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due to greater crossing distances. Diverting traffic may cause impacts at other intersections that
may not be equipped to handle greater traffic volumes.
Regional air quality mitigation measures. Regional measures may be considered to address
nonattainment of pollutants. Regional air quality mitigation measures may include roadway or
transit projects to reduce congestion or inspection and maintenance programs to reduce vehicle
emissions.
SELECTING AN APPROPRIATE METHOD OF ANALYSIS
Most of the air quality and environmental justice assessment methods presented in this chapter
examine each of the major categories of vehicle-generated air pollution individually. Although
these techniques are the ones most readily combined with standard air quality models and
assessment processes, they do not allow practitioners to consider additive or synergistic effects
of pollutants or cumulative effects from all sources. One method, however, does allow for
evaluation of cumulative pollutant levels from all sources. This method can be implemented as
an extension of the most commonly used air quality assessment models. Examples are provided
showing how results for estimated pollutant levels can be evaluated independently of current
criteria pollutant standards.
The methods presented here do not directly address questions about the causal connection
between vehicle-generated pollutants and observed health effects such as asthma or increased
cancer rates. Understanding this connection between environmental air quality (outdoor and
indoor) and health effects is an emerging field of research, although most studies have not
focused solely on transportation sources. This type of research is very time consuming and
requires considerable expertise in health and epidemiology. Because the cost of health effects-
based methods would be beyond the scope of all but a very few, extreme transportation
situations, they are not included in this guidebook. See Carlin and Xia (1996), Hockman and
Morris (1998), and Waller, Louis, and Carlin (1999) for examples of exposure-based
environmental justice research on the health effects of air quality.
METHODS
Table 3-3 provides a summary of the four methods presented in this chapter.
The methods used to analyze air quality impacts from transportation projects range from simple
to complex, and from well understood to experimental and not yet prescribed by regulatory
authority. Micro-scale analyses for transportation-related projects nearly always focus on CO and
possibly PM, whereas regional analyses generally address CO, NO2, PM, and VOC.
Method 1. General air quality review
When projects do not warrant an air quality analysis, the assessment may extend only to a
general discussion about air quality rules and the existing air quality environment. For purposes
of environmental justice assessment, concentrations should be evaluated in terms of potential
NAAQS violations to identify situations where alteration of the planned policy, program, or
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project must be considered. In addition, the net change in concentrations, independent of
NAAQS thresholds, can be determined to identify situations where there would be either
beneficial or adverse effects to protected populations.
Table 3-3.
Summary of methods for analyzing air quality
Assessment Appropriate Use Data Expertise
Method level uses when needs required
1. General air Screening Project/corridor/system Initial analysis and Low Spreadsheet
quality when air quality
review effects are expected to
be minimal
2. Detailed Detailed Project/corridor Project is Medium Modeling
microscale controversial or there (CAL3QHC)
analysis are potential
environmental justice
concerns and more
detail of microscale
effects is required by
local guidance
3. Detailed Detailed Large projects/systems Transportation Medium Modeling
regional conformity analysis is (MOBILE6)
analysis required and there is
potential for
environmental justice
concern
4. Analysis Detailed Project/corridor/system Air quality is a major High Modeling,
using issue with protected database,
pollution population groups and geographic
surfaces previous methods information
have not addressed all systems (GIS),
issues statistical
analysis
When to use. This approach is advised in situations where detailed analysis is not warranted. It
is intended to document both local and regional air quality in an area, as well as the presence of
protected populations. The level of concern that there could potentially be distributive effects to
protected populations is based on review of demographic patterns. Information gathered for this
assessment can be used to perform detailed microscale analysis and detailed regional air quality
analysis if findings indicate the potential for adverse effects to protected populations.
Detailed microscale or regional air quality analyses may not be necessary if the project does not
trigger the requirements for a conformity determination or if the project is within an attainment
area or a maintenance area with specific guidance that does not require a detailed microscale
analysis. A detailed air quality analysis may not be necessary within a nonattainment or
maintenance area if a project is not regionally significant and if the project does not affect any
transportation control measures.
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Even within maintenance or nonattainment areas, programs may be in place that provide
thresholds indicating whether a microscale air quality analysis is needed. An example guidance
would require evaluation of the 10 highest volume intersections in a region. The local guidance
may state that a microscale air quality analysis is required if the highest volume intersection
within the project area is less than the 10th highest intersection volume in the region. Similar
guidance may specify that intersections operating at a particular level of service (LOS) or better
may not require micro-scale air quality analysis.
Analysis. This analysis is a process of documenting regional and local air quality issues, and
then documenting the presence of protected populations in the study area to determine (on a
qualitative basis) whether the proposed transportation system change could potentially cause
distributive effects. Based on local guidance, this analysis may include evaluating the highest
traffic volume intersections within the project area, and/or evaluating the worst intersection LOS.
Step 1 Document regional air quality. The first step is to document regulations and regional
air quality in the study area. Much of the information needed for this step can be obtained from
state and local agencies. At a national level, the EPA provides detailed information on regional
air quality throughout the United States. Available information includes lists of regions that are
in violation of the NAAQS, air pollution data and maps for ozone, PM, NO2, SO2, and air quality
index (AQI) reports.
The AQI is an index for reporting air quality to the public. The AQI is calculated daily for each
criteria pollutant using standard formulas approved by the EPA and is tied to relative levels of
acute health concern. For example, an AQI of 101 to 150 is unhealthy for sensitive groups or
individuals, such as those with asthma. AQI values greater than 150 are considered unhealthy
(151-200), very unhealthy (201-300), or hazardous (301-500) to all persons.
The AQI is calculated on the basis of measurements taken at more than a thousand locations
across the country each day. In large metropolitan areas, state and local agencies must report the
AQI to the public daily. When the AQI is above 100, they must also report which groups, such as
children, people with asthma, or people with heart disease, may be sensitive to the specific
pollutant. Although it is not required, many smaller communities also report the AQI as a public
health service (U.S. EPA 2003a).
