Vallamsundar and Lin
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Using MOVES and AERMOD models for PM2.5 Conformity Hot-Spot Air
Quality Modeling
Suriya Vallamsundar,
PhD Student
Department of Civil and Materials Engineering
University of Illinois at Chicago
842 W. Taylor Street (M/C 246)
Chicago, Illinois 60607-7023
Phone: 224-610-6289
Email: [email protected]
Jie (Jane) Lin*, Ph.D.
Associate Professor
Department of Civil and Materials Engineering
Institute for Environmental Science and Policy
University of Illinois at Chicago
842 W. Taylor Street (M/C 246)
Chicago, Illinois 60607-7023
Phone: 312-996-3068
Fax: 312-996-2426
Email: [email protected]
*Corresponding Author
Submitted to TRB’s 2012 Annual Meeting
Word Count:
Text
Tables (3), Figures(5)
Total
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ABSTRACT
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On March 10, 2006, the U.S. Environmental Protection Agency (USEPA) published a final rule
requiring project level particulate matter (PM) transportation conformity analysis in nonattainment and maintenance areas for “projects of air quality concern”. EPA has released a
public draft on “Transportation Conformity Guidance for Quantitative Hot-spot Analyses in
PM2.5 and PM10 Nonattainment and Maintenance Areas”, in which MOVES and EMFAC in
California are designated as the official mobile emission models. The official air quality models
are AERMOD and CAL3QHCR. The public draft released by EPA requires detailed handling of
emission and air quality data which are new for state DOTs and MPOs. This paper showcases the
use of MOVES and AERMOD for transportation conformity analysis with priority given to the
setup and running of the models with their respective data inputs in accordance with EPA’s
transportation conformity guidance. Details of the input data preparation for MOVES and
AERMOD, MOVES emission factor generation, sensitivity test results from MOVES, and
importance of interagency consultation process are presented. This showcase is an extended
effort for better understanding the conformity process and setting up the models. Results from a
real world case study are presented as an example of the conformity process.
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1. INTRODUCTION
Particulate matter (PM) is fine particles of solid matter suspended in liquid or gas. Based on the
size, PM can be broadly classified into two groups: (i) coarser particles with sizes ranging from
2.5 to 10 µm. (ii) finer particles with sizes up to 2.5 µm. There are many studies in literature
showing a strong association between PM2.5 and adverse health outcomes (1, 2). Finer particles
can have worse health effects because they are made of more toxic metals and cancer causing
organic compounds and can easily pass through the respiratory system due to their size (3).
Kappos et al. (4) found increased exposure to fine PM leads to cardiovascular, respiratory
problems, infant mortality and affects the human immune system. Transportation sources are one
of the major sources contributing to PM emissions. The latest national database summary
prepared by EPA for PM2.5 emissions by source sector shows that road dust accounts for about
21.5% and on-road vehicles account for 3% for calendar year 2005 (5).
In 2006, EPA published a final rule requiring project level hot spot PM transportation
conformity analysis for “projects of air quality concern” in non-attainment and maintenance
areas (6). According to EPA Guidance (7), “projects of air quality concern” are those projects
that involve significant levels of diesel traffic leading to high PM concentrations or any other
projects that are identified by state SIP as a localized air quality concern. Hot spot analysis is an
estimation and comparison of likely future localized PM pollutant concentration with the current
PM concentration and National Ambient Air Quality Standards (NAAQS). This is mainly to
ensure that current and future transportation projects meet the Clear Air Act conformity
requirements (6). The standards to be attained and maintained for PM2.5 for 24 hour period are
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35µg/m and 15µg/m for annual period.
The new PM Hot Spot analyses requires detailed modeling of PM emissions and
concentration levels for transportation projects. These requirements are new for state DOTs and
Metropolitan Planning Organizations (MPOs) and there are not many studies in literature to help
them in this modeling process. The objective of this study is to provide insights into PM hot spot
modeling process with respect to input data preparation, model setup and performance,
importance of interagency consultation process, which in this case involves USEPA, Federal
Highway Administration (FHWA), Illinois Department of Transportation (IDOT), Illinois EPA
(IEPA) and Chicago Metropolitan Agency for Planning (CMAP). A real world case study of I-80
and I-55 interchange near Joliet, Illinois is presented for showcasing the proposed work. The
following section gives the background of MOVES and AERMOD models followed by a
description of relevant work in literature. The fourth and fifth sections describe the model setup
and MOVES sensitivity tests. Finally the sixth section describes the case study followed by
conclusion in the last section.
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2. BACKGROUND
2.1 MOVES Emission Model
The Motor Vehicle Emission Simulator (MOVES) is the new generation EPA’s regulatory
mobile source emissions model. MOVES serves as a single comprehensive system for
estimating emissions from both on-road and non-road mobile sources, and replaces MOBILE as
the officially approved model for developing state implementation plans (SIPs) and regional or
project-level transportation conformity analyses (8).
There are a number of key features which sets MOVES far superior compared to its
predecessor model namely MOBILE. These include modal based approach to estimate emissions,
availability of three scales of analyses, incorporation of MySQL relational database, ability to
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model alternative fuel and vehicle types, estimation of total emissions and emission factors,
sophisticated approach to estimate GHG and energy consumption, inclusion of a number of
pollutants and emission processes. MOVES follows a “modal approach” for emission factor
estimation and calculates emissions using a set of modal functions. MOVES applies a “binning”
approach wherein each vehicle activity is binned or distributed according to different factors
depending on the emission process and pollutant. After distribution of total activity into different
bins, MOVES assigns an emission rate for each unique combination of source and operating
mode bins and the emission rates are aggregated for each vehicle type. A few correction factors
are applied to the emission rates to adjust for the influence of temperature, air conditioning and
fuel effects to obtain the total emissions (8).
