Transportation Conformity Guidance for
Quantitative Hot-spot Analyses in PM2.s
and PMio Nonattainment and
Maintenance Areas
Appendices
Transportation and Climate Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
&EPA
United States
Environmental Protection
Agency
EPA-420-B-15-084
November 2015
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APPENDIX A: CLEARINGHOUSE OF WEBSITES, GUIDANCE, AND OTHER TECHNICAL
RESOURCES FOR PM HOT-SPOT ANALYSES A-l
APPENDIX B: EXAMPLES OF PROJECTS OF LOCAL AIR QUALITY CONCERN B-l
APPENDIX C: HOT-SPOT REQUIREMENTS FOR PM10 AREAS WITH PRE-2006 APPROVED
CONFORMITY SIPS C-l
APPENDIX D: CHARACTERIZING INTERSECTION PROJECTS FOR MOVES D-l
APPENDIX E: [RESERVED] E-l
APPENDIX F: [RESERVED] F-l
APPENDIX G: EXAMPLE OF USING EMFAC2011 FOR A HIGHWAY PROJECT G-l
APPENDIX H: EXAMPLE OF USING EMFAC2011 TO DEVELOP EMISSION FACTORS FOR A
TRANSIT PROJECT H-l
APPENDIX I: ESTIMATING LOCOMOTIVE EMISSIONS 1-1
APPENDIX J: ADDITIONAL REFERENCE INFORMATION ON AIR QUALITY MODELS AND
DATA INPUTS J-l
APPENDIX K: EXAMPLES OF DESIGN VALUE CALCULATIONS FOR PM HOT-SPOT
ANALYSES K-l
APPENDIX L: CALCULATING 24-HOUR PM2.5 DESIGN VALUES USING A SECOND TIER
APPROACH J-l
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Appendix A:
Clearinghouse of Websites, Guidance, and Other Technical
Resources for PM Hot-spot Analyses
A.1 INTRODUCTION
This appendix is a centralized compilation of documents and websites referenced in the
guidance, along with additional technical resources that may be of use when completing
quantitative PM hot-spot analyses. Refer to the appropriate sections of the guidance for
complete discussions on how to use these resources in the context of completing a
quantitative PM hot-spot analysis. 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 TRANSPORTATION CONFORMITY AND CONTROL MEASURE GUIDANCE
The EPA hosts an extensive library of transportation conformity guidance online at:
www.epa.gov/otaq/stateresources/transconf/policy.htm (unless otherwise noted). See in
particular guidance under the heading, "Emission Models and Conformity" as well as
guidance under the heading, "Quantifying Benefits of Control Measures in SIPs and
Conformity." The following specific guidance documents, in particular, may be useful
references when implementing PM hot-spot analyses:
• The most recent version of the MOVES policy guidance, e.g., "Policy Guidance
on the Use of MOVES2014 for State Implementation Plan Development,
Transportation Conformity, and Other Purposes." This document describes how
and when to use the latest version of MOVES for SIP development, transportation
conformity determinations, and other purposes. The most recent version(s)1 of
the MOVES technical guidance, e.g., "MOVES2014 Technical Guidance: Using
MOVES to Prepare Emission Inventories for State Implementation Plans and
Transportation Conformity." This document provides guidance on appropriate
input assumptions and sources of data for the use of MOVES in SIP submissions
and regional emissions analyses for transportation conformity purposes.
EPA and FHWA, "Guidance for the Use of Latest Planning Assumptions in
Transportation Conformity Determinations," EPA-420-B-08-901 (December
2008).
1 More than one version may be available at the same time because of the new emission model grace period
in the conformity regulation at 40 CFR 93.111. During the grace period, more than one version of a model
may be used for conformity.
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"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/smartway/forpartners/technology.htm.
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.
FHWA's transportation conformity site has additional conformity information, including
examples of quantitative PM hot-spot analyses. Available at:
www.fhwa.dot.gov/environment/air quality/conformity/practices/.
A.3 MOVES MODEL TECHNICAL INFORMATION AND USER GUIDES
MOVES, any future versions of the model, the latest user guides, and technical
information can be found at www.epa.gov/otaq/models/moves/index.htm, including the
following:2
• The most recent version of the User Guide, which walks users through various
MOVES examples and provides an overview of menu items and options.
• The most recent version of the User Interface Reference Manual, which provides
details on using the MOVES interface commands, and menu options.
• The most recent version of the Software Design Reference Manual, which
provides background on configuring and installing MOVES and describes
MOVES code structure.
Policy documents and Federal Register announcements related to the MOVES model can
be found on the EPA's website at:
www.epa.gov/otaq/stateresources/transconf/policy.htmtfmodels.
Guidance on using the MOVES model at the project level, as well as illustrative
examples of using MOVES for quantitative PM hot-spot analyses, can be found in
Section 4 of the guidance, in Appendix D, and within EPA's Project Level Training for
Quantitative PM Hot-Spot Analyses, which can be downloaded from
www.epa.gov/otaq/stateresources/transconf/training3day.htm.
2 Note that older model versions and their accompanying documentation can also be found on this EPA
web site, under the link on the left for "Previous MOVES versions."
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A. 4 EMFAC2011 MODEL TECHNICAL INFORMATION, USER GUIDES, AND
OTHER GUIDANCE
EMFAC2011, 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/categories.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.htmtfmodels.
Supporting documentation for EMFAC, including the technical memorandum "Revision
of Heavy Heavy-Duty Diesel Truck Emission Factors and Speed Correction Factors"
cited in Section 5 of this guidance, can be found at
www.arb.ca.gov/msei/supportdocs.htmtfonroad.
Instructions on using the EMFAC model at the project level, as well as examples of using
EMFAC for quantitative PM hot-spot analyses, can be found in Section 5 of the
guidance, in Appendices G and H, and within EPA's Project Level Training for
Quantitative PM Hot-Spot Analyses, which can be downloaded from
www.epa.gov/otaq/stateresources/transconf/training3day.htm. (Be sure to download the
California version of the training course.)
A. 5 DUST EMISSIONS METHODS AND GUIDANCE
Information on calculating emissions from paved roads, unpaved roads, and construction
activities can be found in AP-42, Chapter 13 (Miscellaneous Sources). AP-42 is EPA's
compilation of data and methods for estimating average emission rates from a variety of
activities and sources from various sectors. Refer to EPA's website to access the latest
versions of AP-42 sections and for more information about AP-42 in general:
www. epa. gov/ttn/chief/ap42/index.html.
Guidance on calculating dust emissions for PM hot-spot analyses can be found in Section
6 of the guidance.
A.6 LOCOMOTIVE EMISSIONS GUIDANCE
The following guidance documents, unless otherwise noted, can be found on or through
the EPA's locomotive emissions website at: www.epa.gov/otaq/locomotives.htm:
• "Procedure for Emission Inventory Preparation - Volume IV: Mobile Sources,"
Chapter 6. Available online at:
http://www.epa.gov/otaq/models/nonrdmdl/r92009.pdf. Note that the emissions
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factors listed in Volume IV have been superseded by the April 2009 publication
listed below for locomotives certified to meet EPA standards.
• "Emission Factors for Locomotives," EPA-420-F-09-025 (April 2009). Available
online at: www.epa.gov/otaq/regs/nonroad/locomotv/420f09025.pdf.
• "Control of Emissions from Idling Locomotives," EPA-420-F-08-014 (March
2008).
• "Guidance for Quantifying and Using Long Duration Switch Yard Locomotive
Idling Emission Reductions in State Implementation Plans," EPA-420-B-04-002
(January 2004). Available online at:
www.epa.gov/cleandiesel/documents/420b0937.pdf.
• EPA-verified anti-idle technologies (including technologies that pertain to
locomotives) can be found at:
www.epa.gov/smartwav/forpartners/technology.htm
Guidance on calculating locomotive emissions for PM hot-spot analyses can be found in
Section 6 of the guidance and in Appendix I.
A.7 AlR QUALITY DISPERSION MODEL TECHNICAL INFORMATION AND
USER GUIDES
The latest version of "Guideline on Air Quality Models" (Appendix W to 40 CFR Part
51) (dated 2005 as of this writing) can be found on EPA's SCRAM website at:
www.epa.gov/scramOO l/guidance_permit.htm.
Both AERMOD and CAL3QHCR models and related documentation can be obtained
through EPA's Support Center for Regulatory Air Models (SCRAM) web site at:
www.epa.gov/scramOO 1. In particular, the following guidance may be 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
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• AERMET User Guide
Information on locating and considering air quality monitoring sites can be found in 40
CFR 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 within
EPA's Project Level Training for Quantitative PM Hot-Spot Analyses, which can be
downloaded from www.epa.gov/otaq/stateresources/transconf/training3day.htm.
A. 8 TRANSPORTATION DATA AND MODELING CONSIDERATIONS
The following is a number of technical resources on transportation data and modeling
which may help implementers determine the quality of their inputs and the sensitivity of
various data.
A.8.1 Transportation model improvement
The FHWA Travel Model Improvement Program (TMIP) provides a wide range of
services and tools to help planning agencies improve their travel analysis techniques.
Available online at: www.fhwa.dot.gov/planning/tmip/ .
A.8.2 Speed
"Evaluating Speed Differences between Passenger Vehicles and Heavy Trucks for
Transportation-Related Emissions Modeling." Available online at:
www.ctre.iastate.edu/reports/truck speed.pdf.
A. 8.3 Project level planning
"National Cooperative Highway Research Program (NCHRP) Report 765: Analytical
Travel Forecasting Approaches for Project-Level Planning and Design" describes
methods, data sources, and procedures for producing travel forecasts for highway project-
level analyses. This report provides an update to NCHRP Report 255: Highway Traffic
Data for Urbanized Area Project Planning and Design. Available online at:
http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp rpt 765.pdf.
A. 8.4 Traffic analysis
Traffic Analysis Toolbox website: http://ops.fhwa.dot.gov/trafficanalysistools/.
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"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/traffi canaly si stool s/tat_vol 1 /vol 1 _primer. pdf.
The Highway Capacity Manual Application Guidebook. Transportation Research Board,
Washington, D.C., 2003. Available online at: http://hcmguide.com/.
The Highway Capacity Manual 2010. Transportation Research Board, Washington,
D.C., 2010. Not available online; purchase information available at:
www.trb.org/Main/Blurbs/164718.aspx. As of this writing, the 2010 edition is most
current; the most recent version of the manual, and the associated guidebook, should be
consulted when completing PM hot-spot analyses.
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Appendix B:
Examples of Projects of Local Air Quality Concern
B.I INTRODUCTION
This appendix gives additional guidance on what types of projects may be projects of
local air quality concern requiring a quantitative PM hot-spot analysis under 40 CFR
93.123(b)(l). However, as noted elsewhere in this guidance, PMio nonattainment and
maintenance areas with approved conformity SIPs that include PMio hot-spot provisions
from previous rulemakings must continue to follow those approved conformity SIP
provisions until the SIP is revised; see Appendix C for more information.
B.2 EXAMPLES OF PROJECTS THAT REQUIRE PM HOT-SPOT ANALYSES
EPA noted in the March 2006 final rule that the examples below are considered to be the
most likely projects that would be covered by 40 CFR 93.123(b)(l) and require a PIVh.s
or PMio 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)(l)(i)and(ii)are:
• A project on a new highway or expressway that serves a significant volume of
diesel truck traffic, such as facilities with greater than 125,000 annual average
daily traffic (AADT) and 8% or more of such AADT is diesel truck traffic;
• New exit ramps and other highway facility improvements to connect a highway or
expressway to a major freight, bus, or intermodal terminal;
• Expansion of an existing highway or other facility that affects a congested
intersection (operated at Level-of-Service D, E, or F) that has a significant
increase in the number of diesel trucks; and,
• Similar highway projects that involve a significant increase in the number of
diesel transit busses and/or diesel trucks.
Some examples of projects of local air quality concern that would be covered by 40 CFR
93.123(b)(l)(iii)and(iv)are:
• A major new bus or intermodal terminal that is considered to be a "regionally
significant project" under 40 CFR 93.1012; and,
1 EPA also clarified 93.123(b)(l)(i) in the January 24, 2008 final rule (73 FR 4435-4436).
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40 CFR 93.101 defines a "regionally significant project" as "a transportation project (other than an
exempt project) that is on a facility which serves regional transportation needs (such as access to and from
the area outside of the region, major activity centers in the region, major planned developments such as
new retail malls, sports complexes, etc., or transportation terminals as well as most terminals themselves)
and would normally be included in the modeling of a metropolitan area's transportation network, including
at a minimum all principal arterial highways and all fixed guideway transit facilities that offer an
alternative to regional highway travel."
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• An existing bus or intermodal terminal that has a large vehicle fleet where the
number of diesel buses increases by 50% or more, as measured by bus arrivals.
A project of local air quality concern covered under 40 CFR 93.123(b)(l)(v) could be any
of the above listed project examples.
B.3 EXAMPLES OF PROJECTS THAT DO NOT REQUIRE PM HOT-SPOT
ANALYSES
The March 2006 final rule also provided examples of projects that would not be covered
by 40 CFR 93.123(b)(l) and would not require a PM2.5 or PMio hot-spot analysis (71 FR
12491).
The following are examples of projects that are not a local air quality concern under 40
CFR 93.123(b)(l)(i) and (ii):
• Any new or expanded highway project that primarily services gasoline vehicle
traffic (i.e., does not involve a significant number or increase in the number of
diesel vehicles), including such projects involving congested intersections
operating at Level-of-Service D, E, or F;
• An intersection channelization project or interchange configuration project that
involves either turn lanes or slots, or lanes or movements that are physically
separated. These kinds of projects improve freeway operations by smoothing
traffic flow and vehicle speeds by improving weave and merge operations, which
would not be expected to create or worsen PM NAAQS violations; and,
• Intersection channelization projects, traffic circles or roundabouts, intersection
signalization projects at individual intersections, and interchange reconfiguration
projects that are designed to improve traffic flow and vehicle speeds, and do not
involve any increases in idling. Thus, they would be expected to have a neutral or
positive influence on PM emissions.
Examples of projects that are not a local air quality concern under 40 CFR
93.123(b)(l)(iii) and (iv) would be:
• A new or expanded bus terminal that is serviced by non-diesel vehicles (e.g.,
compressed natural gas) or hybrid-electric vehicles; and,
• A 50% increase in daily arrivals at a small terminal (e.g., a facility with 10 buses
in the peak hour).
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Appendix C:
Hot-Spot Requirements for PMio Areas with Pre-2006
Approved Conformity SIPs
C.I INTRODUCTION
This appendix describes what projects require a quantitative PMio 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 rule defined the current
federal conformity requirements for what projects require a PM hot-spot analysis (i.e.,
only certain highway and transit projects that involve significant levels of diesel vehicle
traffic or any other project identified in the PM SIP as a local air quality concern).2
However, there are some PMio nonattainment and maintenance areas where PMio hot-
spot analyses are required for different types of projects, as described further below.
This appendix will be relevant for only a limited number of PMio nonattainment and
maintenance areas with pre-2006 approved conformity SIPs. This appendix is not
relevant for any PM2.5 nonattainment or maintenance areas, since the current federal
PM2.5 hot-spot requirements apply in all such areas. Project sponsors can use the
interagency consultation process to verify applicable requirements before beginning a
quantitative PMio hot-spot analysis.
C.2 PMio AREAS WHERE THE PRE-2006 HOT-SPOT REQUIREMENTS APPLY
Prior to the March 2006 final rule, the federal conformity rule required some type of hot-
spot analysis for all non-exempt federally funded or approved projects in PMio
nonattainment and maintenance areas. These pre-2006 requirements are in effect for
those states with an approved conformity SIP that includes the pre-2006 hot-spot
requirements.
In PMio areas with approved conformity SIPs that include the pre-2006 hot-spot
requirements, a quantitative PMio hot-spot analysis is required for the following types of
projects:
• Projects which are located at sites at which PMio NAAQS violations have
been verified by monitoring;
• Projects which are located at sites which have vehicle and roadway emission
and dispersion characteristics that are essentially identical to those of sites
1 A "conformity SIP" includes a state's specific criteria and procedures for certain aspects of the
transportation conformity process (40 CFR 51.390).
2 See Section 2.2 and Appendix B of this guidance and the preamble of the March 2006 final rule (71 FR
12491-12493).
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with verified violations (including sites near one at which a violation has been
monitored); and
• New or expanded bus and rail terminals and transfer points which increase the
number of diesel vehicles congregating at a single location.
This guidance should be used to complete any quantitative PMio hot-spot analyses.
In addition, a qualitative PMio hot-spot analysis is required in the pre-2006 hot-spot
requirements for all other non-exempt federally funded or approved projects. For such
analyses, consult the 2006 EPA-FHWA qualitative hot-spot guidance.3
These pre-2006 hot-spot requirements continue to apply in PMio areas with approved
conformity SIPs that include them until the state acts to change the conformity SIP. The
conformity rule at 40 CFR 51.390 states that conformity requirements in approved
conformity SIPs "remain enforceable until the state submits a revision to its [conformity
SIP] to specifically remove them and that revision is approved by EPA."
C.3 REVISING A CONFORMITY SIP
EPA strongly encourages affected states to revise 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 PMio hot-spot requirements in their
conformity SIPs act to revise them to reduce the number of projects where a hot-spot
analysis is required. In affected PMio areas, the current conformity rule's PMio hot-spot
requirements at 40 CFR 93.123(b)(l) and (2) will be effective only when a state either:
• Withdraws the existing provisions from its approved conformity SIP and EPA
approves this SIP revision, or
• Revises its approved conformity SIP consistent with the requirements found at 40
CFR 93.123(b) and EPA approves this SIP revision.
3 "Transportation Conformity Guidance for Qualitative Hot-spot Analyses in PM2 5
Nonattainment and Maintenance Areas," EPA420-B-06-902, found on EPA's website at:
www.epa.gov/otaq/stateresources/transconf/policv/420b06902.pdf.
4 "Transportation Conformity Rule Amendments to Implement Provisions Contained in the 2005 Safe,
Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU); Final
Rule," 73 FR 4420.
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Affected states should contact their EPA Regional Office to proceed with one of these
two options. For more information about conformity SIPs, see EPA's "Guidance for
Developing Transportation Conformity State Implementation Plans (SIPs)," EPA-420-B-
09-001 (January 2009); available online at:
www.epa.gov/otaq/stateresources/transconf/policv/420b09001.pdf
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Appendix D:
Characterizing Intersection Projects for MOVES
D.I INTRODUCTION
This appendix expands upon the discussion in Section 4.2 on how best to characterize
links when modeling an intersection project using MOVES. The MOVES emissions
model allows users to represent intersection traffic activity with a higher degree of
sophistication compared to previous models. This appendix provides several options to
describe vehicle activity to take advantage of the capabilities MOVES offers to complete
more accurate PM hot-spot analyses of intersection projects. MOVES is the approved
emissions model for PM hot-spot analyses in areas outside of California.
Exhibit D-l is an example of a simple signalized intersection showing the links
developed by a project sponsor to represent the two general categories of vehicle activity
expected to take place at this intersection (approaching the intersection and departing the
intersection).
Exhibit D-l. Example of Approach and Departure Links for a Simple Intersection
Approach Link
Departure Link
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When modeling an intersection, each approach link or departure link can be modeled as
one or more links in MOVES depending on the option chosen to enter traffic activity.