To complete Step 1, describe the regional pollutants of highest concern in the study area,
common sources of those pollutants, and provide an indication of the general level of regional air
quality based on state and local information and on information available from the U.S. EPA.
The data developed for this step could include the region's attainment/nonattainment status and
number of days above the NAAQS for the various pollutants.
Step 2 Document local air quality. The second step is to document local air quality rules and
potential locations with relatively higher pollutant levels in the study area. Discuss the
regulations and guides that are used to evaluate local transportation air quality and transportation
conformity. Describe how the attainment status in the area determines the level of air quality
assessment that is necessary. Next, identify locations in the study area where air quality from
mobile and point sources may be of greater concern. At a minimum, you should document areas
where air pollution levels could be higher because of proximity to transportation facilities. These
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locations could be areas near large concentrations of mobile sources, such as freeways, arterials,
and congested intersections. If time and resources allow, it would be advisable to also identify
other significant sources of air pollution that may affect the project area, such as electrical
generation facilities and other point sources.
Step 3 Document protected populations in the project area. Use any of a number of
qualitative or quantitative methods for identifying protected populations in the project area.
Many suitable techniques are discussed in Chapter 2. The best technique to use depends on the
size of the project being evaluated. For policies, programs, and large projects with a sizeable area
of potential effects, it is appropriate to use census data and any of the evaluation techniques that
use census data, such as the Environmental Justice Index (EJI) or threshold analysis. For projects
with smaller study areas, it is more appropriate to use data gathering techniques to characterize
protected populations. Suggested techniques include local knowledge, field surveys, and public
participation-based techniques such as focus groups.
Step 4 Evaluate potential for unequal distributive effects. A map is probably the best way
to evaluate the distribution of air pollution sources in the vicinity of protected populations. Either
desktop methods or GIS can be used to map the location of significant point and mobile sources
depending on available resources, the size of the study area, and the number of sources and
demographic analysis units. Then, overlay the source information on maps showing the location
of protected population groups. Because this is a screening test, the objective is not to identify
statistically significant unequal location patterns. Rather, the intent is to identify areas where
there is spatial clustering of air pollution sources and where protected populations are in very
close proximity to air pollution sources. Document these sites as locations where local air quality
and its effects on protected populations could be of greater concern than in other portions of the
study area.
Commonly used regional air quality assessment methods assume that pollutants at this scale are
distributed relatively uniformly across large areas. Therefore, it will not usually be worthwhile to
evaluate unequal geographic distributions unless the study area includes multiple regions.
Rather, you will need to discuss regional air quality as mentioned above and describe the adverse
effects that may be experienced by various population groups.
Data needs, assumptions, and limitations. This type of analysis requires the following basic
information on the project and the geographic area or region of the project:
· Project traffic study results, including traffic volumes and LOS;
· Maps showing transportation system intersections and links;
· Information on the location of point sources that emit air pollution;
· Regional attainment status, and local project analysis guidance; and
· Information on the location of protected populations.
Information on point sources that emit air pollution can be found in numerous sources. Many
states publish an annual database of permitted facilities. Some of these databases include
information on the level of emissions, making it possible to map not only the locations of
facilities but also to categorize facilities based on the volume and types of pollutants they emit.
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At the national level, the EPA annually releases this type of information in the Toxics Release
Inventory (TRI) (U.S. EPA 2003b).
This technique assumes that proximity to air pollution sources is an indication of possibly higher
local air pollution levels. This may not be a valid assumption in all situations because of factors
such as wind speed and direction, topography, and pollutant dispersion characteristics, among
others. Without further analysis, this technique merely documents local and regional air quality
issues in the study area and identifies locations where air quality may be of greater concern to
protected populations.
Using this method, one approach to evaluating the distributive air quality effects of a project
would be to assess whether the project would affect local air quality in the areas of greater
concern. If so, a more detailed analysis in those areas should be considered. However, if the
project would not have an effect in these areas and air quality is not a major issue to protected
populations in the study area, it should generally be safe to conclude that the project would have
no distributive air quality effects.
Obviously, this method has major limitations. It relies on very basic data to assess the potential
for localized air quality effects, and it assumes that the distribution of regional air quality is
uniform. For areas where the potential for unequal effects is identified, the actual level of effect
cannot be quantified. Because of these limitations, it is safe to assume that any results from this
method of assessment would be challenged if a project were controversial. This technique is
therefore best used as a screening technique only, and further assessment using more robust
techniques should be performed unless no potential for unequal effects is found and the project is
not controversial.
Results and their presentation. The results of this analysis could include a table showing
intersection volumes or LOS, where higher volumes indicate a greater potential for localized air
quality effects. A second table showing total stack and fugitive emissions from point sources
could be used to display sites with high emissions levels. A general discussion of the regional
attainment status and guidance for air quality should be included. Probably the most useful
presentation aid would be a map showing locations of protected populations and nearby mobile
and point sources of air pollution.
Assessment. A general air quality review is intended to disclose local and regional air quality
concerns and regulations. For environmental justice assessment, comparison of mobile and
stationary air quality sources and their proximity to protected populations is included. Due to the
many limitations of this approach, further detailed analysis should be performed in any situation
where protected populations could be affected by the policy, program, or project or where the
transportation system change is controversial. Because no quantitative air quality analysis is
conducted, this technique provides qualitative results and any conclusions are subjective.
Method 2. Detailed microscale analysis
The detailed microscale analysis is an extension of the hot spot analysis used to determine
NAAQS conformance. The additional information and modeling needed to perform
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environmental justice assessment are reasonable and should be within the resources of most
agencies that must currently perform local air quality analysis under Clean Air Act and
transportation conformity guidelines.
