EPA-420-B-10-040

December 2010 Transportation and Regional Programs Division Office of Transportation and Air Quality U.S. Environmental Protection Agency 

# Transportation Conformity Guidance for Quantitative Hot

- spot Analyses in

PM 2.5 

# and PM 10 

# Nonattainment and

Maintenance Areas

Appendicesi APPENDIX A: CLEARINGHOUSE OF WEB SI TES, GUIDANCE, AND OTHER TECHNICAL

RESOURCES FOR PM HOT

- SPOT ANALYSES

......................................................... A-1

APPENDIX B: EXAMPLES OF PROJECTS OF LOCAL AIR QUALITY CONCERN

......................... B-1

APPENDIX C: HO T- SPOT REQUIREMENTS FO R PM10 AREAS WITH PRE

- 2006 APPROVED

CONFORMITY SIPS

....................................................................................................... C-1

APPENDIX D: CHARACTERIZING INTER SECTION PROJECTS FOR MOVES

............................. D-1

APPENDIX E: EXAMPLE QUANTITATIVE PM HOT

- SPOT ANALYSIS OF A H IGHWAY

PROJECT USING MOVES AND CAL3 QHCR

.............................................................. E-1

APPENDIX F: EXAMPLE QUANTIT ATIVE PM HOT

- SP OT ANALYSIS OF A TRA NSIT

PROJECT USING MOVES AND AERM OD

.................................................................. F-1

APPENDIX G: EXAMPLE OF USING EMFAC FOR A HIGHWAY PRO JECT

.................................. G-1

APPENDIX H: EXAMPLE OF USING EMFAC TO DEVELOP EMISSION FACTORS FOR A

TRANSIT PROJECT

....................................................................................................... H-1

APPENDIX I: ESTIMATING LOCOMOTIVE EMISSIONS

.................................................................. I-1

APPENDIX J: ADDITIONAL REFERENCE INFORMA TION ON AIR QUALITY MODELS AND

DATA INPUTS

.................................................................................................................. J-1

APPENDIX K: EXAMPLES OF DESIGN VALUE CALCULATIONS FOR PM HOT

- SPOT

ANALYSES

..................................................................................................................... K-1A

- 1 

## Appendix A:
Clearinghouse of Websites, Guidance, and Other Technical

Resources for PM Hot

- spot Analyses

A.1 I NTRODUCTION

This appendix is a centralized compilation of documents and websites referenced in the guidance, along wi th 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. The references listed are current as of this writing; readers are reminded the check for the latest versions when using them for a particular PM hot-spot analysis.



### A.2 T RANSPORTATION CONFORMITY AND CONTROL MEASURE GUIDANCE

The EPA ho sts an extensive library of transportation conformity guidance online at: www.epa.gov/otaq/stateresources/transconf/policy.htm (unless otherwise noted). The following specific guid ance 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.

• “EPA Releases MOVES2010 Mobile Source Emissions Model: Questions and Answers,” EPA -420-F- 09 -073 (December 2009).

• “EPA Releases MOVES2010a Mobile Source Emissions Model Update: Questions and Answers,” EPA-420-F-10-050 (August 2010).

• “Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity,” EPA -420-B10-023 (December 2009). 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 an d FHWA, “Transportation Conformity Guidance for Qualitative Hot-spot Analyses in PM 2.5 and PM 10 Nonattainment and Maintenance Areas,” EPA -420- B-06-902 (March 2006).A

- 2 • EPA and FHWA, “Guidance for the Use of Latest Planning Assumptions in Transportation Confo rmity De terminations,” EPA-420-B-08-901 (December 2008).

• “Guidance for Developing Transportation Conformity State Implementation Plans,” EPA -420-B-09-001 (January 2009).

• EPA

- verified anti -idle technologies (including technologies that pertain to trucks)

can be found at: www.epa.gov/otaq/smartway/transport/what

- smartway/verified

technologies.htm#idle

.

• 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 and using long duration truck idling

benefits 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/air_quality/conformity/practices/

.



### A.3 M OVES MODEL TECHNICAL INFO RMATION AND USER GUIDES

MOVES, any future versions of the model, the latest user guid es, and t echnical

information can be found at www.epa.gov/otaq/models/moves/index.htm , including the following:

• “ User Guide for MOVES2010a .” This guide provides detailed instructions for

setti ng up and running MOVES2010a . Available at www.epa.gov/otaq/models/moves/index.htm

.

Policy documents and Federal Register announcements related to the MOVES model can

be found on the EPA’s we bsite at: www.epa.gov/otaq/stateresources/transconf/policy.htm#models

.

Guidance on using the MOVES model at the project level, as well as illustrative examples of usin g MOVES for quantitative PM hot-spot analyses, can be found in Section 4 of the guidance and in Appendices D, E and F.A

- 3 

### A.4 E MFAC 2007 MODEL TECHNICAL INFO RMATION 

### , USER GUIDES 

### , AND

OTHER G UIDANCE

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/msei/onroad/latest_version.htm

.

Policy documents and Federal Register announcements related to the EMFAC model can

be found on the EPA’s website at: www.epa.gov/otaq/stateresources/transconf/policy.htm#models

.

Supporting documentation for EMFAC, including the technical memorandum “Revision

o f Heavy Heavy

- Duty Diesel Truck Emissi on Factors and Speed Correction Factors ” cited in Section 5 of this guidance, can be found at www.arb.ca.gov/msei/su pportdocs.htm#onroad

.

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 D UST E MISSIONS METHODS A ND G UIDANCE

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

.

Guidance on calculating dust emissions for PM hot-spot analyses can be found in Section 6 of the guidance.



### A. 6 L OCOMOTIVE E MISSIONS G UIDANCE

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.A

- 4 • “Emissio n Factors for Locomotives,” EPA-420-F-09-025 (April 2009). Available

online at: www.epa.gov/otaq/regs/nonroad/locomotv/420f08014.htm

.

• “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 P lans,” EPA -420-B- 04 -002 (January 2004). Available online at: www.epa.gov/otaq/smartway/documents/420b04002.pdf

.

• EPA

- verified anti -idle technologies (including technolo gies that pertain to locomotives) can be found at: www.epa.gov/otaq/smartway/transport/whatsmartway/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 Appendi x I.



### A.7 A IR QUALITY DISPERSIO N M ODEL T ECHNICAL I NFORMATION 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/scram001/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/scram001 . In particular, the following guidance may be useful when running these models:

• AERMOD Implementation Guide

• AERMOD User Guide ( “User’s Guide for the AMS/EPA Regulatory Model –

AERMOD ”)

• CAL3QHCR User Guide (“User’s Guide to CAL3QHC Version 2.0: A Modeling Methodology for Predicting Pollutant Concentrations Near Roadway Intersections”)

• MPRM User Guide

• AERMET User GuideA

- 5 Information on locating and considering air quality monitoring sites can be found in 40

CF R Part 58 (Ambient Air Quality Surveillance), particularly in Appendices D and E to that part

.

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. Illustrative examples of using an air quality model for a PM hot-spot analysis can be found in Appendices E and F.



### A. 8 T RANSPORTATION DATA AND M ODELING C ONSIDERATIONS

The following is a number of technical resources on transportation data and modeling

wh ich may help implementers determine the quality of their inputs and the sensitivity of

various data.

A. 8.1 Transportation model i mprovement

The FHWA Travel Model Improvement Program (TMIP) provides a wide range of

services and tools to help planning agen cies 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 Em issions 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 an d Design .”

Available online at: http://tmip.fhwa.dot.gov/sites/tmip.fhwa.dot.gov/files/NCHRP_255.pdf

.

A. 8.4 Traffic analysis

Tr affic 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_vol1/vol1_primer.pdf

.

The Highway Capacity Manual Application Guidebook. Transportation Research Board, Washington, D.C., 2003. Available online at: http://hcmguide.com/

.A

- 6 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/Highway_Capacity_Manual_2000_152169.asp

x . As of this writing, the 2000 edition is most current; the most recent version of themanual, and the associated guidebook, should be consulted when completing PM hotspot analyses.B

- 1 

## Appendix B :
Examples of Projects of Local Air Quality Concern

B.1 I NTRODUCTION

This appendix gives additional guidance on what types of projects may be projects of local air qualit y concern requiring a quantitative PM hot-spot analysis under 40 CFR 93.123(b)(1). However, as noted elsewhere in this guidance, PM 10 nonattainment and maintenance areas with approved conformity SIPs that include PM 10 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 E XAMPLES OF PROJECTS THAT REQUIRE PM HOT



### - SPOT ANALYSES

EPA noted in the March 2006 final rule that the examples b elow are considered to be the most likely projects that would be covered by 40 CFR 93.123(b)(1) and require a PM 2.5 or PM 10 hot -spot analysis (71 FR 12491). 1

Some examples of projects of local air quality concern that would be covered by 40 CFR 93.123(b)( 1)(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

dail y 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 trans it busses and/or diesel trucks.

Some examples of projects of local air quality concer n that would be covered by 40 CFR 93.123(b)(1)(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 2

1 EPA also clarified 93.123(b)(1)(i) in the January 24, 2008 final rule (73 FR 4435

- 4436 ). ; and,

2 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 th e 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, includi ng at a minimum all principal arterial highways and all fixed guideway transit facilities that offer an alternative to regional highway travel.”B

- 2 • 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)(1)(v) could be any of the above listed project examples.



### B.3 E XAMPLES 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)(1) and would not require a PM 2.5 or PM 10 hot-spot analysis (71 FR

12491 ).

The following are examples of projects that are not a local air quality concern under 40 CFR 93.123(b)(1)(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 physic ally 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)(1)(iii) and (iv) would be:

• A new or expanded bus terminal that is serviced by non-die sel 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).C

- 1 

## Appendix C :

Ho t- Spot Requirements for PM 10 

## Areas with Pre

- 2006 Approved Conformity SIPs

C.1 I NTRODUCTION

This appendix describes what projects require a quantitative PM 10 hot-spot analysis in those limited cases where a state’s approved conformity SIP is based on pre-2006 conformity requirements. 1 The March 10, 2006 final hot-spot ru le 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 PM 10 nonattainment and maintenance areas where PM 10 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 PM 10 nonattainment and

maintenance areas with pre -2006 approved conformity SIPs. Th is appendix is not relevant for any PM 2.5 nonattainment or maintenance areas, since the current federal

PM 2.5 hot-spot requirements apply in all such areas . Project sponsors can use the interagency consultation process to verify applicable requirements before beginning a quantitative PM 10 hot- spot analysis.



### C.2 P M 10 AREAS WHERE THE PRE



### - 2006 HOT



### - SPOT REQUIREMENTS AP PLY

Prior to the Marc h 2006 final rule, the federal conformity rule required some type of hotspot analysis for all non- exempt federally funded or approved projects in PM 10 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 PM 10 areas with approved conformity SIPs that include the pre-2006 hot-spot requirements, a quantitative PM 10 hot -spot analysis is required for the following types of projects:

• Projects which are located at sites at which PM 10 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

transporta tion conformity process (40 CFR 51.390).

2 See Section 2.2 and Appendix B of this guidance and the pr eamble of the March 2006 fi nal rule (71 FR

12491

- 12493).C

- 2 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 PM 10 hot-spot analyses.

In addition, a qualitative PM 10 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 PM 10 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 R EVISING A CONFORMITY SIP

EPA strong ly encourages affected states to revise pre -2006 provisions and take advantage of the streamlining flexibilities provided by the current Clean Air Act. EPA’s January 2008 final conformity rule significantly streamlined the requirements for conformity SIPs

in 40 CFR 51.390. 4 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 pre -2006 PM 10 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 PM 10 areas, the current conformity rule’s PM 10 hot-spot requirements at 4 0 CFR 93.123(b)(1) 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.

3 “Transportation Conformity Guidance for Qualitative Hot

- spot Analyses in PM 2.5 and PM 10 Nonatt ainment 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.C

- 3 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 Confo rmity State Implementation Plans (SIPs),” EPA -420-B09-001 (January 2009); available online at: www.epa.gov/otaq/stateresources/transconf/policy/420b09001.pdf

.D

- 1 

## Appendix D:

Charac terizing Intersection Projects for MOVES

D.1 I NTRODUCTION

This appendix expands upon the discussion in Section 4.2 on how best to characterize links when modeling an intersection project using MOVES. The MOVES emission s model allows users to represent i ntersection 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 s model for PM hot-spot analyses in areas outside of California.

Exhibit D- 1 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-1. Example of Approach and Departure Links for a Simple IntersectionD

- 2 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-1):

• 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 mor e 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.

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.



### D.2 O PTION 1 

### : U SING AVERAGE SPEED S

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 d eceleration delay, move-up time in a queue, stopped delay, and acceleration delay. Using the intersection example given in Exhibit D

- 1, 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 a ffected by acceleration delay.

Project sponsors can determine congested spe eds by using appropriate methods based on best practices for highway analyses. Some resources are available through FHWA’s Travel Model Improvement Program (TMIP). 1 Methodologies for computing intersection control delay are provided in the Highway Capacity Manual

. 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://144.171.11.107/Main/Public/Blurbs/152169.aspx for details).D

- 3 

### D.3 O PTION 2 

### : U SING L INK D RIVE S CHEDULES

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 duri ng the green signal phase of a traffic light cycle is characterized by a more or less steady cruise speed through the intersection.

E xhibit D-2 . Example Single Vehicle Speed Trajectory Through a Signalized

Intersection

0 5 10 15 20 25 30 35 40 45 50

- 100

- 80

- 60

- 40

- 20 0 20 40 60 80 100Speed (mph)

Distance (m) Cruise Cruise Decelerate Accelerate Idle Red Light Green Light

For the example intersection in Ex hibit D -1, 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 stopp ing at a red signal. Vehicles approaching a red trafficD

- 4 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.