This guidance suggests three possible options for characterizing activity on each
approach and departure link (such as those shown in Exhibit D-l):
• Option 1: Using average speeds
• Option 2: Using link drive schedules
• Option 3: Using Op-Mode distributions
While Option 1 may need to be relied upon more during the initial transition to using
MOVES, as more detailed data are available to describe vehicle activity, users are
encouraged to consider using the Options 2 and 3 to take full advantage of the
capabilities of MOVES.
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 OPTION i: USING AVERAGE SPEEDS
The first option is for the user to estimate the average speeds for each link in the
intersection based on travel time and distance. Travel time should account for the total
delay attributable to traffic signal operation, including the portion of travel when the light
is green and the portion of travel when the light is red. The effect of a traffic signal cycle
on travel time includes deceleration delay, move-up time in a queue, stopped delay, and
acceleration delay. Using the intersection example given in Exhibit D-l, each approach
link would be modeled as one link to reflect the higher emissions associated with vehicle
idling through lower speeds affected by stopped delay; each departure link would be
modeled as one link to reflect the higher emissions associated with vehicle acceleration
through lower speeds affected by acceleration delay.
Project sponsors can determine congested speeds by using appropriate methods based on
best practices for highway analyses. Some resources are available through FHWA's
Travel Model Improvement Program (TMIP).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 2010, which can be obtained from the
Transportation Research Board (see http://www.trb.org/Main/Blurbs/164718.aspx for details).
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D.3 OPTION 2 : USING LINK DRIVE SCHEDULES
A more refined approach is to enter vehicle activity into MOVES as a series of link drive
schedules to represent individual segments of cruise, deceleration, idle, and acceleration
of a congested intersection. A link drive schedule defines a speed trajectory to represent
the entire vehicle fleet via second-by-second changes in speed and highway grade.
Unique link drive schedules can be defined to describe types of vehicle activity that have
distinct emission rates, including cruise, deceleration, idle, and acceleration.
Exhibit D-2 illustrates why using this more refined approach can result in a more detailed
emissions analysis. This exhibit shows the simple trajectory of a single vehicle
approaching an intersection during the red signal phase of a traffic light cycle. This
trajectory is characterized by several distinct phases (a steady cruise speed, decelerating
to a stop for the red light, idling during the red signal phase, and accelerating when the
light turns green). In contrast, the trajectory of a single vehicle approaching an
intersection during the green signal phase of a traffic light cycle is characterized by a
more or less steady cruise speed through the intersection.
Exhibit D-2. Example Single Vehicle Speed Trajectory Through a Signalized
Intersection
-100 -80 -60 -40 -20 0 20
Distance (m)
40
60
80
100
For the example intersection in Exhibit D-l, link drive schedules representing the
different operating modes of vehicle activity on the approach and departure links can be
determined. For approach links, the length of a vehicle queue is dependent on the
number of vehicles subject to stopping at a red signal. Vehicles approaching a red traffic
signal decelerate over a distance extending from the intersection stop line back to the
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stopping distance required for the last vehicle in the queue. The average stopping
distance can be calculated from the average deceleration rate and the average cruise
speed. Similarly, for the departure links, vehicles departing a queue when the light turns
green accelerate over a distance extending from the end of the vehicle queue to the
distance required for the first vehicle to reach the cruise speed, given the rate of
acceleration and cruise speed. Exhibit D-3 provides an illustration of how the different
vehicle operating modes may be apportioned spatially near this signalized intersection.
Exhibit D-3. Example Segments of Vehicle Activity Near a Signalized Intersection
Decelerate
Idle
Accelerate
Cruise
There are other considerations with numerous vehicles stopping and starting at an
intersection over many signal cycles during an hour. For instance, heavy trucks
decelerate and accelerate at slower rates than passenger cars. Drivers tend not to
decelerate at a constant rate, but through a combination of coasting and light and heavy
braking. Acceleration rates are initially higher when starting from a complete stop at an
intersection, becoming progressively lower to make a smooth transition to cruise speed.
In the case of an uncongested intersection, the rates of vehicles approaching and
departing the intersection are in equilibrium. Some vehicles may slow, and then speed up
to join the dissipating queue without having to come to a full stop. Once the queue
clears, approaching vehicles during the remainder of the green phase of the cycle will
cruise through the intersection virtually unimpeded.
In the case of a congested intersection, the rate of vehicles approaching the intersection is
greater than the rate of departure, with the result that no vehicle can travel through
without stopping; vehicles approaching the traffic signal, whether it is red or green, will
have to come to a full stop and idle for one or more cycles before departing the
intersection. The latest Highway Capacity Manual is a good source of information for
vehicle operation through signalized intersections. All assumptions, methods, and data
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underlying the development of link drive schedules should be documented as part of the
PM hot-spot analysis.
The MOVES emission factors for each segment of vehicle activity obtained via
individual link drive schedules are readily transferable to either AERMOD or
CAL3QHCR, as discussed further in Section 7 of the guidance. There will most likely be
a need to divide the cruise and the acceleration segments to account for differences in
approach and departure traffic volumes.
Note: For both free-flow highway and intersection links, users may directly enter output
from traffic simulation models in the form of second-by-second individual vehicle
trajectories. These vehicle trajectories for each road segment can be input into MOVES
using the Link Drive Schedule Importer and defined as unique LinklDs. There are no
limits in MOVES as to how many links 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 OPTION 3: USING OP-MODE DISTRIBUTIONS
A third option is for a user to generate representative Op-Mode distributions for approach
and departure links by calculating the fraction of fleet travel times spent in each mode of
operation. For any given signalized intersection, vehicles are cruising, decelerating,
idling, and accelerating. Op-Mode distributions can be calculated from the ratios of
individual mode travel times to total travel times on approach links and departure links.
This type of information could be obtained from Op-Mode distribution data from (1)
existing intersections with similar geometric and operational (traffic) characteristics, or
(2) output from traffic simulation models for the proposed project or similar projects.
Acceleration and deceleration assumptions, methods, and data underlying the activity-to-
Op-Mode calculations should be documented as part of the PM hot-spot analysis.
The following methodology describes a series of equations to assist in calculating vehicle
travel times on approach and departure links. Note that a single approach and single
departure link should be defined to characterize vehicles approaching, idling at, and
departing an intersection (e.g., there is no need for an "idling link," as vehicle idling is
captured as part of the approach link).
D-5
-------
D. 4.1 Approach links
When modeling each approach link, the fraction of fleet travel times in seconds (s) in
each mode of operation should be determined based on the fraction of time spent
cruising, decelerating, accelerating, and idling:
Total Fleet Travel Time (s) = Cruise Time + Decel Time + Accel Time +
Idle Time
The cruise travel time can be represented by the number of vehicles cruising multiplied
by the length of approach divided by the average cruise speed:
Cruise Time (s) = Number of Cruising Vehicles * (Length of Approach (mi) +
Average Cruise Speed (mi/hr)) * 3600 s/hr
The deceleration travel time can be represented by the number of vehicles decelerating
multiplied by the average cruise speed divided by the average deceleration rate:
Decel Time (s) = Number of Decelerating Vehicles * (Average Cruise Speed
(mi/hr) + Average Decel Rate (mi/hr/s))
The acceleration travel time occurring on an approach link can be similarly represented.
However, to avoid double-counting acceleration activity that occurs on the departure link,
users should multiply the acceleration time by the proportion of acceleration that occurs
on the approach link (Accel Length Fraction on Approach):
Accel Time (s) = Number of Accelerating Vehicles * (Average Cruise Speed
(mi/hr) + Average Accel Rate (mi/hr/s)) * Accel Length Fraction on
Approach
The idle travel time can be represented by the number of vehicles idling multiplied by the
average stopped delay (average time spent stopped at an intersection):
Idle Time (s) = Number of Idling Vehicles * Average Stopped Delay (s)
Control delay (total delay caused by an intersection) may be used in lieu of average
stopped delay, but control delay includes decelerating and accelerating travel times,
which should be subtracted out (leaving only idle time).
After calculating the fraction of time spent in each mode of approach activity, users
should select the appropriate MOVES Op-Mode 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 (OpModelD 11-16, 22-25, 27-30, 33, 35, 37-40);
• Low and moderate speed coasting (OpModelD 11,21);
• Braking (OpModelD 0,501);
D-6
-------
• Idling (OpModelD 1); and
• Tire wear (OpModelD 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 deriving Op-Mode distributions for a link using this
methodology is demonstrated in Step 3 of Appendix F for a bus terminal facility.
D.4.2 Departure links
When modeling each departure link, the fraction of fleet travel times spent in each mode
of operation should be determined based on the fraction of time spent cruising and
accelerating:
Total Fleet Travel Time (s) = Cruise Time + Accel Time
The cruise travel time can be represented by the number of vehicles cruising multiplied
by the travel distance divided by the average cruise speed:
Cruise Time (s) = Number of Cruising Vehicles * (Length of Departure (mi) +
Average Cruise Speed (mi/hr)) * 3600 s/hr
The acceleration travel time occurring during the departure link can be represented by the
number of vehicles accelerating multiplied by the average cruise speed divided by the
average acceleration rate. However, to avoid double-counting acceleration activity that
occurs on the approach link, users should multiply the resulting acceleration time by the
proportion of acceleration that occurs on the departure link (Accel Length Fraction on
Departure):
Accel Time (s) = Number of Accelerating Vehicles * (Average Cruise Speed
(mi/hr) + Average Accel Rate (mi/hr/s)) * Accel Length Fraction on
Departure
After calculating fraction of time spent in each mode of departure activity, users should
select the appropriate MOVES Op-Mode corresponding to each particular type of activity
(see Section 4.5.7 for more information). The operating modes typifying departure links
include:
• Cruise/acceleration (OpModelD 11-16, 22-25, 27-30, 33, 35, 37-40); and
• Tire wear (OpModelD 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.
D-7
-------
Appendix E:
Example Quantitative PM Hot-spot Analysis of a Highway
Project using MOVES and CAL3QHCR
Note: EPA has removed the example in Appendix E because it has been superseded by
the example analyses found in EPA's quantitative PM hot-spot analysis course. The
course materials, including the presentation of the example analysis and all of the files
necessary to repeat the analysis are available for download at:
www.epa.gov/otaq/stateresources/transconf/training3day.htm .
E-l
-------
Appendix F:
Example Quantitative PM Hot-spot Analysis of a Transit
Project using MOVES and AERMOD
Note: EPA has removed the example in Appendix F because it has been superseded by
the example analyses found in EPA's quantitative PM hot-spot analysis course. The
course materials, including the presentation of the example analysis and all of the files
necessary to repeat the analysis are available for download at:
www.epa.gov/otaq/stateresources/transconf/training3day.htm.
F-l
-------
Appendix G:
Example of Using EMFAC2011 for a Highway Project
G.I INTRODUCTION
The purpose of this appendix is to demonstrate the procedures described in Section 5 of
the guidance on using EMFAC2011 to generate emission factors for air quality modeling.
The following example, based on a hypothetical, simplified highway project, illustrates
the modeling steps required for users to run the EMFAC2011-PL tool to develop project-
specific PM running exhaust emission factors using the "simplified approach" described
in Section 5.5 of the guidance.
As discussed in the guidance, application of the simplified approach and use of the
EMFAC2011-PL tool is only appropriate when the project-specific fleet age distribution
does not differ from the EMFAC2011 defaults and the project does not include start or
idling emissions. See Appendix H for an example of using the detailed approach to
modify a default age distribution.
Users will be able to generate running emission factors (in grams/vehicle-mile) in a
single EMFAC2011-PL run; multiple links and calendar years can also be handled within
one run. This example does not include the subsequent air quality modeling; refer to
Appendix E for an example of how to run an air quality model for a highway project for
PM hot-spot analyses.
G.2 PROJECT CHARACTERISTICS
The hypothetical highway project is located in Sacramento County, California. For
illustrative purposes, the project is characterized by a single link with an average link
travel speed for all traffic equal to 65 mph.1 Project-specific age distributions do not
differ from the EMFAC2011 defaults, so a simplified modeling approach using the
EMFAC2011-PL tool will be used to develop a link-specific PIVh.s emission rate.
The proj ect' 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 build scenario
2015 traffic data for this highway project shows that 25% of the total project VMT is
from trucks and 75% from non-trucks. This truck/non-truck fleet mix will be used to
post-process the EMFAC-PL output.
1 These are simplified data to illustrate the use of EMFAC2011; this example does not, for instance,
separate data by peak vs. off-peak periods, divide the project into separate links, or consider additional
analysis years, all of which would likely be required for an actual project.
G-l
-------
G.3 DESCRIBING THE SCENARIO USING THE EMFAc2011-PL TOOL
Based on the project characteristics, it is first necessary to describe the modeling scenario
in the EMFAC2011-PL interface (see Exhibits G-l and G-2).
Exhibit G-l. Basic Inputs in EMFAC2011-PL for the Hypothetical Highway Project
Step
1
2
3
4
5
6
7
8
Input Category
Vehicle Category
Scheme
Region type
Region
CalYr
Season
Vehicle Category
Fuel Type
Speed
Input Data
Truck / Non-Truck
Categories
County
Sacramento
2015
Annual
ALL
TOT
65MPH
Note
Provides rates for truck/non-truck
categories
Per Section 5.5.2 of the guidance
Select from drop-down list
Select from drop-down list
Select from drop-down list
Provides rates for HD and LD
Does not generate separate rates for
gasoline and diesel
Select from drop-down list
G-2
-------
Exhibit G-2. EMFAC2011-PL GUI Showing Selections Made for the Hypothetical
Highway Project
Main Page
EMFAC2011-PL{Verl.l)
Project-level Emission Rates Database
Vehicle Category
Scheme:
Region type:
Reset
*~ EMFAC2011 Vehicle Categories *~ EMFAC2Q07 Vehicle Categories
*" Trucks / Non-Trucks Categories O Trucks 1 / Trucks 2 / Non-Trucks Categories
O Total (Fleet average)
r State r Air Basin r Air District r MPO ff County r GAI
Region Sacramento
CalYr 2015
Season Annual
Download
0 Vehicle Category
0 Fuel Type
ALL
TOT
V
V
0 Speed
65MPH
V
Exit
G-2
-------
G.4 CALCULATING A LINK-SPECIFIC EMISSION RATE FROM
EMFAC2011-PL OUTPUT
After running EMFAC2011-PL, an output Excel file (Exhibit G-3) is produced in the
EMFAC2011-PL folder. From this file, emission rates are appropriately processed to
calculate a single link emission rate appropriate for dispersion modeling. This process is
described below.
Exhibit G-3. EMFAC2011-PL Output File
EMFAC2011-PL Emission Rates - Sacramento County - 2015 Annual (Oct 15, 9.54 AM]Jcls [Compatibilft... - a X
Reqion_TlReqion CalYr
Veh & Tec MdlYr
Non-True* AIIMYr
Trucks -TAIIMYr
Veh Fuel
Non-Truck TOT
Trucks TOT
Speed
65MPH
65 MPH
ROG_RUrT
0.072682 0
.084633 0
Sacramen
Sacramen
2015 Annual
2015 Annual
The next step is to extract the relevant emission rates for post-processing in a separate
Excel worksheet. For running emissions, the Total PIVh.s emission factor (EF) is
calculated as the sum of the running exhaust EF (Exhibit G-4), the brake wear EF, and
the tire wear EF (Exhibit G-5).
Exhibit G-4. Running Exhaust Rates
>®] EMFAC2011-PL Emission Rates - Sacramento County - 2015 Annual (Oct 15. 9.54 AMJ.xls [Compatibility Mode]
K
1 Speed
M N
P
u
R
65 MPH
65 MPH
ROG_RUITOG_RUr- CO_RUNE NOx_RUI\ C02_RUK C02(PavkPM10_RUrPMg_5_RUNEX SOx_RUNEX /?|
0.072682 0.090454 1.714381 0.235209 401.9377 345.6079 0.0024369(~ ~ 0.0022297431 0.003822
0.084633 0.109217 2.117299 1.532394 705.9271 666.0992 0.02495991 0.0229593461 0.007391
STREX EF DLEX EF EVAP
Exhibit G-5. Brake Wear and Tire Wear Rates
i3 EMFAC2011-PL Emission Rates - Sacramento County - 2015 Annual (Oct 15, 9,54 AMl.xIs [Compatibility Mode]
E F
Veh Fuel
Non-Truck TOT
Trucks TOT
__
Veh a Tec MdlYr
Non-Truck AIIMYr
Trucks-TAIIMYr
I J
Speed PM10_PMPM10_PM
AHSpeeds 0.008011 0.038903'
AHSpeeds 0.010303 0.048273
L
PM2 5 PMTW PM2 5 PMBW
0.002002626
) 0.002575764
0.0166726121
0.020G08569I
G-4
-------
These rates are then summed separately for Trucks and Non-Truck categories (shown in
Exhibit G-6).
Exhibit G-6. Calculation of Truck and Non-Truck Total PMi.s EF
Non-trucks
Trucks
Running
Exhaust EF
0.0022297
0.0229593
Tire wear EF
0.0020026
0.0025758
Break wear EF
0.0166726
0.0206886
Total PMi.s EF
0.020905
0.046224
From the calculated Total PIVh.s EF, the truck and non-truck rates are then weighted
together based on the relative VMT for each vehicle type. In this example, trucks
account for 25% of VMT while non-trucks account for 75% of VMT. Exhibit G-7
demonstrates how the EFs are weighted to calculate a single link emission rate.
Exhibit G-7. Calculation of Total PMi.s Link Emission Rate
Non-trucks
Trucks
Total
Emission
Rate
0.020905
0.046224
VMT adjustment
0.75
0.25
Weighted
Emission Rate
0.0156788
0.011556
0.027235
This completes the use of the EMFAC2011-PL tool to determine emissions factors for
this project using the simplified approach. The total running link emission factor of
0.027235 grams per vehicle-mile can be now be used in combination with link length and
link volume as inputs into the selected air quality model, as discussed in Section 7 of the
guidance.
G-5
-------
Appendix H:
Example of Using EMFAC2011 to Develop Emission Factors
for a Transit Project
H.1 INTRODUCTION
The purpose of this appendix is to illustrate the modeling steps required for users to
develop PM idling emission factors for a hypothetical bus terminal project using
EMFAC2011. It also shows how to generate emission factors from EMFAC2011 for a
project that involves a limited selection of vehicle classes (e.g., urban buses) and an age
distribution that differs from the EMFAC2011 defaults.1 Because the project age
distribution differs from the EMFAC2011 defaults, use of the simplified approach and
EMFAC2011-PL tool is not appropriate. Instead, the detailed approach described in
Section 5.6 of the guidance will be used.
This example uses the "Emfac" mode in EMFAC2011-LDV to generate grams per
vehicle-hour (g/veh-hr) emission factors stored in the "Summary Rate" output file (.its
file) suitable for use in the AERMOD air quality model. This example does not include
the subsequent air quality modeling; refer to Appendix F for an example of how to run
AERMOD for a transit project for PM hot-spot analyses.
The assessment of a bus terminal or other non-highway project can involve modeling two
different categories of emissions: (1) the idle and/or start emissions at the project site, and
(2) the running exhaust emissions on the links approaching and departing the project site.
As discussed in Section 5.7.4, EMFAC2011-LVD allows users to generate emission
factors for all of these in a single run. This appendix walks through the steps to model
idle emissions for this hypothetical project. Users will be able to generate idle emission
factors in a single EMFAC2011-LDV model run; multiple calendar years can also be
handled within one model run. As described in the main body of this section, each run
will be specific to either PMio or PM2.s; however, this example is applicable to both.