When to use. A detailed analysis should be conducted for any regionally significant
transportation project within a nonattainment or maintenance area. Because the additional data
requirements necessary to perform environmental justice assessment are relatively minor, this
method can be used to evaluate both projects and plans, such as TIPs or major investment
studies. This analysis should also be conducted if a state agency requires it on the basis of its
SIP, guidance, or rules. The analysis also is needed if the project is controversial and will be
subjected to substantial public scrutiny due to perceived potential air quality impacts. Such an
analysis is especially useful in situations where a general air quality review (Method 1) indicates
the potential for air quality effects to protected populations.
This type of analysis should be conducted (a) to determine that no established criteria pollutant
standards are violated and (b) to evaluate effects to protected populations. Although this form of
detailed assessment is only required in specific circumstances according to the Clean Air Act and
transportation conformity requirements, the method is broadly applicable to all transportation
plans and projects that would benefit from microscale air quality assessment. The microscale
analysis is typically performed at selected worst case, or "hot spot" intersections. Environmental
justice assessment involves evaluating the pattern of where these intersections are located in
relation to the activity spaces of protected population groups and then, at an even finer level, the
effects of hot spot intersections on specific sensitive receptors.
Analysis. The following steps are specific to the environmental justice component of the local
air quality assessment. For an overview of a standard procedure for performing the hot-spot
analysis, see "Microscale air quality assessment," above. FHWA (2001) also provides a detailed
discussion of the requirements for performing hot-spot analysis. Figure 3-3 provides an example
of how a microscale analysis process can be used to evaluate environmental justice.
Step 1 Describe the transportation system change and gather necessary data. The detailed
microscale assessment can be performed for policies, plans, and projects down to the level of a
specific intersection. You must define the nature of the transportation system change and the air
quality objectives by which the change should be evaluated. For general NAAQS conformance
with an environmental justice component, the objective could be stated as "document NAAQS
compliance ensuring that the proposed projects cause no violations at worst case intersections
and in areas where there is possible environmental justice concern." In situations where air
quality is highly controversial and there is a history of concern over impacts to protected
populations, the objective could be to obtain emissions reductions. In this case, the goal would be
to "evaluate project performance and ensure that the transportation facility yields a net emissions
reductions once it is fully implemented."
Once the transportation system change and objectives are described, you must assemble all the
data necessary to describe intersection-related traffic conditions and demographic characteristics
of the intersections. The specific data needed are described later under "Data needs, assumptions,
and limitations."
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Step 2 Review demographics for all intersections. Once each intersection has been assigned
a score characterizing its level of environmental justice concern, rank the intersections either
categorically or numerically. If an environmental justice concern was quantified using a measure
such as the EJI, select a threshold value denoting areas of relatively higher concern based on
expert opinion and knowledge of both system user demographics and activity space
demographics near the intersections. Tabulate the number of intersections with a high level of
environmental justice (EJ) concern.
Step 1
1a
Policy/plan/project/intersection description
1b
Determine air quality/regulatory objectives
1c Assemble all data pertaining to
intersection-related traffic conditions
1d Assemble all data pertaining to
intersection-related demographics
Step 2 Step 3 Step 4
Multiple intersection Multiple intersection 4a Individual
demographic review screening/ranking intersection modeling
4b Assemble data on
traffic, meteorology,
site characteristics,
3a 3c background
Rank top 20
Model top 3 based
by traffic
on traffic volumes 4c
volumes Locate receptors
3b 3d 4d Compute 1-hour
Calculate Model top 3 based peak traffic
LOS on LOS concentration using
CAL3QHC
4e Apply persistence
factor and
background
4f
2a 2b Rank by
Tabulate number of
Model top 3 based concentration
intersections of
on EJ concern
EJ concern
4g
Identify relationship
between EJ score
and concentration
Disproportionate effects
determination
Disproportionate effects
determination
Figure 3-3. Example of a microscale
environmental justice assessment process
Source: FHWA 2001.
The results of this step would lead to a finding of distributive effects to protected populations in
two cases:
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· Fifty percent or more of the affected intersections in the study area are in areas with high
environmental justice concern.
· The proportion of intersections in the study area with high environmental justice concern
is larger than would be expected for the comparison region.
The expected proportion of intersections with high environmental justice concern can be
estimated from reviewing the demographic characteristics of a large sample of similar
intersections in the comparison area. The comparison area should be a jurisdiction large enough
to encompass the entire study area, such as a county, metropolitan area, or state.
Step 3 Screen and rank multiple intersections. As suggested in the FHWA guide, rank the
intersections by traffic volumes and select the top 20 intersections. Then calculate the level of
service for those intersections. Select for individual intersection modeling, at a minimum, the top
three intersections based on traffic volume ranking, the top three intersections based on LOS
ranking, and the top three intersections based on EJ concern ranking. The purpose of adding at
least three intersections of environmental justice concern is to allow comparison of results for
worst case intersections obtained in Step 4.
Step 4 Model individual intersection. The purpose of this step is to compute 1-hour peak
traffic-related concentrations at sensitive receptors through application of a hot-spot model such
as CAL3QHC with the addition of any persistence or background air quality factors. The
screening and ranking step (Step 3) is recommended because the level of modeling effort
increases linearly with the number of intersections being evaluated. By applying the screening
process, you can be reasonably certain that the worst case intersections are being evaluated.
Once you have calculated emissions scores for each modeled intersection, rank the intersections
in order of concentration. Interpretation of the results can be relatively simple, although various
statistical techniques also can be used to compute rank-order correlations and determine
significance of the findings. Initial findings, however, can be made simply by identifying where
the "high-EJ concern" intersections fall in relation to the worst case intersections. In general, a
finding of adverse distributive effects to protected populations would be warranted based on the
following results:
· Fifty percent or more of the worst case intersections are in areas of high environmental
justice concern.
· The proportion of worst case intersections with high environmental justice concern is
larger than would be expected for the comparison region.