E xhibit D-3. Ex ample Segments of Vehicle Activity N ear a Signalized Intersection

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 accel erate 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. A cceleration 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 jo in 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 intersec tion, 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 forD

- 5 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.

The MOVES emiss ion factors for each segment of vehicle activity obtained via individual link drive schedules are readily transferable to either AERMOD or CAL3QHCR, a s 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.

Note: For both free-flow highway and intersection links, u sers 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 LinkIDs. There are no

limits in MOVES as to how many links 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. The sum of emissions from each vehicle trajectory (LinkID) represents the total emission contribution of a given road

segment.



### D.4 O PTION 3 

### : U SING OP



### - M ODE D ISTRIBUTIONS

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) o utput 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 chara cterize 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

- 6 D.4.1 Approach l inks

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 divi ded 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:

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 Fracti on 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 fr action of time spent in each mode of approach activity, users

should select t he appropriate MOVES Op -Mode 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 Mode ID 11-16, 22-25, 27-30, 33, 35, 37-40);

• Low and moderate speed coasting (Op Mode ID 11, 21);

• Braking (Op Mode ID 0, 501 );D

- 7 • Idling (Op Mode ID 1); and

• Tire wear (Op Mode ID 400-416).

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 derivi ng 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 l inks

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

The cruise travel time can be represented by the number of vehicles cruising multiplied by the travel distance divi ded by the average cruise speed:

Cruise Time (s) = Number of Cruising Vehicle s * (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 th e 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 Departu re

After calculating fraction of time spent in each mode of departure activity, users should select the appropriate MOVES Op -Mode corresponding to each particular type of activity (see Section 4.5.7 for more information). The operating modes typifying de parture links

include:

• Cruise/acceleration (Op Mode ID 11-16, 22-25, 27-30, 33, 35, 37-40); and

• Tire wear (Op Mode ID 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.E

- 1 

## Appendix E :
Example Quantitative PM Hot

- spot Analysis of a Highway Project using MOVES and CAL3QHCR

E.1 I NTRODUCTION

Th e 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 compl ete each step of the analysis. This example is limited to showing the build scenario; in practice, project sponsors may also have to 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 p laces 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. In this example, the interagency consultation process is used as needed for evaluating and choosing models, methods, and assumptions, according to the requirements of 40 CFR 93.105(c)(1)(i).



### E .2 P ROJECT 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-1). 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 24-hour PM 2.5 NAAQS and

1997 annual PM 2.5 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 projec t 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 t raffic (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 included in air quality modeling.E

- 2 • The state does not have an adequate or approved SIP budget for either PM 2.5 NAAQS, and neither the E PA nor the state air agency has made a finding that road dust is a significant contributor to the PM 2.5 nonattainment pro blem.

Exhibit E-1. Simple Diagram of the Proposed Highway Project



### E.3 D ETERMINE NEED FOR PM HOT



### - SPOT ANALYSIS 

### ( STEP 1 

### )

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)(1)(i) and Sections 2.2 and Appendix B of the guidance). Therefore, a quantitative PM hot-spot analysis is required.E

- 3 

### E.4 D ETERMINE APPROACH 

### , MODELS 

### , AND DATA 

### ( STEP 

### 2)

E.4.1 Determining geographic area and emission sources to be covered by the a nalysis

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 em ission sources to be included in air quality modeling (see Section 3.3.2).

E.4.2 Deciding the general analysis approach and analysis year(s)

Second, the project sponsor determi nes 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 the PM NAAQS to be evaluated

Because the area has been designated nonattainment for both the 2006 24-hour PM 2.5 NAAQS and 1997 annual PM 2.5 NAAQS, the results of the analysis will have to be compared to both NAAQS (see Section 3.3.4). A ll 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 PM emissions to be modeled

Next, the following directly

- emitted PM 2.5 emissions are determined to be relevant for estimating the emissions in the analysis (see Section 2.5):

• Vehicle exhaust 1

• Brake wear

• Tire wear

E.4.5 Determining the models and methods to be used

Since this project is located outside of California, MOVES is used for emissions modeling. In addition, it is determined that, since this is a highway project with no nearby sources that need to b e included in the air quality modeling , 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).

1 Represented in MOVES as PM total r unning and PM total crankcase running.E

- 4 E.4.6 Obta ining project- specific data

Finally, the project sponsor compiles the data required to use MOVES, including project traffic data, vehicle types and age, and temperature and h umidity 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 CAL3QHCR to 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 required by the conformity rule (see Section 3.3.7).



### E.5 E STIMATE ON



### - ROAD MOTOR VEHICLE E MISSIONS 

### ( 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 proje ct’s on -road emissions:

E.5.1 Characterizing the project in terms of l inks

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 ti ming) 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). Each LinkID is defined with the necessary i nformation for air quality modeling: link length, link width, link volume, as well as link start and end points (x1, y1, x2, y2 coordinates).

Decisions on how to best define links are based on an analysis of vehicle activity and patterns within the proj ect 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 E3.E

- 5 Exhibit E-2. Diagram of Proposed Highway Project Showing Links

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: Q1 (January- March), Q2 (April -June), Q3 (JulySeptember), and Q4 (October- December).E

- 6 Exhibit E -3. Peak -Hour and Average- Hour Traffic Counts for Each Project Link

Freeway Peak Hour Count Average Hour Count Fraction of Total

Passenger Cars 2260 452 0.45

Passenger Trucks 1760 352 0.35

Intercity Buses 36 7 0.01

Short Haul Trucks (gas) 60 12 0.01

Long Haul Trucks (diesel) 944 189 0.19

Total 5060 10 12 1.00

Exit Ramps Peak Hour Count Average Hour Count Fraction of Total

Passenger Cars 124 25 0.22

Passenger Trucks 124 25 0.22

Intercity Buses 8 2 0.01

Short Haul Trucks (gas) 12 2 0.02

Long Haul Trucks (diesel) 300 60 0.53

Total 568 114 1.00

Entrance Ramps Peak Hour Count Average Hour Count Fraction of Total

Passenger Cars 176 35 0.29

Passenger Trucks 148 30 0.24

Intercity Buses 0 0 0.00

Short Haul Trucks (gas) 16 3 0.03

Long Haul Trucks (diesel) 276 55 0.45

Total 616 123 1.00

Arterial Road Peak Hour Count Average Hour Count Fraction of Total

Passenger Cars 124 25 0.22

Passenger Trucks 116 23 0.20

Intercity Buses 12 2 0.02

Short Haul Trucks (gas) 0 0 0.00

Long Haul Trucks (diesel) 316 63 0.56

Total 568 114 1.00

A sign ificant 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 onand off- ramps connecting the freeway to the arterial road suggests approximately h alf 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 mp h, 45 mph, and 45 mph, respectively). Traffic flow through the two intersections north and south of the freeway is controlled by a signalized light with a 60% idle time for vehicles exiting the freeway and 40% idle timeE

- 7 for traffic entering the freeway fr om 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;

• E xiting 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 cru ise speed on the on- ramps;

• Idling activity at both intersection s during the red phase of the traffic light; and

• Vehicles traveling between the north and south intersections at a constant 45 mph.

A s 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 han dle l ink a ctivity

As discussed in Section 4.2 of the guidance, MOVES offers several options for users to apply activity information to each LinkID. 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 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:

• 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 Highway Capacity Manual 2000 , 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 re flect the lower average speed and subsequent higher emissions (as described in Appendix D.2).

• Queue links (links 3, 6, 10, 13, 16, and 19) are given an Op -Mode distribution that represents vehicles decelerating and idling (red light) as well as cruising th rough (green light) (as described in Appendix D.4). Step 1 . First, an Op- Mode distribution is calculated for the link average speed (45 mph).E

- 8 Step 2 . Because this does not adequately account for idling at the intersection, the Op

- Mode fractions are r e- allocated to add in idling. For insta nce, after consulting the Highway Capacity Manual 2000 , for this project scenario the red light timing corresponds to approximately 40% idle time. A fraction of 0.4 for Op- Mode “1” is therefore added to the 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 tru cks, 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)E

- 9 E.5.3 Determining the number of MOVES 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 calc ulate values for all project links for the time period specified in eachrun. The 16 emission factors produced for each link are calculated as grams/vehiclemile, 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 panel, sel ecting 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 p anel, t he custom domain is selected (see Section 4.4.4).

• From the Vehicles/Equipment p anel, appropriate Source T ypes are selected (see

Section 4.4.5).

• From the Road Types panel, Urban Restricted and U rban U nrestricted road types

are selected (see Section 4.4.6).

• From the Pollutants and Processes p anel, the appropriate pollutant/processes are selected according to Section 4.4.7 of the guidance for “highway links.”

• In the Output p anel, a n outpu t 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. T emperature and humidity data from a representative

mete orological 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 a re 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.E

- 10 Exhibit E-5. Temperature and Humidity Input (January 12 a.m.)

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 (Partial)E

- 11 Fuel Supply and Fuel Formulation

In this example, it is determined appropriate to use the default fuel supply and formulation (see Exhib its E -7 and E-8). The default fuel supply and formulation are imported for each respective quarter (January, April, July, and October) and used for the corresponding MOVES runs.

Exhibit E -7. Fuel Supply Table

Exhibit E-8. Fuel Formulation TableE

- 12 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 T ype

The distribution of vehicle types on each link is defined in the Link Source Type table (Ex hibit 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

Links

The Links input table shown in Exhibit E-10 is used to define each individual p roject 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 (other links a re explicitly defined with a link -drive schedule or Op-Mode distribution). Link length and

link volume is entered for each link; however, since the “Emissio n Rates” option is

selected in the Scale p anel , 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

- 13 Exhibit E -10. Links Input (AM Period)

The remaining lin ks are defined with an Op-Mode distribution (Exhibit E- 11) 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 -11 . Operating Mode Distribution Table (Partial)E

- 14 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 Generatin g 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 PollutantID. An aggregate PM 2.5 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: 2

PM aggregate total = (PM total r unning ) + (PM total crankcase running ) + (brake wear) + (tire wear)

The 16 resulting grams/vehicle- mile emission factors (Exhibit E -12) for each link are then ready to be used as input into the CAL3QHCR dispersion model to predict future

PM 2.5 concentrations.

2 EPA is conside ring creating one o r more MOVES scripts that would automate the summing of aggregate emissions when completing project

- level analyses. These scripts would be made available for download on the MOVES website ( www. epa.gov/otaq/models/moves/tools.htm ), when available.E

- 15 Exhibit E -12. Grams/ Vehicle

- Mile Emission Factors Calculated from MOVES Output by Link, Quarter, and HourE

- 16 

### E.6 E STIMATE EMISSIONS FROM ROAD DUST 

### , CONSTRUCTION 

### , AND

ADDITIONAL SOURCES 

### ( 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

PM 2.5 NAAQS, and neither the EP A nor the state air agency have made a finding that road dust emissions are a significant contributor to the air quality problem for either

PM 2.5 NAAQS. Therefore, PM 2.5 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 d ust

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 additional sources of emissions in the project area

I t is determined that the project area in the analysis year does not include locomotive s or other nearby emission sources that have to be considered in the air quality modeling (see Section 6.6).



### E.7 S ELECT 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 PM 2.5 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

be tween 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 eac h of the four quarters being modeled.E

- 17 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, as file requires one to scro ll down the screen): in all, four separate

scenarios.

Exhibit E-13a. CAL3QHCR Quarter 1, 6 a.m. Input File (Partial)**~** highway_jan.INP -Notepad 

### GJ[QJ(g)

File Edit Format View Help

• H01:-Sp01: Highway Example• 60. 175. 0. 0. 41 1 0**~** 1 1 98 12 31 98 

# a 94823 98 94823 98 1 1

. u· .1. -42.9 -20 1.8

• 2. -28.8 -3.1 1.8

• 3. -16.5 29 1.8 .4. -16.5 48.8 1.8

• 5. 29.6 31.8 1.8

• 6. 12.7 -101 1.8

• 7. -14.6 -100.1 1.8

• 8. 15. 5 -152.9 1.8

• 9. -14.6 -154.7 1.8 '10' -12.7 -220.7 1.8 '11' 17.4 -205.6 1.8 '12' -11.8 -265.9 1.8 '13' 21.2 -257.4 1.8 '14' 19.3 -334. 7 1.8 '15' 35.3 -333.7 1.8 '16' 34.4 -317.7 1.8 '17' -21.2 -395.9 1.8 '18' -23.1 -349.7 1.8 '19' 24 -18.1 1.8
'20' -31.6 15.8 1.8 '21' -3.3 -43 5. 5 1.8

'22' 12.7 -7.8 1.8
'23' 24 -379 1.8 .24. 28.7 -411 1.8 '2 5' 4 5. 7 -3 53. 5 1.8
'26' -9.9 -360.1 1.8
'27' -12.7 -320.5 1.8
'28' -42.9 -365.8 1.8
'29' -19.3 -21 1.8
'30' -22.2 -57.7 1.8 '31' 10.8 19.6 1.8
'32' 46.6 20. 5 1.8
'33' 10.8 52. 5 1.8
'34' 13.6 -32.3 1.8 '3 5' 48. 5 -6.8 1.8
'36' -7.1 -386.5 1.8
'37' -43.8 -394 1.8
'38' -6.2 -410 1.8
'39' 43.8 -387.4 1.8
'40' -33. 5 -38.9 1.8 '41' 33.4 -432.7 1. 81 

### 81**~** .::E

- 18 Exhibit E-13b. CAL3QHCR Quarter 1, 6 a.m. Input File (Partial)

Section 7.5 of the guidance recommends that users run the air quality model for five

years of meteorological data when site

- specific 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. 3

3 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 E.7.2 Incorporating meteorological data

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, consistent with the recommendations made in the Section 7

.