This example is intended to help project sponsors understand how to create representative
idle emission factors based on the best available information supplied by EMFAC2011,
thus providing an example of how users may have to adapt the information in
EMFAC2011 to their individual project circumstances.
To estimate idle emissions at a terminal project, the main task will involve modifying the
default vehicle populations and VMT distribution, by vehicle, fuel, and age distribution
embedded in EMFAC2011 to reflect the project-specific bus fleet.
1 This is a highly simplified example showing how to employ EMFAC2011 to calculate idle emission
factors for use in air quality modeling. An actual project would be expected to be significantly more
complex.
H-l
-------
H.2 PROJECT CHARACTERISTICS
A PMio hot-spot analysis is conducted for a planned bus terminal project in Sacramento
County, California. The project's first full year of operation is assumed to be the year
2013. Through the interagency consultation process, it is determined that 2015 should be
the analysis year (based on the project's emissions and background concentrations). The
PM analysis is focused on idle emissions from buses operated in the terminal.
Additionally, all buses in this example operate using diesel fuel and are ten years old (age
10).
It is determined that the appropriate EMFAC2011 vehicle category for the urban transit
buses included in the project is "UBUS-DSL," which is a type found in the
EMFAC2011-LDV module (see Section 5.6.2 of the guidance). Therefore, we will be
applying the EMFAC2011-LDV procedure described in Section 5.7 of the guidance.
H.3 PREPARING EMFAC2011 BASIC INPUTS
Based on the project characteristics, basic inputs and default settings in EMFAC2011-
LDV are first specified (see Exhibit H-l). These basic inputs are similar to those
specified for highway projects. To generate idle emission factors for urban transit buses
(UBUS-DSL) from EMFAC2011-LDV, a speed bin of 5 mph must be selected in the
EMFAC2011-LDV interface.
Exhibit H-l. Basic Inputs in EMFAC2011-LDV for the Hypothetical Highway
Project
Step
1
2
o
3
4
5
6
7
8
9
10
11
12
Input Category
Geographic Area
Calculation Method
Calendar Years
Season or Month
Scenario Title
Model Years
Vehicle Classes
I/M Program Schedule
Temperature
Relative Humidity
Speed
Emfac Rate Files
Output Paniculate
Input Data
County -> Sacramento
Use Average
2015
Annual
Use default
Use default
Use default
Use default
60F
70%RH
Use default
Summary Rates (RTS)
PMio
Note
Select from drop-down list
Default (not visible in the
EMFAC201 1-LDV user interface)
Select from drop-down list
Select from drop-down list
Define default title in the
EMF AC20 1 1 -LD V user interface
Include all model years
Include all vehicle classes
Include all pre-defined I/M program
parameters
Delete all default temperature bins
and input 60
Delete all default relative humidity
bins and input 70
Include speed bin of 5 mph
Select from EMFAC20 1 1 -LD V user
interface
Select from EMFAC20 1 1 -LD V user
interface
H-2
-------
H.4 EDITING EMFAc2011-Lov DEFAULT VMT AND POPULATION TO
REFLECT PROJECT-SPECIFIC BUS FLEET
To generate idle emission factors that reflect the bus terminal project data, vehicle
population and VMT by vehicle class must be modified in the EMFAC2011-LDV user
interface. The EMFAC2011 module has data limitations regarding idle emissions:
among the available vehicle classes in EMFAC2011-LDV, idle emission factors are
available only for the LHDT1, LHDT2, MHDT, HHDT, School Buses, and Other Buses
vehicle types. Although EMFAC2011-LDV does not explicitly provide idle emission
factors for the "UBUS-DSL" class (the class most typically associated with urban transit
buses), as described in Section 5.7.4 of the guidance, the 5 mph emission factors may be
used to represent transit buses by multiplying the rate (grams/vehicle-mile) by 5 miles per
hour, resulting in a grams/veh-hour rate.
Since the fuel use and age distribution of the bus fleet are known, it is necessary to edit
the EMFAC2011-LDV program constants (defaults) to reflect this information. First,
VMT "By Vehicle and Fuel" will be edited to reflect entirely diesel Urban Bus operation
by changing gasoline Urban Bus VMT to "1" (because "0" will cause an error). Next,
Population "By Vehicle and Fuel" will be edited to reflect entirely diesel Urban Bus
operation by changing the number of gasoline Urban Buses to "1". Finally, the
Population "By Vehicle/Fuel/Age" will be edited to reflect the known Urban Bus age
distribution by preserving the number of Urban Buses "age 10", and changing the number
of buses of all other ages to "0" (note this must be done by exporting the default age
distribution to Excel, as explained in Exhibit H-4).
As shown in Figure H-2, VMT is edited to reflect only diesel operation by Urban Buses.
For this example bus terminal, a very low value ("1") is entered into the interface for
gasoline Urban Buses to represent the project-specific fuel data.
-------
Exhibit H-2. Changing EMFAC2011-LDV Default VMT to Reflect Project-Specific
Fuel Use
Editing VMT data for scenario 1: Bus Idle and Start Emission Rates
Total VMT for area
Sacramento County |
Copy with Heading;:
Paste Data Only
EditingMode E diting VM T (vehicle miles traveled per weekday)
Total VMT | By Vehicle Class By Vehicle and Fuel I By Vehicle/Fuel/Hour |
01 -Light-Duty Autos (PC)
02-Light-Duty Trucks (T1)
03-Light-Duty Trucks (T 2)
04 - Medium-Duty Trucks (T3)
05 - Light HD Trucks (T4)
06 • Light HD Trucks (T5)
07 • CAIRP+OOS+IS Trc/Sngl (T6)
OB-Agriculture (T 6)
09-Public* Utility (T 6)
10-Out of State (T 7]
11 -CAIRP(T7)
12-Instate Tractor (T7)
13-Instate Single (T7)
14-Port(Drayage)(T7)
15-Agriculture (T 7)
16 - Public+Util+SolioWaste(T7)
17-Other Buses
18-Urban Buses
19-Motorcycles
20 - School Buses
21 • Motor Homes
Apply
Cancel
Done
Default EMFAC2011-LDV data before modification
H-4
-------
Editing VMT data for scenario 1: Bus Idle and Start Emission Rates
Total VMT for atea
Sacramento County I
Editing Mode
Total VMT I By Vehicle Class By Vehicle and Fuel
Capy 'A'i'.h He.jding- |
Paste D.aia Only I
Editing VMT (vehicle miles traveled per weekday)
I ByVehicle/FueWourl
01 - Light-Duty Autos (PC)
02-Light-Duty Trucks (T1)
03-Light-Duty Trucks (T 2)
04 • Mediurn-DutjJ Trucks (T3J
05 - Light HD Trucks (T4)
06 - Light HD Trucks (T5)
07 - CAIRP+OOS+IS Trc/Sngl (T6)
08-Agriculture (T 6)
09-Public* II Sty (T 6)
10-Out of State (T 7)
11 -CAIRP(T7)
12-Instate Tractor (T 7)
13-Instate Single (T7)
14 - Port (D rayage) (T 7)
15-Agriculture (T 7)
16 • Public+Util+SolioWaste(T7)
17-Other Buses
18-Urban Buses
19-Motorcycles
20-School Buses
21 - Motor Homes
Fuel (l=Ga5/2=Diesel/3=ELectric)
19607716.0
26S4S02.0
6942192.S
5802926.0
112E473.1
96861.8
1476E4.2
0.0
0. 0
0. 0
0.0
0.0
39EOE.8
0.0
0.0
0.0
38112.3
70388.2
3210.E
3167.3
E80E.O
639789.4
1S2682.S
0.0
0.0
0. 0
0. 0
0. 0
0.0
0.0
0.0
0.0
0.0
0.0
90968.4 I
0. 0
0. 0
Apply
Cancel
Done
Modified EMFAC2011-LDV data
H-5
-------
Next, in Exhibit H-3 the default EMFAC2011-LDV vehicle population is similarly edited
to reflect an entirely diesel-fueled bus fleet.
Exhibit H-3. Changing EMFAC2011-LDV Default Population to Reflect Project-
Specific Fuel Use
Editing Population data for scenario 1: Bus Idle and Start Emission Rates
Total Population for area
Sacramento County |
Editing Mode
Total Population
Copy wth Headings
Paste Data Only
Editing Population (registered vehicles with adjustments)
By Vehicle Class By Vehicle and Fuel | By Vehicle/Fuel/Age |
01 -Light-Duty Autos (PC)
02 • Light-Duty Trucks (T1]
03 -Light-Duty Trucks (T2J
04 -Medium-Duty Trucks (T 3)
05- Light HD Trucks [T4)
06- Light HD Trucks (T5)
07 • CAIRP+OOS+IS Tlc/Sngl (T6)
08- Agriculture (T6)
03 - Public + Utility (TG)
10 -Out of State (T 7)
11 -CAIRP(T7)
12 -Instate Tractor (T 7)
13 -Instate Single (T 7)
14-Porl(Draj>age)(T7)
15- Agriculture (T7)
16 - Public+Util+SolidWasle(T7)
17 -Other Buses
18 -Urban Buses
19 -Motorcycles
20 -School Buses
21 -Motor Homes
\
i
f
Fuel (l=Gas/2=Diesel/3=Electric)
1
Z
3
4
5
6
7
3
9
10
11
12
13
14
15
16
17
18
19
20
: !
1 Z
497902.3 1931.0
71285-5 93.8
172196.6 81.2
146270.0 144. 0
26467.5 15029,4
2274. S 3599.7
3183.5 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0-0 0.0
319.7 0.0
0.0 0.0
0.0 0.0
0.0 0.0
769.4 0.0
246 S 457 5
23510.2 0.0
171.7 0.0
5512.6 393. 3
3
420.4
99.0
0.0
0. 0
0.0
0. 0
0. 0
Q.O
0.0
0.0
0. 0
0. 0
0. 0
0. 0
0.0
0. 0
0. 0
0 0
0. 0
0.0
0.0
1
| Done
Default EMFAC2011-LDV data before modification
H-6
-------
Editing Population (registered vehicles with adjustments)
By Vehicle and Fuel | By Vehicle^Fuel/Age ]
Fuel (1=Gas/2=Dii;se]/3=Electric)
Editing Population data for scenario 1: Bus Idle and Start Emission Rates
Total Population for area
Sactarnento County
Editing Mode
Total Population | By Vehicle Class
01 -Light-Duty Autos (PC)
02-Light-Duty Trucks (T1)
03-Light-DutyTrucks(T2)
04 • Medium-Duty Trucks (T3)
05 - Light HD Trucks (T4)
06 - Light HD Trucks (T5)
07 - CAIRPtOOS-HS Trc/Sngl (T6)
08-Agriculture (T 6)
09 - Public+ U Sty (T 6)
10-Out of State (T7)
11 -CAIRP(T7)
12-Instate Tractor (T7)
13-Instate Single (T 7)
14-Port(Drayage)(T7)
15-Agriculture (T 7)
16 - Pubf c+Util+S olioWastefT 7)
17-Other Buses
18 -UrbanBuses
19-Motorcycles
20-School Buses
21 -Motor Homes
497902.3
71285.5
172196.6
146270.0
26467.5
2274.8
31S3.S
0.0
0.0
0.0
0.0
0.0
319.7
0.0
0.0
0.0
769.4
1.0
28510.2
171.7
5512.6
1931.0
93. 8
81. 2
144.0
15029.4
3599.7
0.0
0-0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
704.0
0-0
0. 0
893-3
Apply
Modified EMFAC2011-LDV data
Finally, in Exhibit H-4, it is necessary to export the default age distribution for
modification in Excel. The Urban Bus type has a default age distribution that does not
match the project. To change the default, zeros ("0") are entered for all ages except
"AgelO" to reflect a fleet that is entirely 10 year-old buses. The table is copied and
pasted back into the EMFAC2011-LDV module.
H-7
-------
Exhibit H-4. Changing EMFAC2011-LDV Default Age Distribution to Reflect
Project-Specific Bus Roster
Editing Population data for scenario 1: Bus Idle and Start Emission Rates
Total Population for area
Sacramento County
Editing Mode
Lopy with Headings
Paste Data Only
Editing Population (registered vehicles with adjustments)
Total Population By Vehicle Cla
ss By Vehicle and Fuel By Vehicle/Fuel/Age
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0.0
0.0
0.0
0.0
0.0
0.0
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17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
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19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
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Done
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ill
i
2
3
4
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6
7
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9
10
11
12
13
14
15
16
17
18
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20
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A
Sacramento County Diesel P
01- Light-Duty Autos (PC)
02- Light-Duty Trucks |T1)
03 - Light-Duty Trucks |T2)
04 - Medium-Duty Trucks (T2
05- Light HD Trucks |T4)
06- Light HD Trucks (T5)
07- CAIRP+OOS+IS Trc/Sngl (
OS- Agriculture (TS)
09- Public + Utility (T6)
ID - Out of State (T7)
11-CAIRP|T7)
12 -Instate Tractor (T7j
13 - Instate Single (T7)
14-Port(Drayage) (T7j
15 - Agriculture (T7)
16- Public+Util+SolidWaste(
17 -Other Buses
IS- Urban Buses
IS - Motorcycles
20- School Buses
21- Motor Homes
_ a :
BCDEFGH 1 J K
AgeOl AgeOZ AgeOB Age04 AgeOS AgeQB Age07 AgeOS AgeOS AgelC
133.4758 137.1166 152.1877 15S.1S84 173.6059 179.5487 103.6043 4.97377 0
6.95752 7.152435 7.606674 6.278053 8.160515 5.866775 000
4.866089 9.0509S2 10.413 5.814407 4.593681 6.S01266 000 6. 655
B.03714 7.291153 8.159173 7.372656 7.056864 5.781903 44.25079 3.721883 3.715413 2.745
707.3047 684.5059 650.1201 577.6SS5 531.6475 455.5589 110.2175 491.8519 599.908 1337
171.5638 161.188 160.5263 150.72S7 129.66 109.6431 53.12713 256.6604 243.032 419.S
000000000
000000000
000000000
000000000
000000000
000000000
000000000
000000000
000000000
000000000
000000000
4.634178 13.41263 28.27333 5.890194 000 137.847 9.056062 13. 5E
000000000
000000000
30.27832 28.08069 25.44299 22.05225 19.21754 19.0921 8.860383 44.64226 56.58108 70.4
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01 -Light- 133.4758 137.1166 152.1877 153.1884 173.6059 179.5487 103.6043
02- Light- 6.95752 7.152435 7.606674 6.278053 8.160515 5.866775 0
03 -Light- 4.866089 9.060982 10.413 5.814407 4.593681 6.801266 0
04-Medil 8.03714 7.291153 8.159173 7.372556 7.056864 5.7S1903 44.25079
05- Light 1 707.3047 584.5059 550.1201 577.6385 531.5475 465.5589 110.2175
05- Light 1 171.5588 151.188 160.5263 150.7287 129.55 109.5431 53.12713
07-CAIRP 0
08 - Agrici 0
10 09 -Public 0
!T]lO-Outo- 0
^2]ll-CAIRP 0
13
14
15
16
17
IS
19
20
1
23
12-lnstat 0
13 - Instat 0
14- Port (1 0
15 - Agrio. 0
15 -Public 0
17 -Other 0
18 -Urban 0
19 -Motor 0
20-Schoo 0
21 -Motor 30.27832 28.
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3.721883 3.715413 2.745002 0
491.3519 599.908 1337.003 1116.968
256.6604 243.032 419.9518 341.9187
oooo
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44.64226 55.58108 70.4305 59.19204 64.17:
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H-9
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Editing Population data for scenario 1: Bus Idle and Start Emission Rates
Total Population for area
Copy with Headings
Paste Data Only I
Sacramento County
Editing Mode Editing Population (registered vehicles with adjustments)
Total Population I By Vehicle Class I By Vehicle and Fuel By Vehicle/FuelAge I
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0.0
0.0
0.0
0.0
0.0
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0.0 0.0
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Modified EMFAC2011-LDV data
H-10
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H.6 PROCESSING IDLE EMISSION FACTORS
Urban Buses ("UBUS") is the vehicle class best representing transit buses in this
hypothetical bus terminal project. After the EMFAC2011-LDV run is completed, the
project-specific idle exhaust emission factors are presented in Table 1 of the output
Summary Rates file (.its file) as shown in Exhibit H-5.
Exhibit H-5. EMFAC2011-LDV Output
P sacramento_transit.rts - Notepad - | D || X |
File Edit Format View Help
Pollutant Name: PM10
Humi di ty: 70H
Speed
MPH
0
5
10
IE
20
25
30
35
40
45
50
55
60
65
LDA
0.000
0.009
0.006
0.004
0.003
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.002
0.002
LOT
0.000
0.011
0.007
0.005
0.004
0.003
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
MDT
0.038
0.021
0.015
0.012
0.009
0.007
0.006
0.005
0.005
0.004
0.004
0.004
0.004
0.004
Temperature:
HOT
0.000
0.034 |
0.023
0.016
0.011
0.009
0.008
0.007
0.007
0.007
0.007
0.008
0.010
0.011
UEUS
0.000
0.106 1
0.077
0.058
0.045
0.036
0.030
0.026
0.023
0.022
0.021
0.021
0.021
0.023
A
70F Relative
MCY
0.000
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.001
0.001
0.001
0.001
0.001
0.001
ALL
0.018
0.013
0.009
0.006
0.005
0.004
0.003
0.003
0.002
0.002
0.002
0.002
0.002
0.003
V
As discussed, the Urban Bus type does not have an explicit idle emission rate. Therefore,
the 5 mph emission rate will be used to represent idle operation. As highlighted in
Exhibit H-5, the PMio 5 mph exhaust emission factor for the Urban Buses is 0.106
grams/veh-mile. In order to produce a grams/veh-hour emission factor for use in
AERMOD, this emission factor (0.106 grams/vehicle-mile) is multiplied by 5 miles per
hour. The resulting rate is 0.53 grams/veh-hour. Note that buses typically do not idle for
the entire hour, so this rate should be applied to the actual number of bus idle-hours (i.e.,
[grams/vehicle-hour] x [idling time of each vehicle in fraction of an hour] x [number of
vehicles]) expected in the project area to produce an updated grams/hour rate.
This completes the use of EMFAC2011-LDV for determining idle emission factors for
this project. The grams/hour idle rate can now be input into AERMOD as discussed in
Section 7 of the guidance.
H-ll
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Appendix I:
Estimating Locomotive Emissions
I.I INTRODUCTION
This appendix describes how to quantify locomotive emissions when they are a
component of a transit or freight terminal or otherwise a source in the project area being
modeled. Note that state or local air quality agencies may have experience modeling
locomotive emissions and therefore could be of assistance when quantifying these
emissions for a PM hot-spot analysis.
Generally speaking, locomotive emissions can be estimated in the following manner:
1. Determine where in the project area locomotive emissions should be estimated.
2. Determine when to analyze emissions.
3. Describe the locomotive activity within the project area, including:
• The locomotives present in the project area (the "locomotive roster"); and
• The percentage of time each locomotive spends in various throttle settings
(the "duty cycle").
4. Calculate locomotive emissions using either:
• 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 choose the model and associated method and assumptions used for quantifying
locomotive emissions for PM hot-spot analyses (40 CFR 93.105(c)(l)(i)).
