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Near-road NO2 Monitoring Technical
Assistance Document
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EPA-454/B-12-002
June 2012
Near-road NOi Monitoring Technical Assistance Document
By:
Nealson Watkins
US EPA - OAQPS - AQAD
Research Triangle Park, North Carolina
and
Dr. Richard Baldauf
US EPA - ORD - NRML
Research Triangle Park, North Carolina
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Ambient Air Monitoring Group
Research Triangle Park, NC
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&ER&
United States
Environmental Protection
Agency
Near-Road NOi Monitoring
Technical Assistance Document
June 2012
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Near-Road NC>2 Monitoring TAD Preface
Preface
This document is the June 2012 release of the Near-Road NO2 Monitoring Technical
Assistance Document (TAD). The TAD was developed to aid state and local air monitoring
agencies in the implementation of required near-road NC>2 monitoring stations. The TAD
reflects the collaboration between partner state and local air monitoring agencies and
associations, partnering state departments of transportation, the Federal Highways
Administration, and the EPA. This document also reflects feedback, concepts, and suggestions
from two reviews conducted by the Clean Air Scientific Advisory Committee (CASAC)
Ambient Monitoring and Methods Subcommittee (AMMS).
Questions and comments on this document may be directed to
Nealson Watkins
US EPA - Office of Air and Radiation
Office of Air Quality Planning and Standards
Watkins.nealson@epa.gov
and
Dr. Richard Baldauf
US EPA - Office of Research and Development
National Risk Management Research Laboratory
Baldauf.richard@epa.gov
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Near-Road NC>2 Monitoring TAD Acknowledgments
Acknowledgments
This document reflects collaboration between multiple local, state, and federal entities who
all have a stake in the implementation of required near-road NC>2 monitoring stations, along with
contracted support. The EPA wishes to recognize these partners and thank them for their efforts
in the conception, development, and review of this TAD, which covers a wide range of materials
and concepts.
State and Local Air Agencies/Associations
Broward County (FL) Pollution Prevention Remediation and Air Quality Division
City of Albuquerque Environmental Health Department
Hillsborough County (FL) Environmental Protection Division
Idaho Department of Environmental Quality
Maryland Department of the Environment
National Association of Clean Air Agencies Monitoring Steering Committee
State and Federal Transportation Agencies/Associations
Florida Department of Transportation
Texas Department of Transportation
U.S. Department of Transportation Federal Highways Administration
American Association of State Highway and Transportation Officials
Contract Support
Sonoma Technology, Inc.
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Near-Road NO2 Monitoring TAD Table of Contents
Table of Contents
List of Figures vii
List of Tables viii
Glossary ix
Executive Summary ES-1
Section 1. Background and Near-Road NO2 Network Obj ectives 1
Section 2. Near-Road NO2 Monitoring Technical Assistance Document Objectives
and Content 3
Section 3. Identifying Core Based Statistical Areas with Required Near-Road Monitoring 5
3.1 Identifying CBSA Boundaries 5
3.2 Identifying Census Data 7
3.3 Identifying Roadway Traffic Volumes in Excess of 250,000 AADT 7
3.4 Meeting Requirements in CBSAs Covering Multiple Geo-Political Boundaries
(Multi-Agency/Multi-State) 7
Section 4. Near-Road NO2 TAD Quick-Start 9
4.1 Obtain and Assess AADT, Fleet Mix, and Congestion Data 9
4.2 Consider Physical Site Characteristics 10
4.3 Review Siting Criteria 12
4.4 Prepare Candidate Site Comparison Matrix 12
Section 5. Recommended Traffic Data and Resources for Use in Identifying Candidate Road
Segments for Near-Road NO2 Monitoring 15
5.1 Definitions of Terms 15
5.2 AADT 15
5.3 Sources of AADT Data 16
5.4 Fleet Mix 17
5.5 Sources of Fleet Mix Data 17
5.6 Congestion Patterns 17
5.6.1 Level of Service 18
5.6.2 Volume-to-Capacity Ratio 18
5.6.3 AADT by Lane 18
5.7 Sources of Congestion Pattern Data 19
Section 6. Creating an Initial List of Candidate Road Segments Using Traffic Data 21
in
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Near-Road NO2 Monitoring TAD Table of Contents
6.1 Use AADT to Initially Rank Road Segments 22
6.2 Combine Fleet Mix Data and AADT Data to Rank Road Segments 25
6.3 Calculate FE-AADT for All Segments 28
6.4 Use Congestion Pattern Indicators to Supplement Road Segment Rankings 32
6.5 Rank the Road Segments 35
Section 7. Physical Considerations for Candidate Near-Road Monitoring Sites 37
7.1 Roadway Design 37
7.1.1 At-Grade Roads 38
7.1.2 Below-Grade or Cut-Section Roads 39
7.1.3 Above-Grade or Elevated Roads 39
7.1.4 Relative Desirability in Roadway Designs 41
7.2 Roadside Structures 42
7.3 Vegetation 43
7.4 Terrain 43
7.5 Meteorology 43
Section 8. Siting Criteria 45
Section 9. Using Exploratory Air Quality Monitoring to Identify Roadway Segments for Near-
Road Site Selection Evaluation 47
9.1 Passive Monitoring for Saturation Studies 47
9.2 Stationary Continuous or Integrated Monitoring 48
9.3 Mobile Monitoring 49
Section 10. Using Air Quality Modeling to Identify Roadway Segments for Near-Road Site
Selection Evaluation 51
10.1 The MOVES Model 51
10.2 AERMOD Air Quality Dispersion Model 52
10.2.1 NO2 Chemistry Using PVMRM or OLM Algorithms 52
10.2.2 Including Background and Nearby Sources in Analyses 53
10.3 Resource 53
Section 11. Physical Characteristics of Candidate Near-Road Sites 55
11.1 Road Segment Identification 55
11.2 Road Segment Type 55
11.3 Road Segment End Points 56
11.4 Interchanges 56
11.5 Roadway Design 57
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Near-Road NO2 Monitoring TAD Table of Contents
11.6 Terrain 57
11.7 Roadside Structures 57
11.8 Existing Safety Features 58
11.9 Existing Infrastructure 58
11.10 Surrounding Land Use 58
11.11 Current Road Construction 59
11.12 Frontage Roads 59
11.13 Meteorology 59
Section 12. Monitoring Site Logistics in the Near-Road Environment 61
12.1 Accessing the Right-of-Way 62
12.2 Safety in the Near-Road Environment 64
12.2.1 Terrain 64
12.2.2 Man-Made Barriers 65
12.2.3 Clear Zones 65
12.2.4 Other Safety Considerations 68
12.3 Engaging a Transportation Agency 68
12.3.1 Questions a Transportation Agency May Have 69
12.3.2 Questions to Ask Your State or Local Transportation Agency 69
Section 13. Prioritizing Candidate Near-Road Locations for Monitoring Site Selection 71
13.1 Considering Population Exposure as a Selection Criterion 71
13.2 Unique Locations and Background Source Influences 72
13.3 Confounding Information 73
13.4 Potential for Multi-Pollutant Monitoring 73
13.5 Candidate Site Comparison Matrix 73
Section 14. Final Near-Road Site Selection 77
Section 15. Selecting a Second Near-Road Site 79
Section 16. Multipollutant Monitoring at Near-Road Monitoring Stations 81
16.1 Nitrogen Dioxide (NO2) 82
16.2 Carbon Monoxide (CO) 84
16.3 Ozone 85
16.4 Meteorological Measurements 85
16.5 Air Toxics 86
16.6 Black Carbon and Elemental Carbon 87
16.7 Ultrafme Paniculate Matter 87
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Near-Road NO2 Monitoring TAD Table of Contents
16.8 Traffic Counters and/or Cameras 88
16.9 PMMass 89
16.10 Carbon Dioxide (CO2) 90
16.11 Organic Carbon (OC) 90
Section 17. References 92
Appendix A: Supporting Information on Uncertainties in Traffic Data and Rationale for
Roadway Design Considerations A-l
Appendix B: Using MOVES to Create a Heavy-Duty to Light-Duty NOX Emission Ratio
for Use in this TAD B-l
Appendix C: Modeling C-l
VI
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Near-Road NC>2 Monitoring TAD List of Figures
List of Figures
3-1. Flowchart illustrating the process of identifying whether minimum monitoring
requirements for near-road NO2 monitors apply to individual CBSAs 6
4-1. Candidate road segment ranking process 9
6-1. Candidate road segment ranking process 22
7-1. Wind tunnel study results comparing downwind air pollutant concentrations from a
road with varying topography and roadside structures 41
12-1. Clear zone distance curves (reprinted with permission from AASHTO) 67
vn
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Near-Road NO2 Monitoring TAD List of Tables
List of Tables
4-1. Summary of the traffic-related metrics for candidate site consideration 9
4-2. Summary of physical considerations for candidate near-road sites 11
4-3. Key near-road siting criteria 12
4-4. Suggested data for each candidate site entry in a site comparison matrix 12
6-1. Ranking of road segments by AADT, as described in Step 1, for the Tampa,
Florida, CB SA, using 2009 traffic data available from Florida DOT 24
6-2. Ranking of road segments using fleet mix data as described in Step 2 26
6-3. Ranking of road segments based onFE-AADT as described in Step 3 30
6-4. Ranking of road segments including congestion pattern information per segment 33
6-5. Summary of the traffic-related metrics for candidate site consideration 35
7-1. Summary of physical considerations for candidate near-road sites 37
8-1. Key near-road siting criteria 45
12-1. Terminology used by transportation agencies that is relevant to this section 62
12-2. Clear zone information in U.S. customary units (reprinted with permission from
AASHTO) 66
13-1. Suggested data for each candidate site entry in a site comparison matrix 74
14-1. Near-road site information metadata required in AQS (AA - Basic Site Information
transaction) 77
14-2. Additional near-road site information metadata in AQS (AB Site Street
Information) 78
16-1. CAS AC AAMMS's recommended priorities for multipollutant monitoring at a
near-road site 82
Vlll
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Near-Road NO2 Monitoring TAD
Glossary
Glossary
Term
AADT
AASHTO
AERMOD
AQS
BAM
BC
CAPS
CARS
CASAC AMMS
CBS A
CFR
CO
CO2
CRDS
DNPH
DNSH
DOT
DPM
EC
EMFAC
EPA
FE-AADT
FEM
FHWA
FR
FRM
GC
GC/MS
HC
HD
HDc
HDm
HNO2
HNO3
HPMS
HR-AMS
Definition
Annual average daily traffic
American Association of State Highway and Transportation Officials
AMS/EPA Regulatory Model
Air Quality System
Beta attenuation monitor
Black carbon
Cavity Attenuated Phase Shift
California Air Resources Board
Clear Air Scientific Advisory Committee Ambient Monitoring and Methods
Subcommittee
Core Based Statistical Areas
Code of Federal Regulations
Carbon monoxide
carbon dioxide
Cavity Ring-Down Spectrometer
dinitrophenyl hydrazine
dansylhydrazine
department of transportation
diesel particulate matter
elemental carbon
EMission FACtors
Environmental Protection Agency
fleet equivalent annual average daily traffic
federal equivalent method
Federal Highway Administration
Federal Register
Federal reference method
gas chromatograph
gas chromatograph/mass spectrometer
hydrocarbons
heavy duty
total number of heavy-duty vehicles for a particular road segment
a multiplier that represents the heavy-duty to light-duty NOX emission ratio
for a particular road segment
nitrous acid
nitric acid
Highway Performance Monitoring System
high-resolution aerosol mass spectrometer
IX
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Near-Road NO2 Monitoring TAD
Glossary
IR
LD
LOS
MOVES
MPO
MSAT
N20
NAAQS
NFR
NO
NO2
NOAA
NOx
NOy
NPR
NWS
oc
OLM
OMB
PAH
PAN
PM
PM10
PMiQ-2.5
PM2.5
ppb
PSD
PUF
PVLMRM
REA
ROW
RWIS
SLAMS
S02
SVOC
TAD
TDM
TEOM
v/c
VMT
VOC
infrared
light duty
level of service
Motor Vehicle Emission Simulator
metropolitan planning organization
mobile source air toxic
nitrous oxide
national ambient air quality standard
Notice of Final Rulemaking
nitric oxide
nitrogen dioxide
National Oceanic and Atmospheric Administration's
oxides of nitrogen
total oxides of nitrogen
Notice of Proposed Rulemaking
National Weather Service
organic carbon
Ozone Limiting Method
Office of Management and Budget
polycyclic aromatic hydrocarbon
peroxyacyl nitrates
particulate matter
particles less than or equal to 10 micrometers in aerodynamic diameter
particles between 2.5 and 10 micrometers in diameter
particles less than or equal to 2.5 micrometers in aerodynamic diameter
parts per billion
passive sampling devices
polyurethane foam
Plume Volume Molar Ratio Method
risk and exposure assessment
right of way
Road Weather Information System
State and Local Air Monitoring Station
sulfur dioxide
semi-volatile organic compound
technical assistance document
travel demand model
Tapered Element Oscillating Microbalance
volume to capacity ratio
vehicle miles traveled
volatile organic compound
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Near-Road NC>2 Monitoring TAD Executive Summary
Executive Summary
On February 9, 2010, the U.S. Environmental Protection Agency (EPA) promulgated new
minimum monitoring requirements for the nitrogen dioxide (NO2) monitoring network in support
of a newly revised 1-hour NC>2 National Ambient Air Quality Standards (NAAQS) and the
retained annual NAAQS. In the new monitoring requirements, state and local air monitoring
agencies are required to install near-road NO2 monitoring stations at locations where peak hourly
NC>2 concentrations are expected to occur within the near-road environment in larger urban areas.
State and local air agencies are required to consider traffic volumes, fleet mix, roadway design,
traffic congestion patterns, local terrain or topography, and meteorology in determining where a
required near-road NC>2 monitor should be placed. In addition, there are other factors that affect
the selection and implementation of a near-road monitoring station, including satisfying siting
criteria, favorable site logistics (e.g., gaining access to property and safety), and consideration of
population exposure.
The purpose of this Near-Road NO2 Monitoring Technical Assistance Document (TAD) is to
provide state and local air monitoring agencies with recommendations and ideas on how to
successfully implement near-road NC>2 monitors required by the 2010 revisions to the NC>2
minimum monitoring requirements.
This document also provides information on optional or discretionary multi-pollutant
monitoring in the near-road environment. The establishment of near-road NC>2 monitoring
stations will create an infrastructure that will likely be capable of housing other ambient air
monitoring equipment. Considering the near-road NC>2 monitoring stations for multi-pollutant
monitoring, even though it may not be required, is compatible with the EPA's multi-pollutant
paradigm presented in the Ambient Air Monitoring Strategy for State, Local, and Tribal Air
Agencies document published in 2008 (U.S. Environmental Protection Agency, 2008a), and has
been noted within documents associated with the NC>2 NAAQS revision of 20101 and the Carbon
9 r-M-,
Monoxide (CO) NAAQS review of 2011. The intent of the multi-pollutant paradigm is to
encourage the integration of multiple individual pollutant monitoring networks to broaden the
understanding of air quality conditions and pollutant interactions, furthering the ability to
evaluate air quality models, develop emissions control strategies, and support long-term
scientific studies (including health studies). In light of this potential for multi-pollutant
monitoring at near-road NO2 monitoring stations, this TAD discusses other pollutants of interest
that exist in the near-road environment, including definitions, basis of interest, and measurement
methods.
1 These documents are available on the Internet at http://www.epa.gOv/ttn/naaqs/standards/nox/s noxcr.html.
2 These documents are available on the Internet at http://www.epa.gOv/ttn/naaqs/standards/co/s co index.html.
ES-1
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Near-Road NC>2 Monitoring TAD Section 1: Background
Section 1. Background and Near-Road NO2 Network
Objectives
On February 9, 2010, new minimum monitoring requirements for the nitrogen dioxide (NC>2)
monitoring network were promulgated (75 FR 6474) in support of a revised National Ambient
Air Quality Standard (NAAQS) for NC>2. The NC^NAAQS was revised to include a 1-hour
standard with a 98* percentile form and a maximum allowable NC>2 concentration of 100 ppb
anywhere in an area, while retaining the annual standard of 53 ppb. In the 2009 NC>2 Risk and
Exposure Assessment (found at http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_rea.html)
created during the NAAQS revision process, and as reiterated in the preambles to the Notice of
Proposed Rulemaking (NPR for NO2) (74 FR 34404) and the Notice of Final Rulemaking (NFR
for NO2) (75 FR 6474) on the Primary NAAQS for NO2, the U.S. Environmental Protection
Agency (EPA) recognized that roadway-associated exposures account for a majority of ambient
exposures to peak NO2 concentrations.
In the rulemaking process leading to the recent NC>2 NAAQS revision, it was established that
the combination of higher urban population densities with increased vehicle miles traveled
(VMT), which correspond to on-road mobile source emissions, can result in an increased
potential for exposure and associated risks to human health and welfare. In the NPR for NO2,
when proposing the level of the revised NC>2 NAAQS, the Administrator noted that "the
available evidence and analyses support the importance of roadway-associated NC>2 exposures
for public health. Specifically, the exposure assessment presented in the REA3 estimated that
roadway-associated exposures account for the great majority of exposures to peak NO2
concentrations (REA, Figures 8-17 and 8-18)."
In the NPR for NC>2, the EPA clearly stated that the populations included in that assessment
were people who live, work, play, or go to school near major roads, as well as those people who
spend time commuting on major roads (74 FR 34419). Ultimately, the Administrator followed
through on the proposed approach to setting the level of the NC>2 NAAQS, and promulgated a
revised NC>2 NAAQS in the NFR for NC>2. This revised standard has a high degree of confidence
in providing appropriate public health protection by limiting the higher short-term peak exposure
concentrations expected to occur on and near major roadways, as well as the lower short-term
exposure concentrations expected to occur away from those roadways.
As part of the NC>2 NAAQS revision, the EPA promulgated requirements for near-road NC>2
monitors in urban areas in conjunction. The primary objective of the required near-road NC>2
network is to support the Administrator's approach in revising the NC>2NAAQS discussed above
by focusing monitoring resources on near-road locations where peak, ambient NC>2
concentrations are expected to occur as a result of on-road mobile source emissions. Monitoring
at such a location or locations within a particular urban area will provide data that can be
3 The REA is the EPA's Risk and Exposure Assessment to Support the Review of the NO2 Primary National
Ambient Air Quality Standard (U.S. Environmental Protection Agency, 2008b).
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Near-Road NC>2 Monitoring TAD Section 1: Background
compared to the NAAQS and used to assess exposures for those who live, work, play, go to
school, or commute within the near-roadway environment.
The near-road NC>2 data will provide a clear means to determine whether the NAAQS is
being met within the near-road environment throughout a particular urban area. Near-road NC>2
monitoring sites are to be placed at locations with expected peak NO2 concentrations in the near-
road environment, although the target mobile sources and the roads they travel upon are
ubiquitous throughout urban areas. Because of these two factors, these monitoring data may be
said to represent the relative worst-case population exposures that may be occurring in the near-
road environment throughout an urban area over the averaging times of interest.
Requirements for near-road monitors are based upon population levels and a specific traffic
metric within Core Based Statistical Areas (CBSAs). State and local ambient air monitoring
agencies are required (per 40 Code of Federal Regulations [CFR] Part 58 Appendix D,
Section 4.3.2.a) to use the latest available census figures (e.g., census counts and/or estimates)
and available traffic data in assessing what may be required of them under this new rule.
Further, state and local air agencies are required to consider traffic volumes, fleet mix, roadway
design, traffic congestion patterns, local terrain or topography, and meteorology in determining
where a required near-road NO2 monitor should be placed. In addition to those required
considerations listed above, there are other factors that impact the selection and implementation
of a near-road monitoring station, including satisfying siting criteria, site logistics (e.g., gaining
access to property and safety), and population exposure.
The EPA believes that the site selection requirements and siting criteria for near-road NO2
monitoring stations (presented in the CFR and reiterated in this Technical Assistance Document
[TAD]) provide sufficient flexibility to state and local air agencies for near-road NC>2 monitoring
site implementation. This flexibility should allow state and local air agencies to balance the
over-arching objective of placing monitor probes as near as practicable to highly trafficked roads
where peak NO2 concentrations are expected to occur with the variety of site implementation
issues that exist in the real world. Because of this flexibility, the EPA strongly encourages states
and local agencies to exercise due diligence in selecting and installing required near-road NC>2
monitoring stations and to provide sound rationale for their decisions consistent with their
network design.
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Near-Road NC>2 Monitoring TAD Section 2: Objectives
Section 2. Near-Road NO2 Monitoring Technical Assistance
Document Objectives and Content
The primary objective of this TAD is to provide a set of options, including technical approaches
and rationale, for the near-road N02 monitoring site selection process; these options are
provided to assist state and local air monitoring agencies in implementing required near-road
N02 monitoring stations in a manner that satisfies the requirements and intent of 40 CFR
Part 58.
During the public comment period on the proposed NC>2 rulemaking, and upon the
promulgation of the final NC>2 rulemaking that requires the near-road NC>2 monitoring network
(75 FR 6474), the EPA received feedback from the air monitoring community requesting further
clarification and/or assistance on how the required near-road NC>2 network might best be
implemented. The EPA responded with a commitment to provide assistance in the form of this
Near-Road NO2 Monitoring TAD. The purpose of this TAD is to provide recommendations and
ideas on how to successfully install near-road NC>2 monitors as required by the recent revisions
to the NC>2 monitoring requirements in 40 CFR Part 58 Appendices D and E.
The material supporting this objective is primarily contained in Section 3 through Section 14
of this document. Section 4 provides a quick-start guide to the site selection process. Section 15
presents information on selecting a second near-road monitoring site. Section 16 presents
information on other pollutants of interest in the near-road environment; these pollutants are not
required to be measured unless noted otherwise, but monitoring these pollutants is recommended
for the purpose of addressing issues relevant to science and policy.
The EPA has chosen to elaborate on multi-pollutant monitoring in this TAD because the
characterization of other pollutants and metrics can broaden the understanding of air quality
conditions and pollutant interactions (particularly in the near-road environment), furthering the
ability to evaluate air quality models, develop emission control strategies, and support long-term
scientific studies (including health studies). The information provided about these other
pollutants or metrics of interest includes definitions, reason for interest, and measurement
methods.
In addition to the body of this TAD, the appendices provide a variety of supporting
information and data.
Finally, the EPA notes that the recommendations in this TAD should not be construed as
requirements (requirements are contained within 40 CFR Part 58), but rather as technically-
appropriate approaches to implement required near-road NO2 monitoring stations.
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Near-Road NO2 Monitoring TAD Section 3: Identifying CBS As
Section 3. Identifying Core Based Statistical Areas with
Required Near-Road Monitoring
The first step in implementing required monitoring is for state and local ambient air
monitoring agencies to identify the extent to which the monitoring requirements apply to their
respective territories. Specifically, in 40 CFR Part 58 Appendix D, the EPA requires state and
local air agencies to operate one near-road NC>2 monitor in any CBS A with a population of
500,000 or more persons. Further, those CBSAs with 2,500,000 or more persons, or those
CBSAs with one or more roadway segments carrying traffic volumes of 250,000 or more
vehicles (as measured by annual average daily traffic [AADT] counts), shall have two near-road
NC>2 monitors. State and local ambient air monitoring agencies are required to use the most up-
to-date census information and traffic data in assessing what may be required of them under this
rule, per 40 CFR Part 58 Appendix D, Section 4.3.2.a. The process of identifying minimum
monitoring requirements is shown in Figure 3-1.
3.1 Identifying CBSA Boundaries
CBSAs are made up of whole counties, which may or may not all be within the same state.
CBSA is a collective term for both micropolitan and metropolitan statistical areas.4 Micropolitan
(micro) and metropolitan (metro) statistical areas, and thus CBSAs, are geographic entities
defined by the U.S. Office of Management and Budget (OMB) for use by Federal agencies in
collecting, tabulating, and publishing Federal statistics, such as population.
A micro area contains an urban core in a county or counties of at least 10,000 people, but
fewer than 50,000, while a metro area has one or more counties containing a core urban area of
50,000 people or more. Each micro or metro area consists of one or more counties and includes
the counties containing the core urban area, as well as any adjacent counties that have a high
degree of social and economic integration (as measured by commuting to work) with the urban
core (U.S. Census Bureau, 2005). A full explanation of the development and application can be
found in the Federal Register; specifically, "Standards for Defining Metropolitan and
Micropolitan Statistical areas; Notice" (Office of Management and Budget, 2000).
The use of CBSAs to define areas where near-road monitoring is to occur is appropriate
because they account for populations within core urban areas and their surrounding communities.
These areas are affiliated socially and economically as measured by commuting patterns that
offer a direct relation between traffic and population. Further, because CBSAs include more
than just the core urban areas, it is highly unlikely that roads with high total traffic volumes or
high truck (or heavy-duty vehicle) traffic counts that are not located in the immediate vicinity of
a core urban area would be overlooked and/or excluded in the consideration process for near-
road monitoring.
4 Typically, in ambient air monitoring, metropolitan statistical areas are synonymous with the acronym MSA.
5
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Near-Road NO2 Monitoring TAD
SectionS: Identifying CBS As
Identify Mini mum
Monitoring
Requirements
Identify CBSA
Boundaries
Is population
greater than
or equal to
500,000?
At least one monitor
is required
No monitors are
required
Is population
greater than
or equal to
2,500,000?
A second monitor
is required
Do any road
segments
have AADT
>250,000?
A second monitor
is required
Only one monitor
is required
Figure 3-1. Flowchart illustrating the process of identifying whether minimum monitoring
requirements for near-road N02 monitors apply to individual CBSAs.
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Near-Road NO2 Monitoring TAD Section 3: Identifying CBS As
The list of CBSAs, along with associated population data and estimate information, is
available on the Internet from the U.S. Census Bureau (see Section 3.2).
3.2 Identifying Census Data
The EPA recommends that states and local air agencies use the U.S. Census Bureau as the
source of their population estimates. A convenient list of CBSAs and their estimated populations
have historically been available on the Census Bureau's website in the population estimate
section (http://www.census.gov/popest/). However, the Census Bureau is now encouraging the
public to use the American Fact Finder for population-related information; the American
FactFinder is available at http://factfinder2.census.gov/. The American Fact Finder can be used
to locate population counts and demographic information for a CBS A, including data collected
during the 2010 census. In addition to entire CBSA populations, information is also available at
different geographic levels, such as block, block group, and census tract. The geographic
boundaries of these levels can be found within reference maps on the American Fact Finder
website.
3.3 Identifying Roadway Traffic Volumes in Excess of 250,000 AADT
The EPA recommends that state and local air agencies obtain traffic volume data from a state
or local Department of Transportation (DOT), other local entities such as metropolitan planning
organizations (MPOs), or from the U.S. DOT for the most up-to-date traffic information
available, including AADT data. These data can then be analyzed to ascertain whether one or
more road segments in a CBSA have an AADT count of 250,000 or greater, which would
warrant a second near-road monitor even if that CBSA has a population of fewer than 2,500,000
persons. Section 5.3 provides a list of recommended sources for traffic volume data in the form
of AADT.
3.4 Meeting Requirements in CBSAs Covering Multiple Geo-Political
Boundaries (Multi-Agency/Multi-State)
In a number of cases, a CBSA may cover more than one state, or may cover an area shared
by more than one air monitoring agency, or both. In such cases, state and local air agencies are
encouraged to engage each other, including all of the affected parties, in determining where any
near-road monitoring may be conducted. These discussions are most helpful if conducted before
and during the traffic data analysis process and while determining an initial list of candidate road
segments. This process will ensure that all parties are aware of the most appropriate road
segments to focus upon for identifying candidate near-road monitoring sites. Further, it is
strongly advised that state and local agencies engage associated EPA Regional staff in
identifying ways in which multiple air monitoring agencies can collaborate to satisfy minimum
monitoring requirements for individual CBSAs.
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Near-Road NO2 Monitoring TAD Section 3: Identifying CBS As
While EPA Regional staff should be readily available for consultation and participation in
this process, the state and local air agencies are expected to take the lead on the decision making.
One suggested result of this collaboration should be the inclusion in each affected party's annual
monitoring network plan of the location of all required near-road NC>2 monitors for a given
CBSA, regardless of the operating air agency.
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Near-Road NO2 Monitoring TAD
Section 4: TAD Quick Start
Section 4. Near-Road NO2 TAD Quick-Start
This section is a quick-start guide to the TAD, providing a short, simplified summary of what
is required by rule and the ideas and approaches state and local air agencies may take to
implement required near-road NO2 monitoring sites. This quick-start section highlights key
points presented throughout this TAD; the rationale and supporting details for each key point are
contained in subsequent sections of this document as noted in this quick-start section.
• 4.1 Obtain and Assess AADT, Fleet Mix, and Congestion Data
• 4.2 Consider Physical Site Characteristics
• 4.3 Review Siting Criteria
• 4.4 Prepare Candidate Site Comparison Matrix
4.1 Obtain and Assess AADT, Fleet Mix, and Congestion Data
Table 4-1 presents a summary of traffic information to be used in near-road NO2 monitoring
site consideration. Figure 4-1 presents a flow chart outlining steps to develop a relatively
prioritized list of potential candidate road segments based on available AADT, fleet mix, and
congestion data for an entire CBS A.
Table 4-1. Summary of the traffic-related metrics for candidate site consideration.
Component
AADT
Rationale
Potential Data Sources
Focus on locations with high traffic
volumes
State DOT, local/MPO, or U.S.
Department of Transportation's (U.S.
DOT's) Highway Performance Monitoring
System (HPMS)
Fleet mix
Trucks emit greater amounts of NOX
on an average, per-vehicle basis
State DOT, local/MPO
Fleet Equivalent (FE)
AADT
Single metric accounting for AADT
and fleet mix; used to compare road
segments
Use AADT and fleet mix in Equation 2
(Section 6.2)
Congestion
Frequent acceleration and stopping
can lead to higher emissions per
vehicle
State/local level of service (LOS); state
DOT or HPMS (number of lanes);
congestion maps
To start the site selection process, use the steps illustrated in Figure 4-1 to rank road
segments from highest to lowest Fleet Equivalent AADT (FE-AADT) value. A complete
introduction to the sources, interpretation, and use of these traffic data is presented and discussed
in Section 5.