E.7.3 Placing receptors

Using the guidance given in Section 7.6, receptors are placed at ap propriate locations within the area substantially affected by the project (Exhibit E -14 ). 4 N ote that this grid is shown for illustrative purposes only; placement, location, and spacing of actual receptors should follow the guidance in Section 7.6 . Recept or heights are set at 1.8 meters

. 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 2 002) and

output is produced for all receptors for each of the five years of meteorological data.

Exhibit E -14. Receptor Locations for Air Quality Modeling

4 The number and arrangement of receptors used in this example are simplified for ease of explanation

.E

- 20 

### E.8 D ETERMINE BACKGROUND CONCENTRATIONS FROM NEARBY AND

OTHER EMISSION SOURC ES 

### ( STEP 6 

### )

Through the interagency consultation process, a nearby upwind PM 2.5 monitor that has

been collecting ambient data for both the annual and 24-hour PM 2.5 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 PM 2.5 values a re taken in a four-day/three-day measurement interval. As previously noted, no nearby sources needing to be included in the air quality model are identifie d.

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

- 21 

### E.9 C ALCULATE DESIGN VALU ES AND DETERMINE CONFOR MITY 

### ( 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 with the highest modeled concentrations for the build sce nario are shown in this example. 5 In this step, the guidance from Sections 9.3.2 and 9.3.3 are used to calculate design values from the modeled results and the background concentrations for comparison with the annual and 24-hour PM 2.5 NAAQS.

E.9.1 Determining conformity to the annual PM 2.5 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 Annual Average Background

Concentration

200 8 13.348

200 9 12.785

2010 13.927

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) Annual Average

Modeled

Concentration ( Five

- year A verage) Sum of Background + Project Annual Design Value

13.353 1.580 14.933 14.9

5 In an actual PM hot

- spot analysis, design values would be calculated at additional receptors as described

in Section 9.3.E

- 22 To determine the annual PM 2.5 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 ( project + backgro und ) results in a design value of 14.9 μ g/m 3 . This value at the highest receptor is less than the 1997

annual PM 2.5 NAAQS of 15.0 μ g/m 3 . It can be assumed that all other receptors with lower modeled concentrations will also have design values less than this NAAQS. In this example it is unnecessary to determine appropriate receptors in the build scenario (per Section 9.4 of the guidance) or develop a no -build scenario for the annual PM 2.5 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 PM 2.5 NAAQS

The next step is to calculate a design value to compare with the 2006 24-hour PM 2.5 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, res ulting in 32 values (4 quarters; 8 concentrations/quarter) for each year of data (shown in Exhibit E-17).

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 model ing. 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. Note that, in a real -world situation, this process would be repeated for all receptors in the build

scenario.E

- 23 Exhibit E -17. Highest Daily Background Concentrations for Each Quarter and

Each Year

200 8

Rank Q1 Q2 Q3 Q4

1 20.574 21.262 22.354 20.434

2 20.152 20.823 22.042 20.016

3 19.743 20.398 21.735 19.611

4 19.346 19.985 21.434 1 9.218

5 18.961 19.584 21.140 18.837

6 18.588 19.196 20.851 18.467

7 18.226 18.819 20.568 18.109

8 17.874 18.454 20.291 17.761

200 9

Rank Q1 Q2 Q3 Q4

1 20.195 20.867 21.932 20.058

2 19.784 20.440 21.628 19.651

3 19.386 20.026 21.329 19.257

4 19.000 19.624 21.037 18.875

5 18.625 19.235 20.750 18.504

6 18.262 18.857 20.469 18.145

7 17.910 18.490 20.194 17.796

8 17.568 18.135 19.924 17.457

2010

Rank Q1 Q2 Q3 Q4

1 21.137 21.847 22.980 20.990

2 20.698 21.390 22.655 20.556

3 20.272 20.948 22.336 20.135

4 19.860 20.519 22.023 19.726

5 19.459 20.102 21.717 19.330

6 19.071 19.698 21.417 18.945

7 18.694 19.307 21.123 18.572

8 18.329 18.927 20.834 18.211

Exhibit E -18. Five -year Average 24- hour Modeled Concentrations for Each Quarter (At Example Receptor)

Q1 Q2 Q3 Q4 Five Year Average

Maximum Concentration (At Example Receptor) 10.42 10.62 10.74 10.61E

- 24 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 analy sis shows only a single receptor’s val ues, but project

sponsors should calculate design values at all receptors in the build scenario (see Section 9.3 of the guidance).

Exhibit E -19. Sum of Background and Modeled Concentrations at Example Receptor for Each Quarter

200 8

Rank Q1 Q2 Q3 Q4

1 31.084 31.902 32.994 31.074

2 30.662 31.463 32.682 30.656

3 30.253 31.038 32.375 30.251

4 29.856 30.625 32.074 29.858

5 29.471 30.224 31.780 29.477

6 29.098 29.836 31.491 29.107

7 28.736 29.459 31.208 28.749

8 28.384 29.094 30.931 28.401

200 9

Rank Q1 Q2 Q3 Q4

1 30.705 31.507 32.572 30.698

2 30.294 31.080 32.268 30.291

3 29.896 30.666 31.969 29.897

4 29.510 30.264 31.677 29.515

5 29.135 29.875 31.390 29.144

6 28.772 29.497 31.109 28.785

7 28.420 29.1 30 30.834 28.436

8 28.078 28.775 30.564 28.097

20 10

Rank Q1 Q2 Q3 Q4

1 31.647 32.487 33.620 31.630

2 31.208 32.030 33.295 31.196

3 30.782 31.588 32.976 30.775

4 30.370 31.159 32.663 30.366

5 29.969 30.742 32.357 29.970

6 29.581 30.338 32.057 29.58 5

7 29.204 29.947 31.763 29.212

8 28.839 29.567 31.474 28.851E

- 25 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 2008 2009 2010

1 32.994 32.572 33.620

2 32.682 32.268 33.295

3 32.375 31.969 32.976

4 32.074 31.677 32.663

5 31.902 31.507 32.487

6 31.780 31.390 32.357

7 31.491 31.109 32.057

8 31.463 31.080 32.030

9 31.208 30.834 31.763

10 31.084 30.705 31.647

11 31.074 30.698 31.630

12 31.038 30.666 31.588

13 30.931 30.564 31.474

14 30.662 30.294 31.208

15 30.656 30.291 31.196

16 30.625 30.264 31.159

17 30.253 29.897 30.782

18 30.251 29.896 30.775

19 30.224 29.875 30.742

20 29.858 29.515 30.370

21 29.856 29.510 30.366

22 29.836 29.497 30.338

23 29.477 29.144 29.970

24 29.471 29.135 29.969

25 29.459 29.130 29.947

26 29.107 28.785 29.585

27 29.098 28.775 29.581

28 29.094 28.772 29.567

29 28.749 28.436 29.212

30 28.736 28.420 29.204

31 28.401 28.097 28.851

32 28.384 28.078 28.839E

- 26 Step 7.6 For each year of monitoring data, the value with a rank that corresponds to the projected

98 th 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) u ses the 3 rd highest rank to represent a 98 th percentile. The 3 rd
highest concentration (highlighted in Exhibit E- 20) is referred to as the “projected 98 th
percentile concentration.”

Step 7.7 Steps 7.1 through 7.6 are repeated to calculate a projected 98 th percentile concentration at each receptor based on each year of monitoring data and modeled concentrations.

Step 7.8 For the example receptor, the average of the three projected 98 th percentile concentration s (see Step 7.6) is calculated.

Step 7.9

The resulting value of 32.440 μ g/m 3 is then rounded to the nearest whole μ g/m 3

, resulting
in a design value at the example receptor of 32 μ g/m 3 . At each receptor this process should be repeated. I n the case of this analysis, the example receptor is the rece ptor 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 μ g/m 3 ) is less than the 2006 24-

hour PM 2.5 NAAQ S of 35 μ g/m 3 . S ince 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 C ONSIDER 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 con trol 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 D OCUMENT THE PM H OT



### - SPOT A NALYSIS 

### ( STEP 9 

### )

The final step is to properly document the PM hot-spot analysis in the conformity determination (see Section 3.10).F

- 1 

## Appendix F:
Example Quantitative PM Hot

- spot Analysis of a Transit Project using MOVES and AERMOD

F.1 I NTRODUCTION

Th e 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 s ections 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 calcul ates 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 requi re more documentation of data and decisions. In this example, the interagency consultation process is used as needed for evaluating and choosing models, methods, and assumptions, according to the requirements of 40 CFR 93.105(c)(1)(i).



### F.2 P ROJECT D ESCRIP TION 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 24-hour PM 2.5 NAAQS an d 1997 annual PM 2.5 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

n earby sources of PM 2.5 that need to be 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.F

- 2 • The state does not have an adequate o r approved SIP budget for either PM 2.5 NAAQS, and neither the EPA nor the state air quality agency has made a finding that road dust is a significant contributor to the PM 2.5 nonattainment problem.



### F.3 D ETERMINE NEED FOR PM HOT



### - SPOT A NALYSIS 

### ( STEP 1 

### )

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)(1)(iii) and Section 2.2 and Appendix B of the guidance). Therefore, a quantitative PM hot- spot analysis is required.



### F.4 D ETERMINE APPROACH 

### , MODELS 

### , AND DATA 

### ( STEP 2 

### )

F.4.1 Determining geographic area and emission sources to be covered by the a nalysis

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 em ission sources to be modeled (see Section 3.3.2).

F.4.2 Deciding the 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 i f 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 De termining the PM NAAQS to be evaluated

Because the area has been designated nonattainment for both the 2006 24-hour PM 2.5 NAAQS and 1997 annual PM 2.5 NAAQS, the resul ts of the analysis will be compared to both NAAQS (see Section 3.3.4). A ll fo ur quarters are included in the analysis in order to estimate a year’s worth of emissions for both NAAQS.F

- 3 F.4.4 Deciding on the type of PM emissions to be modeled

Next, the following directly

- emitted PM emissions are determined to be relevant for estimating the emissions in the analysis (see Section 2.5):

• Vehicle exhaust 1

• Brake wear

• Tire wear

F.4.5 Determining the models and methods to be used

Since this project will be located outside of California, MOVES is used for emissions modeling. In addition, i t 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 collecte d, among other reasons (see next step).

F.4.6 Obta ining project- specific 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

co nformity rule (see Section 3.3.7).



### F.5 E STIMATE ON



### - ROAD MOTOR VEHICLE E MISSIONS 

### ( 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 l inks

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-1, 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 u sed to describe vehicles idling at the signalized

1 Represented in MOVES as PM total r unning , PM total crankcase running, PM total ext. idle, and PM total crankcase ext. idle.F

- 4 li ght before exiting the facility. Links 4 through 9 represented bus bays where buses drop-off and pick- up passengers; these are referred to as the terminal links. 2

Exhibit F -1. Diagram of Proposed Bus Terminal Showing Links

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 l inks, 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). To simplify the analysis, the sponsor conservatively assume s weekday traffic for all days of the year, even though the operating plan calls for reduced service on weekends. 3 Identical traffic

volume and activ ity 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: Q1 (January

- March), Q2 (April -June), Q3 (July-September), and Q4 (October- December).

2 The project area in this example is not realistic and has been simplified for demonstration purposes. Analyses of transit facilities will likely include inbound and outbound links beyond what is described in this simplified example, as well as the surrounding area.

3 This decision is made to save time and effort, as it would result in the need for fewer modeling runs.

More a ccurate results would be obtained by treating weekends differently and modeling them using the actual estimated Saturday and Sunday traffic.F

- 5 Exhibit F -2. Average Week day Bus Trips through Transit Terminal for Each Hour

Hour Bus T rips

12am

- 1am 7

1am

- 2am 6

2am

- 3am 6

3am

- 4am 6

4am

- 5am 7

5am

- 6am 9

6am

- 7am 27

7am

- 8am 48

8am

- 9am 39

9am

- 10am 29

10am

- 11am 26

11am

- 12pm 28
12pm

- 1pm 30
1pm

- 2pm 31
2pm

- 3pm 31
3pm

- 4pm 39
4pm

- 5pm 44

5pm

- 6pm 42
6pm

- 7pm 26

7pm

- 8pm 21
8pm

- 9pm 22
9pm

- 10pm 17
10pm

- 11pm 13

11pm - 12am 10

F.5.2 Deciding on how to handle l ink a ctivity

As discussed in Section 4.2 of the guidance, MOVES offers several options for users to apply activity information to each LinkID. 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 entere d 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) a s 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% idlin g (determined after consulting the Highway Capacity Manual 2000 to approximate idle time in an under-capacity scenario) reflecting 50% of buses encountering a red light. AF

- 6 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 MOVES 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 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). Grams/hour

emissions rate s are required to use AERMOD.

F.5.4 De veloping 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 ea ch run is selected (see Section 4.4.3).

• From the Geographic Bounds p anel, the custom domain is selected (see Section 4.4.4).

• From the Vehicles/Equipment p anel, Diesel Transit Buses are selected (see

Section 4.4.5).

• From the Road Types panel, the Urban Unre stricted road type is selected (see Section 4.4.6).