· A violation or exceedance is indicated at any intersection of high environmental justice
concern.
· Projected pollutant concentration increases will be measurable between baseline and
future-year scenarios, and the areas with the greatest increases are characterized as
having high environmental justice concern.
In contrast, results indicating that concentrations are lower at intersections characterized as
having high environmental justice concern when compared to the worst case intersections may
support a finding that protected populations are not adversely or disproportionately affected. A
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finding of beneficial distributive effects would be supported by a result indicating that
concentrations at the modeled intersections with high environmental justice concern will be
measurably lower for future-year scenarios than for the baseline condition.
Data needs, assumptions, and limitations. This analysis requires the following types of
information:
· Background concentration levels (although this can often be estimated);
· Traffic impact study, including traffic volume, intersection geometry, signal timing,
traffic speeds, location of sensitive receivers;
· Regional vehicle parameters and fuel information, including:
Vehicle classification and mileage data,
Vehicle age distribution, and
Vehicle inspection/maintenance program details;
· Specific fuel characteristics (oxygenated fuel, or ethanol); and
· Geometric layout of intersections and locations of sidewalks and nearby sensitive
receptors.
In addition, information is needed to characterize the level of environmental justice concern of
each intersection being analyzed. Any number of methods presented in Chapter 2 can be used to
identify the relative level of environmental justice concern. As an initial screening step, or for
large projects or system-wide analyses to review policies or programs, it would be useful and
possibly necessary to use a census-data-based approach such as the Environmental Justice Index.
However, due to the localized nature of effects being addressed by the microscale analysis, it is
more appropriate to use demographic data collected for specific receptors using field survey,
local knowledge, public input, and other detailed data-gathering techniques.
A microscale intersection analysis is limited by the fact that only discrete receptors are analyzed.
These locations are on such a small scale that the approach does not allow any means of
generalizing or characterizing the overall air quality in an area. Thus, the approach can only be
used to identify if any microscale distributive effects would result from the proposed
transportation system change. It cannot be extended to address the general air quality of the local
study area. It should be noted, however, that this approach does evaluate the maximum impact at
intersections affected by a proposed project. So if the maximum impact is below the NAAQS,
you can be reasonably confident that the project's impacts elsewhere also will be lower than the
NAAQS.
Pollutant concentrations typically decrease very rapidly at greater distances from a roadway, but
it would be difficult to precisely extrapolate results beyond the specific receptors being
evaluated. It could also be possible that a roadway is to be moved closer to a receptor. Under
these conditions, a slight shift in roadway alignment closer to a receptor could result in an
increased concentration, even if an overall air quality benefit is achieved. For these reasons,
occasions may arise where an increase from no-build occurs, but a build analysis verifies that
concentrations do not violate the NAAQS.
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Results and their presentation. Microscale intersection analyses predict the pollutant
concentrations at discrete receptors near worst case intersections and intersections of high
environmental justice concern. The number of receptors can vary significantly (e.g., 1 to 20).
The location of these receptors is usually close (within 200 ft) to the roadway. Most analyses of
this type include a figure showing the receptor locations, along with a table detailing the
concentrations at each receptor. The results will always include the project alternative results and
sometimes existing and no-build scenarios.
Assessment. Policy, program, and project microscale results are most often compared to the
NAAQS. However, results are occasionally compared to a no-build scenario. This microscale
environmental justice assessment method is based on FHWA microscale or "hot spot" NAAQS
conformity analysis, and shows expected concentrations at discreet receptors in parts per million
(ppm). Environmental justice is evaluated by comparing and ranking expected concentrations
and level of environmental justice concern at the worst case intersections as defined by traffic
volumes, LOS, and level of environmental justice concern. If an exceedance of the NAAQS is
predicted or if it is found that areas with relatively greater environmental justice concern
experience relatively higher emissions, mitigation measures should be included to prevent the
exceedances and unequal emissions levels. Mitigation could be required depending on attainment
status and local rules.
Mitigation may include adding intersection capacity with additional traffic lanes, optimizing
signal timing for air quality purposes, or diverting traffic to other locations. Potential impacts
associated with these measures could include right-of-way acquisition, increased pedestrian
conflict areas, or increased traffic volumes at other locations. Mitigation measures often must be
completed before a project is finished and sometimes may be a result of a nearby site
development. Each of these mitigation measures could have environmental justice-related issues
other than air quality that would need to be evaluated using appropriate methods.
Method 3. Detailed regional analysis
When to use. A regional air quality analysis is conducted for regionally significant projects and
when required by local guidance. This type of analysis might be conducted for a major transit
project, for a new freeway connection, or even for adding capacity to a major regional
connection in an urban area. Because this technique provides only regional estimates and does
not provide geographic distinction below the regional level, environmental justice assessment of
regional air quality merely involves documenting the protected populations in the region and the
air quality concerns that have been raised by those populations. Many of the analysis steps and
issues discussed as part of the general air quality review (Method 1) apply to this technique as
well.
Analysis. A detailed regional analysis is carried out in two steps:
Step 1 Perform regional air quality analysis. Regional air quality analyses use regional
travel demand models and MOBILE6 emission modeling. Regional travel demand models
provide traffic volumes and speed on a link level. The MOBILE6 model provides emission rates
that vary by speed and incorporates vehicle age, vehicle classifications, and other operating
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characteristics. By applying these emission rates to each link of the regional travel demand
model, total regional emissions can be determined. Generally, if capacity is added to a
transportation system, delay decreases. While this may result in more trips or longer trips, the
reduced delay results in an overall decrease in emissions because vehicles operate least
efficiently when idling or in congested environments.