1.2 DETERMINING WHERE IN THE PROJECT AREA LOCOMOTIVE
EMISSIONS SHOULD BE ESTIMATED
Under certain circumstances, it is appropriate to model different locations within the
project area as separate sources to characterize differences in locomotive type and/or
activity appropriately. This step is analogous to dividing a highway project into links (as
described in Sections 4.2 and 5.2 of the guidance) and improves the accuracy of
emissions modeling and subsequent air quality modeling. For example, in an intermodal
terminal, emissions from a mainline track (which will have a large percentage of higher
1 These are the two methods described in this appendix; others may be possible. See Appendix 1.5 for
details.
1-1
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speed operations with little idling) should be estimated separately from the associated
passenger or freight terminal (which would be expected to experience low speed
operations and significant idling).
The following activities are among those typically undertaken by locomotives and are
candidates for being modeled as separate sources if they occur at different locations
within the project area:
• Idling within the project area;
• Trains arriving into, or departing from, the project area (e.g., terminal arrival and
departure operations);
• Testing, idling, and service movements in maintenance areas or sheds;
• Switching operations;
• Movement of trains passing through, but not stopping in, the project area.
The project area may also be divided into separate sources if it includes several different
locomotive rosters (see Appendix 1.4.1, below)
1.3 DETERMINING WHEN TO ANALYZE EMISSIONS
The number of hours and days that have to be analyzed depends on the range of activity
expected to occur within the project area. For rail projects where activity varies from
hour to hour, day to day, and possibly month to month, it is recommended that, at a
minimum, project sponsors calculate emissions based on 24 hours of activity for both a
typical weekday and weekend day and for four representative quarters of the analysis
year when comparing emissions to all PIVb.s NAAQS.2 For projects in areas that violate
only the 24-hour PMio or PIVh.sNAAQS, the project sponsor may choose to model only
one quarter, in appropriate cases. See Section 3.3.4 of the guidance for further
information.
These resulting emission rates should be applied to AERMOD and used to calculate
design values to compare with the applicable PM NAAQS as described in Sections 7
through 9 of the guidance.
1.4 DESCRIBING THE LOCOMOTIVE ROSTERS AND DUTY CYCLES
Before calculating locomotive emission rates, it is necessary to know what locomotives
are present in the locations being analyzed in the project area (see Appendix 1.2, above)
and what activities these locomotives are undertaking at these locations. This data will
impact how emissions are calculated.
2 If there is no difference in activity between weekday and weekend activity, it may not be necessary to
examine weekend day activity separately. Similarly, if there is no difference in activity between quarters,
emission rates can be determined for one quarter, which can then be used to represent every quarter of the
analysis year.
1-2
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/. 4.1 Locomotive rosters
Because emissions can vary significantly depending on a locomotive's make, model,
engine, and year of engine manufacture (or re-manufacture), it is important to know what
locomotives are expected to be operating within the project area. Project sponsors should
develop a "locomotive roster" (i.e., a list of each locomotive's make, model, engine, and
year) for the locomotives that will be operating within the specific project area being
analyzed. The more detailed the locomotive roster, the more accurate the estimated
emissions will be.
In some cases, it will be necessary to develop more than one locomotive roster to reflect
the operations in the project area accurately (for example, switcher locomotives may be
confined to one portion of a facility and therefore may be represented by their own
roster). In these situations, users should model areas with different rosters as separate
sources to account for the variability in emissions (see Appendix 1.2).
/. 4.2 Locomotive duty cycles
Diesel locomotive engine power is controlled by "notched" throttles; idling, braking, and
moving the locomotive is conducted by placing the throttle in one of several available
"notch settings."3 A locomotive's "duty cycle" is a description of how much time, on
average, the locomotive spends in each notch setting when operating. Project sponsors
should use the latest locally-generated or project-specific duty cycles whenever possible;
this information may be available from local railway authorities or the state or local air
agency.4 The default duty cycles for line-haul and switch locomotives, found in Tables 1
and 2 of 40 CFR 1033.530 (EPA's regulations on controlling emissions from
locomotives), should be used only if they adequately represent the locomotives that will
be present in the project area and no local or project-specific duty cycles are available.
1.5 CALCULATING LOCOMOTIVE EMISSIONS
Once a project's locomotive rosters and respective duty cycles have been determined,
locomotive emissions can then be calculated for each part of the project area using either
(1) horsepower rating and load factors, or (2) fuel consumption data. These two methods
are summarized below. Unless otherwise determined through consultation, only one
method should be used for a given project.
3 A diesel locomotive typically has eight notch settings for movement (ran notches), in addition to one or
more idle or dynamic brake notch settings. Dynamic braking is when the locomotive engine, rather than
the brake, is used to control speed.
4 The state or local air agency may have previously developed locally-appropriate duty cycles for emissions
inventory purposes.
-------
/. 5.1 Finding emission factors
Regardless of method chosen, locomotive emissions factors will be needed for the
analysis. Locomotive emission factors depend on the type of engine, the power rating of
the locomotive (engine horsepower), and the year of engine manufacture (or re-
manufacture). Default PMio emission factors for line-haul and switch locomotives can be
obtained from Tables 1 and 2 of EPA's "Emission Factors for Locomotives," EPA-420-
F-09-025 (April 2009).5 These PMio emission factors are in grams/horsepower-hour and
can easily be converted to PIVh.s emission factors. However, these are simply default
values; locomotive-specific data may be available from manufacturers and should be
used whenever possible. In addition, see Appendix 1.5.4 for other variables that must be
considered when determining the appropriate locomotive emission factors.
Note that the default locomotive emission factors promulgated by EPA may change over
time as new information becomes available. The April 2009 guidance cited above
contains the latest emission factors as of this writing. Project sponsors should consult the
EPA's website at: www.epa.gov/otaq/locomotives.htm for the latest locomotive default
emission factors and related guidance.
/. 5.2 Calculating emissions using horsepower rating and load factors
One way locomotive emissions can be calculated is to use PIVh.s or PMio locomotive
emission factors, the horsepower rating of the engines found on the locomotive roster,
and engine load factors (which are calculated from the duty cycle).
Calculating Engine Load Factors
The horsepower of the locomotive engines, including the horsepower used in each notch
setting, should be available from the rail operator or locomotive manufacturer.
Locomotive duty cycle data (see Appendix 1.4.2) can then be used to determine how
much time each locomotive spends in each notch setting, including braking and idling.
An engine's "load factor" is the percent of maximum available horsepower it uses over
the course of its duty cycle. In other words, a load factor is the weighted average power
used by the locomotive divided by the engine's maximum rated power.6 Load factors can
be calculated by summing the actual horsepower-hours of work generated by the engine
in a given period of time and dividing it by the engine's maximum horsepower and the
hours during which the engine was being used, with the result expressed as a percentage.
For example, if a 4000 hp engine spends one hour at full power (generating 4000 hp-hrs)
and one hour at 50 percent power (generating 2000 hp-hrs), its load factor would be 75
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 PMio
emission factors for PM2 5 purposes. Note that Table 6 (PMio Emission Factors) should not be used for PM
hot-spot analyses, since these factors are national fleet averages rather than emission factors for any
specific project.
6 "Weighted average power" in this case is the average power used by the locomotive weighted by the time
spent in each notch, as explained further below.
1-4
-------
percent (6000 hp-hrs + 4000 hp + 2 hrs). Note that, in this example, it would be
equivalent to calculate the load factor using the percent power values instead: ((100% * 1
hr) + (50% * 1 hr) + 2 hrs = 75%). To simplify emission factor calculations, it is
recommended that locomotive activity be generalized into the operational categories of
"moving" and "idling," with separate load factors calculated for each.
An engine's load factor is calculated by completing the following steps:
Step 1. Determine the number of notch settings the engine being analyzed has and the
horsepower used by the engine in each notch setting.7 Alternatively, as described above,
the percent of maximum power available in each notch could instead be used.
Step 2. Identify the percentage of time the locomotive being analyzed spends in each
notch setting based on its duty cycle (see Appendix 1.4.2).
Step 3. To make emission rate calculations easier, it is useful to calculate two separate
load factors for an engine: one for when the locomotive is idling and one for when it is
moving.8 Therefore, the percentage of time the locomotive spends in each notch (from
Step 2) needs to be adjusted so that all idling and all moving notches are considered
separately. For example, if a locomotive has just one idle notch setting, it spends 100%
of its idling time in that setting, even if it only idles during part of its duty cycle. While
calculating the time spent idling will usually be simple, for the non-idle (moving) notch
settings some additional adjustment to the locomotive's duty cycle percentages will be
required to determine the time spent in each moving notch as a fraction of total time spent
moving, disregarding any time spent idling.
For example, say a locomotive spends 30% of its time idling and 70% of its time moving
over the course of its duty cycle and that 15% of this total time (idling and moving
together) is spent in notch 2. When calculating the moving load factor, this percentage
needs to be adjusted to determine what fraction of just the 70% of time spent moving is
spent in notch 2. In this example, 15% of the total duty cycle spent in notch 2 would
equal 21.4% (15% * 100% H- 70%) of the locomotive's time when it is not at idle; that is,
whenever 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 generated 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.
1-5
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3000 hp spends 21.4% of its moving time in notch 2 and 78.6% of its moving time in
notch 6, and is known to generate 500 hp while in notch 2 and 2000 hp while in notch 6,
then its weighted average power would be 1679 hp (107 hp (500 hp * 0.214) + 1572 hp
(2000 hp* 0.786)= 1679 hp).
Step 5. The final step is to determine the load factors. This is done by dividing the
weighted average horsepower (calculated in Step 4) by the maximum engine horsepower.
For idling, this should be relatively simple. For example, if there is one idle notch setting
and it is known that a 4000 hp engine uses 20 hp when in its idle notch, then its idle load
factor will be 0.5% (20 hp +• 4000 hp). To determine the load factor for all power
notches, the weighted horsepower calculated in Step 4 should be divided by the total
engine horsepower. For example, if the same 4000 hp engine is determined to use an
average of 1800 hp while in motion (as determined by adjusting the horsepower by the
time spent in each "moving" notch setting in Step 4), then 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. These units can be
converted into grams/second (g/s) emission rates by using the load factor on the engines
and the time spent in each operating mode, as described below.
The first step is to adjust the PM emission factors to reflect how the engine will actually
be operating.9 This is done by multiplying the appropriate PM emission factor by the
idling and moving load factors calculated for that particular engine.10 Next, to determine
the emission rate, this adjusted emission factor is further multiplied by the amount of
time the locomotive spends idling and moving while in the project area.11
For example, if the PM emission factor known to be 0.18 g/bhp-hr, the engine being
analyzed has an idling load factor of 0.5%, and the locomotive is anticipated to idle 24
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 emissions are being used.
11 Note that this may or may not match up with the idle and moving time as described by the duty cycle
used to calculate the load factors, depending on how project-specific that duty cycle is.
1-6
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Emission rates need to be converted into g/s for use by AERMOD, as described further in
Sections 7 through 9 of the guidance. These calculations should be repeated until the
entire locomotive roster is represented in each part of the project area being analyzed.
Appendix 1.7 provides an example of calculating g/s locomotive emission rates using this
methodology.
/. 5.3 Calculating emissions using fuel consumption data
Another method to calculate locomotive emissions involves using fuel consumption data.
Chapter 6.3 of EPA's "Procedure for Emission Inventory Preparation — Volume IV:
Mobile Sources" (reference information provided in Appendix 1.6, below) is a useful
reference and should be consulted when using this method.
Note that, for this method, it may be useful to scale down data already available to the
project sponsor. For example, if rail car miles/fuel consumption is known for trains
operating in situations identical to those being estimated in the project area, this data can
be used to estimate fuel consumption rates for a defined track length within the project
area.
Calculating Average Fuel Consumption
Locomotive fuel consumption is specific to a particular locomotive engine and the
throttle (notch) setting it is using. Data on the fuel consumption of various engines at
different notch settings can often be obtained from the locomotive or engine
manufacturer's specifications. When only partial data is available (e.g., only data for the
lowest and highest notch settings are known), interpolation combined with best available
engineering judgment can be used to determine fuel consumption at the intermediate
notch settings.
A locomotive's average fuel consumption can be calculated by determining how long
each locomotive is expected to spend in each notch setting based on its duty cycle (see
Appendix 1.4.2). This data can be aggregated to generate an average fuel consumption
rate for each locomotive type. See Chapter 6.3 of Volume IV for details on how to
generate this data based on a specific locomotive roster and duty cycle.
Once the average fuel consumption rates have been determined, they should be
multiplied by the appropriate emission factors to determine a composite average hourly
emission rate for each engine in the roster. Since the objective is to determine an average
fuel consumption rate for the entire locomotive roster, this calculation should be repeated
for each engine on the roster at each location analyzed.
If several individual sources will be modeled at different sections of the project area as
described in Appendix 1.2, train schedule data should be consulted to determine the hours
of operation of each locomotive within each section of the project area. Hourly emission
rates per locomotive should then be multiplied by the number of hours the locomotive is
1-7
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operating, for each hour of the day in each section of the project area to provide average
hourly emission rates for each section of the project. These should then be converted to
grams/second for use in AERMOD, as described further in Sections 7 through 9 of the
guidance.
Examples of calculating locomotive emissions using this method can be found in Chapter
6 of Volume IV.
/. 5.4 Factors influencing locomotive emissions and emission factors
The following considerations will influence locomotive emissions regardless of the
method used and should be examined when determining how to characterize locomotives
for emissions modeling or when choosing the appropriate emission factors:
• Project sponsors should be aware of the emission reductions that would result
from remanufacturing existing locomotives (or replacing existing locomotives
with new locomotives) that meet EPA's Tier 3 or Tier 4 emission standards when
they become available. The requirements that apply to existing and new
locomotives were addressed in EPA's 2008 rulemaking entitled "Control of
Emissions of Air Pollution from Locomotive Engines and Marine Compression-
Ignition Engines Less Than 30 liters Per Cylinder" (73 FR 37095). Beginning in
2012 all locomotives will be required to use ultra-low sulfur diesel fuel (69 FR
38958). Additionally, when existing locomotives are remanufactured, certified
remanufacture systems will have to be installed to reduce emissions. Beginning
in 2011, new locomotives must meet tighter Tier 3 emission standards. Finally,
beginning in 2015 even more stringent Tier 4 emission standards for new
locomotives will begin to be phased in.
• For locomotives manufactured before 2005, a given locomotive may be in one of
three possible configurations, depending on when it was last remanufactured: (1)
uncertified; (2) certified to the standards in 40 CFR Part 92; or (3) certified to the
standards in 40 CFR Part 1033. Each of these configurations should be treated as
a separate locomotive type when conducting a PM hot-spot analysis.
• Emissions from locomotives certified to meet Family Emission Limits (FELs)
may differ from the emission standard identified on the engine's Emission
Control Information label. Rail operators will know if their locomotives
participate in this program. Any locomotives in the project area participating in
this program should be identified so that the actual emissions from the particular
locomotives being analyzed are considered in the analysis, rather than the family
emissions level listed on their FEL labels.
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1.6 AVAILABLE RESOURCES
These resources and websites should be checked prior to beginning any PM hot-spot
analysis to ensure that the latest data (such as emission factors) are being used:
• "Emission Factors for Locomotives," EPA-420-F-09-025 (April 2009). Available
online at: www.epa.gov/otaq/locomotives.htm.
• Chapter 6 of "Procedure for Emission Inventory Preparation - Volume IV: Mobile
Sources." Available online at: www.epa.gov/otaq/models/nonrdmdl/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/420fl3050.pdf
• See Section 10 of the guidance for additional information regarding potential
locomotive emission control measures.
1.7 EXAMPLE OF CALCULATING LOCOMOTIVE EMISSION RATES USING
HORSEPOWER RATING AND LOAD FACTOR ESTIMATES
The following example demonstrates how to estimate locomotive emissions using the
engine horsepower rating/load factor method described in Appendix 1.5.2.
The hypothetical proposed project in this example includes the construction of an
intermodal terminal in an area that is designated as nonattainment for both the 1997
annual PM2.5 NAAQS and the 2006 24-hour PM2.5 NAAQS. The terminal in this
example is to be completed and operational in 2013. The hot-spot analysis is performed
for 2015, because it is determined through interagency consultation that this will be the
year of peak emissions, when considering the project's emissions and the other emissions
in the project area.
In this example, the operational schedule anticipates that 32 locomotives will be in the
project area over a 24-hour period, with 16 locomotives in the project area during the
peak hour. Based on the schedule, it is further determined that while in the project area
each train will spend 540 seconds idling and 76 seconds moving.
The locomotive PM2.5 emissions are calculated based on horsepower rating and load
factors.
12 Although the emission factors have been superseded, the remainder of the Volume IV guidance remains
in effect.
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/. 7.1 Calculate idle and moving load factors
As described in 1.5.2, the project sponsor uses a series of steps to calculate load factors.
These steps are described below and the results from each step are shown in table form in
Exhibit I-1.
Step 1: The project sponsor first needs some information about the locomotives expected
to be operating at the terminal in the analysis year.
For each locomotive, the horsepower used by the locomotive in each notch setting as well
as under dynamic braking and at idle must be determined. For the purpose of this
example it is assumed that all of the locomotives that will serve this terminal are very
similar: all use the same horsepower under each of operating conditions, and all have
only one idle and dynamic braking notch setting. The horsepower generated at each
notch setting is obtained from the engine specifications (see second column of Exhibit I-
1). In this case, the rated engine horsepower is 4000 hp (generated at notch 8).
Step 2: The next step is to determine the average amount of time that the locomotives
spend in each notch and expressing the results as a percentage of the locomotive's total
operating time. In this example, it is determined that, based on their duty cycle, the
locomotives that will service this terminal spend 38% of their time idling and 62% of
their time in motion in one of the eight run notch settings or under dynamic braking. The
percentage of time spent in each notch is shown in the third column of Exhibit 1-1.
Step 3: To make emission factor calculations easier, it is decided to calculate separate
idling and moving load factors. The next step, then, is for the project sponsor to calculate
the actual percentage of time that the locomotives spend in each notch, treating idling and
moving time separately. This is done by excluding the time spent idling and
recalculating the percentage of time spent in the other notches (i.e., dynamic braking and
each of the eight notch settings) so that the total time spent in non-idle notches adds to
100%. The results are shown in the fourth column of Exhibit 1-1.
Step 4: The next step is to calculate the weighted average horsepower for this engine
using the horsepower generated in each notch and the percentage of time spent in each
notch as adjusted in Step 3. For locomotives that are idling, this is simply the horsepower
used at idle. For the other notches, the actual horsepower for each notch is determined by
multiplying the horsepower generated in a given notch (determined in Step 1) by the
actual percentage of time that the locomotive is in that notch, as adjusted (calculated in
Step 3). The results are shown in the fifth column of Exhibit 1-1.
Step 5: The final step in this part of the analysis is to determine the idle and moving load
factors. The idle load factor is just the horsepower generated at idle divided by the
maximum engine horsepower, with the result expressed as a percentage. To determine
the moving load factor, the weighted average horsepower for all non-idle notches
(calculated in Step 4) is divided by the maximum engine horsepower, with the result
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expressed as a percentage. The final column of Exhibit 1-1 shows the results of these
calculations, with the idling and moving load factors highlighted.