• From the Pollutants and Processes p anel, 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 w ith gr ams and miles selected as units (see Section 4.4.10).F

- 7 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, respectively ) for the months of January, April, July, and October. T emperature and humidity data from a representative meteorological monitoring sta tion 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 6 a.m.)F

- 8 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 (Partial)F

- 9 Fuel Supply and Fuel Formulation

An appropriate fuel supply and formulation is selected to matc h 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

all transit buses would use the same diesel fuel, so a fraction of 1 is entered for fuel

20011 (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

Exhibit F -6. Fuel Formulation TableF

- 10 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 T ype

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 tr ansit buses are operating on all links. Therefore, a fraction of 1 is entered for S ource 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 TableF

- 11 Links

The L inks 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 term s of miles for ea ch link. The road t ype 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 estim ate in grams/vehicle -hour, the volume (i.e., the number of vehicles) will be divided out during post- processing. Also, because link volume is arbitrary, the Links table shown in Exhibit F-8 can be used for all 16 MOVES

runs.

Exhibit F -8. Links Table

Describing Vehicle Activity

MOVES can capture details about vehicle activity in a number of ways. In this case, it is

decided to use average speeds for L inks 1, 2, and 4 and a det ailed Op

- Mode distribution

for L ink 3 (see Section 4.5.7).

Op -Mode distributions for Links 1 and 2 are calculated within MOVES 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- Mo de distribution to better simulate the queuing and idling that occurs prior to buses exiting the facility at a traffic signal. The s ponsor estimate s that 50% of busesF

- 12 would idle at a red light before exiting the facility, so the idling operation (OpMode I D 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, deceleration, and cruise operating modes). This process requires an additio nal 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 OpMode distributions generated for 5 mph on an urban unrestricted road type are saved inthe MOVES execution database in the MySQL table “opmodedistribution.” The OpMode 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 (P artial)F

- 13 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 a t 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 transitprojects would include rider parking lots and should include these emissions in a PM hotspot 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, t he MOVES

output is located in the MySQL output database table “movesoutput” and sorted by Month, Hour, LinkID, ProcessID, and PollutantID. An aggregate PM 2.5 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 o f the guidance: 4

PM aggregate total = (PM total r unning ) + (PM total 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 Activi tytypeID = 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 p.m. to 6 p.m. has a volume of 42 buses per hour (7 buses per bus bay). If each bus is expected to idle fo r 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):

• 6 a.m. results

– traffic data from 3 a.m. to 9 a.m.

• 12 p.m. results

– traffic data from 9 a.m. to 3 p.m.

• 6 p.m. results

– traffic data from 3 p.m. to 9 p.m.

• 12 p.m. results

– traffic data from 9 p.m. to 3 a.m.

The emission factor results for each quarter are similarly paired with traffic volumes.

4 EPA is conside ring creating one or more MOVES scripts that would automate the summing of aggregate

emissions when completing project

- level analyses. These scripts would be made available for download on the MOVES website ( www.epa.gov/otaq/models/moves/tools.htm ), when available.F

- 14 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

PM 2.5 concentrations.



### F.6 E STIMATE EMISSIONS FROM ROAD DUST 

### , CONSTRUCTION AND

ADDITIONAL SOURCES 

### ( STEP 4 

### )

F.6.1 Estimat ing re-entrained road dust

In this case, this area does not have any adequate or approved SIP budgets for either

PM 2.5 NAAQS, and neither the E PA nor the state air agency has made a finding that road dust emissions are a significant contributor to the air quality problem for either PM 2.5 NAAQS. Therefore, PM 2.5 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 c onstruction d ust

The construction of this project wil l 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 additional sources of emissions in the project area

Through interagency consultation, it i s determined that the project area in the analysis

year do es not include locomotives or other nearby emissions sources that would have to

be included in the air quality modeling (see Section 6.6).



### F.7 S ELECT AN AIR QUALITY MODEL 

### , DATA INPUTS 

### , AND RECEPTOR S 

### ( 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 PM 2.5 concentratio ns 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-1). The emission release height is set to three meters, the approximate exhaust height of mo st transit buses.

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 t emperature effects. Adjustment factors (EMISFACT) are used to model these hourly and quarterly variationsF

- 15 in emission factors. Refer to Appendix J for additional detail on using hour- by -hour emission differences in an AERMOD input file.

Exhibit F-10. AERMOD Input File (Partial) with Seasonal (Quarterly) and Hourly Adjustment Factors (Circled)

F.7.2 Incorporating meteorological data

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 i s obtained from a local airport for calendar years 1998-2002. The appropriate surface roughness is set at 1 meter, consistent with the recommendations made in the AERMOD Implementation Guide.F

- 16 F.7.3 Pla cing receptors

Using the guidance given in Section 7.6, receptors are placed at appropriate locations within the area substantially affected by the project (see Exhibit F -11). 5 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. 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 -11. Area Source and Receptor Locations for Air Quality Mo deling

5 The number and arrangement of receptors used in this example are simplified for ease of explanation;

real

- world projects could expect to see more receptors and include the surrounding area.F

- 17 

### F.8 D ETERMINE BACKGROUND CONCENTRATIONS FROM NEARBY AND

OTHER EMISSION SOURC ES 

### ( STEP 

### 6)

Through the interagency consultation process, a nearby upwind PM 2.5 monitor that has

been collecting ambient data for both the annual and 24-hour PM 2.5 NAA QS 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 PM 2.5 values are provided in a four- day/th ree

- day measurement interval. As previously noted, no ne arby sources requiring 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. PM 2.5 Monitor Data from a Representative Nearby Site (Partial)F

- 18 

### F.9 C ALCULATE DESIGN VALUES AND DETERMINE CONFORMITY 

### ( 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 with the highest modeled concentrations for the build scenario are shown in this example

. 6 In Step 7, the gu idance from Section s 9.3.2 and 9.3.3 are used to calculate design values from the modeled results and the background concentrations for comparison with the annual and 24-hour PM 2.5 NAAQS.

F.9.1 Determining conformity to the annual PM 2.5 NAAQS

First, av erage 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 Annual Average Background

Concentration

200 8 13.348

200 9 12.785

20 10 13.927

Annual Average 13.353

Exhibit F-14. Calculation of Annual Design Value (At Highest Receptor)

Annual Average

Background

Concentration

( Three

- year

Ave rage) Annual Average

Modeled

Concentration ( Five

- year A verage) Sum of Background + Project Annual Design Value

13.353 1.423 14.776 14.8

6 In an actual PM hot

- spot analysis, design values would be calculated at additional rece ptors as described

in Section 9.3.F

- 19 To determine the annual PM 2.5 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 ( project + background ) results in a design

value of 14.8 μ g/m 3

. This value a t the highest receptor is less than the 1997 annual PM 2.5 NAAQS of 15.0 μ g/m 3 . It can be assumed that all other receptors with lower modeled concentrations will also have design values less than the 1997 annual PM 2.5 NAAQS. In this example it is unnecess ary to determine appropriate receptors in the build scenario or develop a no -build scenario for the annual PM 2.5 NAAQS, since the build scenario demonstrates that the hot-spot analysis requirements in the transportation conformity rule are met at all recep tors.

F.9.2 Determining conformity to the 24-Hour PM 2.5 NAAQS

The next step is to calculate a design value to compare with the 2006 24-hour PM 2.5 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, res ulting in 32 values (4 quarters; 8 concentrations/quarter) for each year of data (shown in Exhibit F-15).

Step 7.3 Identify the highest modeled 24-hour concentration in each quarter, averaged across each year of meteorological data. For illustrative purposes, the highest average concentration across five years of meteorological data for a single receptor in e ach quarter is shown in

Exhibit F-16 . Note that, in a real -world situation, this process would be repeated for all receptors in the build scenario.F

- 20 Exhibit F-15. Highest Daily Background Concentrations for Each Quarter and

Each Year

200 8

Rank Q1 Q2 Q 3 Q4

1 20.574 21.262 22.354 20.434

2 20.152 20.823 22.042 20.016

3 19.743 20.398 21.735 19.611

4 19.346 19.985 21.434 19.218

5 18.961 19.584 21.140 18.837

6 18.588 19.196 20.851 18.467

7 18.226 18.819 20.568 18.109

8 17.874 18.454 20.291 17.761

20 0 9

Rank Q1 Q2 Q3 Q4

1 20.195 20.867 21.932 20.058

2 19.784 20.440 21.628 19.651

3 19.386 20.026 21.329 19.257

4 19.000 19.624 21.037 18.875

5 18.625 19.235 20.750 18.504

6 18.262 18.857 20.469 18.145

7 17.910 18.490 20.194 17.796

8 17.568 18.135 1 9.924 17.457

20 10

Rank Q1 Q2 Q3 Q4

1 21.137 21.847 22.980 20.990

2 20.698 21.390 22.655 20.556

3 20.272 20.948 22.336 20.135

4 19.860 20.519 22.023 19.726

5 19.459 20.102 21.717 19.330

6 19.071 19.698 21.417 18.945

7 18.694 19.307 21.123 18.572

8 18.329 18.927 20.834 18.211

Exhibit F -16. Five

- year Average of Highest Modeled Concentrations for Each Quarter (At Example Receptor)

Q1 Q2 Q3 Q4 Five Year Average

Maximum Concentration (At Example Receptor) 6.51 6.64 6.71 6.63F

- 21 Step 7.4 The highes t 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 concentratio ns 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

200 8

Q1 Q2 Q3 Q4

1 27.088 27.901 29.063 26.948

2 26.667 27.462 28.750 26.530

3 26.258 27.037 28.443 26.125

4 25.861 26.624 28.143 25.732

5 25.476 26.22 4 27.848 25.351

6 25.102 25.835 27.560 24.982

7 24.740 25.459 27.277 24.623

8 24.389 25.093 27.000 24.275

200 9

Q1 Q2 Q3 Q4

1 26.709 27.506 28.641 26.572

2 26.298 27.079 28.336 26.166

3 25.900 26.665 28.038 25.772

4 25.514 26.264 27.745 25.389

5 25.140 25.874 27.459 25.019

6 24.776 25.496 27.178 24.659

7 24.424 25.130 26.903 24.310

8 24.082 24.774 26.633 23.971

20 10

Q1 Q2 Q3 Q4

1 27.651 28.486 29.689 27.505

2 27.212 28.030 29.363 27.070

3 26.787 27.587 29.044 26.649

4 26.374 27.158 28.73 2 26.240

5 25.974 26.742 28.426 25.844

6 25.585 26.338 28.125 25.460

7 25.209 25.946 27.831 25.087

8 24.843 25.566 27.543 24.725F

- 22 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 2008 2009 201 0

1 29.063 28.641 29.689

2 28.750 28.336 29.363

3 28.443 28.038 29.044

4 28.143 27.745 28.732

5 27.901 27.506 28.486

6 27.848 27.459 28.426

7 27.560 27.178 28.125

8 27.462 27.079 28.030

9 27.277 26.903 27.831

10 27.088 26.709 27.651

11 27.037 26 .665 27.587

12 27.000 26.633 27.543

13 26.948 26.572 27.505

14 26.667 26.298 27.212

15 26.624 26.264 27.158

16 26.530 26.166 27.070

17 26.258 25.900 26.787

18 26.224 25.874 26.742

19 26.125 25.772 26.649

20 25.861 25.514 26.374

21 25.835 25.496 2 6.338

22 25.732 25.389 26.240

23 25.476 25.140 25.974

24 25.459 25.130 25.946

25 25.351 25.019 25.844

26 25.102 24.776 25.585

27 25.093 24.774 25.566

28 24.982 24.659 25.460

29 24.740 24.424 25.209

30 24.623 24.310 25.087

31 24.389 24.082 24.843

32 24.275 23.971 24.725F

- 23 Step 7.6 For each year of monitoring data, the value with a rank that corresponds to the projected

98 th 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 3 rd highest rank to represent a 98 th percentile. The 3 rd
highest concentration (highlighted in Exhibit F-18) is referred to as the “projected 98 th
percentile concentration.”

St ep 7.7 Steps 7.1 through 7.6 are repeated to calculate a projected 98 th percentile concentration at each receptor based on each year of monitoring data and modeled concentrations.

Step 7.8 For the example receptor, the average of the three projected 98 th percentile concentrations (highlighted in Exhibit F-18) i s calculated.

Step 7.9 The resulting value of 28.508 μ g/m 3 is then rounded to the nearest whole μ g/m 3 resulting
in a design value at the example receptor of 29 μ g/m 3 . At each receptor this process should be repeated. However, in the case of this analysis, the example receptor is the receptor with the highe st 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 μ g/m 3 ) is less than the 2006 24-

hour PM 2.5 NAAQS of 35 μ g/m 3 . S ince 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 scenar io for the 24-hour PM 2.5 NAAQS.



### F.10 C ONSIDER 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 measu res. 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 D OCUMENT 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).G

- 1 

## Appendix G :
Example of Using EMFAC for a Highway Project

G.1 I NTRODUCTION

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 modelin g 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 (.rts 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 PM 10 or PM 2.5 ; 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 P ROJECT C HARACTERISTICS

The hypothetical highway project is located in Sacramento Co unty, 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 emissions and background concentrations). The b uild 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; t his 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

- 2 

### G.3 P REPARING EMFAC B ASIC I NPUTS

Based on the project characteristics, it is first necessary to specify the basic inputs and default settings in EMFAC ( see Exhibit G- 1).

