EMFAC2007, its user guides, and any future versions of the model can be downloaded
from the California Air Resources Board website at:
www. arb. ca. gov/m sei/onroad/1 atest_ver si on. htm.
Supporting documentation for EMFAC, including the technical memorandum "Revision
of Heavy Heavy-Duty Diesel Truck Emission Factors and Speed Correction Factors"
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cited in Section 5 of this guidance, can be found at
www.arb.ca.gov/msei/supportdocs.htmtfonroad.
Instructions on using the EMFAC model at the project level, as well as examples of using
EMFAC for quantitative PM hot-spot analyses, can be found in Section 5 of the guidance
and in Appendices G and H.
A.5 DUST EMISSIONS METHODS AND GUIDANCE
Information on calculating emissions from paved roads, unpaved roads, and construction
activities can be found in AP-42, Chapter 13 (Miscellaneous Sources). AP-42 is EPA's
compilation of data and methods for estimating average emission rates from a variety of
activities and sources from various sectors. Refer to EPA's website to access the latest
versions of AP-42 sections and for more information about AP-42 in general:
www. epa. gov/ttn/chief/ap42/index.html.
Current and future policy documents related to AP-42 and/or road dust emissions can be
found on the EPA's website at:
www.epa.gov/otaq/stateresources/transconf/policy.htmtfmodels, including the following
current guidance:
• "Policy Guidance on the Use of the November 1, 2006, Update to AP-42 for Re-
entrained Road Dust for SIP Development and Transportation Conformity,"
(August 2, 2007).
• "Policy Guidance on the Use of MOBILE6.2 and the December 2003 AP-42
Method for Re-entrained Road Dust for SIP Development and Transportation
Conformity," (February 24, 2004).
Guidance on calculating dust emissions for PM hot-spot analyses can be found in Section
6 of the guidance.
A.6 LOCOMOTIVE EMISSIONS GUIDANCE
The following guidance documents, unless otherwise noted, can be found on or through
the EPA's locomotive emissions website at: www.epa.gov/otaq/locomotives.htm:
• "Procedure for Emission Inventory Preparation - Volume IV: Mobile Sources,"
Chapter 6. Available online at: www.epa.gov/OMS/invntory/r92009.pdf Note
that the emissions factors listed in Volume IV have been superseded by the April
2009 publication listed below for locomotives certified to meet EPA standards.
• "Emission Factors for Locomotives," EPA-420-F-09-025 (April 2009). Available
online at: www.epa.gov/otaq/regs/nonroad/locomotv/420f08014.htm.
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• "Control of Emissions from Idling Locomotives," EPA-420-F-08-014 (March
2008).
• "Guidance for Quantifying and Using Long Duration Switch Yard Locomotive
Idling Emission Reductions in State Implementation Plans," EPA-420-B-04-002
(January 2004). Available online at:
www.epa.gov/otaq/smartway/documents/420b04002.pdf.
• EPA-verified anti-idle technologies (including technologies that pertain to
locomotives) can be found at: www.epa.gov/otaq/smartway/transport/what-
smartwav/verified-technologies.htm#idle.
Guidance on calculating locomotive emissions for PM hot-spot analyses can be found in
Section 6 of the guidance and in Appendix I.
A. 7 AlR QUALITY DISPERSION MODEL TECHNICAL INFORMATION AND
USER GUIDES
The latest version of "Guideline on Air Quality Models" (Appendix W to 40 CFR Part
51) (dated 2005 as of this writing) can be found on EPA's SCRAM website at:
www.epa.gov/scramOO l/guidance_permit.htm.
Both AERMOD and CAL3QHCR models and related documentation can be obtained
through EPA's Support Center for Regulatory Air Models (SCRAM) web site at:
www.epa.gov/scramOO 1. In particular, the following guidance may be particularly useful
when running these models:
• AERMOD Implementation Guide
• AERMOD User Guide ("User's Guide for the AMS/EPA Regulatory Model -
AERMOD")
• CAL3QHCRUser's Guide ("User's Guide to CAL3QHC Version 2.0: A
Modeling Methodology for Predicting Pollutant Concentrations Near Roadway
Intersections")
• MPRM User's Guide
• AERMET User's Guide
Guidance on selecting and using an air quality model for quantitative PM hot-spot
analyses can be found in Sections 7 and 8 of the guidance and in Appendix J. Examples
of using an air quality model for a PM hot-spot analysis can be found in Appendices E
andF.
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A. 8 TRANSPORTATION DATA AND MODELING CONSIDERATIONS
The following is a number of technical resources on transportation data and modeling
which may help implementers determine the quality of their inputs and the sensitivity of
various data.
A.8.1 Transportation model improvement
The FHWA Travel Model Improvement Program (TMIP) provides a wide range of
services and tools to help planning agencies improve their travel analysis techniques.
Available online at: http://tmip.fhwa.dot.gov/.
A.8.2 Speed
"Evaluating Speed Differences between Passenger Vehicles and Heavy Trucks for
Transportation-Related Emissions Modeling." Available online at:
www.ctre.iastate.edu/reports/truck speed.pdf.
A. 8.3 Project level planning
"NCHRP 255: Highway Traffic Data for Urbanized Area Project Planning and Design."
Available online at:
http://tmip.fhwa.dot.gov/sites/tmip.fhwa.dot.gov/files/NCHRP 255.pdf.
A. 8.4 Traffic analysis
Traffic Analysis Toolbox website: http://ops.fhwa.dot.gov/trafficanalysistools/.
"Traffic Analysis Toolbox Volume I: Traffic Analysis Tools Primer." Federal Highway
Administration, FHWA-HRT-04-038 (June 2004). Available online at:
http://ops.fhwa.dot.gov/trafficanalysistools/tat voll/voll_primer.pdf
The Highway Capacity Manual Application Guidebook. Transportation Research Board,
Washington, D.C., 2003. Available online at: http://hcmguide.com/.
The Highway Capacity Manual 2000. Transportation Research Board, Washington,
D.C., 2000. Not available online; purchase information available at:
http://144.171.11.107/Main/Public/Blurbs/Highwav Capacity Manual 2000 152169.asp
x. As of this writing, the 2000 edition is most current; the most recent version of the
manual, and the associated guidebook, should be consulted when completing PM hot-
spot analyses.
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Appendix B:
Examples of Projects of Local Air Quality Concern
B.I INTRODUCTION
This appendix gives additional guidance on what types of projects may be projects of
local air quality concern requiring a quantitative PM hot-spot analysis under 40 CFR
93.123(b)(l). However, as noted elsewhere in this guidance, PMio nonattainment and
maintenance areas with approved conformity SIPs that include PMio hot-spot provisions
from previous rulemakings must continue to follow those approved conformity SIP
provisions until the SIP is revised; see Appendix C for more information.
B.2 EXAMPLES OF PROJECTS THAT REQUIRE PM HOT-SPOT ANALYSES
EPA noted in the March 2006 final rule that the examples below are considered to be the
most likely projects that would be covered by 40 CFR 93.123(b)(l) and require a PM2.5
or PMio hot-spot analysis (71 FR 12491).
Some examples of projects of local air quality concern that would be covered by 40 CFR
93.123(b)(l)(i)and(ii)are:
• A project on a new highway or expressway that serves a significant volume of
diesel truck traffic, such as facilities with greater than 125,000 annual average
daily traffic (AADT) and 8% or more of such AADT is diesel truck traffic;
• New exit ramps and other highway facility improvements to connect a highway or
expressway to a major freight, bus, or intermodal terminal;
• Expansion of an existing highway or other facility that affects a congested
intersection (operated at Level-of-Service D, E, or F) that has a significant
increase in the number of diesel trucks; and,
• Similar highway projects that involve a significant increase in the number of
diesel transit busses and/or diesel trucks.
Some examples of projects of local air quality concern that would be covered by 40 CFR
93.123(b)(l)(iii)and(iv)are:
• A major new bus or intermodal terminal that is considered to be a "regionally
significant project" under 40 CFR 93.101 *; and,
40 CFR 93.101 defines a "regionally significant project" as "a transportation project (other than an
exempt project) that is on a facility which serves regional transportation needs (such as access to and from
the area outside of the region, major activity centers in the region, major planned developments such as
new retail malls, sports complexes, etc., or transportation terminals as well as most terminals themselves)
and would normally be included in the modeling of a metropolitan area's transportation network, including
at a minimum all principal arterial highways and all fixed guideway transit facilities that offer an
alternative to regional highway travel."
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• An existing bus or intermodal terminal that has a large vehicle fleet where the
number of diesel buses increases by 50% or more, as measured by bus arrivals.
A project of local air quality concern covered under 40 CFR 93.123(b)(l)(v) could be any
of the above listed project examples.
B.3 EXAMPLES OF PROJECTS THAT DO NOT REQUIRE PM HOT-SPOT
ANALYSES
The March 2006 final rule also provided examples of projects that would not be covered
by 40 CFR 93.123(b)(l) and would not require a PM2.5 or PMio hot-spot analysis (71 FR
12491).
The following are examples of projects that are not a local air quality concern under 40
CFR93.123(b)(l)(i)and(ii):
• Any new or expanded highway project that primarily services gasoline vehicle
traffic (i.e., does not involve a significant number or increase in the number of
diesel vehicles), including such projects involving congested intersections
operating at Level-of-Service D, E, or F;
• An intersection channelization project or interchange configuration project that
involves either turn lanes or slots, or lanes or movements that are physically
separated. These kinds of projects improve freeway operations by smoothing
traffic flow and vehicle speeds by improving weave and merge operations, which
would not be expected to create or worsen PM NAAQS violations; and,
• Intersection channelization projects, traffic circles or roundabouts, intersection
signalization projects at individual intersections, and interchange reconfiguration
projects that are designed to improve traffic flow and vehicle speeds, and do not
involve any increases in idling. Thus, they would be expected to have a neutral or
positive influence on PM emissions.
Examples of projects that are not a local air quality concern under 40 CFR
93.123(b)(l)(iii) and (iv) would be:
• A new or expanded bus terminal that is serviced by non-diesel vehicles (e.g.,
compressed natural gas) or hybrid-electric vehicles; and,
• A 50% increase in daily arrivals at a small terminal (e.g., a facility with 10 buses
in the peak hour).
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Appendix C:
Hot-Spot Requirements for PM10 Areas with Approved
Conformity SIPs
C.I INTRODUCTION
This appendix describes what projects require a quantitative PMi0 hot-spot analysis in
those limited cases where a state's approved conformity SIP is based on pre-2006
conformity requirements.l The March 10, 2006 final hot-spot rule defined the current
federal conformity requirements for what projects require a PM hot-spot analysis, i.e.,
only certain highway and transit projects that involve significant levels of diesel vehicle
traffic or any other project identified in the PM SIP as a local air quality concern.2
However, there are some PMi0 nonattainment and maintenance areas where PMi0 hot-
spot analyses are required for different types of projects, as described further below.
This appendix will be relevant for only a limited number of PMio nonattainment and
maintenance areas with outdated approved conformity SIPs. This appendix is not
relevant for any PM2.5 nonattainment or maintenance areas, since the current federal
PM2 5 hot-spot requirements apply in all such areas. Project sponsors should use the
interagency consultation process to verify applicable requirements before beginning a
quantitative PMio hot-spot analysis.
C.2 PMio AREAS WHERE THE PRE-2006 HOT-SPOT REQUIREMENTS APPLY
Prior to the March 2006 final rule, the federal conformity rule required some type of hot-
spot analysis for all non-exempt federally funded or approved projects in PMio
nonattainment and maintenance areas. These pre-2006 requirements are in effect for
those states with an approved conformity SIP that includes the pre-2006 hot-spot
requirements.
In PMio areas with approved conformity SIPs that include the pre-2006 hot-spot
requirements, a quantitative PMio hot-spot analysis is required for the following types of
projects:
• Projects which are located at sites at which PMio NAAQS violations have
been verified by monitoring;
• Projects which are located at sites which have vehicle and roadway emission
and dispersion characteristics that are essentially identical to those of sites
1 A "conformity SIP" includes a state's specific criteria and procedures for certain aspects of the
transportation conformity process (40 CFR 51.390).
2 See Sections 1.4 and 2.2 of this guidance and the preamble of the March 10, 2006 final rule for further
information (71 FR 12491-12493).
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with verified violations (including sites near one at which a violation has been
monitored); and
• New or expanded bus and rail terminals and transfer points which increase the
number of diesel vehicles congregating at a single location.
This guidance should be used to complete any quantitative PMio hot-spot analyses.
In addition, a qualitative PMio hot-spot analysis is required in the pre-2006 hot-spot
requirements for all other non-exempt federally funded or approved projects. For such
analyses, consult the 2006 EPA-FHWA qualitative hot-spot guidance.3
These pre-2006 hot-spot requirements continue to apply in PMio areas with approved
conformity SIPs that include them until the state acts to change the conformity SIP. The
conformity rule at 40 CFR 51.390 states that conformity requirements in approved
conformity SIPs "remain enforceable until the state submits a revision to its [conformity
SIP] to specifically remove them and that revision is approved by EPA."
C.3 REVISING A CONFORMITY SIP
EPA strongly encourages affected states to revise outdated provisions and take advantage
of the streamlining flexibilities provided by the current Clean Air Act. EPA's January
2008 final conformity rule4 significantly streamlined the requirements for conformity
SIPs in 40 CFR 51.390. As a result, conformity SIPs are now required to include only
three provisions (consultation procedures and procedures regarding written
commitments) rather than all of the provisions of the federal conformity rule.
EPA recommends that states with outdated PMio hot-spot requirements in their
conformity SIPs act to revise them to reduce the number of projects where a hot-spot
analysis is required. In affected PMio areas, the current conformity rule's PMio hot-spot
requirements at 40 CFR 93.123(b)(l) and (2) will be effective only when a state either:
• Withdraws the existing provisions from its approved conformity SIP and EPA
approves this SIP revision, or
• Revises its approved conformity SIP consistent with the requirements found at 40
CFR 93.123(b) and EPA approves this SIP revision.
Affected states should contact their EPA Regional Office to proceed with one of these
two options. For more information about conformity SIPs, see EPA's "Guidance for
Developing Transportation Conformity State Implementation Plans (SIPs)," EPA-420-B-
3 "Transportation Conformity Guidance for Qualitative Hot-spot Analyses in PM2 5 and PM10
Nonattainment and Maintenance Areas,", EPA420-B-06-902, found on EPA's website at:
www.epa.gov/otaq/stateresources/transconf/policy/420b06902.pdf.
4 "Transportation Conformity Rule Amendments to Implement Provisions Contained in the 2005 Safe,
Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU); Final
Rule," 73 FR 4420.
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09-001 (January 2009); available online at:
www.epa.gov/otaq/stateresources/transconf/policy/420b09001.pdf.
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Appendix D:
Characterizing Intersection Projects for MOVES
D.I INTRODUCTION
This appendix expands upon the discussion in Section 4.2 on how to best characterize
links when modeling an intersection project using MOVES. The MOVES emission
model allows users to represent intersection traffic activity with a higher degree of
sophistication compared to previous models. This appendix provides several options to
describe vehicle activity to take advantage of the capabilities MOVES offers to complete
more accurate PM hot-spot analyses of intersection projects. MOVES is the approved
emission model for PM hot-spot analyses in areas outside if California.
Exhibit D-l is an example of a simple signalized intersection showing the links
developed by a project sponsor to represent the two general categories of vehicle activity
expected to take place at this intersection (approaching the intersection and departing the
intersection).
Exhibit D-l. Example of Approach and Departure Links for a Simple Intersection
Approach Link
Departure Link
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When modeling an intersection, each approach link or departure link can be modeled as
one or more links in MOVES depending on the option chosen to enter traffic activity.
This guidance suggests three possible options for characterizing activity on each
approach and departure link (such as those shown in Exhibit D-l):
• Option 1: Using average speeds
• Option 2: Using link drive schedules
• Option 3: Using Op-Mode distributions
While Option 1 may need to be relied upon more during the initial transition to using
MOVES, as more detailed data are available to describe vehicle activity, users are
encouraged to consider using the Options 2 and 3 to take full advantage of the
capabilities of MOVES. In addition, there may be other options for characterizing
vehicle activity for an intersection; these should be discussed through the interagency
consultation process prior to being used for a particular project.
Once a decision has been made on how to characterize links, users should continue to
develop the remaining MOVES inputs as discussed in Section 4 of the guidance. The
same method of characterizing vehicle activity should be used for all links in both the
build and no-build scenarios.
D.2 OPTION i: USING AVERAGE SPEEDS
The first option is for the user to estimate the average speeds for each link in the
intersection based on travel time and distance. Travel time should account for the total
delay attributable to traffic signal operation, including the portion of travel when the light
is green and the portion of travel when the light is red. The effect of a traffic signal cycle
on travel time includes deceleration delay, move-up time in a queue, stopped delay, and
acceleration delay. Using the intersection example given in Exhibit D-l, each approach
link would be modeled as one link to reflect the higher emissions associated with vehicle
idling through lower speeds affected by stopped delay; each departure link would be
modeled as one link to reflect the higher emissions associated with vehicle acceleration
through lower speeds affected by acceleration delay. A variety of methods are available
to estimate average speed. Project sponsors determine congested speeds by using
appropriate methods based on best practices for highway analyses. Some resources are
available through FHWA's Travel Model Improvement Program (TMIP).l
Methodologies for computing intersection control delay are provided in the Highway
Capacity Manual 2000.2 All assumptions, methods, and data underlying the estimation
of average speeds and delay should be documented as part of the PM hot-spot analysis.
1 See FHWA's TMIP website: http://tmip.fhwa.dot.gov/.
2 Users should consult the most recent version of the Highway Capacity Manual. As of the release of this
guidance, the latest version is the Highway Capacity Manual 2000, which can be obtained from the
Transportation Research Board (see http://144.171.ll.107/Main/Public/Bluibs/152169.aspx for details).
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D.3 OPTION 2: USING LINK DRIVE SCHEDULES
A more refined approach is to enter vehicle activity into MOVES as a series of link drive
schedules to represent individual segments of cruise, deceleration, idle, and acceleration
of a congested intersection. A link drive schedule defines a speed trajectory to represent
the entire vehicle fleet via second-by-second changes in speed and highway grade.
Unique link drive schedules can be defined to describe types of vehicle activity that have
distinct emission rates, including cruise, deceleration, idle, and acceleration.
Exhibit D-2 illustrates why using this more refined approach can result in a more detailed
emissions analysis. This exhibit shows the simple trajectory of a single vehicle
approaching an intersection during the red signal phase of a traffic light cycle. This
trajectory is characterized by several distinct phases (a steady cruise speed, decelerating
to a stop for the red light, idling during the red signal phase, and accelerating when the
light turns green). In contrast, the trajectory of a single vehicle approaching an
intersection during the green signal phase of a traffic light cycle is characterized by a
more or less steady cruise speed through the intersection.
Exhibit D-2. Example Single Vehicle Speed Trajectory Through a Signalized
Intersection
50
45
40
35
* 30
Q.
cu
cu
:r 25
20
15
10
5
0
Green Light
Red Light
Cruise
Decelerate Idle
Accelerate
Cruise
-100 -80 -60 -40 -20 0 20
Distance (m)
40
60
80
100
For the example intersection in Exhibit D-l, link drive schedules representing the
different operating modes of vehicle activity on the approach and departure links can be
determined. For approach links, the length of a vehicle queue is dependent on the
number of vehicles subject to stopping at a red signal. Vehicles approaching a red traffic
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signal decelerate over a distance extending from the intersection stop line back to the
stopping distance required for the last vehicle in the queue. The average stopping
distance can be calculated from the average deceleration rate and the average cruise
speed. Similarly, for the departure links, vehicles departing a queue when the light turns
green accelerate over a distance extending from the end of the vehicle queue to the
distance required for the first vehicle to reach the cruise speed, given the rate of
acceleration and cruise speed. Exhibit D-3 provides an illustration of how the different
vehicle operating modes may be apportioned spatially near this signalized intersection.
Exhibit D-3. Example Segments of Vehicle Activity Near a Signalized Intersection
Decelerate
Idle
Accelerate
Cruise
There are other considerations with numerous vehicles stopping and starting at an
intersection over many signal cycles during an hour. For instance, heavy trucks
decelerate and accelerate at slower rates than passenger cars. Drivers tend not to
decelerate at a constant rate, but through a combination of coasting and light and heavy
braking. And acceleration rates are initially higher when starting from a complete stop at
an intersection, becoming progressively lower to make a smooth transition to cruise
speed. In the case of an uncongested intersection, the rates of vehicles approaching and
departing the intersection are in equilibrium. Some vehicles may slow, and then speed up
to join the dissipating queue without having to come to a full stop. Once the queue
clears, approaching vehicles during the remainder of the green phase of the cycle will
cruise through the intersection virtually unimpeded. In the case of a congested
intersection, the rate of vehicles approaching the intersection is greater than the rate of
departure, with the result that no vehicle can travel through without stopping; vehicles
approaching the traffic signal, whether it is red or green, will have to come to a full stop
and idle for one or more cycles before departing the intersection. The latest Highway
Capacity Manual is a good source of information for vehicle operation through signalized
intersections. All assumptions, methods, and data underlying the development of link
drive schedules should be documented as part of the PM hot-spot analysis.
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The emission factors obtained from MOVES for each segment of vehicle activity
obtained via individual link drive schedules are readily transferable to either AERMOD
or CAL3QHCR, as discussed further in Section 7 of the guidance. There will most likely
be a need to divide the cruise and the acceleration segments to account for differences in
approach and departure traffic volumes.
D. 4 OPTION 3: USING OP-MODE DISTRIBUTIONS
A third option is for a user to generate representative Op-Mode distributions for approach
and departure links by calculating the fraction of fleet travel times spent in each mode of
operation. For any given signalized intersection, vehicles are cruising, decelerating,
idling, and accelerating. Op-Mode distributions can be calculated from the ratios of
individual mode travel times to total travel times on approach links and departure links.
This type of information could be obtained from Op-Mode distribution data from (1)
existing intersections with similar geometric and operational (traffic) characteristics, or
(2) output from traffic simulation models for the proposed project or similar projects.
Acceleration and deceleration assumptions, methods, and data underlying the activity-to-
Op-Mode calculations should be documented as part of the PM hot-spot analysis.
The following methodology describes a series of equations to assist in calculating vehicle
travel times on approach and departure links. Note that a single approach and single
departure link should be defined to characterize vehicles approaching, idling at, and
departing an intersection (e.g., there is no need for an "idling link," as vehicle idling is
captured as part of the approach link).
D. 4.1 Approach links
When modeling each approach link, the fraction of fleet travel times in seconds (s) in
each mode of operation should be determined based on the fraction of time spent
cruising, decelerating, accelerating, and idling:
Total Fleet Travel Time (s) = Cruise Time + Decel Time + Accel Time +
Idle Time
The cruise travel time can be represented by the number of vehicles cruising multiplied
by the length of approach divided by the average cruise speed.
Cruise Time (s) = Number of Cruising Vehicles * (Length of Approach (mi) +
Average Cruise Speed (mi/hr)) * 3600 s/hr
The deceleration travel time can be represented by the number of vehicles decelerating
multiplied by the average cruise speed divided by the average deceleration rate:
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Decel Time (s) = Number of Decelerating Vehicles * (Average Cruise Speed
(mi/hr) + Average Decel Rate (mi/hr/s))
The acceleration travel time occurring on an approach link can be similarly represented.
However, to avoid double counting acceleration activity that occurs on the departure link,
users should multiply the acceleration time by the proportion of acceleration that occurs
on the approach link (Accel Length Fraction on Approach):
Accel Time (s) = Number of Accelerating Vehicles * (Average Cruise Speed
(mi/hr) + Average Accel Rate (mi/hr/s)) * Accel Length Fraction on
Approach
The idle travel time can be represented by the number of vehicles idling multiplied by the
average stopped delay (average time spent stopped at an intersection):
Idle Time (s) = Number of Idling Vehicles * Average Stopped Delay (s)
Control delay (total delay caused by an intersection) may be used in lieu of average
stopped delay, but control delay includes decelerating and accelerating travel times,
which should be subtracted out (leaving only idle time).
After calculating the fraction of time spent in each mode of approach activity, users
should select the appropriate MOVES Op-Mode ID corresponding to each particular type
of activity (see Section 4.5.7 for more information). The operating modes in MOVES
typifying approach links include:
• Cruise/acceleration (Op-Modes 11-16, 22-30, 33, 35-40);
• Low and moderate speed coasting (Op-Modes 11,21);
• Braking (Op-Mode 0);
• Idling (Op-Mode 1); and
• Tire wear (Op-Modes 401-416).
The relative fleet travel time fractions can be allocated to the appropriate Op-Modes in
MOVES. The resulting single Op-Mode distribution accounts for relative times spent in
the different driving modes (cruise, deceleration, acceleration, and idle) for the approach
link. A simple example of deriving Op-Mode distributions for a link using this
methodology is demonstrated in Step 3 of Appendix F for a bus terminal facility.
D.4.2 Departure links
When modeling each departure link, the fraction of fleet travel times spent in each mode
of operation should be determined based on the fraction of time spent cruising and
accelerating:
Total Fleet Travel Time (s) = Cruise Time + Accel Time
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The cruise travel time can be represented by the number of vehicles cruising multiplied
by the travel distance divided by the average cruise speed:
Cruise Time (s) = Number of Cruising Vehicles * (Length of Departure (mi)) /
(Average Cruise Speed (mi/hr)) * 3600 s/hr
The acceleration travel time occurring during the departure link can be represented by the
number of vehicles accelerating multiplied by the average cruise speed divided by the
average acceleration rate. However, to avoid double counting acceleration activity that
occurs on the approach link, users should multiply the resulting acceleration time by the
proportion of acceleration that occurs on the departure link (Accel Length Fraction on
Departure):
Accel Time (s) = Number of Accelerating Vehicles * (Average Cruise Speed
(mi/hr) + Average Accel Rate (mi/hr/s)) * Accel Length Fraction on
Departure
After calculating fraction of time spent in each mode of departure activity, users should
select the appropriate MOVES Op-Mode ID corresponding to each particular type of
activity (see Section 4.5.7 for more information). The operating modes typifying
departure links include:
• Cruise/acceleration (Op-Modes 11-16, 22-30, 33, 35-40); and
• Tire wear (Op-Mode 401-416).
The relative fleet travel time fractions can be allocated to the appropriate Op-Modes. The
resulting single Op-Mode distribution accounts for relative times spent in the different
driving modes (cruise and acceleration) for the departure link.
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Appendix E:
Example Quantitative PM Hot-spot Analysis of a Highway
Project using MOVES and CAL3QHCR
E.I INTRODUCTION
The purpose of this appendix is to demonstrate the procedures for completing a hot-spot
analysis using MOVES and CAL3QHCR following the basic steps described in Section
3. Readers should reference the appropriate sections in the guidance as needed for more
detail on how to complete each step of the analysis. This example is limited to showing
the build scenario; in practice, project sponsors may have to also analyze the no-build
scenario. While this example calculates emission rates using MOVES, EMFAC users
may find the air quality modeling described in this appendix helpful.
Note: The following example of a quantitative PM hot-spot analysis is highly simplified
and intended only to demonstrate the basic procedures described in the guidance. This
example uses default data in places where the use of project-specific data in a real-world
situation would be expected. In addition, actual PM hot-spot analyses could be
significantly more complex, and are likely to require more documentation of data and
decisions.
E.2 PROJECT DESCRIPTION AND CONTEXT
The proposed project is the construction of a highway interchange connecting a four-lane
principle arterial with a six-lane freeway through on-and-off ramps (see Exhibit E-l,
following page). The project is being built to allow truck access to local businesses. The
project is located in an area that was designated nonattainment for the 2006 PM2.5 24-
hour NAAQS and 1997 PM2.5 annual NAAQS.
The following is some additional pertinent data about the project:
• The project is located in a medium-sized city (within one county) in a state other
than California.
• The project is expected to take less than a year to complete and has an estimated
completion date of 2013. The year of peak emissions is expected to be 2015,
when considering the project's emissions and background concentrations.
• In 2015, the average annual daily traffic (AADT) at this location is expected to
exceed 125,000 vehicles and greater than eight percent of the traffic will be
heavy-duty diesel trucks.
• The area surrounding the proposed project is primarily residential, with no nearby
sources that need to be explicitly modeled.
• The state does not have an adequate or approved SIP budget for either PM2.5
NAAQS, and neither the EPA nor the state air agency have made a finding that
road dust is a significant contributor to the PM2.5 nonattainment problem.