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Near-Road NO2 Monitoring TAD
Section 4: TAD Quick Start
Do multiple
segments
have the
same AADT?
Create List of Ranked
Candidate Segments
Assign same rank to segments
with same AADT counts
Proceed with
ranked segment list
Ranked AADT
segments/counts
Step 1. Total Traffic Count
Step 2. Fleet Mix
Step 3. Fleet-Equivalent AADT
Step 4. Congestion
Match HD counts
with AADT by
segment
Proceed with ranked AADT
segments/ counts
Calculate Fleet-
Equivalent AADT for all
segments; use to rank
Congestion
Indicator
Is a congestion
indicator
available for
each segment?
Determine congestion level for each
segment; use to differentiate
comparably ranked segments
Ranked
Candidate
Segments
Proceed with ranked AADT
segments/ counts
Figure 4-1. Candidate road segment ranking process. This flowchart presents the traffic
data evaluation process of providing a prioritized list of candidate road segments (accounting
for traffic volume [AADT], fleet mix, and congestion) for further evaluation as potential near-
road N02 monitoring stations.
4.2 Consider Physical Site Characteristics
Table 4-2 provides a summary of physical site characteristics to be considered, and in some
cases avoided, in the monitor site selection process. The physical considerations discussed
include roadway design, roadside structures, terrain, and meteorology. A complete introduction
10
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Near-Road NO2 Monitoring TAD
Section 4: TAD Quick Start
to and discussion of the physical site considerations that need to be accounted for in near-road
NC>2 monitoring site placement can be found in Section 7.
This TAD recommends that the target distance for near-road N02 monitor probes be within
20 meters of the target road whenever possible.
Table 4-2. Summary of physical considerations for candidate near-road sites.
Physical Site
Component
Roadway
design or
configuration
Roadside
Structures
Terrain
Meteorology
Impact on Site
Selection
Feasibility of
monitor
placements;
affects pollutant
transport and
dispersion.
Feasibility of
monitor
placement;
affects pollutant
transport and
dispersion.
Affects pollutant
dispersion, local
atmospheric
stability.
Affects pollutant
transport and
dispersion.
Desirable
Attributes
At-grade or
nearly at-grade
with immediate
surrounding
terrain.
No barriers
present other
than low(<2 m in
height)
vegetation or
safety features
such as
guardrails.
Flat or gentle
terrain, within a
valley, or along a
road grade.
Relative
downwind
locations; winds
from road to
monitor.
Least Desirable
Attributes
Deep cut-
sections/significant
ly below grade;
significantly above
grade (fill or
bridge); above
grade (bridge).
Presence of sound
walls, mature (high
and thick)
vegetation,
obstructive
buildings.
Along mountain
ridges or peaks,
hillsides, or other
naturally
windswept areas.
Strongly
predominant
upwind positions.
Potential Infor
Field reconnaissance;
imagery.
Field reconnaissance;
imagery.
Field reconnaissance;
elevation models and
files; satellite imagery
mation
satellite
satellite
digital
vegetation
Local data; National Oceanic and
Atmospheric Administration's
(NOAA's) National Weather
Service (NWS); EPA's Air Quality
System (AQS).
11
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Near-Road NO2 Monitoring TAD
Section 4: TAD Quick Start
4.3 Review Siting Criteria
Error! Not a valid bookmark self-reference, provides a summary of near-road NO2 probe
placement. For a complete discussion of siting requirements and associated recommendations,
see Section 7.
Table 4-3. Key near-road siting criteria.
Near-Road NO2 Siting Criteria (per 40 CFR Part 58, Appendix E*
Horizontal spacing
According to 40 CFR Part 58 Appendix E: "As near as practicable to the outside nearest edge
of the traffic lanes of the target road segment; but shall not be located at a distance greater
than 50 meters, in the horizontal, from the outside nearest edge of the traffic lanes of the
target road segment."
This TAD recommends that the target distance for near-road NO2 monitor probes be
within 20 meters of the target road whenever possible.
Vertical spacing Microscale near-road NO2 monitoring sites are required to have sampler inlets between 2
and 7 meters above ground level.
Spacing from
supporting
structures
The probe must be at least 1 meter vertically or horizontally away from any supporting
structure, walls, parapets, penthouses, etc., and away from dusty or dirty areas.
Spacing from For near-road NO2 monitoring stations, the monitor probe shall have an unobstructed air
obstructions flow, where no obstacles exist at or above the height of the monitor probe, or between the
monitor probe and the outside nearest edge of the traffic lanes of the target road segment.
Prepare Candidate Site Comparison Matrix
The candidate site comparison matrix presents and organizes all the data recommended to be
collected to aid in identifying and prioritizing potential near-road monitoring sites. The
individual pieces of information within the matrix are discussed throughout this TAD, from from
Section 5 through Section 13. EPA believes state and local air agency decision-makers can use
these data (suggested to be collected in the site comparison matrix) to adequately characterize
selected near-road monitoring site(s) for public dissemination, such as annual monitoring
network plans and in AQS, as discussed in Section 14.
Table 4-4. Suggested data for each candidate site entry in a site comparison matrix.
Page lot2
Site/Segment
Parameters
Description of Parameter
Location
Road segment name
Is the entry for a specific point along a road segment, or is it representative of a whole
road segment? If the entry is for a point, provide a moniker and the latitude and
longitude. If for a road segment, identify where the segment boundaries occur (such
as an intersection, mile marker, or political boundary).
Given road name and common name (if applicable).
iie~1
Road type
Type of road (controlled access highway, limited access freeway, arterial, etc.).
Road segment end
points
Location of the road segment end points, including any given names, common names,
and the latitude and longitude of each individual end point.
12
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Near-Road NO2 Monitoring TAD
Section 4: TAD Quick Start
Site/Segment
Parameters
Description of Parameter
AADT
AADT, source of data, and vintage.
Table 4-4. Suggested data for each candidate site entry in a site comparison matrix.
Page 2 of 2
Site/Segment
Parameters
FE-AADT
Heavy-duty (HD) vehicle
counts
Congestion information
Roadway design
Terrain
Meteorology
Population exposure
Roadside structures
Safety features
Infrastructure
Interchanges
Surrounding land use
Nearby sources
Current road
construction
Future road construction
Frontage roads
Available space - site
footprint
Property type
Property owner
Likelihood of access
Description of Parameter
FE-AADT, noting HDm value used. (HDm is a multiplier; see Equation 2.) If not using the
national default value, provide the source of data used to calculate the site-specific
value.
HD counts, source of data, and vintage.
Value and type (e.g., LOS, volume-to-capacity ratio , or AADT by lane), data
source, and vintage.
Design type or types present (flat, elevated-fill, cut, etc.). If not flat, identify whether
the configuration is a vertical or sloped boundary. Include the height and degree of
slope if applicable.
Nature of the terrain immediately around the road; also, any larger-scale terrain
features of note.
Predominant winds for a point, and whether the point is relatively upwind or
downwind. For a whole segment, the orientation of the segment to the predominant
winds.
Assessment of population exposure and/or likeness to other road segments
throughout the CBSA.
Presence of any roadside structures and the height, width, and length of those
structures.
Safety features present and the height, width, and length of those features.
Existing infrastructure (light poles, billboards, etc.) and potential site proximity
(distance).
Presence of any interchanges within or at the end points of the target road segment
and potential site proximity (distance), including traffic information if available (AADT,
HD counts, etc.).
Surrounding land use (residential, commercial, etc.); proximity to other large roads;
areas of higher relative road density; and/or locations within or near central business
districts or urban downtown areas.
Nearby NOX sources (type, tonnage, etc.) and potential site proximity (distance).
Visible or known road construction at the candidate site or along the target road
segment.
Transportation agency plans for any future road construction (including time frame for
completion).
Presence of frontage roads; are those roads included as part of the target road
segment?
Limitations in the space available for a multipollutant monitoring station.
Is the property a right-of-way (ROW), or is it private property?
Who manages or owns the property under evaluation?
Level of confidence and any uncertainties regarding the acquisition of access to a
particular property.
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Near-Road NO2 Monitoring TAD
Section 4: TAD Quick Start
Site/Segment
Parameters
Other details/local
knowledge
Description of Parameter
Other pertinent details that may have bearing on why a particular candidate site may
or may not be selected, such as information that reflects a state or local agencies' own
knowledge of the area or roads under consideration.
14
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Near-Road NC>2 Monitoring TAD Section 5: Data and Resources
Section 5. Recommended Traffic Data and Resources for Use
in Identifying Candidate Road Segments for Near-
Road NO2 Monitoring
The key first step in identifying candidate NC>2 near-road monitoring sites is to collect and
analyze traffic data. Traffic data indicate the level and type of activity on a given road that can
be used to compare anticipated pollutant emissions among multiple road segments in a CBSA.
This section summarizes the data and sources that are recommended for use in generating a
list of candidate road segments for evaluation as potential near-road NC>2 monitoring sites. The
purpose of using these recommended data is ultimately to aid in identifying locations where the
highest motor vehicle emissions leading to peak near-road NO2 concentrations are likely to
occur.
In addition to the descriptions in this section, Appendix A provides more detailed
information on the variability and some of the uncertainties of these parameters.
5.1 Definitions of Terms
In traffic analysis, a number of terms are used, often interchangeably, to describe a road. The
terms can vary by individual transportation agencies (e.g., DOTs and other transportation
authorities); these terms are presented, defined, and discussed throughout this TAD as needed.
In general, a road can be defined as an open way for the passage of vehicles, persons, or
animals on land. In this TAD, the term "road" or "roadway" includes the entire cross section or
travel corridor (i.e., both directions of the primary travel lanes, plus any ramps, special use lanes,
and included frontage roads) of any open ways for passage (over land and water) that may
otherwise be labeled as a road, street, collector, arterial, highway, expressway, toll-way,
parkway, freeway, or other such commonly used terms.
Further, the near-road NC>2 monitoring site selection process suggested in this TAD calls for
the evaluation of individual sections or lengths of a road where information about the traffic on
the road is known and characterized. In this TAD, the term "road segment" or "roadway
segment" is defined as a length of road between two points along a road. Road segments are
typically defined by transportation agencies as the sections between end points. End points are
located at intersections, highway exits, highway mile markers, geo-political boundaries, or other
features where traffic volumes or patterns are likely to change.
5.2 AADT
AADT is a measure of the total volume of traffic on a roadway segment (typically in both
directions unless specified otherwise) for one year divided by the number of days in the year.
AADT can be used to identify the relative traffic activity and corresponding potential for
15
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Near-Road NC>2 Monitoring TAD Section 5: Data and Resources
pollutant emissions experienced along roads. Generally, AADT is representative of the traffic
volume along a given length of road or individual road segment.
Some traffic data sources may present traffic counts at point locations instead of specifically
representing a defined length of road. In these cases, the length and nature of individual road
segments and their boundaries may need to be clarified for use in the recommended near-road
NC>2 monitoring site selection process as presented in this TAD.
Traffic volume data are typically collected by state DOTs and other local sources, such as
MPOs. AADT data sets generally comprise two types of data, recorded and estimated, which are
merged to create a full data set. Because measurements are not made on every road segment in
an urban area each year, AADT can vary from year to year, depending on actual changes in
traffic volumes and when certain road segments are individually measured or estimated.
However, for this TAD, data users may treat the measured and estimated data equally as long as
they are using the latest available data (typically provided annually). In addition to traditional
AADT data, metropolitan area urban travel demand models (TDMs) can also be consulted to
estimate future traffic volumes on these segments if needed.
5.3 Sources of AADT Data
State or local traffic volume data sets are created by state DOTs and sometimes MPOs.
Often, these data are available on DOT or MPO websites, and likely represent the most up-to-
date traffic counts available. In addition to state or local sources, a national source for traffic
data is the Highway Performance Monitoring System (HPMS), managed by the U.S. DOT.
HPMS (http://www.fhwa.dot.gov/policy/ohpi/hpms/index.cfm) AADT data can be downloaded
in shapefile format, either as one national file or by region, from the Bureau of Transportation
Statistics' National Transportation Atlas Database
(http://www.bts.gov/publications/national transportation atlas database/). (Shapefiles contain
data within a geospatial format, and thus are displayed as map features.)
One key issue regarding the use of data from the HPMS is that the data may be one or more
years older than data provided by state and local sources. This potential discrepancy in data
source date is because the data in the HPMS originate from state DOTs and other local sources,
and must be collected, reviewed, and otherwise processed by the U.S. DOT before being
presented in the HPMS. It is thus recommended that state and local air agencies first attempt to
obtain and use the most recent data set available from their state DOT or other local sources. In
the event that sufficient traffic data are not available from state or local sources, it is then
recommended that state and local air agencies use the most recent data available in the HPMS for
use in the near-road NO2 monitoring site selection process.
Traffic volume data varies by location, but is most often provided as "counts" for particular
road segments. The format of these count data may be available within an interactive interface
or may come as downloadable tables, images, or shapefiles. To use shapefiles, state and local air
agencies will need to use a mapping software program such as Esri's ArcGIS. Regardless of the
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Near-Road NC>2 Monitoring TAD Section 5: Data and Resources
formatting, state and local air agencies are encouraged to migrate the available data into a
spreadsheet or database for use in a comparative process that is discussed in Section 6.
5.4 Fleet Mix
While AADT describes the total volume of traffic on a road, fleet mix data provide specific
counts, or percentages of total traffic volume, of the different types of vehicles that comprise the
total traffic volume. Most commonly, fleet mix data differentiate between light-duty (LD)
vehicles (e.g., gasoline fueled) and HD vehicles (e.g., diesel fueled). The manner in which fleet
data are defined depends on the monitoring methods employed by the state or local
transportation agency. In most cases, LD and HD vehicles are differentiated by either weight or
length of vehicles on the road. The number of axles can also be used to differentiate HD from
LD vehicles.
Understanding the number or percentage of HD vehicles within the total traffic volume is
important because the difference in the amount of nitrogen oxides (NOX) emitted on a per-vehicle
basis between the two vehicle types varies greatly. On a per vehicle basis, HD vehicles typically
emit much higher amounts of NOX than LD vehicles. Since these NOX emissions include both
nitric oxide (NO) (which readily converts to NO2 in the near-road environment in the presence of
ozone and also can be oxidized to NO2 through other photochemical processes) and directly-
emitted NO2, these emission differences are important in identifying locations where peak NO2
concentrations may occur. For all vehicles, NOX emissions vary by vehicle type, load, speed, and
highway grade. For more information on on-road mobile source NOX emissions based upon
EPA's Motor Vehicle Emission Simulator (MOVES), see Appendix B.
5.5 Sources of Fleet Mix Data
Similar to AADT data, fleet mix data are typically collected by state DOTs or possibly by
MPOs. However, fleet mix data are not measured and disseminated as routinely as AADT data.
Thus, fleet mix data availability is much more variable from state to state or between individual
CBS As. Further, fleet mix data may not be available for individual road segments, but instead it
may only be available for larger domains such as counties or urban areas. In cases where fleet
mix data are available by segment, the data are often available with related total AADT count
data and files, the sources of which are discussed in Section 5.3. Similar to the recommendations
for AADT, state and local air agencies are encouraged to migrate the available fleet mix data into
a spreadsheet or database (one that also contains AADT data) for use in the comparative process
that is discussed in Section 6.
5.6 Congestion Patterns
Congestion patterns are an important factor in the near-road NO2 monitoring site selection
process because traffic congestion can lead to vehicle operating conditions, particularly stop-and-
go traffic, where per-vehicle emissions may increase (as compared to vehicles operating at
17
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Near-Road NC>2 Monitoring TAD Section 5: Data and Resources
steady-state highway speeds). Congestion pattern data can be presented in multiple forms.
Three example metrics of congestion data that can be used for consideration during the near-road
NC>2 monitoring site selection process are discussed in Sections 5.6.1 through 5.6.3.
5.6.1 Level of Service
The first example of congestion pattern data is the Level of Service (LOS) metric. The LOS
system describes the effectiveness of a transportation facility, such as a road segment. LOS is
determined for individual road segments by the evaluation of multiple pieces of traffic
information, including time-resolved traffic counts, traffic speeds, and the relative frequency of
occurrence of congested conditions. The LOS is presented as a qualitative measure, using a
letter grading system.
In the LOS grading framework, the grading ranges from A to F, where A describes a road
segment with free-flowing traffic conditions, with speeds at or above the posted limit. On the
other end of the spectrum, the letter grade F represents the lowest measure of efficiency for a
road segment, which has traffic subjected to forced or impeded flows, congestion, and frequent
slowing and stopping during peak hours of use. As a result, when considering the candidacy of
individual road segments as permanent near-road NO2 monitoring sites, congestion patterns can
be considered in a qualitative manner. In the case of using LOS data to consider congestion
patterns, those road segments with higher relative congestion (e.g., a worse letter grade) may
have relatively higher NO2 emissions per vehicle than road segments that are otherwise similar,
but that have less congestion.
In some cases, LOS may be available for both directions of a particular road segment, and/or
may be presented with multiple classifications based on season or time of day. In such cases, the
EPA suggests that the worst letter grade LOS be used to represent that particular segment when
making comparisons with other road segments. We believe this is an appropriate approach
considering that the NO2 NAAQS is a 1-hour standard, and the objective of the monitoring effort
is to characterize the peak NO2 concentrations that are occurring in the area. The greatest effects
of traffic congestion on peak NO2 concentrations, represented by LOS, are likely to occur during
the worst indicated LOS conditions.
5.6.2 Volume-to-Capacity Ratio
A second metric that can be used as a congestion pattern indicator is the ratio. The
ratio compares peak traffic volumes on a road segment with the capacity of the road based on the
number of lanes. This calculation typically takes into account the larger size of HD vehicles and
focuses on traffic conditions during peak hours of operation.
5.6.3 AADT by Lane
If LOS or data are not available, a third metric for assessing possible congestion is a
simple calculation to determine the "AADT by lane" for individual road segments. This
indicator can be determined by dividing the total AADT by the number of lanes on a road
18
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Near-Road NC>2 Monitoring TAD Section 5: Data and Resources
segment. In the absence of LOS or information, AADT by lane can be used to aid in
understanding the potential congestion of a road segment by accounting for how much traffic
volume is using a given number of available driving lanes. A larger number of vehicles per lane
indicates a greater potential for traffic congestion. However, AADT by lane is not based on the
multiple metrics that LOS and are based upon, and should be viewed only as a rough
surrogate for what those data might represent for a given road segment. Thus, AADT by lane is
suggested for use only if LOS or data are not available. The method of calculating AADT by
lane is discussed in Section 5.7.
5.7 Sources of Congestion Pattern Data
LOS and data, if available, are determined and disseminated by state DOTs or MPOs.
However, these metrics are not as common as AADT or even fleet mix data and thus may not be
available for all CBSAs where near-road NO2 monitoring is required. If these data are not
available from state DOTs or MPOs, the use of AADT by lane or some other similar congestion
pattern metric is recommended as a surrogate. To determine AADT by lane, knowledge of the
number of lanes on a road segment, along with the total number of vehicles on that segment, is
required. If those data are known, the AADT by lane can be estimated by using Equation 1.
(1)
where AADT is the actual total traffic volume on the road segment (in both directions), and
the number of lanes is the total number of lanes (in both directions) on that road segment.
19
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Near-Road NO2 Monitoring TAD Section 6: Candidate Road Segments
Section 6. Creating an Initial List of Candidate Road
Segments Using Traffic Data
The site selection process for required near-road NO2 monitors, per 40 CFR Part 58
Appendix D, includes the ranking of road segments in a CBSA by AADT, followed by the
consideration of five other factors, which include fleet mix and congestion patterns, in the site
selection process. The other three factors (roadway design, terrain, and meteorology) are
discussed in Section 7.
This section presents a process by which state and local air agencies may use available traffic
data to create an initial list of candidate road segments for further evaluation as potential near-
road monitoring sites. In this process, the EPA believes that a state or local agency will be
satisfying a portion of the minimum monitoring requirements by ranking road segments using
AADT, and by considering both fleet mix and congestion patterns as factors in the ranking
process. The purpose of this process is to identify road segments, ranked by relative priority,
where peak NC>2 concentrations attributable to on-road mobile sources are most likely to occur in
a CBSA on a routine basis.
Figure 6-1 presents a visualization of the traffic data evaluation process. The four steps
presented in this section are illustrated as a flowchart, providing alternate evaluation paths that
depend on what traffic data is available. After creating a prioritized list of road segments for
further evaluation, and while working with partners and stakeholders (e.g., EPA Regions,
transportation agencies), state and local air agencies will be in a position to consider the three
remaining factors required of them in the CFR, which are roadway design, terrain, and
meteorology, plus the additional case-by-case factors that will affect where near-road NC>2
monitoring sites might be placed.
21
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Near-Road NO2 Monitoring TAD
Section 6: Candidate Road Segments
Do multiple
segments
have the
same AADT?
Create List of Ranked
Candidate Segments
Assign same rank to segments
with same AADT counts
Proceed with
ranked segment list
Ranked AADT
segments/counts
Match HD counts
with AADT by
segment
Calculate Fleet-
Equivalent AADT for all
segments; use to rank
Congestion
Indicator
Determine congestion level for each
segment; use to differentiate
comparably ranked segments
Step 1. Total Traffic Count
Step 2. Fleet Mix
Step 3. Fleet-Equivalent AADT
Step 4. Congestion
Proceed with ranked AADT
segments/ counts
Is a congestion
indicator
available for
each segment?
Ranked
Candidate
Segments
Proceed with ranked AADT
segments / counts
Figure 6-1. Candidate road segment ranking process. This flowchart presents the traffic data
evaluation process for determining a prioritized list of candidate road segments (accounting for
traffic volume [AADT], fleet mix, and congestion) for further evaluation as potential near-road
N02 monitoring stations.
6.1 Use AADT to Initially Rank Road Segments
The first step in the traffic data evaluation process is to satisfy the requirement in 40 CFR
Part 58, Appendix D, Section 4.3, to rank road segments in a CBSA based on the total traffic
volume, represented by AADT. The intent of this first step is to begin to focus the evaluation
22
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Near-Road NO2 Monitoring TAD Section 6: Candidate Road Segments
process on road segments that are more likely to have higher potential for NOX emissions due to
their higher volumes of traffic.
STEP 1 - Generate a list of road segments in the CBSA in descending order, where the segment
with the highest AADT is ranked first. This list should include at a minimum the road segment
ID, location information, road information, and AADT value. In situations where two or more
road segments have the same AADT value, those segments should be assigned the same
numerical ranking.
Table 6-1 is an example of road segments ranked by AADT for the Tampa, Florida, CBSA
using 2009 data from the Florida DOT. The roadway name is listed in the first column, the
physical end points of the road segment are listed in columns two and three, AADT for the road
segment is listed in the fourth, and the segment's rank is listed in the final column. Although all
road segments were ranked in the development of this example, for illustrative purposes, only the
top segments are listed here. State and local agencies should rank those segments for which data
are available for each CBSA in this initial step.
23
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Near-Road NO2 Monitoring TAD
Section 6: Candidate Road Segments
Table 6-1. Ranking of road segments by AADT, as described in Step 1, for the Tampa, Florida,
CBSA, using 2009 traffic data available from Florida DOT. Note that all available road segments
within the CBSA were ranked; however, to conserve space within this document, only the top
segments are shown.
Roadway
1-275
1-275
1-275
1-275
1-275
1-275
1-4
1-275
1-275
SR-60
1-275
1-275
1-275
1-275
1-4
1-275
1-275
1-275
1-4
1-4
1-4
1-275
1-4
1-275
1-275
1-275
1-275
1-275
1-4
1-4
1-4
1-4
1-75
1-4
From
Bridge No-100128
CR587/WESTSHORE BLVD
S600/U92/DALE MABRY
Bridge No-100138
Bridge No-100110
SLIGH AVE
10320000/10320001
Bridge No-100120
FLORIBRASKAAVE
SR616
SR 600 /HILLS AVE
Bridge No-100203
SR580/BUSCH BLVD
Bridge No-100219
Bridge No-100658
EAST END BR 150107
4TH ST N
Columbus Dr
US 301 /SR 43
1-75/SR 93A
Bridge No-100115
SR 688/Ulmerton Rd
Mango Rd
GANDYBLVD/SR694
38TH AVE N
ROOSEVELT BL/SR 686
54TH AVE N
22NDAVEN
SR574/ML KING BLVD
US 41/SR 599/50TH ST
MCINTOSH RD
ORIENT RD
GIBSONTON DR
Bridge No-100599
To
Bridge No-100110
Bridge No-100120
Bridge No-100128
10320000/10320001
Bridge No-100138
Bridge No-100219
Bridge No-100658
S600/U92/DALE MABRY
Bridge No-100203
SR 93/1-275
SLIGH AVE
SR 600 /HILLS AVE
Bridge No-100231
SR580/BUSCH BLVD
US 41/SR 599/50TH ST
Bridge No-100115
END BRIDGE 150107
FLORIBRASKAAVE
1-75/SR 93A
Mango Rd
CR587/WESTSHORE BLVD
4TH ST N
MCINTOSH RD
ROOSEVELT BL/SR 686
54TH AVE N
N/A
GANDY BLVD/SR 694
38TH AVE N
ORIENT RD
SR574/MLKING BLVD
Bridge No-100599
US 301 /SR 43
SR 43 /US 301
S566/THONOTOSASSA RD
AADT
192,000
176,500
170,500
169,000
169,000
167,000
164,000
163,000
160,500
158,000
156,500
153,500
151,500
151,500
151,000
147,000
147,000
147,000
136,500
136,500
135,500
130,000
127,000
123,000
123,000
123,000
123,000
123,000
122,000
121,000
117,932
113,000
111,500
110,000
AADT
Rank
1
2
3
4
4
5
6
7
8
9
10
11
12
12
13
14
14
14
15
15
16
17
18
19
19
19
19
19
20
21
22
23
24
25
24
-------
Near-Road NO2 Monitoring TAD Section 6: Candidate Road Segments
6.2 Combine Fleet Mix Data and AADT Data to Rank Road Segments
As discussed in Section 5.4, the fleet mix metric accounts for the amount of HD vehicles on a
roadway, or the ratio of HD vehicles to LD vehicles on a road. Fleet mix is an important factor
because HD vehicles emit higher amounts of NOX on a per vehicle basis than LD vehicles.
Therefore, accounting for fleet mix in the near-road NC>2 monitoring site selection process more
accurately focuses the search on road segments where potential on-road emissions may more
consistently lead to peak NC>2 concentrations in the near-road environment.
If fleet mix data are available on a segment by segment basis, or some other categorization
up to a county by county characterization, proceed with Step 2. If you do not have fleet mix data
that is differentiated within a CBSA, skip ahead to Step 4 in Section 6.4.
STEP 2 - Link the total volume of heavy-duty vehicles to the AADT list generated in Step 1,
matching the two data sets by segment. If another form of fleet mix distribution is available
(such as county level data), assign the available values or percentages to all the corresponding
road segments.
Table 6-2 has been updated from Table 6-1 and contains a new column for HD vehicles for
each of the initial road segments found in Step 1 and presented in Table 6-1. For illustration
purposes, the rows were re-ranked based on HD vehicle counts. In this example, the twenty-
eighth-ranked AADT road segment had the highest HD counts. The top-ranked total AADT
road segment from Step 1 has the twenty-seventh-highest rank based solely on HD counts.
Again, this list is arbitrarily cut off to conserve space in this document; all segments with
available data should be included in this step.
25
-------
Table 6-2. Ranking of road segments using fleet mix data as described in Step 2. This modified version of Table 6-1 illustrates the
ranking of road segments based on fleet mix for the Tampa, Florida, CBSA, using 2009 traffic data available from Florida DOT. For
illustrative purposes, this table was re-ranked by heavy duty vehicle AADT.
Page lof 2
to
Roadway From
1-4 Bridge No-100607
1-4 Bridge No-100605
1-4 Bridge No-100599
1-4 S566/THONOTOSASSA RD
1-4 US 301/SR 43
1-4 1-75/SR 93A
1-75 PASCOCOLINE
1-75 N/A
1-4 MCINTOSH RD
1-75 GIBSONTON DR
1-4 10320000/10320001
1-4 Bridge No-100658
1-75 HILLSBOROUGHCOLINE
1-4 SR574/MLKING BLVD
1-75 SR 582 / FOWLER AVE
1-75 SR 400/1-4
1-75 SR574/M L KING BLVD
1-75 CR 582A/FLETCHER AVE
1-4 Mango Rd
1-75 SR674/E COLLEGE AVE
1-75 Bridge No-100363
To
HILLS/POLK CO LINE
Bridge No-100607
S566/THONOTOSASSA RD
Bridge No-100605
1-75/SR 93A
Mango Rd
US 98 / CORTEZ BLVD
HERNANDOCOLINE
Bridge No-100599
SR 43/US 301
Bridge No-100658
US 41/SR 599/50TH ST
CR54
ORIENT RD
CR582A/FLETCHERAVE
SR 582/FOWLER AVE
SR 400/1-4
C581/BRUCE B.DOWNS B
MCINTOSH RD
Bridge No-100363
GIBSONTON DR
AADT
105,000
103,000
110,000
98,000
136,500
136,500
40,500
40,500
117,932
111,500
164,000
151,000
68,500
122,000
108,500
108,500
108,500
90,500
127,000
67,000
89,000
AADT Heavy Duty Heavy Duty Vehicle
Rank Vehicle AADT AADT Rank
28
29
25
30
15
15
102
102
22
24
6
13
49
20
26
26
26
34
18
51
35
15,719
15,388
15,279
14,396
14,073
13,172
12,859
12,859
12,595
12,577
12,251
12,050
11,542
11,236
10,579
10,579
10,579
10,498
10,465
10,285
10,217
1
2
3
4
5
6
7
7
8
9
10
11
12
13
14
14
14
15
16
17
18
-------
to
Table 6-2. Ranking of road segments using fleet mix data as described in Step 2. This modified version of Table 6-1 illustrates the
ranking of road segments based on fleet mix for the Tampa, Florida, CBSA, using 2009 traffic data available from Florida DOT. For
illustrative purposes, this table was re-ranked by heavy duty vehicle AADT.