Exhibit 1-1. Calculating Locomotive Load Factors
Notch
Setting
Step 1:
Horsepower
(hp)
used in
notch
Step 2:
Average %
time spent
in notch
Step 3:
Reweighted
time spent in
each notch
(adjusted so
that non-idle
notches add to
100%)
Step 4:
Time-
weighted
hp used,
based on
time
spent in
notch
Step 5:
Load
factors
(idle and
moving)
Idling load factor:
Idle
14
38.0%
100.0%
14.0
0.4%
Moving load factor:
Dynamic
Brake
1
2
3
4
5
6
7
8
Total
136
224
484
984
1149
1766
2518
3373
4,000
12.5%
6.5%
6.5%
5.2%
4.4%
3.8%
3.9%
3.0%
16.2%
62.0%
20.2%
10.5%
10.5%
8.4%
7.1%
6.1%
6.3%
4.8%
26.1%
100.0%
27.5
23.5
50.8
82.7
81.6
107.8
158.6
161.9
1,044.0
1,752.4
43.8%
/. 7.2 Using the load factors to calculate idle and moving emission rates
Now that the idle and moving load factors have been determined, the gram/second (g/s)
emission rates can be calculated for the idling and moving locomotives.
First, the project sponsor would determine how many locomotives are projected to be
idling and how many are projected to be in motion during the peak hour of operation and
over a 24-hour period. As previously noted, it is anticipated that 32 locomotives will be
in the project area over a 24-hour period, with 16 locomotives in the project area during
the peak hour. It was further determined that, while in the project area, each train will
spend 540 seconds idling and 76 seconds moving.
For the purpose of this example, it has been assumed that each locomotive idles for the
same amount of time and is in motion for the same amount of time. Note that, in this
case, the number of locomotives considered "moving" will be double the actual number
of locomotives present in order to account for the fact that each locomotive moves twice
through the project area (as it arrives and departs the terminal).
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Next, the project sponsor would determine the PIVb.s 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 PMio emission factors in terms
of grams/brake horsepower-hour (g/bhp-hr) for line haul locomotives that are typically
used by commuter railroads. Emission factors are presented for uncontrolled
locomotives, locomotives manufactured to meet Tier 0 through Tier 4 emission
standards, and locomotives remanufactured to meet more stringent emission standards.
It's important to determine the composition of the fleet of locomotives that will use the
terminal in the year that is being analyzed so that the emission factors in Table 1 can be
used in the calculations. This information would be available from the railway operator.
In this example, we are assuming that all of the locomotives meet the Tier 2 emission
standard. However, an actual PM hot-spot analysis would likely have a fleet of
locomotives that meets a combination of these emission standards. The calculations
shown below would have to be repeated for each different standard that applies to the
locomotives in the fleet.
The final step in these calculations is to use the information shown in Exhibit 1-1 and the
other project data collected to calculate the PIVh.s emission rates for idling and moving
locomotives during both the peak hour and over a 24-hour basis.13
Calculating Peak Hour Idling Emissions
The following calculation would be used to determine the idling emission rate during the
peak hour of operation:14
PM2.5 Emission Rate = (16 trains/hr) * (1 hr/3,600 s) * (540 s/train) * (4,000 hp) *
(0.004) * (0.18 g/bhp-hr) * (1 hr/3,600 s) * (0.97)
PM2.5 Emission Rate = 0.0019 g/s
Where:
• Trains per hour =16 (number of trains present in peak hour)
• Idle time per train = 540 s (from anticipated schedule)
• Locomotive horsepower = 4,000 hp (from engine specifications)
• Idle load factor = 0.004 (0.4%, calculated in Exhibit 1-1)
• Tier 2 Locomotive Emission Factor = 0.18 g/bhp-hr (from "Emission
Factors for Locomotives")
• Ratio of PM2.5 to PMio = 0.97 (from "Emission Factors for Locomotives")
13 Peak hour emission rates will not be necessary for all analyses; however, for certain projects that involve
very detailed air quality modeling analyses, peak hour emission rates may be necessary to more accurately
reflect the contribution of locomotive emissions to air quality concentrations in the project area.
14 Note that, for the calculations shown here, any units expressed in hours or days need to be converted to
seconds since a g/s emission rate is required for AERMOD.
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Calculating 24-hour Moving Emissions
Similarly, the following equation would be used to calculate the moving emission rate for
the 24-hour period:
PM2.5 Emission Rate = (64 trains/day) * (76 s/train) * (1 day/86,400 s) * (4,000 hp) *
(0.438) * (0.18 g/bhp-hr) * (lhr/3,600 s) * (0.97)
PM2.5 Emission Rate = 0.0048 g/s
Where:
• Trains per day = 64 (double the actual number of trains present over 24
hours to account for each train moving twice through the project area)
• Moving time per train = 76 s (from anticipated schedule)
• Locomotive horsepower = 4,000 hp (from engine specifications)
• Moving load factor = 0.438 (43.8%, calculated in Exhibit 1-1)
• Tier 2 Locomotive Emission Factor = 0.18 g/bhp-hr (from "Emission
Factors for Locomotives")
• Ratio of PM2.5 to PMio = 0.97 (from "Emission Factors for Locomotives")
A summary of the variables used in the above equations and the resulting emission rates
can be found in Exhibit 1-2, below.
Exhibit 1-2. PMi.5 Locomotive Emission Rates
Operational
Mode
Idle
Moving
Number of
Locomotives
Peak
hour
16
32
24
hours
32
64
Time/
Train
(s)
540
76
PM25
Emission
Factor
(g/bhp-hr)
0.18
0.18
Calculated
Peak Hour
Emission Rate
(g/s)
0.0019
0.057
Calculated
24-hour
Emission
Rate
(g/s)
0.00016
0.0048
These peak and 24-hour emission rates can now be used in air quality modeling for the
project area, as described in Sections 7 through 9 of the guidance.
Note that, since this area is designated as nonattainment for both the 1997 annual PM2.5
NAAQS and the 2006 24-hour PM2.5NAAQS, the results 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 PM2.5 NAAQS, all four quarters will need to be included in
the analysis to estimate a year's worth of emissions. If there is no change in locomotive
activity across quarters, the emission rates calculated here could be used for each quarter
of the year (see Appendix 1.3).
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Appendix J:
Additional Reference Information on Air Quality Models and
Data Inputs
J.I INTRODUCTION
This appendix supplements Section 7's discussion of air quality models. Specifically,
this appendix describes how to configure AERMOD and CAL3QHCR for PM hot-spot
analysis modeling, as well as additional information on handling the data required to run
the models for these analyses. This appendix is not intended to replace the user guides
for air quality models, but discuss specific model inputs, keywords, and formats for PM
hot-spot modeling. This appendix is organized so that it references the appropriate
discussions in Section 7 of the guidance.
J.2 SELECTING AN APPROPRIATE AIR QUALITY MODEL
The following discussion supplements Section 7.3 of the guidance and describes how to
appropriately configure AERMOD and CAL3QHCR when completing a PM hot-spot
analysis. Users should also refer to the model user guides, as appropriate.
J. 2.1 Using AERMOD for PM hot-spot analyses
There are no specific commands unique to transportation projects that are necessary when
using AERMOD. By default, AERMOD produces output for particulate matter in units
of micrograms per cubic meter of air (|j,g/m3). All source types in AERMOD require that
emissions are specified in terms of emissions per unit time, although AREA-type sources
also require specification of emissions per unit time per unit area. AERMOD has no
specific traffic queuing mechanisms. Emissions output from MOVES, EMFAC, AP-42,
and other types of methods should be formatted as described in the AERMOD User
Guide.1
J. 2.2 Using CAL3QHCRfor 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.
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in the CAL3QHCR User Guide.2 In addition, the following guidance is provided when
using CAL3QHCR for a PM hot-spot analysis:
Specifying the Right Pollutant
When using CAL3QHCR for PM hot-spot analyses, the MODE keyword must be used to
specify analyses for PM so that concentrations are described in micrograms per cubic
meter of air (|j,g/m3) rather than parts per million (ppm).
Entering Emission Rates
MOVES emission rates for individual roadway links are based on the Op-Mode
distribution associated with each link and are able to include emissions resulting from
idling. MOVES-based emission factors that incorporate relevant idling time and other
delays should be entered in CAL3QHCR using the EFL keyword. Therefore, within
CAL3QHCR, the IDLFAC keyword's emission rates should be set to zero, because the
effects of idling are already included within running emissions. (Note that if a non-zero
emission rate is used in CAL3QHCR, the model will treat idling emission rates separately
from running emission rates.) The same recommendation applies when using emission
rates calculated by EMFAC.
Assigning Speeds
Although the user guide for CAL3QHCR specifies that the non-queuing links should be
assigned speeds in the absence of delay caused by traffic signals, the user should use
speeds that reflect delay when using CAL3QHCR for a hot-spot analysis. Since MOVES
emission factors already include the effects of delay (i.e., Op-Mode distributions that are
user-specified or internally calculated include the effects of delay), the speeds used in
CAL3QHCR links will already reflect the relevant delay on the link over the appropriate
averaging time. The same recommendation applies when using EMFAC.
Using the Queuing Algorithm
When applying CAL3QHCR for the analysis of highway and intersection projects, its
queuing algorithm should not be used.3 This includes the CAL3QHCR keywords
NLANE, CAVG, RAVG, YFAC, IV, and IDLFAC. As discussed in Sections 4 and 5,
idling vehicle emissions should instead be accounted for by properly specifying links for
emission analysis and reflecting idling activity in the activity patterns used for MOVES
or EMFAC modeling.
2 The CAL3QHCR user guide and other model documentation can be found on EPA's SCRAM website:
www.epa.gov/scram001/dispersion_prefrec.htm#cal3qhc.
3 CALSQHCR's algorithm for estimating the length of vehicle queues associated with intersections is based
on the 1985 Highway Capacity Manual, which is no longer current. Furthermore, a number of other
techniques are now available that can be used to estimate vehicle queuing around intersections.
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J.3 CHARACTERIZING EMISSION SOURCES
The following discussion supplements Section 7.4 of the guidance and describes in more
detail how to characterize sources in AERMOD and CAL3QHCR, 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 AERMOD and CAL3QHCR user guides and EPA's
quantitative PM hot-spot analysis training course, available for download at
http://www.epa.gov/otaq/stateresources/transconf/projectlevel-hotspot.htmtftraining .
J. 3.1 Physical characteristics and locations of sources in AERMOD
The following discussion gives guidance on how to best characterize a source.
AERMOD includes different commands (keywords) for area, volume, and point sources.
When modeling roadway links, experience in the field has shown that area sources may
be easier to characterize correctly compared to volume sources. It is acceptable to use
either area or volume sources to simulate roadways in AERMOD. Users may want to be
particularly mindful of making errors when using volume sources.4
Modeling Area Sources
AERMOD can represent rectangular, polygon-shaped, and circular area sources using the
AREA, AREAPOLY, AREACIRC, or LINE keywords.5 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. Evenly-distributed ground-level
sources might also be modeled as area sources. EPA recommends that the LINE source
keyword be used for modeling roadway sources as it greatly simplifies defining the
physical location and orientation of sources.
AERMOD requires the following information when modeling an area source using the
LINE source keyword:
• The emission rate per unit area (mass per unit area per unit time);
• The coordinates of midpoint of the ends (Xi,Yi, X2,Y2)
• The width of the source in meters;
• The initial vertical dimension of the area source plume and initial vertical
dispersion coefficient; and
• The release height above the ground.
To estimate the width of the source, one of the following options should be used:
4 For additional information on issues related to applying volume sources, see slides 16-19 in EPA's "PM
Hot-spot Modeling: Lessons Learned in the Field" presentation found on:
http ://www. epa. gov/otaq/stateresources/transconf/proj ectlevel-hotspot. htm#training
5 Sources defined by the LINE keyword are still area sources and are equivalent to rectangular AREA
sources.
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a) The width of the traveled way, typically 3.7 m (12 ft) per lane for a high-speed,
high volume roadway and 3.3 m (11 ft) per lane for an arterial/collector; or
b) The width of the traveled way (all travel lanes) + 6 meters.6
A typical approach is to assume the initial vertical dimension is about 1.7 times the
average vehicle height, to account for the effects of vehicle-induced turbulence. For
light-duty vehicles, this is about 2.6 meters, using an average vehicle height of 1.53
meters or 5 feet. For heavy-duty vehicles, this is about 6.8 meters, using an average
vehicle height of 4.0 meters. 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, respectively, 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
(GZO), termed Szinit in AERMOD, be estimated by dividing the initial vertical dimension
by 2.15. For typical light-duty vehicles, this corresponds to a Szinit (ozo) of 1.2 meters.
For typical heavy-duty vehicles, the initial value of Szinit (ozo) is 3.2 meters.
The source release height (Relhgt), which is the height at which wind effectively begins
to affect the plume, may be estimated as the midpoint of the initial vertical dimension. In
other words, Relhgt is the initial vertical dimension multiplied by 0.5. As noted above,
most road links will consist of a combination of light-duty and heavy-duty traffic. For
each roadway source, the source release height (Relhgt) should be based on the same
initial vertical dimension used for calculated its Szinit, as described above.
Another way of dealing with Szinit and/or release height (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 the same space, but
would have emission rates and Szinit and Relhgt parameters that are specific to light-duty
or heavy-duty traffic.
Also, AERMOD allows 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
6 Option (a) is based on the AASHTO "Green Book," A Policy on Geometric Design of Highways and
Streets, available from AASHTO's on-line bookstore
(https://bookstore.transportation.org/collection detail.aspx?ID=110'): Option (b) is based on the Haul Road
Workgroup Final Report (December 2011), found on the web at
http://www.epa.gov/ttn/scram/reports/Haul Road Workgroup-Final Report Package-20120302.pdf.
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throughout a day. Users should consult the latest information on AERMOD when
beginning a PM hot-spot analysis.
Groups of idling vehicles may also be modeled as one or more area sources. In those
cases, the initial vertical dimension of the source, dispersion coefficients, and release
heights should be calculated assuming that the vehicles themselves are inducing no
turbulence. Source characterization should be based on the type of vehicles idling, e.g., if
the vehicles idling are primarily heavy-duty trucks, then the release height would be 4
meters.
Modeling Volume Sources
Another option for modeling sources in a PM hot-spot analysis is to use volume sources.
When modeling highway and intersection links, experience in the field has shown that
area sources may be easier to characterize compared to volume sources. Project sponsors
using volume sources should seek the assistance of their EPA Region through the
interagency consultation process, based on 40 CFR 93.105(c)(l)(i). Consulting with EPA
on parameters that will be used to describe the sources may save time in avoiding errors.
Examples of project sources that may be modeled with volume sources could 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.7
AERMOD can also approximate a highway using a series of adjacent volume sources
(see the AERMOD User Guide for suggestions), but as noted above, EPA recommends
using area sources rather than volume sources to represent highways. 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).
When using volume sources, users need to provide the following information:
• The emission rate (mass per unit time, such as g/s);
• The initial lateral dispersion coefficient determined from the initial lateral
dimension (width) of the volume;
• 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).8 See the above discussion on area sources
for guidance on defining release height and initial vertical dispersion coefficients.
7 See Section 6 and Appendix I for information regarding calculating locomotive emissions.
8 The vehicle-induced turbulence around roadways with moving traffic suggests that prior to transport
downwind, a roadway plume has an initial size; that is, the emissions from the tailpipe are stirred because
the vehicle is moving and therefore the plume "begins" from a three-dimensional volume, rather than from
a point source (the tailpipe).
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The goal of using volume sources to represent a roadway is to create a uniform emissions
characterization. Ensure that volume sources are not spaced too widely along the
roadway. Adjacent volume sources should overlap and the distance between them should
be equal to the width of the source, as described in the AERMOD user's guide. Any
other approximation of roadways with volume sources will result in adjacent receptors
being over or under-estimated depending on their proximity to the center of the volume
source.
To specify the initial lateral dispersion coefficient (oyo), referred to as Syinit in
AERMOD, the AERMOD User Guide recommends dividing the initial width by 2.15.
This is to ensure that the overlapping distributions from adjacent volume sources simulate
a line source of emissions.
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.
Source characterization should be based on the type of vehicles idling, e.g., if the vehicles
idling are primarily heavy-duty trucks, then the release height would be 4 meters.
In addition, when the source-receptor spacing in AERMOD is shorter than the distance
between adjacent volume sources, AERMOD may produce aberrant results. Therefore,
ensure that no receptors are placed within a distance of (2.15 x Syinit + 1 meter) of the
center of a volume source, known as the "receptor exclusion zone." As a practical
recommendation, when using volume sources to simulate a roadway where receptors are
placed five meters from the edge of the roadway, the width of a volume source should be
less than eight meters. This will ensure that no receptors fall within the receptor
exclusion zone. If the width of the roadway is larger than eight meters, it is
recommended that additional volume sources be defined (e.g., separate each lane of
traffic), or area sources be used.
Modeling Point Sources
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.
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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
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. 3.2 Placement and sizing of sources within AERMOD
There are several general considerations with regard to placing and sizing sources within
AERMOD.
First, area, volume, 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
appropriately capture the characteristics of these emissions (such as downwash).
Third, the initial dimensions and other parameters of each source should be as realistic as
is feasible. Chapter 3 of the AERMOD User Guide includes recommendations for how
to appropriately characterize the shape of area and volume sources.
Finally, if nearby sources are 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 to
appropriately represent their emissions within AERMOD. For instance, evenly-
distributed ground-level sources might also be modeled as area sources, while a nearby
power plant stack might be modeled as a point source.
J. 3.3 Timing of emissions in AERMOD
Within AERMOD, emissions that vary across a year should be described with the
EMISFACT keyword (see Section 3.3.5 of the AERMOD User Guide). The number of
quarters that need to be analyzed may vary based on a particular PM hot-spot analysis.
See Section 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 patterns of emission variations throughout a year. Note that AERMOD
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defines seasons in the following manner: winter (December, January, February), spring
(March, April, May), summer (June, July, August), and fall (September, October,
November). Emission data obtained from MOVES or EMFAC should be appropriately
matched with the relevant time periods in AERMOD. For example, if four MOVES or
EMFAC runs are completed (one for each quarter of a year), there are emission estimates
corresponding to four months of the year (January, April, July, October) and peak and
average periods within each day. In such a circumstance, January runs should be used to
represent all AERMOD winter months (December, January, February), April runs for all
spring months (March, April, May), July runs for all summer months (June, July,
August), and October for all fall months (September, October, November).
If separate weekend emission rates are available, season-specific weekday runs should be
used for the Monday-Friday entries; weekend runs would be assigned to the Saturday and
Sunday entries. The peak/average runs for each day should be mapped to the AERMOD
entry hours corresponding to the relevant time of day from the traffic analysis. Qflag can
be used to represent emission rates that vary by season, hour of day, and day of the week.
Consult the AERMOD User Guide for details.
J. 3.4 Physical 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:9
• The coordinates (X, Y) of the endpoints of each link;10
• The width of the "highway mixing zone" (see below);
• The type of link ("at grade," "fill," "bridge," or "depressed");
• The height of the roadway relative to the surrounding ground (not to exceed ±10
meters);11 and
• The hourly flow of traffic (vehicles per hour).
CAL3QHCR treats the area over each roadway link as a "mixing zone" that accounts for
the area of turbulent air around the roadway resulting from vehicle-induced turbulence.
The width of the mixing zone is an input to the model. Users should specify the width of
a link in CAL3QHCR as the width of the traveled way (traffic lanes, not including
9 Traffic engineering plans and diagrams may include information such as the number, width, and
configuration of lanes, turning channels, intersection dimensions, and ramp curvature, as well as
operational estimates such as locations of weave and merge sections and other descriptions of roadway
geometry that may be useful for specifying sources.