E-l
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Exhibit E-l. Simple Diagram of the Proposed Highway Project
400 meters
E.3 DETERMINE NEED FOR ANALYSIS (STEP i)
Through interagency consultation, the proposed project is determined to be of local air
quality concern under the conformity rule because it is a new freeway project with a
significant number of diesel vehicles (see 40 CFR 93.123(b)(l)(i) and Sections 1.4 and
3.2 and Appendix B of the guidance). Therefore, a quantitative PM hot-spot analysis is
required.
E.4 DETERMINE APPROACH, MODELS, AND DATA (STEP 2)
E. 4.1 Determining geographic area and emission sources to be covered by the analysis
First, the interagency consultation process is used to ensure that the project area is
defined so that the analysis includes the entire project, as required by 40 CFR
93.123(c)(2). As previously noted, it is also determined that, in this case, there are no
nearby emission sources to be explicitly modeled (see Section 3.3.2).
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E. 4.2 Deciding on general analysis approach and analysis year(s)
Second, the project sponsor determines that the preferred approach in this case is to
model the build scenario first, completing a no-build scenario only if necessary.
In addition, it is determined that the year of peak emissions (within the timeframe of the
current transportation plan) is mostly likely to be 2015. Therefore, 2015 is selected as the
year of the analysis, and the analysis considers traffic data from 2015 (see Section 3.3.3).
E. 4.3 Determining which PMNAAQS to be evaluated
Because the area has been designated nonattainment for both the 2006 PM2.s 24-hour
NAAQS and 1997 PM2.5 annual NAAQS, the results of the analysis will have to be
compared to both NAAQS (see Section 3.3.4). All four quarters are included in the
analysis in order to estimate a year's worth of emissions for both NAAQS.
E.4.4 Deciding on the type of PMemissions to be modeled
Next, through interagency consultation, the following directly-emitted PM2.5 emissions
are determined to be relevant for estimating the emissions in the analysis (see Section
3.3.5):
• Vehicle exhaust1
• Brake wear
• Tire wear
E. 4.5 Determining the models and methods to be used
Since this project is located outside of California, MOVES2010 is used for emissions
modeling. In addition, it is determined that, since this is a highway project with no
nearby sources that need to be explicitly modeled, either AERMOD or CAL3QHCR
could be used for air quality modeling (see Section 3.3.6). In this case, CAL3QHCR is
selected. Making the decision on what air quality model to use at this stage is important
so that the appropriate data are collected, among other reasons (see next step).
E. 4.6 Obtaining project-specific modeling data
Finally, the project sponsor compiles the data required to use MOVES, including project
traffic data, vehicle types and age, and temperature and humidity data for the months and
hours to be modeled (specifics on the data collected are described in the following steps).
In addition, information necessary to use CAL3QHCRto model air quality is gathered,
including meteorological data and information on representative air quality monitors.
The sponsor also ensures the latest planning assumptions are used and that data used for
the analysis are consistent with that used in the latest regional emissions analysis, as
Represented in MOVES as PMtotai mnnmg and PMtotai crankcase mmmg
E-2
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required by the conformity rule (see Section 3.3.7). The interagency consultation process
is used to discuss the data for the PM hot-spot analysis.
E.5 ESTIMATE ON-ROAD MOTOR VEHICLE EMISSIONS (STEP 3)
Having completed the analysis preparations described above, the project sponsor then
follows the instructions provided in Section 4 of the guidance to use MOVES to estimate
the project's on-road emissions:
E. 5.1 Characterizing the project in terms of links
As described in Section 4.2 of the guidance, links are defined based on the expected
emission rate variability across the project. Generally, a highway project like the one
proposed in this example can be broken into four unique activity modes:
• Freeway driving at 55 mph;
• Arterial cruise at 45 mph;
• Acceleration away from intersections to a cruising speed of 45/55 mph; and
• Cruise, deceleration, and idle/cruise (depending on light timing) at intersections.
Following the guidance given in Section 4.2, 20 links are defined for MOVES and
CAL3QHCR modeling, each representing unique geographic and activity parameters (see
Exhibits E-2 and E-3, following pages). Each LinkID is defined with the necessary
information for air quality modeling: link length, link width, link volume, as well as link
start and end points (xl, yl, x2, y2 coordinates).
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Exhibit E-2. Diagram of Proposed Highway Project Showing Links
LINK11
LINK4
f A,
400 meters
Decisions on how to best define links are based on an analysis of vehicle activity and
patterns within the project area. AADT is calculated from a travel demand model for
passenger cars, passenger trucks, intercity buses, short haul trucks, and long haul trucks.
From these values, both an average-hour and peak-hour volume is calculated. The
average and peak-hour vehicle counts for each part of the project are shown in Exhibit E-
3.
Based on the conditions in the project area, for this analysis peak traffic is assumed to be
representative of morning rush hour (AM: 6 a.m. to 9 a.m.) and evening rush hour (PM: 4
p.m. to 7 p.m.), while average hour traffic represents all other hours: midday (MD: 9 a.m.
to 4 p.m.), and overnight (ON: 7 p.m. to 6 a.m.) Identical traffic volume and speed
profiles are assumed for all quarters of the year. Quarters are defined as described in
Section 3.3.4 of the guidance: Ql (January-March), Q2 (April-June), Q3 (July-
September), and Q4 (October-December).
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Exhibit E-3. Peak-Hour and Average-Hour Traffic Counts for Each Project Link
Freeway
Passenger Cars
Passenger Trucks
Intercity Buses
Short Haul Trucks (gas)
Long Haul Trucks (diesel)
Total
Exit Ramps
Passenger Cars
Passenger Trucks
Intercity Buses
Short Haul Trucks (gas)
Long Haul Trucks (diesel)
Total
Entrance Ramps
Passenger Cars
Passenger Trucks
Intercity Buses
Short Haul Trucks (gas)
Long Haul Trucks (diesel)
Total
Arterial Road
Passenger Cars
Passenger Trucks
Intercity Buses
Short Haul Trucks (gas)
Long Haul Trucks (diesel)
Total
Peak Hour Count
2260
1760
36
60
944
5060
Peak Hour Count
124
124
8
12
300
568
Peak Hour Count
176
148
0
16
276
616
Peak Hour Count
124
116
12
0
316
568
Average Hour Count
452
352
7
12
189
1012
Average Hour Count
25
25
2
2
60
114
Average Hour Count
35
30
0
3
55
123
Average Hour Count
25
23
2
0
63
114
Fraction of Total
0.45
0.35
0.01
0.01
0.19
1.00
Fraction of Total
0.22
0.22
0.01
0.02
0.53
1.00
Fraction of Total
0.29
0.24
0.00
0.03
0.45
1.00
Fraction of Total
0.22
0.20
0.02
0.00
0.56
1.00
A significant amount of traffic using the project is expected to be diesel trucks. While
the freeway contains approximately 19% diesel truck traffic, traffic modeling for the on-
and off-ramps connecting the freeway to the arterial road suggests approximately half of
vehicles are long-haul diesel trucks.
The average speeds on the freeway, arterial, and on/off-ramps are anticipated to be
identical in the analysis year for both peak and average hours and assumed to
approximately reflect the speed limit (55 mph, 45 mph, and 45 mph, respectively).
Traffic flow through the two intersections is controlled by a signalized light with a 60%
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wait time (that is, 60% idle) for vehicles exiting the freeway and 40% wait time for traffic
entering the freeway from the arterial road or traveling north and south on the arterial
road passing over the freeway. The total project emissions, therefore, are determined to
be a function of:
• Vehicles traveling east and west on the freeway at a relatively constant 55 mph;
• Exiting vehicles decelerating to a stop at either the north or south signalized
intersection (or continuing through if the light is green);
• Vehicles accelerating away from the signalized intersections north and south, as
well as accelerating to a 55 mph cruise speed on the on-ramps;
• Idling activity at both intersections during the red phase of the traffic light; and
• Vehicles traveling between the north and south intersections at a constant 45 mph.
As there is no new parking associated with the project (e.g., parking lots), there are no
start emissions to be considered. Additionally, there are no trucks parked or "hoteling" in
extended idle mode anywhere in the project area, so extended idle emissions do not need
to be calculated.
E. 5.2 Deciding how to handle link activity
As discussed in Section 4.2 of the guidance, MOVES offers several options for users to
apply activity information to each LinklD. For illustrative purposes, based on the
available information for the project (in this case, average speed, link average and peak
volume, and red-light idle time) several methods of deriving Op-Mode distributions are
employed in this example, as described below.
The links parameter table in Exhibit E-4 (following page) shows the various methods that
activity is entered into MOVES for each link. The column "MOVES activity input"
describes how the Op-Mode distribution is calculated for each particular link (again, in a
real-world situation, only one method would be used for all links):
• Freeway links (links 1 and 4) are defined through a 55 mph average speed input,
from which MOVES calculated an Op-Mode distribution (as described in
Appendix D.2).
• Arterial cruise links (links 12 and 18) and links approaching an intersection queue
(links 2, 5, 9 and 15) are defined through a link-drive schedule with a constant
speed of 45 mph; indicating vehicles are cruising at 45 mph, with no acceleration
or deceleration (as described in Appendix D.3).
• Links representing vehicles accelerating away from intersections (links 7, 8, 11,
14, 17, 20) are given "adjusted average speeds" calculated from guidance in the
2000 Highway Capacity Manual, based on the link cruise speed (45 mph or 55
mph), red-light timing, and expected volume to capacity ratios. The adjusted
average speeds (16.6 mph or 30.3 mph) are entered into MOVES, which
calculates an Op-Mode distribution to reflect the lower average speed and
subsequent higher emissions (as described in Appendix D.2).
• Queue links are given an Op-Mode distribution that represents vehicles
decelerating and idling (red light) as well as cruising through (green light) (as
described in Appendix D.4).
E-7
-------
PUBLIC DRAFT-MAY 2010
1. First, an Op-Mode is calculated for the link average speed (45 mph).
2. Because this does not adequately account for idling at the intersection, the
Op-Mode fractions are re-allocated to add in idling. For instance, after
consulting the 2000 Highway Capacity Manual, for this project scenario,
the red light timing corresponds to approximately 40% idle time. A
fraction of 0.4 for Op-Mode "1" is added to Op-Mode distribution
calculated from the 45 mph average speed in Step 1.
The resulting Op-Mode distribution represents all activity on a queuing
intersection link.
The length of the queue links are estimated as a function of the length of three trucks, one
car, and one passenger truck with two meters in between each car and five meters in
between each truck.
Departure links on the arterial road are assumed to have a link length of 125 meters
(estimated to be the approximate distance that vehicles accelerate to a 45 mph cruising
speed). The departure links from the intersection to the on-ramp are assumed to have a
link length of 200 meters (estimated to be the approximate distance that vehicles
accelerate to a 55 mph cruising speed).
Exhibit E-4. Link Parameters (Peak Traffic)
1
2
3 "
A J
B J
C
D
E I
adj
F I 6
] H |
J L
J i K L —
average
inkID
1
2
Tl 3
5
6
7
8
9
10
11
12
13"
14
15
16
17
4
5
6
7
8
9
10
11
12
13
14
15
16
18] 17
l9~| 18
'"20
21
22
23
'24
25
IB"
27
28
19
20
nkLengtllinkwidth
935
250
87
940
220
87
450
520
75
61
125
190
61
125'
75
61
125
189
61 '
125
12
9
9
12
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
linkVolume
5060
568
568
5060
568
568
B16
616
568
568
568
568
568
568
568
568
568
568
568
568
inkAvgSpeed spe
55 n/a
45 n/a
45 n/a
55 n/a
45 n/a
45 n/a
45
45
45 n/a
45 n/a
45
45 n/a
45 n/a
45'
45 n/a
45 n/a
45
45 n/a
45 n/a
45 1
ed linkDescription
EB highway
EB off-ramp cruise
EB off-ramp queue
WB highway
WB off-ramp cruise
WB off-ramp queue
16.6 EB on-ramp
16.6 WB on-ramp
sNB cruise
sNB queue
30.3 sNB depart
NB connect
nNB queue
30.3 nNB depart
nSB cruise
nSB queue
30.3 nSB depart
SB connect
sSB queue
30.3lsSB depart
MOVES activity input x1
average speed
linkdrive schedule
avg spd/opMode
average speed
linkdrive schedule
avg spd/opMode
adj. average speed
adj. average speed
linkdrive schedule
avg spd/opMode
adj. average speed
linkdrive schedule
avg spd/opfvlode
adj. average speed
linkdrive schedule
avg spd/opMode
adj. average speed
linkdrive schedule
avg spd/opMode
adj. average speed
y
-422
-337
-89
358
315
96
19
-14
26
18
12
9
2
1
-10
-8
-9
-7
-3
2
H < f M \link / ' |<
x2 y2
-469 367 32
-424 -89 -386
-386 -3 -372
44 -440 -453
29 96 13
13 10 7
-367 300 -13
2 -360 -386
-507 18 -433
-433 12 -371
-371 9 -246
-246 2 -56
-56 1 5
5' 1 130
142 -8 68
68 -9 6
6 -7 -122
-122 -3 -311
-311 2' -371
-371 12 -501
V
> I '
E-S
-------
PUBLIC DRAFT-MAY 2010
E. 5.3 Determining the number of MO VES runs
Following the guidance given in Section 4.3, it is determined that 16 MOVES runs
should be completed to produce emission factors that show variation across four hourly
periods (12 a.m., 6 a.m., 12 p.m., and 6 p.m., corresponding to overnight, morning,
midday, and evening traffic scenarios, respectively) and four quarterly periods
(represented by the months of January, April, July, and October; see Section 3.3).
MOVES will calculate values for all project links for the time period specified in each
run. The 16 emission factors produced for each link are calculated as grams/vehicle-
mile, which will then be paired with corresponding traffic volumes (peak or average
hour, depending on the hour) and used in CAL3QHCR.
E. 5.4 Developing basic run specification inputs
When configuring MOVES for the analysis, the project sponsor follows Section 4.4 of
the guidance, including, but not limited to, the following:
• From the Scale menu, selecting the "Project" domain; in addition, choosing
output in "Emission Rates," so that emission factors will be in grams/vehicle-mile
as needed for CAL3QHCR (see Section 4.4.2).
• From the Time Spans Panel, the appropriate year, month, day, and hour for each
run is selected (see Section 4.4.3).
• From the Geographic Bounds Panel, the custom domain is selected (see Section
4.4.4).
• From the Vehicles/Equipment Panel, appropriate Source Types are selected (see
Section 4.4.5).
• From the Road Types Panel, Urban Restricted and Unrestricted road types are
selected (see Section 4.4.6).
• From the Pollutants and Processes Panel, appropriate pollutant/processes are
selected according to Section 4.4.7 of the guidance for "highway links."
• In the Output Panel, an output database is specified with grams and miles selected
as units (see Section 4.4.10).
E. 5.5 Entering project details using the Project Data Manager
Meteorology
As described previously, it is determined that MOVES should be run 16 times to reflect
the following scenarios: 12 a.m., 6 a.m., 12 p.m., and 6 p.m. (corresponding to overnight,
morning, midday, and evening traffic scenarios, respectfully) for the months of January,
April, July, and October. Through the interagency consultation process, temperature and
humidity data from a representative meteorological monitoring station are obtained and
confirmed to be consistent with data used in the regional emissions analysis from the
currently conforming transportation plan and TIP (see Section 4.5.1). Average values for
each hour and month combination are used for each of the 16 MOVES runs. As an
example, temperature and humidity values for 12 a.m. January are shown in Exhibit E-5
(following page).
E-9
-------
PUBLIC DRAFT-MAY 2010
Exhibit E-5. Temperature and Humidity Input (January 12 a.m.)
9 met janl 2am. xls
A
B
D
monthID zonelD hourlD tennperaturrelHumidity
1 990010 1 26.2 75.4
M \ZoneMonthHour / HourOfAi]<
Age Distribution
Section 4.5.2 of the guidance specifies that default data should be used only if an
alternative local dataset cannot be obtained and the regional conformity analysis relies on
national defaults. However, for the sake of simplicity only, in this example the national
default age distribution for 2015 is used for all vehicles and all runs (see Exhibit E-6).
Exhibit E-6. Age Distribution Table
11, age dist.xls
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
N 4
A
sourceTyp
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
B | C
yearlD agelD
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
> H (\sourceTypeAgeDistribu1
r-. 1 p— I r— \ ... ..'
D 1 E | F | A
ageFraction
0.0599
0.0609
0.0616
0.0622
0.0620
0.0579
0.0559
0.0556
0.0578
0.0584
0.0591
0.0558
0.0497
0.0461
0.0404
0.0339
0.0286
0.0215
0.0163
0.0125
0.0109
0.0089 v
tion£]<~ > j ~
E-10
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PUBLIC DRAFT-MAY 2010
Fuel Supply and Fuel Formulation
In this example, it is determined appropriate to use the default fuel supply and
formulation (see Exhibits E-7 and E-8). The default fuel supply and formulation are
input for each respective quarter (January, April, July, and October) and used for the
corresponding MOVES runs.
Exhibit E-7. Fuel Supply Table
fuelsupplyJan.xls
A
B
D
G "T
countylD fuelYearlD tnonthGroLfuelFormul marketShsmarketShareCV
99001 2015 1 1054 1 0.5
99001 2015 1 3043 1 0.5
H 4 > H ]\FuelSupply/ County ~£ FuelFormulati j <
Exhibit E-8. Fuel Formulation Table
3, fuelforrnjan.xls
A
B
D
1_ fuelFormullfuelSubtyp RVP sulfurLevel ETOHVolu MTBEVolu ETBEVolu TAMEVolu an
3011
3043
3100
3113
3281
3300
3337
3450
3468
20
20
20
20
20
20
20
20
20
20
20
11
43
100
113
281
300
337
450
468
0
H\FuelFormulation/ FuelSubtype /
E-ll
-------
PUBLIC DRAFT-MAY 2010
Inspection and Maintenance (I/M)
As there is no PM emissions benefit in MOVES for I/M programs, this menu item is
skipped (see Section 4.5.4).
Link Source Type
The distribution of vehicle types on each link is defined in the Link Source Type table
(Exhibit E-9) following the guidance in Section 4.5.5. The fractions are derived from the
vehicle count estimates in Exhibit E-3.
Exhibit E-9. Link Source Type Table
linklD
J B [ C |
sourceTyp sourceTypeHourFraction
D
9
10
11
12
13
14
15
16
JZJ
18 |
H 4
Links
21
31
41
61
62
21
31
41
61
62
21
31
41
61
62
21
31
0.45
0.35
0.01
0.01
0.19
0.22
0.22
0.01
0.02
0.53
0.22
0.22
0.01
0.02
0.53
0.45
0.35
H \HnkSourceTypeHoiir / Sourc |
> I
The Links input table shown in Exhibit E-10 (following page) is used to define each
individual project link in MOVES. Road Types 4 and 5 indicate Urban Restricted
(freeway) and Urban Unrestricted (arterial) road types, respectively; these correspond to
the two road types represented in this example. The average speed is entered for all links,
but only used to calculate Op-Mode distributions for links 1, 4, 7, 8, 11, 14, 17, and 20
(others links are explicitly defined with a link-drive schedule or Op-Mode distribution).
Link length and link volume is entered for each link; however, since the "Emission
Rates" option is selected in the Scale Panel, MOVES will produce grams/vehicle-mile.
The volume and link length will become relevant when running the air quality model
later in this analysis.
E-12
-------
PUBLIC DRAFT-MAY 2010
Exhibit E-10. Links Input (AM Period)
JlinkID countylD zonelD roadTypelD linkLength linkVolums
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
99001
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
990010
0.58
0.16
0.05
0.58
0.14
0.05
0.28
0.32
0.05
0.04
0.08
0.12
0.04
0.08
0.05
0.04
0.08
8.12
8.04
8.08
5060
568
568
5060
568
568
618
616
568
568
568
568
568
568
568
568
568
568
568
568
linkAvgSpeed linkDescription
55 EB highway
45 EB off-ramp cruise
45 EB off-ramp queue
55 WB highway
45 WB off-ramp cruise
45 WB off-ramp queue
16.6 EB on-ramp
16.6 WB on-ramp
45 sNB cruise
45 sNB queue
30.3 sNB depart
45 MB connect
45 nNB queue
30.3 nNB depart
45 nSB cruise
45 nSB queue
30.3 nSB depart
45 SB connect
45 sSB queue
30.3 sSB depart
H \linkX County / RoadType ^Zone /
The remaining links are defined with an Op-Mode distribution (Exhibit E-l 1) calculated
separately, as discussed earlier. Operating modes used in this analysis vary by both link
and source type, but not by hour or day.
Exhibit E-ll. Operating Mode Distribution Table
B
sourceTyp hourDaylD linkID polProces
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
21 15 1
1C
. 11 ic .1 14
H \0p_mpdex Sheet2 /Sheets /
9101
9101
9101
9101
9101
9101
9190
9190
9190
9190
9190
9190
11001
11001
11001
11001
11001
11001
11015
11015
11015
11015
11015
11015
140.17
opModelD
35
40
38
39
0
33
35
40
38
39
0
33
35
40
38
39
0
33
35
40
38
39
0
33
opModeFraction
0.2
0.28
0.08
0.08
0.2
0.16
0.2
0.28
0.08
0.08
0.2
0.16
0.2
0.28
0.08
0.08
0.2
0.16
0.2
0.28
0.08
0.08
0.2
0.16
r<
>i
E-13
-------
PUBLIC DRAFT-MAY 2010
Off-Network
As it was determined that there are no off-network links (such as parking lots or truck
stops) that would have to be considered using the Off-Network Importer, there is no need
to use this option in this example.
E.5.6 Generating emission factors for use in air quality modeling
After generating the run specification and entering the required information into the
Project Data Manager as described above, MOVES is run 16 times, once for each unique
hour/month combination. Upon completion of each run, the MOVES output is located in
the MySQL output database table "rateperdistance" and sorted by Month, Hour, LinkID,
ProcessID, and PollutantlD. An aggregate PM2 5 emission factor is then calculated by the
project sponsor for each Month, Hour, and LinkID combination using the following
equation and the guidance given in Section 4.4.7 of the guidance:
PMaggregate total = (PMtotal running) + (PMtotal crankcase running) + (brake Wear) + (tire Wear)
The 16 resulting grams/vehicle-mile emission factors (Exhibit E-12, following page) for
each link are then ready to be used as input into the CAL3QHCR dispersion model to
predict future PM2 5 concentrations.
E-14
-------
PUBLIC DRAFT-MAY 2010
Exhibit E-12. Grams/Vehicle-Mile Emission Factors Calculated from MOVES
Output by Link, Quarter, and Hour
' ;5|§ J^gljI^iifJir^^M^ •jjs.-iSfHP- ljgg§jjj_^ \
I 1
2
3
"4
5
6
7
8
"9
10
11
12
13
14
15
16
17
18
19'
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
(37
38
39"
40'
41
42
H 4
A | B
linkID linkLength (miles)
1 0.58
2 0.16
3 0.05
4 0.58
5 0.14
6 0.05
7 0.28
8 0.32
9 0.05
10 0.04
11 0.08
12 0.12
13 0.04
14 0.08
15 0.05
16 0.04
17 0.08
18 0.12
19 0.04
20 0.08
linkID linkLength (miles)
1 0.58
2 0.16
3 0.05
4 0.58
5 0.14
6 0.05
7 0.28
8 0.32
9 0.05
10 0.04
11 0.08
12 0.12
13 0.04
14 0.08
15 0.05
16 0.04
17 0.08
18 0.12
19 0.04
20 0.08
> H \ Output Xgramspervf
C D E F
Jan12am Jan6am Jan12pm Jan6pm
0.121 0.128 0.113 0.111
0.374 0.374 0.373 0.373
0.260 0.265 0.255 0.254
0.121 0.128 0.113 0.111
0.374 0.374 0.373 0.373
0.260 0.265 0.255 0.254
0.539 0.552 0.524 0.522
0.539 0.552 0.524 0.522
0.399 0.399 0.398 0.398
0.336' 0.342' 0.328 0.327
0.364 0.370' 0.357 0.356
0.399 0.399 0.398 0.398
0.336 0.342 0.328 0.327
0.364 0.370 0.357 0.356
0.399 0.399 0.398 0.398
0.336 0.342 0.328 0.327
0.364 0.370 0.357 0.356
0.399 0.399 0.398 0.398
0.336 0.342 0.328 0.327
0.364 0.370 0.357 0.356
Jul12am JuEam Jul12pm Jul6pm
0.085 0.086 0.084 0.084
0.369 0.369 0.369 0.369
0.238 0.239 0.237 0.237
0.085 0.086 0.084 0.084
0.369 0.369 0.369 0.369
0.238 0.239 0.237 0.237
0.469 0.472 0.468 0.468
0.469 0.472 0.468 0.468
0.394 0.394 0.394 0.394
0.304 0.305' 0.303 0.303
0.332' 0.333 0.331 0.331
0.394 0.394 0.394' 0.394
0.304 0.305 0.303 0.303
0.332 0.333 0.331 0.331
0.394 0.394 0.394 0.394
0.304 0.305 0.303 0.303
0.332 0.333 0.331 0.331
0.394 0.394 0.394 0.394
0.304 0.305 0.303 0.303
0.332 0.333 0.331 0.331
£ i H | I | J ! — I
Apr12am Apr6am Apr12pm Apr6pm
0.098 0.103 0.090 0.089
0.371 0.372 0.371 0.370
0.246 0.249 0.242 0.241
0.098 0.103 0.090 0.089
0.371 0.372 0.371 0.370
0.246 0.249 0.242 0.241
0.498 0.507 0.484 0.482
0.498 0.507 0.484 0.482
0.396 0.397 0.396 0.395
0.316 0.320 0.309 0.308
0.346 0.350 0.340 0.339
0.396 0.397 0.396 0.395
0.316 0.320 0.309 0.308
0.346 0.350 0.340 0.339
0.396 0.397 0.396 0.395
0.316 0.320 0.309 0.308
0.346 0.350 0.340 0.339
0.396 0.397 0.396 0.395
0.316 0.320 0.309 0.308
0.346 0.350 0.340 0.339
Oct12am OctGam Oct12pm Oct6pm
0.096 0.099 0.088 0.088
0.370 0.371 0.370 0.370
0.244 0.247 0.240 0.240
0.096 0.099 0.088 0.088
0.370 0.371 0.370 0.370
0.244 0.247 0.240 0.240
0.489 0.495 0.475 0.476
0.489 0.495 0.475 0.476
0.395 0.396 0.394 0.395
0.313 0.316 0.306 0.307
0.340 0.343 0.334 0.334
0.395 0.396 0.394 0.395
0.313 0.316 0.306 0.307
0.340 0.343 0.334 0.334
0.395 0.396 0.394 0.395
0.313 0.316 0.306 0.307
0.340 0.343 0.334 0.334
0.395 0.396 0.394 0.395
0.313 0.316 0.306 0.307
0.340 0.343 0.334 0.334
V
shiclemile/ CAL3QHCR Inputs / < ! > '•
E-15
-------
PUBLIC DRAFT-MAY 2010
E.6 ESTIMATE DUST AND OTHER EMISSIONS (STEP 4)
E. 6.1 Estimating re-entrained road dust
In this case, this area does not have any adequate or approved SIP budgets for either
PM2 5 NAAQS, and neither the EPA nor the state air agency have made a finding that
road dust emissions are a significant contributor to the air quality problem for either
PM2 5 NAAQS. Therefore, PM25 emissions from road dust do not need to be considered
in this analysis (see Sections 2.5.3 and 6.2).
E. 6.2 Estimating transportation-related construction dust
The construction of this project will not occur during the analysis year. Therefore,
emissions from construction dust are not included in this analysis (see Sections 2.5.5 and
6.4).
E. 6.3 Estimating other sources of emissions in the project area
Through interagency consultation, it is determined that the project area in the analysis
year does not include locomotives or other nearby emission sources that have to be
considered in the analysis (see Section 6.6).
E.7 SELECT AN AIR QUALITY MODEL, DATA INPUTS, AND RECEPTORS
(STEP 5)
E. 7.1 Characterizing emission sources
As discussed previously, the CAL3QHCR model is selected to estimate PM2.5
concentrations for this analysis (see Section 7.3). Each link is defined in CAL3QHCR
with coordinates and dimensions matching the project parameters (shown in Exhibit E-4).