Page 2 of 2
Roadway
From
1-75 SB 1-275
1-75 HILLSBOROUGH COUNTY
1-75 C581/BRUCE B.DOWNS B
1-75 SR52
1-275 FLORIBRASKAAVE
1-275 EAST END BR 150107
1-275 4TH ST N
1-4 US 41/SR 599/50TH ST
1-75 MANATEE CO LINE
1-275 S600/U92/DALE MABRY
1-275 SLIGH AVE
1-275 Bridge No-100128
RTE-41 OLD COLUMBUS DR(UNS)
1-275 Bridge No-100138
1-275 Bridge No-100110
1-275 SR 688/Ulmerton Rd
1-4 ORIENT RD
1-275 Bridge No-100120
1-275 Bridge No-100203
1-275 SR 600/HILLS AVE
1-75 N/A
1-75 SR 43/US 301
1-75 SR 60/ADAMO DR
To
NB 1-275
JNTY SB 1-275
/NSB PASCOCOLINE
N/A
Bridge No-100203
7 Bridge No-100115
END BRIDGE 150107
ST SR574/MLKING BLVD
SR 674/E COLLEGE AVE
BRY Bridge No-100128
Bridge No-100219
Bridge No-100110
(UNS) N 48TH ST
10320000/10320001
Bridge No-100138
I 4TH ST N
US 301 /SR 43
S600/U92/DALE MABRY
SR 600 /HILLS AVE
SLIGH AVE
SR 60/ADAMO DR
N/A
SR 574/M L KING BLVD
AADT
60,500
60,500
60,500
40,000
160,500
147,000
147,000
121,000
55,500
170,500
167,000
192,000
107,500
169,000
169,000
130,000
113,000
163,000
153,500
156,500
92,500
92,500
92,500
AADT Heavy Duty Heavy Duty Vehicle
Rank Vehicle AADT
60
60
60
103
8
14
14
21
68
3
5
1
27
4
4
17
23
7
11
10
33
33
33
9,462
9,462
9,462
9,304
9,229
9,026
9,026
9,014
8,919
8,713
8,684
8,467
8,342
8,298
8,298
8,281
8,215
7,824
7,736
7,669
7,530
7,530
7,530
AADT Rank
19
19
19
20
21
22
22
23
24
25
26
27
28
29
29
30
31
32
33
34
35
35
35
-------
Near-Road NO2 Monitoring TAD Section 6: Candidate Road Segments
6.3 Calculate FE-AADT for All Segments
Although comparing Table 6-1 to Table 6-2 is informative, it is not easy to simultaneously
compare the ranked lists between both AADT and fleet mix. In order to more easily compare
one road segment to another, particularly when those road segments have a varied amount of
both total traffic volume and HD vehicle volume, the EPA recommends the use of FE-AADT, a
unique metric that accounts for both total traffic volume and fleet mix for comparison purposes .
STEP 3 - Calculate the Fleet Equivalent AADT values for each road segment using Equation 2 (if
using locally derived HD to LD NOX emission ratios) or Equation 3 (if using the national default
HD to LD ratio of 10). Re-prioritize the candidate site list based upon FE-AADT, where the road
segment with the highest FE-AADT value is ranked first and subsequent road segments are
presented in descending order.
With FE-AADT, roads can be re-ranked in an order that reflects both AADT and fleet mix (if
information on the amount of heavy-duty vehicles that are present on each individual road
segment are available) within one numerical value. Re-ranking by FE-AADT presents a
prioritized list of road segments that are more likely representative of estimated or potential NOX
emissions than either AADT or fleet mix alone. The determination of FE-AADT per segment
depends on three factors:
1. total traffic volume, presented as AADT counts,
2. fleet mix, presented as HD vehicle counts, and
3. the heavy-duty to light-duty vehicle NOX emission ratio.
Equation 2 can be used to calculate an FE-AADT value for each road segment.
(2)
where AADT is the total traffic volume count for a particular road segment, HDC is the total
number of heavy-duty vehicles for a particular road segment, and HDm is a multiplier that
represents the heavy-duty to light-duty NOX emission ratio for a particular road segment.
The HDm multiplier can be obtained several ways. One option is to determine HDm from
national average motor vehicle emission factors, resulting in the same HDm value being used for
all road segments being characterized in a CBSA as described below. Using this option results
in a value for HDm, which should be suitable for most situations. Alternatively, the HDm value
can be derived from local vehicle speed and/or emissions estimates for a given CBSA that can
28
-------
Near-Road NO2 Monitoring TAD Section 6: Candidate Road Segments
provide a specific HDm value across the CBS A, or provide HDm values for individual road
segments.
For this TAD, we have used the national default approach. Based on information derived
from EPA's MOVES, the EPA suggests that the national default for HDm be 10. This HDm value
is used in all examples where FE-AADT is calculated in this TAD. In using a national default
where HDm equals 10, the NOX emissions from one FID vehicle are assumed to be equivalent to
the NOX emissions from 10 LD vehicles operating on the same road segment and under the same
environmental and relative operating conditions. When using the national default HDm of 10,
Equation 2 can be simplified to Equation 3.
(3)
The details on the rationale for the national default HDm value of 10, as well as guidance for
local municipalities to calculate their own HDm value, are included in Appendix B. If air
agencies have appropriate on-road vehicle fleet mix and speed characterizations for roads in their
jurisdictions, they may choose to calculate their own ratio, which may be more accurate for their
particular road segment(s) of interest. For example, state and local agencies may choose to
calculate a local HDm value or values based on information for a specific road segment (e.g., a
given segment experiences higher congestion with lower average vehicle speeds or may have a
higher percentage of older diesel trucks or buses). Agencies may also consider calculating a
local //Devalue for a particular season, such as when air quality violations occur in only one
season at an existing area-wide NC>2 monitor.
Table 6-3 has been updated from Table 6-2 with an additional column to reflect FE-AADT.
The road segments have been re-ranked based on the FE-AADT value. In this example, the
sixth-ranked AADT (tenth-ranked HD) segment has moved to the first-ranked position. Two
notable changes are that the first-ranked AADT (twenty-seventh-ranked HD) segment is only
moved down to second, and that the first-ranked HD segment (twenty-eighth-ranked AADT) is
now ranked eighth. This table illustrates that accounting for higher per-vehicle NOx emission
rates for HD vehicles using the heavy-duty to light-duty NOx emissions ratio (described in
Equations 2 and 3) has a significant effect on the ranking of the road segments for further
consideration as candidate near-road NO2 monitoring sites, with the magnitude of this effect
dependent on the HDm value(s) chosen.
29
-------
Table 6-3. Ranking of road segments based on FE-AADT as described in Step 3. The listed road segments from the Tampa CBSA are
ranked by FE-AADT, which was calculated using 2009 traffic data available from Florida DOT.
Page lof 2
Roadway From
1-4
1-275
1-4
1-4
1-4
1-275
1-4
1-4
1-275
1-275
1-275
1-275
1-275
1-4
1-275
1-4
1-275
1-275
10320000/10320001
Bridge No-100128
US 301 /SR 43
Bridge No-100658
1-75/SR 93A
S600/U92/DALE
MABRY
Bridge No-100599
Bridge No-100607
SLIGH AVE
Bridge No-100138
Bridge No-100110
FLORIBRASKAAVE
CR587/WESTSHORE
BLVD
Bridge No-100605
Bridge No-100120
MCINTOSH RD
EAST END BR 150107
4TH ST N
Bridge No-100658
Bridge No-100110
1-75/SR 93A
US 41/SR 599/50TH ST
Mango Rd
Bridge No-100128
S566/THONOTOSASSA
RD
HILLS/POLK CO LINE
Bridge No-100219
10320000/10320001
Bridge No-100138
Bridge No-100203
Bridge No-100120
Bridge No-100607
S600/U92/DALE
MABRY
Bridge No-100599
Bridge No-100115
END BRIDGE 150107
AADT
164,000
192,000
136,500
151,000
136,500
170,500
110,000
105,000
167,000
169,000
169,000
160,500
176,500
103,000
163,000
117,932
147,000
147,000
Heavy Duty Heavy Duty
„ . Vehicle Vehicle AADT FE-AADT rc'MMLM
Rank AAr^T „ , Rank
AADT Rank
6
1
15
13
15
3
25
28
5
4
4
8
2
29
7
22
14
14
12,251
8,467
14,073
12,050
13,172
8,713
15,279
15,719
8,684
8,298
8,298
9,229
7,413
15,388
7,824
12,595
9,026
9,026
10
27
5
11
6
25
3
1
26
29
29
21
36
2
32
8
22
22
274,259
268,203
263,157
259,450
255,048
248,917
247,511
246,471
245,156
243,682
243,682
243,561
243,217
241,492
233,416
231,287
228,234
228,234
1
2
3
4
5
6
7
8
9
10
10
11
12
13
14
15
16
16
-------
Table 6-3. Ranking of road segments based on FE-AADT as described in Step 3. The listed road segments from the Tampa CBSA are
ranked by FE-AADT, which was calculated using 2009 traffic data available from Florida DOT.
Page 2 of 2
Roadway
1-4
1-275
1-75
1-4
1-275
1-4
1-275
1-275
SR-60
1-275
1-275
1-75
1-75
1-75
1-4
1-4
1-4
1-75
S566/THONOTOSASS
ARD
SR 600 /HILLS AVE
GIBSONTON DR
Bridge No-100605
SLIGH AVE
SR 43 /US 301
SR 574/ML KING BLVDORIENT RD
Bridge No-100203
Mango Rd
SR580/BUSCH BLVD
Bridge No-100219
SR616
Columbus Dr
SR688/Ulmerton Rd
SR 582 /FOWLER AVE
SR 400/1-4
SR574/M LKING
BLVD
US 41/SR 599/50TH
ST
Bridge No-100115
ORIENT RD
CR582A/FLETCHER
AVE
SR 600 /HILLS AVE
MCINTOSH RD
Bridge No-100231
SR 580 /BUSCH BLVD
SR 93/1-275
FLORIBRASKAAVE
4TH ST N
CR 582A/FLETCHER
AVE
SR 582 /FOWLER AVE
SR 400/1-4
SR 574/ML KING BLVD
CR587/WESTSHORE
BLVD
US 301 /SR 43
C581/BRUCE
B.DOWNSB
AADT
98,000
156,500
111,500
122,000
153,500
127,000
151,500
151,500
158,000
147,000
130,000
108,500
108,500
108,500
121,000
135,500
113,000
90,500
AADT
Rank
30
10
24
20
11
18
12
12
9
14
17
26
26
26
21
16
23
34
Heavy Duty
Vehicle
AADT
14,396
7,669
12,577
11,236
7,736
10,465
7,105
7,105
5,941
7,159
8,281
10,579
10,579
10,579
9,014
7,371
8,215
10,498
LJ r\
Heavy Duty
Vehicle AADT
4
34
9
13
33
16
39
39
42
38
30
14
14
14
23
37
31
15
FE-AADT
227,564
225,521
224,693
223,124
223,124
221,185
215,445
215,445
211,469
211,431
204,529
203,711
203,711
203,711
202,126
201,839
186,935
184,982
FE-AADT
Rank
17
18
19
20
20
21
22
22
23
24
25
26
26
26
27
28
29
30
-------
Near-Road NO2 Monitoring TAD Section 6: Candidate Road Segments
6.4 Use Congestion Pattern Indicators to Supplement Road Segment
Rankings
The EPA does not recommend that any of the congestion indicators be used in a quantitative
manner to further re-rank or re-prioritize the whole list of candidate road segments resulting from
Step 3 (or Step 1 if fleet mix data are not available). This recommendation is made because of
the relatively higher potential for incomplete data and overall uncertainties in congestion pattern
indicators. Instead, such data are believed to be more useful as a qualitative measure by which
one road segment might be selected over other relatively similar candidate road segments in the
overall selection process. In such a situation, it is recommended that when using LOS data, a
higher priority should be placed on road segments with a lower or worse LOS, where A is the
highest (or best) LOS grade and F is the lowest (or worst) LOS grade. If LOS is not available,
but either ratios or "AADT by lane" is available for use, a higher priority should be placed
on road segments with higher or AADT per lane values.
STEP 4 - Add the congestion indicator (LOS, , or AADT by lane value from Equation 2, if
available) to the candidate site list. These data will be used in the overall evaluation process,
and can be used as a qualitative metric to aid in selecting one candidate road segment over
other similarly ranked candidates.
Table 6-4 has been updated from Table 6-3 with a column displaying congestion information
in the form of LOS letter grades. The LOS for the example was gathered from five different data
sources. Table 6-4 shows that a majority of the higher ranked FE-AADT segments have an LOS
value of F, which indicates that these segments are also some of the most congested. As a result,
there is little discernible difference among the higher FE-AADT ranked candidate sites for the
Tampa CBSA example based on the congestion indicators.
32
-------
Table 6-4. Ranking of road segments including congestion pattern information per segment. The last column here was added to
Table 6-3 from the Tampa, Florida, CBSA. In this example, LOS data were available from Florida DOT. The segments are still ranked
by FE-AADT. Note that LOS data that were made available span several years; however, we have treated the data equally in this
example.
Page lof 2
AADT
Heavy
AADT Duty
Rank Vehicle
AADT
10320000/10320001
Bridge No-100128
Bridge No-100658
Bridge No-100110
164,000
192,000
12,251
Duty
Vehicle
AADT
Rank
10
FE-
AADT
AADT
Rank
274,259
8,467
27
268,203
(Year)
F (2005)
F (2005)
US 301/SR43
1-75/SR 93A
136,500
15
14,073
263,157
F (2008)
1-4
Bridge No-100658
US 41/SR 599/50TH ST
151,000
13
12,050
11
259,450
F (2005)
1-75/SR 93A
Mango Rd
136,500
15
13,172
255,048
F (2008)
S600/U92/DALE MABRY
Bridge No-100128
170,500
8,713
25
248,917
F (2005)
Bridge No-100599
S566/THONOTOSASSA RD 110,000
25
15,279
247,511
F (2008)
Bridge No-100607
HILLS/POLK CO LINE
105,000
28
15,719
246,471
F (2008)
SLIGH AVE
Bridge No-100219
167,000
8,684
26
Bridge No-100138
10320000/10320001
169,000
8,298
29
245,156
243,682
10
F (2005)
F (2005)
Bridge No-100110
Bridge No-100138
169,000
8,298
29
243,682
10
D (2005)
FLORIBRASKAAVE
Bridge No-100203
CR587/WESTSHORE BLVD
Bridge No-100120
Bridge No-100605
Bridge No-100607
160,500
176,500
103,000
9,229
21
243,561
11
7,413
36
243,217
12
29
15,388
241,492
13
F (2005)
F (2005)
F (2008)
Bridge No-100120
S600/U92/DALE MABRY
163,000
7,824
32
233,416
14
F (2005)
MCINTOSH RD
Bridge No-100599
117,932
22
12,595
231,287
15
F (2008)
EAST END BR 150107
Bridge No-100115
147,000
14
9,026
22
228,234
16
E (2005)
D (2008)
4TH ST N
END BRIDGE 150107
147,000
14
9,026
22
228,234
16
S566/THONOTOSASSA RD Bridge No-100605
98,000
30
14,396
227,564
17
F (2008)
SR 600/HILLS AVE
SLIGH AVE
156,500
10
7,669
34
225,521
18
F (2005)
GIBSONTON DR
SR43/US 301
111,500
24
12,577
224,693
19
C (2008)
SR574/ML KING BLVD
ORIENT RD
122,000
20
11,236
13
223,124
20
E (2008)
Bridge No-100203
SR 600/HILLS AVE
153,500
11
7,736
33
223,124
20
F (2005)
-------
Table 6-4. Ranking of road segments including congestion pattern information per segment; the last column here was added to
Table 6-3 from the Tampa, Florida, CBSA. In this example, LOS data were available from Florida DOT. The segments are still ranked
by FE-AADT. Note that LOS data that were made available span several years; however, we have treated the data equally in this
example.
Page 2 of 2
AADT
Heavy
AADT Duty
Rank Vehicle
AADT
Heavy
Duty
Vehicle
AADT
Rank
FE-
AADT
FE-
AADT
Rank
(Year)
MCINTOSH RD
127,000
18
10,465
16
221,185
21
F (2008)
SR580/BUSCHBLVD
Bridge No-100231
151,500
12
7,105
39
215,445
22
E (2005)
Bridge No-100219
SR580/BUSCHBLVD
151,500
12
7,105
39
215,445
22
F (2005)
SR-60
SR616
SR 93/1-275
158,000
5,941
42
211,469
23
C (2005)
1-275
Columbus Dr
FLORIBRASKAAVE
147,000
14
7,159
38
211,431
24
F (2005)
1-275
SR688/Ulmerton Rd
4TH ST N
130,000
17
8,281
30
204,529
25
D (2007)
1-75
SR 582 / FOWLER AVE
CR582A/FLETCHERAVE
108,500
26
10,579
14
203,711
26
F (2008)
1-75
SR 400/1-4
SR 582/FOWLER AVE
108,500
26
10,579
14
203,711
26
F (2008)
1-75
SR 574/M L KING BLVD
SR 400/1-4
108,500
26
10,579
14
203,711
26
F (2008)
1-4
US 41/SR 599/50TH ST
SR574/ML KING BLVD
121,000
21
9,014
23
202,126
27
1-275
Bridge No-100115
CR587/WESTSHORE BLVD 135,500
16
7,371
37
201,839
28
F (2008)
F (2005)
1-4
ORIENT RD
US 301/SR43
113,000
23
8,215
31
186,935
29
E (2008)
1-75
CR582A/FLETCHERAVE
C581/BRUCE B.DOWNS B 90,500
34
10,498
15
184,982
30
F (2008)
1-275
GANDYBLVD/SR694
ROOSEVELT BL/SR 686
123,000
19
6,876
41
184,884
31
A, B, or C (2007)
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Near-Road NO2 Monitoring TAD
Section 6: Candidate Road Segments
6.5 Rank the Road Segments
Completion of as many of the steps in Sections 6.1 through 6.4 as possible (based on
available traffic data) results in a prioritized list of candidate road segments in which the highest-
ranked road segments are expected to be the locations where traffic volume, fleet mix, and
congestion patterns combine to contribute to a greater potential for, and/or more frequent
occurrences of, peak NO2 concentrations in the near-road environment.
The parameters described in this section, along with their recommended sources and/or
methods of calculation, are needed to produce the list of prioritized candidate road segments.
Table 6-5 summarizes these parameters. This list is recommended as a guide to subsequent
evaluation processes (described in the following sections of this document) to determine where
permanent near-road NO2 monitoring stations will be installed.
Table 6-5. Summary of the traffic-related metrics for candidate site consideration.
Component
AADT
Rationale
Potential Sources
Focus on locations with high traffic
volumes
State DOT, local/MPO, or US DOT's
HPMS
Fleet mix
Trucks emit greater amounts of NOX
on an average, per-vehicle basis
State DOT, local/MPO
FE-AADT
Single metric to compare road
segments, accounting for AADT and
fleet mix
Use AADT and fleet mix in Equation 2
(Section 6.2)
Congestion
Frequent acceleration and stopping
can lead to higher per-vehicle
emissions
State/local LOS; state DOT or HPMS
(number of lanes); congestion maps
35
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
Section 7. Physical Considerations for Candidate Near-Road
Monitoring Sites
Once an initial list of candidate sites is created, whether through a process such as that
described in Section 6, through use of methods such as monitoring or modeling as described in
Sections 9 and 10, or via other approaches, select segments must be further evaluated to
determine adequacy for a near-road monitoring station. Specifically, candidate road segments
need to be inspected to account for roadway design, terrain, and meteorological factors (covered
in this section), and also for safety and logistical considerations, and possibly for population
exposure potential (covered in subsequent sections). This section provides a review of the three,
non-traffic related data considerations listed in the CFR: roadway design (including related
roadside structures), terrain, and meteorology.
Table 7-1 provides an overview of the physical characteristics that need to be considered in
evaluating candidate sites, including positive and negative attributes. Additional details on these
characteristics are included in the sections that follow.
Table 7-1. Summary of physical considerations for candidate near-road sites.
Physical
Site
Component
Roadway
design or
configuration
Roadside
Structures
Terrain
Meteorology
Impact on Site
Selection
Feasibility of monitor
placements; affects
pollutant transport
and dispersion.
Feasibility of monitor
placement; affects
pollutant transport
and dispersion.
Affects pollutant
dispersion, local
atmospheric stability.
Affects pollutant
transport and
dispersion.
Desirable
Attributes
At-grade or nearly at-
grade with immediate
surrounding terrain.
No barriers present
other than low (<2 m
in height) vegetation
or safety features such
as guardrails.
Flat or gentle terrain,
within a valley, or
along road grade.
Relative downwind
locations-winds from
road to monitor.
Least Desirable
Attributes
Deep cut-
sections/significantly
below grade;
significantly above
grade (fill or bridge);
above grade (bridge).
Presence of sound
walls, mature (high and
thick) vegetation,
obstructive buildings.
Along mountain ridges
or peaks, hillsides, or
other naturally
windswept areas.
Strongly predominant
upwind positions.
Potential
Information
Sources
Field reconnaissance;
satellite imagery.
Field reconnaissance;
satellite imagery.
Field reconnaissance;
digital elevation
models and
vegetation files;
satellite imagery.
Local data;
NOAA/NWS; AQS.
7.1 Roadway Design
The design (or configuration) of a roadway can influence the amount of emissions generated
from motor vehicles and the transport and dispersion of those emissions along and/or away from
37
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
the road. Roadway design includes features of the road itself, such as the slope or grade of a
roadbed (which is often a reflection of local terrain or topography), the presence of access ramps,
intersections, interchanges, or other such locations where traffic may merge or disperse, and a
roadbed's position relative to the immediate surrounding terrain.
In particular, road grades create an increased load on vehicles ascending a grade, leading to
increased exhaust emissions as the vehicle does more work to continue its forward motion. In
addition, the presence of ramps, intersections, and lane merge locations can lead to increased but
localized emissions due to the propensity for acceleration and the potential for stop-and-go
vehicle operations resulting from traffic congestion.
The relative position of a road to the immediate terrain around the roadway can have a
significant influence on pollutant transport and dispersion along and/or away from the source
road. The three general types of roadway design discussed here are at-grade, below-grade or cut-
sections, and above-grade or elevated roads.
7.1.1 At-Grade Roads
At-grade roads are those where the roadway surface (on which the vehicles are travelling) is
generally at the same elevation as the immediate surrounding terrain. In any particular wind
condition (e.g., winds
parallel or normal to the
road), at-grade roads will
experience the least amount
of influence on pollutant
dispersion among all
roadway design types, not
accounting for other
structures or obstacles
(discussed in Section 7.2)
that can exist in the near-
road environment.
Photo courtesy of Eric Stuve, OKRoads.com.
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
7.1.2 Below-Grade or Cut-Section Roads
Cut-section roads are those where the roadway surface elevation is below the surrounding
terrain. A cut-section road can have vertical or sloped walls; the walls can be natural or man-
made. Under perpendicular wind conditions (normal to the road), cut-section roads tend to cause
lofting of the traffic
plume as wind flows
through, up, and out
of the depressed road
canyon. With wind
conditions parallel or
near-parallel to the
source road, on-road
emissions may be
funneled downwind
for some distance
with emissions
contained in the road
canyon, akin to what
happens in an urban street canyon. Channeling of winds may also occur within the cut section as
a result of turbulence and wind flow generated from the vehicles operating on the road.
7.1.3 Above-Grade or Elevated Roads
Elevated roadways are those where the roadway surface is higher than the surrounding
topography. Elevated roads can be elevated primarily in two ways:
1. roads built on an earthen berm or other solid material, where such earth or material
may be referred to as "fill," with no open space underneath the road surface for
airflow, and
2. roads built on pilings or supports with open space underneath, where air may flow
both above and beneath the road surface, such as a bridge.
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
7.1.3.2 Elevated Roads Over Solid Fill Material
Elevated roads over solid fill material can have similar dispersion patterns as at-grade roads
with winds normal to the road, since shear forces can draw the traffic plume back to the surface,
downwind of a sloped fill section. However, some fill configurations (e.g., those with vertical or
sharply sloped walls - shown below) can cause the traffic plume to loft above the ground
immediately adjacent to the vertical or sharply sloped wall (where eddy formation immediately
downwind of the roadbed is occurring), with the core of the emission plume impacting the
ground further downwind from the vertical or sharply sloped wall.
Imagery © 2012 Google Maps.
7.1.3.3 Elevated Roads Which Are Open Underneath
Elevated roads which are open underneath can have enhanced dispersion of on-road
emissions with all wind directions. In these cases, emissions are more readily dispersed due to
the increased dilution air
(moving above and below
the roadbed) and from the
turbulence caused by the
elevated road structure
itself. Because of this,
ground-level
concentrations downwind
of the elevated roadbed
may not be as high as
concentrations found near
at-grade roadside locations
or near similar roads which
are elevated on fill.
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
7.1.4 Relative Desirability in Roadway Designs
The general understanding of the effect of roadway design on emissions dispersion has been
derived through review of near-road field studies and the use of wind tunnel facilities. For
example, Figure 7-1 shows results from a wind tunnel study comparing roadway configurations
and changes in near-road air pollutant concentrations (along a path normal to the source
roadway) that illustrates these effects. These results show that some roadway designs are more
desirable than others, considering the goal of monitoring peakNC>2 concentrations.
20
15
u 10
0
0
10
Flat terrain
Noise barrier, upwind only
Noise barrier, upwind and downwind
Depressed roadway (6m), vertical walls
Depressed roadway (6m), sloped walls
Elevated roadway (6m), sloped walls
Depressed roadway (6m), sloped, with barriers
20
X/H
30
40
Figure 7-1. Wind tunnel study results comparing downwind air pollutant concentrations from a
road with varying topography and roadside structures. The distance downwind (x-axis; X) is
expressed in multiples of the height of the noise barrier studied (H; 6 m). Multiplying the x-axis
values by 6 provides an estimate of downwind concentrations at distances in units of meters.
The ground-level concentrations have been non-dimensionalized to represent inert pollutant
dispersion. This figure was obtained from Baldauf et al. (2009), with Heist et al. (2009)
providing additional details on the wind tunnel studies conducted.
Figure 7-1 highlights how roadside features can affect downwind pollutant concentrations
under wind conditions normal to the source road. Notably, flat terrain, which is representative of
at-grade roads, shows the least disruption in dispersion, where relative ground-level
concentrations are highest as on-road emissions are dispersed (with a Gaussian-type gradient),
and concentrations decrease with increasing distance from the source roadway. At-grade road
configurations would have the least complicated dispersion scenarios to consider while targeting
maximum NC>2 concentrations, and thus be the most desirable setting for near-road NC>2
monitoring stations.
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
The second most desirable near-road monitoring location is adjacent to elevated roads on fill
material with gently sloped walls, where maximum concentrations are very close to
concentrations found with at-grade locations.
Those roadway designs that may be less preferable when considering near-road NO2 monitor
locations would be those where a site would be adjacent to elevated roads that are open
underneath, or cut (or depressed) road beds (where deeper cuts or depressions likely present
increasingly more significant impacts or complications on pollutant dispersion).
Recommendations on siting a monitor probe near above- and below-grade roads are discussed in
Section 8.
7.2
Roadside Structures
In addition to the manner in which roadway design affects pollutant transport and dispersion,
roadside structures may be present that also affect near-road pollutant concentrations. These
structures include sound walls or noise barriers, vegetation, and buildings. Physical barriers
affect pollutant concentrations around the structure by blocking initial dispersion and increasing
turbulence and
initial mixing of
the emitted
pollutants. In
wind tunnel
studies such as
that reported by
Baldauf et al.
(2009), sample
road
configurations
with noise barriers
are shown to have
the largest
impacts on
pollutant
dispersion,
relative to flat, at-grade roadway designs. In other situations, such as when winds blow along the
roadway, roadside structures may channel emissions downwind, without much dispersion
occurring normal to the road. Therefore, even if siting criteria can be met at a site, the EPA
suggests that monitor placement adjacent to these structures be avoided when possible,
particularly if other, similar candidate near-road locations without roadside structures are
available.
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Near-Road NO2 Monitoring TAD
Section 7: Siting Criteria
7.3 Vegetation
Vegetation along a road segment can
affect on-road pollutant transport and
dispersion. Winds flowing through
vegetative structures can experience
increased mixing and dilution due to the
complex system of branches and leaves,
while also leading to calmer winds
behind the vegetation compared with
similar winds in an open field situation.
In addition, the branches and leaves can
provide surfaces for particle deposition
through impaction or diffusion as
pollutants are transported through the
vegetation.
Imagery © 2012 Google Maps.
7.4
Terrain
As mentioned in Section 7.1 (on roadway design), local topography is often a part of
roadway design and can greatly influence pollutant transport and dispersion. However, large-
scale terrain features beyond the local roadway configuration may also affect where peak NO2
concentrations from on-road mobile sources can occur. The consideration of large-scale terrain
in the siting process is more of a case-by-case issue for individual sites. In consideration of this
issue, the EPA believes that state and local air agencies likely have a good understanding of
large-scale terrain impacts on pollution dispersion in or within a CBSA, because these impacts
would not be unique to near-road emissions, but would also affect wider-scale ambient
monitoring.
Larger scale terrain needs to be considered in the near-road NO2 monitoring site selection
process, as appropriate. One example could be to identify multiple air basins within a single
CBSA (if present), and consider how those individual basins may affect pollutant build-up and
dispersion. Another example might be to consider roads through valleys, where, due to the
increased potential for inversion conditions within the valley, higher near-road NO2
concentrations may be found than concentrations measured at sites on the tops of hills, along
hillsides, or in open terrain.