Step 2 Document air quality concerns of protected populations. Once the regional air
quality analysis is completed, present the findings to protected population groups and address
any questions or concerns. Except in cases where the geographic scope of the policy, program, or
project covers more than one air quality region, it is very difficult to assess unequal distributive
effects using this method. That does not mean, however, that regional air quality has no
environmental justice implications. Because unequal effects cannot be determined in most cases
and regional air quality is similar across broad areas, the environmental justice assessment effort
should focus on sharing information with protected population groups and understanding any air
quality concerns that these groups may have. To understand how protected populations perceive
regional air quality and its health effects, consider either conducting a survey or focus groups.
You must be sure to obtain the demographic characteristics of each participant that are relevant
to characterizing protected populations. Recommended survey questions are presented in
Chapter 2.
Data needs, assumptions, and limitations. This type of analysis requires the following
information:
· Results of applying a regional travel demand model,
· MOBILE6 modeling variables and regional characteristics, and
· Information on protected populations.
The models that must be used (according to regulation) assume a uniform distribution of the
vehicle fleet across a region. The greatest limitation of this type of analysis for environmental
justice assessment is that it provides no geographic distinction for results. As a result, extension
of the technique to evaluate environmental justice is merely a matter of disclosure. You can do
little more than share the results with protected population groups and understand their most
important air quality concerns and how those concerns may be different (e.g., lesser, greater, or
focused on specific health effects such as asthma) from the general population. Suggestions for
understanding locations in a study area where there may be relatively higher pollution
concentrations are explained under Method 1, but can be applied here as well.
Results and their presentation. A regional air quality analysis generally includes a description
of the air quality status of the region, a description of the guidance, and a tabular presentation of
total pollutant emissions. The pollutants include CO, NO2, PM, and VOC. The results of the
analysis would include a no-build and build comparison of annual tons of pollutant. Again,
transportation projects usually represent an increase in capacity, which usually results in reduced
pollutant emissions because free flowing traffic pollutes less than stop-and-go traffic.
Assessment. This method can provide regional estimates of air quality impacts that would result
from a proposed major transportation project, but it cannot give an indication of how protected
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for each zip code in the study area. Technology groups are combinations of vehicle
characteristics and operating characteristics that have been identified through empirical research
as being highly predictive. For engine start and engine off modes, vehicle fleet characteristics are
estimated for zonal analysis units such as TAZs and/or zip codes. Estimates for operating
vehicles (i.e., vehicles in motion) associated with road segments are based on analysis of travel
demand model links. In MEASURE, the mix of vehicles on various roadways is identified by
developing estimates of local and regional fleet characteristics based on zip-code-level vehicle
registration data.
Step 1c. Estimate vehicle activity. Use the travel demand model to predict regional travel,
including the number and location of peak hour or daily trip origins, road segment volumes, and
average speeds based on volume-to-capacity ratios. Use this information to estimate distributions
of speed and acceleration, and vehicle mode operations.
Step 1d. Predict facility-level emissions. Transportation "facilities" include major roads, minor
roads, and trip origin zones. Engine start emissions are linked to the zone (i.e., TAZ, census
block group, or census block) of origin. Minor roads not usually included in travel demand
models are assigned running exhaust emissions as zonal averages based on travel time, local road
speeds, fleet composition estimates, and vehicle miles traveled. Major roads included in the
travel demand model are assigned running exhaust emissions as linear (i.e., road segment)
averages based on speed and acceleration characteristics and fleet composition estimates.
Step 1e. Generate the mobile emissions inventory. This step involves converting the facility-
based emissions estimates generated in Step 1d to grid-cell-based emissions estimates. Convert
estimates for each polygon facility (origin zone or minor road zone) to a rate expressed as
grams/square kilometer, and convert major road facilities to a rate expressed as grams per
kilometer. The polygon and line facilities are overlaid on the output grid cells and the rates are
allocated to the grid cells based on the proportion of a facility that falls into each cell. Compute
emissions per grid cell by multiplying the rates by the areas and lengths of the facility/grid cell
portions.
Step 1f. Apply photochemical model (Optional). Use an appropriate photochemical model to
generate estimates of ground-level pollutant concentrations across the study area. The ground-
level pollutant concentrations are based on meteorological conditions and the emissions
estimates obtained for each grid cell. The physical properties of the pollutant and distance decay
functions are also accounted for in the model. Perform this step if you wish to evaluate the
distribution of ground-level pollutant concentrations rather than transportation-related emissions
(the output of Step 1e). By incorporating emissions information for point sources into the
photochemical model, you can evaluate cumulative ground-level concentrations in addition to
ground-level concentrations from transportation-related sources. However, application of
photochemical modeling requires collection and processing of far more input data (including
regional speciated emission inventories and three-dimensional fields and boundary conditions of
all meteorological and air quality parameters) than any of the other methods described here.
Therefore, this step generally would be impractical for evaluating individual transportation
projects.
Step 1g (SAVIAH example). Data collection and preparation. Unlike a model-based method
that estimates emissions based on roadway geometry, traffic volumes, and vehicle fleet
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emissions characteristics, statistical methods predict population surfaces by fitting regression
models to observations at monitoring sites based on known values for predictor variables.
Because existing monitoring sites (such as stations used to evaluate regional air quality and to
develop AQI scores) are relatively sparse in most areas, you may want to consider additional
monitoring over a prolonged period of time (i.e., many weeks or months). A larger monitoring
network and a larger number of samples will yield a more accurate regression model. In addition,
you will need to collect the necessary information to compute predictor variables. Commonly
used information is listed in "data needs, assumptions, and limitations."
Step 1h. Conduct exploratory study. The result of a multivariate analysis is a best-fit curve
allowing estimates of the response variable to be derived from known values of predictor
variables. The purpose of the exploratory study is to reduce the set of candidate predictor
variables described in Step 1g to the set to be used in the regression model. Highly
intercorrelated predictor variables should be eliminated, as should other variables found to have
low correlation with the response variable. Use a variable reduction procedure to develop
correlation matrices and identify candidate predictors that should be retained in the final model.