10 In CAL3QHCR, the Y-axis is aligned due north.
11 The CALINE3 dispersion algorithm in CAL3QHCR is sensitive to the height of the road. In particular,
the model treats bridges and above-grade "fill" roadways differently. It also handles below-grade roadways
with height of less than zero (0) meters as "cut" sections. Information on the topological features of the
project site is needed to make such a determination. Note that in the unusual circumstance that a roadway
is more than ten meters below grade, CALINE3 has not been evaluated, so CAL3QHCR is not
recommended for application. In this case, the relevant EPA Regional Office should be consulted for
determination of the most appropriate model.
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shoulders) plus three meters on either side. Users should treat divided highways as two
separate links. See Section 7.6 of the guidance for more information on placing
receptors.
J. 3.5 Timing of emissions in CAL3QHCR
The CAL3QHCR User Guide describes two methods for accepting time-varying
emissions and traffic data; these are labeled the "Tier I" and "Tier IF' approaches.12
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
emissions data will not be so detailed, but the Tier II approach can accommodate
emissions data similar to that described in Sections 4 and 5 of the guidance. The
CAL3QHCR Tier I approach should not be used, as it employs only one hour of
emissions and traffic data and therefore cannot accommodate the emissions data required
in a PM hot-spot analysis.
Through the IPATRY keyword, CAL3QHCR allows up to seven 24-hour profiles
representing hour-specific emission, traffic, and signalization (ETS) data for each day of
the week. Depending on the number of MOVES runs, the emission factors should be
mapped to the appropriate hours of the day. For example, peak traffic emissions data for
each day would be mapped to the CAL3QHCR entry hours corresponding to the relevant
times of day (in this case, the morning and afternoon peak traffic periods). If there are
more MOVES runs than the minimum specified in the Section 4, they should be modeled
and linked to the correct days and hours using FPATRY.
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.4 INCORPORATING METEOROLOGICAL DATA
This discussion supplements Section 7.5 of the guidance and describes in more detail
how to handle meteorological data in AERMOD and CAL3QHCR. Section 7.2.3 of
Appendix W to 40 CFR Part 51 provides the basis for determining the urban/rural status
of a source. Consult the AERMOD Implementation Guide for instructions on what type
of population data should be used in making urban/rural determinations.
J. 4.1 Specifying urban or rural sources in AERMOD
As described in Section 7 of the guidance, AERMOD employs nearby population as a
surrogate for the magnitude of differential urban-rural heating (i.e., the urban heat island
12 This nomenclature is unrelated to EPA's motor vehicle emission standards and the design value
calculation options described in Section 9 of this guidance.
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effect). When modeling urban sources in AERMOD, users should use the URBANOPT
keyword to enter this data.
When considering urban roughness lengths, users should consult the AERMOD
Implementation Guide. Any application of AERMOD that utilizes a value other than 1
meter for the urban roughness length should be considered a non-regulatory application
and would require appropriate documentation and justification as an alternate model (see
Section 7.3.3 of the guidance).
For urban applications using representative National Weather Service (NWS)
meteorological data, consult the AERMOD Implementation Guide. For urban
applications using NWS data, the URBANOPT keyword should be selected, regardless of
whether the NWS site is located in a nearby rural or urban setting. When using site-
specific meteorological data in urban applications, consult the AERMOD Implementation
Guide.
J. 4.2 Specifying urban or rural sources in CAL3QHCR
CAL3QHCR requires that users specify the run as being rural or urban using the "RU"
keyword.13 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 MODELING COMPLEX TERRAIN
This discussion supplements Section 7.5 of the guidance and describes in more 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 most 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,
AERMOD will tend to underestimate concentrations from low-level, non-buoyant
13 Specifying urban modeling with the "RU" keyword converts stability classes E and F to D.
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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 guides and the latest AERMOD Implementation Guide
for information on obtaining and processing relevant terrain data.
J.5.2 CAL3QHCR
CAL3QHCR does not handle complex terrain. No action is therefore required.
J.6 RUNNING THE MODEL AND OBTAINING RESULTS
This discussion supplements Section 7.7 of the guidance and describes in more detail
how to handle data outputs in AERMOD and CAL3QHCR. AERMOD and CAL3QHCR
produce different output file formats, which must be post-processed in different ways to
enable calculation of design values 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 multiple years of
meteorological data, so multiple output files may be required (unless the meteorological
files have been joined prior to running AERMOD - which is recommended for most
analyses).
For the annual PM2.5 design value calculations described in Section 9.3.2, averaging
times should be specified that allow calculation of the annual average concentrations at
each receptor. For example, when using five years of meteorological data, the ANNUAL
averaging time should be specified using the AVERTIME keyword in the CO pathway.
For the OU pathway, a POSTFILE keyword should be defined to obtain the annual
average concentrations at each receptor.
For the 24-hour PM2.5 design value calculations described in Section 9.3.3, the
RECTABLE keyword should be used to obtain the average 98th percentile concentration
at each receptor. The eighth high value should be requested, because this would be the
98th percentile concentration for the year, that is, of 365 values. In conjunction with
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defining PM2.5 in the POLLUTED keyword of the Control pathway, the concentrations
generated in the output will be an average across N-years of meteorological data. If five
years of meteorological data were used, the output will be calculated as the average 98th
percentile value, and can be added directly to the 98th percentile background
concentration to determine the 24-hr PM2.5 design value for a first tier approach
(described in Section 9 and Appendix K).
See Appendix L for information on using AERMOD for a second tier design value
approach.
For the 24-hour PMio calculations, the RECTABLE keyword may be used to obtain the
sixth highest 24-hour concentrations over the entire modeling period (assuming five years
of meteorological data were used). The output will be calculated as the sixth high value
at each receptor and can be added directly to the appropriate background concentration
(i.e., fourth-, third-, second-highest, or highest, based on Exhibit 9-6) to determine the 24-
hr PMio design value (described in Section 9 and Appendix K).
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 PIVh.s design values using CAL3QHCR output, some post-
processing is required. CALSQHCR's output file refers to certain data under the display:
"THE HIGHEST ANNUAL AVERAGE CONCENTRATIONS." If four quarters of
emission data are separately run in CAL3QHCR, each quarter's outputs listed under
"THE HIGHEST ANNUAL AVERAGE CONCENTRATIONS" are actually quarterly-
average concentrations. As described in Section 7, per year of meteorological data,
CAL3QHCR should be run for as many quarters as analyzed using MOVES and
EMFAC, as CAL3QHCR accepts only a single quarter's emission factors per input file.
Calculating 24-hour PIVh.s design values under a first tier approach is described in
Section 9.3.3. To get annual average modeled concentrations for a first tier approach
(Step 1), the third-highest 24-hour concentrations in each quarter and year of
meteorological data should be identified. Within each year of meteorological data, the
eighth-highest 24-hour concentration from the 12 values (the top three for each of four
quarters) at each receptor should be identified. For a first tier approach, at each receptor,
the eighth-high concentration (98th percentile from 365 values) from each year of
meteorological data should be averaged together. See Appendix L for information on
using CAL3QHCR for a second tier design value approach.
When calculating 24-hour PMio design values, it is necessary to estimate the sixth-
highest concentration in each year if using five years of meteorological data. For each
period of meteorological data, CAL3QHCR outputs the six highest 24-hour
concentrations. To estimate the sixth-highest concentration, for each receptor, the six
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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. These concentrations, at each receptor
can be added directly to the appropriate monitor value for the 24-hour background
concentration from three years of monitoring data, based on Exhibit 9-6 (as described in
Section 9 and Appendix K).
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Appendix K:
Examples of Design Value Calculations for PM Hot-spot
Analyses
K.1 INTRODUCTION
This appendix supplements Section 9's discussion of calculating and applying design
values for PM hot-spot analyses. 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 PM2.5 NAAQS, the 2006 and
1997 24-hour PM2.5 NAAQS, and the 1987 24-hour PMio 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.
These illustrative example calculations demonstrate the basic procedures described in the
guidance and therefore are simplified in the number of receptors considered and other
details that would occur in an actual PM hot-spot analysis. Where users would have to
repeat steps for additional receptors, it is noted. These examples are organized according
to the build/no-build analysis steps that are described in Sections 2 and 9 of this guidance.
The final part of this appendix provides mathematical formulas that describe the design
value calculations discussed in Section 9 and this appendix.
K.2 PROJECT DESCRIPTION AND CONTEXT FOR ALL EXAMPLES
For the following examples, a PM hot-spot analysis is being done for an expansion of an
existing highway with a significant increase in the number of diesel vehicles (40 CFR
93.123(b)(l)(i)). The highway expansion will serve an expanded freight terminal. The
traffic at the terminal will increase as a result of the expanded highway project's increase
in truck traffic, and therefore the freight terminal is projected to have higher emissions
under the build scenario than under the no-build scenario. The freight terminal is not part
of the project; 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 l-in-3 day sampling schedule. In this example, the three
most recent years of monitoring data are from 2008, 2009, and 2010. Since 2008 is a
leap year (366 days), 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, there may be fewer
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However, through interagency consultation, it is determined that the freight terminal's
emissions are not already captured by this air quality monitor. AERMOD has been
selected as the air quality model to estimate PM concentrations produced by the project
(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 calculations, in this appendix such measures are not considered
until after the calculations are done.
K.3 EXAMPLE: ANNUAL PMI.S NAAQS
K.3.1 General
This example illustrates the approach to calculating design values for comparison to the
annual PIVb.s NAAQS, as described in Section 9.3.2. The annual PIVb.s design value is
the average of three consecutive years' annual averages. The design value for
comparison is rounded to the nearest tenth of a ug/m3 (nearest 0.1 ug/m3). For example,
15.049 rounds to 15.0, and 15.050 rounds to 15.1.3
Each year's annual average concentrations include contributions from the project, any
nearby sources modeled, and background concentrations. For air quality monitoring
purposes, the annual PIVh.s NAAQS is met when the three-year average concentration is
less than or equal to the current annual PIVb.s NAAQS (i.e., 15.0 ug/m3):
Annual PIVb.s design value = ([Yl] average + [Y2] average + [Y3] average) + 3
Where:
[Yl] = Average annual PIVh.s concentration for the first year of air quality
monitoring data
[Y2] = Average annual PIVh.s concentration for the second year of air quality
monitoring data
[Y3] = Average annual PIVh.s 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 could not be used in this particular PM hot-spot analysis, since air quality
modeling included the project and a nearby source. See Section 7.3 of the guidance for further information.
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.
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For this example, the project described in Appendix K.2 is located in an annual
NAAQS nonattainment area. This example illustrates how an annual PIVh.s design value
could be calculated at the same receptor in the build and no-build scenarios, based on air
quality modeling results and air quality monitoring data. In an actual PM hot-spot
analysis, design values would be calculated at additional receptors, as described further in
Section 9.3.2.
K.3.2 Build scenario
For the build scenario, the PIVh.s impacts from the project and from the nearby source are
estimated with AERMOD at all receptors.4
Steps 1-2. Because AERMOD is used for this project, Step 1 is skipped. The receptor
with the highest average annual concentration, using five years of meteorological data, is
identified directly from the AERMOD output. This receptor's average annual
concentration is 3.603 ug/m3.
Step 3. Based on the three years of measurements at the background air quality monitor,
the average monitored background concentrations in each quarter is determined. Then,
for each year of background data, the four quarters are averaged to get an average annual
background concentration (last column of Exhibit K-l). These three average annual
background concentrations are averaged, and the resulting value is 11.582 ug/m3, as
shown in Exhibit K-l:
Exhibit K-l. Background Concentrations
Background
Concentrations
2008
2009
2010
Ql
13.013
14.214
11.890
Q2
17.037
14.872
16.752
Q3
8.795
7.912
9.421
Q4
8.145
7.639
9.287
3 -year average:
Average
Annual
11.748
11.159
11.838
11.582
Step 4. The 3-year average annual background concentration (from Step 3) is added to
the average annual modeled concentration from the project and nearby source (from Step
2):
11.582 + 3.603 = 15.185
Step 5. Rounding to the nearest 0.1 ug/m3 produces a design value of 15.2 ug/m3.
4 As noted above, there is a single nearby source that is projected to have higher emissions under the build
scenario than the no-build scenario as a result of the project and its impacts are not expected to be captured
by the monitor chosen to provide background concentrations. Therefore, emissions from the project and
this nearby source are both included in the AERMOD output.
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In this example, the concentration at the highest receptor is estimated to exceed the 1997
annual PM2.5NAAQS of 15.0
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 PIVh.s 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
Hg/m3.
Step 10. The background concentrations from the representative monitor are unchanged
from the build scenario, so the average annual modeled concentration of 3.521 is added to
the 3 -year average annual background concentrations of 1 1.528 |j,g/m3 from Step 3:
11.582 + 3.521 = 15.103
Step 11. Rounding to the nearest 0. 1 |j,g/m3 produces a design value of 15. 1 |j,g/m3.
In this example, the design value at the receptor in the build scenario (15.2 ug/m3) is
greater than the design value at the same receptor in the no-build scenario (15.1 ug/m3).5
In an actual PM hot-spot analysis, design values would also be compared between build
and no-build scenarios at all receptors in the build scenario that exceeded the annual
PM2.5 NAAQS. The interagency consultation process would then be used to discuss next
steps, e.g., appropriateness of receptors. Refer to 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.
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K.4 EXAMPLE: 24-HOUR PMI.S NAAQS
K.4.1 General
This example illustrates a first tier approach to calculating design values for comparison
with the 24-hour PIVh.s NAAQS. As discussed in Section 9, while either approach is
acceptable, EPA recommends beginning with a first tier approach as there are very few
cases where a second tier approach would not produce a more conservative design value.
See Appendix L for information on using a second tier approach.
5
The 24-hour design value is the average of three consecutive years' 98th percentile PIVb.
concentration of 24-hour values for each of those years. For air quality monitoring
purposes, the NAAQS is met when that three-year average concentration is less than or
equal to the currently applicable 24-hour PIVh.s NAAQS for a given area's nonattainment
designation (35 |j,g/m3 for nonattainment areas for the 2006 PIVh.s NAAQS and 65 |j,g/m3
for nonattainment areas for the 1997 PIVh.s NAAQS).6 The design value for comparison
to any 24-hour PIVh.s NAAQS is rounded to the nearest 1 |j,g/m3 (i.e., decimals 0.5 and
greater are rounded up to the nearest whole number, and any decimal lower than 0.5 is
rounded down to the nearest whole number). For example, 35.499 rounds to 35 ng/m3,
while 35.500 rounds to 36.7
For this example, the project described in Appendix K.2 is located in a nonattainment
area for the 2006 24-hour PIVh.s NAAQS. This example presents the first 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. In an actual PM
hot-spot analysis, design values would be calculated at additional receptors, as described
further in Section 9.3.3.
K.4.2 Build scenario
PM2.5 contributions from the project and the nearby source are estimated together with
AERMOD in each of four quarters using meteorological data from five consecutive
years, using a 24-hour averaging time. As discussed in Appendix K.2 above, the one
nearby source (the freight terminal) was included in air quality modeling.
Under a first tier analysis, the average 98th percentile modeled 24-hour concentrations at
a given receptor are added to the average 98th percentile 24-hour background
concentrations, regardless of the quarter in which they occur. The average 98th percentile
6 There are only two PMis areas where conformity currently applies for both the 1997 and 2006 24-hour
NAAQS. While both 24-hour NAAQS must be considered in these areas, in practice if the more stringent
2006 24-hour PM2 5 NAAQS is met, then the 1997 24-hour PM2 5 NAAQS is met as well.
7 A sufficient number of decimal places (3-4) should be retained during intermediate calculations for design
values, so that there is no possibility of intermediate rounding or truncation affecting the final result.
Rounding should only occur during final design value calculations, pursuant to Appendix N to 40 CFR Part
50.
K-5
-------
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 98th percentile modeled 24-hour
concentration is identified. This was obtained directly from the AERMOD output.8 For
this example, the data from this receptor is shown in Exhibit K-2. Exhibit K-2 shows the
98th percentile 24-hour concentration for each year of meteorological data used.. The
average concentration of these outcomes, 3.710 |j,g/m3 (highlighted in Exhibit K-2), is the
highest, compared to the averages at all of the other receptors.
Exhibit K-2. Modeled 98th Percentile PMi.s Concentrations from Project and
Nearby Source
Year
Met Year 1
Met Year 2
Met Year 3
Met Year 4
Met Year 5
Average
98th Percentile
PM25
Concentration
3.413
2.846
3.671
4.951
3.667
3.710
Step 2. The average 98th percentile 24-hour background concentration for a first tier
analysis is calculated using the 98th percentile 24-hour concentrations of the three most
recent years of monitoring data from the representative air quality monitor selected (see
Appendix K.2). Since the background monitor is on a l-in-3 day sampling schedule, it
made either 122 or 121 measurements per year during the three most recent years.
According to Exhibit 9-5, with this number of monitored values per year, the 98th
percentile is the third highest concentration. Exhibit K-3 depicts the top eight monitored
concentrations (in ng/m3) of the monitor throughout the years employed for estimating
background concentrations.
8 If CAL3QHCR were being used, some additional processing of model output would be needed. Refer to
Section 9.3.3.
K-6
-------
Exhibit K-3. Top Eight Monitored Concentrations in the Three Most Recent Years
Rank
1
2
3
4
5
6
7
8
Yearl
34.123
31.749
31.443
30.809
30.219
30.134
30.099
28.481
Year 2
33.537
32.405
31.126
30.819
30.487
29.998
29.872
28.937
YearS
35.417
31.579
31.173
31.095
30.425
30.329
30.193
28.751
The third-ranked concentration of each year (highlighted in Exhibit K-3) is the 98th
percentile value. These are averaged:
(31.443 +31. 126 + 31. 173) -3 = 31.247
Step 3 . Then, the 98th percentile average 24-hour modeled concentration for this receptor
(from Step 1) is added to the average 98th percentile 24-hour background concentration
(from Step 2):
3.710 + 31.247 = 34.957
Rounding to the nearest whole number results in a 24-hour PIVh.s design value of 35
Hg/m3.
This concentration is equal to the 2006 24-hour PIVh.s NAAQS (35 ng/m3), and therefore
this analysis demonstrates that conformity is met.
If the project had not passed the initial build comparison, the project sponsor has two
options:
1 . Repeat the first tier analysis for the no-build scenario at all receptors that
exceeded the NAAQS in the build scenario. If the calculated design value for the
build scenario is less than or equal to the design value for the no-build scenario at
all of these receptors, then the project conforms;9 or
2. Conduct a second tier approach - See Appendix L.
K-7
-------
K.5 EXAMPLE: 24-HOUR PMIO NAAQS
K.5.1 General
This example illustrates calculating design values for comparison with the 24-hour PMio
NAAQS, as described in Section 9.3.4. The 24-hour PMio design value is based on the
expected number of 24-hour exceedances of 150 ng/m3, averaged over three consecutive
years. For air quality monitoring purposes, the NAAQS is met when the number of
exceedances is less than or equal to 1.0. The 24-hour PMio design value is rounded to the
nearest 10 ng/m3. For example, 155.500 rounds to 160, and 154.999 rounds to 150.10
The 24-hour PMio design value is calculated at each air quality modeling receptor by
directly adding the sixth-highest modeled 24-hour concentration (if using five years of
meteorological data) to the appropriate monitor value for the 24-hour background
concentration (from three years of monitored data), based on Exhibit 9-6.