Exhibit G-1. Basic Inputs in EMFAC for the Hypothetical Highway Project

Step Input Category Input Data Note

1 Geographic Area County  Sacramento Select fro m drop

- down list

Calculation Method Use Average Default (not shown in the EMFAC user interface)

2 Calendar Years 20 15 Select from drop

- down list

3 Season or Month Annual Select from drop

- down list

4 Scenario Title Use default Define default title in t he EMFAC

user interface

5 Model Years Use default Include all model years

6 Vehicle Classes Use default Include all vehicle classes

7 I/M Program Schedule Use default Include all pre

- defined I/M program

parameters

8 Temperature 60F Delete all default t emperature bins and input 60

9 Relative Humidity 70%RH Delete all default relative humidity bins and input 70

10 Speed Use default Include all speed bins from 5 mph to 65 mph

11 Emfac Rate Files Summary Rates (RTS) Select from EMFAC user interface

12 O utput Particulate PM 10 Select from EMFAC user interfaceG

- 3 

### G.4 E DITING EMFAC DEFAULT VMT D ISTRIBUTIONS

The next step is to calculate the EMFAC default s for trucks and non-trucks. As shown in Exhibit G-2, EMFAC’s 13 vehicle classes are grouped into truc ks and non-trucks to match the project

- specific traffic data. Specifically, Light -Duty Autos, Light- Duty Trucks (T1 and T2) and Motorcycles are grouped together to represent the “non- truck” class. All other vehicle classes (Medium -Duty Trucks, Light HD Trucks (T4 and T5), Medium HD Trucks, Heavy HD Trucks, Other Buses, Urban Buses, School Buses, and

Motor Ho mes) 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 mile s, respectively.

Exhibit G -2. Example Highway Project Pre- Populated VMT for 13 Default Vehicle

Classes

EMFAC Vehicle Class EMFAC default VMT

01

- Light

- Duty Autos (PC) 15

, 271

, 757

02

- Light

- Duty Trucks (T1) 3

, 340

, 492

03

- Light

- Duty Trucks (T2) 7

, 266

, 306

04

– Medium

- Duty Trucks (T3)* 3

, 535

, 454

05

- Light HD Trucks (T4)* 816

, 278

06

- Light HD Trucks (T5)* 302

, 809

07

– Medium HD Trucks (T6)* 698

, 543

08

- Heavy HD Trucks (T7)* 704

, 156

09

- Other Buses* 49

, 590

10

- Urban Buses* 40

, 198

11

– Motorcyc les 256

, 367

12

- School Buses* 31

, 176

13

- Motor Homes* 91

, 341

Truck VMT 6

, 269

, 545

Non

- truck VMT 26

, 134

, 922

TOTAL 32,404,46 7

* Classified as t rucks to match project

- specific data

The next step is to calculate percentage VMT for trucks and non- truc ks and their respective adjustment factors to match project

- specific VMT distributions, as shown in Exhibit G-3. The default VMT percentages for trucks (19%) and non- trucks (81%) are much different from what the project traffic data suggest (25% and 75% i n 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).G

- 4 Exhibit G -3. Calculation of Adjustment Fac tors for Truck and Non

- Truck VMT

VMT Column A

% of total VMT (EMFAC default) Column B

% of total VMT (Project

- specific) Adjustment Factor (AF)*

T rucks 6, 269

, 545 19% 2 5% 1.29

N on

- trucks 26

, 134

, 922 81% 7 5% 0.93

Sum 32

, 404

, 46 7 100% 100%

* Adjustment fa ctor is equal to the ratio between project

- specific % VMT (Column B) and

EMFAC default % VMT (Column A), for truck s and non

- truck s , respectively

.

Multiplying the EMFAC default VMT by the calculated adjustment factors (AF) for each vehicle class will prod uce 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 (2 5% of the t otal VMT). The non-truck VMT is 24,303,350 (7 5% 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; press ing the “Apply” button

accept s the changes.

Exhib it G -4. Example Adjusted VMT for 13 Default Vehicle Classes

Vehicle Class Default

VMT % VMT by

vehicle class Adjusted

VMT Adjusted % VMT by

vehicle class (default VMT*AF)

01

- Light

- Duty Autos (PC) 15,271,757 47.1% 14,201,491 43.8%

02

- Light

- Dut y Trucks (T1) 3,340,492 10.3% 3,106,386 9.6%

03

- Light

- Duty Trucks (T2) 7,266,306 22.4% 6,757,073 20.9%

04

- Medium

- Duty Trucks (T3)* 3,535,454 10.9% 4,568,294 14.1%

05

- Light HD Trucks (T4)* 816,278 2.5% 1,054,743 3.3%

06

- Light HD Trucks (T5)* 302 ,809 0.9% 391,271 1.2%

07

- Medium HD Trucks (T6)* 698,543 2.2% 902,614 2.8%

08

- Heavy HD Trucks (T7)* 704,156 2.2% 909,867 2.8%

09

- Other Buses* 49,590 0.2% 64,077 0.2%

10

- Urban Buses* 40,198 0.1% 51,941 0.2%

11

– Motorcycles 256,367 0.8% 238,400 0.7%

12

- School Buses* 31,176 0.1% 40,284 0.1%

13

- Motor Homes* 91,341 0.3% 118,025 0.4%

Truck 6,269,545 19.4% 8,101,117 25.0%

Non

- truck 26,134,922 80.7% 24,303,350 75.0%

TOTAL 32,404, 46 7 100.0% 32,404, 46 7 100.0%

* Classified as t ruck s to match pr oject

- specific dataG

- 5 

### G.5 G ENERATING LINK



### - SPECIFIC E MISSION F ACTORS

After the EMFAC run is completed, the project- specific running exhaust emission factors are presented in Table 1 of the output Summary Rates file (.rts file). As highlighted in Exhibit G-5, the PM 10 running exhaust emission factor is 0.040 g/mi under the associated speed bin of 65 mph. Tire wear and brake wear PM 10 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,

th e 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 onEMFAC default VMT distribution) is equal to 0.056 g/mi. It is lower than the projectsp ecific 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

This completes the use of EMFAC for determining emis sions 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 guida nce.H

- 1 

## Appendix H:
Example of Using EMFAC to Develop Emission Factors for a Transit Project

H.1 I NTRODUCTION

The purpose of this appendix is to illustrate the modeling steps required for users to change EMFAC’s default s 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 (.rts file) suitable for use in the AERMOD air quality model. This example does not include the subsequen t air quality modeling ; r efer 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 emissi on 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 t o 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 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. A s des cribed in the main body of this section, each run will be specific to either PM 10 or PM 2.5 ; however, this example is applicable to both.

1 Th is 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

- 2 

### H.2 P ROJECT C HARACTERISTICS

A PM 10 hot-spot analysis is conducted for a planned bus terminal project in Sacramen to 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 s 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 P REPARING EMFAC BASIC INPUTS 

### ( APPLICABLE TO BOTH I DLE AND

START EMISSIONS E STIMATION 

### )

Based on the project characteristics, basic inputs and default settings in EMFAC are first specified (see Exhibit H- 1). 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 -1. Basic Inputs in EMFAC for the Hypothetical Highway Project

Step Input Category Input Data Note

1 Geographic Area County  Sacramento Select from drop

- down list

Calculation Method Use Average Default (not visible in the EMFAC user interface)

2 Calendar Years 20 15 Select from drop

- down list

3 Season or Month Annual Select from drop

- down list

4 Scenario Title Use default Define default title in the EMFAC

user interface

5 Model Years Use default Include all model years

6 Vehicle Classes Use default Include all vehicle classes

7 I/M Program Schedule Use default Incl ude all pre

- defined I/M program

parameters

8 Temperature 60F Delete all default temperature bins and input 60

9 Relative Humidity 70%RH Delete all default relative humidity bins and input 70

10 Speed Use default Include speed bin of 0 mph

11 Emfac Rate Files Summary Rates (RTS) Select from EMFAC user interface

12 Output Particulate PM 10 Select from EMFAC user interfaceH

- 3 

### H.4 E DITING EMFAC DEFAULT POPULATION DISTRIBUTIONS TO OBTAIN

IDLE EMISSION FACTOR S

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 shows the EMFAC interface before and after vehicle populat ion by vehicle class is changed.

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 E xhibit H

- 2. Changing EMFAC Vehicle Population Distributions to Estimate Idle

Emission Factors

Default EMFAC data before modification

Modified EMFAC dataH

- 5 

### H.5 E DITING EMFAC DEFAULT TRIP D ISTRIBUTIONS 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 vehic le class must be modified in the EMFAC user interface. For this example bus term inal 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 shows the EMFAC interface before and after vehicle trip distributions by vehicle class are changed.H

- 6 Exhibit H

- 3. Changing EMFAC Trip Distributions to Estimate Start Emission

Factors

Default EMFAC data before modification

Modifi ed EMFAC dataH

- 7 

### H.6 G ENERATING IDLE AND START E MISSION F ACTORS

“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 facto rs are presented in Table 1 of the output Summary Rates file (.rts file). As shown in Exhibit H-4, the PM 10 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 ).

In order to produce a grams/hour emission factor for use in AERMOD, several postprocessing calculations are necessary. First, the idle emission factor (0.734 grams/idlehour) 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 I dling E mission F actors in EMFACH

- 8 Exhibit H-5. Generating S tart E mission F actors in EMFAC 2

This completes the use of EMFAC for determining start and idle emission factors for this project. The ag gregate grams/hour value for starts and idle can now be input into AERMOD, as discussed in Section 7 of the guidance.

2 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 “ U rban B uses ” . EMFAC collapsed the 13 vehicle classes to six v ehicle groups in the output file. The collapsed output provides start

emission factors for the “U rban B us es” in the UBUS category and because fleet activity was composed entirely from th is vehicle class, the start emission factors for UBUS and ALL are essentially the same.I

- 1 

## Appendix I :
Estimating Locomotive Emissions

I.1 I NTRODUCTION

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 proje ct area being

modeled. Note that state or local air quality agencies may have experience modeling locomotive emissions and therefore could be of assistance when quantifying these

emissio ns 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 (the “duty cycle”).

4. Calculate locomotive emissions using ei ther:

• Horsepower rating and load factors, or

• Fuel consumption data. 1

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 to evaluate

and choo se the model and associated method and assumptions used for quantifying locomotive emissions for PM hot-spot analyses (40 CFR 93.105(c)(1)(i)).



### I.2 D ETERMINING WHERE IN THE PROJECT AREA LOCOMOTIVE

EMISSIONS SHOULD BE ESTIMATED

Under certain circumstan ces, 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 I.5 for

details.I

- 2 speed operatio ns 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 include s several different locomotive rosters (see Appendix I.4.1, below)



### I.3 D ETERMINING WHEN TO A NALYZE 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 proje cts 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 represent ative quarters of the analysis year when comparing emissions to all PM 2.5 NAAQS. 2 For projects in areas that violate only the 24-hour PM 10 or PM 2.5 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.



### I.4 D ESCRIBING 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 I.2, above)

and what activities these locomotives are underta king 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.I

- 3 I.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 impo rtant 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 I.2 ).

I.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 po ssible; 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 emission s from locomotives), should be used only if they adequately represent the locomotives that will be present in the project area and no local or project- specific duty cycles are available.



### I.5 C ALCULATING LOCOMOTIV E 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. Unless otherwise determined through consultation, only one method should be used for a given project.

3 A diesel locomotive typically has eight not ch settings for movement (run 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 de veloped locally

- appropriate duty cycles for emissions inventory purposes.I

- 4 I.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 ofthe locomotive (engine horsepower), and the year of engine manufacture (or remanufacture). Default PM 10 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 PM 10 emission factors are in grams/horsepower -hour and can easily be converted to PM 2.5 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 I.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.

I.5.2 Calculating e miss ions using horsepower rating and load factors

One way locomotive emissions can be calculated is to use PM 2.5 or PM 10 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 L oad F actors

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 I.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 th e engine’s maximum rated power. 6

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 PM 10 emission factors for PM 2.5 purposes. Note that Table 6 (PM 10 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. 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 and the hours during which the engine was b eing 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

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.I

- 5 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 locomot ive 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 locom otive being analyzed spends in each notch setting based on its duty cycle (see Appendix I.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% ÷ 70%) of the locomotive’s time when it is not at idle; that is,

when ever it is moving, this 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 (calculated in Step 3) by the horsepower generat ed by the engine at that notch setting (determined in Step 1). For example, if the locomotive with a rated engine power of

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.I

- 6 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 avera ge 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 lo ad 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” n otch setting in Step 4), then the 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. Th ese 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 act ually 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 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).

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 emissio ns 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.I

- 7 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 I.7 provides an example of calculating g/s locomotive emission rates using this methodology.

I.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 informati on provided in Appendix I.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 cons umption 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 A verage F uel 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 intermediat e 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 I.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 shou ld be repeated for each engine on the roster at each location analyzed.

If several individual sources will be modeled at different sections of the project area as described in Appendix I.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 isI

- 8 operating, for each hour of the day in each section of the project area to provi de 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 meth od can be found in Chapter

6 of Volume IV

.

I.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 ch aracterize 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

wit h 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 Loc omotive Engines and Marine CompressionIgnition 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 rem anufactured, 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 P art 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 i dentified 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.I

- 9 

### I.6 A VAILABLE RESOURCES

These resources and websites should be check ed 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.



### I.7 E XAMPLE OF C ALCULATING L OCOMOTIVE E MISSION R ATES USING

HORSEPOWER RATING AND LOAD F ACTOR E STIMATES

The following example demonstrates how to estimate locomotive emissions using the engine horsepower rating/load factor method described in Appendix I.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 PM 2.5 NAAQS and the 2006 24-hour PM 2.5 NAAQS. Th e 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 sch edule, it is further determined that while in the project area each train will spend 540 seconds idling and 76 seconds moving.

12 Although the emission factors have been sup erseded, the remainder of the Volume IV guidance remains

in effect.I

- 10 The locomotive PM 2.5 emissions are calculated based on horsepower rating and load

factors.

I.7.1 Calculate idle and moving load factors

As described in I.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.

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 ge nerated at eachnotch setting is obtained from the engine specifications (see second column of Exhibit I1). 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 thei r 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 I-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 don e 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 I-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 lo comotive is in that notch, as adjusted (calculated in Step 3). The results are shown in the fifth column of Exhibit I-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 t he horsepower generated at idle divided by the maximum engine horsepower, with the result expressed as a percentage. To determineI

- 11 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 expressed as a percentage. The final column of Exhibit I-1 shows the results of these calculations, with the idling and moving load factors highlighted.