The necessary inputs for link length, traffic volume, and corresponding link emission
factor are also added using the CAL3QHCR Tier II approach. Each MOVES emission
factor (12 a.m., 6 a.m., 12 p.m., and 6 p.m.) and traffic volume (average or peak) for each
link is applied to multiple hours of the day, as follows:
• Morning peak (AM) emissions based on traffic data and meteorology occurring
between 6 a.m. and 9 a.m.;
• Midday (MD) emissions based on data from 9 a.m. to 4 p.m.;
• Evening peak (PM) emissions based on data from 4 p.m. to 7 p.m.;
• Overnight (ON) emissions based on data from 7 p.m. to 6 a.m.
In addition, these factors are applied to each of the four quarters being modeled.
E-16
-------
PUBLIC DRAFT-MAY 2010
CAL3QHCR scenarios are built to model traffic conditions for all 24 hours of a weekday
in each quarter (partial elements of the CAL3QHCR input file can be found in Exhibits
E-13a and 13b): in all, four separate scenarios.
Exhibit E-13a. CAL3QHCR Quarter 1, 6 a.m. Input File (Partial)
P highway_jan.lNP - Notepad
File Edit
Format View Help
'Hot-Spot Highway Exampl
1 1 98
94823
1 1 'U
1'
2'
3'
4'
5'
6'
7'
8'
9'
10'
11'
12'
13'
14'
15'
16'
17'
18'
19'
20'
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22'
23'
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33'
34'
35'
36'
37'
38'
39'
40'
41'
12 31 98
^8 94823 98
-42.9 -20
-28.8 -3.1
-16.5 29
-16.5 48.8
29.6 31.8
12.7 -101
-14.6 -100.1
15.5 -152.9
-14.6 -154.7
-12.7 -220.7
17.4 -205.6
-11.8 -265.9
21.2 -257.4
19.3 -334.7
35.3 -333.7
34.4 -317.7
-21.2 -395.9
-23.1 -349.7
24 -18.1
-31.6 15.8
-3.3 -435.5
12.7 -7.8
24 -379
28.7 -411
45.7 -353.5
-9.9 -360.1
-12.7 -320.5
-42.9 -365.8
-19.3 -21
-22.2 -57.7
10.8 19.6
46.6 20.5
10.8 52.5
13.6 -32.3
48.5 -6.8
-7.1 -386.5
-43.8 -394
-6.2 -410
43.8 -387.4
-33.5 -38.9
33.4 -432.7
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60". 175"; 6". 0". 41 1 0 A~
8
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V
E-17
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PUBLIC DRAFT-MAY 2010
Exhibit E-13b. CAL3QHCR Quarter 1, 6 a.m. Input File (Partial)
H highway_jan.lNP -Notepad
File Edit Format
View Help
2 'p' *|
1111111
'Example Highway Project'
1 1
1 EB highway'
' EB off-ramp
3 1
1 EB off-ramp
4 1
'WB highway'
5 1
'WB off -ramp
6 1
'WB off -ramp
7 1
1 EB on-ramp'
8 1
'WB on-ramp'
9 1
1 sNB cruise'
10 1
1 SNB queue'
11 1
1 SNB depart '
12 1
1 NB connect '
13 1
1 nNB queue'
14 1
' nNB depart '
15 1
1 nSB cruise'
16 1
1 nSB queue'
17 1
' nSB depart '
18 1
1 SB connect '
19 1
' sSB queue'
20 1
' SSB depart '
01 0.0
1
2
3
4
5
6
7
8
9
10
11
'ag'
cruise'
queue'
'ag'
cruise'
queue'
'ag'
'ag'
'ag'
'ag'
'ag'
'br1
'ag'
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'ag'
'ag'
5060
568
568
5060
568
568
616
616
568
568
568
-422
'ag'
'ag'
358
'ag1
'ag'
19
-14
26
18
12
9
2
1
-10
-8
-9
-7
-3
2
0.1214
0.3737
0.2605
0.1214
0.3737
0.2605
0. 5392
0. 5392
0.3985
0.3356
0.3642
20
367
-337
-89
-440
315
96
300
-360
18
12
9
2
1
1
-8
-9
-7
-3
2
12
-469
-89
-3
44
96
10
-367
2
-507
-433
-371
-246
-56
5
142
68
6
-122
-311
-371
32
-424
-386
-453
29
13
-13
-386
-433
-371
-246
-56
5
130
68
6
-122
-311
-371
-501
0
-386
-372
0
13
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0 9
0 9
12
0 9
0 9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
V
E-18
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PUBLIC DRAFT-MAY 2010
Section 7.5 of the guidance recommends that users run the air quality model for five
years of meteorological data when on-site meteorology data is not available. Since
CAL3QHCR can only process one year of meteorological data for each run, each
quarterly scenario is run for five years of meteorological data for a total of 20 runs.2
E. 7.2 Incorporating meteorological data
Through the interagency consultation process, a representative set of meteorology data,
as well as an appropriate surface roughness are selected (see Section 7.5). The
recommended five years of meteorological data are obtained from a local airport for
calendar years 1998-2002. A surface roughness of 175 cm is selected for the site; this is
consistent with the recommendations made in the Section 7 of the guidance.
E. 7.3 Specifying receptors
Using the interagency consultation process and the guidance given in Section 7.6,
receptors are placed in appropriate areas within the area substantially affected by the
project (Exhibit E-14, following page). Receptor heights are set at 1.8 meters (the
approximate height at which a person breathes). Additionally, a background
concentration of "0" is input into the model. Representative background concentrations
are added later (see Step 7).
CAL3QHCR is then run with five years of meteorological data (1998 through 2002) and
output is produced for all receptors for each of the five years of meteorological data.
2 As explained in Section 7, AERMOD allows five years of meteorological data to be modeled in a single
run (see Section 7.5.3)
E-19
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PUBLIC DRAFT-MAY 2010
Exhibit E-14. Receptor Locations for Air Quality Modeling
400 meters
E.8 DETERMINE BACKGROUND CONCENTRATIONS (STEP 6)
Through the interagency consultation process, a nearby upwind PM2.5 monitor that has
been collecting ambient data for both the annual and 24-hour PM2.5 NAAQS is
determined to be representative of the background air quality at the project location. The
most recent data set is used (in this case, calendar year 2008 through 2010) and average
24-hour PM2.5 values are taken in a four-day/three-day measurement interval. As
previously noted, no nearby sources requiring explicit modeling are identified.
Note: This is a highly simplified situation for illustrative purposes; refer to Section 8 of
the guidance for additional considerations for how to most accurately reflect background
concentrations in a real-world scenario.
E-20
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PUBLIC DRAFT-MAY 2010
E.9 CALCULATE DESIGN VALUES AND COMPARE BUILD AND NO-BUILD
SCENARIO RESULTS (STEP 7)
With both CAL3QHCR outputs and background concentrations now available, the
project sponsor can calculate the design values. For illustrative purposes, calculations for
a single receptor for the build scenario are shown in this example, but any analysis should
be done at all receptors for comparison with the relevant NAAQS. In this step, the
guidance from Section 9.3.2 and 9.3.3 is used to calculate design values from the
modeled results and the background concentrations for comparison with the 24-hour and
annual PM2.5 NAAQS.
E.9.1 Determining conformity to the annual PM2.s NAAQS
First, average background concentrations are determined for each year of monitored data
(shown in Exhibit E-15).
Exhibit E-15. Annual Average Background Concentration for Each Year
Monitoring
Year
2008
2009
2010
Annual Average
Background
Concentration
13.348
12.785
13.927
E-21
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PUBLIC DRAFT-MAY 2010
The three-year average background concentration is then calculated (see Exhibit E-16).
Exhibit E-16. Calculation of Annual Design Value (At Highest Receptor)
Annual Average
Background
Concentration
(Three-year
Average)
13.353
Annual Average
Modeled
Concentration
(Five-year
Average)
1.580
Sum of
Background +
Project
14.933
Annual Design Value
14.9
To determine the annual PM2 5 design value, the annual average background
concentration is added to the five-year annual average modeled concentration (at the
receptor with the highest annual average concentration from the CAL3QHCR output).
This calculation is shown in Exhibit E-16. The sum (background + project) results in a
design value of 14.9 ng/rn3. This value at the highest receptor is less than the 1997
NAAQS of 15.0 ng/rn3. It can be assumed that all other receptors with lower modeled
concentrations will also have design values less than the 1997 NAAQS. In this example
it is unnecessary to determine appropriate receptors in the build scenario or develop a no-
build scenario for the annual PM2.5 NAAQS, since the build scenario demonstrates that
the hot-spot analysis requirements in the transportation conformity rule are met at all
receptors.
E.9.2 Determining conformity to the 24-Hour PM2.s NAAQS
The next step is to calculate a design value to compare with the 2006 24-hour PM2.5
NAAQS through a "Second Tier" analysis as described in Section 9.3.3. For ease of
explanation, this process has been divided into individual steps, consistent with the
guidance.
Step 7.1
The number of background measurements is counted for each year of monitored data
(2008 to 2010). Based on a 4-day/3-day measurement interval, the dataset has 104 values
per year.
Step 7.2
For each year of monitored concentrations, the eight highest daily background
concentrations for each quarter are determined, resulting in 32 values (4 quarters; 8
concentrations/quarter) for each year of data (shown in Exhibit E-17, following page).
Step 7.3
Identify the highest-predicted modeled concentration resulting from the project in each
quarter, averaged across each year of meteorological data used for air quality modeling.
For illustrative purposes, the highest average concentration across five years of
meteorological data for a single receptor in each quarter is shown in Exhibit E-18
(following page). Note that, in a real-world situation, this process would be repeated for
all receptors in the build scenario.
E-22
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PUBLIC DRAFT-MAY 2010
Exhibit E-17. Highest Daily Background Concentrations for Each Quarter and
Each Year
2008
Rank
1
2
3
4
5
6
7
8
Q1
20.574
20.152
19.743
19.346
18.961
18.588
18.226
17.874
Q2
21.262
20.823
20.398
19.985
19.584
19.196
18.819
18.454
Q3
22.354
22.042
21.735
21.434
21.140
20.851
20.568
20.291
Q4
20.434
20.016
19.611
19.218
18.837
18.467
18.109
17.761
2009
Rank
1
2
3
4
5
6
7
8
Q1
20.195
19.784
19.386
19.000
18.625
18.262
17.910
17.568
Q2
20.867
20.440
20.026
19.624
19.235
18.857
18.490
18.135
Q3
21.932
21.628
21.329
21.037
20.750
20.469
20.194
19.924
Q4
20.058
19.651
19.257
18.875
18.504
18.145
17.796
17.457
2010
Rank
1
2
3
4
5
6
7
8
Q1
21.137
20.698
20.272
19.860
19.459
19.071
18.694
18.329
Q2
21.847
21.390
20.948
20.519
20.102
19.698
19.307
18.927
Q3
22.980
22.655
22.336
22.023
21.717
21.417
21.123
20.834
Q4
20.990
20.556
20.135
19.726
19.330
18.945
18.572
18.211
Exhibit E-18. Five-year Average 24-hour Modeled Concentrations for Each Quarter
(At Example Receptor)
Five Year Average
Maximum
Concentration (At
Example Receptor)
Q1
10.42
Q2
10.62
Q3
10.74
Q4
10.61
E-23
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PUBLIC DRAFT-MAY 2010
Step 7.4
The highest modeled concentration in each quarter (from Step 7.3) is added to each of the
eight highest monitored concentrations for the same quarter for each year of monitoring
data (from Step 7.2). As shown in Exhibit E-19, this step results in eight concentrations
in each of four quarters for a total of 32 values for each year of monitoring data. As
mentioned, this example analysis shows only a single receptor's values, but project
sponsors should calculate design values at all receptors in the build scenario.
Exhibit E-19. Sum of Background and Modeled Concentrations at Example
Receptor for Each Quarter
2008
Rank
1
2
3
4
5
6
7
8
Q1
31.084
30.662
30.253
29.856
29.471
29.098
28.736
28.384
Q2
31.902
31.463
31.038
30.625
30.224
29.836
29.459
29.094
Q3
32.994
32.682
32.375
32.074
31.780
31.491
31.208
30.931
Q4
31.074
30.656
30.251
29.858
29.477
29.107
28.749
28.401
2009
Rank
1
2
3
4
5
6
7
8
Q1
30.705
30.294
29.896
29.510
29.135
28.772
28.420
28.078
Q2
31.507
31.080
30.666
30.264
29.875
29.497
29.130
28.775
Q3
32.572
32.268
31.969
31.677
31.390
31.109
30.834
30.564
Q4
30.698
30.291
29.897
29.515
29.144
28.785
28.436
28.097
2010
Rank
1
2
3
4
5
6
7
8
Q1
31.647
31.208
30.782
30.370
29.969
29.581
29.204
28.839
Q2
32.487
32.030
31.588
31.159
30.742
30.338
29.947
29.567
Q3
33.620
33.295
32.976
32.663
32.357
32.057
31.763
31.474
Q4
31.630
31.196
30.775
30.366
29.970
29.585
29.212
28.851
E-24
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PUBLIC DRAFT-MAY 2010
Step 7.5
As shown in Exhibit E-20, for each year of monitoring data, the 32 values from Step 7.4
are ordered together in a column and assigned a yearly rank for each value, from 1
(highest concentration) to 32 (lowest concentration).
Exhibit E-20. Ranking Sum of Background and Modeled Concentrations at
Example Receptor for Each Year of Background Data
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
2008
32.994
32.682
32.375
32.074
31.902
31.780
31.491
31.463
31.208
31.084
31.074
31.038
30.931
30.662
30.656
30.625
30.253
30.251
30.224
29.858
29.856
29.836
29.477
29.471
29.459
29.107
29.098
29.094
28.749
28.736
28.401
28.384
2009
32.572
32.268
31.969
31.677
31.507
31.390
31.109
31.080
30.834
30.705
30.698
30.666
30.564
30.294
30.291
30.264
29.897
29.896
29.875
29.515
29.510
29.497
29.144
29.135
29.130
28.785
28.775
28.772
28.436
28.420
28.097
28.078
2010
33.620
33.295
32.976
32.663
32.487
32.357
32.057
32.030
31.763
31.647
31.630
31.588
31.474
31.208
31.196
31.159
30.782
30.775
30.742
30.370
30.366
30.338
29.970
29.969
29.947
29.585
29.581
29.567
29.212
29.204
28.851
28.839
E-25
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Steps 7.1 through 7.6 are repeated to calculate a projected 98th percentile concentration at
For the example receptor, the average of the three projected 98th percentile concentrations
PUBLIC DRAFT-MAY 2010
Step 7.6
For each year of monitoring data, the value with a rank that corresponds to the projected
98th percentile concentration is determined. As discussed in Section 9, an analysis
employing 101-150 background values for each year (as noted in Step 7.1, this analysis
uses 104 values per year) uses the 3rd highest rank to represent a 98th percentile. The 3rd
highest concentration (highlighted in Exhibit E-20) is referred to as the "projected 98th
percentile concentration."
Step 7.7
Steps 7.1
each receptor based on each year of monitoring data and modeled concentrations.
Step 7.8
For the e
(see Step 7.6) is calculated.
Step 7.9
The resulting value of 32.440 |J,g/m3 is then rounded to the nearest whole ng/m3, resulting
in a design value at the example receptor of 32 ng/rn3. At each receptor this process
should be repeated. In the case of this analysis, the example receptor is the receptor with
the highest design value in the build scenario.
Step 7.10
The design values calculated at each receptor are compared to the NAAQS. In the case
of this example, the highest 24-hour design value (32 |J,g/m3) is less than the 2006 PM2 5
24-hour NAAQS of 35 |J,g/m3. Since this is the design value at the highest receptor, it
can be assumed that the conformity requirements are met at all receptors in the build
scenario. Therefore, it is unnecessary for the project sponsor to calculate design values
for the no-build scenario for the 24-hour NAAQS.
E.10 CONSIDER MITIGATION AND CONTROL MEASURES (STEP 8)
In this case, the project is determined to conform. In situations when this is not the case,
it may be necessary to consider additional mitigation or control measures. If measures
are considered, additional air quality modeling would need to be completed and new
design values calculated to ensure that conformity requirements are met. See Section 10
for more information, including some specific measures that might be considered.
E. 11 DOCUMENT THE PM HOT-SPOT ANALYSIS (STEP 9)
The final step is to properly document the PM hot-spot analysis in the conformity
determination (see Section 3.10).
E-26
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PUBLIC DRAFT-MAY 2010
Appendix F:
Example Quantitative PM Hot-spot Analysis of a Transit
Project using MOVES and AERMOD
F.I INTRODUCTION
This purpose of this appendix is to demonstrate the procedures for completing a hot-spot
analysis using MOVES and AERMOD following the basic steps described in Section 3.
Readers should reference the appropriate sections in the guidance as needed for more
detail on how to complete each step of the analysis. This example is limited to showing
the build scenario; in practice, project sponsors may have to also analyze the no-build
scenario. While this example calculates emission rates using MOVES, EMFAC users
may find the air quality modeling described in this appendix helpful.
Note: The following example of a quantitative PM hot-spot analysis is highly simplified
and intended only to demonstrate the basic procedures described in the guidance. This
example uses default data in places where the use of project-specific data in a real-world
situation would be expected. In addition, actual PM hot-spot analyses could be
significantly more complex, and are likely to require more documentation of data and
decisions.
F.2 PROJECT DESCRIPTION AND CONTEXT
The proposed project is a new regionally significant bus terminal that would be created
by taking a downtown street segment one block in length and reserving it for bus use
only. It would be an open-air facility containing six "sawtooth" lanes where buses enter
to load and unload passengers. The terminal is designed to handle about 575 diesel buses
per day with up to 48 buses in the peak hour. The project is located in an area designated
nonattainment for the 2006 PM2.5 24-hour NAAQS and 1997 PM2.5 annual NAAQS.
The following is some additional pertinent data about the project:
• The proposed project is located in a medium-size city (within one county) in a
state other than California.
• The project is expected to take less than a year to complete and has an estimated
completion date of 2013. The year of peak emissions is expected to be 2015,
when considering the project's emissions and background concentrations.
• The area surrounding the proposed project is primarily commercial, with no
nearby sources of PM2.5 that need to be explicitly modeled. This assumption is
made to simplify the example. In most cases, transit projects include parking lots
with emissions that would be considered in a PM hot-spot analysis.
• The state does not have an adequate or approved SIP budget for either PM2 5
NAAQS, and neither the EPA nor the state air quality agency has made a finding
that road dust is a significant contributor to the PM2.5 nonattainment problem.
F-l
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PUBLIC DRAFT-MAY 2010
F.3 DETERMINE NEED FOR ANALYSIS (STEP i)
Through interagency consultation, the proposed project is determined to be of local air
quality concern under the conformity rule because it is a new bus terminal that has a
significant number of diesel vehicles congregating at a single location (see 40 CFR
93.123(b)(l)(iii) and Sections 1.4 and 3.2 of the guidance). Therefore, a quantitative PM
hot-spot analysis is required.
F.4 DETERMINE APPROACH, MODELS, AND DATA (STEP 2)
F. 4.1 Determining geographic area and emission sources to be covered by the analysis
First, the interagency consultation process is used to ensure that the project area is
defined so that the analysis includes the entire project, as required by 40 CFR
93.123(c)(2). As previously noted, it is also determined that, in this case, there are no
nearby emission sources to be explicitly modeled (see Section 3.3.2).
F. 4.2 Deciding on general analysis approach and analysis year(s)
The project sponsor then determines that the preferred approach in this case is to model
the build scenario first, completing a no-build scenario only if necessary.
The year of peak emissions (within the timeframe of the current transportation plan) is
determined to be 2015. Therefore, 2015 is selected as the year of the analysis, and the
analysis will consider traffic data from 2015 (see Section 3.3.3).
F. 4.3 Determining which PMNAAQS to be evaluated
Because the area has been designated nonattainment for both the 2006 NAAQS and 1997
NAAQS, the results of the analysis will have to be compared to both NAAQS (see
Section 3.3.4). All four quarters are included in the analysis in order to estimate a year's
worth of emissions for both NAAQS.
F-2
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PUBLIC DRAFT-MAY 2010
F.4.4 Deciding on the type of PM emissions to be modeled
Next, through interagency consultation the following directly-emitted PM emissions are
determined to be relevant for estimating the emissions in the analysis (see Section 3.3.5):
• Vehicle exhaust1
• Brake wear
• Tire wear
F. 4.5 Determining the models and methods to be used
Since this project will be located outside of California, MOVES2010 is used for
emissions modeling. In addition, it is determined that, since this is a terminal project, the
appropriate air quality model to use would be AERMOD (see Section 3.3.6). Making the
decision on what air quality model to use at this stage is important so that the appropriate
data are collected, among other reasons (see next step).
F. 4.6 Obtaining project-specific modeling data
Finally, having selected a model and a general modeling approach, the project sponsor
compiles the data required to use MOVES, including project traffic data, vehicle types
and age, and temperature and humidity data for the months and hours to be modeled
(specifics on the data collected are described in the following steps). In addition,
information required to use AERMOD to model air quality is gathered, including
meteorological data and information on representative air quality monitors. The sponsor
ensures the latest planning assumptions are used and that data used for the analysis are
consistent with that used in the latest regional emissions analysis, as required by the
conformity rule (see Section 3.3.7). The interagency consultation process is used to
discuss the data for the analysis.
F.5 ESTIMATE ON-ROAD MOTOR VEHICLE EMISSIONS (STEP 3)
Having completed the analysis preparations described above, the project sponsor then
follows the instructions provided in Section 4 of the guidance to use MOVES to estimate
the on-road emissions from this terminal project:
F. 5.1 Characterizing the project in terms of links
Using the guidance described in Section 4.2, a series of links are defined in order to
accurately capture the activity at the proposed terminal. As shown in Exhibit F-l
(following page), two one-way running links north and south of the facility ("Link 1" and
"Link 2") are defined to describe buses entering and exiting the terminal. A third
running/idle link (shown as "Link 3" to the north of the facility), is used to describe
vehicles idling at the signalized light before exiting the facility. Links 4 through 9
Represented in MOVES as PMtotai mnnmg, PMtotai crankcase rmmmg, PMtotai ext ldie> and PMtotai crankcase ext. idle.
F-:
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PUBLIC DRAFT-MAY 2010
represented bus bays where buses drop-off and pick-up passengers; these are referred to
as the terminal links.
Exhibit F-l. Diagram of Proposed Bus Terminal Showing Links
50 feet
The running links have the following dimensions:
Link 1: 200 feet long by 24 feet wide
Link 2: 160 feet long by 24 feet wide
Link 3: 40 feet long by 24 feet wide
Additionally, the dimensions of the six terminal links (Links 4 through 9) are 60 feet long
by 12 feet wide. These links are oriented diagonally from southwest to northeast. The
queue link (Link 3) is defined with a length of 40 feet, based on the average length of a
transit bus.
After identifying and defining the links, traffic conditions are estimated for the project in
the analysis year of 2015. The terminal was presumed to be in operation all hours of the
year. Based on expected terminal operations, the anticipated future traffic volumes are
available for each hour of an average weekday (see Exhibit F-2, following page). To
simplify the analysis, the sponsor conservatively assumes weekday traffic for all days of
the year, even though the operating plan calls for reduced service on weekends.2
Identical traffic volume and activity profiles are assumed for all quarters of the year.
Quarters are defined for this analysis as described in Section 3.3.4 of the guidance: Ql
(January-March), Q2 (April-June), Q3 (July-September), and Q4 (October-December).
2 This decision, which would be discussed through interagency consultation, is made to save time and
effort, as it would result in the need for fewer modeling runs. More accurate results would be obtained by
treating weekends differently and modeling them using the actual estimated Saturday and Sunday traffic.
F-4
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PUBLIC DRAFT-MAY 2010
Exhibit F-2. Average Weekday Bus Trips through Transit Terminal for Each Hour
Hour
12am - 1am
1am - 2am
2am - Sam
Sam - 4am
4am - Sam
Sam - 6am
6am - 7am
7am - Sam
Sam - 9am
9am - 10am
10am - 11am
11am - 12pm
12pm - 1pm
1pm - 2pm
2pm - 3pm
3pm - 4pm
4pm - 5pm
5pm - 6pm
6pm - 7pm
7pm - 8pm
8pm - 9pm
9pm - 10pm
10pm - 11pm
11pm - 12am
Bus Trips
7
6
6
6
7
9
27
48
39
29
26
28
30
31
31
39
44
42
26
21
22
17
13
10
F.5.2 Deciding on how to handle link activity
As discussed in Section 4.2 of the guidance, MOVES offers several options for users to
apply activity information to each LinklD. For illustrative purposes, based on the
available information for the project (average speed, hourly bus volume, idle time, and
fraction of vehicles encountering a red-light) several methods of deriving Op-Mode
distributions are employed in this example, as described below.
• Links 1 and 2 represent buses driving at an average of 5 mph through the
terminal, entering and exiting the bus bays. An average speed of 5 mph is entered
into the MOVES "links" input, which calculates an Op-Mode distribution to
reflect the MOVES default 5 mph driving pattern.
• The queue link (Link 3) is given an Op-Mode distribution that represents buses
decelerating, idling, and accelerating (red light) as well as cruising through (green
light). First, an Op-Mode distribution is calculated for the link average speed (5
mph). Because this does not adequately account for idling at the intersection, the
Op-Mode fractions are re-allocated to add in 50% idling (determined after
consulting the 2000 Highway Capacity Manual to approximate idle time in an
under-capacity scenario) reflecting 50% of buses encountering a red light. A
F-5
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PUBLIC DRAFT-MAY 2010
fraction of 0.5 for Op-Mode "1" is added to the re-allocated 5 mph average speed
Op-Mode distribution. The resulting Op-Mode distribution represents all activity
on a queuing intersection link.
• The bus bays (Links 4 through 9) are represented by a single link (modeled in
MOVES as "LinkID 4") and activity is defined in the Links table by an average
speed of "0", representing exclusively idle activity.
F. 5.3 Determining the number of MO VES runs
Following the guidance given in Section 4.3, it is determined that 16 MOVES runs
should be completed to produce emission factors that show variation across four hourly
periods (12 a.m., 6 a.m., 12 p.m., and 6 p.m., corresponding to overnight, morning,
midday, and evening traffic scenarios, respectfully) and four quarterly periods
(represented by the months of January, April, July, and October; see Section 3.3).
MOVES would calculate values for all project links for the time period specified in each
run. Although traffic data is available for 24 hours, the emission factors produced from
the 16 scenarios would be post-processed into grams/vehicle-hour and further converted
to grams/hour emission factors that vary based on the hour-specific vehicle count. This
methodology avoids running 24 hourly scenarios for four quarters (96 runs). A
grams/hour emissions rate is required to use AERMOD.
F. 5.4 Developing basic run specification inputs
When configuring MOVES for the analysis, the project sponsor follows Section 4.4 of
the guidance, including, but not limited to, the following:
• From the Scale menu, selecting the "Project" domain; in addition, choosing
output in "Inventory" so that total emission results are produced for each link,
which is equivalent to a grams/hour/link emission factor needed by AERMOD
(see Section 4.4.2).
• From the Time Spans Panel, the appropriate year, month, day, and hour for each
run is selected (see Section 4.4.3).
• From the Geographic Bounds Panel, the custom domain is selected (see Section
4.4.4).
• From the Vehicles/Equipment Panel, Diesel Transit Buses are selected (see
Section 4.4.5).
• From the Road Types Panel, the Urban Restricted road type is selected (see
Section 4.4.6).
• From the Pollutants and Processes Panel, appropriate pollutant/processes are
selected according to Section 4.4.7 of the guidance for "highway links".
• In the Output Panel, an output database is specified with grams and miles selected
as units (see Section 4.4.10).
F-6
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PUBLIC DRAFT-MAY 2010
F. 5.5 Entering project details using the Project Data Manager
Meteorology
As described previously, it is determined that MOVES should be run 16 times to reflect
the following scenarios: 12 a.m., 6 a.m., 12 p.m., and 6 p.m. (corresponding to overnight,
morning, midday, and evening traffic scenarios, respectfully) for the months of January,
April, July, and October. Through the interagency consultation process, temperature and
humidity data from a representative meteorological monitoring station are obtained and
confirmed to be consistent with data used in the regional emissions analysis from the
currently conforming transportation plan and TIP (see Section 4.5.1). Average values for
each hour and month combination are used for each of the 16 MOVES runs. As an
example, temperature and humidity values for 12 a.m. January are shown in Exhibit F-3.
Exhibit F-3. Temperature and Humidity Input (January 12 a.m.)