7.5 Meteorology
Evaluating historical meteorological data should be useful in determining whether certain
candidate locations might experience a higher proportion of direct traffic emissions impacts from
a given road segment due to the local winds. More specifically, an evaluation of local
meteorology may provide some indication of which side of a candidate segment might
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Near-Road NC>2 Monitoring TAD Section 7: Siting Criteria
experience a higher proportion of direct traffic emission impacts. In the process of identifying
near-road gradients, most research studies showing elevated pollutant concentrations near roads
have focused on measurements in situations where winds were from the road to the downwind
monitor or receptor (typically along a line normal to the roadbed). These studies indicate that
monitor placement very near the road, on the relative downwind side of the target road, is
typically appropriate when attempting to measure peak concentrations.
In addition to considering relatively small-scale impacts on a candidate road segment
(upwind/downwind), state and local air agencies may also consider other meteorological
impacts, such as the frequency of inversions, which can lead to increased potential for pollutant
build-up due to limited atmospheric mixing.
In the preamble to the NFR for NC>2, it is discussed that downwind monitoring is not
required, but the EPA strongly encourages it. There were several reasons for the decision not to
make downwind monitoring a requirement. Some evidence suggests that wind direction may not
always be a major factor in peak concentrations close to a major roadway. Often, peak NO2
concentrations may occur during stable, low-wind-speed conditions when wind direction is less
influential. Further, in some situations the turbulence created by vehicles on the road can lead to
"upwind meandering" of pollutants, so that a monitor upwind of the target road would still be
characterizing a portion of the on-road emission plume. Finally, the EPA did not want such a
restrictive siting criterion in place which may not always be applicable.
There are situations where meteorological patterns may warrant very strong consideration for
the relative downwind side of a target road segment. One example might be a road that runs
relatively parallel to a coastline, where diurnal wind patterns, such as a sea breeze/land breeze
set-up, may significantly affect pollutant buildup and dispersion along that roadway. Another
example might be roads in valley areas which are subject to air flows driven by diurnal mountain
air flow patterns. Thus, historical wind directions and local knowledge of wind patterns should
be considered in establishing NO2 monitoring sites.
In most cases, monitor placement on the climatologically downwind side of a road segment
is preferred; however, the EPA stresses that meteorology should not preclude consideration of
sites located in the predominant climatologically upwind direction if applicable site access,
safety, and other logistical issues are still favorable when considering a relatively high ranked
candidate road segment.
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Near-Road NO2 Monitoring TAD
Section 8: Siting Criteria
Section 8. Siting Criteria
The primary requirements related to horizontal and vertical probe placement for near-road
NC>2 monitors are specified by the EPA in 40 CFR Part 58, Appendix E. For horizontal
placement (with respect to the target roadway), near-road NC>2 monitor probes are required to be
installed so ".. .the monitor probe shall be as near as practicable to the outside nearest edge of the
traffic lanes of the target road segment; but shall not be located at a distance greater than 50
meters, in the horizontal, from the outside nearest edge of the traffic lanes of the target road
segment."
This TAD recommends that the target distance for near-road N02 monitor probes be within
20 meters of the target road whenever possible.
The key component of this passage is that the monitoring probes are to be placed "as near as
practicable" to the target road segment. Baldauf et al. (2009) notes that a distance of 10 to
20 meters should be considered for near-roadway monitoring; the EPA strongly encourages state
and local agencies to place near-road NC>2 monitor probes within 20 meters from target road
segments when possible. Key requirements from 40 CFR Part 58, Appendix E, are shown in
Table 8-1
Table 8-1. Key near-road siting criteria.
Near-Road NO2 Siting Criteria (per 40 CFR Part 58, Appendix E)
Horizontal spacing
According to 40 CFR Part 58 Appendix E: "As near as practicable to the outside
nearest edge of the traffic lanes of the target road segment; but shall not be
located at a distance greater than 50 meters, in the horizontal, from the outside
nearest edge of the traffic lanes of the target road segment."
This TAD recommends that the target distance for near-road NO2 monitor probes
be within 20 meters of the target road whenever possible.
Vertical spacing
Microscale near-road NO2 monitoring sites are required to have sampler inlets
between 2 and 7 meters above ground level.
Spacing from supporting
structures
The probe must be at least 1 meter vertically or horizontally away from any
supporting structure, walls, parapets, penthouses, etc., and away from dusty or
dirty areas.
Spacing from obstructions
For near-road NO2 monitoring stations, the monitor probe shall have an
unobstructed air flow between the monitor probe and the outside nearest edge of
the traffic lanes of the target road segment, where no obstacles exist at or above
the height of the monitor probe.
Vertical placement requirements of near-road NC>2 monitoring probes are "... to have the
sampler inlet between 2 and 7 meters above ground level." There are several situations where
the limits of the allowable vertical range for inlet probe heights may be appropriate. For
example, if a candidate monitoring site is nearly at-grade with the target road, or if the target
45
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Near-Road NC>2 Monitoring TAD Section 8: Siting Criteria
road is a cut-section road, the state and local air agency should consider placing the inlet probe
closer to the 2-meter height limit above ground level. This recommendation is based on the
information presented in Section 7.1, where the impact of the roadway designs will likely lead to
peak concentrations more frequently occurring closer to ground level. Further, monitor probe
placement at or near a 2-meter height above ground level is generally considered to be at or near
"breathing height," which is a human exposure consideration.
Alternatively, if a near-road monitoring station is being considered for placement adjacent to
an elevated fill section of road where the elevated roadbed has vertical or sharply sloped walls,
the state or local air agency should consider placing the inlet probe higher in the 2 to 7 meter
range above the ground level so that the sampler inlet might be closer to the elevation of the
target road surface and out of a possible eddy cavity. This follows the rationale, as discussed in
Section 7.1, that emissions plumes from elevated roads may have peak concentrations aloft when
winds are normal to the roadway (due to eddy formation immediately downwind of the roadbed)
with the core of the emission plume impacting the ground relatively further downwind from the
edge of the road. In this situation, depending on the relative difference in height between the
target road surface, ground-level at the monitor probe location, and the steepness of the grade
between the two locations, the state or local air agency should consider placing the monitor
probe slightly higher and further away from the target road. This placement avoids situations in
which the inlet probe may be in the eddy cavity downwind of the elevated road structure, causing
the emission plume to potentially pass over the inlet probe.
According to 40 CFR Part 58 Appendix E, near-road NC>2 monitor probes need to be spaced
away from certain supporting structures and have an open, unobstructed fetch to the target road
segment. In a majority of monitoring sites, gas analyzer inlet probes, such as those used for
NC>2, are placed on a monitoring shelter or on a tower on or adjacent to a monitoring shelter.
However, for some monitoring site configurations, inlet probes may be placed upon walls,
parapets, or other existing infrastructure, which could include a noise barrier in the near-road
environment. In these cases, the probe must be at least 1 meter vertically and/or horizontally
away from any supporting structure, and away from dusty or dirty areas. Further, for near-road
NC>2 monitors, there will likely be some distance between the target road segment and the NO2
inlet probe. It is required that there be an unobstructed air flow, or open fetch, where no
obstacles exist at or above the height of the monitor probe and the outside nearest edge of the
traffic lanes of the target road segment. Technically speaking, open fetch would be observed
along a path directly between the road and the NO2 inlet, normal to the roadbed. However, as
the EPA noted in the preamble of the NFR for NC>2, the NC>2 inlet will likely be influenced by
various parts of the target road segment that are at a relative angles compared to the normal
transect between the road and the NC>2 inlet. Because of this, a desirable characteristic is to have
increasingly open areas without obstructions along the length of the road segment on either side
of the monitoring station. Therefore, when considering site locations, the recommended
approach is for state and local air agencies to consider more than one linear pathway between the
target road segment and the monitor probe to have open fetch characteristics, and to choose sites
where the monitor probe will be increasingly clear of obstructions.
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Near-Road NC>2 Monitoring TAD Section 9: Exploratory Monitoring
Section 9. Using Exploratory Air Quality Monitoring to
Identify Roadway Segments for Near-Road Site
Selection Evaluation
To provide increased confidence of the likelihood for measuring peakNC>2 concentrations at
a particular location, agencies may elect to conduct air quality monitoring to either identify
candidate near-road monitoring sites or evaluate candidate monitoring sites identified through
the process described earlier in this TAD. A variety of fixed and/or mobile monitoring
techniques can be used to accomplish this task, and they can be used in a variety of applications,
including a saturation study, a more limited and focused monitoring campaign, or through
mobile monitoring. The methods that could be used in such exploratory monitoring campaigns
include passive devices or active devices that provide integrated or continuous measurements.
• Saturation studies typically involve the use of a large number of low-cost, portable
samplers to "saturate" an area with sampling devices in order to identify the spatial
variability of pollutant concentrations. In this case, the application could be to deploy
many samplers or devices at a number of roadside locations to estimate which roadways
might have relatively higher pollutant levels.
• Focused exploratory monitoring studies might use a an approach to create data for
comparison or evaluation at a smaller number of sites, such as those derived from the
process in Section 6 using traffic data, and subsequently considering the results of
physical reconnaissance.
• Mobile exploratory monitoring utlizes instrumented vehicles or moveable platforms to
measure pollutant concentrations at multiple locations. In this case, mobile monitoring
could potentially be used to determine spatial variability of pollutant concentrations
among a number of road segments.
These methods may be used exclusively or in combination to aid in the site selection process.
Passive Monitoring for Saturation Studies
Passive sampling devices (PSDs) for the measurement of NC^have been used widely in
saturation sampling applications, and in more focused applications, including near-road
monitoring studies. Using PSDs is a relatively inexpensive method, requiring only modest
hardware and infrastructure to use in the field, with the greatest expense being laboratory
analysis of the exposed sampling media. PSDs are small, lightweight, and do not require power
to operate. These characteristics allow PSDs to be more easily deployed in saturation monitoring
campaigns, where a relatively large number can be deployed near numerous road segments at
almost any location.
The primary limitation to these devices for near-road applications is the long exposure times
needed to collect a sufficient sample for analysis. In typical urban areas, these samplers typically
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Near-Road NC>2 Monitoring TAD Section 9: Exploratory Monitoring
are exposed to ambient air for at least three or more days, and traditionally are exposed for week-
long or multi-week periods. Because of this, PSDs are not able to directly reveal those locations
experiencing short-term, 1-hour average NC>2 concentration peaks. However, it can be useful to
use PSDs to differentiate the variability in long-term concentrations among candidate near-road
monitoring locations as part of the near-road site selection process. Several studies have
compared PSDs with Federal Reference Method (FRM) and other real-time monitors, with the
accuracy and precision of these devices varying by application and the time averaging periods
evaluated.
The EPA believes that even though sample data are collected over longer time periods, PSDs
can still be used in a comparative manner to help identify those road segments which may have a
relatively higher probability of experiencing peak NC>2 concentrations on shorter time intervals.
A number of references (such as those listed below) can be consulted for more information on
how to conduct passive sampling for a near-road evaluation, advantages and disadvantages of
this approach, and the precision and accuracy that can be anticipated when conducting this type
of project. Some of these references focus on NC>2 applications; however, passive samplers can
also be used for other contaminants if multi-pollutant monitoring is desired. Resources include
the following materials:
• Quality Assurance Project Plan: Use of Passive Sampling Devices (PSDs) in a Near-Road
Monitoring Environment, http://www.epa.gov/ttnamtil/files/nearroad/20110428qapp.pdf
• New York City Community Air Survey,
http://www.nyc.gov/html/doh/html/eode/nyccas. shtml
• Mukerjee S., Oliver K.D., Seila R.L., Jacumin H.H. Jr, Croghan C., Daughtrey E.H. Jr,
Neas L.M., Smith L.A. (2009) "Field comparison of passive air samplers with reference
monitors for ambient volatile organic compounds and nitrogen dioxide under week-long
integrals" J. Environ. Monit., 2009, 11(1), 220-227
9.2 Stationary Continuous or Integrated Monitoring
Several small, lightweight, and portable NO2 analyzers are commercially available that may
be useful for conducting a saturation study, or a more focused study on a small set of road
segments, to further evaluate potential near-road monitoring sites. Many of these samplers are
battery-operated, with many of the same advantages of PSDs, including the flexibility of making
monitoring possible at almost any location. These samplers cost more than PSDs; however, most
cost significantly less than an FRM sampler. These samplers can also collect real-time, near-
real-time, or otherwise integrated NC>2 data, so the data collected from these samplers may be
more comparable to the 1-hour time average of the NO2 NAAQS than data provided by PSDs.
However, these sampling techniques are relatively new compared to FRM and PSD samplers,
so the precision and accuracy of these devices is often uncertain and not well characterized,
especially for near-road applications. Thus, if these samplers are chosen for the purpose of
establishing a near-road NO2 monitoring site, care must be taken to ensure that the precision and
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Near-Road NC>2 Monitoring TAD Section 9: Exploratory Monitoring
accuracy of these devices are well characterized, which would include collocated sampling with
an FRM or Federal equivalent method (FEM) sampler, collocation of two or more of the portable
devices, and rotation of the portable samplers to evaluate potential individual sampler bias.
9.3 Mobile Monitoring
The use of mobile monitoring platforms for research and exploratory monitoring has
increased in recent years. Mobile monitoring entails the placement of air quality sampling
systems on board a moveable platform (e.g., car, truck, bicycle, or cart). This technique allows
for a greater spatial coverage of monitoring over fairly short time periods. For some
applications, the mobile platform is continuously moving, with short-term air quality
measurements collected during this movement. This mobility provides a broad spatial coverage
of an area of interest over a short time. In other applications, the moveable platform is rotated
from location to location, collecting short- or longer-term measurements at each spot, where the
time-averaged measurements are typically on the order of minutes to hours at each location.
One limitation of mobile monitoring is the lack of simultaneous monitoring at multiple sites;
with mobile monitoring, there is the potential of missing maximum concentrations over the entire
area of interest if changes occur in the strength or location of emissions over short time periods.
Another limitation is that mobile measurements may not be easily correlated to the maximum 1-
hour average concentrations of interest for the NC>2 NAAQS if collected over short time
periods(l- to 5-second average concentrations). To address these limitations, some studies have
incorporated the use of multiple mobile monitoring platforms, or have employed integrated
mobile and stationary monitoring for reference.
In general, mobile monitoring studies tend to be much more expensive than PSD or other
saturation studies using portable equipment. While few NC>2 mobile monitoring studies have
been conducted due to the lack of continuous instrumentation, these studies will likely increase
with the availability of new real-time NO2 monitors. Mobile monitoring may also be useful for
conducting multi-pollutant assessments, since a number of air quality samplers can be placed on
a mobile platform for simultaneous use.
If state or local agencies consider the use of mobile monitoring as a tool to assist in
determining where a near-road NC>2 station might best be located, a number of key concepts and
measurement routines should be considered. These concepts and routines include repeating
travel loops over the course of hours and/or days, and determining how much data collection will
be sufficient for comparison purposes. These concepts are described in peer-reviewed literature,
such as the Hagler et al. (2010) article (and references within); such literature should be
considered as a template for how a mobile monitoring study might be conducted.
Resources include the following materials:
• Hagler G.S.W., Thoma E.D., and Baldauf R.W. (2010) High-resolution mobile
monitoring of carbon monoxide and ultrafme particle concentrations in a near-road
environment. J. Air & Waste Manage 60(3), 328-336.
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• Westerdahl D., Fruin S., Sax T., Fine P.M., and Sioutas C. (2005) Mobile platform
measurements of ultrafine particles and associated pollutant concentrations on freeways
and residential streets in Los Angeles. Atmos. Environ. 39(20), 3597-3610. Available on
the Internet at http://www.sciencedirect.com/science/article/B6VH3-4G4NOFIK-
l/2/2fl05ea20bb843af35c9586cOf810cac.
• Westerdahl D., Wang X., Pan X., and Zhang K.M. (2009) Characterization of on-road
vehicle emission factors and microenvironmental air quality in Beijing, China. Atmos.
Environ. 43(3), 697-705. Available on the Internet at
http://www.sciencedirect.com/science/article/pii/S1352231008009011.
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Near-Road NO2 Monitoring TAD Section 10: Air Quality Modeling
Section 10. Using Air Quality Modeling to Identify Roadway
Segments for Near-Road Site Selection Evaluation
Air quality modeling can be used in several different ways to aid the near-road site selection
process. One use is to conduct dispersion modeling of several candidate near-road sites, such as
those identified through traffic data evaluation and subsequent physical reconnaissance. The
model output could be used to provide further confidence of the likelihood of measuring peak
NC>2 concentrations by comparing the relative concentrations among the modeled road segments.
Another use of modeling could be to further refine locations for near-road stations along
individual road segments as necessary. A third application of modeling, although potentially
time intensive, could be to model a larger number of road segments to identify those segments
where peak NC>2 concentrations might be expected. This third application could possibly be
performed in lieu of the traffic analysis suggested in Section 6 in order to generate a prioritized
list of road segments for further evaluation. All three of these air quality modeling applications
require the use of both a vehicle emissions model and an air quality dispersion model.
This section describes these applications in terms of EPA regulatory models (e.g., MOVES
for vehicle emissions and the AMS/EPA Regulatory Model [AERMOD] for dispersion). We
note that California maintains the EMission FACtors (EMFAC) model for predicting vehicle
emissions in that state, and the California Air Resources Board (CARB) guidance should be
consulted for using that model.
10.1 The MOVES Model
EPA's MOVES is a computer model that estimates emissions from on-road motor vehicles,
including cars, trucks, buses, and motorcycles.5 MOVES was released in December 2009 and
replaces MOBILE6.2, EPA's previous emissions model.6 MOVES is based on an extensive
review of in-use vehicle emissions data collected and analyzed since the release of MOBILE6.2.
MOVES estimates emissions of NOX and other pollutants based on vehicle type, age, and
activity. MOVES accounts for variations in speed, temperature, and other factors, and can do so
at a high level of geographic resolution. Accordingly, MOVES can incorporate a wide array of
vehicle activity for each road segment.
MOVES includes various emission processes (running, start, brake wear, tire wear, extended
idle, and crankcase) that are applicable in different contexts. Because the emphasis in this TAD
is on high-traffic road segments, the emphasis of emissions modeling should focus on the NOX
5 See EPA's MOVES website for further information on downloading MOVES, the MOVES User Guide, and
other technical documentation: http://www.epa. gov/otaq/models/moves/index.htm. For guidance documents, see
http://www.epa.gov/otaq/stateresources/transconf/pohcv.htm#project.
6 This document uses "MOVES" to refer genetically to any approved version of the MOVES model. Unless
EPA notes otherwise, this guidance is applicable to current and future versions of the MOVES model.
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emission processes prevalent on roadway segments: running exhaust and crankcase.7 For other
pollutants, such as particulate matter (PM), hydrocarbons (HC), and CO, other emission
processes are important. See Appendix C for further information on using MOVES for project-
level analyses.
10.2 AERMOD Air Quality Dispersion Model
This section provides guidance to state and local agencies that choose to use air quality
modeling to further inform the implementation of near-road NO2 monitors. The information
provided here, along with the more detailed information in Appendix C, covers the selection of
an air quality model, modeling domain (including receptor placement), characterization of
emission sources, meteorological inputs, and inclusion of background concentrations.
For this TAD, AERMOD was selected as the regulatory dispersion model. Promulgated in
2005, AERMOD is EPA's preferred near-field dispersion model for a wide range of regulatory
applications in all types of terrain based on extensive development and performance evaluation.
AERMOD is the recommended model for most mobile source modeling scenarios.8 In regard to
NO2 mobile sources, AERMOD was used for the NO2 Risk and Exposure Assessment (U.S.
EPA, 2008) and performed generally well.
10.2.1 NO2 Chemistry Using PVMRM or OLM Algorithms
NO to NO2 conversion can be modeled explicitly in AERMOD using one of two methods,
the Plume Volume Molar Ratio Method [PVMRM; (Hanrahan, 1999a, b); (Cimorelli et al.,
2004)] or the Ozone Limiting Method (OLM).9 These methods use NO2/NOX emitted ratios and
background ozone concentrations to convert NO to NO2 within AERMOD.
On March 1, 2011, EPA issued "Additional Clarification Regarding Application of Appendix
W Modeling Guidance for the 1-hour NO2 National Ambient Air Quality Standard" (U.S.
Environmental Protection Agency, 2011) to provide clarification and guidance on the use of
Appendix W guidance for the 1-hour NO2 standard, including guidance for the implementation
of PVMRM and OLM in AERMOD, inclusion of background concentrations, and other
modeling guidance. Much of the information noted in the memorandum is presented in
Appendix C.
7 If other transportation facilities are evaluated (e.g., diesel truck or bus activity at terminals), then additional
emission processes would be considered in MOVES.
8 For example, EPA cites AERMOD as a recommended model when completing PM hot-spot analyses for
transportation conformity analyses of highway and transit projects. See EPA's Transportation Conformity
Guidance for Quantitative Hot-spot Analyses in PM2.s and PM10 Nonattainment and Maintenance Areas (U.S.
Environmental Protection Agency, 2010a, b).
9 Appendix C of this TAD also discusses two conservative methods of calculating NO2 concentrations based on
information in Appendix W, Modeling Guidance.
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For more information regarding the use of PVMRM and OLM, consult the following
resources:
• the AERMOD User's Guide and addendum (Cimorelli et al., 2004),
• the material in Appendix C in this document, and
• the March 1, 2011 EPA memorandum, "Additional Clarification Regarding Application
of Appendix W Modeling Guidance for the 1-hour NC>2 National Ambient Air Quality
Standard," which offers more guidance and background information on these two
algorithms (U.S. Environmental Protection Agency, 2011).
10.2.2 Including Background and Nearby Sources in Analyses
While this section of the TAD has emphasized mobile source emissions, two other
components usually considered in a modeling exercise are the inclusion of background
concentrations and the modeling of nearby sources, including stationary sources and other
mobile sources. The inclusion of background concentrations will affect the magnitude of
cumulative (all sources) concentrations. Inclusion of nearby sources will also affect the
magnitude of cumulative concentrations, and may also change the location of maximum modeled
concentrations or design values. Also, as described in Appendix C, the inclusion of additional
sources can also affect the competition among sources for ozone when using the PVMRM or
OLM algorithms in AERMOD. More information on how to handle background and nearby
source in a near-road centric modeling effort is presented in Appendix C.
10.3 Resource
U.S. Environmental Protection Agency (1985) Guideline for determination of good engineering
practice stack height (technical support document for the stack height regulations, revised).
Technical memorandum by the Office of Air and Radiation, Office of Air Quality Planning
and Standards, Research Triangle Park, NC, EPA-450/4-80-023R, June. Available on the
Internet at http://www. epa. gov/ttn/scram/guidance/guide/gep. pdf.
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Near-Road NC>2 Monitoring TAD Section 11: Physical Characteristics
Section 11. Physical Characteristics of Candidate Near-Road
Sites
Using the list of prioritized candidate near-road segments produced in the process discussed
in Section 6, and any supplemental exploratory monitoring and/or modeling that may have been
conducted, state and local air agencies can perform a more detailed evaluation of potential near-
road sites to further characterize and prioritize candidate segments. Such characterizations and
evaluations can be carried out through the use of electronic data resources including satellite
imagery (e.g., Google Earth), mapping resources (e.g., Bing Maps or Google Maps), and/or
ArcGIS, for example. In addition, the EPA also advises that state and local air agencies conduct
physical reconnaissance to characterize candidate sites. This section provides a suggested
checklist for state and local air agencies to use in their reconnaissance. The EPA notes that some
information suggested to be gathered to characterize any given road segment may already be
reflected in prior traffic data analysis.
11.1 Road Segment Identification
The road segment identifier is most likely part of the traffic analysis data. However, in some
cases the identifying terms in the traffic analysis may not be the most commonly used or known
terms. To better understand and communicate information about candidate near-road sites, it
may be necessary to correlate the assigned roadway identifiers with other useful identifying
information and/or commonly used names for these traffic facilities. For example, using the
Tampa, Florida, example from Section 6 (Table 6-4, row 2), it is rather common knowledge that
1-275 indicates "Interstate 275." However, it might be useful during the evaluation process if the
1-275 road segment was listed as "I-275/US 93," since US Highway 93 also uses the same
corridor for the road segment used in this particular example (but is not listed in the traffic data
table).
Another example of additional useful road segment identification is adding a commonly
referenced road name, such as the Kennedy Expressway in the Chicago, Illinois, area. In this
example, while the road identifier may be "I-90/I-94" for a road segment, the segment may be
more commonly known as the Kennedy Expressway.
When applicable, EPA recommends that state and local air agencies use the combination of
the given road identifiers along with more useful identification and/or commonly used labeling
to aid in the site identification process. More detailed names make it easier for interested parties
to identify and understand which road segments are being described or characterized.
11.2 Road Segment Type
During reconnaissance, it should be noted whether the road is a controlled access roadway,
limited access expressway, limited or full access arterial, or other type of road. Controlled
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access roads (also referred to as freeways or sometimes expressways) are divided highways with
full control of access. The control of access is established two ways:
1. by a lack of access to the roadway by any adjoining property (e.g., no driveways), and
2. by free-flowing traffic (i.e., traffic flow is unhindered because there are no traffic
signals or intersections that might cause traffic to stop).
Access to these roads is typically provided by on- and off-ramps at interchanges with other
roads. Limited-access roads may have traffic signals, intersections, and access to adjoining
properties; however, these access points are limited in number and location.
Understanding the type of road for a candidate road segment can help determine the
likelihood of safe, feasible monitoring shelter access. Controlled and limited access segments
should not be avoided for monitoring site consideration; however, the evaluation of these
segments should consider how potential monitoring sites will be accessed and maintained.
11.3 Road Segment End Points
Similar to the suggestion to use additional information and common names or labels for road
segment identification, it may also be helpful to use more descriptive language to describe, in
name and location, those transportation facilities or boundaries that denote the end points to any
given road segment (e.g., intersections, highway exits, highway mile markers, geo-political
boundaries). For example, using the Tampa, Florida, example from Section 6, the highest-
ranked FE-AADT road segment on 1-275 (Table 6-4, row 2) has end points at Bridge No-100128
and Bridge No-100110. Such traffic facility infrastructure identifications may not be commonly
known or easily translated into physical and geographical locations to aid in understanding the
extent of the road segment. To improve understanding and communication of information about
candidate near-road sites, it may be necessary to combine the DOT-assigned traffic facility
identifiers with any other commonly used names for these traffic facilities. In this example, it
may be more useful to label Bridge 100128 as the "interchange of I-275/US 93 with Business
41/SR 685" and Bridge 100110 as the location where "Armenia Avenue crosses 1-275." The use
of more commonly used or readily understood labeling can aid in the site identification process,
making it easier for interested parties to identify and understand the location of a candidate road
segment.
11.4 Interchanges
State and local air agencies should note the presence of interchanges or road junctions within
or at the ends of a particular road segment. Information could include the identification of the
intersecting or connecting road(s) and the type of interchange. There are multiple types of
interchanges, including four-way (i.e., cloverleaf, stack, and diamonds) and three-way
interchanges. A robust but unofficial resource on the types of interchanges transportation
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agencies use in building transportation facilities can be found at
http://en.wikipedia.org/wiki/Interchange_(road).
11.5 Roadway Design
As discussed in Section 7.1, the roadway design can have a significant impact on pollutant
transport and dispersion. During the reconnaissance of a road segment, state and local air
agencies should note the design of the candidate road segment (e.g., at-grade; above-grade—on
fill or open underneath; below-grade; or even a mix) including the notation of changes in design
and the related local terrain along the length of the segment (if present).
In those cases where the road is above or below grade, air monitoring agencies should
attempt to characterize the nature of the cut road or elevated road. For example, if a road is
below grade, estimate the depth of the cut below the surrounding terrain and note the type of
walls (i.e., sloped or vertical). For elevated roads, note whether the road is on a bridge or fill
section, the height of the roadbed above the surrounding land, and, for a fill section, whether the
road is supported by vertical or sloped walls.
11.6 Terrain
Akin to roadway design, state and local air agencies should note the following about terrain
relative to their candidate sites:
• the type of terrain on which the candidate road lies
• the terrain immediately adjacent to the candidate road segment
• any larger-scale terrain features within which the candidate road may lie
• any larger-scale terrain features that may potentially influence the candidate road
One example of a terrain feature to note is whether the road segment is along a grade for its
entire length, or a portion of its length. Another example might be noting a road segment's
proximity to hills, bluffs, canyons, ridges, bodies of water, or other topographical features that
can influence local meteorology.
11.7 Roadside Structures
As discussed in Section 7.2, roadside structures can have a significant effect on pollutant
transport and dispersion. Further, roadside structures can seriously impact the candidacy of a
road segment for near-road monitoring. During the reconnaissance of a road segment, state and
local agencies should note all roadside structures throughout the length of the candidate road
segment. Notation on the existence, type, location, length, and approximate height of any
structures should be captured for any sound walls, vegetation, earthen berms, buildings, or other
structures along each side of the segment.
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11.8 Existing Safety Features
Safety in the near-road environment is a very important consideration in the installation of a
near-road monitoring station (a more detailed discussion on safety issues is presented in
Section 12.2). Safety of the travelling public on the road, the air monitoring staff members who
service a near-road monitoring station, and the monitoring station itself should be a top priority.
During the reconnaissance of a candidate road segment, state and local air agencies should take
note of existing safety features on each side of the road along the road segment, including
ditches, berms, guard rails, cable barriers, jersey barriers (temporary and permanent concrete
barriers), or other features. Placement of a monitoring station behind such safety features would
be preferable when possible.
11.9 Existing Infrastructure
Existing structures, traffic related monitoring systems, and other highway maintenance
facilities may already exist in the near-road environment along some candidate road segments.