Exhibit I -1. Calculating Locomotive Load Factors

Notch

Set ting 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 (i dle and moving)

Idling load factor:

Idle 14 38.0% 100.0% 14.0 0.4%

Moving load factor:

Dynamic

Brake 136 12.5% 20.2% 27.5

1 224 6.5% 10.5% 23.5

2 484 6.5% 10.5% 50.8

3 984 5.2% 8.4% 82.7

4 1149 4.4% 7.1% 81.6

5 1766 3.8% 6.1% 107.8

6 2518 3 .9% 6.3% 158.6

7 3373 3.0% 4.8% 161.9

8 4,000 16.2% 26.1% 1,044.0

Total 62.0% 100.0% 1,752.4 43.8%

I.7.2 Using the load factors to calculate idle and moving emission r ates

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 numberI

- 12 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).

Next, the project sponsor would determine the PM 2.5 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 PM 10 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 emissio n factors in Table 1 can be

used in the calculations. This information would be available from the rail way 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 calcul ations is to use the information shown in Exhibit I-1 and the other project data collected to calculate the PM 2.5 emission rates for idling and moving locomotives during both the peak hour and over a 24-hour basis. 13

Calculating Peak H our Idling Emission s

The following calculation would be used to determine the idling emission rate during the peak hour of operation: 14

PM 2.5 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)

PM 2.5 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 I-1)

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 e mission rate is required for AERMOD.I

- 13 • Tier 2 Locomotive Emission Factor = 0.18 g/bhp- hr (from “Emission Factors for Locomotives”)

• Ratio of PM 2.5 to PM 10 = 0.97 (from “Emission Factors for Locomotives”)

Calculating 24-hour Moving E missions

Similarly, the following equation would be used to calculate the moving emission rate for the 24-hour period:

PM 2.5 Emission Rate = (64 trains/day) * (76 s/train) * (1 day/86,400 s) * (4,000 hp) * (0.438) * (0.18 g/bhp-hr) * (1hr/3,600 s) * (0.97)

PM 2.5 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 e ngine specifications)

• Moving load factor = 0.438 (43.8%, calculated in Exhibit I-1)

• Tier 2 Locomotive Emission Factor = 0.18 g/bhp- hr (from “Emission Factors for Locomotives”)

• Ratio of PM 2.5 to PM 10 = 0.97 (from “Emission Factors for Locomotives”)

A summ ary of the variables used in the above equations and the resulting emission rates can be found in Exhibit I-2, below.

Exhibit I-2. PM 2.5 Locomotive Emission Rates

Operational

Mode Number of

Locomotives Time/

Train PM 2.5 Emission

Factor Calculated

Peak Hour

Emission Rate Calculated

24- hour

Emission

Rate

Peak

hour 24

hours (s) (g/bhp- hr) (g/s) (g/s)

Idle 16 32 540 0.18 0.0019 0.00016 Moving 32 64 76 0.18 0.057 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 thr ough 9 of the guidance.

Note that, since this area is designated as nonattainment for both the 1997 annual PM 2.5 NAAQS and the 2006 24-hour PM 2.5 NAAQS, the resul ts of the analysis will be compared to both NAAQS (see Section 3.3.4 of the guidance). Since the area is in

nonattainment of the annual PM 2.5 NAAQS, all four quarters will need to be included inI

- 14 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 I.3 ).J

- 1 

## Appendix J :
Additional Reference Information on Air Quality Models and Data Inputs

J.1 I NTRODUCTIO N

This appendix supplements Section 7’s discussion of a ir 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

discu ssions in Section 7 of the guidance.



### J.2 S E LECTING 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 ( μ g/m 3 ). 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. 1

J.2.2 Using CAL3QHCR for 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/scram001/dispersion_prefrec.htm#aermod

.J

- 2 in the CAL3QHCR User Guide. 2 In addition, the following guidance is provided when using CAL3QHCR for a PM hot-spot analysis:

Specifying the R ight 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 ( μ g/m 3 ) rather than parts per million (ppm).

Entering E mission R ates

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 em ission 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 inc luded within running emissions. (Note that if a non- zero emission rate is used in CAL3QHCR, the model will treat idling emission rates separate ly from running emission rates.) The same recommendation applies when using emission rates calculated by EMFAC.

Assigning S peeds

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 anal ysis. 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 CAL3QH C R links will already reflect the relevant delay on the link over the appropriate averaging time. The same recommendation applies when using EMFAC.

Using the Q ueuing 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 ac tivity 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 CAL3QHCR’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. Furthe rmore, a number of other techniques are now available that can be used to estimate vehicle queuing around intersections.J

- 3 

### J.3 C HARACTERIZING 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 Phy sical characteristics and locations of sources in CAL3QHCR

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 hei ght of the roadway relative to the surrounding ground (not to exceed ±10 meters); 6

• The hourly flow of traffic (vehicles per hour). and

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

sepa rate links. See Section 7.6 of the guidance for more information on placing

receptors.

J.3.2 Timing of emissions in CAL3Q HC R

The CAL3QHCR User G uide describes two methods for accepting time

- varying emissions and traffic data; these are labeled the “Tier I” and “Tier II” approaches. 7

4 Traffic engineering plans and diagrams may include information such as the number, width, and configuration of lanes, turning chann els, 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 CALINE3 d ispersion 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) mete rs 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 CAL3QHC R is not recommended for application. In this case, 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 and the design value

calcu lation options described in Section 9 of this guidance.J

- 4 Project -level PM hot-spot modeling should use the Tier II method, which can accommodate different hourly emission patterns for each day of the week. Most

emissi ons 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 o f 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 Secti on 4, they should be 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 guidan ce 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 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 u nit time, such as g/s);

• The initial lateral dispersion coefficient determined from the initial lateral dimension (width) of the volume;

8 See Section 6 and Appendix I for information regarding calculating locomotive emissions.J

- 5 • The initial vertical dispersion coefficient determined from the initial vertical dimension (height) of the volume; and

• The source release height of the volume source center, (i.e., meters above the ground).

Within AERMOD, the volume source algorithms are applicable to line sources with some initial plume depth (e.g., highways, rail lines). 9 There are three inputs needed to characterize the initial size of a roadway plume :

1. Initial lateral dispersion coefficient ( σ yo , Sy init ) . First, estimate the initial lateral dimension (or width ) of the volume source. One of the following options can be

used : a) The average vehicle width plus 6 meters , when modeling a single lane of traffic; b) The road width multiplied by 2; or c) A set width, such as 10 meters per lane of traffic

.

To specify the initial lateral dispersion coefficient ( σ yo ) , referred to as Syinit in AERMOD, the AERMOD User Guide recommends dividing the initial width by 2.15. This is to ensure that the overlapping distributions from adjacent volume sources

simulate a line source of emissions.

2. Initial vertical dispersion coefficient ( σ zo , Szinit ) . First, estimat e the initial vertical dimension (height) of the plume for volume sources. A typical approach 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 . F or heavy -duty vehicles, this is about 6.8 meters, using an average vehicle height of 4.0 meters. Since most road links will consist of a combination of light-duty and heavy- duty traffic, the initial vertical dimension should be a combination of their respective values. There are two options

available to estimate initial vertical dimension: a) 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, res pectively, the initial vertical dimension would be (0.4 * 2.6) + (0.6 * 6.8) = 5.1 meters. b) Alternatively, the initial vertical dimension may be estimated using a traffic volume weighted approach based on light-duty and heavy-duty vehicle

fractions.

The AERMOD User Guide recommends that the initial vertical dispersion coefficient ( σ zo ) , termed Szinit in AERMOD, be estimated for a surface -based volume source by dividing the initial vertical dimension by 2.15. For typical light -duty vehicles, this

9 The vehicle

- induced turbulence around roadways with moving traffic suggests that prior to transport

downwin d, 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).J

- 6 correspo nds to a Szinit (σ

zo ) of 1.2 meters. For typical heavy

- duty vehicles, the initial

value of Szinit (σ

zo ) is 3.2 meters.

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. Since most road links will consist of a combination of light-duty and heavy-duty traffic, the source release height should be a combination of their respective values. There are two options available to estimate source release height: a) Estimate using an emissions -weighted average. For a 40% light- duty and 60% heavy-duty emissions share, the source release height would be (0.4 * 1.3) + (0.6 * 3.4) = 2.6 meters. b) Alternatively, the source release height may be estimated using a traffic volume weighted approach based on light-duty and heavy-duty YHKLFOHIUDFWLRQV

.

Another way of dea ling 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 overlapping 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.

Also, AERMOD (version dated 09292) allows Syinit

, Szinit , and Relhgt 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.

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.

When using adjacent volume sources to represent emissions from a source such as a roadway, a sufficient number of volume sources should be employed to represent a consistent density of emissions for a single link in a MOVES or EMFAC analysis. In addition, when the source- re ceptor spacing in AERMOD is shorter than the distance between adjacent volume sources, AERMOD may produce aberrant results. In the present version of the model, receptors within a volume source in AERMOD are assigned concentrations of zero. When volume sources are used and publicly- accessible locations are closer to a source than the distance between adjacent volume sources, it is recommended that smaller volume sources be used with shorter spacing between them.

For example, for such a segment along a highway segment, individual lanes might be modeled discretely, rather than using a single volume source for all lanes. This will reduce the spacing between volume sources and increase the quality of results closest to a source. Receptors near area and point sources are not affected by this concern.J

- 7 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 sour ces using the AREA, AREAPOLY, or AREACIRC keywords. Sources that may be modeled as area sources may include areas within which emissions occur relatively evenly , such as a single link modeled using MOVES or EMFAC. 10 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.

In using a series of area sources to represent emissions of a roadway, the release height and initial vertical dimension of the plume should be calculated as described above for

volume sources.

Modeling Point S ources

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.

For projects with emissions on or near rooftops, such as bus terminals or garages, building downwash should also be modeled for the relevant sources. The potential for building downwash should also be addressed for nearby sources whose emissions are on or near rooftops in the project area. Building downwash occurs when air moving over a

10 At present, the ar ea 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.J

- 8 building mixes to the ground on the “downwind” side of the building. AERMOD includes algorithms to model the effects of building downwash on plumes from nearby or adjacent point sources. Consult the AERMOD User Guide for additional detail on how to enter building information.

J.3.4 Placement and sizing of s ources w ithin AERMOD

There are several general considerations with regard to pla cing 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 buses enter and exit a bus terminal from a single driveway, 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

approp riately 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 app ropriately characterize the shape of area and volume sources.

Finally, if nearby sources are to be included in air quality modeling (see discussion in Section 8 of the guidance), a combination of all these source types may be needed toappropriately represent their emissions within AERMOD. For instance, evenlydistributed 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, emiss ions 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 2.5 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.

The Qflag parameter under EMISFACT may be used with a secondary keyword to describe different pattern s 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 estimatesJ

- 9 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 tha t vary by season, hour of day, and day of the week.

Consult the AERMOD User Guide for details.



### J.4 I NCORPORATING 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 represent ative 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 set ting. When using sitespecific meteorological data in urban applications, consult the AERMOD Implementation

Guide.J

- 10 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. 11 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 M ODELING C OMPLEX T ERRAIN

This discussion supplements Section 7.5 of the guidance and describes in mor e detail how to address complex terrain in AERMOD and CAL3QHCR. In most situations, the project area should be modeled as having flat terrain. Additional detail on how this should be accomplished in each model is found below. However, in some situations a project area may include complex terrain, such that sources and receptors included in the model are found at different heights.

J.5.1 AERMOD

This guidance reflects the AERMOD Implementation Guide as of March 19, 2009. Analysts should consult the mos t recent AERMOD Implementation Guide for the latest guidance on modeling complex terrain.

For most highway and transit projects, the analyst should apply the non-DFAULT option

in AERMOD and assume flat, level terrain. In the AERMOD input file, the FLAT option should be used in the MODELOPT keyword. This recommendation is made to avoid underestimating concentrations in two circumstances likely to occur with the low

elevation, non-buoyant emissions from transportation projects. First, in DFAULT mode,

AERM OD will tend to underestimate concen trations from low-level, non-buoyant sources where there is up

- sloping terrain with downwind receptors uphill since the

DFAULT downwind horizontal plume will pass below the actual receptor elevation. Second, in DFAULT mode, AERMOD will tend to underestimate concentrations when a plume is terrain

- following. Therefore , the FLAT option should be selected in most cases.

There may be some cases where significant concentrations result from nearby elevated sources. In these cases, interagency consultation should be used on a case- by

- case basis to determine whether to include terrain effects and use the DFAULT option. In those cases, AERMAP should be used to prepare input files for AERMOD; consult the AERMOD and AERMAP user g uides and the latest AERMOD Implementation Guide for information on obtaining and processing relevant terrain data.

11 Specifying urban modeling with the “ RU ” keyword converts stability classes E and F to D.J

- 11 J.5.2 CAL3QHCR

CAL3QHCR does not handle complex terrain. No action is therefore required.



### J. 6 R UNNING 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 cal culation of design values as described in Section 9.3 of the guidance. This guidance is applicable regardless of how many quarters are being modeled.

J. 6.1 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 multi ple 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 PM 2.5 design value calculations described in Section 9.3.2, averaging times should be spe cified 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 PM 2.5 design value calculations des cribed 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. Not e users should also specify a 24-hour averaging time with the CO AVERTIME command as well.

Another option for calculating 24-hour PM 2.5 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 ne eded to calculate design values and 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 PM 10 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).J

- 12 EPA is actively workin g 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/scram001/

.

J. 6.2 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 PM 2.5 design values using CAL3QHCR output, some postprocessing 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, CAL3Q HCR should be run for as many quarters as analyzed using MOVES and EMFAC, as CAL3QHCR accepts only a single quarter’s emission factors per input file.