H metjanl 2am.xls
__A_
monthID zonelD hourlD
1 990010
temperaturrelHumidity
1 26.2 75.4
H \ZoneMonthHour / HourOf Ai | <
F-7
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PUBLIC DRAFT-MAY 2010
Age Distribution
Section 4.5.2 of the guidance specifies that default data should be used only if an
alternative local dataset cannot be obtained and the regional conformity analysis relies on
national defaults. However, for the sake of simplicity only, in this example the national
default age distribution for 2015 is used for all vehicles and all runs (see Exhibit F-4). As
discussed in the guidance, transit agencies should be able to provide a fleet-specific age
distribution, and the use of fleet-specific data is always recommended (and would be
expected in a real-world scenario) because emission factors vary significantly depending
on the age of the fleet.
Exhibit F-4. Age Distribution Table
1| agedist.xls (K*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
H 4
A
sourceTyp
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
B C
yearlD agelD
2015 0
2015 1
2015 2
2015 3
2015 4
2015 5
2015 6
2015 7
2015 8
2015 9
2015 10
2015 11
2015 12
2015 13
2015 14
2015 15
2015 16
D E | F | -
ageFraction
0.052013
0.052432
0.051104
0.050951
0.0509
0.050341
0.045595
0.038191
0.034719
0.038183
0.043573
0.043438
0.051516
0.047071
0.043819
0.035929
0.031348 v
* H \sguirceTypeAge^ > J
F-8
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PUBLIC DRAFT-MAY 2010
Fuel Supply and Fuel Formulation
An appropriate fuel supply and formulation is selected to match the project area's diesel
use. In MOVES, diesel fuel formulation is constant across all quarters, so one fuel
supply/fuel formulation combination is used for all MOVES runs. Also, it is known that
100% of the transit buses would use diesel fuel, so a fraction of 1 is entered for fuel 3043
(ultra-low-sulfur diesel fuel) in the Fuel Supply Table. In the case of this example, the
default fuel supply/formulation matches the actual fuel supply/formulation, so it is
therefore appropriate to use the default in the analysis (see Exhibits F-5 and F-6).
Exhibit F-5. Fuel Supply Table
3 fuelsupplyJan.xls
1
2
3
4
5
6
7
N 4
A
count}
9£
9£
> H
B
D
G -i1
fuelYearlD monthGroLfuelFormul marketShsmarketShareCV
2015 1 1054 1 0.5
2015 1 3043 1 0.5
M KFuelSupply/ County ^ FyelFormulati j <
Exhibit F-6. Fuel Formulation Table
HI fuelformjan.xls
B
D
fuelFormullfuelSubtyp RVP
sulfurLevel ETOHVolu MTBEVoluETBEVolu TAMEVoluare
3011
3043
3100
3113
3281
3300
3337
3450
3468
20
20
20
20
20
20
20
20
20
20
20
11
43
100
113
281
300
337
450
468
0
w \FuelFormulatign / FueJSubtype /
J<
F-9
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PUBLIC DRAFT-MAY 2010
Inspection and Maintenance (I/M)
As there is no PM emissions benefit in MOVES for I/M programs, this menu item is
skipped (see Section 4.5.4).
Link Source Type
The distribution of vehicle types on each link is defined in the Link Source Type table
following the guidance in Section 4.5.5. Given that the project will be a dedicated transit
bus terminal this analysis assumes only transit buses are operating on all links.
Therefore, a fraction of 1 is entered for Source Type 42 (Transit Buses) for each LinkID
indicating 100% of vehicles using the project are transit buses (see Exhibit F-7).
Exhibit F-7. Link Source Type Table
9 Linksource.xls
linkID sourceTypelD sourceTypeHourFraction
1 42 1
2 42 1
3 42 1
4 42 1
10
11
H \linkSourceTypeHour / Sourcel |
F-10
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PUBLIC DRAFT-MAY 2010
Links
The links table (see Exhibit F-8) is populated with parameters for the four defined links
of the bus terminal: three running links (Links 1-3) and one idle link (representing the
terminal links). The link length is entered in terms of miles for each link. The road type
for the four links is classified as "5" (Urban Unrestricted). The entrance and exit links
(Links 1 and 2) are given an average speed of 5 mph. The queue link (Link 3) is given an
average speed of 2.5 mph, representing 50% of the vehicle operating hours in idling
mode and 50% operating hours traveling at 5 mph. Although MOVES is capable of
calculating emissions from an average speed (as is done for Links 1 and 2), the specific
activity on Link 3 is directly entered with an Op-Mode distribution. LinkID 4 is given a
link average speed of "0" mph, which indicates entirely idle operation. Link volume
(which represents the number of buses per hour) is entered for each link; however, since
the goal of the analysis is to produce an estimate in grams/vehicle-hour, the volume (i.e.,
the number of vehicles) will be divided out during post-processing.
Exhibit F-8. Links Table
i A I B I C I D
linkID countylD zonelD roadTypelClinkLength linkVolume
1 1 99001 990010 5 0.038
1 2 99001 990010 5 0.030
i 3 99001 990010 5 0.008
1 4 99001 990010 1 0.011
G [ H
NnkAvgSpeed linkDescription
48 5 Entrance Link
24 5 Exit Approach Link
24 2.5 Left Turn Exit Link
8 0 Terminals
l
M \link/ County jf Roadfype
J<
Describing Vehicle Activity
MOVES can capture details about vehicle activity in a number of ways. In this case, it is
decided to provide a detailed Op-Mode distribution for each link (see Section 4.5.7).
Op-Mode distributions for Links 1 and 2 are calculated based on a 5 mph average speed.
The MOVES model calculates a default Op-Mode distribution based on average speed
and road type (for these links, 5 mph on Road Type 5). Link 3 is given a unique Op-
Mode distribution to better simulate the queuing and idling that occurs prior to buses
exiting the facility at a traffic signal. The sponsor estimates that 50% of buses would idle
at a red light before exiting the facility, so the idling operation (OpMode ID 1) is
assumed to be 0.5 for Link 3. The remaining 50% is re-allocated based on the default 5
mph Op-Mode distribution calculated for Links 1 and 2 (which includes acceleration,
F-ll
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PUBLIC DRAFT-MAY 2010
deceleration, and cruise operating modes). This process requires an additional MOVES
run to extract the default 5 mph Op-Mode distribution from the MOVES execution
database. By selecting "save data" for the "Operating Mode Distribution Generator
(Running OMDG)" under the MOVES "Advanced Performance Features" panel, the Op-
Mode distributions generated for 5 mph on an urban unrestricted road type are saved in
the MOVES execution database in the MySQL table "opmodedistribution." The Op-
Mode distribution used in the analysis for Link 3 is partially shown in Exhibit F-9.
Exhibit F-9. Link 3 (Queue Link) Op-Mode Distribution Input Table (Partial)
f|!fp,prowie,xb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
37
H 4
A B C
; D E
F ! G I H I I —
sourceTyp hourDaylD linkID polProcessopModelD opModeFraction
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
42 75
3 9101
3 9190
3 11001
3 11015
3 11017
3 11090
3 11101
3 11115
3 11117
3 11190
3 11201
3 11215
3 11217
3 11290
3 11501
3 11515
3 11517
3 11590
3 11609
3 11710
3 9101
3 9190
3 11001
3 11015
3 11017
3 11090
3 11101
3 11115
3 11117
3 11190
47 75 3 11?m
^ H:\opModeDistributionX! HourDay / Operating!^
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
1 0.5
11 0.25
11 0.25
11 0.25
11 0.25
11 0.25
11 0.25
11 0.25
11 0.25
11 0.25
11 0.25
11 075 ,v
lode < : > |. ;
F-12
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PUBLIC DRAFT-MAY 2010
Off-Network
As it is assumed that there are no off-network links (such as parking lots or truck stops)
that would have to be considered using the Off-Network Importer (bus idling at the
terminal is captured by the terminal links), there is no need to use this option in this
example. As noted earlier, this assumption is made to simplify the example. Most transit
projects would include rider parking lots and should include these emissions in a PM hot-
spot analysis.
F.5.6 Generating emission factors for use in air quality modeling
After generating the run specification and entering the required information into the
Project Data Manager as described above, MOVES is run 17 times: 16 runs (four hours
of the day for four quarters of the year) plus an initial run to generate the Op-Mode
distribution for 5 mph as discussed earlier. Upon completion of each run, the MOVES
output is located in the MySQL output database table "movesoutput" and sorted by
Month, Hour, LinkID, ProcessID, and PollutantlD. An aggregate PM2.5 emission factor
is then calculated by the project sponsor for each Month, Hour, and LinkID combination
using the following equation and the guidance given in Section 4.4.7 of the guidance:
PMaggregate total = (PMtotal running) + (PMtotal crankcase running) + (brake Wear) + (tire Wear)
For each link, the total emissions are divided by the number of vehicles on each link (as
reported in the "movesactivityoutput" table ActivitytypelD = 6) to produce a
grams/vehicle-hour value. This value is then multiplied by the number of buses on each
link, for each of the 24 hours where data are available (see Exhibit F-2).
The emission factor (grams/vehicle-hour) for LinkID 4 (links 4 through 9) is converted
into grams per vehicle-minute, and then multiplied by the total idle time for each unique
hour. For instance, the hour from 5 pm to 6 pm has a volume of 42 buses per hour (7
buses per bus bay). If each bus is expected to idle for 60 seconds each hour, the total idle
time for each bus bay for that hour would be 7 minutes per hour. If MOVES calculated a
PM emission factor of 2.0 grams per vehicle-minute, the emission factor for each bus bay
link under this scenario would be 14.0 grams/hour.
To account for temperature changes throughout the day, emission factors are evenly
paired with corresponding traffic volumes (six hours per period):
• 6am results - traffic data from 3am to 9am
• 12pm results - traffic data from 9am to 3 pm
• 6pm results - traffic data from 3pm to 9 pm
• 12pm results - traffic data from 9pm to 3am
The emission factor results for each quarter are similarly paired with traffic volumes.
F-13
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PUBLIC DRAFT-MAY 2010
The 96 resulting grams/hour emission factors (24 hours each for four quarters) for each
link are then ready to be used as an input to the AERMOD dispersion model to predict
future PM2.5 concentrations.
F.6 ESTIMATE DUST AND OTHER EMISSIONS (STEP 4)
F. 6.1 Estimating re-entrained road dust
In this case, this area does not have any adequate or approved SIP budgets for either
PM2 5 NAAQS, and neither the EPA nor the state air agency have made a finding that
road dust emissions are a significant contributor to the air quality problem for either
PM2 5 NAAQS. Therefore, PM25 emissions from road dust do not need to be considered
in this analysis (see Sections 2.5.3 and 6.2).
F. 6.2 Estimating transportation-related construction dust
The construction of this project will not occur during the analysis year. Therefore,
emissions from construction dust are not included in this analysis (see Sections 2.5.5 and
6.4).
F. 6.3 Estimating other sources of emissions in the project area
Through interagency consultation, it is determined that the project area in the analysis
year does not include locomotives or other nearby emissions sources that would have to
be considered in the analysis (see Section 6.6).
F.7 SELECT AN AIR QUALITY MODEL, DATA INPUTS, AND RECEPTORS
(STEP 5)
F. 7.1 Characterizing emission sources
Because this is a transit terminal project, EPA's AERMOD model is determined to be the
appropriate dispersion model to use for this analysis (see Section 7.3). AERMOD is run
to estimate PM2.5 concentrations in and around the bus terminal project. Each link is
represented in AERMOD as an "Area Source" with dimensions matching the project
description (see Exhibit F-l). The emission release height is set to three meters, the
approximate exhaust height of most transit buses.
F. 7.2 Incorporating meteorological data
Through the interagency consultation process, a representative set of meteorology data,
as well as an appropriate surface roughness are selected (see Section 7.5). The
recommended five years of meteorological data is obtained from a local airport for
F-14
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PUBLIC DRAFT-MAY 2010
calendar years 1998-2002. Additionally, surface roughness is set at 1 meter, consistent
with the recommendations made in the "AERMOD Implementation Guide."
Emission factors generated from the MOVES runs are added to the AERMOD input file
(see Exhibit F-10). For this analysis, emissions vary significantly from hour to hour due
to fluctuating bus volumes as well as from daily and quarterly temperature effects.
Adjustment factors are used to model these hourly and quarterly variations in emission
factors.
Exhibit F-10. AERMOD Input File (Partial) with Seasonal (Quarterly) and Hourly
Adjustments (Circled)
File Edit Format
CO STARTING
CO TITLEONE Transit Example
CO MODELOPT DFAULT CONC
CO RUNORNOT RUN
CO AVERTIME 24 ANNUAL
CO POLLLITID OTHER
co FINISHED]
SO STARTING
SO ELEVLJNIT
SO LOCATION
SO LOCATION
SO LOCATION
SO LOCATION
SO LOCATION
SO LOCATION
SO LOCATION
SO LOCATION
SO LOCATION
SO SRC PAR AM
SO SRC PAR AM
SO SRC PAR AM
SO SRCPARAM
SO SRCPARAM
SO SRCPARAM
SO SRCPARAM
SO SRCPARAM
SO SRCPARAM
SO ARE AVERT
SO ARE AVERT
SO AREAVERT
SO AREAVERT
SO AREAVERT
SO AREAVERT
SO EM IS FACT
SO EM IS FACT
SO EM IS FACT
SO EM IS FACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
METERS
LINKl
LINK2
LINK3
LINKS
LINK4
LINK?
LINKS
LINK9
LINK6
LINKl
LINK2
LINK3
LINKS
LINK4
LINK7
LINKS
LINK9
LINKS
LINKS
LINK4
LINK?
LINKS
LINK9
LINK6
LINKl
LINKl
LINKl
LINKl
LINKl
LINKl
LINK
LINIj
Lit*
LItfiKI
LIWK1
LINK2
AREA
AREA
AREA
AREAPOLY
AREAPOLY
AREAPOLY
AREAPOLY
AREAPOLY
AREAPOLY
?E-06 3
4E-06
4E-06
2E-06
2E-06
2E-06
2E-06
2E-06
2E-G6
-156.1 -47.4
-163.5 -47.4
-140. 5 -47.4
-132.9 -47.4
-125.3 -47.3
-148.0 -47.4
SEASHR
SEASHR
SEAS
-166.7
-166.6
-118.1
-156.1
-163.5
-140. 5
-132.9
-125.3
-148
60.6
48.
12.
-50.9
-33.2
-33.2
-47.4
-47.4
-47.4
-47.4
-47.3
-47.4
3.
3.
3.
0.0
0.0
6 0.0
-152. 5 -47.
-159.9 -47.
-136.9 -47.
-129.3 -47.
-121.7 -47.
-144
-141.3
-148.7
-125.7
-118.1
-110.5
-133.2
EASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
SEASHR
ASHR
0.65
79 0
86
0.72
72 0.65
F-15
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PUBLIC DRAFT-MAY 2010
F. 7.3 Specifying receptors
Using the interagency consultation process and the guidance given in Section 7.6,
receptors are placed in appropriate areas within the area substantially affected by the
project (see Exhibit F-l I).3 It is determined in this instance to locate receptors around
the perimeter of the project in increments of five meters as well as within the passenger
loading areas adjacent to the bus bays. Receptor heights are set at 1.8 meters (the
approximate height at which a person breathes). A background concentration of "0" is
input into the model. Representative background concentrations are added at a later step
(see Step 7).
AERMOD is run using five years of meteorological data and output produced for all
receptors for each of the five years of meteorological data.
Exhibit F-ll. Area Source and Receptor Locations for Air Quality Modeling
UNK
3 The number and arrangement of receptors used in this example are simplified for ease of explanation;
real-world projects could expect to see a significantly larger number of receptors.
F-16
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PUBLIC DRAFT-MAY 2010
F.8 DETERMINE BACKGROUND CONCENTRATIONS (STEP 6)
Through the interagency consultation process, a nearby upwind PM2.s monitor that has
been collecting ambient data for both the annual and 24-hour PM2.5 NAAQS is
determined to be representative of the background air quality at the project location (see
Exhibit F-12). The most recent data set is used (in this case, calendar year 2008 through
2010) and average 24-hour PM2.5 values are provided in a four-day /three-day
measurement interval. As previously noted, no nearby sources requiring explicit
modeling are identified.
Note: This is a highly simplified situation for illustrative purposes; refer to Section 8 of
the guidance for additional considerations for how to most accurately reflect background
concentrations in a real-world scenario.
Exhibit F-12. PM2.s Monitor Data from a Representative Nearby Site (Partial)
_9_
jm
Jl
Jl
Jl
Jl
J5_
16
JZ.
Jl.
Ji
20
J1_
_22_
_23_
J£
25
j|
_27_
^
_29_
_3Q_
^1
M •
Month Day
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
4
4
4
4
Year
1
5
8
12
15
19
22
26
29
2
5
9
12
16
19
23
26
1
4
8
11
15
18
22
25
29
1
5
8
12
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
w \MnoitomgJData
Date PM2.5
1/1/2008
1/5/2008
1/8/2008
1/12/2008
1/15/2008
1/19/2008
1/22/2008
1/26/2008
1/29/2008
2/2/2008
2/5/2008
2/9/2008
2/12/2008
2/16/2008
2/19/2008
2/23/2008
2/26/2008
3/1/2008
3/4/2008
3/8/2008
3/11/2008
3/15/2008
3/18/2008
3/22/2008
3/25/2008
3/29/2008
4/1/2008
4/5/2008
4/8/2008
4/12/2008
24-hour Output |
Concentration
23.08
5.69
12.19
6.71
7.26
17.92
11.90
14.37
16.54
7.40
13.63
19.15
12.65
14.77
11.08
18.00
21.62
14.65
6.93
19.03
20.66
11.99
4.71
11.05
15.64
6.64
11.68
5.04
10.11
11.96
F-17
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PUBLIC DRAFT-MAY 2010
F.9 CALCULATE DESIGN VALUES AND COMPARE BUILD AND NO-BUILD
SCENARIO RESULTS (STEP 7)
With both MOVES outputs and background concentrations now available, the project
sponsor can calculate the design values. For illustrative purposes, calculations for a
single receptor for the build scenario are shown in this example, but any analysis should
be done at all receptors for comparison with the relevant NAAQS. In Step 7, the
guidance from Section 9.3.2 is used to calculate design values from the modeled results
and the background concentrations for comparison with the 24-hour and annual PM2.5
NAAQS.
F.9.1 Determining conformity to the annual PM2.s NAAQS
First, average background concentrations are determined for each year of monitored data
(shown in Exhibit F-13). The three-year average background concentration is then
calculated (see Exhibit F-14).
Exhibit F-13. Annual Average Background Concentration for Each Year
Monitoring
Year
2008
2009
2010
Annual
Average
Annual Average
Background
Concentration
13.348
12.785
13.927
13.353
Exhibit F-14. Calculation of Annual Design Value (At Highest Receptor)
Annual Average
Background
Concentration
(Three-year
Average)
13.353
Annual Average
Modeled
Concentration
(Five-year
Average)
1.423
Sum of
Background +
Project
14.776
Annual Design Value
14.8
F-18
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PUBLIC DRAFT-MAY 2010
To determine the annual PM2.5 design value, the annual average background
concentration is added to the five-year annual average modeled concentration (at the
receptor with the highest annual average concentration from the AERMOD output). This
calculation is shown in Exhibit F-14. The sum (background + project) results in a design
value of 14.8 ng/m3. This value at the highest receptor is less than the 1997 PM2.5 annual
NAAQS of 15.0 ug/m3. It can be assumed that all other receptors with lower modeled
concentrations will also have design values less than the 1997 PM2.5 annual NAAQS. In
this example it is unnecessary to determine appropriate receptors in the build scenario or
develop a no-build scenario for the annual PM2.5 NAAQS, since the build scenario
demonstrates that the hot-spot analysis requirements in the transportation conformity rule
are met at all receptors.
F.9.2 Determining conformity to the 24-Hour PM2.5NAAQS
The next step is to calculate a design value to compare with the 2006 24-hour PM2.5
NAAQS through a "Second Tier" analysis as described in Section 9.3.3. For ease of
explanation, this process has been divided into individual steps, consistent with the
guidance.
Step 7.1
The number of background measurements is counted for each year of monitored data
(2008 to 2010). Based on a 4-day/3-day measurement interval, the dataset has 104 values
per year.
Step 7.2
For each year of monitored concentrations, the eight highest daily background
concentrations for each quarter are determined, resulting in 32 values (4 quarters; 8
concentrations/quarter) for each year of data (shown in Exhibit F-15, following page).
Step 7.3
Identify the highest-predicted modeled concentration resulting from the project in each
quarter, averaged across each year of meteorological data used for air quality modeling is
identified. For illustrative purposes, the highest average concentration across five years
of meteorological data for a single receptor in each quarter is shown in Exhibit F-16
(following page). Note that, in a real-world situation, this process would be repeated for
all receptors in the build scenario.
F-19
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PUBLIC DRAFT-MAY 2010
Exhibit F-15. Highest Daily Background Concentrations for Each Quarter and
Each Year
2008
Rank
1
2
3
4
5
6
7
8
Q1
20.574
20.152
19.743
19.346
18.961
18.588
18.226
17.874
Q2
21.262
20.823
20.398
19.985
19.584
19.196
18.819
18.454
Q3
22.354
22.042
21.735
21.434
21.140
20.851
20.568
20.291
Q4
20.434
20.016
19.611
19.218
18.837
18.467
18.109
17.761
2009
Rank
1
2
3
4
5
6
7
8
Q1
20.195
19.784
19.386
19.000
18.625
18.262
17.910
17.568
Q2
20.867
20.440
20.026
19.624
19.235
18.857
18.490
18.135
Q3
21.932
21.628
21.329
21.037
20.750
20.469
20.194
19.924
Q4
20.058
19.651
19.257
18.875
18.504
18.145
17.796
17.457
2010
Rank
1
2
3
4
5
6
7
8
Q1
21.137
20.698
20.272
19.860
19.459
19.071
18.694
18.329
Q2
21.847
21.390
20.948
20.519
20.102
19.698
19.307
18.927
Q3
22.980
22.655
22.336
22.023
21.717
21.417
21.123
20.834
Q4
20.990
20.556
20.135
19.726
19.330
18.945
18.572
18.211
Exhibit F-16. Five-year Average of Highest Modeled Concentrations for Each
Quarter (At Example Receptor)
Five Year Average
Maximum
Concentration (At
Example Receptor)
Q1
6.51
Q2
6.64
Q3
6.71
Q4
6.63
F-20
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PUBLIC DRAFT-MAY 2010
Step 7.4
The highest modeled concentration in each quarter (from Step 7.3) is added to each of the
eight highest monitored concentrations for the same quarter for each year of monitoring
data (from Step 7.2). As shown in Exhibit F-17, this step results in eight concentrations
in each of four quarters for a total of 32 values for each year of monitoring data. As
mentioned, this example analysis shows only a single receptor's values, but project
sponsors should calculate design values at all receptors in the build scenario.
Exhibit F-17. Sum of Background and Modeled Concentrations at Example
Receptor for Each Quarter
2008
1
2
3
4
5
6
7
8
Q1
27.088
26.667
26.258
25.861
25.476
25.102
24.740
24.389
Q2
27.901
27.462
27.037
26.624
26.224
25.835
25.459
25.093
Q3
29.063
28.750
28.443
28.143
27.848
27.560
27.277
27.000
Q4
26.948
26.530
26.125
25.732
25.351
24.982
24.623
24.275
2009
1
2
3
4
5
6
7
8
Q1
26.709
26.298
25.900
25.514
25.140
24.776
24.424
24.082
Q2
27.506
27.079
26.665
26.264
25.874
25.496
25.130
24.774
Q3
28.641
28.336
28.038
27.745
27.459
27.178
26.903
26.633
Q4
26.572
26.166
25.772
25.389
25.019
24.659
24.310
23.971
2010
1
2
3
4
5
6
7
8
Q1
27.651
27.212
26.787
26.374
25.974
25.585
25.209
24.843
Q2
28.486
28.030
27.587
27.158
26.742
26.338
25.946
25.566
Q3
29.689
29.363
29.044
28.732
28.426
28.125
27.831
27.543
Q4
27.505
27.070
26.649
26.240
25.844
25.460
25.087
24.725
F-21
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PUBLIC DRAFT-MAY 2010
Step 7.5
As shown in Exhibit F-18, for each year of monitoring data, the 32 values from Step 7.4
are ordered together in a column and assigned a yearly rank for each value, from 1
(highest concentration) to 32 (lowest concentration).
Exhibit F-18. Ranking Sum of Background and Modeled Concentrations at
Example Receptor for Each Year of Background Data
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
2008
29.063
28.750
28.443
28.143
27.901
27.848
27.560
27.462
27.277
27.088
27.037
27.000
26.948
26.667
26.624
26.530
26.258
26.224
26.125
25.861
25.835
25.732
25.476
25.459
25.351
25.102
25.093
24.982
24.740
24.623
24.389
24.275
2009
28.641
28.336
28.038
27.745
27.506
27.459
27.178
27.079
26.903
26.709
26.665
26.633
26.572
26.298
26.264
26.166
25.900
25.874
25.772
25.514
25.496
25.389
25.140
25.130
25.019
24.776
24.774
24.659
24.424
24.310
24.082
23.971
2010
29.689
29.363
29.044
28.732
28.486
28.426
28.125
28.030
27.831
27.651
27.587
27.543
27.505
27.212
27.158
27.070
26.787
26.742
26.649
26.374
26.338
26.240
25.974
25.946
25.844
25.585
25.566
25.460
25.209
25.087
24.843
24.725
F-22
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PUBLIC DRAFT-MAY 2010
Step 7.6
For each year of monitoring data, the value with a rank that corresponds to the projected
98th percentile concentration is determined. As discussed in Section 9, an analysis
employing 101-150 background values for each year (as noted in Step 7.1, this analysis
uses 104 values per year) uses the 3rd highest rank to represent a 98th percentile. The 3rd
highest concentration (highlighted in Exhibit F-18) is referred to as the "projected 98th
percentile concentration."
Steps 7.1 through 7.6 are repeated to calculate a projected 98th percentile concentration at
For the example receptor, the average of the three projected 98th percentile concentrations
Step 7.7
Steps 7.1
each receptor based on each year of monitoring data and modeled concentrations.
Step 7.8
For the e
(highlighted in Exhibit F-18) is calculated.
Step 7.9
The resulting value of 28.508 |J,g/m3 is then rounded to the nearest whole ng/m3 resulting
in a design value at the example receptor of 29 ng/rn3. At each receptor this process
should be repeated. However, in the case of this analysis, the example receptor is the
receptor with the highest design value in the build scenario.
Step 7.10
The design values calculated at each receptor are compared to the NAAQS. In the case
of this example, the highest 24-hour design value (29 |J,g/m3) is less than the 2006
NAAQS of 35 |J,g/m3. Since this is the design value at the highest receptor, it can be
assumed that the conformity requirements are met at all receptors in the build scenario.
Therefore, it is unnecessary for the project sponsor to calculate design values for the no-
build scenario for the 24-hour PM2.5 NAAQS.
F.10 CONSIDER MITIGATION AND CONTROL MEASURES (STEP 8)
In this case, the project is determined to conform. In situations when this is not the case,
it may be necessary to consider additional mitigation or control measures. If measures
are considered, additional air quality modeling would need to be completed and new
design values calculated to ensure that conformity requirements are met. See Section 10
for more information, including some specific measures that might be considered.
F. 11 DOCUMENT THE PM HOT-SPOT ANALYSIS (STEP 9)
The final step is to properly document the PM hot-spot analysis in the conformity
determination (see Section 3.10).
F-23
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PUBLIC DRAFT-MAY 2010
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F-24
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PUBLIC DRAFT-MAY 2010
Appendix G:
Example of Using EMFAC for a Highway Project
G.I INTRODUCTION
The purpose of this appendix is to demonstrate the procedures described in Section 5 of
the guidance on using EMFAC2007 to generate emission factors for air quality modeling.
The following example, based on a hypothetical highway project, illustrates the modeling
steps required for users to change EMFAC's default VMT distribution and to develop
project-specific PM running exhaust emission factors. This example uses the "Emfac"
mode in EMFAC2007 (v2.3) to generate gram per mile (g/mi) emission factors stored in
the "Summary Rate" output file (.its file) suitable for use in an air quality model. Users
will be able to generate running emission factors in a single EMFAC model run; multiple
calendar years can also be handled within one model run. As described in the main body
of this section, each run will be specific to either PMio or PM2.5; however this example is
applicable to both. This example does not include the subsequent air quality modeling;
refer to Appendix E for an example of how to run an air quality model for a highway
project for PM hot-spot analyses.