For this example, the project described in Appendix K.2 is located in a nonattainment
area for the 24-hour PMio NAAQS. This example presents build scenario results for a
single receptor to illustrate how the calculations should be made based on air quality
modeling results and air quality monitoring data.
K.5.2 Build Scenario
Step 1. From the air quality modeling results from the build scenario, the sixth-highest
24-hour concentration is identified at each receptor. These sixth-highest concentrations
are the sixth highest that are modeled at each receptor, regardless of year of
meteorological data used.11 AERMOD was configured to produce these values.
Step 2. The sixth-highest modeled concentrations (i.e., the concentrations at Rank 6) are
compared across receptors, and the receptor with the highest value at Rank 6 is identified.
For this example, the highest sixth-highest 24-hour concentration at any receptor is
15.218 |j,g/m3. (That is, at all other receptors, the sixth-highest concentration is less than
15.218 |j,g/m3.) Exhibit K-4 shows the six highest 24-hour concentrations at this
receptor.
10 This rounding convention comes from Appendix K to 40 CFR Part 50. A sufficient number of decimal
places (3-4) in modeling results should be retained during intermediate calculations for design values, so
that there is no possibility of intermediate rounding or truncation affecting the final result. Rounding to the
nearest 10 ug/m3 should only occur during final design value calculations, pursuant to Appendix K to 40
CFR Part 50. Monitoring values typically are reported with only one decimal place.
11 The six highest concentrations could occur anytime during the five years of meteorological data. They
may be clustered in one or two years, or they may be spread out over several, or even all five, years of the
meteorological data.
K-8
-------
Exhibit K-4. Receptor with the Highest Sixth-Highest 24-Hour Concentration
(Build Scenario)
Rank
1
2
O
4
5
6
Highest 24-Hour
Concentrations
17.012
16.709
15.880
15.491
15.400
15.218
Step 3. In this example, the background monitor collects data every third day (l-in-3
sampling) and has a total of 360 daily readings in the most recent three year period. The
appropriate 24-hour background concentration from the three most recent years of
monitoring data is identified based on Exhibit 9-6. The information in Exhibit 9-6 has
been repeated in Exhibit K-5 below, along with the highest four values from the
background monitor:
Exhibit K-5: Highest Values from the Chosen Background Monitor (360 Readings
in the Most Recent Three Year Period)
Number of Background
Concentration Values
from the Monitor
<347
348 - 695
696 - 1042
1043 - 1096
Monitor Value Used
for Design Value
Calculation
Highest Monitor Value
Second Highest Value
Third Highest Value
Fourth Highest Value
Highest Values from
the Chosen
Background Monitor
112.490
86.251
75.821
75.217
Because the monitor has 360 readings in the most recent three-year period, the second-
highest 24-hour background concentration is used for the design value calculation. The
second-highest value is 86.251 |j,g/m3.
Step 4. The sixth-highest 24-hour modeled concentration of 15.218 |j,g/m3 from the
highest receptor (from Step 2) is added to the second-highest 24-hour background
concentration of 86.251 |j,g/m3 (from Step 3):
15.218 + 86.251 = 101.469
Step 5. This sum is rounded to the nearest 10 ng/m3, which results in a design value of
100
This result is then compared to the 24-hour PMio NAAQS. In this case, the concentration
calculated at all receptors is less than the 24-hour PMio NAAQS of 150 ng/m3, therefore
the analysis shows that the project conforms. However, if the design value for this
K-9
-------
receptor had been greater than 150 |J,g/m3, the remainder of the steps in Section 9.3.4
would be completed. That is, build scenario design values for each receptor would be
calculated (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.12
12 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-10
-------
Appendix L:
Calculating 24-hour PM2.5 Design Values
Using a Second Tier Approach
L.I INTRODUCTION
As described in Section 9, design values for the 24-hr PIVh.s NAAQS may be calculated
using either a first tier or second tier approach. Generally, the first tier approach involves
adding the 98th percentile monitored data directly to each receptor's 98th percentile
modeled concentrations. The second tier approach requires developing a 98th percentile
background concentration for each quarter. Those values are then read into the
AERMOD input file and used to calculate an appropriate 98th percentile design value for
each receptor - done entirely within the model. EPA believes that most analyses should
be done with a first tier approach, as described in Section 9 and demonstrated in
Appendix K. The first tier approach requires much less processing of monitoring data
and modeled concentrations. However, users may choose to follow the second tier
approach to meet conformity requirements if through interagency consultation it is
determined that a first tier approach is overly conservative. The second tier process
includes the following general steps:
1) Calculate quarterly 98th percentile values from the monitoring data
2) Add quarterly background concentrations to AERMOD input file
3) Run AERMOD to generate 98th percentile concentrations at each receptor
This process differs from the methodology described for the first tier approach, as well as
PM2.5 annual and PMio design value calculations. Notably, background is handled first,
then added into the AERMOD input file. AERMOD will automatically generate the
appropriate 98th percentile design value.
Note that CAL3QHCR cannot be used in a second tier approach. The model only
produces the highest six values for each quarter; and to accurately calculate the 98th
percentile across four quarters, it is necessary to obtain the highest eight values in each
quarter. Any analysis done with a second tier design value approach should use
AERMOD.
The remainder of Appendix L describes an example of a second tier design value
approach, as well as the steps involved with adding background concentrations to an
AERMOD input file.
L.2 PREPARING MONITORING DATA
This appendix provides an illustrative example of the calculations and data sorting
recommendations for the background monitoring data to be used in a second tier
L-l
-------
modeling approach.1 In this example, it was determined through interagency consultation
that the impacts from the project's PIVh.s emissions were most prominent during the cool
season and were not temporally correlated with background PIVh.s levels that were typical
highest during the warm season. So, combining the modeled and monitored contributions
through a first tier approach was determined to be potentially overly conservative.
The example provided is from an idealized Federal Reference Method (FRM)
monitoring site that operates on a daily (1-in-l day) frequency with 100% data
completeness. In this case, the annual 98th percentile concentration is the 8th highest
concentration of the year. In most cases, the FRM monitoring site will likely operate on a
l-and-3 day frequency and will also likely have missing data due to monitor maintenance
or collected data not meeting all of the quality assurance criteria. Please reference
Section 9 (Exhibit 9.5) and Appendix N to 40 CFR Part 50 to determine the appropriate
98th percentile rank of the monitored data based on the monitor sampling frequency and
valid number of days sampled during each year. The appropriate seasonal (or quarterly)
background concentrations to be included as input to the AERMOD model per a second
tier approach are as follows:
Step 1 - Start with the most recent three years of representative background PM2.5
ambient monitoring data that are being used to develop the monitored background PM2.5
design value. In this example, the three years are labeled Year 1, Year 2, and Year 3.
Step 2 - For each year, determine the appropriate rank for the daily 98th percentile PM2.5
concentration. Again, this idealized example is from a 1-in-l day monitor with 100% data
completeness. So, the 8th highest concentration of each year is the 98th percentile PM2.5
5
concentration. The 98th percentile PM2.5 concentration for Year 1 is highlighted in Exhibit
L-l. The full concentration data from Year 2 and Year 3 are not shown across the steps in
this Appendix for simplicity, but would be similar to that of Year 1.
Step 3 - Remove from further consideration in this analysis the PM2.5 concentrations
from each year that are greater than the 98th percentile PM2.5 concentration. In the case
presented for a 1-in-l day monitor, the top 7 concentrations are removed. If the monitor
were a l-in-3 day monitor, only the top 2 concentrations would be removed. The
resultant dataset after the top 7 concentrations have been removed from further
consideration in this analysis for Year 1 is presented in Exhibit L-2.
Step 4 - For each year, divide the resultant annual dataset of the monitored data equal to
or less than the 98th percentile PM2.5 concentration into each season (or quarter). For
Year 1, the seasonal subsets are presented in Exhibit L-3.
Step 5 - Determine the maximum PM2.5 concentration from each of the seasonal (or
quarterly) subsets created in Step 4 for each year. The maximum PM2.5 concentration
from each season for Year 1 is highlighted in both Exhibits L-3 and L-4.
1 This example has been adapted from the 2014 Guidance for PM2s Permit Modeling, available at:
http://www.epa.gov/ttn/scram/guidance/guide/Guidance_for_PM25_Permit_Modeling.pdf
L-2
-------
Step 6 - Average the seasonal (or quarterly) maximums from Step 5 across the three
years of monitoring data to create the four seasonal background PIVh.s concentrations to
be included as inputs to the AERMOD model. These averages for the Year 1, Year 2,
and Year 3 dataset used in this example are presented in Exhibit L-4. As noted above,
the full concentration data only from Year 1 is shown in the exhibits in this appendix for
simplicity, but the seasonal maximums from Years 2 and 3 presented in Exhibit L-4 were
determined by following the previous five steps, similar to that of Year 1.
L-3
-------
Exhibit L-l. Year 1 Daily PMi.5 Concentrations
Date
1-Jan
2-Jan
3-Jan
4-Jan
5-Jan
6-Jan
7-Jan
8-Jan
9-Jan
10-Jan
11- Jan
12-Jan
13-Jan
14-Jan
15-Jan
16-Jan
17-Jan
18-Jan
19-Jan
20-Jan
21-Jan
22-Jan
23-Jan
24-Jan
25-Jan
26-Jan
27-Jan
28-Jan
29-Jan
30-Jan
31- Jan
1-Feb
2-Feb
3-Feb
4-Feb
5-Feb
6-Feb
7-Feb
8-Feb
9-Feb
10-Feb
1 1-Feb
12-Feb
13-Feb
14-Feb
15-Feb
Cone.
10.4
5.4
10.0
16.4
11.2
11.1
10.2
11.4
8.1
9.4
5.7
8.9
18.1
11.0
11.8
10.7
10.0
15.6
18.0
6.6
7.4
13.5
16.0
9.4
12.6
13.6
16.1
10.0
10.4
6.9
4.9
5.4
7.1
10.9
12.1
17.1
10.3
4.0
9.7
11.5
3.0
5.5
18.9
17.6
11.2
14.4
Date
16-Feb
17-Feb
18-Feb
19-Feb
20-Feb
21-Feb
22-Feb
23-Feb
24-Feb
25-Feb
26-Feb
27-Feb
28-Feb
29-Feb
1-Mar
2-Mar
3-Mar
4-Mar
5-Mar
6-Mar
7-Mar
8-Mar
9-Mar
10-Mar
1 1-Mar
12-Mar
13-Mar
14-Mar
15-Mar
16-Mar
17-Mar
18-Mar
19-Mar
20-Mar
21-Mar
22-Mar
23-Mar
24-Mar
25-Mar
26-Mar
27-Mar
28-Mar
29-Mar
30-Mar
3 1-Mar
1-Apr
Cone.
15.1
11.8
3.4
4.5
4.8
11.9
20.1
11.4
19.3
18.2
12.8
5.5
9.7
12.1
9.6
5.6
12.5
7.1
4.9
9.9
11.2
5.5
8.8
11.0
12.1
9.7
15.1
21.6
16.6
7.9
9.6
10.3
8.4
4.9
8.7
13.3
12.2
10.3
11.9
20.1
22.5
18.2
10.8
6.4
3.3
7.8
Date
2-Apr
3-Apr
4-Apr
5-Apr
6-Apr
7-Apr
8-Apr
9-Apr
10-Apr
11- Apr
12-Apr
13-Apr
14-Apr
15-Apr
16-Apr
17-Apr
18-Apr
19-Apr
20-Apr
21-Apr
22-Apr
23-Apr
24-Apr
25-Apr
26- Apr
27-Apr
28-Apr
29-Apr
30-Apr
1-May
2-May
3-May
4-May
5-May
6-May
7-May
8-May
9-May
10-May
1 1-May
12-May
13-May
14-May
1 5-May
16-May
17-May
Cone.
10.5
8.2
9.7
6.9
6.3
7.9
9.8
16.5
13.3
11.0
8.8
6.3
5.1
7.9
8.2
14.7
22.5
12.8
6.9
7.5
6.0
9.1
10.3
12.0
12.5
11.3
7.6
7.4
11.4
12.6
10.0
11.2
10.4
15.7
16.1
16.8
14.5
11.7
9.0
6.7
7.9
8.3
12.2
13.1
8.8
8.2
Date
18-May
19-May
20-May
21-May
22-May
23-May
24-May
25-May
26-May
27-May
28-May
29-May
30-May
3 1-May
1-Jun
2-Jun
3-Jun
4-Jun
5-Jun
6-Jun
7-Jun
8-Jun
9-Jun
10-Jun
11-Jun
12-Jun
13-Jun
14-Jun
15-Jun
16-Jun
17-Jun
18-Jun
19-Jun
20-Jun
21-Jun
22-Jun
23-Jun
24-Jun
25-Jun
26-Jun
27-Jun
28-Jun
29-Jun
30-Jun
1-Jul
2-Jul
Cone.
11.1
7.7
13.6
12.1
10.0
13.3
11.2
17.7
14.2
15.4
13.9
9.3
14.5
20.5
15.3
11.5
17.9
21.1
17.9
17.6
15.0
22.3
21.9
21.6
19.4
21.2
29.1
15.6
14.8
17.8
12.6
10.5
15.0
22.7
18.7
15.2
16.8
15.1
20.7
23.0
17.8
12.4
12.7
8.9
7.1
13.8
Date
3-Jul
4-Jul
5-Jul
6-Jul
7-Jul
8-Jul
9-Jul
10-Jul
11-Jul
12-Jul
13-Jul
14-Jul
15-Jul
16-Jul
17-Jul
18-Jul
19-Jul
20-Jul
21-Jul
22-Jul
23-Jul
24-Jul
25-Jul
26-Jul
27-Jul
28-Jul
29-Jul
30-Jul
31-Jul
1-Aug
2-Aug
3-Aug
4-Aug
5-Aug
6-Aug
7-Aug
8-Aug
9-Aug
10-Aug
11-Aug
12-Aug
13-Aug
14-Aug
15-Aug
16-Aug
17-Aug
Cone.
17.1
19.8
14.3
11.5
14.3
12.2
11.1
9.7
16.4
21.5
25.1
11.7
18.9
28.9
27.6
12.8
6.2
20.1
26.5
16.9
12.8
7.9
15.7
24.9
22.2
17.5
19.1
21.1
18.0
16.3
19.3
17.9
25.1
29.3
19.1
14.0
10.8
15.0
21.7
14.3
14.7
13.0
13.5
17.5
23.9
18.4
Date
18-Aug
19-Aug
20-Aug
21-Aug
22-Aug
23-Aug
24-Aug
25-Aug
26-Aug
27-Aug
28-Aug
29-Aug
30-Aug
31-Aug
1-Sep
2-Sep
3-Sep
4-Sep
5-Sep
6-Sep
7-Sep
8-Sep
9-Sep
10-Sep
11-Sep
12-Sep
13-Sep
14-Sep
15-Sep
16-Sep
17-Sep
18-Sep
19-Sep
20-Sep
21-Sep
22-Sep
23-Sep
24-Sep
25-Sep
26-Sep
27-Sep
28-Sep
29-Sep
30-Sep
1-Oct
2-Oct
Cone.
18.7
21.5
20.1
18.4
16.7
13.8
19.0
17.6
15.4
12.6
12.1
10.1
17.2
19.9
19.4
18.2
24.0
15.4
12.4
12.5
15.8
23.4
11.5
6.0
11.8
10.7
7.6
7.5
7.1
7.7
11.3
16.8
14.8
8.0
10.8
14.5
21.2
8.6
1.2
16.0
12.1
18.0
17.8
16.4
12.3
8.2
Date
3-Oct
4-Oct
5-Oct
6-Oct
7-Oct
8-Oct
9-Oct
10-Oct
11-Oct
12-Oct
13-Oct
14-Oct
15-Oct
16-Oct
17-Oct
18-Oct
19-Oct
20-Oct
21-Oct
22-Oct
23-Oct
24-Oct
25-Oct
26-Oct
27-Oct
28-Oct
29-Oct
30-Oct
31-Oct
1-Nov
2-Nov
3-Nov
4-Nov
5-Nov
6-Nov
7-Nov
8-Nov
9-Nov
10-Nov
11-Nov
12-Nov
13-Nov
14-Nov
15-Nov
16-Nov
17-Nov
Cone.
12.3
19.5
23.7
19.8
21.7
12.2
5.1
10.2
10.7
5.6
5.9
9.7
12.8
16.4
12.0
7.9
6.6
8.1
12.2
4.6
6.1
4.6
4.5
10.5
6.4
4.6
5.6
7.6
11.2
16.2
17.3
18.3
8.9
5.8
8.6
15.0
8.3
10.0
12.8
11.8
14.8
14.5
7.7
3.6
4.6
7.8
Date
18-Nov
19-Nov
20-Nov
21-Nov
22-Nov
23-Nov
24-Nov
25-Nov
26-Nov
27-Nov
28-Nov
29-Nov
30-Nov
1-Dec
2-Dec
3-Dec
4-Dec
5-Dec
6-Dec
7-Dec
8-Dec
9-Dec
10-Dec
1 1-Dec
12-Dec
13-Dec
14-Dec
15-Dec
16-Dec
17-Dec
18-Dec
19-Dec
20-Dec
21-Dec
22-Dec
23-Dec
24-Dec
25-Dec
26-Dec
27-Dec
28-Dec
29-Dec
30-Dec
3 1-Dec
Cone.
4.4
8.2
11.1
5.3
8.9
14.0
12.7
9.7
12.8
16.6
17.2
16.6
4.5
7.5
10.6
16.7
12.5
7.3
10.4
13.4
10.5
9.3
6.5
3.0
3.5
10.2
17.6
12.4
9.7
7.0
7.9
6.9
8.1
4.9
7.7
7.7
10.5
6.5
7.6
13.3
6.4
3.7
4.7
4.4
Annual 98th Percentile Concentration (highlighted green value) = 25.1
L-3
-------
Exhibit L-2: Year 1 Daily PMi.5 Concentrations Less Than or Equal to the 98th Percentile
Date
1-Jan
2-Jan
3-Jan
4-Jan
5-Jan
6-Jan
7-Jan
8-Jan
9-Jan
10-Jan
11-Jan
12-Jan
13-Jan
14-Jan
15-Jan
16-Jan
17-Jan
18-Jan
19-Jan
20-Jan
21-Jan
22-Jan
23-Jan
24-Jan
25-Jan
26-Jan
27-Jan
28-Jan
29-Jan
30-Jan
31- Jan
1-Feb
2-Feb
3-Feb
4-Feb
5-Feb
6-Feb
7-Feb
8-Feb
9-Feb
10-Feb
1 1-Feb
12-Feb
13-Feb
14-Feb
15-Feb
Cone.