Comrie and Diem (2001), for example, used principal component analysis to evaluate an initial
set of candidate predictor variables. Although it is beyond the scope of this guidebook to provide
an in-depth discussion of variable reduction, numerous books on applied regression analysis are
available, such as Neter et al. (1996).
Although this approach is not always applied, it may be worthwhile to consider developing
multiple regression models, one for each commonly observed set of meteorological conditions.
Comrie and Diem (2001), for example, developed independent regression models for four
distinct clusters of CO monitoring data, where observed concentrations, temperature ranges,
wind speeds, and atmospheric pressure were relatively constant within each cluster.
Step 1i. Refine and select model. The result of Step 1h is a small subset of candidate regression
models with a limited number of explanatory variables that provide good predictive ability. Step
1i results in selection of the final model, based on review of residual plots and analyses to
identify lack of fit, outliers, and influential observations.
The SAVIAH study (Briggs et al. 1997), for example, developed regression models to predict
NO2 concentrations in Huddersfield, the United Kingdom; Amsterdam, the Netherlands; and
Prague, the Czech Republic. In each city, the regression models were based on local predictors
that provided results with the most predictive power. However, two constraints were placed on
the regression models developed for each city. First, the models had to include terms for traffic
volume, land cover, and topography because each of these is known to affect pollutant
dispersion. Second, a common GIS-based buffering approach was used to compute predictor
variables that had a spatial component, such as total traffic volume within 300 meters of a
monitoring site. The resulting regression equation for average annual NO2 concentration in
Huddersfield included traffic volume within 300 meters, high-density housing, and industrial
land use proportions within 300 meters, altitude, and sampler height.
Step 1j. Apply the model. Based on computed predictor variable values for each grid cell, use the
selected regression equation to compute predicted pollutant concentrations. This yields a
pollution surface for the entire study area.
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The output of either the model-based or statistical approach is a pollution surface that provides
estimates of either emissions or pollutant concentrations for each cell in the study area grid. This
output is then combined with a population surface to perform an environmental justice
assessment.
Step 2 Develop population surface. A population surface is a raster, or grid-cell-based,
representation of a population. Population surfaces are produced in GIS using any of various
algorithms to convert census polygon-based, or zonal, population data to a grid cell-based
format. A population surface is the best form of demographic data to use with model-based and
statistical air quality prediction techniques because their results are also grid-based. By using the
same set of grid cells to produce pollution surfaces and population surfaces, you create an
information-rich dataset that can be evaluated to assess unequal distributive effects to protected
populations. More detail on the process for developing population surfaces is provided in
Chapter 2.
The output of this step is a set of GIS grids depicting the distribution of protected populations,
nonprotected or other populations, and total population. Protected populations can include race,
income, age, sex, or any other protected population group. It is also possible to produce a
"protected population surface" that is merely the sum of all population groups of interest. From
these datasets, standard map algebra routines can be used to compute estimates of affected
populations and population percentages for the entire study area or for smaller areas within a
larger region.
Step 3 Overlay pollution surface and population surface. The next step is to overlay the
pollution surface created in Step 1 with the various population surfaces created in Step 2. You
can do this using GIS and relational database software. Both tabular and map-based results are
needed. Figure 3-6 depicts the process of combining population surfaces and pollution surfaces
and the resulting outputs.
Step 4 Visualize results. The power of this technique is the rich dataset it produces for
evaluating geographically distributed effects. With additional processing of time-series data,
such as prebuild, build, and future-year scenarios, temporal environmental justice aspects can
also be assessed. Because of the richness of the dataset, it is both unwise and unnecessary to base
conclusions about distributive effects on a single statistical test or data visualization approach.
Rather, it is important to analyze and visualize the data in many different ways. This will lead to
a detailed understanding of distributive effects patterns and effective control measures for
altering present or future distributive patterns.
The following assessment and data visualization routines are described below:
· Relative emissions burden calculation,
· Relative pollution burden graphs,
· Regional effects mapping and analysis, and
· Local effects analysis.
Relative emissions burden calculation. Relative emissions burden is defined as the ratio
between (a) the average ground-level pollutant concentration (or emissions level) available to
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members of protected population groups and (b) the concentration available to members of non-
protected population groups. Note that this is a measure of pollutant "availability," not exposure.
"Burden" therefore means that pollutants are present and available for persons to be exposed to.
The relative emissions burden evaluation was first used in 1998 by the U.S. EPA to determine if
pollutant distributions in the Louisiana Industrial Corridor disproportionately affected minority
populations (U.S. EPA 1998b).
From Step 1
Inputs
Zonal census Zonal census
blocks block groups Pollution
surface
Grid cells
Zonal/Surface conversion
Population
surfaces
Protected Nonprotected Total
population (PP) population (NPP) population (TP)
Overlay
Tabulated population characteristics and
concentrations for each grid cell
Outputs
Average emissions
burden for Map PP and pollutant Comparison charts
PP, NPP, and TP concentration
Determine distributive
effects and evaluate
significance
Figure 3-6. GIS process for combining pollution surfaces
and population surfaces
The EPA approach based estimates of emissions burden on proximity to pollution sources and on
estimates of pollutant emissions in pounds. The approach did not account for pollutant dispersion
based on meteorological and chemical properties, nor did it develop estimates of ground-level
pollutant concentrations. Unlike the approach presented here, the U.S. EPA study used zonal
census data, buffers of pollutant-emitting facilities, and information on emissions volumes to
evaluate distributive emissions patterns. One benefit of working with population surfaces is that
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calculation of relative emissions burden is relatively simple and straightforward when compared
to working with vector GIS datasets.