These pieces of infrastructure may provide a leveraging opportunity for a near-road monitoring
site at a location that may already be accessible, have safety features, have power, and/or have
other utilities, which might ease the installation of a possible near-road NC>2 station. Such
infrastructure can include sign supports (traffic or billboard), light poles, automatic traffic
counters, traffic camera installations, dynamic message signs, Road Weather Information System
(RWIS) installations, rest stops, or other such locations. Additionally, depending upon
individual state participation, state and local air agencies may be able to identify the location and
nature of some RWIS infrastructures through U.S. DOT's Clarus System website
(http://www.clarus-system.com/).
11.10 Surrounding Land Use
State and local air agencies should note the general or mixed use of land (e.g., urban or
suburban residential, commercial, industrial, agricultural, forested) around candidate road
segments during the reconnaissance process. Specific information (such as the presence of
schools, hospitals, and low-rise or high-rise buildings) is also useful to note. In addition to the
traditional land use categories noted above, state and local air agencies should also determine
and note (through field reconnaissance and possibly emissions inventory review) whether any
significant emissions sources (off-road mobile or stationary sources) are nearby. Beyond
traditional land use characterization, the EPA also suggests that state and local air agencies
identify the proximity of a candidate road segment to other heavily trafficked roads, areas of
higher relative road density, and/or locations within or near central business districts or urban
downtown areas.
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11.11 Current Road Construction
The potential for future road construction on candidate road segments is discussed in
Section 12. However, during reconnaissance of candidate road segments, state and local air
agencies should note any ongoing road construction along with any immediately apparent
preparations for road construction.
11.12 Frontage Roads
During candidate road segment analysis, the presence of frontage roads should be noted.
Frontage roads, also called service roads or access roads, typically run parallel to major
highways, and may or may not be considered part of the major highway. Frontage roads can be
(but aren't necessarily) controlled access or limited access roads, and are often one-way roads
with traffic flowing in the same direction of the adjacent lanes of the partnering main-line travel
lanes. They can provide access to property adjacent to major roads and connect these properties
with roads which have direct access to the main roadway. Frontage roads can also provide a
means for traffic in and around the properties adjacent to a major road to access that road, most
often at interchanges.
11.13 Meteorology
State and local agencies should attempt to understand the general climatological wind rose
for candidate road segments, which can be used to aid in the determination of what might be
dominant upwind and downwind locations along a particular segment. Local data, such as that
collected by the air agency themselves, is preferable, along with any other local and such
collected by the air agencies themselves regional weather and climatological data collected by
NOAA.
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Near-Road NO2 Monitoring TAD Section 12: Site Logistics
Section 12. Monitoring Site Logistics in the Near-Road
Environment
Key components in determining whether a candidate near-road monitoring site is truly
feasible include determining whether
• an air monitoring agency will be able to access the desired location
• the site will be safe for site operators and the public during routine operations
• there is sufficient availability of power and telecommunications services (or the ability to
procure and install those services)
According to 40 CFR Part 58, Appendix E, section 6.4(a), ".. .the monitor probe shall be as
near as practicable to the outside nearest edge of the traffic lanes of the target road segment; but
shall not be located at a distance greater than 50 meters, in the horizontal, from the outside
nearest edge of the traffic lanes of the target road segment." With emphasis on being "as near as
practicable" to the target road segment, a number of candidate near-road sites are expected to fall
within right-of-way properties under the jurisdiction and maintenance of state or local DOTs or
other transportation authorities, collectively referred to as transportation agencies. This section
provides background information regarding the access of right-of-ways, including associated
terminology, safety guidelines, procedures, and expectations regarding the access to such
properties. This section also includes suggestions on engaging and collaborating with
transportation agencies to access highway property for installation of a near-road monitoring
station in a right-of-way.
If a candidate near-road site is accessible without the state having to use the right-of-way
(i.e., on property not otherwise managed or governed by a transportation agency), states will
more than likely be able to treat the site access investigation as they would for any other
traditional ambient air monitoring site. In these cases, the EPA still encourages states to make
special accommodations and considerations for safety such as those presented within this
section. Terminology specific to this section is provided in Table 12-1.
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Section 12: Site Logistics
Table 12-1. Terminology used by transportation agencies that is relevant to this section.
Definition
Air rights
Air space lease
Easement
Federal-aid Highway
Federal-aid Highway
System
Right-of-way (ROW)
The term "air rights" is a legal term used to describe that area above (e.g., air
space) or below the plane of the transportation facility and located within the
right-of-way boundaries under authority of the appropriate highway agency. Air
rights typically include access to a parcel of ground within the right-of-way.
The agreement between the managing transportation authority and another
entity dictating the length and terms by which the requesting entity may have
access to highway air rights.
An easement is a right to use property belonging to someone else, for a stated
purpose, without owning that property.
According to 23 CFR 470.103, federal-aid highways are those that are part of the
federal-aid highway system and all other public roads not classified as local
roads or rural minor collectors.
According to 23 CFR 470.103, the federal-aid highway system means the
National Highway System and the Dwight D. Eisenhower National System of
Interstate and Defense Highways (the "Interstate System"). Specific information
on the National Highway System and the Interstate System can be found at
http://www.fhwa.dot.gov/planning/nhs/.
The right-of-way is a type of easement that gives someone the right to travel
across property owned by someone else. In situations dealing with ROWs along
major highways, the use of ROW space is typically governed or managed by
state or local DOTs or other transportation authorities.
12.1 Accessing the Right-of-Way
The feasibility of a potential near-road NO2 monitoring site depends upon the determination
of whether a given location can be accessed. If the prospective location is within the ROW of an
existing road, state and local air agencies will need to engage their respective transportation
agencies to gain access to the air rights of that property. This access would most likely be
accomplished through a permitting process that would ultimately lead to the development and
establishment of an air space lease (or permit).
The right to use space within the ROW by public entities or private parties for interim non-
highway uses may be granted in air space leases, as long as such uses will not interfere with
• the construction, operation, or maintenance of the transportation facility;
• anticipated future transportation needs; or
• the safety and security of the facility for both highway and non-highway users.
State and local air agencies considering potential near-road sites within the ROW need to
work with their companion transportation agencies to consider near and long-term construction
plans, potential interference with routine highway operations and maintenance due to the
presence of a monitoring station, safety, and security of the highway ROW during the
development of the lease agreement. The permitting and lease agreement process is likely
different from state to state, or from one urban area to another; however, this process will likely
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involve similar factors and take time to complete before physical access is granted to the state or
local air agency. The U.S. Federal Highway Administration (FHWA) maintains information on
air space access on the Internet at http://www.fhwa.dot.gov/realestate/airguide.htm.
When considering a site within the ROW, state and local air agencies should consider several
factors that may affect the ease of negotiating an air space lease.
The first factor is physical access. It is anticipated that transportation agencies will prefer
that any potential near-road NC>2 monitoring site in the ROW be planned so that the site is or will
be made accessible from outside the ROW, or have accommodations that preclude the need to
access the site from the primary travel lanes of the target road.
If it is determined during the evaluation of a candidate site that the installation of a locked
access point (such as a gate) is required to access the ROW, if the candidate site is an interstate
facility, the state or local transportation agency must submit justifications and obtain approval
from FHWA, which is a formal federal action. FHWA's policies on changes in access to the
interstate highway ROW are maintained on the Internet
at http://www.fhwa.dot.gov/programadmin/fraccess.cfm. This requirement does not preclude the
establishment of a monitoring station where access is only feasible from the target highway;
however, an approach requiring the use of a new locked access point may be more preferable to a
transportation agency in an air space lease negotiating process than a plan relying upon access
solely from the target road.
A second factor to be considered for site feasibility and the impact on negotiating an air
space lease is the availability of utilities. State and local air agencies need to determine whether
utilities are already present, need to be relocated, or need to be installed to support the air
monitoring station. Any activity to change or install utilities will require approval from the
managing transportation agency. If the road segment in question is part of a federal-aid
highway, the state or local transportation agency must ensure that any permits to install
necessary utilities must comply with the appropriate federal regulation and FHWA policies.
However, identifying potential site locations adjacent to, or otherwise near, existing
infrastructure within the ROW with existing power may make it possible to avoid some
permitting procedures and possibly reduce utility-related installation costs. More information on
utility considerations, particularly with respect to bringing utilities into the ROW, can be found
at http://www.fhwa.dot.gov/programadmin/utility.cfm
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12.2 Safety in the Near-Road Environment
The EPA stresses that safety is a top priority in all field operations.
Near-road NC>2 monitoring sites must be safely sited for both the traveling public on the
roadway and the personnel operating the monitoring site. Near-road monitoring sites must be
safely and legally accessible to station operators, and not pose safety hazards to drivers,
pedestrians, or nearby residents. Safety hazards to drivers can include obstructions to sight lines
and distractions, which can lead to accidents. Safety hazards to pedestrians include obstructions
that block safe movement along the road or walkways. Safety hazards to monitoring site
operators include factors which inhibit the safe entrance to or egress from a site and factors that
could allow vehicles to encroach upon and damage the site infrastructure. Since near-road NO2
monitoring sites may be located on ROWs maintained by a transportation agency, as discussed
above, it is anticipated that state and local air agencies will engage their respective transportation
agency regarding access to such locations. During discussions on the potential access and use of
locations within the ROW, safety should be a primary concern.
Transportation agencies deal with multiple roadway safety issues when building and
maintaining traffic facilities. FHWA maintains a safety program addressing safety issues; more
information can be found at http://safety.fhwa. dot, gov. However, of the multiple safety
categories that are dealt with, the one category that may be most relevant with regard to the near-
road NC>2 monitoring network is "roadway departure" safety. FHWA defines a roadway
departure accident as a non-intersection crash which occurs after a vehicle crosses an edge line
or a center line, or otherwise leaves the traveled way. Since near-road NO2 monitoring stations
are not on the road, but relatively near the outside edge of travel lanes, the roadway departure of
vehicles likely poses the biggest safety risk to the travelling public, the air monitoring staff
working at a near-road site, and the monitoring site infrastructure. Depending upon roadway
design and terrain, there are multiple means by which transportation agencies can improve or
increase safety within the ROW or at the edge of ROW space. Examples include roadway
paving techniques (e.g., rumble strips or safety edging), increased pavement friction, the use of
retaining barriers, and maintenance of open areas within the ROW called "clear zones". With
respect to near-road NO2 monitoring stations, existing safety features provided by the local
terrain, man-made barriers, or clear zones should be considered as positive attributes to a
potential site in the site selection process.
12.2.1 Terrain
The terrain of a road segment can, in some cases, increase safety by reducing roadway
departures that impact a near-road monitoring site. Such examples are ditches or berms (made of
earthen fill) that might exist between the roadway and the monitoring station. So long as these
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terrain features do not obstruct the fetch between the monitor probe and the target road, they may
be viewed as positive attributes for a given candidate road site.
12.2.2 Man-Made Barriers
Man-made barriers or retainers in the ROW come in many forms, most of which can
generically be referred to as longitudinal barriers. FHWA maintains a list of crash-worthy
longitudinal barriers on the internet at
http://safety.fhwa.dot.gov/roadway_dept/policy_guide/road_hardware/barriers/. Some examples
of the many individual types of longitudinal barriers available include temporary and permanent
concrete barriers, multiple configurations of steel and/or wood guardrails, water-filled barriers,
and cable barriers. The presence of any type of longitudinal barrier, so long as it does not
obstruct the fetch between the monitor probe and the target road, may be viewed as a positive
attribute for a given candidate near-road site.
12.2.3 Clear Zones
Clear zones are defined by FHWA to be an unobstructed, traversable roadside area that
allows a driver to stop safely or regain control of a vehicle that has left the roadway. The width
of the clear zone (e.g., the distance between the outside edge of the road to an obstacle) is based
on risk, which is derived from a roadway's traffic volume, design speeds, and the slope of the
underlying and adjacent terrain. In practice, a clear zone is free of obstructions (including safety
barriers) and denotes an area or distance from the road that a near-road monitoring station would
be placed outside of, measured from the outside edge of the travel lanes. As a rule of thumb,
highways with no natural or man-made obstructions alongside the travel lanes typically might
have a prescribed clear zone on the order of about 9 to 10 meters (30 feet), with maximum clear
zone recommendations of approximately 13 meters (46 feet) for elevated fill roads.
Although FHWA provides a summarization of clear zones on the Internet
(http://safety.fhwa.dot.gov/roadway_dept/clear_zones/#zones), clear zone guidance is created by
the American Association of State Highway and Transportation Officials (AASHTO).
AASHTO's Roadside Design Guide [also known as the "green book"; (American Association of
State Highway and Transportation Officials, 2011)] contains more specific information on clear
zones, providing variable clear zone distances based on traffic volume, speeds, and roadside
geometry. Table 12-2 and Figure 12-1, which are reprinted here with permission of AASHTO,
show information on clear zones. As stated in the Roadside Design Guide, the table and curves
depicted were based on limited empirical data; the clear zone distances suggest only an
approximate center of a range of distances to be considered and not a precise or absolute
distance. In addition, clear zone distance recommendations do not preclude the establishment of
monitoring stations at closer distances to the road; however, stations located within the clear
zone will likely require the installation of safety devices (as previously discussed).
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Table 12-2. Clear zone information in U.S. customary units (reprinted with permission from
AASHTO).
Design
Speed
(mph)
Design
ADTa
Foreslopes
1V:6H or 1V:5H to
Flatter 1V:4H
Backslopes
1V:3HC 1V:3H
1V:5H to 1V:6H or
1V:4H Flatter
A^ ^n
cc
fin
fiR vn
Under 750
750-1,500
1,500-6,000
Over 6,000
Under 750
750-1,500
1,500-6,000
Over 6,000
Under 750
750-1,500
1,500-6,000
Over 6,000
Under 750
750-1,500
1,500-6,000
Over 6,000
Under 750
750-1,500
1,500-6,000
Over 6,000
7-10
10-12
12-14
14-16
10-12
14-16
16-18
20-22
12-14
16-18
20-22
22-24
16-18
20-24
26-30
30-32d
18-20
24-26
28-32d
30-34d
7-10
12-14
14-16
16-18
12-14
16-20
20-26
24-28
14-18
20-24
24-30
26-32d
20-24
26-32d
32-40d
36-44d
20-26
28-36d
34-42d
38-46d
7-10
10-12
12-14
14-16
8-10
10-12
12-14
14-16
8-10
10-12
14-16
16-18
10-12
12-14
14-18
20-22
10-12
12-16
16-20
22-24
7-10
10-12
12-14
14-16
8-10
12-14
14-16
18-20
10-12
14-16
16-18
20-22
12-14
16-18
18-22
24-26
14-16
18-20
22-24
26-30
7-10
10-12
12-14
14-16
10-12
14-16
16-18
20-22
10-12
16-18
20-22
22-24
14-16
20-22
24-26
26-28
14-16
20-22
26-28
28-30
a ADT = average daily traffic.
b The two most common slopes used in road construction are foreslopes and backslopes. The foreslope extends from the outside
of the shoulder to the bottom of the ditch. The backslope extends from the top of the cut at the existing grade to the bottom of the
ditch. The amount of slope in either the foreslope or backslope is the ratio of the horizontal distance (H) to the vertical
distance (V).
0 Since recovery is less likely on the unshielded, traversable 1V: 3H slopes, fixed objects should not be present in the vicinity of
the toe of these slopes. Recovery of high-speed vehicles that encroach beyond the edge of the shoulder may be expected to occur
beyond the toe of the slope. Determination of the width of the recovery area at the toe of the slope should take into consideration
right-of-way availability, environmental concerns, economic factors, safety needs, and crash histories. Also, the distance
between the edge of the through-traveled lane and the beginning of the IV: 3H slope should influence the recovery area provided
at the toe of the slope. While the application may be limited by several factors, the foreslope parameters which may enter into
determining a maximum desirable recovery area are illustrated in AASHTO's Roadside Design Guide.
d Where site-specific investigation indicates a high probability of continuing crashes, or such occurrences are indicated by crash
history, the designer may provide clear-zone distances greater than the clear zone shown in this table. Clear zones may be
limited to 30 feet for practicality and to provide a consistent roadway template if previous experience with similar projects or
designs indicates satisfactory performance.
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Section 12: Site Logistics
3H:1V -
EXAMPLE #1
6H:1V FORESLOPE uj
(FILL SLOPE)
OOmph
5000 vpd
ANSWER:
CLEAR ZONE
WIDTH = 30 ft
•SEE SECTION 3.3.4.
FOR DISCUSSION ON VARIABLE
SLOPE DETERMINATION.
EXAMPLE *2
6H:1VBACKSLOPE
(CUT SLOPE)
60mph
750 vpd
ANSWER:
CLEAR ZONE
WIDTH = 20 ft
OVER 6000 DESIGN APT )
0' 10' 20' |30'| 40' 50' 60' 70' 80 901 1001
1500-6000 DESIGN AD?) 1 ' ' I ' ' I ' ' I ' ' I ' ' I ' ' I ' ' I ' ' I ' ' I
d 10 201 301 40 50' 60' 70 80' 90'
750-1500 DESIGN ADT )—M—'—I'M
' 0' Iff 20'
I ' ' I ' M ' 'I ' M
30' 40' 50 60' 70'
UNDER 750 DESIGN APT) h
0' 10' 20' 30' 40'
CLEAR ZONE DISTANCE [ft]
50'
Figure 12-1. Clear zone distance curves (reprinted with permission from AASHTO). For
definitions, see the footnotes for Table 12-2.
State or local transportation agency might have approved design manuals that contain
specific safety criteria and/or guidance that could be applied to any particular candidate road
segment under evaluation. In addition to using the preceding material from FHWA and
AASHTO, a state and local air agency may also benefit from asking their respective state or
local transportation agency about such manuals.
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Near-Road NO2 Monitoring TAD Section 12: Site Logistics
12.2.4 Other Safety Considerations
The EPA stresses that safety is a top priority in all field operations. Ambient air monitoring
operations in the near-road environment present additional safety issues that must be addressed
in the site selection process. Because of this priority, the EPA recommends that state and local
air agencies fully evaluate the presence of existing protection or safety features along candidate
road segments. If appropriate safety features are not present, the state or local air agency will
need to consult with their DOT to determine whether there is a need for safety features, and if so,
what the design and installation process might be for infrastructure additions or enhancement to
ensure safety for all highway and monitoring station users.
For example, if a longitudinal barrier does not exist along a candidate site, state and local air
agencies should bear in mind that, with permission from and in coordination with their
companion transportation agency, they can procure and install longitudinal barriers specifically
to protect the near-road monitoring site. The EPA has learned that these barriers are relatively
inexpensive compared to other typical site installations.
Further, given the importance of protecting the public, air monitoring staff persons, and the
site infrastructure, air agencies can also consider allowable safety measures beyond standard
practice to further reduce safety-related risk.
12.3 Engaging a Transportation Agency
State and local air agencies will need to obtain permission from the appropriate
transportation agency if a monitoring site is to be located within an ROW. Most often, this
permission will come in the form of an air space lease (or permit) negotiated with the managing
transportation agency. The following sets of questions and issues are intended to prepare state
and local air agencies to engage transportation agencies, including those that will need to be
answered among the state or local air agency, the state or local transportation agency, and
potentially FFIWA.
• Who is the public authority responsible for the ROW?
• What are the transportation agency requirements for considering and approving leases (or
permits) to allow for the subject installation?
• Is the near-road site within an interstate highway ROW? If so, the request for a lease or
permit to the responsible state or local transportation agency responsible for the ROW
must include information that the FHWA requires to be addressed in the review and
approval of such an action. These issues will likely include the air rights agreement,
locked-gate access (as necessary), and compliance with applicable utilities
accommodation and relocation policy.
• What other policies, procedures, standards, leases/permits required or desired by the
managing transportation agency will need to be addressed?
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In addition to the overarching questions listed above, there are some questions that
transportation agencies may have about a potential ambient air monitoring station, and some
suggested questions that air agencies should consider asking their transportation agency
regarding individual candidate road segments, as discussed next.
12.3.1 Questions a Transportation Agency May Have
There are a number of questions that state or local transportation agencies may have when
first approached by a state or local air agency regarding the placement of an ambient air
monitoring station in the near-road environment. Some questions might include, but are not
limited to, the following:
• Who will own the monitoring equipment?
• How long would the air monitoring site be used/needed?
• What are the physical dimensions of the monitoring site and shelter?
(State and local air agencies need to consider the potential for multi-pollutant monitoring
when preparing this information. Multipollutant monitoring in near-road NC>2 monitoring
sites is discussed in Section 13.4 and Section 16.)
• What type of structure (shelter) will be installed at the site? (Pictures showing the
structure are useful.)
• How often would air monitoring staff need to access the site?
• If there are no existing utilities at the candidate site location, who will prepare the request
for permit, and subsequently pay for the installation of required utilities?
• Who will be financially responsible for the upkeep of the monitoring station? This
includes routine operations and the inspection, maintenance, and security of the site.
• Who would be responsible for any closure, removal, and relocation of the station, if
necessary?
12.3.2 Questions to Ask Your State or Local Transportation Agency
There are a variety of questions that state and local air agencies may want to ask their partner
transportation agencies about the long-term feasibility and access of a site within the highway
ROW. Some general questions an air agency might want to ask include:
• What, if any, construction is planned along the candidate road segment that might affect
traffic operations on the road, the safety of the monitoring site, or safe and efficient
access to the monitoring site?
• What, if any, construction is planned on nearby road segments or to the CBS A
transportation network that might impact traffic operations along the candidate site road
segment?
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Near-Road NO2 Monitoring TAD Section 12: Site Logistics
• In the future, if access to the monitoring site is either temporarily or permanently affected
by a highway project, what contingencies might be available for alternative access to the
site, or what alternative sites could be used along the same road segment?
• Will an air space lease, if awarded, be a one-time process, or will that lease need to be
renewed regularly? If the lease requires renewal, are there any particular criteria that
might cause the renewal to be disapproved in the future?
• If a near-road station is to be installed in the ROW, under what conditions should or
could safety features be added to a road segment? If a clear zone is currently in use, is
that sufficient, or do additional safety features need to be installed? If additional safety
features need to be installed, will that be allowed?
• If safety feature installation or improvements are desired, what types of features are
available to be considered for installation (such as guardrails or barriers)?
• Are there any other safety provisions that an air agency would need to conform to if they
routinely access and work on and within a monitoring station in the ROW?
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Near-Road NO2 Monitoring TAD Section 13: Prioritizing Locations
Section 13. Prioritizing Candidate Near-Road Locations for
Monitoring Site Selection
The EPA expects that state and local air agencies will be in possession of sufficient
information to begin making informed decisions regarding the selection of near-road NC>2
monitoring sites by
1. following the traffic analysis procedures to aid development of a prioritized list of
candidate road segments (described in Section 6), and
2. evaluating select candidate road segments through reconnaissance, possible use of
optional evaluation tools (e.g., exploratory monitoring or modeling), and possible
discussions with respective transportation agencies (discussed in in Section 7 through
Section 12).
The EPA expects that state and local air agencies will have a variety of options once they
compile candidate site information. It is important to recall that the objective is to monitor in
locations that are as near as practicable to roads where peak, ground-level NC>2 concentrations
are expected to occur. However, even with all the factors that can affect whether candidate near-
road locations are feasible accounted for, undoubtedly some air agencies will have multiple
candidate sites to choose from. These air agencies will have to begin narrowing their options by
placing weight on one or more road segment characteristics over others. It is at this point in the
site selection process that a number of other factors should be considered: population exposure,
unique and background source influences, confounding data, and the potential for multi-pollutant
monitoring. To assist with this, the EPA suggests using a site comparison matrix to aid in the
site selection decision process. A matrix will help ensure that all available information is
presented in a format easy for decision makers to review.
13.1 Considering Population Exposure as a Selection Criterion
According to 40 CFR Part 58, Appendix D, Section 4.3.2(a)(l), "where a state or local air
monitoring agency identifies multiple acceptable candidate sites where maximum hourly NC>2
concentrations are expected to occur, the monitoring agency shall consider the potential for
population exposure in the criteria utilized to select the final site location." Therefore, when
considering all the available information (particularly AADT, fleet mix, congestion patterns,
roadway design, terrain, meteorology, and siting criteria) to determine which candidate locations
are suitable for a required near-road NC>2 station, population exposure should subsequently be
considered. Specifically, among a pool of otherwise similar candidate near-road sites, the site
that may represent a higher population exposure, or exposures to susceptible or vulnerable
populations, should be given increased consideration.
Population exposure can be considered in a number of ways, not all of which can be listed
here. In some cases, the consideration of population exposure may be relatively straight-
forward. A hypothetical example might involve two segments, one in a rural or less populated
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Near-Road NO2 Monitoring TAD Section 13: Prioritizing Locations
area of the CBSA and one located in a more urbanized or more densely populated area. In this
example, the higher population exposure would lead a state or local air agency to give greater
weight to the more urbanized site(s).
However, the EPA anticipates that in more cases than not, such a simple example will not be
the reality for state and local air agencies. In more complicated situations, the use of publicly
available demographic and socioeconomic data for the populations living along and near
candidate road segments can be used to aid in considering population exposure as an additional
selection criterion. One example might be to use census block information, particularly focusing
on those census blocks that contain, or are adjacent to, candidate road segments, or are otherwise
able to be spatially connected to one or more candidate road segments. The official source for
census block data is the U.S. Census Bureau's American Fact Finder website
(http://factfmder2.census.gov/). Data can be downloaded from the FactFinder site and these data
can then be associated with spatial files located at http://www.census.gov/geo/www/tiger/ and
finally displayed within GIS software. The instructions for downloading and spatially
associating census data for use in GIS are maintained at
http ://www. census, gov/geo/www/tiger/wwtl/wwtl .html.
An alternative data source and analysis tool for spatially utilizing census data in the near-road
site selection process is gCensus, located at http://gecensus.stanford.edu/gcensus/. While not
officially endorsed, gCensus provides census-level demographic information that can be
downloaded and visualized in Google Earth. Further, since available socioeconomic data can be
used by state and local air agencies, the EPA encourages state and local air agencies to determine
sites that are located in areas with susceptible and vulnerable populations.
13.2 Unique Locations and Background Source Influences
In the evaluation process, state and local air agencies may encounter situations where certain
road segments of interest have characteristics that make the location a unique near-road location
that has elevated pollutant concentrations. In such cases, the pollutant concentrations are not
representative of other near-road locations across the CBSA. The unique characteristics of these
locations could be due to the close proximity of a substantial stationary source, non-road mobile
sources, or roadway design features (such as tunnel entrances and exits or toll plazas). In
situations where a state or local air agency has a choice between road segments that otherwise
have similar potential for peak NC>2 concentrations, the air agencies should place a higher weight
on sites that are most influenced by typical roadway activity rather than those that are heavily
influenced by unique sources or features. This approach increases the probability that the chosen
site can represent a larger population exposure within and across CBSAs.
The EPA recognizes that state and local air agencies will likely have a good understanding of
whether candidate near-road NC>2 monitoring sites have unique characteristics that do or do not
represent the CBSA that those sites are within. The EPA encourages state and local air agencies
to use their local knowledge in site selection and to engage the EPA Regional staff for assistance
in evaluating such a situation as necessary.
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Near-Road NO2 Monitoring TAD Section 13: Prioritizing Locations
13.3 Confounding Information
There may be instances where state and local air agencies have data or insights that indicate
that certain candidate near-road sites may be preferable to those identified through other
technical analyses, including those suggested in this TAD. Such data may be from one or more
sources, including, but not necessarily limited to, traffic data analysis and projections,
exploratory monitoring, modeling, or local knowledge. In cases where measured data (of
sufficient amount, quality, and confidence) are available as compared to estimated, modeled, or
otherwise approximated information, measured data may be more reliable, unless those data are
suspect due to poor quality assurance or other reasons. An example of such a situation might be
a comparison between exploratory monitored data and modeled data, where the exploratory
monitoring would provide measured data that may differ and have higher confidence that the
modeled results. Ultimately, the EPA expects air agencies to use their best judgment when
presented with confounding information to make the best decision for their individual case. The
EPA also encourages state and local air agencies to engage the EPA Regional staff for assistance
in evaluating any such situation.
13.4 Potential for Multi-Pollutant Monitoring
Other than NC>2, a number of pollutants and measureable metrics of interest that exist in the
near-road environment are discussed in some detail in Section 16. Although this document
specifically provides suggestions on siting required near-road NC>2 monitors, the EPA strongly
encourages state and local air agencies to consider the potential of a site to house other pollutant
monitors and measurement devices. This would specifically be accomplished in the site
selection process by considering the footprint and layout of the infrastructure of a near-road
monitoring station.
The EPA believes that the footprint of a typical NCore station may be a conservative
approximation of a multipollutant site footprint. A typical NCore station houses analyzers for
CO, ozone, sulfur dioxide (802), total oxides of nitrogen (NOy), a variety of PM instruments
(including PM with an aerodynamic diameter of 2.5 um and less [PM2.s] and lead samplers), and
meteorological gear, along with all the associated support equipment. Although this NCore-type
footprint can be bigger than a single pollutant shelter, the EPA believes, based on research
experiences, that installing a site with room for multi-pollutant monitoring efforts should not
typically create additional burden or restrictions for site installation versus a single gas pollutant
monitoring shelter.
13.5 Candidate Site Comparison Matrix
The EPA recommends that, upon the completion of traffic data analysis, field
reconnaissance, and other evaluation efforts, state and local air agencies consider compiling their
research for use in the final stages of the site selection process. A suggested approach is to
create a candidate site comparison matrix. Such a matrix would consolidate the data collected in
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Section 13: Prioritizing Locations
the evaluation process and present that information in a comparable format, creating a foundation
for the rationale of why one site might be selected over another. The matrix would likely aid
state air staff and other decision makers, and will also be a useful source of data supporting site
selection discussion with the EPA, other stakeholders, and possibly the public. Further, the EPA
anticipates that the matrix will be a useful reference for users of the data, who may want to
analyze the data for applications beyond NAAQS compliance.