G.2 PROJECT CHARACTERISTICS
The hypothetical highway project is located in Sacramento County, California. For
illustrative purposes, the project is characterized by a single link with an average link
travel speed for all traffic equal to 65 mph.1 The project's first full year of operation is
assumed to be the year 2013. Through the interagency consultation process, it is
determined that 2015 should be the analysis year (based on the project's emission and
background concentrations). The build scenario 2015 traffic data for this highway
project shows that 25% of the total project VMT is from trucks and 75% from non-trucks.
1 These are simplified data to illustrate EMFAC's use; this example does not, for instance, separate data by
peak vs. off-peak periods, divide the project into separate links, or consider additional analysis years, all of
which would likely be required for an actual project.
G-l
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PUBLIC DRAFT-MAY 2010
G.3 PREPARING EMFAC BASIC INPUTS
Based on the project characteristics, it is first necessary to specify the basic inputs and
default settings in EMFAC (see Exhibit G-l).
Exhibit G-l. Basic Inputs in EMFAC for the Hypothetical Highway Project
Step
1
2
3
4
5
6
7
8
9
10
11
12
Input Category
Geographic Area
Calculation Method
Calendar Years
Season or Month
Scenario Title
Model Years
Vehicle Classes
I/M Program Schedule
Temperature
Relative Humidity
Speed
Emfac Rate Files
Output Paniculate
Input Data
County -> Sacramento
Use Average
2015
Annual
Use default
Use default
Use default
Use default
60F
70%RH
Use default
Summary Rates (RTS)
PM10
Note
Select from drop-down list
Default (not shown in the EMFAC
user interface)
Select from drop-down list
Select from drop-down list
Define default title in the EMFAC
user interface
Include all model years
Include all vehicle classes
Include all pre-defined I/M program
parameters
Delete all default temperature bins
and input 60
Delete all default relative humidity
bins and input 70
Include all speed bins from 5 mph to
65 mph
Select from EMFAC user interface
Select from EMFAC user interface
G-2
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PUBLIC DRAFT-MAY 2010
G.4 EDITING EMFAC DEFAULT VMT DISTRIBUTIONS
The next step is to calculate the EMFAC defaults for trucks and non-trucks. As shown in
Exhibit G-2, EMFAC's 13 vehicle classes are grouped into trucks and non-trucks to
match the project-specific traffic data. Specifically, Light-Duty Autos, Light-Duty
Trucks (Tl and T2), and Motorcycles are grouped together to represent the "non-truck"
class. All other vehicle classes (Medium-Duty Trucks, Light FID Trucks (T4 and T5),
Medium FID Trucks, Heavy HD Trucks, Other Buses, Urban Buses, School Buses, and
Motor Homes) are classified as "trucks." The total pre-populated VMT for truck and
non-truck for this highway project are 6,269,545 miles and 26,134,922 miles,
respectively.
Exhibit G-2. Example Highway Project Pre-Populated VMT for 13 Default Vehicle
Classes
EMFAC Vehicle Class
01 - Light-Duty Autos (PC)
02 - Light-Duty Trucks (Tl)
03 - Light-Duty Trucks (T2)
04 - Medium-Duty Trucks (T3)*
05 - Light HD Trucks (T4)*
06 - Light HD Trucks (T5)*
07 - Medium HD Trucks (T6)*
08 - Heavy HD Trucks (T7)*
09 - Other Buses*
10 - Urban Buses*
1 1 - Motorcycles
12 - School Buses*
13 - Motor Homes*
Truck VMT
Non-truck VMT
TOTAL
EMFAC default VMT
15,271,757
3,340,492
7,266,306
3,535,454
816,278
302,809
698,543
704,156
49,590
40,198
256,367
31,176
91,341
6,269,545
26,134,922
32,404,467
Classified as trucks to match project-specific data
The next step is to calculate percentage VMT for trucks and non-trucks and their
respective adjustment factors to match project-specific VMT distributions as shown in
Exhibit G-3 (following page). The default VMT percentages for trucks (19%) and non-
trucks (81%) are much different from what the project traffic data suggest (25% and 75%
in the build scenario). Therefore the EMFAC default VMT for each vehicle class is
scaled down for non-trucks and scaled up for trucks, respectively, based on the calculated
adjustment factors (0.93 and 1.29).
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PUBLIC DRAFT-MAY 2010
Exhibit G-3. Calculation of Adjustment Factors for Truck and Non-Truck VMT
Trucks
Non-trucks
Sum
VMT
6,269,545
26,134,922
32,404,467
Column A
% of total VMT
(EMFAC default)
19%
81%
100%
Column B
% of total VMT
(Project-specific)
25%
75%
100%
Adjustment Factor
(AF)*
1.29
0.93
* Adjustment factor is equal to the ratio between project-specific % VMT (Column B) and
EMFAC default % VMT (Column A), for trucks and non-trucks, respectively.
Multiplying the EMFAC default VMT by the calculated adjustment factors (AF) for each
vehicle class will produce updated VMT numbers that reflect project-specific information
in terms of truck and non-truck VMT percentage. As shown in Exhibit G-4, when the
adjusted VMT values for the truck group are added up, the sum is equal to 8,101,117
(which is 25% of the total VMT). The non-truck VMT is 24,303,350 (which accounts for
75% of the total VMT). Note that the overall VMT before and after the adjustment stays
constant. Next, the adjusted VMT values are entered into the EMFAC interface; pressing
the "Apply" button accepts the changes.
Exhibit G-4. Example Adjusted VMT for 13 Default Vehicle Classes
Vehicle Class
01 - Light-Duty Autos (PC)
02 - Light-Duty Trucks (Tl)
03 - Light-Duty Trucks (T2)
04 - Medium-Duty Trucks
(T3)*
05- Light HD Trucks (T4)*
06 - Light HD Trucks (T5)*
07 - Medium HD Trucks (T6)*
08 - Heavy HD Trucks (T7)*
09 - Other Buses*
10 - Urban Buses*
1 1 - Motorcycles
12 - School Buses*
13 - Motor Homes*
Truck
Non-truck
TOTAL
Default VMT
15,271,757
3,340,492
7,266,306
3,535,454
816,278
302,809
698,543
704,156
49,590
40,198
256,367
31,176
91,341
6,269,545
26,134,922
32,404,467
% VMT by
vehicle class
47.1%
10.3%
22.4%
10.9%
2.5%
0.9%
2.2%
2.2%
0.2%
0.1%
0.8%
0.1%
0.3%
19.4%
80.7%
100.0%
Adjusted
VMT
(default
VMT*AF)
14,201,491
3,106,386
6,757,073
4,568,294
1,054,743
391,271
902,614
909,867
64,077
51,941
238,400
40,284
118,025
8,101,117
24,303,350
32,404,467
Adjusted %
VMT by
vehicle class
43.8%
9.6%
20.9%
14.1%
3.3%
1.2%
2.8%
2.8%
0.2%
0.2%
0.7%
0.1%
0.4%
25.0%
75.0%
100.0%
Classified as trucks to match project-specific data
G-4
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PUBLIC DRAFT-MAY 2010
G.5 GENERATING LINK-SPECIFIC EMISSION FACTORS
After the EMFAC run is completed, the project-specific running exhaust emission factors
are presented in Table 1 of the output Summary Rates file (.its file). As highlighted in
Exhibit G-5, the PMi0 running exhaust emission factor is 0.040 g/mi under the associated
speed bin of 65 mph. Tire wear and brake wear PMio emission factors are 0.009 g/mi
and 0.013 g/mi, respectively, and do not vary by speed. For the one link in this example,
the total running link emission factor is 0.062 g/mi, which is the sum of these three
emission factors. For comparison, the total running link emission factor (based on
EMFAC default VMT distribution) is equal to 0.056 g/mi. It is lower than the project-
specific emission factor because the EMFAC default includes a smaller proportion of
truck VMT than this hypothetical highway project.
Exhibit G-5. Generating Running Exhaust Emission Factors in EMFAC
Title Sacramento County Subarea Annual CYr 2015
version Emfac2007 V2 . 3 Nov l 200S
Run Date 2010/02/04 14:54:50
Seen Year 2015 — All model years In the range 1971
season Annual
Area Sacramento
Default Title
to 2015 selected
Year 2015 -- Model Years 1971 to 2015 Inclusive -- Annual
Emfac2007 Emission Factors: V2.3 NOV l 2006
county Average Sacramento county Average
Table 1: Running Exhaust Emissions (grams/mile; graras/i dl e-hour)
Pollutant Name: PMIO Temperature: SOF Relative Humidity: 7 OK
speed
MPH
0
5
10
15
20
25
30
35
40
45
50
55
60
65
IDA
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
051
033
023
017
013
010
009
OOS
007
007
007
OOS
009
LOT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
092
061
042
031
024
019
016
015
014
013
014
015
017
MDT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
064
096
OS4
045
034
026
021
01S
016
015
015
015
016
01S
HOT
1
1
1
0
0
0
0
0
0
0
0
0
0
0
297
442
Oil
697
510
427
365
323
29S
290
299
324
364
420
UEUS
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
457
32S
245
1S9
152
126
109
097
090
OS6
085
087
093
MCY
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
056
044
036
031
02S
027
027
028
030
034
040
050
065
ALL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
093
160
109
076
056
045
038
033
030
029
029
031
035
040
This completes the use of EMFAC for determining emissions factors for this project.
The total running link emission factor of 0.062 grams per vehicle-mile can be now be
used in combination with link length and link volume as inputs into the selected air
quality model, as discussed in Section 7 of the guidance.
G-5
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G-6
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PUBLIC DRAFT-MAY 2010
Appendix H:
Example of Using EMFAC to Develop Emission Factors for a
Transit Project
H.1 INTRODUCTION
The purpose of this appendix is to illustrate the modeling steps required for users to
change EMFAC's defaults and to develop project-specific PM idling and start exhaust
emission factors for a hypothetical bus terminal project. It also shows how to generate
emission factors from EMFAC for a project that involves a limited selection of vehicle
classes (e.g., urban buses).1 This example uses the "Emfac" mode in EMFAC2007 (v2.3)
to generate grams per hour (g/hr) and grams per trip start (g/trip) emission factors stored
in the "Summary Rate" output file (.its file) suitable for use in the AERMOD air quality
model. This example does not include the subsequent air quality modeling; refer to
Appendix F for an example of how to run AERMOD for a transit project for PM hot-spot
analyses.
The assessment of a bus terminal or other non-highway project can involve modeling two
different categories of emissions: (1) the start and idle emissions at the project site, and
(2) the running exhaust emissions on the links approaching and departing the project site.
This example is intended to help project sponsors understand how to create representative
idle and start emission factors based on the best available information supplied by
EMFAC, thus providing an example of how users may have to adapt the information in
EMFAC to their individual project circumstances.
As a preliminary note, the reader should understand that to estimate idle emissions, the
main task will involve modifying the default vehicle populations, by vehicle class,
embedded in EMFAC. When estimating start emissions, users will be modifying the
default vehicle trips, also by vehicle class. This appendix walks through the steps to
model its idle and start emissions for this hypothetical project. Users will be able to
generate idle and start emission factors in a single EMFAC model run; multiple calendar
years can also be handled within one model run. As described in the main body of this
section, each run will be specific to either PMi0 or PM2.s; however, this example is
applicable to both.
1 This is a highly simplified example showing how to employ EMFAC to calculate idle and start emission
factors for use in air quality modeling. An actual project would be expected to be significantly more
complex.
H-l
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PUBLIC DRAFT-MAY 2010
H.2 PROJECT CHARACTERISTICS
A PMio hot-spot analysis is conducted for a planned bus terminal project in Sacramento
County, California. The project's first full year of operation is assumed to be the year
2013. Through the interagency consultation process, it is determined that 2015 should be
the analysis year (based on the project's emission and background concentrations). The
PM analysis focuses on idle and start emissions from buses operated in the terminal. It is
assumed that these buses correspond to the "Urban Buses" vehicle class specified in
EMFAC and their average soak time is 540 minutes (all buses are parked overnight
before trip starts).
H.3 PREPARING EMFAC BASIC INPUTS (APPLICABLE TO BOTH IDLE AND
START EMISSIONS ESTIMATION)
Based on the project characteristics, basic inputs and default settings in EMFAC are first
specified (see Exhibit H-l). These basic inputs are similar to those specified for highway
projects. To generate idle emission factors from EMFAC, a speed bin of 0 mph must be
selected in the EMFAC interface.
Exhibit H-l. Basic Inputs in EMFAC for the Hypothetical Highway Project
Step
1
2
o
6
4
5
6
7
8
9
10
11
12
Input Category
Geographic Area
Calculation Method
Calendar Years
Season or Month
Scenario Title
Model Years
Vehicle Classes
I/M Program Schedule
Temperature
Relative Humidity
Speed
Emfac Rate Files
Output Paniculate
Input Data
County -> Sacramento
Use Average
2015
Annual
Use default
Use default
Use default
Use default
60F
70%RH
Use default
Summary Rates (RTS)
PM10
Note
Select from drop-down list
Default (not visible in the EMFAC
user interface)
Select from drop-down list
Select from drop-down list
Define default title in the EMFAC
user interface
Include all model years
Include all vehicle classes
Include all pre-defined I/M program
parameters
Delete all default temperature bins
and input 60
Delete all default relative humidity
bins and input 70
Include speed bin of 0 mph
Select from EMFAC user interface
Select from EMFAC user interface
H-2
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PUBLIC DRAFT-MAY 2010
H.4 EDITING EMFAC DEFAULT POPULATION DISTRIBUTIONS TO OBTAIN
IDLE EMISSION FACTORS
To generate idle emission factors that reflect the bus terminal project data, vehicle
population by vehicle class must be modified in the EMFAC user interface. EMFAC has
data limitations regarding idle emissions: among the 13 vehicle classes in EMFAC, idle
emission factors are available only for LHDT1, LHDT2, MHDT, HHDT, School Buses,
and Other Buses. Although EMFAC does not provide idle emission factors for the
"Urban Buses" class (the class most typically associated with transit buses), the idle
emission factors for "Other Buses" may be used to represent transit buses.
Note that only the "Other Buses" vehicle population will affect idle emissions in this
example; however, the "Urban Buses" class also needs to be included at this point to
address idling and starting emission factors in one single run. Thus, except for "Other
Buses" and "Urban Buses," all other vehicle classes are eliminated in EMFAC by
inputting very low values (such as "1"; entering "0" is not allowed in EMFAC). Exhibit
H-2 (following page) shows the EMFAC interface before and after vehicle population by
vehicle class is changed.
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PUBLIC DRAFT-MAY 2010
Exhibit H-2. Changing EMFAC Vehicle Population Distributions to Estimate Idle
Emission Factors
Editing Population data for scenario 1: Sacramento County Subarea Annual CYr 2015 ,
Total Population for area
Sacramento County
Copy with Heading^ Paste Data Only
Editing Mode Editing Population (registered vehicles with adjustments)
T otal Population By Vehicle Class By Vehicle and Fuel I By Vehide/Fuel/Age I
01 -Light-Duty Autos (PC)
02-Light-Duty Trucks (T1)
03 • Light-Duty Trucks (T2)
04 - Medium-Duty Trucks (T3)
05 - Light HD Trucks (T4)
06 - Light HD Trucks (T5)
07 - Medium HD Trucks (T6)
08 - Heavy HD Trucks (T7)
09 • Other Buses
10-Urban Buses
11 - Motorcycles
12-School Buses
13-Motor Homes
102814.
219099.
20420.
8291.
15362.
5148.
1098.
371.
34494.
855.
8415.
Done
Default EMFAC data before modification
Editing Population data for scenario 1: Sacramento County Subarea Annual CYr 2015 .
Total Population for area
Sacramento County
Copy with Headings) Paste Data Only
Total Population By Vehicle Class By Vehicle and Fuel | By Vehicle/Fuel/Age |
01 -Light-Duty Autos (PC)
02 -Light-Duty Trucks (T1)
03 - Light-Duty Trucks (T2)
04 - Medium-Duty Trucks (T3)
05 - Light HD Trucks (T4)
06 - Light HD Trucks (T5)
07 - Medium HD Trucks (T6)
08 - Heavy HD Trucks (T7)
09 • Other Buses
10 -Urban Buses
11 - Motorcycles
12 -School Buses
13 - Motor Homes
1
1.
1.
1.
1.
1.
1.
1.
1098.
371.
1.
1.
1.
Done
Modified EMFAC data
Note: In this bus terminal example, start emissions are available for "urban buses";
however, idle emission factors are only available for "other buses. " Therefore, users
will access emission factor information for both "other" and "urban" buses, and the
population data for these fleets are left intact (see modified version of Exhibit H-2).
H-4
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PUBLIC DRAFT-MAY 2010
H.5 EDITING EMFAC DEFAULT TRIP DISTRIBUTIONS TO OBTAIN START
EMISSION FACTORS
After users modify the population distribution in EMFAC, the new population
distribution will be used by EMFAC to create vehicle trip distributions. The new
distribution will affect the EMFAC data displayed during the trip distribution
modification steps described below. Users need to manually update the trip distributions
through the EMFAC user interface to obtain project-specific start emission factors.
Average start emission factors in EMFAC depend on the number of trips made by a
particular vehicle class and the corresponding soak time. To generate project-specific
start emission factors, the number of trips by vehicle class must be modified in the
EMFAC user interface. For this example bus terminal project, a very low value ("1") is
entered into the interface for all vehicle classes except for "Urban Buses" to represent the
project-specific data. Exhibit H-3 (following page) shows the EMFAC interface before
and after vehicle trip distributions by vehicle class are changed.
H-5
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PUBLIC DRAFT-MAY 2010
Exhibit H-3. Changing EMFAC Trip Distributions to Estimate Start Emission
Factors
Editing Trips-per-Day data for scenario 1: Sacramento County Subarea Annual CYr 20.
Total Trips-per-Day for area
Sacramento County
Copjji with Headi^ngJ Paste Da
Editing Mode Editing Trips-per-Day (starts per weekday)
Total Trips-per-Day By Vehicle Class | By Vehicle and Fuel I By Vehicle/Fuel/Hour 1
01 -Light-Duty Autos (PC)
02 - Light-Duty T tucks (T1)
03-Light-DutyTrucks(T2)
04 - Medium-Duty Trucks (T3)
05 • Light HD Trucks (T 4)
06- Light HD Trucks (T5)
07- Medium HD Trucks (T6)
08- Heavy HD Trucks (T7)
09 - Other Buses
10 -Urban Buses
11 - Motorcycles
12 -School Buses
13 -Motor Homes
6.
6.
6.
6.
28.
24.
32.
11.
39322.
1485.
r *
4.
0.
Done
Default EMFAC data before modification
Editing Trips per Day data for scenario 1: Sacramento County Subarea Annual CYr 20..
Total Trips-per-Day for area
Sacramento County
Copy with Headingj Paste Data Only
Editing Mode Editing Trips-per-Day (starts per weekday)
Total Trips-per-Day By Vehicle Class | By Vehicle and Fuel ] By Vehicle/Fuel/Hour ]
01 -Light-Duty Autos (PC)
02 -Light-DutyT rucks (T1)
03-Light-DutyTrucks(T2)
04-Medium-DutyTrucks(T3]
05 - Light HD Trucks (T4)
06- Light HD Trucks (T5)
07-
08
Medium HD Trucks (T6)
- Heavy HD Trucks (T7)
09 - Other Buses
10- Urban Buses
11 -Motorcycles
12 -School Buses
13 -Motor Homes
i
1.
1.
1-
1.
1-
1.
1.
1.
1485.
1-
1.
0.
Done
Modified EMFAC data
H-6
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PUBLIC DRAFT-MAY 2010
H.6 GENERATING IDLE AND START EMISSION FACTORS
"Urban Buses" is the vehicle class best representing transit buses in this hypothetical bus
terminal project. After the EMFAC run is completed, the project-specific idle exhaust
emission factors are presented in Table 1 of the output Summary Rates file (.its file). As
shown in Exhibit H-4, the PMi0 idle exhaust emission factor for the example bus terminal
project (0.734 grams/idle-hour) can be found under the 0 mph speed bin for the HDT
vehicle class (associated with "Other Buses" because EMFAC does not provide "Urban
Buses" idle emission factors). The start emission factor for vehicle class "Urban Buses"
(0.011 g/trip) is presented in Table 2 under the 540-min time bin in the column "All" or
"UBUS" (see Exhibit H-5, following page).
In order to produce a grams/hour emission factor for use in AERMOD, several post-
processing calculations are necessary. First, the idle emission factor (0.734 grams/idle-
hour) is multiplied by the number of vehicle idle-hours. Next, the start emissions can be
calculated by multiplying the start emission factor (0.011 grams/trip) by the number of
starts expected in a given hour. If the area being modeled has both idling and starts, these
values can be summed to produce an aggregate grams/hour value.
Exhibit H-4. Generating Idling Emission Factors in EMFAC
Title Sacramento County Subarea Annual CYr 2015 Default Title
Version Emfac2007 V2 . 3 Nov 1 200S
Run Date 2010/02/04 14:54:50
Seen Year 2015 — All model years in the range 1971 to 2015 selected
Season Annual
Area Sacramento
^Tt***^*1^^**frlt^l£l^*^l£l^*1^*^*1?*1^*1^*l^*^* 1^*^*1^
Year 2015 — Model Years 1971 to 2015 inclusive — Annual
Emfaczoo? Emission Factors: V2.3 NOV l 2006
County Average Sacraiento County Average
Table 1: Running Exhaust Emissions (grams/mile; grams/idle-hour}
Pollutant Name: PH10 Temperature: 60F Relative Humidity: 70«
speed
MPH
0
5
10
15
20
25
30
35
40
45
50
55
60
65
LDA
0
0
0
0
0
0
0
0
0
0
0
000
051
033
023
017
013
010
OO9
008
007
007
0. 007
0
0
DOS
009
LOT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
083
055
03S
028
022
018
015
013
013
012
013
014
016
HDT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
220
082
OSS
042
033
026
022
018
016
015
014
014
014
015
HCT
0.734
0.479
0.373
0.297
0.242
0. 203
0.173
0.152
0.136
0. 124
0.117
0.112
0.110
0.110
UBUS
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
457
328
245
189
152
126
109
097
090
OSS
OSS
087
093
MCY
0
0
0
0
0
0
0
0
0
0
0
0
0
0
000
056
044
036
031
028
027
027
028
030
034
040
050
065
ALL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
406
468
352
273
218
180
152
132
118
109
103
100
100
102
H-7
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PUBLIC DRAFT-MAY 2010
Exhibit H-5. Generating Start Emission Factors in EMFAC
Title Sacramento County Subarea Annual CYr 2015
Version Emfac2007 V2 . 3 Nov 1 2006
Run Date 2010/02/04 14:54:50
Seen Year 2015 -- All model years in the range 1971
Season Annual
Area Sacramento
Default Title
to 2015 selected
Year 2015 — Model Years 1971 to 2015 inclusive — Annual
Emfac2007 Emission Factors: V2 . 3 Nov 1 2006
County Average Sacramento County Average
Table 2: starting Emissions (grams/trip)
Pollutant Name: PM10 Temperature: 60F Relative Humidity: ALL
Time
mi n
5
10
20
30
40
50
60
120
180
240
300
3 SO
420
480
540
600
660
720
LDA
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
001
001
002
004
004
005
006
009
010
010
Oil
Oil
012
012
013
013
013
013
LDT
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
001
O02
004
005
007
ooe
010
014
015
016
017
01S
019
019
020
020
021
021
MDT
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
001
002
003
O'OS
006
OOS
009
012
013
014
014
015
015
016
016
017
017
017
HDT
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
O'Ol
002
002
003
0'03
004
004
006
007
007
OOS
OOS
008
009
009
009
010
010
UBUS
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
001
002
003
004
005
006
007
009
00'9
010
010
010
Oil
Oil
Oil
012
012
012
MCY
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
014
013
010
Q'OS
006
005
004
010
015
020
024
027
030
033
035
036
037
037
ALL
0.001
0.002
0 . 003
0.004
0.005
0.006
0.007
0.009
0 . 009
0 . 010
0.010
0.010
0.011
0.011
0.011
0.012
0.012
0.012
This completes the use of EMFAC for determining start and idle emission factors for this
project. The aggregate grams/hour value for starts and idle can now be input into
AERMOD, as discussed in Section 7 of the guidance.
Note that the start emission factors for UBUS and ALL are identical in this exhibit because the user
modified the number of trips by vehicle class to include activity from only "Urban Buses". EMFAC
collapsed the 13 vehicle classes to six vehicle groups in the output file. The collapsed output provides start
emission factors for the "Urban Buses" in the UBUS category and because fleet activity was composed
entirely from this vehicle class, the start emission factors for UBUS and ALL are essentially the same.
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PUBLIC DRAFT-MAY 2010
Appendix I:
Estimating Locomotive Emissions
I.I INTRODUCTION
This appendix describes how to quantify locomotive emissions when they are a
component of a transit or freight terminal or otherwise a source in the project area being
modeled. Note the state air quality agencies may have experience modeling locomotive
emissions and therefore could be of assistance when quantifying these emissions for a
PM hot-spot analysis.
Generally speaking, locomotive emissions can be estimated in the following manner:
1. Determine where in the project area locomotive emissions should be estimated.
2. Determine when to analyze emissions.
3. Describe the locomotive activity within the project area, including:
• The locomotives present in the project area (the "locomotive roster"); and
• The percentage of time each locomotive spends in various throttle settings
(its "duty cycle").
4. Calculate locomotive emissions using either:
• Horsepower rating and load factors, or
• Fuel consumption data.l
The estimated locomotive emission rates that result from this process would then be used
for air quality modeling. The interagency consultation process must be used when
calculating locomotive emissions (40 CFR 93.105(c)(l)(i)), including determining which
method may be most appropriate for a given project.
1.2 DETERMINING WHERE IN THE PROJECT AREA LOCOMOTIVE
EMISSIONS SHOULD BE ESTIMATED
Under certain circumstances, it is appropriate to model different locations within the
project area as separate sources to characterize differences in locomotive type and/or
activity appropriately. This step is analogous to dividing a highway project into links (as
described in Sections 4.2 and 5.2 of the guidance) and improves the accuracy of
emissions modeling and subsequent air quality modeling. For example, in an intermodal
terminal, emissions from a mainline track (which will have a large percentage of higher
1 These are the two methods described in this appendix; others may be possible. See Appendix 1.5 for
details.
1-1
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PUBLIC DRAFT-MAY 2010
speed operations with little idling) should be estimated separately from the associated
passenger or freight terminal (which would be expected to experience low speed
operations and significant idling).
The following activities are among those typically undertaken by locomotives and are
candidates for being modeled as separate sources if they occur at different locations
within the project area:
• Idling within the project area;
• Trains arriving into, or departing from, the project area (e.g., terminal arrival and
departure operations);
• Testing, idling, and service movements in maintenance areas or sheds;
• Switching operations;
• Movement of trains passing through, but not stopping in, the project area.
The project area may also be divided into separate sources if it includes several different
locomotive rosters (see Appendix 1.4.1, below)
1.3 DETERMINING WHEN TO ANALYZE EMISSIONS
The number of hours and days that have to be analyzed depends on the range of activity
expected to occur within the project area. For rail projects where activity varies from
hour to hour, day to day, and possibly month to month, it is recommended that, at a
minimum, project sponsors calculate emissions based on 24 hours of activity for both a
typical weekday and weekend day and for four representative quarters of the analysis
year when comparing emissions to all PM2.5 NAAQS.2 For projects in areas that violate
only the 24-hour PMi0 or PM25 NAAQS, the project sponsor may choose to model only
one quarter, in appropriate cases. See Section 3.3.4 of the guidance for further
information.
These resulting emission rates should be applied to AERMOD and used to calculate
design values to compare with the applicable PM NAAQS as described in Sections 7
through 9 of the guidance.
1.4 DESCRIBING THE LOCOMOTIVE ROSTERS AND DUTY CYCLES
Before calculating locomotive emission rates, it is necessary to know what locomotives
are present in the locations being analyzed in the project area (see Appendix 1.2, above)
and what activities these locomotives are undertaking at these locations. This data will
impact how emissions are calculated.
2 If there is no difference in activity between weekday and weekend activity, it may not be necessary to
examine weekend day activity separately. Similarly, if there is no difference in activity between quarters,
emission rates can be determined for one quarter, which can then be used to represent every quarter of the
analysis year.