10.4
5.4
10.0
16.4
11.2
11.1
10.2
11.4
8.1
9.4
5.7
8.9
18.1
11.0
11.8
10.7
10.0
15.6
18.0
6.6
7.4
13.5
16.0
9.4
12.6
13.6
16.1
10.0
10.4
6.9
4.9
5.4
7.1
10.9
12.1
17.1
10.3
4.0
9.7
11.5
3.0
5.5
18.9
17.6
11.2
14.4
Date
16-Feb
17-Feb
18-Feb
19-Feb
20-Feb
21-Feb
22-Feb
23-Feb
24-Feb
25-Feb
26-Feb
27-Feb
28-Feb
29-Feb
1-Mar
2-Mar
3-Mar
4-Mar
5-Mar
6-Mar
7-Mar
8-Mar
9-Mar
10-Mar
1 1-Mar
12-Mar
13-Mar
14-Mar
1 5-Mar
16-Mar
17-Mar
18-Mar
19-Mar
20-Mar
21-Mar
22-Mar
23-Mar
24-Mar
25-Mar
26-Mar
27-Mar
28-Mar
29-Mar
30-Mar
3 1-Mar
1-Apr
Cone.
15.1
11.8
3.4
4.5
4.8
11.9
20.1
11.4
19.3
18.2
12.8
5.5
9.7
12.1
9.6
5.6
12.5
7.1
4.9
9.9
11.2
5.5
8.8
11.0
12.1
9.7
15.1
21.6
16.6
7.9
9.6
10.3
8.4
4.9
8.7
13.3
12.2
10.3
11.9
20.1
22.5
18.2
10.8
6.4
3.3
7.8
Date
2-Apr
3- Apr
4- Apr
5- Apr
6- Apr
7-Apr
8-Apr
9- Apr
10-Apr
11-Apr
12-Apr
13-Apr
14-Apr
15- Apr
16- Apr
17-Apr
18-Apr
19-Apr
20-Apr
21-Apr
22-Apr
23- Apr
24- Apr
25- Apr
26-Apr
27-Apr
28-Apr
29-Apr
30-Apr
1-May
2-May
3-May
4-May
5-May
6-May
7-May
8-May
9-May
10-May
1 1-May
12-May
13-May
14-May
15-May
16-May
17-May
Cone.
10.5
8.2
9.7
6.9
6.3
7.9
9.8
16.5
13.3
11.0
8.8
6.3
5.1
7.9
8.2
14.7
22.5
12.8
6.9
7.5
6.0
9.1
10.3
12.0
12.5
11.3
7.6
7.4
11.4
12.6
10.0
11.2
10.4
15.7
16.1
16.8
14.5
11.7
9.0
6.7
7.9
8.3
12.2
13.1
8.8
8.2
Date
18-May
19-May
20-May
21-May
22-May
23-May
24-May
25-May
26-May
27-May
28-May
29-May
30-May
3 1-May
1-Jun
2-Jun
3-Jun
4-Jun
5-Jun
6-Jun
7-Jun
8-Jun
9-Jun
10-Jun
11-Jun
12-Jun
13-Jun
14-Jun
15-Jun
16-Jun
17-Jun
18-Jun
19-Jun
20-Jun
21-Jun
22-Jun
23-Jun
24-Jun
25-Jun
26-Jun
27-Jun
28-Jun
29-Jun
30-Jun
1-Jul
2-Jul
Cone.
11.1
7.7
13.6
12.1
10.0
13.3
11.2
17.7
14.2
15.4
13.9
9.3
14.5
20.5
15.3
11.5
17.9
21.1
17.9
17.6
15.0
22.3
RC
21.6
19.4
21.2
RC
15.6
14.8
17.8
12.6
10.5
15.0
22.7
18.7
15.2
16.8
15.1
20.7
23.0
17.8
12.4
12.7
8.9
7.1
13.8
Date
3-Jul
4-Jul
5-Jul
6-Jul
7-Jul
8-Jul
9-Jul
10-Jul
11-Jul
12-Jul
13-Jul
14-Jul
15-Jul
16-Jul
17-Jul
18-Jul
19-Jul
20-Jul
21-Jul
22-Jul
23-Jul
24-Jul
25-Jul
26-Jul
27-Jul
28-Jul
29-Jul
30-Jul
31-Jul
1-Aug
2-Aug
3-Aug
4-Aug
5-Aug
6-Aug
7-Aug
8-Aug
9-Aug
10-Aug
11-Aug
12-Aug
13-Aug
14-Aug
15-Aug
16-Aug
17-Aug
Cone.
17.1
19.8
14.3
11.5
14.3
12.2
11.1
9.7
16.4
21.5
RC
11.7
18.9
RC
RC
12.8
6.2
20.1
RC
16.9
12.8
7.9
15.7
24.9
22.2
17.5
19.1
21.1
18.0
16.3
19.3
17.9
25.1
RC
19.1
14.0
10.8
15.0
21.7
14.3
14.7
13.0
13.5
17.5
23.9
18.4
Date
18-Aug
19-Aug
20-Aug
21-Aug
22-Aug
23-Aug
24-Aug
25-Aug
26-Aug
27-Aug
28-Aug
29-Aug
30-Aug
31-Aug
1-Sep
2-Sep
3-Sep
4-Sep
5-Sep
6-Sep
7-Sep
8-Sep
9-Sep
10-Sep
11-Sep
12-Sep
13-Sep
14-Sep
15-Sep
16-Sep
17-Sep
18-Sep
19-Sep
20-Sep
21-Sep
22-Sep
23-Sep
24-Sep
25-Sep
26-Sep
27-Sep
28-Sep
29-Sep
30-Sep
1-Oct
2-Oct
Cone.
18.7
21.5
20.1
18.4
16.7
13.8
19.0
17.6
15.4
12.6
12.1
10.1
17.2
19.9
19.4
18.2
24.0
15.4
12.4
12.5
15.8
23.4
11.5
6.0
11.8
10.7
7.6
7.5
7.1
7.7
11.3
16.8
14.8
8.0
10.8
14.5
21.2
8.6
1.2
16.0
12.1
18.0
17.8
16.4
12.3
8.2
Date
3-Oct
4-Oct
5-Oct
6-Oct
7-Oct
8-Oct
9-Oct
10-Oct
11-Oct
12-Oct
13-Oct
14-Oct
15-Oct
16-Oct
17-Oct
18-Oct
19-Oct
20-Oct
21-Oct
22-Oct
23-Oct
24-Oct
25-Oct
26-Oct
27-Oct
28-Oct
29-Oct
30-Oct
31-Oct
1-Nov
2-Nov
3-Nov
4-Nov
5-Nov
6-Nov
7-Nov
8-Nov
9-Nov
10-Nov
11-Nov
12-Nov
13-Nov
14-Nov
15-Nov
16-Nov
17-Nov
Cone.
12.3
19.5
23.7
19.8
21.7
12.2
5.1
10.2
10.7
5.6
5.9
9.7
12.8
16.4
12.0
7.9
6.6
8.1
12.2
4.6
6.1
4.6
4.5
10.5
6.4
4.6
5.6
7.6
11.2
16.2
17.3
18.3
8.9
5.8
8.6
15.0
8.3
10.0
12.8
11.8
14.8
14.5
7.7
3.6
4.6
7.8
Date
18-Nov
19-Nov
20-Nov
21-Nov
22-Nov
23-Nov
24-Nov
25-Nov
26-Nov
27-Nov
28-Nov
29-Nov
30-Nov
1-Dec
2-Dec
3-Dec
4-Dec
5-Dec
6-Dec
7-Dec
8-Dec
9-Dec
10-Dec
1 1-Dec
12-Dec
13-Dec
14-Dec
15-Dec
16-Dec
17-Dec
18-Dec
19-Dec
20-Dec
21-Dec
22-Dec
23-Dec
24-Dec
25-Dec
26-Dec
27-Dec
28-Dec
29-Dec
30-Dec
3 1-Dec
Cone.
4.4
8.2
11.1
5.3
8.9
14.0
12.7
9.7
12.8
16.6
17.2
16.6
4.5
7.5
10.6
16.7
12.5
7.3
10.4
13.4
10.5
9.3
6.5
3.0
3.5
10.2
17.6
12.4
9.7
7.0
7.9
6.9
8.1
4.9
7.7
7.7
10.5
6.5
7.6
13.3
6.4
3.7
4.7
4.4
RC =
Annual 98th Percentile Concentration (highlighted green value) = 25.1
Above 98th Percentile and Removed from Consideration (highlighted peach values)
L-4
-------
Exhibit L-3. Year 1 Daily PMi.5 Concentrations Less Than or Equal to the 98th Percentile by Quarter
Season / Quarter 1
Date
1-Jan
2-Jan
3-Jan
Wan
5-Jan
6-Jan
7-Jan
8-Jan
9-Jan
10-Jan
11-Jan
12-Jan
13-Jan
14-Jan
15- Jan
16-Jan
17-Jan
18- Jan
19-Jan
20-Jan
21-Jan
22-Jan
23-Jan
24-Jan
25-Jan
26-Jan
27-Jan
28-Jan
29-Jan
30-Jan
31-Jan
1-Feb
2-Feb
3-Feb
4-Feb
5-Feb
6-Feb
7-Feb
8-Feb
9-Feb
10-Feb
1 1-Feb
12-Feb
13-Feb
14-Feb
1 5-Feb
Cone.
10.4
5.4
10.0
16.4
11.2
11.1
10.2
11.4
8.1
9.4
5.7
8.9
18.1
11.0
11.8
10.7
10.0
15.6
18.0
6.6
7.4
13.5
16.0
9.4
12.6
13.6
16.1
10.0
10.4
6.9
4.9
5.4
7.1
10.9
12.1
17.1
10.3
4.0
9.7
11.5
3.0
5.5
18.9
17.6
11.2
14.4
Date
16-Feb
17-Feb
1 8-Feb
19-Feb
20-Feb
21-Feb
22-Feb
23-Feb
24-Feb
25-Feb
26-Feb
27-Feb
28-Feb
29-Feb
1-Mar
2-Mar
3-Mar
4-Mar
5-Mar
6-Mar
7-Mar
8-Mar
9-Mar
10-Mar
1 1-Mar
12-Mar
13-Mar
14-Mar
15-Mar
16-Mar
17-Mar
18-Mar
19-Mar
20-Mar
21-Mar
22-Mar
23-Mar
24-Mar
25-Mar
26-Mar
27-Mar
28-Mar
29-Mar
30-Mar
31-Mar
Seasonal / Quarterly Maximum
Cone.
15.1
11.8
3.4
4.5
4.8
11.9
20.1
11.4
19.3
18.2
12.8
5.5
9.7
12.1
9.6
5.6
12.5
7.1
4.9
9.9
11.2
5.5
8.8
11.0
12.1
9.7
15.1
21.6
16.6
7.9
9.6
10.3
8.4
4.9
8.7
13.3
12.2
10.3
11.9
20.1
22.5
18.2
10.8
6.4
3.3
22.5
Season / Quarter 2
Date Cone.
1-Apr
2-Apr
3-Apr
4-Apr
5-Apr
6-Apr
7-Apr
8-Apr
9-Apr
10-Apr
11-Apr
12-Apr
13-Apr
14-Apr
15-Apr
16-Apr
17-Apr
18-Apr
19-Apr
20-Apr
21-Apr
22-Apr
23-Apr
24-Apr
25-Apr
26-Apr
27-Apr
28-Apr
29-Apr
30-Apr
1-May
2-May
3-May
4-May
5-May
6-May
7-May
8-May
9-May
10-May
1 1-May
12-May
13-May
14-May
15-May
16-May
7.8
10.5
8.2
9.7
6.9
6.3
7.9
9.8
16.5
13.3
11.0
8.8
6.3
5.1
7.9
8.2
14.7
22.5
12.8
6.9
7.5
6.0
9.1
10.3
12.0
12.5
11.3
7.6
7.4
11.4
12.6
10.0
11.2
10.4
15.7
16.1
16.8
14.5
11.7
9.0
6.7
7.9
8.3
12.2
13.1
8.8
Date Cone.
17-May
1 8-May
19-May
20-May
21-May
22-May
23-May
24-May
25-May
26-May
27-May
28-May
29-May
30-May
3 1-May
1-Jun
2-Jun
3-Jun
4-Jun
5-Jun
6-Jun
7-Jun
8-Jun
9-Jun
10-Jun
11-Jun
12-Jun
13-Jun
14-Jun
15-Jun
16-Jun
17-Jun
18-Jun
19-Jun
20-Jun
21-Jun
22-Jun
23-Jun
24-Jun
25-Jun
26-Jun
27-Jun
28-Jun
29-Jun
30-Jun
Seasonal / Quarterly Maximum
8.2
11.1
7.7
13.6
12.1
10.0
13.3
11.2
17.7
14.2
15.4
13.9
9.3
14.5
20.5
15.3
11.5
17.9
21.1
17.9
17.6
15.0
22.3
RC
21.6
19.4
21.2
RC
15.6
14.8
17.8
12.6
10.5
15.0
22.7
18.7
15.2
16.8
15.1
20.7
23.0
17.8
12.4
12.7
8.9
23.0
Season / Quarter 3
Date Cone.
1-Jul
2-Jul
3-Jul
4-Jul
5-Jul
6-Jul
7-Jul
8-Jul
9-Jul
10-Jul
11-Jul
12-Jul
13-Jul
14-Jul
15-Jul
16-Jul
17-Jul
18-Jul
19-Jul
20-Jul
21-Jul
22-Jul
23-Jul
24-Jul
25-Jul
26-Jul
27-Jul
28-Jul
29-Jul
30-Jul
31-Jul
1-Aug
2-Aug
3-Aug
4-Aug
5-Aug
6-Aug
7-Aug
8-Aug
9-Aug
10-Aug
11-Aug
12-Aug
13-Aug
14-Aug
15-Aug
7.1
13.8
17.1
19.8
14.3
11.5
14.3
12.2
11.1
9.7
16.4
21.5
RC
11.7
18.9
RC
RC
12.8
6.2
20.1
RC
16.9
12.8
7.9
15.7
24.9
22.2
17.5
19.1
21.1
18.0
16.3
19.3
17.9
25.1
RC
19.1
14.0
10.8
15.0
21.7
14.3
14.7
13.0
13.5
17.5
Date Cone.
16-Aug
17-Aug
18-Aug
19-Aug
20-Aug
21-Aug
22-Aug
23-Aug
24-Aug
25-Aug
26-Aug
27-Aug
28-Aug
29-Aug
30-Aug
31-Aug
1-Sep
2-Sep
3-Sep
4-Sep
5-Sep
6-Sep
7-Sep
8-Sep
9-Sep
10-Sep
11-Sep
12-Sep
13-Sep
14-Sep
15-Sep
16-Sep
17-Sep
18-Sep
19-Sep
20-Sep
21-Sep
22-Sep
23-Sep
24-Sep
25-Sep
26-Sep
27-Sep
28-Sep
29-Sep
30-Sep
Seasonal / Quarterly Maximum
23.9
18.4
18.7
21.5
20.1
18.4
16.7
13.8
19.0
17.6
15.4
12.6
12.1
10.1
17.2
19.9
19.4
18.2
24.0
15.4
12.4
12.5
15.8
23.4
11.5
6.0
11.8
10.7
7.6
7.5
7.1
7.7
11.3
16.8
14.8
8.0
10.8
14.5
21.2
8.6
1.2
16.0
12.1
18.0
17.8
16.4
25.1
Season / Quarter 4
Date Cone.
1-Oct
2-Oct
3-Oct
4-Oct
5-Oct
6-Oct
7-Oct
8-Oct
9-Oct
10-Oct
11-Oct
12-Oct
13-Oct
14-Oct
15-Oct
16-Oct
17-Oct
18-Oct
19-Oct
20-Oct
21-Oct
22-Oct
23-Oct
24-Oct
25-Oct
26-Oct
27-Oct
28-Oct
29-Oct
30-Oct
31-Oct
1-Nov
2-Nov
3-Nov
4-Nov
5-Nov
6-Nov
7-Nov
8-Nov
9-Nov
10-Nov
11-Nov
12-Nov
13-Nov
14-Nov
15-Nov
12.3
8.2
12.3
19.5
23.7
19.8
21.7
12.2
5.1
10.2
10.7
5.6
5.9
9.7
12.8
16.4
12.0
7.9
6.6
8.1
12.2
4.6
6.1
4.6
4.5
10.5
6.4
4.6
5.6
7.6
11.2
16.2
17.3
18.3
8.9
5.8
8.6
15.0
8.3
10.0
12.8
11.8
14.8
14.5
7.7
3.6
Date Cone.
16-Nov
17-Nov
18-Nov
19-Nov
20-Nov
21-Nov
22-Nov
23-Nov
24-Nov
25-Nov
26-Nov
27-Nov
28-Nov
29-Nov
30-Nov
1-JJec
2-JJec
3-Dec
4-JJec
5-JJec
6-JJec
7-Dec
8-JJec
9-JJec
10-Dec
11-Dec
12-Dec
13-Dec
14-Dec
15-Dec
16-Dec
17-Dec
18-Dec
19-Dec
20-Dec
21-Dec
22-Dec
23-Dec
24-Dec
25-Dec
26-Dec
27-Dec
28-Dec
29-Dec
30-Dec
31-Dec
Seasonal / Quarterly Maximum
4.6
7.8
4.4
8.2
11.1
5.3
8.9
14.0
12.7
9.7
12.8
16.6
17.2
16.6
4.5
7.5
10.6
16.7
12.5
7.3
10.4
13.4
10.5
9.3
6.5
3.0
3.5
10.2
17.6
12.4
9.7
7.0
7.9
6.9
8.1
4.9
7.7
7.7
10.5
6.5
7.6
13.3
6.4
3.7
4.7
4.4
23.7
Seasonal/Quarterly Maximum Concentration (highlighted blue values)
RC = Above 98th Percentile and Removed from Consideration (highlighted peach values)
L-5
-------
Exhibit L-4: Resulting Average of Seasonal (or Quarterly) Maximums from Year 1
for Inclusion into AERMOD
Seasonal / Quarterly Average Highest Monitored Concentration
(From Annual Datasets Equal To and Less Than the 98th
Percentile)
Year 1
Year 2
Year3
Average
Ql
22.5
21.1
20.7
21.433
Q2
23.0
20.7
22.6
22.100
Q3
25.1
21.2
23.5
23.267
Q4
23.7
19.8
20.7
21.400
Note, the complete datasets for Year 2 and Year 3 are not shown in this appendix but
would follow the same steps as for Year 1.
L.3 RUNNING AERMOD
After calculating the seasonal 98th percentile background concentrations, the four average
seasonal values (shown in the last row of Exhibit L-4) can be added to the AERMOD
input file. There are four important steps to follow when creating an input file consistent
with the second tier design value approach.
1) AERMOD must be run with five years of concatenated met data (assuming the
use of an off-site monitor). This allows for the calculation of the 98th percentile
value across all years of data.
2) Ensure that "PM2.5" is listed for the POLLUTE) keyword in the CO pathway.
This will trigger calculations in AERMOD that automatically average across five
years of meteorological data to determine the 98th percentile concentration at each
receptor.
3) Add a line in the SO pathway with the keyword BACKGRND, followed by
SEASON. This will allow the definition of four seasonal values. For the example
shown above in Appendix L.2, the appropriate line in AERMOD would be:
SO BACKGRND SEASON 21.433 22.100 23.267 21.400
Also, ensure that BACKGRND is added to the SRCGROUP line of the SO
pathway.
4) Finally, since the 98th percentile of 365 days is the eighth highest day, use the
RECTABLE keyword of the OU pathway to define the "8th" highest value to
report.
L-6
-------
After running AERMOD, the RECTABLE generated will report 98th percentile
concentrations, averaged across five years of meteorological data, for each receptor.
These values can be compared directly to the NAAQS, or in the case of a build/no-build
analysis, the values at the same receptor in the build scenario.
L-7
------- |