The candidate site comparison matrix is recommended to include, at a minimum,
• traffic data
• field information (e.g., type of road);
• site feasibility information, such as permission for, or lack of, access to individual
candidate sites;
• probable distance between the inlet probe and the outside edge of the target road;
• safety issues (if applicable); and
• any other collected ambient data and/or modeled data for the site.
The matrix can be used to represent individual points along a road segment or for whole road
segments under consideration. Such a detailed matrix could have several candidate locations that
are available along the same target road segment. Table 13-1 includes a list of variables that
could be included in the matrix.
Table 13-1. Suggested data for each candidate site entry in a site comparison matrix.
Site/Segment
Parameters
Location
Page lof 2
•escription of Parameter
Is the entry for a specific point along a road segment, or is it representative of an
entire road segment? For a point, provide a moniker and the latitude and longitude.
For a road segment, identify where the segment boundaries occur (e.g., intersection,
mile marker, political boundary).
Road segment name
Given road name and common name (if applicable).
Road type
Type of road (controlled access highway, limited access freeway, arterial, etc.).
Road segment end
points
Location of the road segment end points, including any given names, common names,
and the latitude and longitude of each individual end point.
AADT
AADT, source of data, and vintage.
HD counts
Provide HP counts (if available), source of data, and vintage.
FE-AADT
Provide FE-AADT (if available), noting HDm value used. If not using the national default
value for HDm, provide the source of data used to calculate the site-specific value.
Congestion information Value and type (e.g., LOS, , or AADT by lane), data source, and vintage.
Roadway design
Design type or types present (flat, elevated-fill, cut, etc.). If not flat, identify whether
the configuration is a vertical or sloped boundary. Include the height (and degree of
slope if applicable).
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Table 13-1. Suggested data for each candidate site entry in a site comparison matrix.
Page 2 of 2
Description of Parameter
Terrain
Nature of the terrain immediately around the road; also, any larger-scale terrain
features of note.
Meteorology
Population exposure
Roadside structures
Safety features
Infrastructure
Interchanges
Surrounding land use
Nearby sources
Current road
construction
Future road construction
Frontage roads
Available space-site
footprint
Property type
Property owner
Likelihood of access
Other details/local
knowledge
For a point, the predominant winds and whether the point is relatively upwind or
downwind. For a whole segment, the orientation of the segment to the predominant
winds.
Assessment of population exposure and/or likeness to other road segments
throughout the CBSA.
Presence of any roadside structures and their height, width, and length.
Safety features present and their height, width, and length.
Existing infrastructure (light poles, billboards, etc.) and potential site proximity
(distance).
Presence of any interchanges within or at the end points of the target road segment
and potential site proximity (distance), including traffic information if available (AADT,
HD counts, etc.).
Surrounding land use (residential, commercial, etc.). Also, proximity to other large
roads, areas of higher relative road density, and/or locations within or near central
business districts or urban downtown areas.
Nearby NOX sources if applicable (type, tonnage, etc.) and potential site proximity
(distance).
Visible or known road construction at the candidate site location or along the target
road segment.
Transportation agency plans (if known) for any future road construction (including
time frame for completion).
Presence of frontage roads, and whether those roads are included as part of the target
road segment.
Limitations in the space available for a multipollutant monitoring station.
Is it ROW or private property?
Who manages or owns the property under evaluation?
Level of confidence and any uncertainties regarding the acquisition of access to a
particular property.
Any other pertinent details that may have bearing on why a particular candidate site
may or may not be selected. This can include information that reflects a state or local
agency's own knowledge of the area or roads under consideration.
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Near-Road NO2 Monitoring TAD
Section 14: Final Site Selection
Section 14. Final Near-Road Site Selection
The EPA expects that state and local air agencies will engage the EPA Regional staff during
the site selection process (as needed) and when a site has been selected it will be reflected in
annual monitoring plans. At a minimum, the EPA Regions will provide feedback on any near-
road site selection listed in the annual monitoring plan before issuing a network plan approval
letter, as is typically done. The availability of data supporting the rationale behind selection of a
site, such as that within the candidate site comparison matrix, will facilitate the review process.
Once a location has been selected for the installation, the EPA suggests that state and local
air agencies prepare and include site record metadata about the near-road location (along with
monitor record data) which would eventually be input into AQS for inclusion in annual
monitoring plans. AQS manuals and guidelines, including information on required and optional
metadata fields associated with monitoring sites and monitor records, are maintained by EPA at
http://www.epa.gov/ttn/airs/airsaqs/manuals/. For new near-road NO2 monitoring sites, the EPA
requires that certain metadata are entered into AQS, as is the case for any new State and Local
Air Monitoring Station (SLAMS) site. The new site information should be added to AQS online
or via the AQS metadata form (AA Basic Site Information transaction) with an action indicator
of "I" for "insert." If using a batch transaction, refer to the AA Basic Site Information for
formatting; the required fields are presented in Table 14-1.
Table 14-1. Near-road site information metadata required in AQS (AA- Basic Site Information
transaction).
AQS Metadata (AA - Basic Site Information)
Transaction Type
Action Indicator
State Code or Tribal Indicator
County Code or Tribal Code
Site ID
Latitude
Longitude
UTM Zone
UTM Easting
UTM Northing
^^H
Horizontal Datum
Source Scale
Horizontal Accuracy
Vertical Measure
Time Zone
Agency Code
Street Address
Land Use Type
Location Setting
Date Site Established
Horizontal Collection Method
In addition to the basic site information required for every new SLAMS site in AQS, the
EPA strongly suggests that air agencies also populate the AB Site Street Information metadata
fields for near-road NO2 monitoring sites. This can be done online in AQS via the
Maintain Site -^ Tangent Roads tab or via the AB Site Street Information transaction with an
action indicator of "I" for "insert." If using a batch transaction, refer to the AB Site Street
Information for formatting; the required fields are presented in Table 14-2.
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Section 14: Final Site Selection
Table 14-2. Additional near-road site information metadata in AQS (AB Site Street
Information).
AQS Metadata (AB Site Street Information)
Transaction Type
Action Indicator
State Code or Tribal Indicator
County Code or Tribal Code
Site ID
Tangent Street Number3
Street Name
Road Type
Traffic Count
Year of Traffic Count
Direction from Site to Street
Source of Traffic Count
a Tangent Street Number (also called Tangent Road Number) is merely a unique identifier supplied by the user (i.e., "1", "2",...,
"99"); it does not refer to a physical street number.
For traffic-related data fields, state and local air agencies should utilize the data gathered as
part of the site selection process. For location-oriented data fields (e.g., Street/Road Name) the
EPA suggests that these fields reflect the target road segment. The EPA recognizes that a site
may not have a traditional street number if it is within the ROW of a major interstate or freeway.
In such cases, try to use an appropriate descriptor as allowable.
Before a site can go into "production" status on AQS (meaning it can be seen by public
users), it must have at least one monitor associated with it. This is accomplished by populating
monitor record fields, as is done for any SLAMS monitor. Within the multiple data input
formats that exist for monitor record fields, the EPA suggests that state and local air agencies
ensure that the following fields for near-road NO2 monitors be populated as noted:
• Monitor Objective - at least reflect that it is "source oriented."
• CBSA Represented - reflect the CBSA that the monitor is within.
• Distance from Monitor to Tangent Road - as accurately as possible, reflect the distance,
in the horizontal, between the inlet probe and the outside nearest edge of the target road
segment. This will be a highly visible and often used piece of metadata.
• Dominant Source - reflect that the "mobile source" category is the dominant source.
The EPA believes that a full and accurate characterization of the monitoring site and the
monitor itself will greatly improve the usefulness of data at near-road NO2 monitoring stations
for subsequent analyses.
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Near-Road NC>2 Monitoring TAD Section 15: Second Sites
Section 15. Selecting a Second Near-Road Site
According to 40 CFR Part 58 Appendix D, CBS As meeting one of the following criteria are
required to have a second near-road NC>2 monitoring site:
• the CBSA has a population of 2.5 million or more persons
• the CBSA has a population of 500,000 or more persons and has one or more road
segments of 250,000 AADT or greater
The EPA prescribes that these second near-road NC>2 monitoring sites be selected so that sites
are differentiated from the first near-road NO2 monitoring site by one or more factors affecting
traffic emissions and/or pollutant transport (such as fleet mix, congestion patterns, terrain,
geographic area within the CBSA), or by different route, interstate, or freeway designation.
The data gathered to select the initial near-road NC>2 monitoring site will be very useful in
determining where to place a second site. The EPA's primary recommendation for a second site
is to attempt to have the second site represent as many of the characteristics listed above
differently from the first site, without sacrificing the objective of measuring relative peakNC>2
concentrations in the near-road environment. In some cases, this could allow for the
consideration of sites that may have characteristics that make the location more unique.
Examples include, but are not limited to, a site with potentially higher population exposure
(including vulnerable and susceptible populations); a site with a different fleet mix (e.g., high LD
component); a site closer to an area-wide monitoring location, as applicable (for relative gradient
assessment); or a site with relatively unique features.
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Section 16. Multipollutant Monitoring at Near-Road
Monitoring Stations
The EPA has expressed the intention of pursuing the integration of monitoring networks and
programs by encouraging multipollutant monitoring wherever possible. This intention is
evidenced by actions taken in the 2006 monitoring rule that created the NCore network, the
expression of the multipollutant paradigm in the 2008 Ambient Air Monitoring Strategy for
State, Local, and Tribal Air Agencies, and through recent rulemakings where minimum
monitoring requirements have been proposed in a manner that would either require or strongly
encourage multipollutants monitoring within SLAMS. Multipollutant monitoring is viewed by
the EPA as a means to broaden the understanding of air quality conditions and pollutant
interactions, furthering our capability to evaluate air quality models, develop emission control
strategies, and support research, including health studies.
This section of the TAD discusses a number of pollutants that are of interest in the near-road
environment due to their direct emission by on-road mobile sources, or due to their formation
from or interaction with on-road mobile source emissions. The Clear Air Scientific Advisory
Committee (CASAC) Ambient Monitoring and Methods Subcommittee (AMMS), in their
review of the EPA's "Near-road Guidance Document - Outline" and "Near-road Monitoring
Pilot Study Objectives and Approach" (CASAC AMMS review dated November 24, 201010),
stated that "CASAC recognizes the importance for public health of better characterizing near-
road pollutant concentrations." Subsequently, the CASAC AMMS held public teleconferences
(September 29th, 2011, and November 17th, 2011) to discuss their review of the August 11th,
2011, draft version of this TAD.11 In response to a question regarding the relative priority for
measuring other pollutants or other metrics of interest in the near-road environment, should state
and local air monitoring agencies choose to invest in additional measurements, the CASAC
AMMS suggested three pollutant groups, with relative priority (i.e., primary, secondary, and
tertiary), to provide some guidance to air agencies choosing to conduct monitoring (Table 16-1).
These pollutants or metrics are discussed in this section, including definitions, basis of interest,
and measurement methods.
10 Located on the Internet at
http://vosemite.epa.gov/sab/sabproduct.nsf/ACDlBD26412312DC852577E500591B37/$File/EPA-CASAC-ll-001-
unsigned.pdf.
11 Located on the Internet at
http://vosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/beal681fl769e6fl8525778
1005fcf8d!OpenDocument&TableRow=2.0#2.
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Table 16-1. CASAC AAMMS's recommended priorities for multipollutant monitoring at a near-
road site.
_ . NO and NO2 (where NO2 is required), CO (required in a subset of near-road NO2 monitorine
Pnmarv -t * j . , / • j j • j j- t- »
sites), ozone, and meteorology (wind speed, wind direction).
Air toxics (at least benzene, toluene, ethyl benzene, and xylenes), black carbon, ultrafine
particle size distribution (preferred) or ultrafine particle number concentration, and traffic
counters (if the site is not already in proximity of a fixed transportation agency traffic
counting device).
Tertiary PM2.5, PM10.2.5, CO2, and organic and elemental carbon (OC and EC, respectively).
a PM10 is particulate matter with an aerodynamic diameter of 10 |im or less. PM10.2 5 is participate matter with an aerodynamic
diameter less than 10 |im and more than 2.5 |im; it is also called coarse particulate matter
16.1 Nitrogen Dioxide (NO2)
NC>2 is an important target of ambient air monitoring because of its adverse impact on human
health. Scientific evidence links short-term NO2 exposures with adverse respiratory effects,
including airway inflammation in healthy people and increased respiratory symptoms in people
with asthma. Further, some health studies have linked NC>2 exposure to increased visits to
emergency departments and increased hospital admissions for respiratory issues.
NO2 is one of a number of oxidized nitrogen species. Scientifically, NO and NC>2 are
collectively referred to as NOx, where NO + NO2 = NOx. However, there are other oxidized
nitrogen species in the ambient air, including nitric acid (HNOs), nitrous acid (HNO2), nitrous
oxide (N2O), peroxyacyl nitrates (PANs), and organic nitrates. These "other" oxidized nitrogen
species are collectively known as NOz. The entire family of oxidized nitrogen species is known
as NOy, which may operationally include some particulate nitrate species with respect to
measurements of NOy, where NOy = NOx + NOz.
NO2 is the key focus of this document because of its role as the indicator of the NAAQS for
the oxides of nitrogen, and the requirement to measure NO2 in the near-road environment where,
as noted in Section 1, the EPA recognizes that roadway-associated exposures account for a
majority of ambient exposures to peak NO2 concentrations. The near-road environment is of
interest because motor vehicles are significant contributors to the total NOx and NO2 inventory in
the United States. In tailpipe emissions, which are primary emissions, the majority of NOx
exhaust is in the form of NO. NO2 quickly forms by photochemical reaction in the ambient air
from the reaction of NO and ozone, and also through other photochemical processes. Although
all motor vehicles emit NOx, heavy-duty (diesel) vehicles emit more NOx per vehicle than
gasoline-powered vehicles. Further, primary NOx emissions from newer-technology heavy-duty
diesel engines with after-treatment devices may contain a much greater percentage of NO2 in the
exhaust (although the total amount of NOx is reduced) than older diesel engines. Thus, NO and
NO2 will be present in varying concentrations in the near-road microenvironment.
In the current SLAMS network, NO2 is almost exclusively measured using the
chemiluminescent NOx analyzer, which is an FRM. In the chemiluminescent FRM, NO2 is
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measured by difference, as the NC>2 analyzer is only capable of measuring NO directly. The
analyzer directly measures NO by introducing ozone to the sample stream, where the reactions
between the two compounds release energy in the form of light (chemiluminescence). In order
to determine the amount of NO2 in the ambient air, the analyzer will first detect the amount of
NO in the ambient air.
Next, the analyzer re-routes the incoming ambient air stream through a heated converter
(usually containing molybdenum) which reduces the NO2 in the air stream to NO. The analyzer
then detects all NO in that air stream that has passed through the converter. The ambient NO2
concentration is determined by subtracting the original amount of NO measured in the un-
converted air from the amount measured in the air that was passed through the heated converter,
where the available ambient NO2 was reduced to NO.
A known drawback to the traditional chemiluminescent measurement technique is that other
NOz species, if present in the heated converter, will also be reduced to NO; this means that
reduction by the heated converter is not specific to just NO2. Thus, if a significant amount of
NOz species are present in the ambient air, some of those species may make it to the heated
converter and erroneously be counted as NO2 when the analyzer determines the NO2
concentration by difference. The EPA does not believe this measurement bias is a significant
concern in the near-road environment (and typically in many urban sites) because the gaseous
NOy species present are dominated by NO and NO2, due to the proximity to the emission source
(e.g., vehicles on the road). This measurement bias is of greater concern when measuring so-
called "aged" air masses, where there has been time for NOx emissions to be further oxidized
into other NOz species.
There are two type of chemiluminescent analyzers in the market today: the standard analyzer,
and a more recently commercialized "trace-level" line of analyzers. Standard analyzers have
levels of detection on the order of 0.4 ppb, while the trace-level analyzers have levels of
detection down to approximately 0.05 ppb. While trace-level analyzers are strongly encouraged
for use in the NO2 monitoring network when possible, standard NO2 analyzers are expected to
be sufficient for use in the near-road environment, where ambient NO2 levels are expected to be
relatively higher than at other locations more representative of area-wide spatial scales.
There are other techniques that are commercially available to measure NO2; however, as of
the production of this document, there are no other approved methods for NO2 measurement by
which the data could be used to determine compliance with the NAAQS. One of the available
methods is a photolytic-chemiluminescent analyzer. This method, like the traditional
chemiluminescent analyzer, can only directly measure NO. However, the photolytic-
chemiluminescent analyzer uses a photolytic converter (instead of a heated metal converter) to
reduce NO2 to NO for measurement. The advantage that this method has over the traditional
chemiluminescent analyzer is that the photolytic converter is much more specific to NO2, and
does not reduce other NOz species to NO, removing the potential for bias if NOz species are
present.
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Other commercially available methods to measure NC>2 include the Cavity Ring-Down
Spectrometer (CRDS) and the Cavity Attenuated Phase Shift (CAPS) technology. These devices
are laser light (at a specific wavelength) based devices that utilize NC>2 absorption to determine
the NC>2 concentration in the sampled air. The EPA plans to continue to work with academia and
measurement technology vendors to improve measurement techniques, and increase the
accuracy, precision, specificity, and speed of these next-generation measurement technologies.
Eventually, the EPA hopes that the advancement of such measurement technologies will lead to
their consideration as reference or equivalent methods.
16.2 Carbon Monoxide (CO)
CO is a colorless, odorless gas emitted from combustion processes. CO can cause harmful
health effects by reducing oxygen delivery to the body's organs (like the heart and brain) and
tissues. In addition, CO is a useful indicator of the transport and dispersion of inert, primary
combustion emissions from on-road mobile sources, as CO does not react in the near-road
environment. The majority of CO emissions come from mobile sources, where approximately
60% of the national inventory is attributable to on-road mobile sources (per the 2008 National
Emissions Inventory).
While all motor vehicles emit CO, the majority of mobile source CO is from light-duty,
gasoline-powered vehicles, where LD vehicles produce more CO per vehicle than HD vehicles.
Ambient CO measurements are routinely taken by analyzers using gas filter correlation
methodology, which relies on infrared (IR) absorption of CO at a specific radiation wavelength.
This method has been in use since the 1970s; the current ambient CO compliance monitoring
network wholly comprises analyzers based on this infrared absorption method. Of these
analyzers, there are currently two types of IR absorption analyzers in use: the standard analyzer,
and a more recently commercialized "trace-level" line of analyzers. Standard analyzers have
levels of detection on the order of 0.5 to 1 ppm, while the trace-level analyzers have levels of
detection down to approximately 0.04 ppm. While trace-level analyzers are strongly encouraged
for use in the CO monitoring network when possible, standard CO analyzers are expected to be
sufficient for use in the near-road environment, where ambient CO levels are expected to be
relatively higher than at other locations more representative of area-wide spatial scales.
On August 12, 2011, the EPA promulgated minimum monitoring requirements for CO in the
near-road environment. According to 40 CFR Part 58 Appendix D, one CO monitor is required
to be co-located with a near-road NO2 monitor in CBSAs having populations of 1 million or
more persons. State and local air agencies can make a request to the EPA Regional
Administrator for permission to place a required CO monitor at another near-road location in that
CBS A if they can provide quantitative evidence in support of that request. However, the EPA
expects that in most cases, state and local agencies will find it advantageous to leverage the near-
road NO2 infrastructure and also conduct multipollutant monitoring at their near-road NO2
monitoring site.
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16.3 Ozone
Ozone is not usually emitted directly into the air, but created at ground-level by a chemical
reaction between NOX and volatile organic compounds (VOCs) in the presence of sunlight. NOX
and VOCs are emitted by mobile sources, among others. Ozone can trigger a variety of
respiratory health problems; worsen bronchitis, emphysema, and asthma; reduce lung function;
and inflame the linings of the lungs. Ozone is routinely measured in situ using photometry or
chemiluminescence on a sub-hourly to hourly sampling frequency. To date, ozone
measurements have not typically been collected for near-road applications. The presence of
elevated NO concentrations in the near-road microenvironment typically leads to lower ozone
concentrations due to "ozone scavenging" as part of the formation of NO2 from NO and ozone.
However, ozone measurements may be useful in the near-road environment for increasing the
understanding of NO2 concentrations, NO2 formation behavior, and broader photochemistry
processes in the near-road environment. Further, ozone monitoring in the near-road environment
may support health studies investigating the role of ozone and other co-pollutants on adverse
health effects, given the potentially lower concentrations of this pollutant relative to other
pollutants in this microenvironment.
The EPA notes that while the measurement of ozone in the near-road environment would
provide data that could be useful for furthering the understanding of photochemistry in the near-
road environment, and would also provide data that could be compared with the NAAQS, the
monitor would not meet minimum monitoring requirements for ozone (which calls for area-wide
monitors) as prescribed in 40 CFR Part 58 Appendix D.
16.4 Meteorological Measurements
Meteorological data measured onsite at a near-road monitoring station can provide important
information that can be used to characterize the pollutant data being measured at the station. As
part of the CAS AC AMMS review, the panel stated that "the AMMS believes meteorological
parameters (wind speed and direction) should be one of the highest tier measurements considered
as part of [the near-road NO2] network." A key advantage to having meteorological data
collected onsite would be the ability to correlate the occurrence of peak NO2 concentrations (and
other pollutant peaks) to wind conditions. Data analysis of the collected pollutant data will be
greatly enhanced by knowing whether winds are calm, parallel to the road, or at any other angle
making the monitoring site relatively upwind or downwind when peak NO2 concentrations are
measured.
Although meteorological measurements were originally proposed in the NPR for NO2 to be
required at near-road NO2 monitoring sites, the EPA did not ultimately require them within 40
CFR Part 58. However, the EPA strongly encourages states to measure meteorological
parameters at near-road sites whenever possible. The EPA suggests that if meteorological
measurements are made, state and local air agencies at a minimum measure wind speed, wind
direction, temperature, and relative humidity (which match those parameters required at NCore
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stations). If possible, other measurements, such as precipitation, solar radiation, and barometric
pressure, among others, should be considered as well. More information on meteorological
parameters, measurement techniques, and related quality assurance can be found in EPA's
Quality Assurance Handbook for Air Pollution Measurement Systems, Volume IV:
Meteorological Measurements (U.S. Environmental Protection Agency, 2008c).
16.5 Air Toxics
In addition to some criteria pollutants, motor vehicles emit a large number of other
compounds which can cause adverse health effects, such as air toxics (hazardous air pollutants).
A discussion and listing of potential air toxics of concern for near-road monitoring can be found
in the EPA's Mobile Source Air Toxics (MSAT) Rule (U.S. Environmental Protection Agency,
2007). These pollutants include VOCs, semi-volatile organic compounds (SVOCs), and organic
and inorganic PM constituents. Reasons for monitoring these pollutants for a near-road program
include concerns about adverse human health effects and ecological effects, and the need to
provide data for evaluating the effectiveness of mobile source control programs.
Air toxics span the entire range of pollutants present in the atmosphere; they are present as
particles, gases, and in semi-volatile form. No one measurement method captures all air toxics
of interest in a near-road environment. This section discusses potential monitoring activities for
a number of classes of air toxics, but a discussion of all potential air toxic compounds identified
in the MSAT rule is beyond the scope of this document.
Typical VOCs of concern for near-road monitoring include, but are not limited to, benzene,
toluene, ethyl benzene, xylenes, styrene, 1,3-butadiene, and various aldehydes. These air toxics
can contribute to long-term health issues (e.g., cancer) and are also ozone precursors. A more
detailed listing of potential VOCs of health concern is included in the MSAT Rule.
VOCs are typically measured by collecting ambient air using evacuated canister sampling
and subsequently analyzing the samples using a gas chromatograph (GC). For evacuated
canister sampling, depending on the collection equipment used, the sample collection time can
vary from an instantaneous grab sample to averaging times of more than 24 hours. Auto-GCs
can be used to measure specific VOC pollutant concentrations semi-continuously at a monitoring
site. A number of manufacturers also advertise semi-continuous analyzers for one or more
VOCs of interest using various GC technologies.
Aldehydes emitted from motor vehicles include, but are not limited to, formaldehyde,
acetaldehyde, and acrolein. A more detailed listing of aldehydes with potential health concerns
is included in the MSAT Rule. These pollutants are also formed through secondary production
in the atmosphere. Aldehydes are typically measured using cartridges containing dinitrophenyl
hydrazine (DNPH). In addition, other methods, including evacuated canisters and cartridges
with dansylhydrazine (DNSH), have been used to measure ambient concentrations of some of
these compounds. Sample collection periods of 24 hours or more are typically required for
assessing ambient aldehyde concentrations, although a few manufacturers advertise semi-
continuous analyzers for select compounds. Accurate acrolein measurements remain a
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challenge. Measurements of these pollutants have indicated that concentrations are elevated near
heavily trafficked roads.
SVOCs present in near-road emissions are naphthalene and other polycyclic aromatic
hydrocarbons (PAHs). SVOC sampling is done using XAD/polyurethane foam (XAD/PUF)
cartridges within high-volume samplers over 24-hour sampling periods. The XAD/PUF
cartridges are extracted and analyzed using a gas chromatograph with a mass spectrometer
(GC/MS).
Toxic metals, along with other elements (such as soil components), are present in PM2.s and
PMio samples. These toxic metals can be emitted from brake wear, tire wear, engine wear, and
oil and lubricant combustion. Inorganic PM samples are usually collected on filters using high-
volume samplers and longer sampling times to collect sufficient mass for elemental analyses.
Higher frequency monitoring methods, sub-hourly to multiple hours, are developed, but are not
widely used. Concentration gradients of these toxic metals near roads have not been widely
studied in real time. Metal deposition has been shown to have a similar gradient to other motor-
vehicle-related pollutants near roads.
16.6 Black Carbon and Elemental Carbon
The graphitic-containing portion of PM, represented by black carbon (BC) or EC, also
referred to as "soot," is emitted in motor vehicle exhaust. BC and EC are of interest because
they serve as a measure of diesel particulate matter (DPM). Although BC and EC are primarily
associated with emissions from heavy-duty diesel engines, a portion of all motor vehicle
combustion emissions contains these constituents. Long-term (i.e., chronic) inhalation of diesel
exhaust (a combination of gases and particles) is likely to pose a lung cancer hazard to humans,
as well as damage the lung in other ways (depending on the nature of the exposure). Short-term
(i.e., acute) exposures can cause irritation and inflammatory symptoms of a transient nature,
these being highly variable across the population
(http://cfpub. epa.gov/ncea/cfm/recordisplay. cfm?deid=29060).
BC and EC are operationally defined. BC is measured through light absorption, while EC is
measured thermally. BC measurements can be made sub-hourly to hourly, while EC
measurements are typically hourly to daily.
Other sources of BC or EC, such as wood smoke, exist in urban areas, but emissions from
motor vehicles usually dominate these sources in near-road air quality.
16.7 Ultrafine Particulate Matter
PM emitted through the combustion process occurs initially in the ultrafine size range (i.e.,
less than 0.1 um in diameter); a very large number of these small particles are emitted. Despite
the large number of ultrafine particles emitted, the impact on PM mass is negligible. Research
has shown that particle number concentration measurements often provide a good indication of
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primary PM exhaust emissions from motor vehicles. Several health studies suggest that ultrafine
particles may lead to adverse health effects. A number of devices exist to measure particle
number concentrations, ranging from inexpensive industrial hygiene monitors to research-grade
ambient air monitors (e.g., condensation particle counter, differential mobility analyzer). Most
of these devices can provide number concentration measurements in near real-time, while some
devices are capable of providing particle number concentrations within specific size bins. When
comparing measurements from different devices, any differences in particle size ranges detected
must be noted. Measurements show that as the distance or transit time from emission to
sampling increases, the size distribution shifts to larger particle diameters.
16.8 Traffic Counters and/or Cameras
Traffic counting devices and/or traffic cameras provide other non-pollutant measurements
that could be useful to an air agency to aid in characterizing measured pollutants at a near-road
monitoring site. Understanding traffic behavior can help analysts better understand measured
pollutant concentrations, such as the correlation of peak NO2 readings to time periods when
traffic is heaviest and/or experiencing increased congestion. There are a number of direct-
measure methods used to characterize traffic, including over-the-road tube counters, embedded
devices such as contact closure loops for counts or piezoelectric sensors used for weigh-in-
motion, speed cameras and red-light cameras, and vehicle classification and count devices.
These methods are applied by placing the sensors on or within the road surface of active travel
lanes.
Unless a transportation agency has installed (or plans to install) on-road or embedded devices
on a target road segment, the EPA does not encourage air agencies to pursue the use of such
methods to gather traffic count information. However, there are remote sensing methods
available to characterize traffic that use radar- or camera-based technology. These methods can
be installed alongside roads (such as on a meteorological tower or monitoring shelter), and can
focus on the target road segment. The sophistication of remotely sensing instruments is variable,
but the EPA suggest that if such a device is investigated for use, a minimum requirement should
be total traffic counts for at least an hourly interval. Other data metrics that would also be useful
include the ability to segregate HD from LD vehicle counts, and those methods with sub-hourly
time resolution capability.
The EPA envisions any air agency collecting such data should do so only for the internal
purpose of analyzing air quality data, and not to broadcast traffic data publicly in a manner that is
independent of their local transportation agency. In some cases, the local transportation agency
might be in a position to collaborate with an air agency that is looking to collect traffic data for a
particular road segment where no traffic data are currently being collected. In such a case, there
may be a synergistic advantage to the agencies; the air agency can gather traffic data for air
quality analysis with the support of a transportation agency, while the transportation agency may
gain another source of traffic data for their use as well, at no cost to them. The EPA encourages
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state and local air agencies to pursue such collaboration if traffic data collection is conducted at
near-road monitoring sites.