1-2
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PUBLIC DRAFT'-MAY 2010
1.4.1 Locomotive rosters
Because emissions can vary significantly depending on a locomotive's make, model,
engine, and year of engine manufacture (or re-manufacture), it is important to know what
locomotives are expected to be operating within the project area. Project sponsors should
develop a "locomotive roster" (i.e., a list of each locomotive's make, model, engine, and
year) for the locomotives that will be operating within the specific project area being
analyzed. The more detailed the locomotive roster, the more accurate the estimated
emissions will be.
In some cases, it will be necessary to develop more than one locomotive roster to reflect
the operations in the project area accurately (for example, switcher locomotives may be
confined to one portion of a facility and therefore may be represented by their own
roster). In these situations, users should model areas with different rosters as separate
sources to account for the variability in emissions (see Appendix 1.2.3).
1.4.2 Locomotive duty cycles
Diesel locomotive engine power is controlled by "notched" throttles; idling, braking, and
moving the locomotive is conducted by placing the throttle in one of several available
"notch settings."3 A locomotive's "duty cycle" is a description of how much time, on
average, the locomotive spends in each notch setting when operating. Project sponsors
should use the latest locally-generated or project-specific duty cycles whenever possible;
this information may be available from local railway authorities or the state or local air
agency.4 The default duty cycles for line-haul and switch locomotives found in Tables 1
and 2 of 40 CFR 1033.530 (EPA's regulations on controlling emissions from
locomotives), should be used only if it is agreed through interagency consultation that
they adequately represent the locomotives that will be present in the project area and no
local or project-specific duty cycles are available.
1.5 CALCULATING LOCOMOTIVE EMISSIONS
Once a project's locomotive rosters and respective duty cycles have been determined,
locomotive emissions can then be calculated for each part of the project area using either
(1) horsepower rating and load factors, or (2) fuel consumption data. These two methods
are summarized below.
The interagency consultation process must be used to evaluate and choose the method
and data used for quantifying locomotive emissions for PM hot-spot analyses (40 CFR
3 A diesel locomotive typically has eight notch settings for movement (ran notches), in addition to one or
more idle or dynamic brake notch settings. Dynamic braking is when the locomotive engine, rather than
the brake, is used to control speed.
4 The state or local air agency may have previously developed locally-appropriate duty cycles for emissions
inventory purposes.
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PUBLIC DRAFT-MAY 2010
93.105(c)(l)(i)). Unless otherwise determined through consultation, only one method
should be used for a given project.
/. 5.1 Finding emission factors
Regardless of method chosen, locomotive emissions factors will be needed for the
analysis. Locomotive emission factors depend on the type of engine, the power rating of
the locomotive (engine horsepower), and the year of engine manufacture (or re-
manufacture). Default PMio emission factors for line-haul and switch locomotives can be
obtained from Tables 1 and 2 of EPA's "Emission Factors for Locomotives," EPA-420-
F-09-025 (April 2009).5 These PMio emission factors are in grams/horsepower-hour and
can easily be converted to PM2.5 emission factors. However, these are simply default
values; locomotive-specific data may be available from manufacturers and should be
used whenever possible. In addition, see Appendix 1.5.4 for other variables that must be
considered when determining the appropriate locomotive emission factors.
Note that the default locomotive emission factors promulgated by EPA may change over
time as new information becomes available. The April 2009 guidance cited above
contains the latest emission factors as of this writing. Project sponsors should consult the
EPA's website at: www.epa.gov/otaq/locomotives.htm for the latest locomotive default
emission factors and related guidance.
/. 5.2 Calculating emissions using horsepower rating and load factors
One way locomotive emissions can be calculated is to use PM2.5 or PMio locomotive
emission factors, the horsepower rating of the engines found on the locomotive roster,
and engine load factors (which are calculated from the duty cycle).
Calculating Engine Load Factors
The horsepower of the locomotive engines, including the horsepower used in each notch
setting, should be available from the rail operator or locomotive manufacturer.
Locomotive duty cycle data (see Appendix 1.4.2) can then be used to determine how
much time each locomotive spends in each notch setting, including braking and idling.
An engine's "load factor" is the percent of maximum available horsepower it uses over
the course of its duty cycle. In other words, a load factor is the weighted average power
used by the locomotive divided by the engine's maximum rated power.6 Load factors
can be calculated by summing the actual horsepower-hours of work generated by the
engine in a given period of time and dividing it by the engine's maximum horsepower
5 Table 1 of EPA's April 2009 document includes default emission factors for higher power cycles
representative of general line-haul operation; Table 2 includes emission factors for lower power cycles used
for switching operations. The April 2009 document also includes information on how to convert PM10
emission factors for PM2 5 purposes. Note that Table 6 (PM10 Emission Factors) should not be used for PM
hot-spot analyses, since these factors are national fleet averages rather than emission factors for any
specific project.
6 "Weighted average power" in this case is the average power used by the locomotive weighted by the time
spent in each notch, as explained further below.
1-4
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PUBLIC DRAFT-MAY 2010
and the hours during which the engine was being used, with the result expressed as a
percentage. For example, if a 4000 hp engine spends one hour at full power (generating
4000 hp-hrs) and one hour at 50 percent power (generating 2000 hp-hrs), its load factor
would be 75 percent (6000 hp-hrs + 4000 hp + 2 hrs). Note that, in this example, it
would be equivalent to calculate the load factor using the percent power values instead:
((100% * 1 hr) + (50% * 1 hr) + 2 hrs = 75%). To simplify emission factor calculations,
it is recommended that locomotive activity be generalized into the operational categories
of "moving" and "idling," with separate load factors calculated for each.
An engine's load factor is calculated by completing the following steps:
Step 1. Determine the number of notch settings the engine being analyzed has and the
horsepower used by the engine in each notch setting.7 Alternatively, as described above,
the percent of maximum power available in each notch could instead be used.
Step 2. Identify the percentage of time the locomotive being analyzed spends in each
notch setting based on its duty cycle (see Appendix 1.4.2).
Step 3. To make emission rate calculations easier, it is useful to calculate two separate
load factors for an engine: one for when the locomotive is idling and one for when it is
moving.8 Therefore, the percentage of time the locomotive spends in each notch (from
Step 2) needs to be adjusted so that all idling and all moving notches are considered
separately. For example, if a locomotive has just one idle notch setting, it spends 100%
of its idling time in that setting, even if it only idles during part of its duty cycle. While
calculating the time spent idling will usually be simple, for the non-idle (moving) notch
settings some additional adjustment to the locomotive's duty cycle percentages will be
required to determine the time spent in each moving notch as a fraction of total time spent
moving, disregarding any time spent idling.
For example, say a locomotive spends 30% of its time idling and 70% of its time moving
over the course of its duty cycle and that 15% of this total time (idling and moving
together) is spent in notch 2. When calculating the moving load factor, this percentage
needs to be adjusted to determine what fraction of just the 70% of time spent moving is
spent in notch 2. In this example, 15% of the total duty cycle spent in notch 2 would
equal 21.4% (15% * 100% H- 70%) of the locomotive's time when it's not at idle; that is,
when moving, the locomotive spends 21.4% of its time in notch 2. This calculation is
repeated for each moving notch setting. The result will be the fraction of time spent in
each notch when considering idle and moving modes of operation separately.
Step 4. The next step is to calculate what fraction of maximum available horsepower is
being used based on the time spent in each notch setting as was calculated in Step 3. This
is determined by summing the product of the percentage of time spent in each notch
7 For locomotives that are equipped with multiple dynamic braking notches and/or multiple idle notches, it
may be necessary to assume a single dynamic braking notch and a single idle notch, depending on what
information is available about the particular engine.
8 In this case, "moving" refers to all non-idle notch settings: that is, dynamic braking and all run notches.
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(calculated in Step 3) by the horsepower generated by the engine at that notch setting
(determined in Step 1). For example, if the locomotive with a rated engine power of
3000 hp spends 21.4% of its moving time in notch 2 and 78.6% of its moving time in
notch 6, and is known to generate 500 hp while in notch 2 and 2000 hp while in notch 6,
then its weighted average power would be 1679 hp (107 hp (500 hp * 0.214) + 1572 hp
(2000 hp * 0.786) = 1679 hp).
Step 5. The final step is to determine the load factors. This is done by dividing the
weighted average horsepower (calculated in Step 4) by the maximum engine horsepower.
For idling, this should be relatively simple. For example, if there is one idle notch setting
and it is known that a 4000 hp engine uses 20 hp when in its idle notch, then its idle load
factor will be 0.5% (20 hp + 4000 hp). To determine the load factor for all power
notches, the weighted horsepower calculated in Step 4 should be divided by the total
engine horsepower. For example, if the same 4000 hp engine is determined to use an
average of 1800 hp while in motion (as determined by adjusting the horsepower by the
time spent in each "moving" notch setting in Step 4), then its moving load factor would
be 45% (1800 hp - 4000 hp).
The resulting idling and moving load factors represent the average amount of the total
engine horsepower the locomotive is using when idling and moving, respectfully. These
load factors can then be used to modify PM emission factors and generate emission rates
as described below.
Generating Emission Rates Based on Load Factors
As noted above, EPA's "Emission Factors for Locomotives" provides emission factors in
grams/brake horsepower-hour. This will also likely be the case with any specific
emission factors obtained from manufacturer's specifications. These units can be
converted into grams/second (g/s) emission rates by using the load factor on the engines
and the time spent in each operating mode, as described below.
The first step is to adjust the PM emission factors to reflect how the engine will actually
be operating.9 This is done by multiplying the appropriate PM emission factor by the
idling and moving load factors calculated for that particular engine.10 Next, to determine
the emission rate, this adjusted emission factor is further multiplied by the amount of
time the locomotive spends idling and moving while in the project area.11
For example, if the PM emission factor known to be 0.18 g/bhp-hr, the engine being
analyzed has an idling load factor of 0.5%, and the locomotive is anticipated to idle 24
9 Because combustion characteristics of an engine vary by throttle notch position, it is appropriate to adjust
the emission factor to reflect the average horsepower actually being used by the engine.
10 Project sponsors are reminded to check www.epa.gov/otaq/locomotives.htm to ensure the latest default
emission factors for idle and moving emissions are being used.
11 Note that this may or may not match up with the idle and moving time as described by the duty cycle
used to calculate the load factors, depending on how project-specific that duty cycle is.
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minutes per hour in the project area, then the resulting emission rate would be 0.035
grams/hour (0.18 g/bhp-hr * 0.5% * 0.4 hours).
Emission rates need to be converted into g/s for use by AERMOD, as described further in
Sections 7 through 9 of the guidance. These calculations should be repeated until the
entire locomotive roster is represented in each part of the project area being analyzed.
Appendix 1.7 provides an example of calculating g/s locomotive emission rates using this
methodology.
/. 5.3 Calculating emissions using fuel consumption data
Another method to calculate locomotive emissions involves using fuel consumption data.
Chapter 6.3 of EPA's "Procedure for Emission Inventory Preparation — Volume IV:
Mobile Sources" (reference information provided in Appendix 1.6, below) is a useful
reference and should be consulted when using this method.
Note that, for this method, it may be useful to scale down data already available to the
project sponsor. For example, if rail car miles/fuel consumption is known for trains
operating in situations identical to those being estimated in the project area, this data can
be used to estimate fuel consumption rates for a defined track length within the project
area.
Calculating Average Fuel Consumption
Locomotive fuel consumption is specific to a particular locomotive engine and the
throttle (notch) setting it is using. Data on the fuel consumption of various engines at
different notch settings can often be obtained from the locomotive or engine
manufacturer's specifications. When only partial data is available (e.g., only data for the
lowest and highest notch settings are known), interpolation combined with best available
engineering judgment can be used to determine fuel consumption at the intermediate
notch settings.
A locomotive's average fuel consumption can be calculated by determining how long
each locomotive is expected to spend in each notch setting based on its duty cycle (see
Appendix 1.4.2). This data can be aggregated to generate an average fuel consumption
rate for each locomotive type. See Chapter 6.3 of Volume IV for details on how to
generate this data based on a specific locomotive roster and duty cycle.
Once the average fuel consumption rates have been determined, they should be
multiplied by the appropriate emission factors to determine a composite average hourly
emission rate for each engine in the roster. Since the objective is to determine an average
fuel consumption rate for the entire locomotive roster, this calculation should be repeated
for each engine on the roster at each location analyzed.
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If several individual sources will be modeled at different sections of the project area as
described in Appendix 1.2, train schedule data should be consulted to determine the hours
of operation of each locomotive within each section of the project area. Hourly emission
rates per locomotive should then be multiplied by the number of hours the locomotive is
operating, for each hour of the day in each section of the project area to provide average
hourly emission rates for each section of the project. These should then be converted to
grams/second for use in AERMOD, as described further in Sections 7 through 9 of the
guidance.
Examples of calculating locomotive emissions using this method can be found in Chapter
6 of Volume IV.
/. 5.4 Factors influencing locomotive emissions and emission factors
The following considerations will influence locomotive emissions regardless of the
method used and should be examined when determining how to characterize locomotives
for emissions modeling or when choosing the appropriate emission factors:
• Project sponsors should be aware of the emission reductions that would result
from remanufacturing existing locomotives (or replacing existing locomotives
with new locomotives) that meet EPA's Tier 3 or Tier 4 emission standards when
they become available. The requirements that apply to existing and new
locomotives were addressed in EPA's 2008 rulemaking entitled "Control of
Emissions of Air Pollution from Locomotive Engines and Marine Compression-
Ignition Engines Less Than 30 liters Per Cylinder" (73 FR 37095). Beginning in
2012 all locomotives will be required to use ultra-low sulfur diesel fuel (69 FR
38958). Additionally, when existing locomotives are remanufactured, certified
remanufacture systems will have to be installed to reduce emissions. Beginning
in 2011, new locomotives must meet tighter Tier 3 emission standards. Finally,
beginning in 2015 even more stringent Tier 4 emission standards for new
locomotives will begin to be phased in.
• For locomotives manufactured before 2005, a given locomotive may be in one of
three possible configurations, depending on when it was last remanufactured: (1)
uncertified; (2) certified to the standards in 40 CFR Part 92; or (3) certified to the
standards in 40 CFR Part 1033. Each of these configurations should be treated as
a separate locomotive type when conducting a PM hot-spot analysis.
• Emissions from locomotives certified to meet Family Emission Limits (FELs)
may differ from the emission standard identified on the engine's Emission
Control Information label. Rail operators will know if their locomotives
participate in this program. Any locomotives in the project area participating in
this program should be identified so that the actual emissions from the particular
locomotives being analyzed are considered in the analysis, rather than the family
emissions level listed on their FEL labels.
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1.6 AVAILABLE RESOURCES
These resources and websites should be checked prior to beginning any PM hot-spot
analysis to ensure that the latest data (such as emission factors) are being used:
• "Emission Factors for Locomotives," EPA-420-F-09-025 (April 2009). Available
online at: www.epa.gov/otaq/locomotives.htm.
• Chapter 6 of "Procedure for Emission Inventory Preparation - Volume IV: Mobile
Sources." Available online at: www.epa.gov/OMS/invntory/r92009.pdf. Note
that, as of this writing, the emission factors listed in Volume IV have been
superseded by the April 2009 publication listed above for locomotives certified to
meet current EPA standards.12
• "Control of Emissions from Idling Locomotives," EPA-420-F-08-014, March
2008. Available online at:
www.epa.gov/otaq/regs/nonroad/locomotv/420f08014.htm.
• See Section 10 of the guidance for additional information regarding potential
locomotive emission control measures.
1.7 EXAMPLE OF CALCULATING LOCOMOTIVE EMISSION RATES USING
HORSEPOWER RATING AND LOAD FACTOR ESTIMATES
The following example demonstrates how to estimate locomotive emissions using the
engine horsepower rating/load factor method described in Appendix 1.5.2.
The hypothetical proposed project in this example includes the construction of an
intermodal terminal in an area that is designated as nonattainment for both the 1997
annual PM2.5 NAAQS and the 2006 24-hour PM2.5 NAAQS. The terminal in this
example is to be completed and operational in 2013. The hot-spot analysis is performed
for 2015, because it is determined through interagency consultation that this will be the
year of peak emissions, when considering the project's emissions and the other emissions
in the project area.
In this example, the operational schedule anticipates that 32 locomotives will be in the
project area over a 24-hour period, with 16 locomotives in the project area during the
peak hour. Based on the schedule, it is further determined that while in the project area
each train will spend 540 seconds idling and 76 seconds moving.
It's decided to calculate the locomotive PM2 5 emissions rates based on horsepower rating
and load factors.
12 Although the emission factors have been superseded, the remainder of the Volume IV guidance remains
in effect.
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1.7.1 Calculate idle and moving load factors
As described in 1.5.2, the project sponsor uses a series of steps to calculate load factors.
These steps are described below and the results from each step are shown in table form in
Exhibit I-1 (following page).
Step 1: The project sponsor first needs some information about the locomotives expected
to be operating at the terminal in the analysis year.
For each locomotive, the horsepower used by the locomotive in each notch setting as well
as under dynamic braking and at idle must be determined. For the purpose of this
example it is assumed that all of the locomotives that will serve this terminal are very
similar: all use the same horsepower under each of operating conditions, and all have
only one idle and dynamic braking notch setting. The horsepower generated at each
notch setting is obtained from the engine specifications (see second column of Exhibit I-
1). In this case, the rated engine horsepower is 4000 hp (generated at notch 8).
Step 2: The next step is to determine the average amount of time that the locomotives
spend in each notch and expressing the results as a percentage of the locomotive's total
operating time. In this example, it is determined that, based on their duty cycle, the
locomotives that will service this terminal spend 38% of their time idling and 62% of
their time in motion in one of the eight run notch settings or under dynamic braking. The
percentage of time spent in each notch is shown in the third column of Exhibit 1-1.
Step 3: To make emission factor calculations easier, it is decided to calculate separate
idling and moving load factors. The next step, then, is for the project sponsor to calculate
the actual percentage of time that the locomotives spend in each notch, treating idling and
moving time separately. This is done by excluding the time spent idling and
recalculating the percentage of time spent in the other notches (i.e., dynamic braking and
each of the eight notch settings) so that the total time spent in non-idle notches adds to
100%. The results are shown in the fourth column of Exhibit 1-1.
Step 4: The next step is to calculate the weighted average horsepower for this engine
using the horsepower generated in each notch and the percentage of time spent in each
notch as adjusted in Step 3. For locomotives that are idling, this is simply the horsepower
used at idle. For the other notches, the actual horsepower for each notch is determined by
multiplying the horsepower generated in a given notch (determined in Step 1) by the
actual percentage of time that the locomotive is in that notch, as adjusted (calculated in
Step 3). The results are shown in the fifth column of Exhibit 1-1.
Step 5: The final step in this part of the analysis is to determine the idle and moving load
factors. The idle load factor is just the horsepower generated at idle divided by the
maximum engine horsepower, with the result expressed as a percentage. To determine
the moving load factor, the weighted average horsepower for all non-idle notches
(calculated in Step 4) is divided by the maximum engine horsepower, with the result
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expressed as a percentage. The final column of Exhibit 1-1 shows the results of these
calculations, with the idling and moving load factors highlighted.
Exhibit 1-1. Calculating Locomotive Load Factors
Notch
Setting
Step 1:
Horsepower
(hp)
used in
notch
Step 2:
Average %
time spent
in notch
Step 3:
Reweighted
time spent in
each notch
(adjusted so
that non-idle
notches add to
100%)
Step 4:
Time-
weighted
hp used,
based on
time
spent in
notch
Step 5:
Load
factors
(idle and
moving)
Idling load factor:
Idle
14
38.0%
100.0%
14.0
0.4%
Moving load factor:
Dynamic
Brake
1
2
3
4
5
6
7
8
Total
136
224
484
984
1149
1766
2518
3373
4,000
12.5%
6.5%
6.5%
5.2%
4.4%
3.8%
3.9%
3.0%
16.2%
62.0%
20.2%
10.5%
10.5%
8.4%
7.1%
6.1%
6.3%
4.8%
26.1%
100.0%
27.5
23.5
50.8
82.7
81.6
107.8
158.6
161.9
1,044.0
1,752.4
43.8%
/. 7.2 Using the load factors to calculate idle and moving emission rates
Now that the idle and moving load factors have been determined, the gram/second (g/s)
emission rates can be calculated for the idling and moving locomotives.
First, the project sponsor would determine how many locomotives are projected to be
idling and how many are projected to be in motion during the peak hour of operation and
over a 24-hour period. As previously noted, it is anticipated that 32 locomotives will be
in the project area over a 24-hour period, with 16 locomotives in the project area during
the peak hour. It was further determined that, while in the project area, each train will
spend 540 seconds idling and 76 seconds moving.
For the purpose of this example, it has been assumed that each locomotive idles for the
same amount of time and is in motion for the same amount of time. Note that, in this
case, the number of locomotives considered "moving" will be double the actual number
of locomotives present in order to account for the fact that each locomotive moves twice
through the project area (as it arrives and departs the terminal).
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Next, the project sponsor would determine the PM2 5 emission factor to be used in this
analysis for 2015. These emission factors can be determined from the EPA guidance
titled "Emission Factors for Locomotives."
Table 1 of "Emission Factors for Locomotives" presents PMi0 emission factors in terms
of grams/brake horsepower-hour (g/bhp-hr) for line haul locomotives that are typically
used by commuter railroads. Emission factors are presented for uncontrolled
locomotives, locomotives manufactured to meet Tier 0 through Tier 4 emission
standards, and locomotives remanufactured to meet more stringent emission standards.
It's important to determine the composition of the fleet of locomotives that will use the
terminal in the year that is being analyzed so that the emission factors in Table 1 can be
used in the calculations. This information would be available from the railway operator.
In this example, we are assuming that all of the locomotives meet the Tier 2 emission
standard. However, an actual PM hot-spot analysis would likely have a fleet of
locomotives that meets a combination of these emission standards. The calculations
shown below would have to be repeated for each different standard that applies to the
locomotives in the fleet.
The final step in these calculations is to use the information shown in Exhibit 1-1 and the
other project data collected to calculate the PM2 5 emission rates for idling and moving
locomotives during both the peak hour and over a 24-hour basis.13
Calculating Peak Hour Idling Emissions
The following calculation would be used to determine the idling emission rate during the
peak hour of operation:14
PM2.5 Emission Rate = (16 trains/hr) * (1 hr/3,600 s) * (540 s/train) * (4,000 hp) *
(0.004) * (0.18 g/bhp-hr) * (1 hr/3,600 s) * (0.97)
PM2.5 Emission Rate = 0.0019 g/s
Where:
• Trains per hour =16 (number of trains present in peak hour)
• Idle time per train = 540 s (from anticipated schedule)
• Locomotive horsepower = 4,000 hp (from engine specifications)
• Idle load factor = 0.004 (0.4%, calculated in Exhibit 1-1)
• Tier 2 Locomotive Emission Factor = 0.18 g/bhp-hr (from "Emission
Factors for Locomotives")
• Ratio of PM2 5 to PMi0 = 0.97 (from "Emission Factors for Locomotives")
13 Peak hour emission rates will not be necessary for all analyses; however, for certain projects that involve
very detailed air quality modeling analyses, peak hour emission rates may be necessary to more accurately
reflect the contribution of locomotive emissions to air quality concentrations in the project area.
14 Note that, for the calculations shown here, any units expressed in hours or days need to be converted to
seconds since a g/s emission rate is required for AERMOD.
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Calculating 24-hour Moving Emissions
Similarly, the following equation would be used to calculate the moving emission rate for
the 24-hour period:
PM2.5 Emission Rate = (64 trains/day) * (76 s/train) * (1 day/86,400 s) * (4,000 hp) *
(0.438) * (0.18 g/bhp-hr) * (lhr/3,600 s) * (0.97)
PM2.5 Emission Rate = 0.0048 g/s
Where:
Trains per day = 64 (double the actual number of trains present over 24
hours to account for each train moving twice through the project area)
Moving time per train = 76 s (from anticipated schedule)
Locomotive horsepower = 4,000 hp (from engine specifications)
Moving load factor = 0.438 (43.8%, calculated in Exhibit 1-1)
Tier 2 Locomotive Emission Factor = 0.18 g/bhp-hr (from "Emission
Factors for Locomotives")
Ratio of PM2 5 to PMi0 = 0.97 (from "Emission Factors for Locomotives")
A summary of the variables used in the above equations and the resulting emission rates
can be found in Exhibit 1-2, below.
Exhibit 1-2. PMi.s Locomotive Emission Rates
Operational
Mode
Idle
Moving
Number of
Locomotives
Peak
hour
16
32
24
hours
32
64
Time/
Train
(s)
540
76
PM25
Emission
Factor
(g/bhp-hr)
0.18
0.18
Calculated
Peak Hour
Emission Rate
(g/s)
0.0019
0.057
Calculated
24-hour
Emission
Rate
(g/s)
0.00016
0.0048
These peak and 24-hour emission rates can now be used in air quality modeling for the
project area, as described in Sections 7 through 9 of the guidance. Note that, since this
area is designated as nonattainment for both the 1997 annual PM2 sNAAQS and the 2006
24-hour PM2.5NAAQS, the results of the analysis will have to be compared to both
NAAQS (see Section 3.3.4 of the guidance). Since the area is in nonattainment of the
annual PM25 NAAQS, all four quarters will need to be included in the analysis to
estimate a year's worth of emissions. If there is no change in locomotive activity across
quarters, the emission rates calculated here could be used for each quarter of the year (see
Appendix 1.3).
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Appendix J:
Additional Reference Information on Air Quality Models and
Data Inputs
J.I INTRODUCTION
This appendix supplements Section 7's discussion of air quality models. Specifically,
this appendix describes how to configure AERMOD and CAL3QHCR for PM hot-spot
analysis modeling, as well as additional information on handling the data required to run
the models for these analyses. This appendix is not intended to replace the user guides
for air quality models, but discuss specific model inputs, keywords, and formats for PM
hot-spot modeling. This appendix is organized so that it references the appropriate
discussions in Section 7 of the main guidance document.
J.2 SELECTING AN APPROPRIATE AIR QUALITY MODEL
The following discussion supplements Section 7.3 of the guidance and describes how to
appropriately configure AERMOD and CAL3QHCR when completing a PM hot-spot
analysis. Users should also refer to the model user guides, as appropriate.
J.2.1 Using AERMOD for PM hot-spot analyses
There are no specific commands unique to transportation projects that are necessary when
using AERMOD. By default, AERMOD produces output for particulate matter in units
of micrograms per cubic meter of air (|j,g/m3). All source types in AERMOD require that
emissions are specified in terms of emissions per unit time, although AREA-type sources
also require specification of emissions per unit time per unit area. AERMOD has no
specific traffic queuing mechanisms. Emissions output from MOVES, EMFAC, AP-42,
and other types of methods should be formatted as described in the AERMOD User
Guide. *
J. 2.2 Using CALSQHCRfor PM hot-spot analyses
CAL3QHCR is an extension of the CAL3QHC model that allows the processing of a full
year of hourly meteorological data, the varying of traffic-related inputs by hour of the
week, and calculation of long-term average concentrations. It also will display the five
highest concentration days for the time period being modeled. Emissions output from
MOVES, EMFAC, AP-42, and other emission methods should be formatted as described
1 Extensive documentation is available describing the various components of AERMOD, including user
guides, model formulation, and evaluation papers. See EPA's SCRAM website for AERMOD
documentation: www.epa.gov/scramOOI/dispersion prefrec.htm#aermod
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in the CAL3QHCR User Guide.2 In addition, the following guidance is provided when
using CAL3QHCR for a PM hot-spot analysis:
Specifying the Right Pollutant
When using CAL3QHCR for PM hot-spot analyses, the MODE keyword must be used to
specify analyses for PM so that concentrations are described in micrograms per cubic
meter of air (|j,g/m3) rather than parts per million (ppm).
Entering Emission Rates
MOVES emission rates for individual roadway links are based on the Op-Mode
distribution associated with each link and are able to include emissions resulting from
idling. MOVES-based emission factors that incorporate relevant idling time and other
delays should be entered in CAL3QHCR using the EFL keyword. Therefore, within
CAL3QHCR, the IDLFAC keyword's emission rates should be set to zero, because the
effects of idling are already included within running emissions. (Note that if a non-zero
emission rate is used in CAL3QHCR, the model will treat idling emission rates separately
from running emission rates). The same recommendation applies when using emission
rates calculated by EMFAC.