Calculating 24 -hour PM 2.5 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

ave raged together. Und er 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 PM 2.5 design valu e calculations.

In calculating 24 -hour PM 10 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

highest concentration should be identified. This concentration, at each receptor, is used

in calculations of the PM 10 design value described in Section 9.3.4.K

- 1 

## Appendix K :
Examples of Design Value Calculations for PM Hot

- spot Analyses

K.1 I NTRODUC TION

This appendix supplements Section 9’s discussion of calculating and applying design values for PM hot-spot analyses. While this guidance can apply to any PM NAAQS, this appendix provides examples of how to calculate design values for the PM NAAQS in effect at the time the guidance was issued (the 1997 annual PM 2.5 NAAQS, the 2006 and

1997 24-hour PM 2.5 NAAQS, and the 1987 24-hour PM 10 NAAQS ). The design values in this appendix are calculated 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

.

Th ese 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 P ROJECT 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)(1)(i)). The highway expansion will serve an expanded frei ght 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; however, it is a nearby source that will be included in the air quality modeling, as described further below.

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 1- 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

lea p year (366 days), for this example, there are 122 monitored values in that year and 121 values for both 2009 and 2010 (365 days each). 1

1 Note that the number of air quality monitoring measurements may vary by year. For example, with 1

- in

3 measurements, there could be 122 or 121 measurements in a year with 365 days. Or, the re may be fewerK

- 2 However, through interagency consultation, it is determined that the freight terminal’s emissions are not already ca ptured by this air quality monitor. AERMOD has been selected as the air quality model to estimate PM concentrations produced by the project (the highway expansion) and the nearby source (the freight terminal). 2 There are five years of representative off

- site meteorological data being used in this analysis.

As discussed in Section 2.4, a project sponsor could consider mitigation and control measures at any point in the process. However, since the purpose of these examples is to show the design value ca lculati ons, in this appendix such measures are not considered

until after the calculations are done.



### K.3 E XAMPLE 

### : ANNUAL PM 2.5 NAAQS

K.3.1 General

This example illustrates the approach to calculating design values for comparison to the

annual PM 2.5 NAAQS, as described in Section 9.3.2. The annual PM 2.5 design value is the average of three consecutive years’ annual averages. The design va lue for comparison is rounded to the nearest tenth of a μg/m 3 (nearest 0.1 μg/m 3 ). For example, 15.049 rounds to 15.0, and 15.050 rounds to 15.1. 3

Each year’s annual average concentrations include contributions from the project, any nearby sources mode led , and background concentrations. For air quality monitoring purposes, the annual PM 2.5 NAAQS is met when the three

- year average concentration is less than or equal to the current annual PM 2.5 NAAQS (i.e., 15.0 μg/m 3 ) :

Annual PM 2.5 design value = ([Y1] average + [Y2] average + [Y3] average) ÷ 3

Where: [Y1] = Average annual PM 2.5 concentration for the first year of air quality monitoring data [Y2] = Average annual PM 2.5 concentration for the second year of air quality monitoring data [Y3] = Average annual PM 2.5 concentration for the third year of air quality monitoring data

actual monitored values if sampling was not conducted on some scheduled days or the measured value was invalidated due to quality assurance concerns. The actual number of samples with valid data should be

used.

2 EPA notes that CAL3QHCR co uld 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.

3 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.K

- 3 For this example, the project described in Appendix K.2 is located in an annual PM 2.5 NAAQS nonattainment area. This example illustrates how an annual PM 2.5 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 fur ther in

Section 9.3.2.

K.3.2 Build scenario

For the build scenario, the PM 2.5 impacts from the project and from the nearby source are estimated with AERMOD at all receptors. 4

Step s 1-2 . Because AERMOD is used for this project, Step 1 is skipped. Th e 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 μg/m 3

.

Step 3 . Based on the three years of measuremen ts 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-1). These three average annual background concentrations are averaged, and the resulting value is 11.582 μg/m 3 , as shown in Exhibit K-1:

Exhibit K -1. Background Concentrations

Background

Concentrations Q1 Q2 Q3 Q4 Average

Annual

2008 13. 013 17.037 8.795 8.145 11.748

2009 14.214 14.872 7.912 7.639 11.159

2010 11.890 16.752 9.421 9.287 11.838

3

- year average: 11.582

Step 4 . The 3-year average annual background concentration (from Step 3) is added to the average annual modeled concentra tion from the project and nearby source (from Step 2):

11.582 + 3.603 = 15.185

Step 5 . Rounding to the nearest 0.1 μ g/m 3 produces a design value of 15.2 μ g/m 3

.

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 cho sen to provide background concentrations. Therefore, emissions from the project and this nearby source are both included in the AERMOD output.K

- 4 In this example, the concentration at the highest receptor is estimated to exceed the

current annual PM 2.5 NAAQS of 15.0 μ g/m 3

.

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.

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 PM 2.5 NAAQS (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

μ g/m 3

.

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 μ g/m 3 from Step 3:

11.582 + 3.521 = 15.103

Step 11 . Rounding to the nearest 0.1 μ g/m 3 produces a design value of 15.1 μ g/m 3

.

In this example, the design value at the receptor in the build scenario (15.2 μg/m 3 ) is greater than the design value at the same recept or in the no- build scenario (15.1 μg/m 3 ). 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

PM 2.5 NAAQS. The interagency consultation process would then be used to discuss next steps, e.g., appropriateness of receptors. Refer to Sections 9.2 and 9.4 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 shown above would not need to be recalculated since the no-build scenario would not change.

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.K

- 5 

### K.4 E XAMPLE 

### : 24



### - HOUR PM 2.5 NAAQS

K.4.1 General

This example illustrates the two

- tiered approach to calculating design values for comparison with the 24-hour PM 2.5 NAAQS, as described in Section 9.3.3. The 24-hour design value is the average of three consecutive years’ 98 th percentile PM 2.5 concentration of 24-hour values for each of those years. For air quality monitoring purposes, the NAAQS is met whe n that three- year average concentration is less than or equal to the currently applicable 24-hour PM 2.5 NAAQS for a given area’s nonattainment designation (35 μ g/m 3 for nonattainment areas for the 2006 PM 2.5 NAAQS and 65 μ g/m 3 for

nonattainment areas for the 1997 PM 2.5 NAAQS). 6 The design value for comparison to
any 24 -hour PM 2.5 NAAQS is rounded to the nearest 1 μ g/m 3 (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 μ g/m 3

, 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 PM 2.5 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

PM 2.5 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 ( the freight terminal) was included in air quality modeling.

6 There are only two PM 2.5 areas where conformity currently appl ies 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 PM 2.5 NAAQS is met, then the 1997 24

- hour PM 2.5 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, purs uant to Appendix N to 40 CFR Part

50.K

- 6 First Tier Analysis

Und er a first tier analysis, the average highest modeled 24 -hour concentrations at a given receptor are added to the average 98 th 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.

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 exampl e, 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 μ g/m 3

(highlighted in Exhibit K-2), is the highest, compared to the averages at all of the other

receptors.

Exhibit K -2. Modeled PM 2.5 Concentrations from Project and Nearby Source

Year Highest PM 2.5 Concentration

Met Year 1 6.413

Met Year 2 5.846

Me t Year 3 6.671

Met Year 4 7.951

Met Year 5 6.667 Average 6.710

Step 2 . The average 98 th percentile 24 -hour background concentration for a first tier analysis is calculated using the 98 th percentile 24 -hour concentrations of the three most

recent year s of monitoring data from the representative air quality monitor selected (see Appendix K.2). Since the background monitor is on a 1- 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 98 th percentile is the third highest concentration. Exhibit K-3 depicts the top eight monitored concentrations (in μ g/m 3 ) of the monitor throughout the years employed for estimating backgroun d concentrat ions.

8 If CAL3QHCR were being used, some additional processing of model output would be needed. Refer to

Section 9.3.3.K

- 7 Exhibit K -3. Top Eight Monitored Concentrations in Years 2008 – 2010

Rank 2008 2009 2010

1 34.123 33.537 35.417

2 31.749 32.405 31.579

3 31.443 31.126 31.173

4 30.809 30.819 31.095

5 30.219 30.487 30.425

6 30.134 29.998 30.329

7 30.099 29. 872 30.193

8 28.481 28.937 28.751

The third-ranked concentration of each year (highlighted in Exhibit K-3) is the 98 th
percentile value. These are averaged: (3 1.443 + 31.126 + 31.173) ÷ 3 = 31.247 μ g/m 3

.

Step 3 . Then, the highest average 24-hour modeled concentration for this receptor (from Step 1) is added to the average 98 th percentile 24 -hour background concentration (from Step 2):

6.710 + 31.247 = 37.957 μ g/m 3

.

Rounding to the nearest whole number results in a 24-hour PM 2.5 design value of 38

μ g/m 3

.

Because this concentration is greater than the 2006 24-hour PM 2.5 NAAQS (35 μ g/m 3 ), 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

• Conduct a second tier analysis. or

In this example, the next step chosen is to conduct a second tier analysis.

Second Tier Analysis

In a second tier analysis, the highest modeled concentrations are not added to the 98 th
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. Im pacts from the project, nearby sources, and other background concentrations are calculated on a quarterly basis before determining the 98 th

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

- 8 percentile concentration resulting from these inputs. The steps presented below follow the steps described in Secti on 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.

Exhibit K -4. Eight Highest 24- hour Background Concentrations By Quarter for

Each Year

Year Rank of Background

Concentration Q1 Q2 Q3 Q4

2008 1 27.611 31.749 34.123 30.099

2 25.974 30.219 31.443 28.096

3 25.760 30.134 30.809 26.990

4 25.493 28.368 28.481 25.649

5 25.099 27.319 27.372 25.526

6 24.902 25.788 25.748 25.509

7 24.780 25.564 25.288 25.207

8 23.287 24.794 24.631 24.525

2009 1 26.962 32.405 33.537 31.126

2 24.820 30.487 30.819 28.553

3 24 .330 28.937 29.998 25.920

4 23.768 27.035 29.872 25.856

5 23.685 25.880 25.596 25.565

6 23.287 25.867 25.148 24.746

7 23.226 25.254 24.744 24.147

8 22.698 24.268 24.267 23.142

2010 1 27.493 31.579 35.417 30.425

2 24.637 31.173 31.095 26.927

3 24.637 30.193 30.329 26.263

4 24.392 27.994 28.751 25.684

5 24.050 25.439 26.084 25.170

6 23.413 24.253 24.890 24.254

7 22.453 23.006 24.749 23.425

8 22.061 21.790 22.538 22.891K

- 9 Step 3 . The highest modeled 24-hour concentrations in each quar ter 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 con centrations. This step would be repeated for each receptor in an actual PM hot

- spot analysis.

Exhibit K -5. Highest Modeled 24- hour Concentrations Within Each Quarter (Build Scenario)

Q1 Q2 Q3 Q4

Met Year 1 6.413 3.332 6.201 6.193

Met Year 2 3.229 3 .481 5.846 4.521

Met Year 3 6.671 3.330 5.696 6.554

Met Year 4 7.095 3.584 7.722 7.951

Met Year 5 6.664 4.193 4.916 6.667 Average 6.014 3.584 6.076 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 PM 2.5 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) i s 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

- 10 Exhibit K-6. Sum of Modeled and Monitored Concentrations (Build Scenario)

Year Rank of Background

Concentration Q1 Q2 Q3 Q4

2008 1 33.625 35.333 40.200 36.476

2 31.989 33.803 37.520 34.474

3 31.774 33.718 36.886 33.368

4 31.507 31.952 34.557 32.026

5 31.113 30.903 33.448 31.903

6 30.916 29.372 31.824 31.886

7 30.794 29.148 31.365 31.584

8 29.301 28.378 30.707 30.902

2009 1 32.976 35.989 39.613 37.503

2 30.835 34.071 36.895 34.931

3 30.344 32.521 36.074 32.297

4 29.782 30.619 35.948 32.233

5 29.700 29.464 31.672 31.942

6 29.301 29.451 31.225 31.124

7 29.240 28.838 30.820 30.524

8 28.712 27 .852 30.343 29.520

2010 1 33.507 35.163 41.493 36.802

2 30.651 34.757 37.172 33.304

3 30.651 33.777 36.405 32.640

4 30.406 31.578 34.827 32.062

5 30.064 29.022 32.160 31.547

6 29.428 27.837 30.966 30.631

7 28.468 26.590 30.825 29.803

8 28.07 5 25.374 28.614 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

- 11 Exhibit K -7. Eight Highest Concentrations in Each Year, Ranked from Highest to

Lowest (Bu ild Scenario)

Year μg/m 3 Yearly

Rank Quarter Quarterly

Rank

2008 40.200 1 Q3 1

37.520 2 Q3 2

36.886 3 Q3 3

36.476 4 Q4 1

35.333 5 Q2 1

34.557 6 Q3 4

34.474 7 Q4 2

33.803 8 Q2 2

2009 39.613 1 Q3 1

37.503 2 Q4 1

36.895 3 Q3 2

36.074 4 Q3 3

35.989 5 Q2 1

35.948 6 Q3 4

34.931 7 Q4 2

34.071 8 Q2 2

2010 41.493 1 Q3 1

37.172 2 Q3 2

36.802 3 Q4 1

36.405 4 Q3 3

35.163 5 Q2 1

34.827 6 Q3 4

34.757 7 Q2 2

33.777 8 Q2 3

Step s 6-7 . The value that represents the 98 th 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 98 th percentile is the 3 rd highest concentration for that year. Therefore, for this example, the 3 rd highest 24-hour concentration of each year, highlighted in Exhibit K-7, represents the 98 th percentile value for that year.