Relative emissions burden is computed using the following equation:
N
PCi × NPPi / ( NPP )
i= 1
R=
N
PCi × NNP / ( NNP )
i
i= 1
where
R = relative emissions burden
N = number of cells in grid
PC = pollutant concentration
NPP = number of persons in protected population group
NNP = number of persons in non-protected population group
Evaluation of results is also straightforward. If R is greater than 1.0, the average level of
emissions experienced by members of protected population groups is greater than the average
level experienced by members of other population groups. If, for example, R is computed to be
1.25, this means that the average emissions burden to members of protected populations is 25
percent greater than the average emissions burden to members of other population groups.
Although it is worthwhile to evaluate relative emissions burden, comparison of average burden
levels across a study area is only of limited value in determining to what extent there may be
unequal distributive patterns and where those patterns are evident. This more detailed evaluation
is better performed by evaluating the distributions in charts and maps. Examples are provided
below.
Relative pollution burden graphs. When evaluating the codistribution of air quality effects and
protected populations, visualizing the information patterns in graphs and maps is often the most
insightful form of analysis. To produce graphs, the pollution surface must be combined with a
population surface, which yields an estimate of pollutant concentration and protected population
characteristics for each grid cell. This dataset can be used to identify any areas where standards
may be violated and to identify locations where strategies to reduce pollutant concentrations are
required.
For purposes of environmental justice assessment, it is also important to understand that many
communities may have concern over pollutant concentrations that meet regulatory air quality
standards. This could be because a community feels that the NAAQS are not protective enough
of sensitive individuals or of individuals that receive greater exposure due to lifestyle. Or, it
could be that a community is concerned about the additive and synergistic effects of exposure to
multiple pollutants.
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Evaluating distributive effects at concentration levels below the NAAQS is also a useful
approach for dealing with concerns about measurement error. It is often practical to evaluate
effects above the NAAQS, as well as to identify a "threshold of concern" for values that
approach the NAAQS. The threshold of concern can be based on policy, expert information, or
even community input.
Figure 3-7 depicts results of an analysis to estimate annual average PM10 concentrations (see
page 61) in a five-county metropolitan region. The annual average NAAQS for PM10 is 50
micrograms per cubic meter (µg/m3). A decision was made, based on public input, that areas
with concentrations above a 40 µg/m3 threshold of concern should also be evaluated for
distributive effects. The graphs show the regional pattern of concentrations available to protected
population groups compared to other individuals. The top chart shows that a greater proportion
of protected population groups reside in areas with concentrations between 31 and 60
µg/m3meter. The bottom chart shows that a greater number of persons in protected populations
also reside within areas with concentrations in this range.
It is important when using this technique to compare to the rest of the population both (a) the
percent of the protected population and (b) the total number of individuals in the protected
population group that would experience adverse effects. This can be done by preparing one set of
graphs with percent of population as the vertical axis and another set of graphs with the number
of persons as the vertical axis (as in Figure 3-7). Both evaluations are necessary to determine
distributive effects because in certain study areas a majority of the affected population may
belong to protected groups. In the case of this particular dataset, evaluations by number of
persons and population proportions both show patterns of unequal distribution. For other study
areas, however, this may not be the case. This topic is addressed again in Chapter 10.
Regional effects mapping and analysis. The graphic visualization of pollution and population
surface results presented in Figure 3-7 is a very useful assessment technique for characterizing
the distribution of pollutant levels among population groups. The graphs are even more useful,
however, when combined with maps showing the pollution surface overlaid on the population
surface. Figure 3-8 provides an example for the same five-county area discussed above. The top
map depicts areas where there is a high proportion of protected population. The bottom map
depicts areas where a large total number of members of protected population groups reside. As
with the graphs, it is necessary to evaluate the population distribution patterns in both ways. The
benefit of using both maps and graphs is that one form of visualization overcomes the limitations
of the other: maps are very good at depicting geographic patterns, but all detail is lost when you
try to quantify the geographic patterns. On the other hand, by viewing the graphs it is easy to
speak in quantitative terms about the disproportionate pattern that seems evident in the map. If
you rely on just the graphs, however, it is easy enough to understand that a disproportionate
pattern exists but impossible, without the map, to determine where the patterns are located.
Regional analysis is thus a combination of determining relative emissions burden and visualizing
any potential disparities using graphs and maps. In the examples provided above, it could be
concluded that areas where annual average PM10 concentrations exceed 40 µg/m3 are of concern
and that strategies must be implemented to reduce these concentration levels. It could also be
concluded that there is an environmental justice issue because those concentration levels burden
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a greater proportion of the protected population and a greater number of individuals in protected
population groups. This form of analysis is useful for policies, programs, long-range planning,
and regionally significant projects.
Proportion of Specific Population Group
45
Protected Population
40
Other Population
35
Percent of Population
30 Threshold Above
of Concern PM10 NAAQS
25
Unequal exposure
at levels between
20
31 & 60 micrograms/cubic meter
15
10
5
0
0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65
PM10 Concentration (micrograms/cubic meter)
Total Persons
200,000
180,000 Protected Population
Other Population
160,000
140,000
Threshold Above
Population Count
120,000 of Concern PM10 NAAQS
100,000 Unequal exposure
at levels between
31 & 60 micrograms/cubic meter
80,000
60,000
40,000
20,000
0
0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65
PM10 Concentration (micrograms/cubic meter)
Figure 3-7. Distribution of annual average PM10 concentrations
in a regional study area
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Percent of Total Population
Number of Persons
Figure 3-8. PM10 concentration and protected population patterns
in a regional study area
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By performing prebuild and postbuild modeling runs, this method also allows you to evaluate
policy, program, and project impacts. With some change in current regulation, the geographic
variability of pollutant concentrations that results from this method would also give
transportation planners a much broader set of control measures to consider for achieving
transportation conformity.