Assigning Speeds
Although the user guide for CAL3QHCR specifies that the non-queuing links should be
assigned speeds in the absence of delay caused by traffic signals, the user should use
speeds that reflect delay when using CAL3QHCR for a hot-spot analysis. Since MOVES
emission factors already include the effects of delay (i.e., Op-Mode distributions that are
user-specified or internally calculated include the effects of delay), the speeds used in
CAL3QCFIR links will already reflect the relevant delay on the link over the appropriate
averaging time. The same recommendation applies when using EMFAC.
Using the Queuing Algorithm
When applying CAL3QHCR for the analysis of highway and intersection projects, its
queuing algorithm should not be used.3 This includes the CAL3QHCR keywords
NLANE, CAVG, RAVG, YFAC, IV, and IDLFAC. As discussed in Sections 4 and 5,
idling vehicle emissions should instead be accounted for by properly specifying links for
emission analysis, and reflecting idling activity in the activity patterns used for MOVES
or EMFAC modeling.
2 The CAL3QHCR user guide and other model documentation can be found on EPA's SCRAM website:
www.epa.gov/scram001/dispersion_prefrec.htm#cal3qhc
3 CALSQHCR's algorithm for estimating the length of vehicle queues associated with intersections is based
on the 1985 Highway Capacity Manual, which is no longer current. Furthermore, a number of other
techniques are now available that can be used to estimate vehicle queuing around intersections.
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J.3 CHARACTERIZING EMISSION SOURCES
The following discussion supplements Section 7.4 of the guidance and describes in more
detail how to characterize sources in CAL3QHCR and AERMOD, including the physical
characteristics, location, and timing of sources. This discussion assumes the user is
familiar with handling data in these models, including the use of specific keywords. For
additional information, refer to the CAL3QHCR and AERMOD user guides.
J. 3.1 Physical characteristics and locations of sources in CAL3QCHR
CAL3QHCR characterizes highway and intersection projects as line sources. The
geometry and operational patterns of each roadway link are described using the following
variables, which in general may be obtained from engineering diagrams and design plans
of the project:4
• The coordinates (X, Y) of the endpoints of each link;5
• The width of the "highway mixing zone" (see below);
• The type of link ("at grade," "fill," "bridge," or "depressed");
• The height of the roadway relative to the surrounding ground (not to exceed ±10
meters);6 and
• The hourly flow of traffic (vehicles per hour).
CAL3QHCR treats the area over each roadway link as a "mixing zone" that accounts for
the area of turbulent air around the roadway resulting from vehicle-induced turbulence.
The width of the mixing zone is an input to the model. Users should specify the width of
a link in CAL3QHCR as the width of the traveled way (traffic lanes, not including
shoulders) plus three meters on either side. Users should treat divided highways as two
separate links. See Section 7.6 of the guidance for more information on placing
receptors.
J. 3.2 Timing of emissions in CAL3QCHR
The CAL3QHCR user's guide describes two methods for accepting time-varying
emissions and traffic data; these are labeled the "Tier I" and "Tier II" approaches.7
Project-level PM hotspot modeling should use the Tier II method, which can
4 Traffic engineering plans and diagrams may include information such as the number, width, and
configuration of lanes, turning channels, intersection dimensions, and ramp curvature, as well as
operational estimates such as locations of weave and merge sections and other descriptions of roadway
geometry that may be useful for specifying sources.
5 In CAL3QHCR, the Y-axis is aligned due north.
6 The C ALINES dispersion algorithm in CAL3QHCR is sensitive to the height of the road. In particular,
the model treats bridges and above-grade "fill" roadways differently. It also handles below-grade roadways
with height of less than zero (0) meters as "cut" sections. Information on the topological features of the
project site is needed to make such a determination. Note that in the unusual circumstance that a roadway
is more than ten meters below grade, CALINE3 has not been evaluated, so CAL3QHCR is not
recommended for application. In that circumstance, the relevant EPA Regional Office should be consulted
for determination of the most appropriate model.
7 This nomenclature is unrelated to EPA's motor vehicle emission standards.
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accommodate different hourly emission patterns for each day of the week. Most
emissions data will not be so detailed, but the Tier II approach can accommodate
emissions data similar to that described in Sections 4 and 5 of the guidance. The
CAL3QHCR Tier I approach should not be used, as it employs only one hour of
emissions and traffic data and therefore cannot accommodate the emissions data required
in a PM hot-spot analysis.
Through the IPATRY keyword, CAL3QHCR allows up to seven 24-hour profiles
representing hour-specific emission, traffic, and signalization (ETS) data for each day of
the week. Depending on the number of MOVES runs, the emission factors should be
mapped to the appropriate hours of the day. For example, peak traffic emissions data for
each day would be mapped to the CAL3QHCR entry hours corresponding to the relevant
times of day (in this case, the morning and afternoon peak traffic periods). If there are
more MOVES runs than the minimum specified in the Section 4, they should be
explicitly modeled and linked to the correct days and hours using IPATRY.
As described in Section 7 of the guidance, the number of CAL3QHCR runs required for a
given PM hot-spot analysis will vary based on the amount of meteorological data
available.
J. 3.3 Physical characteristics and locations of sources in AERMOD
The following discussion gives guidance on how to best characterize a source.
AERMOD includes different commands (keywords) for volume, area, and point sources.
Modeling Volume Sources
Many different sources in a project undergoing a PM hot-spot analysis might be modeled
as volume sources. Examples include areas designated for truck or bus queuing or idling
(e.g., off-network links in MOVES), driveways and pass-throughs in transit or freight
terminals, and locomotive emissions.8 AERMOD can also approximate a highway "line
source" using a series of adjacent volume sources (see the AERMOD user guide for
suggestions). Certain nearby sources that have been selected to be explicitly modeled
may also be appropriately treated as a volume source (see Section 8 of the guidance for
more information on considering background concentrations from other sources).
Volume source parameters are entered using the source parameter (SRCPARAM)
keyword in the AERMOD input file. This requires the user to provide the following
information:
• The emission rate (mass per unit time, such as g/s);
• The initial lateral dimension (width) of the volume, and the initial lateral
dispersion coefficient;
• The initial vertical dimension (height) of the volume and initial vertical dispersion
coefficient; and
See Section 6 and Appendix I for information regarding calculating locomotive emissions.
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• The source release height of the volume source center, (i.e., meters above the
ground).
Within AERMOD, the volume source algorithms are most applicable to line sources with
some initial plume depth (e.g., highways, rail lines).9 There are three methods available
to characterize the initial size of a roadway plume:
1. Initial lateral dimension and dispersion coefficient (oyo). To estimate the initial
lateral dimension (or width) of the volume source, you could use one of the following
approaches:
• Use the average vehicle width plus 6 meters, when modeling a single lane of
traffic;
• Use road width multiplied by 2; or
• Use a set width, such as 10 meters per lane of traffic.
To specify the initial lateral dispersion coefficient (oyo), referred to as Syinit in
AERMOD, the AERMOD User Guide recommends dividing the initial width by 2.15.
2. Initial vertical dimension and dispersion coefficient (o70). A typical approach to
estimating the initial vertical dimension (height) of the plume for volume sources is to
assume it is about 1.7 times the average vehicle height, to account for the effects of
vehicle-induced turbulence:
• For light-duty vehicles, this is about 2.6 meters, using an average vehicle
height of 1.53 meters or 5 feet;
• For heavy-duty vehicles, this is about 6.8 meters, using an average vehicle
height of 4.0 meters;
• For mixed fleets, estimate the initial vertical dimension using an emissions-
weighted average. For example, if light-duty and heavy-duty vehicles
contribute 40% and 60% of the emissions of a given volume source,
respectively, the initial vertical dimension would be 0.4 * 2.6 + 0.6 * 6.8 = 5.1
meters.
The AERMOD User Guide recommends that the initial vertical dispersion coefficient
(GZO), termed Szinit in AERMOD, be estimated by dividing the initial vertical
dimension of the source by 2.15. For typical light-duty vehicles, this corresponds to
an Szinit (ozo) of 1.2 meters. For typical heavy-duty vehicles, the initial value of
Szinit (GZO) is 3.2 meters10.
9 The vehicle-induced turbulence around roadways with moving traffic suggests that prior to transport
downwind, a roadway plume has an initial size - that is, the emissions from the tailpipe are stirred because
the vehicle is moving and therefore the plume "begins" from a three-dimensional volume, rather than from
a point source (the tailpipe).
10 At this time, AERMOD (version dated 09292) allows the initial dimensions and release heights of
volume sources to change by hour of the day, which may be considered if the fraction of heavy-duty
vehicles is expected to significantly change throughout a day. Users should consult the latest information
on AERMOD when starting a PM hot-spot analysis.
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3. Source release height. The source release height (Relhgt in AERMOD), which is
the height at which wind effectively begins to affect the plume, may be estimated
from the midpoint of the initial vertical dimension:
• For moving light-duty vehicles, this is about 1.3 meters.
• For moving heavy-duty vehicles, it is 3.4 meters.
Similar to the initial vertical dimension of a volume source, the release height of
mixed fleets may be estimated using an emissions-weighted average. For a 40%/60%
light-duty/heavy-duty emissions share, the source release height would be 0.4 * 1.3 +
0.6* 3.4 = 2.6 meters.
Another way of dealing with Syinit, Szinit, and/or Relhgt parameters that change as a
result of different fractions of light-duty and heavy-duty vehicles is to create two versions
of each roadway source, corresponding to either light-duty and heavy-duty traffic. These
two sources could be superimposed in space, but have emission rates and Syinit, Szinit,
and Relhgt parameters that are specific to light-duty or heavy-duty traffic.
Finally, groups of idling vehicles may also be modeled as one or more volume sources.
In those cases, the initial dimensions of the source, dispersion coefficients, and release
heights should be calculated assuming that the vehicles themselves are inducing no
turbulence.
Consult the AERMOD User Guide and AERMOD Implementation Guide for details in
applying AERMOD to roadway sources.
Modeling Area Sources
AERMOD can represent rectangular, polygon-shaped, and circular area sources using the
AREA, AREAPOLY, or AREACIRC keywords. Sources that may be modeled as area
sources may include areas within which emissions occur relatively evenly.n Evenly-
distributed ground-level sources might also be modeled as area sources.
AERMOD requires the following information when modeling an area source:
• The emission rate per unit area (mass per unit area per unit time);
• The release height above the ground;
• The length of the north-south side of the area;
• The length of the east-west side of the area (if the area is not a square);
• The orientation of the rectangular area in degrees relative to north; and
• The initial height (vertical dimension) of the area source plume.
Modeling Point Sources
11 At present, the AERMOD Implementation Guide recommends that, where possible, a volume source
approximation be used to model area sources, because area sources in AERMOD do not include
AERMOD's "plume meander approach." Consult the latest version of the AERMOD Implementation
Guide for the most current information on when volume sources or area sources are most appropriate.
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It may be appropriate to model some emission sources as fixed point sources, such as
exhaust fans or stacks on a bus garage or terminal building. If a source is modeled with
the POINT keyword in AERMOD, the model requires:
• The emission rate (mass per unit time);
• The release height above the ground;
• The exhaust gas exit temperature;
• The stack gas exit velocity; and,
• The stack inside diameter in meters.
These parameters can often be estimated using the plans and engineering diagrams for
ventilation systems.
J. 3.4 Placement and sizing of sources within AERMOD
There are several general considerations with regard to placing and sizing sources within
AERMOD.
First, volume, area, and point sources should be placed in the locations where emissions
are most likely to occur. For example: if, within, a bus terminal, buses enter and exit
from a single driveway within the terminal yard, the driveway should be modeled using
one or more discrete volume or area sources in the location of that driveway, rather than
spreading the emissions from that driveway across the entire terminal yard.
Second, for emissions from the sides or tops of buildings (as may be found from a bus
garage exhaust fan), it may be necessary to use the BPIPPRIME utility in AERMOD to
appropriately capture the characteristics of these emissions (such as downwash).
Third, the initial dimensions and other parameters of each source should be as realistic as
is feasible. Chapter 3 of the AERMOD User Guide includes recommendations for how
to appropriately characterize the shape of area and volume sources.
Finally, if nearby sources are explicitly modeled (see discussion in Section 8 of the
guidance), a combination of all these source types may be needed to appropriately
represent their emissions within AERMOD. For instance, evenly-distributed ground-
level sources might also be modeled as area sources, while a nearby power plant stack
might be modeled as a point source.
J. 3.5 Timing of emissions in AERMOD
Within AERMOD, emissions that vary across a year should be described with the
EMISFACT keyword (see Section 3.3.5 of the AERMOD User Guide). The number of
quarters that need to be analyzed may vary based on a particular PM hot-spot analysis.
See Section 3 of the guidance for more information on when PM emissions need to be
evaluated, and Sections 4 and 5 of the guidance on determining the number of MOVES
and EMFAC runs.
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The Qflag parameter under EMISFACT may be used with a secondary keyword to
describe different patterns of emission variations throughout a year. Note that AERMOD
defines seasons in the following manner: winter (December, January, February), spring
(March, April, May), summer (June, July, August), and fall (September, October,
November). Emission data obtained from MOVES or EMFAC should be appropriately
matched with the relevant time periods in AERMOD. For example, if four MOVES or
EMFAC runs are completed (one for each quarter of a year), there are emission estimates
corresponding to four months of the year (January, April, July, October) and peak and
average periods within each day. In such a circumstance, January runs should be used to
represent all AERMOD winter months (December, January, February), April runs for all
spring months (March, April, May), July runs for all summer months (June, July,
August), and October for all fall months (September, October, November). If separate
weekend emission rates are available, season-specific weekday runs should be used for
the Monday-Friday entries; weekend runs would be assigned to the Saturday and Sunday
entries. The peak/average runs for each day should be mapped to the AERMOD entry
hours corresponding to the relevant time of day from the traffic analysis. Qflag can be
used to represent emission rates that vary by season, hour of day, and day of the week.
Consult the AERMOD User Guide for details.
J.4 INCORPORATING METEOROLOGICAL DATA
This discussion supplements Section 7.5 of the guidance and describes in more detail
how to handle meteorological data in AERMOD and CAL3QHCR. Section 7.2.3 of
Appendix W to 40 CFR Part 51 provides the basis for determining the urban/rural status
of a source. Consult the AERMOD Implementation Guide for instructions on what type
of population data should be used in making urban/rural determinations.
J. 4.1 Specifying urban or rural sources in AERMOD
As described in Section 7 of the guidance, AERMOD employs nearby population as a
surrogate for the magnitude of differential urban-rural heating (i.e., the urban heat island
effect). When modeling urban sources in AERMOD, users should use the URBANOPT
keyword to enter this data.
When considering urban roughness lengths, users should consult the AERMOD
Implementation Guide. Any application of AERMOD that utilizes a value other than 1
meter for the urban roughness length should be considered a non-regulatory application,
and would require appropriate documentation and justification as an alternate model (see
Section 7.3.3 of the guidance).
For urban applications using representative National Weather Service (NWS)
meteorological data, consult the AERMOD Implementation Guide. For urban
applications using NWS data, the URBANOPT keyword should be selected, regardless of
whether the NWS site is located in a nearby rural or urban setting. When using site-
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specific meteorological data in urban applications, consult the AERMOD Implementation
Guide.
J. 4.2 Specifying urban or rural sources in CAL3QHCR
CAL3QHCR requires that users specify the run as being rural or urban using the "RU"
keyword.12 Users should make the appropriate entry depending if the source is
considered urban or rural as described in Section 7.5.5 of the guidance.
J.5 RUNNING THE MODEL AND OBTAINING RESULTS
This discussion supplements Section 7.7 of the guidance and describes in more detail
how to handle data outputs in AERMOD and CAL3QHCR. AERMOD and CAL3QHCR
produce different output file formats, which must be post-processed in different ways to
enable calculation of design values, described in Section 9.3 of the guidance. This
guidance is applicable regardless of how many quarters are being modeled.
J.5.1 AERMOD output
AERMOD requires that users specify the type and format of output files in the main input
file for each run. See Section 3.7 of the AERMOD User Guide for details on the various
output options. Output options should be specified to enable the relevant design value
calculations required in Section 9.3. Note that many users will have multiple years of
meteorological data, so multiple output files may be required (unless the meteorological
files have been joined prior to running AERMOD).
For the annual PM2.5 design value calculations described in Section 9.3.2, averaging
times should be specified that allow calculation of the annual average concentrations at
each receptor. For example, when using five years of meteorological data, the PERIOD
averaging time could be specified using the CO AVERTIME keyword.
For the 24-hour PM2.s design value calculations described in Section 9.3.3, the
DAYTABLE option provides output files with 24-hour concentrations at each receptor
for each day processed. Users should flag the quarter and year for each day listed in the
DAYTABLE that AERMOD generates. Note users should also specify a 24-hour
averaging time with the CO AVERTIME command as well.
Another option for calculating 24-hour PM2 5 design values is with a POSTFILE, a file of
results at each receptor for each day processed. By specifying a POSTFILE with a 24-
hour averaging time, a user can generate a file of daily concentrations for each day of
meteorological data. When using this option, users should specify a POSTFILE with a
24-hour averaging time to generate the outputs needed to calculate design values, and
Specifying urban modeling with the "RU" keyword converts stability classes E and F to D.
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flag the quarter and year for each day listed in the POSTFILE that AERMOD generates.
Note that POSTFILE output files can be very large.
For the 24-hour PMi0 calculations described in Section 9.3.4, the RECTABLE keyword
may be used to obtain the six highest 24-hour concentrations over the entire modeling
period. A RECTABLE is a file summarizing the highest concentrations at each receptor
over an averaging period (e.g., 24 hours) across a modeling period (e.g., 5 years).
EPA is actively working towards a post-processing tool for AERMOD that will provide
the appropriate modeling metrics that may then be combined with background
concentrations for comparisons to the PM NAAQS. EPA will announce these new
options as they become available on EPA's SCRAM website at:
www. epa. gov/scramOO 1 /.
J. 5.2 CAL3QHCR output
For each year of meteorological data and quarterly emission inputs, CAL3QHCR reports
the five highest 24-hour concentrations and the quarterly average concentrations in its
output file.
For calculating annual PM2.5 design values using CAL3QHCR output, some post-
processing is required. CAL3QHCR's output file refers to certain data under the display:
"THE HIGHEST ANNUAL AVERAGE CONCENTRATIONS." If four quarters of
emission data are separately run in CAL3QHCR, each quarter's outputs listed under
"THE HIGHEST ANNUAL AVERAGE CONCENTRATIONS" are actually quarterly-
average concentrations. As described in Section 7, per year of meteorological data,
CAL3QHCR should be run for as many quarters as analyzed using MOVES and
EMFAC. CAL3QHCR accepts only a single quarter's emission factors per input file.
Calculating 24-hour PM2.5 design values under a first or second tier analysis is described
in Section 9.3.3. To get annual average modeled concentrations for a first tier analysis
(Step 1), the highest 24-hour concentrations in each quarter and year of meteorological
data should be identified. Within each year of meteorological data, the highest 24-hour
concentration at each receptor should be identified. For a first-tier analysis, at each
receptor, the highest concentrations from each year of meteorological data should be
averaged together. Under a second tier analysis, at each receptor, the highest modeled
concentration in each quarter, from each year of meteorological data, should be averaged
together. These average highest 24-hour concentrations in each quarter, across multiple
years of meteorological data, are used in second tier PM2.5 design value calculations.
In calculating 24-hour PMio design values, it is necessary to estimate the sixth-highest
concentration in each year if using five years of meteorological data. For each period of
meteorological data, CAL3QHCR outputs the five highest 24-hour concentrations. To
estimate the sixth-highest concentration at a receptor, the five highest 24-hour
concentrations from each quarter and year of meteorological data should be arrayed
together and ranked. From all quarters and years of meteorological data, the sixth-
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highest concentration should be identified. This concentration, at each receptor, is used
in calculations of the PMi0 design value described in Section 9.3.4.
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Appendix K:
Examples of Design Value Calculations for PM Hot-spot
Analyses
K.1 INTRODUCTION
This appendix supplements Section 9's discussion of calculating and applying design
values for PM hot-spot analyses. Specifically, this appendix provides examples of how
to calculate design values for the annual PM2.5 NAAQS, the 24-hour PM2.5 NAAQS, and
the 24-hour PMi0 NAAQS using the steps described in Section 9.3. Readers should
reference the appropriate sections of the guidance as needed for more detail on how to
complete each step of these analyses.
These illustrative example calculations demonstrate the basic procedures described in the
guidance and therefore are simplified in the number of receptors considered and other
details that would occur in an actual PM hot-spot analysis. Where users would have to
repeat steps for additional receptors, it is noted. These examples are organized according
to the build/no-build analysis steps that are described in Sections 2 and 9 of this guidance.
The final part of this appendix provides mathematical formulas that describe the design
value calculations discussed in Section 9 and this appendix.
K.2 PROJECT DESCRIPTION AND CONTEXT FOR ALL EXAMPLES
For the following examples, a PM hot-spot analysis is being done for an expansion of an
existing highway with a significant increase in the number of diesel vehicles (40 CFR
93.123(b)(l)(i)). The highway expansion will serve an expanded freight terminal. The
traffic at the terminal will increase as a result of the expanded highway project's increase
in truck traffic, and therefore the freight terminal is projected to have higher emissions
under the build scenario than under the no-build scenario. The freight terminal is not part
of the project; it is a nearby source.
The air quality monitor selected to represent background concentrations from other
sources is a Federal Equivalent Method (FEM) monitor that is 300 meters upwind of the
project. The monitor is on a l-in-3 day sampling schedule. In this example, the three
most recent years of monitoring data are from 2008, 2009, and 2010. Since 2008 is a
leap year (366 days), there are 122 monitored values in that year and 121 values for both
2009 and 2010 (365 days each).
However, through interagency consultation, it is determined that the freight terminal's
emissions are not already captured by this air quality monitor. AERMOD has been
selected as the air quality model to estimate PM concentrations produced by the project
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(the highway expansion) and the nearby source (the freight terminal).l There are five
years of representative off-site meteorological data being used in this PM hot-spot
analysis.
K.3 EXAMPLE: ANNUAL PM2.5 NAAQS
K. 3.1 General
This example illustrates the approach to calculating design values for comparison to the
annual PM2.5 NAAQS, as described in Section 9.3.2. The annual PM2.5 design value is
the average of three consecutive years' annual averages. The design value for
comparison is rounded to the nearest tenth of a ug/m3 (nearest 0.1 ug/m3). For example,
15.049 rounds to 15.0, and 15.050 rounds to 15.1.2
Each year's annual average concentrations include contributions from the project, any
explicitly modeled nearby sources, and background concentrations. For air quality
monitoring purposes, the annual PM2.5 NAAQS is met when the three-year average
concentration is less than or equal to the current annual PM2.5 NAAQS (i.e., 15.0 ug/m3):
Annual PM2 5 design value = ([Yl] average + [Y2] average + [Y3] average) + 3
Where:
[Yl] = Average annual PM25 concentration for the first year of air quality
monitoring data3
[Y2] = Average annual PM2.5 concentration for the second year of air quality
monitoring data
[Y3] = Average annual PM2 5 concentration for the third year of air quality
monitoring data
For this example, the project described in Appendix K.2 is located in an annual PM2.5
NAAQS nonattainment area. This example illustrates how an annual PM2 5 design value
could be calculated at the same receptor in the build and no-build scenarios, based on air
quality modeling results and air quality monitoring data. In an actual PM hot-spot
analysis, design values would be calculated at additional receptors, as described further in
Section 9.3.2.
1 EPA notes that CAL3QHCR could not be used in this particular PM hot-spot analysis, since air quality
modeling included the project and a nearby source. See Section 7.3 of the guidance for further information.
2 A sufficient number of decimal places (3-4) should be retained during intermediate calculations for design
values, so that there is no possibility of intermediate rounding or truncation affecting the final result.
Rounding to the tenths place should only occur during final design value calculations, pursuant to
Appendix N to 40 CFR Part 50.
3 The number of air quality monitoring measurements may vary by year.
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K. 3.2 Build scenario
For the build scenario, the PM2 5 impacts from the project and from the nearby source are
estimated with AERMOD at all receptors.4
Steps 1-2. Because AERMOD is used for this project, Step 1 is skipped. The receptor
with the highest average annual concentration, using five years of meteorological data, is
identified directly from the AERMOD output. This receptor's average annual
concentration is 3.603 ug/m3.
Step 3. Based on the three years of measurements at the background air quality monitor,
the average monitored background concentrations in each quarter is determined. Then,
for each year of background data, the four quarters are averaged to get an average annual
background concentration (last column of Exhibit K-l). These three average annual
background concentrations are averaged, and the resulting value is 11.582 ug/m3, as
shown in Exhibit K-l:
Exhibit K-l. Background Concentrations
Background
Concentrations
2008
2009
2010
Ql
13.013
14.214
11.890
Q2
17.037
14.872
16.752
Q3
8.795
7.912
9.421
Q4
8.145
7.639
9.287
3 -year average:
Average
Annual
11.748
11.159
11.838
11.582
Step 4. The 3-year average annual background concentration (from Step 3) is added to
the average annual modeled concentration from the project and nearby source (from Step
2):
11.582 + 3.603 = 15.185
Step 5 . Rounding to the nearest 0. 1 ug/m3 produces a design value of 15.2
In this example, the concentration at the highest receptor is estimated to exceed the
current annual PM2.5 NAAQS of 15.0 ug/m3.
Steps 6-8: Since the design value in Step 5 is greater than the NAAQS, design value
calculations are then completed for all receptors in the build scenario, and receptors with
design values above the NAAQS are identified. After this is done, the no-build scenario
is modeled for comparison.
4 As noted above, there is a single nearby source that is projected to have higher emissions under the build
scenario than the no-build scenario as a result of the project and its impacts are not expected to be captured
by the monitor chosen to provide background concentrations. Therefore, emissions from the project and
this nearby source are both included in the AERMOD output.
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K.3.3 No-build scenario
The no-build scenario, i.e., the existing highway and freight terminal without the
proposed highway and freight terminal expansion, is modeled at all of the receptors in the
build scenario, but design values are only calculated in the no-build scenario at receptors
where the design value for the build scenario is above the annual PM2.5NAAQS (from
Steps 6-8 above).
Step 9. For this example, the receptor with the highest average annual concentration in
the build scenario is used to illustrate the no-build scenario design value calculation. The
average annual concentration modeled at this receptor in the no-build scenario is 3.521
Hg/m3.
Step 10. The background concentrations from the representative monitor are unchanged
from the build scenario, so the average annual modeled concentration of 3.521 is added to
the 3-year average annual background concentrations of 11.528 |J,g/m3 from Step 3:
11.582 + 3.521 = 15.103
Step 11. Rounding to the nearest 0.1 |J,g/m3 produces a design value of 15.1 |J,g/m3.
In this example, the design value at the receptor in the build scenario (15.2 ug/m3) is
greater than the design value at the same receptor in the no-build scenario (15.1 ug/m3).5
In an actual PM hot-spot analysis, design values would also be compared between build
and no-build scenarios at all receptors in the build scenario that exceeded the annual
PM2.5 NAAQS. The interagency consultation process would then be used to discuss next
steps, e.g., appropriateness of receptors. Refer to Section 9.2 for additional details.
If it is determined that conformity requirements are not met at all appropriate receptors,
the project sponsor should then consider additional mitigation or control measures, as
discussed in Section 10. After measures are selected, a new build scenario that includes
the controls should be modeled and new design values calculated. Design values for the
no-build scenario in Appendix K.3.3 above would not need to be recalculated since the
no-build scenario would not change.
K.4 EXAMPLE: 24-HOUR PM2.sNAAQS
K. 4.1 General
This example illustrates the two-tiered approach to calculating design values for
comparison with the 24-hour PM2.5 NAAQS, as described in Section 9.3.3. The 24-hour
design value is the average of three consecutive years' 98th percentile PM2.5 concentration
5 Values are compared after rounding. As long as the build design value is no greater than the no-build
design value after rounding, the project would meet conformity requirements at a given receptor, even if
the pre-rounding build design value is greater than the pre-rounding no-build design value.