16.9 PM Mass
PM is a complex mixture of small particles and liquid droplets comprised of sulfates, nitrates,
acids, ammonium, EC, OC compounds, trace elements such as metals, and water. The size of
particles is directly linked to their potential for causing health problems. Particles that are 10 um
in diameter or smaller (PMio) are of concern because those are the particles that generally pass
through the throat and nose and enter the lungs. Once inhaled, these particles can affect the heart
and lungs and cause serious health effects. Motor vehicles emit significant amounts of PM
through combustion, brake wear, and tire wear. Motor vehicles may also contribute to elevated
near-road PM concentrations by re-suspending dust present on the road surface.
In the United States, the NAAQS regulates ambient concentrations of PMio and PM2.5. Both
these PM size fractions are emitted by motor vehicles.
In general, PM from combustion is in the PM2.5 size fraction, with combustion-emitted
particles typically being ultrafine particles. These ultrafine particles contribute little to ambient
PM2.5 mass concentrations, contribute significantly to particle number concentrations, and can
affect the chemical composition of the PM2.5 mass collected near the road relative to urban
background conditions.
Ultrafine particles tend to exist as disaggregated particles for very short periods of time (i.e.,
minutes) and rapidly coagulate into accumulation-mode particles (0.3 to 0.7 um). Accumulation-
mode PM that is secondarily formed from motor vehicle combustion emissions may have a
greater effect on PM2.5 mass concentrations in or near the near-road environment.
PM emitted through mechanical processes of vehicle operation (e.g., brake wear, tire wear,
re-suspended road dust) will tend to be in the PMio size fraction and can lead to elevated mass
concentrations. As a result, both PMio and PM2.5 mass (and also the coarse fraction, PMio-2.s)
measurements and any speciation of PM mass at near-road sites can further the understanding of
concentrations, properties, and behavior of PM in the near-road environment.
Currently, a large majority of PMio mass measurements and a slight majority of PM2.5 mass
measurements use filter-based, gravimetric analyses over a 24-hour sample collection period.
Diurnal variations in traffic and meteorology can have a tremendous effect on near-road air
quality, an effect that is not identifiable in 24-hour average measurements. Thus, continuous or
semi-continuous (i.e., hourly or sub-hourly) PM measurements may be considered to provide
useful time-resolved information. Continuous particle measurement methods include the use of
Beta attenuation, Tapered Element Oscillating Microbalances (TEOMs), and optical (light
scattering) measurements (e.g., nephelometry). However, care must be taken in choosing a
sampling method. Optical PM mass samplers, for example, typically cannot detect particles
smaller than approximately 0.2-0.5 um in diameter. Because of this, these measurement devices
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may not detect or characterize a significant amount of the PM mass related to motor vehicle
combustion emissions.
In addition, some continuous PM samplers heat the inlet air prior to analysis. Since motor
vehicle PM emissions contain a significant amount of semi-volatile organic compounds, these
samplers may have the potential to underestimate the PM mass in the near-road environment by
volatilizing organic PM prior to detection.
16.10 Carbon Dioxide (CO2)
Fossil fuel combustion is the primary source of CC>2 emissions, with the transportation sector
contributing about 33% of U.S. CC>2 emissions. CC>2 is of concern as the most important
greenhouse gas contributing to climate change. Continuous CC>2 measurements are typically
made using a non-dispersive infrared system with which sub-hourly sampling duration can be
achieved. CC>2 concentrations can be elevated near roads, so high-resolution measurement
methods with good precision (high signal-to-noise ratios) would be needed to quantify near road
impacts to relative background concentrations.
16.11 Organic Carbon (OC)
OC is a complicated mixture of thousands of individual molecules and is a combination of
both primary particulate emissions and gaseous precursors that can form secondary aerosol.
Some of the OC compounds, such as PAHs, are known or suspected carcinogens. OC is often
the largest component of PM in urban areas in the western United States, and especially in near-
roadway environments. Motor vehicle fuel combustion is an important contributor of OC.
OC is typically measured by collecting PM on filters and then thermally quantifying the OC
portion of the PM. These measurements are most commonly performed daily, but continuous
instruments that allow for 1-hour time resolution are in use. Other sources of OC exist in urban
areas (such as wood smoke, industrial processes, biogenic emissions). Little is known about OC
concentration gradients from roadways; however, emissions from motor vehicles are expected to
have a significant contribution to PM in near-road air quality.
To further investigate the OC mass, samples can be collected and analyzed for a wide range
of organic species. These speciated organic PM samples are most often collected on filters
backed by a cartridge to collect gas-phase constituents. Sample collection typically uses high-
volume samplers to maximize the amount of PM mass obtained for detailed chemical and
physical analysis; thus, collection times can be from 24-hours to over a week, or samples are
consolidated to collect an ample amount of mass. Detailed speciation of organic PM compounds
present in near-road samples can be useful in conducting source apportionment studies to
estimate the effects of traffic emissions on near-road PM concentrations, although the long
sample averaging times required for this analysis may limit the ability to discern differences in
vehicle activity on organic PM air quality impacts. Alternatively, higher time resolution
measurements can be made with instruments such as the high-resolution aerosol mass
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spectrometer (HR-AMS), where, for example, 2-minute resolved data can be gathered. While
specific molecules are not quantified, the amount of primary versus secondary OC can be
quantified, and as well as local versus regional influences.
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Section 17. References
American Association of State Highway and Transportation Officials (2011) Roadside design
guide, Fourth edition, American Association of State Highway and Transportation
Officials, 356.
Baldauf R., Watkins N., Heist D., Bailey C., Rowley P., and Shores R. (2009) Near-road air
quality monitoring: factors affecting network design and interpretation of data. Air
Quality, Atmosphere, and Health, 2, 1, 1-9 (DOT: 10.1007/s 11869-009-0028-0).
Available on the Internet at http://www.daham.org/bin/Near-
road%20air%20qualitv%20monitoring%20Factors%20affecting%20network.pdf.
Cimorelli A.J., Perry S.G., Venkatram A., Weil J.C., Paine R.J., Wilson R.B., Lee R.F., Peters
W.D., Erode R.W., and Paumier J.O. (2004) AERMOD: description of model
formulation. Report by the U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Emissions Monitoring and Analysis Division, Research Triangle
Park, NC, EPA-454/R-03-004, September. Available on the Internet at
http://www.epa.gov/scramOO l/7thconf/aermod/aermod_mfd.pdf
Hagler G.S.W., Thoma E.D., and Baldauf R.W. (2010) High-resolution mobile monitoring of
carbon monoxide and ultrafine particle concentrations in a near-road environment.
Journal of Air and Waste Management Association, 60, 3, 328-336.
Hanrahan P.L. (1999a) The plume volume molar ratio method for determining NO2/NOX ratios in
modeling - part I: methodology. Journal of the Air & Waste Management Association,
49, 11, 1324-1331.
Hanrahan P.L. (1999b) The plume volume molar ratio method for determining NO2/NOX ratios
in modeling - part II: evaluation studies. Journal of the Air & Waste Management
Association, 49, 11, 1332-1338.
Heist O.K., Perry S.G., and Brixey L.A. (2009) A wind tunnel study of the effect of roadway
configurations on the dispersion of traffic-related pollution. Atmospheric Environment,
43,5101-5111.
Office of Management and Budget (2000) Standards for defining metropolitan and micropolitan
statistical areas (notice). Federal Register, 65, 249, 82228-82238 (FRDOC# 00-32997).
Available on the Internet at http://www.gpo.gov/fdsvs/pkg/FR-2000-12-27/pdf/00-
32997.pdf
U.S. Census Bureau (2005) Metropolitan and micropolitan statistical areas. Available on the
Internet at http://www.census.gov/population/metro/.
U.S. Environmental Protection Agency (2007) Control of hazardous air pollutants from mobile
sources: final rule to reduce mobile source air toxics. Available on the Internet at
http://epa.gov/otaq/regs/toxics/420f07017.htm. EPA420-F-07-017, February.
U.S. Environmental Protection Agency (2008a) Ambient air monitoring strategy for state, local,
and tribal air agencies. Report by the Office of Air Quality Planning and Standards,
Research Triangle Park, NC, December.
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Near-Road NO2 Monitoring TAD Section 17: References
U.S. Environmental Protection Agency (2008b) Risk and exposure assessment to support the
review of the NO2 primary National Ambient Air Quality Standard. Report prepared by
the Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-
452/R-08-008a, November. Available on the Internet at
http://www.epa.gov/ttnnaaqs/standards/nox/data/20081121 NO2 REA final.pdf.
U.S. Environmental Protection Agency (2008c) Quality assurance handbook for air pollution
measurement systems Volume IV: meteorological measurements version 2.0 (final).
Prepared by the U.S. Environmental Protection Agency, Office of Air Quality Planning
and Standards, Air Quality Assessment Division, Research Triangle Park, NC, EPA-
454/B-08-002, March. Available on the Internet at
http://www.epa.gov/ttn/amtic/files/ambient/met/Volume%20IV Meteorological Measure
ments.pdf.
U.S. Environmental Protection Agency (2010a) Transportation conformity guidance for
quantitative hot-spot analyses in PM2.5 and PMio nonattainment and maintenance areas.
Guidance document prepared by the Transportation and Regional Programs Division,
Office of Transportation and Air Quality, EPA-420-B-10-040, December. Available on
the Internet at http://www.epa.gov/otaq/stateresources/transconf/policy/420bl0040.pdf.
U.S. Environmental Protection Agency (201 Ob) Transportation conformity guidance for
quantitative hot-spot analyses in PM2.5 and PMio nonattainment and maintenance areas:
Appendices. Guidance document prepared by the Transportation and Regional Programs
Division, Office of Transportation and Air Quality, EPA-420-B-10-040, December.
Available on the Internet at
http://www.epa.gov/otaq/stateresources/transconf/policy/420bl0040-appx.pdf
U.S. Environmental Protection Agency (2011) Additional clarification regarding application of
Appendix W modeling guidance for the 1-hour NO2 National Ambient Air Quality
Standard. Tyler Fox memorandum prepared by the Office of Air Quality Standards and
Planning, Research Triangle Park, NC, March 1. Available on the Internet at
http://www.epa.gov/ttn/scram/Additional_Clarifications_AppendixW_Hourly-NO2-
NAAQS FINAL 03-01-2011.pdf.
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Near-Road NC>2 Monitoring TAD Appendix A: Supporting Information
Appendix A. Supporting Information on Uncertainties in Traffic Data and
Rationale for Roadway Design Considerations
The HPMS and Traffic Demand Modeling applications contain uncertainties that need to be
understood to properly interpret these data when they are used to identify suitable road segments
for NC>2 near-road NAAQS monitoring. These uncertainties relate to the type and frequency of
traffic data collected, location of sampling, and the characterization of vehicle type with these
systems.
A.I Measurement and Frequency Uncertainties
Measurement types include fixed, automated sensors and temporary devices that can be
deployed for short periods of time on a given road section. Data collected during relatively short
duration campaigns can sometimes be used to represent longer periods of time, such as annual
averages.
A.2 Fixed Measurement Systems
Options available for fixed, long-term measurements of traffic volume are automated traffic
recorders and weigh-in-motion devices. These sampling devices typically operate for over a
year, so these measurements can be directly related to an AADT value.
A.3 Temporary Measurement Systems
Pneumatic tubes can be used for short-term measurements of traffic volumes. When these
devices are used for traffic measurements, expansion factors must be used to estimate AADT
volumes on that road segment over longer time periods (i.e., "expand" a short-term traffic
volume into a long-term value). These expansion factors can be related to maximum hourly
traffic volumes or the overall number of days of sampling conducted with the temporary devices.
If these devices are used, the state DOT or local planning organization should be consulted to
determine the most appropriate expansion factors to use for their given location and time period
of sampling.
A.4 Sampling Location Uncertainties
Since resource restrictions do not allow for the siting of traffic counting devices at all
locations, there are uncertainties associated with the estimation of traffic volumes along
unmonitored roadway segments.
A.5 Vehicle Characterization Uncertainties
The measurement devices described above have limitations in differentiating the mix of
vehicles present on the roadway. Many devices separate light-duty from heavy-duty vehicles
using length factors. These lengths can be misclassified due to a number of factors, including
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Near-Road NC>2 Monitoring TAD Appendix A: Supporting Information
tailgating vehicles and trucks with multiple axles, although misclassification depends on the
measurement device.
In addition, these devices cannot differentiate between vehicles operating on gasoline and
vehicles operating on diesel. While this differentiation is not critical for highway planning,
understanding the distribution of gasoline versus diesel vehicles can be very important for
emissions and air quality characterization. In the United States, most light-duty vehicles (less
than 20 feet in length) operate on gasoline, while most heavy-duty vehicles (greater than 40 feet
in length) operate on diesel fuel. Medium-duty vehicles (between 20 and 40 feet in length) can
operate on either gasoline or diesel fuels, and present the highest uncertainties when looking at
traffic counts related to air pollutant emissions and fuel use.
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Near-Road NO2 Monitoring TAD
Appendix B: Using MOVES
Appendix B.
Using MOVES to Create a Heavy-Duty to Light-Duty NOX
Emission Ratio for Use in this TAD
As described in Section 6 of the TAD, the HDm ratio of 10 was chosen as the national default
value to weight the contribution of heavy-duty vehicle emissions compared with emission rates
from light-duty vehicles for use in creating FE-AADT values. This ratio was chosen using
national default emission values for both heavy- and light-duty vehicles, and represents a
realistic ratio of average heavy-duty to light-duty vehicle emissions nationally for typical
highway driving conditions. Actual emission rates can vary based on a number of factors,
including the vehicle technology, fuel burned, vehicle speed, vehicle load, and ambient
temperature. Thus, a single HDm value cannot capture all of the variability that can be
experienced among differing vehicle types. There may be situations under which a state may
choose to calculate a local HDm value or local values based on information for a specific road
segment or for a particular season, for use in calculating FE AADT as discussed in Section 6 of
the TAD.
Table B-l lists average motor vehicle emission rates using national default values of fleet
distribution and speed for the year 2010 as provided by EPA's Office of Transportation and Air
Quality (OTAQ); these data are from running the MOVES emissions model. The year 2010 was
chosen based on the likely year of traffic data available to state and local air agencies during the
initial process of identifying candidate sites. Note that these emission factors represent hot,
stabilized running conditions only; cold starts are not included, since these events are unlikely to
occur during highway or other high-volume roadway driving activities. In addition, the default
temperature and humidity used to calculate this ratio for January and July represented national
averages. As shown in this table, an HDm value of 10 provides a representative approximation of
the heavy-duty to light-duty vehicle emissions ratio using national default values. A simplified
HDm factor of 10 signifies a ratio of a combination of heavy-duty and light-duty vehicles
commonly found on U.S. highways at typical highway operating speeds.
Table B-l. Average motor vehicle emissions rates within two seasonally representative months
using national default values of fleet distribution and speed for 2010.
Month Vehicle Type "^^N""* Hfc R*o
January
July
Heavy Duty
Light Duty
Heavy Duty
Light Duty
10.09
0.92
8.47
0.91
10.96
9.33
For areas that have data on road segment congestion or fleet mix, a more detailed assessment
can be made using MOVES emissions results. Figure B-l and Figure B-2 provide examples of
how emission rates vary by vehicle speed and type. These graphs compare emission rates for
vehicles commonly using U.S. highways, with separate graphs provided for January and July of
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Near-Road NO2 Monitoring TAD
Appendix B: Using MOVES
2010 (to highlight emission differences between colder and warmer ambient temperatures). As
shown, the ratio of emissions can vary widely among vehicle types and speeds.
January
•Motorcycle
•Passenger Car
•Passenger Truck
Light Commercial
Truck
•Transit Bus
School Bus
•Motor Home
•Short-Haul Truck
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
Speed (mph)
Figure B-l. Average NOX emission rates by vehicle type and speed for January 2010 (from
MOVES).
July
•Motorcycle
•Passenger Car
•Passenger Truck
Light Commercial
Truck
•Transit Bus
School Bus
•Motor Home
•Short-Haul Truck
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
Speed (mph)
Figure B-2. Average NOX emission rates by vehicle type and speed for July 2010 (from MOVES).
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Near-Road NO2 Monitoring TAD Appendix BC: Modeling
Appendix C. Modeling
This appendix offers specific guidance to those state and local agencies choosing to use
modeling to further inform the implementation of near-road NC>2 monitors. This appendix offers
guidance on the selection of an air quality model, modeling domain (including receptor
placement), characterization of emissions sources, meteorological inputs, and inclusion of
background concentrations.
C.I Guidance on Air Emissions Models
The following sections provide an overview of using MOVES for project-level analyses.12
This guidance is based on
1. the MOVES User Guide (U.S. Environmental Protection Agency, 2010a),
2. Section 4 of EPA's Transportation Conformity Guidance for Quantitative Hot-spot
Analyses in PM2.5 andPMio Nonattainment and Maintenance Areas (U.S.
Environmental Protection Agency, 201 Ob), and
3. EPA's "Using MOVES in Project-Level Carbon Monoxide Analyses"13 (U.S.
Environmental Protection Agency, 2010c).
Interested agencies should consult these documents for further details when performing
MOVES project-level analyses.14 For guidance documents, see
http://www.epa.gov/otaq/stateresources/transconf/policy.htmtfproject.
C.I.I Geographic Scale of Analysis
MOVES can be used to model emissions for different geographic scales. For analyzing
individual road segments, the "Project" scale of MOVES should be employed. The "County"
and "National" scales are not suitable for analyzing individual road segments. See Section C.I.3
for further information on specifying the Project scale in a MOVES "Run Specification."
At the Project scale, MOVES represents emissions of a particular roadway as a series of
"links." The purpose of defining a roadway as one or more MOVES links is to accurately
capture emissions where they occur. Generally, links specified for a roadway should include
segments with similar traffic characteristics and vehicle activity. Using link-specific vehicle
activity and other inputs, MOVES calculates emissions from every link of a project for a given
hour. There are no limits to the number of links that can be defined in MOVES.
12 This appendix uses "MOVES" to refer genetically to any approved version of the MOVES model. This
guidance is applicable to current and future versions of the MOVES model, unless EPA notes otherwise.
13 For the user guide, see http://www.epa.gov/otaq/models/moves/index.htmfaser: other guidance documents
are available at http://www.epa.gov/otaq/models/moves/index.htm. For guidance documents, see
http://www.epa.gov/otaq/stateresources/transconf/policv.htmfaroject.
14 Note that these technical guidance documents were developed to address other requirements. However,
certain sections of the guidance may be applicable when completing analyses of transportation projects for other
purposes, such as when completing NO2 modeling as described in this appendix.
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Using the Project scale and the Project Data Manager, users can enter data that applies to the
project being analyzed. Modeling to support siting of near-road NC>2 monitors should
incorporate the most recently available data. MOVES also includes a default database of
meteorology, fleet, activity, fuel, and control program data for the entire United States. The
information in the default database comes from a variety of sources; these data may not
necessarily be the most accurate or up-to-date information available. For some needed inputs,
such as fuel information, it may be appropriate to use the national defaults.
C.I.2 Time Period of Analysis
When MOVES is run at the Project scale, it estimates emissions for only the hour specified
by the user. State and local agencies may have activity data collected over a range of possible
temporal resolutions. Multiple MOVES runs can be completed to represent emissions during
different time periods. In most cases, traffic data will represent weekdays, which should be so
indicated in MOVES. The year, month, and hour should be defined for each MOVES run. Since
modeling will be used to compare potential impacts at multiple sites, it is important that the same
modeling years are evaluated for each road segment.
C.1.3 Developing a MOVES Run Specification (RunSpec)
A MOVES RunSpec is a computer file in XML format that can be edited and executed
directly or with the MOVES Graphical User Interface (GUI). MOVES needs the user to set up a
RunSpec to define the place and time of the analysis, as well as the vehicle types, road types,
fuel types, and the emissions-producing processes and pollutants that will be included in the
analysis.
A RunSpec is entered through the Navigation Panel of the MOVES GUI (shown in
Figure C-l)
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Appendix BC: Modeling
Vehiclestquipment
Road Type
Pollutants And Proces
Manage Input Data Set
Figure C-l. The MOVES opening screen; the Navigation Panel is on the left. Image from the
MOVES2010a user guide (U.S. Environmental Protection Agency, 2010a).
To create a project-level RunSpec, a user moves through the relevant tabs and fills in data
appropriate for each item.
• Description - Users may enter up to 5,000 characters of text.
• Scale - Users must specify the "Project" scale. In this panel, the user also should select
output as "Inventory" (grams per hour per link) if using American Meteorological
Society/Environmental Protection Agency Regulatory Model (AERMOD) to complete
the air quality modeling.
• Time Spans - Here, users specify the hour, day, month, and year. Also, users specify
time aggregation (select "hour" for analysis of individual road links).
• Geographic Bounds - Users define the county being modeled. County information in
MOVES determines some default information in the analysis.
• Vehicles/Equipment - Users specify the vehicle and fuel types to be included. MOVES
includes 13 "source use types," such as "passenger car" and "long-haul combination
truck."
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• Road Type - Users define the types of roads included in the run. Road types determine
which default vehicle driving cycles MOVES assigns to vehicles. In most scenarios
evaluated for NO2 near-road monitoring, the urban restricted access road type will be
used, although a state or local agency may be interested in comparing impacts from other
road types and segments.
• Pollutants and Processes - Users identify the pollutants and emission processes to be
calculated by MOVES. For NO2 modeling, the user should identify "Oxides of
Nitrogen," "Nitrogen Oxide," and "Nitrogen Dioxide." "Running Exhaust" and
"Crankcase Running" emission processes should be selected for modeling individual road
links.15
• Manage Input Data Sets - This panel is not used in most project-level analyses. The
Project Data Manager is used for creating input databases. (For more information, see
Section C.I.4.)
• Strategies - Users can model alternative control strategies that affect the composition of
the vehicle fleet. In most situations, the state or local agency should use the same
strategies for all road segments analyzed unless existing information is known regarding
a difference in applicable strategies among different road segments.
• Output - Users specify output formats. Under "General Output," users should select
"grams," "miles," and "joules" for output units, and "Distance Traveled" and
"Population" to obtain vehicle volume information for each link modeled and to provide
details for evaluating MOVES results. Under "Output Emissions Detail," emissions by
hour and link are required for use in AERMOD (discussed in Section C.3).
• Advanced Performance Features - This panel is not used in most project-level analyses.
C.1.4 Entering Project Details Using the Project Data Manager
Once the choices for establishing a RunSpec have been set, the user should create appropriate
input databases using the Project Data Manager, which can be accessed from the Pre-Processing
menu item on the menu bar on top of the GUI. The Project Data Manager has a series of tabs
through which site-specific data are entered:
• Links - MOVES represents individual road segments as "links." Use this importer to
define individual road links, which must be assigned a unique identification (ID). This
importer requires information on the link's length, traffic volume, average speed, and
road grade.
• Link Source Type - Users enter the fraction of link's traffic volume represented by each
vehicle type (source type).
15 If other transportation facilities are evaluated (e.g., diesel truck or bus activity at terminals), then additional
emission processes would be considered in MOVES.
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• Link Drive Schedule - An optional importer that imports a 1-second time series driving
trace (speed and road grade) intended to represent vehicle driving behavior on the road
link modeled.
• Operating Mode Distribution - Users specify the distribution of operating modes for
source types, hour/day combinations, roadway links, and pollutant/process combinations
that are included in the run specification. This importer is considered an advanced option
that requires detailed vehicle activity data, and is typically not used.
• Off-Network - Users specify vehicle populations and activity for locations where vehicles
park, start, and/or idle for extended periods of time, such as parking lots or truck stops.
• Age Distribution - Used to enter information on the distribution of vehicle ages (agelD)
within the calendar year and vehicle type.
• Fuel Supply and Fuel Formulation - Used to provide fuels and fuel mix in the area
modeled. These inputs should generally be the same for all road segments in an area.
• Meteorology - Used to specify temperature and humidity data for the month and hour
modeled in the MOVES RunSpec.
• Inspection and Maintenance - In general, inspection and maintenance (I/M) programs
apply to vehicle fleets throughout certain nonattainment and maintenance areas.
C.1.5 MOVES Output Format
MOVES produces an output database that contains a line for each year, hour, link, pollutant,
process, fuel, and model year (if selected). MOVES produces either "inventories" (mass
emissions per hour) or emission rates. Inventory output can be used in AERMOD directly (with
additional source characterization as described next). MOVES emission rates must be post-
processed to obtain emission rates suitable for use in AERMOD.
C.2 Guidance on Air Quality Models
The guidance in this section is based on and is consistent with EPA's Guideline on Air
Quality Models, also published as Appendix W of Title 40 CFR Part 51 (U.S. Environmental
Protection Agency, 1993, 2005). Appendix W is the primary source of information on the
regulatory application of air quality models for State Implementation Plan (SIP) revisions for
existing sources and for New Source Review and Prevention of Significant Deterioration
programs. Because air quality modeling to inform the implementation of NO2 near-road
monitors needs to employ air quality dispersion models16 that properly address NOi emissions,
such modeling should rely upon the principles and techniques in Appendix W.
Appendix W was originally published in April 1978 and was incorporated by reference in the
regulations for the Prevention of Significant Deterioration of Air Quality, Title 40, CFR Sections
16 Dispersion modeling uses mathematical formulations to characterize the atmospheric processes that disperse a
pollutant emitted by a source. Based on emissions and meteorological inputs, a dispersion model can be used to
predict concentrations at selected downwind receptor locations.
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Near-Road NO2 Monitoring TAD Appendix BC: Modeling
51.166 and 52.21 in June 1978 [43 FR 26382-26388]. The purpose of Appendix W guidelines is
to promote consistency in the use of modeling within the air quality management process. These
guidelines are periodically revised to ensure that new model developments or expanded
regulatory requirements are incorporated.
Clarifications and interpretations of modeling procedures become official EPA guidance
through several courses of action:
1. the procedures are published as regulations or guidelines;
2. the procedures are formally transmitted as guidance to Regional Office managers;
3. the procedures are formally transmitted as guidance to Regional Modeling Contacts as a
result of a Regional consensus on technical issues; or
4. the procedures are a result of decisions by the EPA's Model Clearinghouse that
effectively establish national precedent.
Formally located in the Air Quality Modeling Group (AQMG) of EPA's Office of Air
Quality Planning and Standards (OAQPS), the Model Clearinghouse is the single EPA focal
point for the review of criteria pollutant modeling techniques for specific regulatory applications.
Model Clearinghouse and related Clarification memoranda involving decisions with respect to
interpretation of modeling guidance are available at the Support Center for Regulatory
Atmospheric Modeling (SCRAM) website.17
Recently issued EPA guidance of relevance for consideration in modeling for attainment and
maintenance demonstrations includes
• "Applicability of Appendix W Modeling Guidance for the 1-hour NOi NAAQS" June 28,
2010—confirming that Appendix W guidance is applicable for New Source
Review/Prevention of Significant Deterioration permit modeling for the new NOi
NAAQS (U.S. Environmental Protection Agency, 2010d).
• "Additional Clarification Regarding Application of Appendix W Modeling Guidance for
the 1-hour NO2 National Ambient Air Quality Standard" March 1, 2011- provides
additional guidance regarding NO2 permit modeling and also relevant to modeling for
implementation of NO2 near-road monitors (U.S. Environmental Protection Agency,
2011 a).
• "Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM2.5 and
PMio Nonattainment and Maintenance Areas" - provides guidance on hot-spot analyses
1 & -^^
for PM2.5 and PMio and has applicable guidance relevant to NO2 (U.S. Environmental
Protection Agency, 201 Ob, e).
The following sections refer to the relevant sections of Appendix W and other existing
guidance, with summaries as necessary. Please refer to those original guidance documents for
17 The SCRAM website is available at http://www.epa.gov/ttn/scram/
18 Hereafter referred to as "PM hot-spot guidance."
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full discussion, and consult with the appropriate EPA Regional Modeling Contact if questions
arise about interpreting modeling techniques and procedures.19
C.3 Model Selection
Preferred air quality models for use in regulatory applications are addressed in Appendix A
of EPA's Guideline on Air Quality Models. If a model is to be used for a particular application,
the user should follow the guidance on the preferred model for that application. As long as they
are used as indicated in each model summary of Appendix A, these models may be used without
an area-specific formal demonstration of applicability. Further recommendations for the
application of these models to specific source problems are found in subsequent sections of
Appendix W.
As described in the PM hot-spot guidance (U.S. Environmental Protection Agency, 201 Ob, e)
EPA's preferred near-field dispersion model, AERMOD (U.S. Environmental Protection
Agency, 2004a, 201 Ib) has been recommended for use in PM hot-spot analyses and is applicable
as well to NC>2 modeling.
For most scenarios to be considered as part of this TAD, AERMOD should be used. In 2005,
after extensive development and performance evaluation, EPA promulgated AERMOD as the
Agency's preferred near-field dispersion modeling for a wide range of regulatory applications in
all types of terrain.
AERMOD performed generally well in the NO2Risk and Exposure Assessment (U.S.
Environmental Protection Agency, 2008a) and is the recommended model for most mobile
90 r-r-, -m—,
source modeling scenarios. The guidance discussed here focuses on the use of AERMOD for
mobile source modeling.
The AERMOD modeling system includes several components, which fall into one of two
categories: regulatory and non-regulatory. The regulatory components are
• AERMOD: the dispersion model (U.S. Environmental Protection Agency, 2004a, 201 Ib)
• AERMAP: the terrain processor for AERMOD (U.S. Environmental Protection Agency,
20lie, 2004b)
• AERMET: the meteorological data processor for AERMOD (U.S. Environmental
Protection Agency, 2004c, 20lid)
The non-regulatory components are
• AERSURFACE: the surface characteristics processor for AERMET (U.S.
Environmental Protection Agency, 2008b)
19 For a list of regional modeling contacts by EPA Regional Office, see the SCRAM website:
http://www.epa.gov/ttn/scram/guidance cont regions.htm.
20 For example, EPA cites AERMOD as a recommended model when completing PM hot-spot analyses for
transportation conformity purposes. See Section 7.3 of EPA's PM hot-spot guidance (U.S. Environmental
Protection Agency, 2010e).