Local effects analysis. The pollution surface technique can be used to evaluate regionally
significant projects and transportation corridors. However, for smaller projects with the potential
only for localized effects, it is more appropriate to combine regional analysis of the pollution
surface with microscale analysis (Method 2). Regional pollution surface analysis is useful to help
understand whether the area of effects is in a location where pollutant concentrations are
predicted to be relatively high. Microscale analysis can then be used to evaluate the localized
effects of small projects.
To evaluate localized effects of regionally significant projects and transportation corridors, the
project area of effects must be identified. Using the pollution surface technique, a reasonable
approach is to apply a dispersion model to identify the area within which the project would have
a measurable (or meaningful) effect on concentrations. This level would depend on the pollutant
being evaluated as well as on the sensitivity of the model. For CO, the area potentially affected
by the project would be highly localized, whereas for PM the area of effects could be far-
reaching.
Figure 3-9 shows the distribution of predicted postimplementation annual average PM10
concentrations. The regional pollution surface described above was used to tabulate results
within the project area of effects defined for two alternative alignments. Alternative 1 is depicted
in the top two graphs and Alternative 2 in the bottom two graphs. For each alternative, percent of
population is depicted in the left-hand chart, and population count in the right-hand chart.
A review of these graphs indicates that Alternative 1 may be preferable from an environmental
justice perspective because there would be no unequal impact to protected population groups
above 40 µg/m3. In contrast, Alternative 2 would result in a greater proportion of protected
population groups being exposed at these levels. Both the total number of persons and the total
number of persons in protected population groups in areas with concentrations above 40 µg/m3
would be similar under both alternatives.
The biggest drawback of the pollution surface approach is that it requires a great deal of time and
effort to produce the necessary data. That is why, although limited in their predictive power, the
previous three methods are likely to be used in all but the most controversial situations. Both
statistical and model-based methods for estimating the regional and local variability in air quality
are highly experimental. Unlike the micro-scale analysis and regional air quality assessment
methods, this method has not yet been mandated, or even widely accepted, as part of the
regulatory process.
The assessment of relative emissions burden is not the same as determining if people in the study
area are exposed or if the level of exposure varies by person. To measure exposure, you must
consider factors such as the amount of time persons spend outdoors and the ventilation properties
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of buildings. Tests to evaluate the statistical significance of the relative emissions burden statistic
(R) are available, but their application is problematic. This is because in most situations the
sample set, in this case grid cells, will be large and even slight differences in average emissions
burden would be deemed significant. It is also uncertain whether you can assume that the sample
population is normally distributed about the mean, which is a requirement of most parametric
tests. Because of these concerns, it is better to merely evaluate the value of R to determine the
direction of the difference in average burden levels.
Proportion of Specific Population Group Total Persons
Alternative 1 Alternative 1
45 14,000
Protected Population Protected Population
40
Other Population 12,000 Other Population
35
10,000
Percent of Population
30 Threshold Above Threshold Above
Population Count
of Concern PM10 NAAQS of Concern PM10 NAAQS
8,000
25
No unequal exposure No unequal exposure
at levels above at levels above
20 6,000
40 micrograms/cubic meter 30 micrograms/cubic meter
15
4,000
10
2,000
5
0 0
0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65
PM10 Concentration (micrograms/cubic meter) PM10 Concentration (micrograms/cubic meter)
Alternative 2 Alternative 2
40 3,000
Protected Population Protected Population
35
Other Population Other Population
2,500
30
Percent of Population
Threshold Above 2,000 Threshold Above
25
Population Count
of Concern PM10 NAAQS of Concern PM10 NAAQS
Unequal exposure Unequal exposure
20 1,500
at levels between at levels between
30 & 50 micrograms/cubic meter 6 & 55 micrograms/cubic meter
15
1,000
10
500
5
0 0
0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65
PM10 Concentration (micrograms/cubic meter) PM10 Concentration (micrograms/cubic meter)
Figure 3-9. Comparison of alternative project alignments
Data needs, assumptions, and limitations of population surfaces are discussed in Chapter 2.
Results and their presentation. The graphics included in the discussion of Step 4, above,
provide examples of results and how they may be presented. Depending on the audience, it may
be necessary to simplify the presentation of the graphs and maps.
Data needs, assumptions, and limitations. Application of a dispersion model such as
MEASURE would require the following data to be collected (Bachman et al. 2000):
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· Spatial character information · Vehicle mode activity information
-- Land use boundaries -- Idle
-- Census blocks -- Cruise
-- TAZs -- Acceleration
-- Roads -- Deceleration
-- Travel demand model network -- Starts
-- Output grid cells -- Engine off
· Temporal character information · Trip generation information
-- Hour of the day -- Land use
· Vehicle technology information -- Housing units
-- Model year -- Socioeconomic characteristics
-- Engine displacement -- Home-based work trips
-- Transmission type -- Home-based shopping trips
-- Fuel delivery technology -- Home-based school trips
-- Supplemental air injection system -- Home-based other trips
-- Catalyst configuration -- Nonhome-based trips
-- Exhaust gas recirculation
Application of a regression model such as the one implemented in the SAVIAH project would
generate the following data needs (Briggs et al. 1997):
· Road network · Topography
· Road type · Concentrations from monitoring sites
· Distance-to-road · Sample height
· Traffic volume · Site exposure
· Land use/land cover · Topographical exposure
Assessment. Analysis of local and regional air quality using pollution surfaces and population
surfaces is a very useful technique for evaluating distributive air quality effects to protected
populations. Pollution surfaces can be developed using models that extend commonly used
regulatory models to account for geographic variability in pollutant concentrations. Data
visualization techniques using graphs and maps and findings based on expert opinion and public
input generally are recommended over statistical tests that reduce the information-rich dataset to
a single test for statistical significance. Although these methods are not widely used in current
practice, considerable research has been performed. The drawback of this method is that it is
extremely data intensive and has not yet received the level of regulatory approval that has been
given to traditional microscale and regional air quality assessment methods. Given that
techniques for understanding the geographic variability of air quality are important both for
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