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of 24-hour values for each of those years. For air quality monitoring purposes, the
NAAQS is met when that three-year average concentration is less than or equal to the
currently applicable 24-hour PM2.5 NAAQS for a given area's nonattainment designation
(35 |J,g/m3 for nonattainment areas for the 2006 PM2 5 NAAQS and 65 |J,g/m3 for
nonattainment areas for the 1997 PM2.5 NAAQS).6 The design value for comparison to
any 24-hour PM2.5 NAAQS is rounded to the nearest 1 ng/m3 (i.e., decimals 0.5 and
greater are rounded up to the nearest whole number, and any decimal lower than 0.5 is
rounded down to the nearest whole number). For example, 35.499 rounds to 35 ng/m3,
while 35.500 rounds to 36.7
For this example, the project described in Appendix K.2 is located in a nonattainment
area for the 2006 24-hour PM2.5 NAAQS. This example presents first tier and second tier
build scenario results for a single receptor to illustrate how the calculations should be
made based on air quality modeling results and air quality monitoring data. It also shows
second tier no-build scenario results for this same receptor. In an actual PM hot-spot
analysis, design values would be calculated at additional receptors, as described further in
Section 9.3.3.
As explained in Section 9.3.3, project sponsors can start with either a first or second tier
analysis. This example begins with a first tier analysis. However, it would also be
acceptable to begin with the second tier analysis and skip the first tier altogether.
K. 4.2 Build scenario
PM2.5 contributions from the project and the nearby source are estimated together with
AERMOD in each of four quarters using meteorological data from five consecutive
years, using a 24-hour averaging time. As discussed in Appendix K.2 above, the one
nearby source (i.e., the freight terminal) was included in air quality modeling.
First Tier Analysis
Under a first tier analysis, the average highest modeled 24-hour concentrations at a given
receptor are added to the average 98th percentile 24-hour background concentrations,
regardless of the quarter in which they occur. The average highest modeled 24-hour
concentrations are produced by AERMOD, using five years of meteorological data in one
run.
6 There are only two PM2 5 areas where conformity currently applies for both the 1997 and 2006 24-hour
NAAQS. While both 24-hour NAAQS must be considered in these areas, in practice if the more stringent
2006 24-hour PM2 5 NAAQS is met, then the 1997 24-hour PM2 5 NAAQS is met as well.
7 A sufficient number of decimal places (3-4) should be retained during intermediate calculations for design
values, so that there is no possibility of intermediate rounding or truncation affecting the final result.
Rounding should only occur during final design value calculations, pursuant to Appendix N to 40 CFR Part
50.
K-5
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PUBLIC DRAFT-MAY 2010
Step 1. The receptor with the highest average modeled 24-hour concentration is
identified. This was obtained directly from the AERMOD output.8 For this example, the
data from this receptor is shown in Exhibit K-2. Exhibit K-2 shows the highest 24-hour
concentration for each year of meteorological data used, regardless of the quarter in
which they were modeled. The average concentration of these outcomes, 6.710 ng/m3
(highlighted in Exhibit K-2), is the highest, compared to the averages at all of the other
receptors.
Exhibit K-2. Modeled PM2.s Concentrations from Project and Nearby Source
Year
Met Year 1
Met Year 2
Met Year 3
Met Year 4
Met Year 5
Average
Highest PM2.5
Concentration
6.413
5.846
6.671
7.951
6.667
6.710
-,th
Step 2. The average 98 percentile 24-hour background concentration for a first tier
analysis is calculated using the 98th percentile 24-hour concentrations of the three most
recent years of monitoring data from the representative air quality monitor selected (see
Appendix K.2). Since the background monitor is on a l-in-3 day sampling schedule, it
made either 122 or 121 measurements per year during 2008 - 2010. According to Exhibit
9-5, with this number of monitored values per year, the 98th percentile is the third highest
concentration. Exhibit K-3 depicts the top eight monitored concentrations (in ng/m3) of
the monitor throughout the years employed for estimating background concentrations.
The values at Rank 3, highlighted, are the 98th percentile concentrations:
Exhibit K-3. Top Eight Monitored Concentrations in Years 2008 - 2010
Rank
1
2
3
4
5
6
7
8
2008
34.123
31.749
31.443
30.809
30.219
30.134
30.099
28.481
2009
33.537
32.405
31.126
30.819
30.487
29.998
29.872
28.937
2010
35.417
31.579
31.173
31.095
30.425
30.329
30.193
28.751
8 If CAL3QHCR were being used, some additional processing of model output would be needed. Refer to
Section 9.3.3.
K-6
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PUBLIC DRAFT-MAY 2010
The third-ranked concentration of each year (highlighted in Exhibit K-3) is the 98th
percentile value. These are averaged:
(31.443+31.126+ 31.173)-3= 31.247
Step 3. Then, the highest average 24-hour modeled concentration for this receptor (from
Step 1) is added to the average 98th percentile 24-hour background concentration (from
Step 2):
6.710 + 31.247 = 37.957
Rounding to the nearest whole number results in a 24-hour PM2.5 design value of 38
Hg/m3.
Because this concentration is greater than the 2006 24-hour PM2.5 NAAQS (35 ng/m3),
this first tier analysis does not demonstrate that conformity is met. As described in
Section 9.3.3, the project sponsor has two options:
• Repeat the first tier analysis for the no-build scenario at all receptors that
exceeded the NAAQS in the build scenario. If the calculated design value for the
build scenario is less than or equal to the design value for the no-build scenario at
all of these receptors, then the project conforms;9 or
• Conduct a second tier analysis.
In this example, the next step chosen is a second tier analysis.
Second Tier Analysis
In a second tier analysis, the highest modeled concentrations are not added to the 98th
percentile background concentrations on a yearly basis. Instead, a second tier analysis
uses the average of the highest modeled 24-hour concentration within each quarter of
each year of meteorological data. Impacts from the project, nearby sources, and other
background concentrations are calculated on a quarterly basis before determining the 98th
percentile concentration resulting from these inputs. The steps presented below follow
the steps described in Section 9.3.3.
Step 1. The first step is to count the number of measurements for each year of
monitoring data used for background concentrations. As described in Appendix K.2 and
in Step 2 of the first tier analysis above, there are 122 monitored values during 2008, 121
values during 2009, and 121 values during 2010.
Step 2. For each year of monitoring data, the eight highest 24-hour background
concentrations in each quarter are determined. The eight highest concentrations in each
quarter of 2008, 2009, and 2010 are shown in Exhibit K-4.
9 In certain cases, project sponsors can also decide to calculate the design values for all receptors in the
build and no-build scenarios and use the interagency consultation process to determine whether a "new"
violation has been relocated (see Section 9.2).
K-7
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PUBLIC DRAFT-MAY 2010
Exhibit K-4. Eight Highest 24-hour Background Concentrations By Quarter for
Each Year
Year
2008
2009
2010
Rank of
Background
Concentration
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Ql
27.611
25.974
25.760
25.493
25.099
24.902
24.780
23.287
26.962
24.820
24.330
23.768
23.685
23.287
23.226
22.698
27.493
24.637
24.637
24.392
24.050
23.413
22.453
22.061
Q2
31.749
30.219
30.134
28.368
27.319
25.788
25.564
24.794
32.405
30.487
28.937
27.035
25.880
25.867
25.254
24.268
31.579
31.173
30.193
27.994
25.439
24.253
23.006
21.790
Q3
34.123
31.443
30.809
28.481
27.372
25.748
25.288
24.631
33.537
30.819
29.998
29.872
25.596
25.148
24.744
24.267
35.417
31.095
30.329
28.751
26.084
24.890
24.749
22.538
Q4
30.099
28.096
26.990
25.649
25.526
25.509
25.207
24.525
31.126
28.553
25.920
25.856
25.565
24.746
24.147
23.142
30.425
26.927
26.263
25.684
25.170
24.254
23.425
22.891
Step 3. The highest modeled 24-hour concentrations in each quarter are identified at each
receptor. Exhibit K-5 presents the highest 24-hour concentrations within each quarter at
one receptor (for each of the five years of meteorological data used in air quality
modeling) as well as the average of these quarterly concentrations. This step would be
repeated for each receptor in an actual PM hot-spot analysis.
K-8
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PUBLIC DRAFT-MAY 2010
Exhibit K-5. Highest Modeled 24-hour Concentrations Within Each Quarter (Build
Scenario)
Met Year 1
Met Year 2
Met Year 3
Met Year 4
Met Year 5
Average
Ql
6.413
3.229
6.671
7.095
6.664
6.014
Q2
3.332
3.481
3.330
3.584
4.193
3.584
Q3
6.201
5.846
5.696
7.722
4.916
6.076
Q4
6.193
4.521
6.554
7.951
6.667
6.377
The average highest concentrations on a quarterly basis (i.e., the values highlighted in
Exhibit K-5) constitute the contributions of the project and nearby source to the projected
24-hour PM2.5 design value, and are used in subsequent calculations.
Step 4. For each receptor, the highest modeled 24-hour concentration in each quarter
(from Step 3) is added to each of the eight highest monitored concentrations for the same
quarter for each year of monitoring data (from Step 2). To obtain this result, the average
highest modeled concentration for each quarter, found in the last row of Exhibit K-5, is
added to each of the eight highest background concentrations in each quarter in Exhibit
K-4. The results are shown in Exhibit K-6.
K-9
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PUBLIC DRAFT-MAY 2010
Exhibit K-6. Sum of Modeled and Monitored Concentrations (Build Scenario)
Year
2008
2009
2010
Rank of
Background
Concentration
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Ql
33.625
31.989
31.774
31.507
31.113
30.916
30.794
29.301
32.976
30.835
30.344
29.782
29.700
29.301
29.240
28.712
33.507
30.651
30.651
30.406
30.064
29.428
28.468
28.075
Q2
35.333
33.803
33.718
31.952
30.903
29.372
29.148
28.378
35.989
34.071
32.521
30.619
29.464
29.451
28.838
27.852
35.163
34.757
33.777
31.578
29.022
27.837
26.590
25.374
Q3
40.200
37.520
36.886
34.557
33.448
31.824
31.365
30.707
39.613
36.895
36.074
35.948
31.672
31.225
30.820
30.343
41.493
37.172
36.405
34.827
32.160
30.966
30.825
28.614
Q4
36.476
34.474
33.368
32.026
31.903
31.886
31.584
30.902
37.503
34.931
32.297
32.233
31.942
31.124
30.524
29.520
36.802
33.304
32.640
32.062
31.547
30.631
29.803
29.269
Step 5. The 32 values from each year in Exhibit K-6 are then ranked from highest to
lowest, regardless of the quarter from which each value comes. This step is shown in
Exhibit K-7. Note that only the top eight values are shown for each year instead of the
entire set of 32. Exhibit K-7 also displays the quarter from which each concentration
comes and the value's rank within its quarter.
K-10
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PUBLIC DRAFT-MAY 2010
Exhibit K-7. Eight Highest Concentrations in Each Year, Ranked from Highest to
Lowest (Build Scenario)
Year
2008
2009
2010
ug/m3
40.200
37.520
36.886
36.476
35.333
34.557
34.474
33.803
39.613
37.503
36.895
36.074
35.989
35.948
34.931
34.071
41.493
37.172
36.802
36.405
35.163
34.827
34.757
33.777
Yearly
Rank
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Quarter
Q3
Q3
Q3
Q4
Q2
Q3
Q4
Q2
Q3
Q4
Q3
Q3
Q2
Q3
Q4
Q2
Q3
Q3
Q4
Q3
Q2
Q3
Q2
Q2
Quarterly
Rank
1
2
3
1
1
4
2
2
1
1
2
3
1
4
2
2
1
2
1
O
1
4
2
O
Steps 6-7. The value that represents the 98th percentile 24-hour concentration is
determined, based on the number of background concentration values there are. As
described in Step 1, there are 122 monitored values for the year 2008 and 121 values for
both 2009 and 2010. According to Exhibit 9-7 in Section 9.3.3, for a year with 101-150
samples per year, the 98th percentile is the 3rd highest concentration for that year.
Therefore, for this example, the 3rd highest 24-hour concentration of each year,
highlighted in Exhibit K-7, represents the 98th percentile value for that year.
Step 8. At each receptor, the average of the three 24-hour 98th percentile concentrations
is calculated. For the receptor in this example, the average is:
(36.886 + 36.895 + 36.802) - 3 = 36.861
Step 9. The average for the receptor in this example from Step 8 (36.861 ng/m3) is then
rounded to the nearest whole number (37 |J,g/m3) and compared to the 2006 24-hour
K-ll
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PUBLIC DRAFT-MAY 2010
PM2.5NAAQS (35 ng/m3). In an actual PM2.5 hot-spot analysis, the design value
calculations need to be repeated for all receptors, and compared to the NAAQS.
The design value at the receptor in this example is higher than the relevant 24-hour PM2.5
NAAQS. Since one (and possibly more) receptors have design values greater than the
24-hour PM2 5 NAAQS, the project will only conform if the design value in the no-build
scenario are less than the design value in the build scenario at each receptor. Therefore,
the no-build scenario needs to be modeled for comparison, as described further below.
K.4.3 No-build scenario
The no-build scenario is described in Section 9.3.3 as Step 10:
• Step 10. Using modeling results for the no-build scenario, repeat steps 3 through
9 for all receptors with a design value that exceeded the PM2.5 NAAQS in the
build scenario. The result will be a 24-hour PM2 5 design value at such receptors
for the no-build scenario.
For this part of the example, air quality modeling is completed for the no-build scenario
for the same receptor as the build scenario. Steps 1 and 2 for the build scenario do not
need to be repeated, since the background concentrations in the no-build scenario are
identical to those in the build scenario. Exhibit K-4, which shows the eight highest
monitored concentrations in each quarter over three years, therefore can also be used for
the no-build scenario.
Step 3. For the same receptor examined above in the build scenario, the highest modeled
24-hour concentrations for the no-build scenario are calculated for each quarter, using
each year of meteorological data used for air quality modeling. Exhibit K-8 provides
these concentrations, as well as the quarterly averages (highlighted).
Exhibit K-8. Highest Modeled 24-hour Concentrations Within Each Quarter (No-
Build Scenario)
Met Year 1
Met Year 2
Met Year 3
Met Year 4
Met Year 5
Average
Ql
6.757
3.402
7.029
7.476
7.022
6.337
Q2
3.383
3.535
3.381
3.639
4.258
3.639
Q3
6.725
6.340
6.177
8.374
5.331
6.589
Q4
6.269
4.577
6.635
8.048
6.748
6.455
Step 4. The highest modeled 24-hour concentration in each quarter (i.e., the values in the
last row of Exhibit K-8) are added to each of the eight highest concentrations for the
same quarter for each year of monitoring data (found in Exhibit K-4), and the resulting
values are shown in Exhibit K-9.
K-12
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PUBLIC DRAFT-MAY 2010
Exhibit K-9. Sum of Modeled and Monitored Concentrations (No-Build Scenario)
Year
2008
2009
2010
Rank of
Background
Concentration
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Ql
33.948
32.312
32.097
31.830
31.436
31.239
31.117
29.624
33.299
31.158
30.667
30.105
30.023
29.624
29.563
29.035
33.830
30.974
30.974
30.729
30.387
29.751
28.791
28.398
Q2
35.389
33.858
33.774
32.007
30.959
29.428
29.204
28.433
36.044
34.126
32.576
30.674
29.520
29.506
28.894
27.907
35.218
34.812
33.832
31.633
29.078
27.893
26.645
25.429
Q3
40.713
38.033
37.399
35.070
33.961
32.337
31.878
31.220
40.127
37.408
36.587
36.461
32.185
31.738
31.333
30.856
42.007
37.685
36.918
35.340
32.674
31.479
31.338
29.127
Q4
36.555
34.552
33.446
32.104
31.981
31.964
31.662
30.980
37.581
35.009
32.375
32.311
32.020
31.202
30.602
29.598
36.880
33.382
32.719
32.140
31.625
30.709
29.881
29.347
Step 5. The 32 values from each year in Exhibit K-9 are ranked from highest to lowest,
regardless of the quarter from which each value comes. This step is shown in Exhibit K-
10. Note that only the top eight values are shown for each year instead of the entire set of
32.
K-13
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PUBLIC DRAFT-MAY 2010
Exhibit K-10. Eight Highest Concentrations in Each Year, Ranked from Highest to
Lowest (No-Build Scenario)
Year
2008
2009
2010
ug/m3
40.713
38.033
37.399
36.555
35.389
35.070
34.552
33.961
40.127
37.581
37.408
36.587
36.461
36.044
35.009
34.126
42.007
37.685
36.918
36.880
35.340
35.218
34.812
33.832
Yearly
Rank
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Quarter
Q3
Q3
Q3
Q4
Q2
Q3
Q4
Q3
Q3
Q4
Q3
Q3
Q3
Q2
Q4
Q2
Q3
Ql
Ql
Q4
Q4
Ql
Q4
Q4
Quarterly
Rank
1
2
3
1
1
4
2
5
1
1
2
3
4
1
2
2
7
O
2
8
6
1
2
O
Steps 6-7. Based on the number of background measurements available per year in this
example (122 for 2008 and 121 for both 2009 and 2010, as discussed in the analysis of
the build scenario), Exhibit 9-7 in Section 9.3.3 indicates that the 3rd highest 24-hour
concentration in each year represents the 98th percentile concentration for that year. The
third highest concentrations are highlighted in Exhibit K-10.
Step 8. For this receptor, the average of the Rank 3 concentrations in 2008, 2009, and
2010 is calculated:
(37.399 + 37.408 + 36.918) - 3 = 37.242
Step 9. The average for the receptor in this example from Step 8 (37.242 |J,g/m3) is
rounded to the nearest whole ng/m3 (37 ng/m3).
K-14
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PUBLIC DRAFT-MAY 2010
In this example, the design value at this receptor for both the build and no-build scenarios
is 37 ng/m3, which is greater than the 2006 24-hour NAAQS (35 mg/m3). However, the
build scenario's design value is equal to the design value in the no-build scenario.10 For
the project to conform, the build design values must be less than or equal to the no-build
value for all the receptors that exceeded the NAAQS in the build scenario. Assuming
that this is the case at all other receptors, the proposed project in this example would
therefore demonstrate conformity.
K.5 EXAMPLE: 24-HOUR PMio NAAQS
K.5.1 General
This example illustrates calculating design values for comparison with the 24-hour
NAAQS, as described in Section 9.3.4. The 24-hour PMio design value is based on the
expected number of 24-hour exceedances of 150 |J,g/m3, averaged over three consecutive
years. For air quality monitoring purposes, the NAAQS is met when the number of
exceedances is less than or equal to 1.0. The 24-hour PMio design value is rounded to the
nearest 10 |J,g/m3. For example, 155.511 rounds to 160, and 154.999 rounds to 150.u
The 24-hour PMio design value is calculated at each air quality modeling receptor by
directly adding the sixth-highest modeled 24-hour concentration (if using five years of
meteorological data) to the highest 24-hour background concentration (from three years
of monitored data).
For this example, the project described in Appendix K.2 is located in a nonattainment
area for the 24-hour PMio NAAQS. This example presents build scenario results for a
single receptor to illustrate how the calculations should be made based on air quality
modeling results and air quality monitoring data. In an actual PM hot-spot analysis,
design values would be calculated at additional receptors, as described in Section 9.3.4.
10 Values are compared after rounding. As long as the build design value is no greater than the no-build
design value after rounding, the project would meet conformity requirements at a given receptor, even if
the pre-rounding build design value is greater than the pre-rounding no-build design value.
1: A sufficient number of decimal places (3-4) in modeling results should be retained during intermediate
calculations for design values, so that there is no possibility of intermediate rounding or truncation
affecting the final result. Rounding to the nearest 10 ug/m3 should only occur during final design value
calculations, pursuant to Appendix K to 40 CFR Part 50. Monitoring values typically are reported with
only one decimal place.
K-15
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PUBLIC DRAFT-MAY 2010
K.5.2 Build Scenario
Step 1. From the air quality modeling results from the build scenario, the sixth-highest
24-hour concentration is identified at each receptor. These sixth-highest concentrations
are the sixth highest that are modeled at each receptor, regardless of year of
meteorological data used.12 AERMOD was configured to produce these values.
Step 2. The sixth-highest modeled concentrations (i.e., the concentrations at Rank 6) are
compared across receptors, and the receptor with the highest value at Rank 6 is identified.
For this example, the highest sixth-highest 24-hour concentration at any receptor is
15.218 |J,g/m3. (That is, at all other receptors, the sixth-highest concentration is less than
15.218 |j,g/m3.) Exhibit K-l 1 shows the six highest 24-hour concentrations at this
receptor.
Exhibit K-ll. Receptor with the Highest Sixth-Highest 24-Hour Concentration
(Build Scenario)
Rank
1
2
O
4
5
6
Highest 24-Hour
Concentrations
17.012
16.709
15.880
15.491
15.400
15.218
Step 3. The highest 24-hour background concentration from the three most recent years
of monitoring data (2008, 2009, and 2010) is identified. In this example, the highest 24-
hour background concentration from these three years is 86.251 |J,g/m3.
Step 4. The sixth-highest 24-hour modeled concentration of 15.218 |J,g/m3 from the
highest receptor (from Step 2) is added to the highest 24-hour background concentration
of 86.251 ng/m3 (from Step 3):
15.218 + 86.251 = 101.469
Step 5. This sum is rounded to the nearest 10 ng/m3, which results in a design value of
100 ng/m3.
This result is then compared to the 24-hour PMio NAAQS. In this case, the concentration
calculated at all receptors is less than the 24-hour PMi0 NAAQS of 150 ng/m3, therefore
12 The six highest concentrations could occur anytime during the five years of meteorological data. They
may be clustered in one or two years, or they may be spread out over several, or even all five, years of the
meteorological data.
K-16
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PUBLIC DRAFT-MAY 2010
the analysis shows that the project conforms. However, if the design value for this
receptor had been greater than 150 |J,g/m3, the remainder of the steps in Section 9.3.4
would be completed: build scenario design values for each receptor would be calculated
(Steps 6-7 in Section 9.3.4); for all those that exceed the NAAQS, the no-build design
values would also be calculated (Steps 8-10 in Section 9.3.4) and build and no-build
design values compared.13
K.6 MATHEMATICAL FORMULAS FOR DESIGN VALUE CALCULATIONS
K. 6.1 Introduction
This part of the appendix includes mathematical formulas to represent the calculations
described narratively in Section 9.3. This information is intended to supplement Section
9, which may be helpful for certain users.
Appendix K.6 relies on conventions of mathematical and logical notation that are
described after the formulas are presented. Several symbols are used that may be useful
to review prior to reading the individual formulas.
Notation symbols
• x - a single bar over variable x represents a single arithmetic mean of that
variable
• x - double bars over variable x represents an "average of averages"
• x - a "hat" over variable x represents the arithmetic of multiple high
concentration values from different years, either from monitoring data or from
modeling results
Logical symbols
• Vx - an upside down A before variable x means "for all" values of x
• e x - an " e " before variable x means "in x"
• Vx e y - means "for all x in y"
The following information present equations for calculating design values for the PM2.5
annual NAAQS, 24-hour PM2.5 NAAQS, and 24-hour PMio NAAQS. The equations are
organized into the sets that are referenced in Section 9.3.
13 Values are compared after rounding. As long as the build design value is no greater than the no-build
design value after rounding, the project would meet conformity requirements at a given receptor, even if
the pre-rounding build design value is greater than the pre-rounding no-build design value.
K-17
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PUBLIC DRAFT -MAY 20 10
K. 6.2 Equation Set 1: Annual PM2. 5 design value
Formulas
When using CAL3QHCR, ptk = Pifle
Definitions
bt = average of three consecutive years' average annual background concentrations at
receptor /'
bim = quarterly-weighted average annual background concentrations at receptor /' during
monitoring year m
bijm = quarterly average background concentration at receptor /', during quarter y' in
monitoring year m
ci = annual PM2.5 design value at receptor /'
/' = receptor
j = quarter
k = year of meteorological data
/ = length in years of meteorological data record
m = year of background monitoring data
pik = average modeled quarterly average concentrations at receptor / for meteorological
year k. When using AERMOD, it is presumed that AERMOD's input file is used
to specify this averaging time. When using CAL3QHCR with a single quarter of
meteorological data, pik must be calculated using each pijk for each quarter of
meteorological year k.
pijk = quarterly average concentration at receptor /' for quarter y, in meteorological data
year k. This variable is the product of CAL3QHCR when run with a single
quarter of meteorological data. pik can be calculated directly using AERMOD
without explicitly calculating pijk .
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PUBLIC DRAFT-MAY 2010
K. 6.3 Equation Set 2: 24-Hour PM2.s design value (First Tier Analysis)
Formulas
ct=bt+pt
&,„ = VA,._ e m
3 A
X"1 im*rm
m=\
I
sing CAL3QHCR), which compresses to:
Pi = 2 (when using AERMOD with maximum concentration by year)
k=\ '
Definitions
bt = the average of 98th percentile 24-hour concentrations from three consecutive years of
monitoring data
bijm = daily 24-hour background concentration at receptor /', during quarter y' in monitoring
year m
bim = Vbi}.m &m = All 24-hour background concentration measurements in year m
bimfr = The 24-hour period within year m whose concentration rank among all 24-hour
measurements in year m is rm (this represents the 98th percentile of 24-hour
background concentrations within one year.)
ci = 24-hour PM2 5 design value at receptor /
/' = receptor
j = quarter
k = year of meteorological data
/ = length in years of meteorological data record
m = year of background monitoring data
max^ = maximum predicted 24-hour concentration within meteorological year k
max;t = maximum predicted 24-hour concentration within quarter y' within
meteorological year k
Pi = average of highest predicted concentrations from each year modeled with the / years
from which meteorological data are used (>5 years for off-site data, >1 year for
on-site data)
pijk = modeled daily 24-hour concentration at receptor /', in quartery and meteorological
year &
plk = modeled daily 24-hour concentration at receptor /', in meteorological year k
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PUBLIC DRAFT-MAY 2010
rm = concentration rank of bim corresponding to 98th percentile of all bim in year m, based
on number of background concentration measurements per year (nm). rm is given
by the following table:
nm
1-50
51-100
101-150
151-200
201-250
251-300
301-350
351-366
rm
1
2
3
4
5
6
7
8
K. 6.4 Equation Set 3: 24-Hour PM2.5 design value (Second Tier Analysis)
Formulas
cijm = bijm + Pi,, for the eight (8) highest^ in quarter^ in monitoring year m
Definitions
bijm = daily 24-hour background concentration at receptor /', during quarter7' in monitoring
year m
ci = 24-hour PM2.5 design value at receptor /'
ctjm = The set of all sums of modeled concentrations (ptj) with background
concentrations from quarter7' and monitoring year m, using the eight highest
background concentrations (bljm) for the corresponding receptor, quarter, and
monitoring year.
cim = Vcijm <=m = the set of all cimj corresponding to monitoring year m
cim-rm = predicted 98th percentile total concentration from the project, nearby sources, and
background measurements from year m. Given by the value of cim whose
concentration rank in year m is rm, using background measurements from year m.
i = receptor
7 = quarter
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PUBLIC DRAFT-MAY 2010
k = year of meteorological data
/ = length in years of meteorological data record
m = year of background monitoring data
max;yt = maximum predicted 24-hour concentration within quarter y within
meteorological year k
pijk = Predicted daily 24-hour concentration at receptor /', during quarter j, based on data
from meteorological year k
pi} = Average highest 24-hour modeled concentration (pijk ) using /years of
meteorological data
rm = concentration rank ofcim corresponding to 98th percentile of all cim in year m, based
on number of background concentration measurements per year (nm~). rm is given
by the following table:
nm
1-50
51-100
101-150
151-200
201-250
251-300
301-350
351-366
rm
1
2
O
4
5
6
7
8
K. 6. 5 Equation Set 5: 24-Hour PMw design value
Formulas
b =
m=\
Pi = Pil.n
I
Pn =\JP,k
k=\
Definitions
ci = 24-hour PMio design value
bt = maximum monitored 24-hour PMio background concentration at within bin
bim = the set of all monitored 24-hour PMio background concentrations at receptor /'
within monitoring year m
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bin = the set of all bim within monitoring years n
i = receptor
k = year of meteorological data
/ = length in years of meteorological data record.
maxin = the maximum monitored 24-hour background concentration at receptor /' within
monitoring years n
n = the set of all years of monitoring data, m = {1,2,3}
Pi = Pu.r, = modeled 24-hour PMi0 concentration with concentration rank of r/ among all
concentrations modeled using /years of meteorological data
pa = set of all modeled 24-hour concentrations at receptor /' across / years of
meteorological data
ri = l+ 1 (for example, r/ = 6 when using 5 years of meteorological data)
z
(Jca = the set (finite union) of all ca with integer values of a = {!,...,z}
0=1
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