Step 8 . At each receptor, the average of the three 24 -hour 98 th percentile concentrations is calculated. For the receptor in this example, the average is: (36.886 + 36.895 + 36.802) ÷ 3 = 36.861K

- 12 Step 9 . The average for the receptor in this example from Step 8 (36.861 μ g/m 3 ) is then
rounded to the nearest whole number (37 μ g/m 3 ) and compared to the 2006 24-hour

PM 2.5 NAAQS (35 μ g/m 3 ).

The design value at the receptor in this example is higher than the relevant 24 -hour PM 2.5 NAAQS. In an ac tual PM 2.5 hot-spot analysis, the design value calculations need to be repeated for all receptors, and compared to the NAAQS. Since one (and possibly more) receptors have design values greater than the 24 -hour PM 2.5 NAAQS, the project will only conform if the design value in the build scenario is less than or equal to the design value in the no-build scenario for all receptors that exceeded the NAAQS in the build

scenario . Therefore, the no-build scenario needs to be modeled for comparison, as

described further below. Because the build scenario was modeled with a second tier analysis, the no-build scenario must also be modeled with a second tier analysis.

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 PM 2.5 NAAQS in the

build scenario. The result will be a 24-hour PM 2.5 design value at such receptors

for the no-build scen ario.

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-bu ild 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 )

Q1 Q2 Q3 Q4

Met Year 1 6.757 3.383 6.725 6.269

Met Year 2 3.402 3.535 6.340 4.577

Met Year 3 7.029 3.381 6.177 6.63 5

Met Year 4 7.476 3.639 8.374 8.048

Met Year 5 7.022 4.258 5.331 6.748 Average 6.337 3.639 6.589 6.455K

- 13 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.

Exhibit K -9. Sum of Modeled and Monitored Concentrations (No

- Build Scenario)

Year Rank of Backgroun d

Concentration Q1 Q2 Q3 Q4

2008 1 33.948 35.389 40.713 36.555

2 32.312 33.858 38.033 34.552

3 32.097 33.774 37.399 33.446

4 31.830 32.007 35.070 32.104

5 31.436 30.959 33.961 31.981

6 31.239 29.428 32.337 31.964

7 31.117 29.204 31.878 31. 662

8 29.624 28.433 31.220 30.980

2009 1 33.299 36.044 40.127 37.581

2 31.158 34.126 37.408 35.009

3 30.667 32.576 36.587 32.375

4 30.105 30.674 36.461 32.311

5 30.023 29.520 32.185 32.020

6 29.624 29.506 31.738 31.202

7 29.563 28.894 31.333 30.602

8 29.035 27.907 30.856 29.598

2010 1 33.830 35.218 42.007 36.880

2 30.974 34.812 37.685 33.382

3 30.974 33.832 36.918 32.719

4 30.729 31.633 35.340 32.140

5 30.387 29.078 32.674 31.625

6 29.751 27.893 31.479 30.709

7 28.791 26.645 31 .338 29.881

8 28.398 25.429 29.127 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 K10. Note that only the top eight values are shown for each year instead of the entire set of

32.K

- 14 Exhibit K -10. Eight Highest Concentrations in Each Year, Ranked from Highest to

Lowest (No

- Build Scenario)

Year μg/m 3 Yearly

Rank Quarter Quarterly

Rank

2008 40.713 1 Q3 1

38.033 2 Q3 2

37.399 3 Q3 3

36.555 4 Q4 1

35.389 5 Q2 1

35.070 6 Q3 4

34.552 7 Q4 2

33.961 8 Q3 5

2009 40.127 1 Q3 1

37.581 2 Q4 1

37.408 3 Q3 2

36.587 4 Q3 3

36.461 5 Q3 4

36.044 6 Q2 1

35.009 7 Q4 2

34.126 8 Q2 2

2010 42.007 1 Q3 7

3 7.685 2 Q1 3

36.918 3 Q1 2

36.880 4 Q4 8

35.340 5 Q4 6

35.218 6 Q1 1

34.812 7 Q4 2

33.832 8 Q4 3

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 3 rd highest 24-hour concentration in each year represents the 98 th 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 μ g/m 3 ) is

rounded to the nearest whole μ g/m 3 (37 μ g/m 3 ).K

- 15 In this example, the design value at this receptor for both the build and no-build scenarios

is 37 μ g/m 3 , which is greater than the 2006 24- hour NAAQS (35 mg/m 3 ). 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 E XAMPLE 

### : 24



### - HOUR PM 10 NAAQS

K.5.1 General

This example illustrates calculating design values for comparison with the 24 -hour PM 10 NAA QS, as described in Section 9.3.4. The 24-hour PM 10 design value is based on the expected number of 24- hour exceedances of 150 μ g/m 3 , averaged over three consecutive years. For air quality monitoring purposes, the NAAQS is met when the number of

exceedan ces is less than or equal to 1.0. The 24-hour PM 10 design value is rounded to the

nearest 10 μ g/m 3 . For example, 155.511 rounds to 160, and 154.999 rounds to 150. 11

The 24-hour PM 10 design value is calculated at each air quality modeling receptor by

di rectly 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 PM 10 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.

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.

11 A sufficient number of decimal places (3

- 4) in m odeling 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/m 3 should only occur during final desig n value calculations, pursuant to Appendix K to 40 CFR Part 50. Monitoring values typically are reported with only one decimal place.K

- 16 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, rega rdless 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 μ g/m 3 . (That is, at all other receptors, the sixth-highest concentration is less than

15.218 μ g/m 3 .) Exhibit K -11 shows the six highest 24-hour concentrations at this

receptor.

Exhibit K -11 . Receptor with the Highest Sixth

- Highest 24-Hour Concentration (Build Scenario)

Rank Highest 24

- Hour

Concentration s
1 1 7.0 12

2 1 6 .709

3 1 5 .880

4 1 5

. 4 91

5 1 5

. 4 00

6 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 μ g/m 3

.

Step 4 . The sixth-highest 24-hour modeled concentration of 15.218 μ g/m 3 from the highest receptor (from Step 2) is added to the highest 24-hour background concentration

of 86.251 μ g/m 3 (from Step 3):

15.218 + 86.251 = 101.469

Step 5 . This sum is rounded to the nearest 10 μ g/m 3 , which results in a design value of

100 μ g/m 3

.

12 T he 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

- 17 This result is then compared to the 24-hour PM 10 NAAQS. In this case, the concentration calculated at all receptors is less than the 24

- hour PM 10 NAAQS of 150 μ g/m 3 , ther efore the analysis shows that the project conforms. However, if the design value for this receptor had been greater than 150 μ g/m 3 , the remainder of the steps in Section 9.3.4 would be completed. That is, build scenario design values for each receptor would be

calculate d (Steps 6-7 in Section 9.3.4) and, for all those that exceed the NAAQS, the no

build design values would also be calculated ( Steps 8 -10 in Section 9.3.4). The build and no-build design values would then be compared. 13



### K.6 M ATHEMATICAL FORMULAS FOR DESIGN VALUE CALCULATIONS

K.6.1 In troduction

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 help ful 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 ˆ

- a “hat” over variable x represents the arithmetic of multiple high

concentration values from different years , either from monitoring data or from

model ing results

Logical symbols

• x ∀ - an upside down A before variable x means “for all” values of x

• x ∈ - an “ ∈ ” before variable x means “in x”

• yx ∈∀

- means “for all x in y”

The following information present equations for calculating design values for the PM 2.5 annual NAAQS, 24-hour PM 2.5 NAAQS, and 24 -hour PM 10 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

- 18 K.6.2 Equation Set 1: Annual PM 2.5 design value

Formulas

iii pbc +=



# ∑

= = 3

1 3

m im

i b b



# ∑

= = 4

1 4 j ijm

im b b



## ∑

= = l

k ik

i l p p

1

Wh en using CAL3QHCR, 

## ∑

= = 4

1 4 j ijk

ik p p

Definitions

i b = average of three consecutive years’ average annual background concentrations at

receptor i

im b = quarterly

- weighted average annual background concentrati ons at receptor i during

monitoring year m

ijm b = quarterly average background concentration at receptor i , during quarter j in

monitoring year m

i c = annual PM 2.5 design value at receptor i

i = receptor j = quarter

k = year of meteorological data

l = length in years of meteorological data record

m = year of background monitoring data

ik p = average modeled quarterly average concentrations at receptor i 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,
ik p must be calculated using each ijk p for each quarter of

meteorolog ical year k

.

ijk p = quarterly average concentration at receptor i for quarter j , in meteorological data

year k . This variable is the product of CAL3QHCR when run with a single quarter of meteorological data.
ik p can be calculated directly using AERMOD

without explicitly calculating ijk p

.K

- 19 K.6.3 Equation Set 2: 24- Hour PM 2.5 design value (First Tier Analysis)

Formulas

iii pbc ˆ ˆ
ˆ +=

mbb ijm im ∈∀=



# ∑

= •

= 3

1 3 ˆ

m r im

i m b b



## ∑

= = l

k ijkjk k

i l p p

1 )]( [maxmax

ˆ (when using CAL3QHCR), which compresses to:



## ∑

= = l

k ik k

i l p p

1 )( max

ˆ (when using AERMOD with maximum concentration by year)

Definitions

i b ˆ = the average of 98 th percentile 24 -hour concentrations from three consecutive ye ars of

monitoring data

ijm b = daily 24 -hour background concentration at receptor i, during quarter j in monitoring

year m
mbb ijm im ∈∀= = All 24 -hour background concentration measurements in year m

m r im b

• = The 2 4-hour period within year m whose concentration rank among all 24-hour

measurements in year m is r m (t his represents the 98 th percentile of 24 -hou r background concentrations within one year.)

i c ˆ = 24 -hour PM 2.5 design value at receptor i

i = receptor j = quarter

k = year of meteorological data

l = length in years of meteorological data record

m = year of background monitoring data

k max = maximum predicted 24 -hour concentration within meteorological year k

jk max = maximum predicted 24 -hour concentration within quarter j within

meteorological year k

i p ˆ = average of highest predicted concentrations from each year modeled with the l years from which meteorological data are used ( ≥5 years for off

- site data, ≥1 year for

on- site data)

ijk p = modeled daily 24-hour concentration at receptor i , in quarter j and meteorological

year k

ik p = modeled daily 24-hour concentration at receptor i , in meteorol ogical year kK

- 20 r

m = concentration rank of b im corresponding to 98 th percentile of all b im in year m, based on number of background concentration measurements per year ( n m )

. r m is given by the following table:

n m r m 1

- 50 1

51

- 100 2

101

- 150 3

151

- 200 4

201

- 250 5

251

- 300 6

301

- 350 7

351

- 366 8K

- 21 K.6.4 Equation Set 3: 24-Hour PM 2.5 design value ( Second Tier A nalysis)

Formulas



## ∑

= •

= 3

1 3 ˆ

m r im

i m c

c

mcc ijm im ∈∀=

ijijm ijm pbc ˆ += , for the eight (8) highest ijm b in quarter j in monitoring year m 

## ∑

= = l

k ijkjk
ij l p p

1 )( max

ˆ

Definitions

ijm b = daily 24 -hour background concentration at receptor i, during quarter j in monitoring

year m

i c ˆ = 24 -hour PM 2.5 design value at receptor i

ijm c = The set of all sums of modeled concentrations ( ij p ˆ ) with background

concentrations from quarter j and monitoring year m, using the eight highest background concentrations ( ijm b ) for the corres ponding receptor, quarter, and

monitoring year.

mcc ijm im ∈∀= = the set of all c imj corresponding to monitoring year m

m r im c

• = predicted 98 th percentile total concentration from the project, nearby sources, and

background measurem ents from year m. Given by the value of c im whose concentration rank in year m is r m , using background measurements from year m

. i = receptor j = quarter

k = year of meteorological data

l = length in years of meteorological data record

m = year of backg round monitoring data

jk max = maximum predicted 24 -hour concentration within quarter j within

meteorological year k

ijk p = Predicted daily 24 -hour concentration at receptor i , during quarter j , based on data

from meteoro logical year k

ij p ˆ = Average highest 24 -hour modeled concentration ( ijk p ) using l years of

meteorological dataK

- 22 r

m = concentration rank of c im corresponding to 98 th percentile of all c im in year m, based on number of background concentration measurements per year ( n m )

. r m is given by the following table:

n m r m 1

- 50 1

51

- 100 2

101

- 150 3

151

- 200 4

201

- 250 5

251

- 300 6

301

- 350 7

351

- 366 8K

- 23 K.6.5 Equation S et 5 : 24

- Hour PM 10 design value

Formulas

iii pbc ~ ~

~ +=

i b ~ = )( max inin b


## 

3

1 = =

m im in bb

l r il i pp

• = ~



##  l

k ik il pp

1 = =

Definitions

i c ~ = 24 -hour PM 10 design value

i b ~ = maximum monitored 24-hour PM 10 background concentration at within b in
b im = the set of all monitored 24-hour PM 10 background concentrations at receptor i within monitoring year m b in = the set of all b im within monitoring years n
i = receptor

k = year of meteorological data

l = length in years of meteorological data record

.

max in = the maximum monitored 24 -hour background concentration at receptor i within monitoring years n

n = the set of all years of monitoring data, m = {1,2,3}

l r il i pp

• = ~ = modeled 24-hour PM 10 con centration with concentration rank of r l among all

concentrations modeled using l years of meteorological data

il p = set of all modeled 24 -hour concentrations at receptor i across l years of

meteorological data

r l = l + 1 (for example

, r l = 6 when using 5 years of meteorological data) 

## 

z

a a c

1 = = the set (finite union) of all c a with integer values of a = { 1,...,z }