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Near-Road NO2 Monitoring TAD Appendix BC: Modeling
• AERSCREEN: a recently released screening version of AERMOD (U.S. Environmental
Protection Agency, 201 le)
• BPIPPRIME: the building input processor (U.S. Environmental Protection Agency,
2004d)
Before running AERMOD, the user should become familiar with the user's guides associated
with the modeling components listed above and with the AERMOD Implementation Guide
(AIG) (U.S. Environmental Protection Agency, 2009). The AIG lists several recommendations
for applications of AERMOD which would be applicable for NO2 roadway modeling.
C.4 Receptor Placement
The receptor grid is unique to each particular situation. It depends on the size of the
modeling domain, number of modeled sources, and complexity of terrain. Receptors should be
placed in areas that have representative ambient air. Receptor placement should be of sufficient
density to provide the resolution needed to detect significant gradients in concentrations of the
pollutants of concern, with receptors placed closer together near the source to detect local
gradients and placed farther apart away from the source. In addition, the user should place
receptors at key locations, such as at monitor locations for comparison to monitored
concentrations for model evaluation purposes.
Generally, the receptor network should cover the modeling domain. However, for the
purpose of the modeling discussed in this TAD, receptors may not have to be placed throughout
the domain, but only near the roadways; i.e., receptors may not have to be placed out to one or
five kilometers from the roadways for road comparison purposes in an effort to identify or aid in
the identification of candidate near-road monitoring sites. Refer to Section 7.6 of the PM hot-
spot guidance for additional guidance on placing receptors near roadways, and to the AERMOD
User's Guide and Addendum for receptor inputs into AERMOD. Receptors may also be placed
in locations that may represent potential monitoring sites as outlined in Section 6 of this TAD.
C.5 PVMRM and OLM
As outlined in Section 5.2.4 of Appendix W, there is a three-tiered approach to estimating
NO2 concentrations from AERMOD.
• The first tier, the most conservative, is to assume total conversion of NO to NO2.
• The second, less conservative tier is to apply a representative equilibrium NO2/NOX ratio
to modeled concentrations to yield NO2 concentrations.
• The third tier is to use a detailed analysis on a case-by-case basis, using PVMRM
(Hanrahan, 1999a, b; Cimorelli et al., 2004) or OLM.
In the March 1, 2011, memorandum "Additional Clarification Regarding Application of
Appendix W Modeling Guidance for the 1-hour NO2 National Ambient Air Quality Standard,"
clarification was provided for tiers 2 and 3. A summary is provided here, but users are strongly
encouraged to read the memorandum for details. For modeling of mobile NOX emissions, it is
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expected that the tier 3 approach would be implemented to account for the chemical
transformations from NOX to NO2, and that NOX will not be treated as an inert pollutant.
For the tier 3 approach, the March 1, 2011, memorandum gave clarification about the use of
PVMRM and OLM. The clarifications are summarized in Section 10.2.1 of this TAD, but again
the user is strongly encouraged to read the March 1, 2011, memorandum in full, and to consult
the AERMOD User's Guide (U.S. Environmental Protection Agency, 2004a) and addendum
(U.S. Environmental Protection Agency, 201 Ib) for details about the implementation of
PVMRM and OLM in AERMOD.
C.5.1 Source Characterization
As described in the Appendix J of the PM hot-spot guidance (U.S. Environmental Protection
Agency, 2010c), road segments can be characterized as either elongated area sources (AERMOD
source type AREA) or a series of volume sources (AERMOD source type VOLUME). Refer to
that appendix for more information about these source characterizations and their use in near-
roadway modeling. For general information about these source types, refer to the AERMOD
User's Guide and addendum (U.S. Environmental Protection Agency, 2004a, 201 Ib). As noted
in Section 10.2.1 of this TAD, if modeling roadway segments with PVMRM, it is recommended
that the user represent the roadway as a series of volume sources.
C.5.2 Inclusion of Nearby Sources
The inclusion of stationary sources or other nearby mobile sources in modeling of NOX
mobile emissions should be considered carefully. Such inclusion is complicated given the nature
of the pollutant, the form of the NO2 NAAQS standard, and the purpose of the modeling.
Sometimes, moderate or large stationary sources or other major roadways may be located within
a few kilometers of a targeted major roadway. Inclusion of other sources in mobile source
modeling may heavily influence the characterization of the near-road environment and change
the spatial distribution and magnitude of modeled concentrations; the inclusion of these other
sources is discussed in this section.
If road segments are modeled without any consideration of nearby sources, the modeled peak
concentrations of NOX will usually be near the road segments. If road segments are modeled as
elongated areas sources, the maximum concentration will often occur near the ends of segments
as the wind blows along the source. However, if other sources are included in the modeling
results, and the sources produce sufficiently large enough concentrations, the peak
concentrations' locations may shift toward those sources away from the roads of interest, thus
affecting the decision on where to place near-road monitors. Also, those sources could influence
the near-road environment; the measurements from any monitor placed near the road may be
influenced by those sources.
Another implication of inclusion or non-inclusion of other sources in the modeling is in the
use of PVMRM or OLM in modeling NO-to-NO2 conversion in AERMOD. If the other sources
are included in the same model run with the road segment sources of interest, there are more
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sources competing for the input ozone to convert NO to NC>2. The additional sources can lead to
a different final result than if they were not included in the model run.
A recommendation is to model the road segment or segments of interest along with any
nearby sources that may influence the near-road environment around the road segment(s) of
interest, and to model with the OLM option with OLMGROUP ALL selected. For model output,
create multiple source groups with the SRCGROUP keyword and output design values for each
source group to analyze the effects of the other sources. Note that the grouping of sources for
SRCGROUP is independent of the grouping for OLM; for more information, see Section 2.5.5 of
the AERMOD User's Guide Addendum (U.S. Environmental Protection Agency, 201 Ib).
For example, if an area contains a road segment of interest and three stationary sources are
nearby, then all sources can be modeled with OLM and using the OLMGROUP ALL option.
Two source groups can be created: (1) a source group for the road segment only, and (2) a
source group representing contributions from all sources (SRCGROUP ALL). The user can then
output concentrations for design values for the road-only source group and values for the total
source group (see Section C.8 for output options for design value calculations).
The user can then analyze those results to see the effects of the stationary sources near the
roadway and use that information to inform the monitor siting decision or inform the peak
concentration analysis. The user can use design values based on the road segment to refine the
monitor siting location.
C.5.3 Urban/Rural Classification
For any dispersion modeling exercise, the urban or rural classification of a source is
important in determining the boundary layer characteristics that affect the model's prediction of
downwind concentrations. Figure C-2 gives examples of maximum 1-hour concentration
profiles within 100 meters for a road segment represented by an area source (Figure C-2a) and a
volume source (Figure C-2b) based on urban versus rural designation. The urban population
used for the examples is 100,000. For both cases, the urban concentrations are much lower than
the rural concentrations. These profiles show that the urban or rural designation of a source can
be quite important.
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Appendix BC: Modeling
AREA
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Distance (m)
VOLUME
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Distance (m)
Figure C-2. Urban (red) and rural (blue) concentration profiles for (a) area source release, and
(b) volume source release.
Determining whether a source is urban or rural can be done using the methodology outlined
in Section 7.2.3 of Appendix W and recommendations outlined in Sections 5.1 through 5.3 in the
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AIG (U.S. Environmental Protection Agency, 2009). In summary, there are two methods of
urban/rural classification described in Section 7.2.3 of Appendix W.
• The first method of urban determination is a land use method (Appendix W,
Section 7.2.3c). In the land use method, the user analyzes the land use within a 3 km
radius of the source using the meteorological land use scheme described by Auer (1978).
Using this methodology, a source is considered urban if the land use types—II (heavy
industrial), 12 (light-moderate industrial), Cl (commercial), R2 (common residential),
and R3 (compact residential)—are 50% or more of the area within the 3 km radius circle.
Otherwise, the source is considered a rural source.
• The second method uses population density and is described in Section 7.2.3d of
Appendix W. As with the land use method, a circle of 3 km radius is used. If the
population density within the circle is greater than 750 people/km2, then the source is
considered urban. Otherwise, the source is modeled as a rural source. Of the two
methods, the land use method is considered more definitive (Section 7.2.3e,
Appendix W).
Caution should be exercised with either classification method. As stated in Section 5.1 of the
AIG (U.S. Environmental Protection Agency, 2009), when using the land use method, a source
may be in an urban area but located close enough to a body of water or other non-urban land use
category to result in an erroneous rural classification for the source. In Section 5.1, the AIG
cautions users against using the land use scheme on a source-by-source basis, and instead advises
considering the potential for urban heat island influences across the full modeling domain.
When using the population density method, Section 7.2.3e of Appendix W states, "Population
density should be used with caution and should not be applied to highly industrialized areas
where the population density may be low and thus a rural classification would be indicated, but
the area is sufficiently built-up so that the urban land use criteria would be satisfied..." With
either method, Section 7.2.3(f) of Appendix W recommends modeling all sources within an
urban complex as urban, even if some sources within the complex would be considered rural
using either the land use or population density method.
Another consideration that may need attention is when the user is including stationary
sources in the modeling exercise (which is discussed in Section 5.1 of the AIG, regarding tall
stacks located within or adjacent to small- to moderate-size urban areas). In such cases, the stack
height or effective plume height for very buoyant sources may extend above the urban boundary
layer height.
The application of the urban option in AERMOD for these types of sources may artificially
limit the plume height. AERMOD calculates boundary layer heights that are due to thermal and
mechanical turbulence forcing separately. In urban areas, there is constant thermal forcing at
night, whereas in rural areas, nocturnal mixing is purely mechanical in nature. The use of the
urban option may not be appropriate for buoyant sources, since the actual plume is likely to be
transported over the urban boundary layer. Section 5.1 of the AIG gives details on determining
whether a tall stack should be modeled as urban or rural, based on comparing the stack or
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effective plume height to the urban boundary layer height and equation 104 of the AERMOD
formulation document (Cimorelli et al., 2004). This equation is as follows:
where z;uc is the height of the nocturnal boundary layer due to convection effects alone, P is
population, and z;uo is a reference height of 400 m corresponding to a reference population P0 of
2,000,000 people.
If a stack is a buoyant release type, the plume may extend above the urban boundary layer
and may be best characterized as a rural source, even if it is near an urban complex. Exclusion
of these elevated sources from application of the urban option would need to be justified for each
case in consultation with the appropriate reviewing authority.
AERMOD requires the input of urban population when using the urban option. Population
can be entered to one or two significant digits (i.e., an urban population of 1,674,365 can be
entered as 1,700,000). Users can enter multiple urban areas and populations using the
URBANOPT keyword in the runstream file (U.S. Environmental Protection Agency, 2004a,
201 Ib). If multiple urban areas are entered, AERMOD requires that each urban source be
associated with a particular urban area; otherwise, AERMOD model calculations will abort.
Urban populations can be determined by using a method described in Section 5.2 of the AIG
(U.S. Environmental Protection Agency, 2009).
C.6 Meteorological Inputs
This section gives guidance on the selection of meteorological data for input into AERMOD.
Much of the guidance from Section 8.3 of Appendix W is applicable to NO2 near-road modeling
and is summarized here. In Section C.6.2.2, the use of a new tool, AERMINUTE (U.S.
Environmental Protection Agency, 201 If), is introduced. AERMINUTE is an AERMET pre-
processor that calculates hourly averaged winds from ASOS (Automated Surface Observing
System) 1-minute winds.
C.6.1 Surface Characteristics and Representativeness
The selection of meteorological data that are input into a dispersion model should be
considered carefully. The selection of data should be based on spatial and climatological
(temporal) representativeness (Appendix W, Section 8.3). The representativeness of the data is
based on the following:
• proximity of the meteorological monitoring site to the area under consideration,
• complexity of terrain,
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• exposure of the meteorological site, and
• period of time over which data are collected.
Sources of meteorological data are NWS stations; site-specific or onsite data; and other
sources, such as universities, Federal Aviation Administration (FAA), and military stations.
Appendix W addresses spatial representativeness issues in Sections 8.3.a and 8.3.c.
Spatial representativeness of the meteorological data can be adversely affected by large
distances between the source and receptors of interest and the complex topographic
characteristics of the area (Appendix W, Section 8.3.a and 8.3.c). If the modeling domain is
large enough that conditions vary drastically across the domain, then the selection of a single
station to represent the domain should be carefully considered. Also, care should be taken when
selecting a station if the area has complex terrain. While a source and meteorological station
may be geographically close, if there is complex terrain between them, conditions at the
meteorological station may not be representative of the source. An example would be a source
located on the windward side of a mountain chain with a meteorological station a few kilometers
away on the leeward side of the mountain.
Spatial representativeness for offsite data should also be assessed by comparing the surface
characteristics (albedo, Bowen ratio, and surface roughness) of the meteorological monitoring
site and the analysis area. When processing meteorological data in AERMET (U.S.
Environmental Protection Agency, 2004c, 20 lid) the surface characteristics of the
meteorological site should be used [Section 8.3.c of Appendix W and the AERSURFACE User's
Guide (U.S. Environmental Protection Agency, 2008b)]. Spatial representativeness should also
be addressed for each meteorological variable separately. For example, temperature data from a
meteorological station several kilometers from the analysis area may be considered adequately
representative, while it may be necessary to collect wind data near the plume height (Section
8.3.c of Appendix W).
Surface characteristics can be calculated in several ways. For details, see Section 3.1.2 of the
AIG (U.S. Environmental Protection Agency, 2009). EPA has developed a tool, AERSURFACE
(U.S. Environmental Protection Agency, 2008b), to aid in determining surface characteristics.
The current version of AERSURFACE uses 1992 National Land Cover Data. Note that the use
of AERSURFACE is not a regulatory requirement; however, the methodology outlined in
Section 3.1.2 of the AIG should be followed unless an alternative method can be justified.
C.6.2 Meteorological Data
Appendix W states in Section 8.3.1.1 that the user should acquire enough meteorological data
to ensure that worst-case conditions are adequately represented in the model results.
Appendix W states that five years of NWS meteorological data or at least one year of site-
specific data should be used (Section 8.3.1.2, Appendix W); the data used should adequately
represent the study area. If one or more years (including partial years) of site-specific data are
available, those data are preferred.
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While the form of the NC>2 NAAQS uses three years of monitoring data, this does not
preempt the use of five years of NWS data or at least one year of site-specific data in the
modeling. The five-year average based on the use of NWS data, or an average across one or
more years of available site specific data, serves as an unbiased estimate of the three-year
average for purposes of modeling demonstrations of compliance with the NAAQS [see the
June 28, 2010, Clarification Memorandum on "Applicability of Appendix W Modeling Guidance
for the 1-hour NC>2 National Ambient Air Quality Standard" (U.S. Environmental Protection
Agency, 2010d)]. See the memorandum for more details on the use of five years of NWS data or
at least one year of site-specific data and applicability to the NAAQS.
C.6.2.1 NWS Data
NWS data are available from the National Climatic Data Center (NCDC) in many formats,
with the most common one in recent years being the Integrated Surface Hourly data (ISH). Most
available formats can be processed by AERMET. As stated in Section C.6.1, when using data
from an NWS station alone or with site-specific data, the data should be spatially and temporally
representative of conditions at the modeled sources.
A recently discovered issue with ASOS is that 5-second wind data that are used to calculate
the 2-minute average winds are truncated rather than rounded to whole knots. For example, a
wind of 2.9 knots is reported as 2 knots, not 3 knots. To account for this truncation of NWS
winds (either standard observation or AERMINUTE output), an adjustment of /^ knot or
0.26 m/s is added to the winds in stage 3 AERMET processing. For more details, refer to the
AERMET User's Guide Addendum (U.S. Environmental Protection Agency, 201 Id) and/or the
appropriate EPA Regional Modeling Contact.
C.6.2.2 AERMINUTE
In AERMOD, concentrations are not calculated for variable wind (i.e., missing wind
direction) and calm conditions, resulting in zero concentrations for those hours. Since the NC>2
NAAQS is a 1-hour standard, these light wind conditions may be the controlling meteorological
circumstances because of the limited dilution that occurs under low wind speeds, which can lead
to higher concentrations. The exclusion of a greater number of instances of near-calm conditions
from the modeled concentration distribution may therefore lead to underestimation of daily
maximum 1-hour concentrations when calculating the design value.
To address the issues of calm and variable winds associated with the use of NWS
meteorological data, EPA has developed a preprocessor to AERMET, called AERMINUTE
(U.S. Environmental Protection Agency, 201 If) that can read 2-minute ASOS winds and
calculate an hourly average. Beginning with year 2000 data, NCDC has made the 1-minute wind
data, reported every minute from the ASOS network, freely available. The AERMINUTE
program reads these 2-minute winds and calculates an hourly average wind. In AERMET, these
hourly averaged winds replace the standard observation time winds obtained from the archive of
meteorological data. This approach results in a lower number of calms and missing winds and
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an increase in the number of hours used in averaging concentrations. For more details regarding
the use of NWS data in regulatory applications, see Section 8.3.2 of Appendix W; for more
information about the processing of NWS data in AERMET and AERMINUTE, see the
AERMET (U.S. Environmental Protection Agency, 2004c, 201 Id) and AERMINUTE User's
Guides (U.S. Environmental Protection Agency, 201 If).
C.6.2.3 Site-Specific Data
The use of site-specific meteorological data is the best way to achieve spatial
representativeness in the modeling. AERMET can process a variety of formats and variables for
site-specific data. The use of site-specific data for regulatory applications is discussed in detail
in Section 8.3.3 of Appendix W. Due to the range of data that can be collected onsite and the
range of formats of data input to AERMET, the user should consult Appendix W, the AERMET
User's Guide (U.S. Environmental Protection Agency, 2004c, 20lid), and Meteorological
Monitoring Guidance for Regulatory Modeling Applications (U.S. Environmental Protection
Agency, 2000). Also, when processing site-specific data for an urban application, Section 3.3 of
the AERMOD Implementation Guide offers recommendations for data processing. In summary,
in order to avoid double-counting the effects of enhanced turbulence due to the urban heat island,
the guide recommends that site-specific turbulence measurements should not be used when
applying AERMOD's urban option.
C.6.2.4 Upper-Air Data
AERMET requires full upper air soundings to calculate the convective mixing height. For
AERMOD applications in the United States, the early morning sounding, usually the 1200 UTC
(Universal Time Coordinate) sounding, is typically used for this purpose. Upper air soundings
can be obtained from the Radiosonde Data of North America CD for the period 1946-1997.
Upper air soundings for 1994 through the present are also available as free downloads from the
Radiosonde Database Access website. Users should choose all levels or mandatory and
significant pressure levels21 when selecting upper air data. Selecting mandatory levels only
would not be adequate for input into AERMET, as the use of just mandatory levels would not
provide an adequate characterization of the potential temperature profile.
C.7 Background Concentrations
Background concentrations are often included in a modeling analysis to account for natural
sources or sources not explicitly modeled. Given the nature of the modeling described in this
TAD, either for comparing road segments or refining monitor locations, inclusion of background
concentrations may not be necessary, but best professional judgment should be used. Section 8.2
21 By international convention, mandatory levels are in millibars: 1,000; 850; 700; 500; 400; 300; 200; 150;
100; 50; 30; 20; 10; 7 5; 3; 2; and 1. Significant levels may vary depending on the meteorological conditions at the
upper-air station.
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of Appendix W gives more detailed general guidance regarding background concentrations. The
March 1, 2011, memorandum also gives guidance specific to NO2 and is summarized here:
• The June 28, 2010, memorandum initially discussed a "first tier" option of adding the
maximum 1-hour background NC>2 concentration from a representative monitor to the
modeled design value. This option may be applied without further justification.
• The March 1, 2011, memorandum recognized that the above approach may be overly
conservative and may be prone to reflecting source-oriented impacts from nearby
sources, thus increasing chances of double counting.
• The March 1, 2011, memorandum discussed a second, less conservative form of
application of a uniform background by using monitored design values from the most
recent three years of monitor data.
• Also discussed in the March 1, 2011, memorandum is the use of temporally varying
background concentrations; i.e., using the 98th percentile of concentrations by season and
hour of day. The memorandum also discussed including a day-of-week component to
background concentrations for mobile sources.
The user is strongly encouraged to read the March 1, 2011, memorandum for full details
about background concentrations.
For the purposes of the modeling discussed in this TAD, inclusion of background
concentrations may not be necessary. If the purpose of the modeling is to compare relative
impacts of road segments, including background concentrations may not be necessary, since the
purpose of the modeling is not a cumulative impact analysis. However, if the purpose of the
modeling is to inform monitor siting or to identify peaks, then background concentrations, as
well as emissions from stationary sources, should be included (see Section C.6) in order to fully
characterize the air quality situation).
C.8 Running AERMOD and Implications for Design Value Calculations
Recent enhancements to AERMOD include options that aid in calculating design values for
comparison with the NC>2 NAAQS. These enhancements include
• The output of daily maximum 1 -hour concentrations by receptor for each day in the
modeled period for a specified source group. This is the MAXDAILY output option in
AERMOD.
• The output, for each rank specified on the RECTABLE output keyword, of daily
maximum 1-hour concentrations by receptor for each year for a specified source group.
This is the MXDYBYYR output option.
• The MAXDCONT option, which shows the contribution of each source group to the high
ranked values for a specified target source group, paired in time and space. The user can
specify a range of ranks to analyze, or specify an upper bound rank (i.e., 8th highest) and
a lower threshold value (such as the NAAQS) for the target source group. The model
will process each rank within the range specified, but will stop after the first rank (in
descending order of concentration) that is below the threshold, specified by the user. A
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warning message will be generated if the threshold is not reached within the range of
ranks analyzed (based on the range of ranks specified on the RECTABLE keyword). For
more details about the enhancements, see the AERMOD User's guide Addendum (U.S.
Environmental Protection Agency, 201 Ic).
C.9 References
Auer Jr. A.H. (1978) Correlation of land use and cover with meteorological anomalies. Journal
of Applied Meteorology, 17, 5, 636-643.
Cimorelli A.J., Perry S.G., Venkatram A., Weil J.C., Paine R.J., Wilson R.B., Lee R.F., Peters
W.D., Erode R.W., and Paumier J.O. (2004) AERMOD: description of model
formulation. Report by the U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Emissions Monitoring and Analysis Division, Research Triangle
Park, NC, EPA-454/R-03-004, September. Available on the Internet at
http://www.epa.gov/scramOO l/7thconf/aermod/aermod_mfd.pdf
Hanrahan P.L. (1999a) The plume volume molar ratio method for determining NO2/NOX ratios in
modeling - part I: methodology. Journal of the Air & Waste Management Association,
49, 11, 1324-1331.
Hanrahan P.L. (1999b) The plume volume molar ratio method for determining NO2/NOX ratios
in modeling - part II: evaluation studies. Journal of the Air & Waste Management
Association, 49, 11, 1332-1338.
U.S. Environmental Protection Agency (1993) Guideline on air quality models (revised),
including Supplements A and B. U.S. Environmental Protection Agency, Research
Triangle Park, NC EPA-450/2-78-027R (see also 40 CFR Part 51 Appendix W), July 20.
U.S. Environmental Protection Agency (2000) Meteorological monitoring guidance for
regulatory modeling applications. Office of Air Quality Planning and Standards,
Research Triangle Park, NC, Document EPA-454/R-99-005, February. Available on the
Internet at http://www.epa.gov/scram001/guidance/met/mmgrma.pdf.
U.S. Environmental Protection Agency (2004a) User's guide for the AMS/EPA regulatory model
(AERMOD). Office of Air Quality Planning and Standards, Research Triangle Park, NC,
EPA-454/B-03-001, September.
U.S. Environmental Protection Agency (2004b) User's guide for the AERMOD terrain
preprocessor (AERMAP). Emissions, Monitoring, and Analysis Division, Office of Air
Quality Planning and Standards, Research Triangle Park, NC, EPA-454/B-03-003,
October. Available on the Internet at
http://www.epa.gov/scram001/7thconf/aermod/aermapugb2.pdf.
U.S. Environmental Protection Agency (2004c) User's guide for the AERMOD meteorological
preprocessor (AERMET). Office of Air Quality Planning and Standards, Research
Triangle Park, NC, EPA-454/B-03-002, November.
U.S. Environmental Protection Agency (2004d) User's guide to the building profile input
program, revised. Office of Air Quality Planning and Standards, Research Triangle Park,
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NC, EPA-454/R-93-038, April 21. Available on the Internet at
http://www.epa.gov/scram001/userg/relat/bpipdup.pdf.
U.S. Environmental Protection Agency (2005) Revision to the guideline on air quality models:
adoption of a preferred general purpose (flat and complex terrain) dispersion model and
other revisions; Final Rule (40 CFR Part 51). Federal Register, 70, 216. Available on the
Internet at http://www. epa. gov/ttn/scram/guidance/guide/appw_05. pdf.
U.S. Environmental Protection Agency (2008a) Risk and exposure assessment to support the
review of the NO2 primary National Ambient Air Quality Standard. Report prepared by
the Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-
452/R-08-008a, November. Available on the Internet at
http://www.epa.gov/ttnnaaqs/standards/nox/data/20081121 NO2 REA final.pdf.
U.S. Environmental Protection Agency (2008b) AERSURFACE user's guide. Prepared by the
Office of Air Quality Planning and Standards, Air Quality Assessment Division, Air
Quality Modeling Group, Research Triangle Park, NC, EPA-454/B-08-001, January.
Available on the Internet at
http://www.epa.gov/ttn/scram/7thconf/aermod/aersurface_userguide.pdf.
U.S. Environmental Protection Agency (2009) AERMOD implementation guide. Office of Air
Quality Planning and Standards, Air Quality Assessment Division, Research Triangle
Park, NC, March 19. Available on the Internet at
http://www.epa.gov/scramOO l/7thconf/aermod/aermod_implmtn_guide_19March2009.pd
f.
U.S. Environmental Protection Agency (2010a) Motor vehicle emission simulator (MOVES)
user guide for MOVES2010a. Assessment and Standards Division, Office of
Transportation and Air Quality, Research Triangle Park, NC, EPA-420-B-10-036,
August. Available on the Internet at
http://www.epa.gov/otaq/models/moves/MOVES201 Oa/420b 10036.pdf: additional
resources are available at http://www.epa.gov/otaq/models/moves/index.htm.
U.S. Environmental Protection Agency (201 Ob) Transportation conformity guidance for
quantitative hot-spot analyses in PM2.5 and PMio nonattainment and maintenance areas.
Guidance document prepared by the Transportation and Regional Programs Division,
Office of Transportation and Air Quality, EPA-420-B-10-040, December. Available on
the Internet at http://www.epa.gov/otaq/stateresources/transconf/policy/420bl0040.pdf.
U.S. Environmental Protection Agency (2010c) Using MOVES in project-level carbon monoxide
analyses. Report by the Transportation and Regional Programs Division, Office of
Transportation and Air Quality, Research Triangle Park, NC, EPA-420-B-10-041,
December. Available on the Internet at
http://www.epa.gov/otaq/stateresources/transconf/policy/420bl0041.pdf.
U.S. Environmental Protection Agency (2010d) Applicability of Appendix W modeling guidance
for the 1-hour NO2 National Ambient Air Quality Standard. Tyler Fox technical
memorandum, Research Triangle Park, NC, June 28. Available on the Internet at
http://www.epa.gov/ttn/scram/ClarificationMemo AppendixW Hourly-NO2-
NAAQS FINAL 06-28-2010.pdf
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U.S. Environmental Protection Agency (2010e) Transportation conformity guidance for
quantitative hot-spot analyses in PM2.5 and PMio nonattainment and maintenance areas:
Appendices. Guidance document prepared by the Transportation and Regional Programs
Division, Office of Transportation and Air Quality, EPA-420-B-10-040, December.
Available on the Internet at
http://www.epa.gov/otaq/stateresources/transconf/policy/420bl0040-appx.pdf
U.S. Environmental Protection Agency (201 la) Additional clarification regarding application of
Appendix W modeling guidance for the 1-hour NC>2 National Ambient Air Quality
Standard. Tyler Fox memorandum prepared by the Office of Air Quality Standards and
Planning, Research Triangle Park, NC, March 1. Available on the Internet at
http://www.epa.gov/ttn/scram/Additional Clarifications AppendixW Hourly-NO2-
NAAQS FINAL 03-01-2011.pdf
U.S. Environmental Protection Agency (201 Ib) Addendum: user's guide for the AMS/EPA
regulatory model - AERMOD (EPA-454/B-03-001, September 2004). Updated user
instructions by the Office of Air Quality Planning and Standards, Research Triangle Park,
NC, EPA-454/B-03-001, February. Available on the Internet at
http://www.epa.gov/ttn/scram/models/aermod/aermod userguide addendum_vl 1059 dra
ft.pdf.
U.S. Environmental Protection Agency (201 Ic) Addendum: user's guide for the AERMOD
terrain preprocessor (AERMAP). EPA-454/B-03-003, March. Available on the Internet at
http://www.epa.gov/ttn/scram/models/aermod/aermap/aermap userguide.zip.
U.S. Environmental Protection Agency (201 Id) Addendum: user's guide for the AERMOD
meteorological preprocessor (AERMET). EPA-454/B-03-002, February. Available on the
Internet at http://www.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.zip.
U.S. Environmental Protection Agency (201 le) AERSCREEN user's guide. Office of Air
Quality Planning and Standards, Air Quality Assessment Division, Air Quality Modeling
Group, Research Triangle Park, NC, EPA-454/B-11-001, March. Available on the
Internet at http://www.epa.gov/ttn/scram/models/screen/aerscreen_userguide.pdf.
U.S. Environmental Protection Agency (201 If) AERMINUTE user's instructions (draft).
Available on the Internet at
http://www.epa.gov/ttn/scram/models/aermod/aerminute userguide vll059 draft.pdf.
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United States Office of Air Quality Planning and Publication No. EPA-
Environmental Protection Standards 454/B-12-002
Agency Air Quality Assessment Division June 2012
Research Triangle Park, NC
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