2.2 Air Dispersion Models
Air dispersion models are used to determine how air-borne pollutants disperse in the atmosphere
and how their concentration dilutes over distance and time. EPA recommends using either
AERMOD or CAL3QHCR for highway and intersection projects, but using only AERMOD for
transit, freight, terminal projects and projects that involve both highway/ intersection and
terminals and/ or nearby sources (7). Both AERMOD and CAL3QHCR are Gaussian based
models and are derived for steady state conditions. The dispersion in Gaussian models are
estimated with a Gaussian equation which incorporates factors that account for the rate the plume
disperses in each direction, reflection from the ground and plume rise (9).
AERMOD was developed as a replacement for EPA’s Industrial Source Complex Model
by incorporating the planetary boundary layer (PBL) (10). PBL is the turbulent air layer next to
the earth’s surface which is affected by the surface heating, drag, turbulence and friction due to
its contact with the planetary surface (11). There are two types of PBL, namely (1) Convective
boundary layer (CBL) driven by surface heating (2) Stable boundary layer (SBL) driven by
surface cooling. AERMOD utilizes a Gaussian distribution in both horizontal and vertical
direction in SBL similar to CAL3QHCR but uses a Gaussian distribution in the horizontal but biGaussian in the vertical direction and the concentration is calculated as a weighted average of
two distributions in CBL (10).
3. RELEVANT WORK
With MOVES being a new model, there have been few studies in literature assessing MOVES
performance. Studies (12, 13) compared the macroscopic scale of MOVES and MOBILE
showed that the difference in emission estimates is attributed to inclusion of alternative fuel
types, newer technology vehicles in fleet mix by MOVES. Song et al. (14) compared
macroscopic scale of MOVES with EMFAC and showed that CO2, CH4 emission difference to
depend on vehicle activity and base emission rates respectively. Vallamsundar et al. (15)
compared mesoscopic scale of MOVES with MOBILE and found lower estimates from
MOBILE compared to MOVES which is attributed to underlying base emission rates.
There are a number of studies in literature mostly related to the sensitivity testing and
performance of AERMOD. Zou et al. (16) evaluated the sensitivity of AERMOD and found the
effect of urban/ rural dispersion coefficients, terrain conditions to have limited influence on
model’s performance. Studies (17, 18, 19) compared the effect of each surface characteristic on
AERMOD concentrations and found the Bowen ratio to have little effect and surface roughness
to have the greatest effect on model concentrations. Schroeder et al., (20) found out the location
and type of land use around meteorological data location to significantly affect surface roughness
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length. It is worth noting that most of these studies have focused mostly on industrial sources and
hence there is a gap in the current literature on roadway sources. With respect to model
comparison, a number of studies compare AERMOD with its predecessor ISC. Studies (21, 22)
found that compared to ISC, AERMOD generally tends to generate lower concentration results.
Chen et al. (23) compared CALINE4, CAL3QHCR and AERMOD for near road PM2.5 and
found CALINE, CAL3QHCR results matched the observed concentrations moderately well but
AERMOD under estimated PM2.5. Donaldson et al. (24) found that CALPUFF predictions of
fugitive PM lower than that of AERMOD using a combination of area and volume sources.
AERMOD can model roadway line source as a series of volume or area sources (25).
According to (26), volume source are more appropriate for line sources, which have some initial
plume depth (rail lines, conveyor belts) and area sources are more appropriate for near ground
level sources with no plume rise (viaduct, storage piles). Schewe et al. (27) performed a
comparison between area and volume source types for fugitive PM concentrations for a
hypothetical study location in Evansville, Indiana .The authors found higher concentrations from
volume source characterization compared to area sources which they attributed to the way each
source characterization calculates the initial plume dispersion and transport. EPA study (28)
found that modeling roadway line sources as volume sources is indistinguishable from modeling
them as area sources with an initial vertical dispersion parameter.
This study is motivated to provide an overview of the PM hot spot process with detailed
explanation of each step in the process. The scope of this study is restricted to modeling annual
PM2.5 for highway and arterial projects in the two non-attainment areas for annual PM2.5 in
Illinois namely Chicago and Metro-East. MOVES emission factors are developed for a range of
scenarios which are discussed in section 4. The roadway sources are modeled using AERMOD
Area source approach. The EFs obtained from MOVES are converted into a format compatible
for AERMOD’s area source characterization. Using the traffic activity, local specific data and
emission factors from MOVES, AERMOD computes the pollutant concentration. Details on
AERMOD model set up are discussed in section 5.
4. EMISSION MODELING
MOVES emission factors are developed for a range of scenarios in Chicago and Metro East
areas based on interagency consultation process. The first subsection describes the input data;
second subsection presents the sensitivity tests; the third subsection presents the details of the EF
generation.
4.1 MOVES Input Data
Most of the MOVES input data for the project scale was obtained from IEPA and IDOT. Table 1
lists the input data utilized for MOVES Project scale.
TABLE1 Inputs data for MOVES Project scale
Input Item
Description
Source
Link
Roadway link characteristics. 1. Link Length
2. Traffic volume for each link
3. Average traffic speed
4. Grade
Link Drive Schedule/
Vehicle Activity. Either of
Average speed is used for
Opmode Distribution
average speed, link drive
describing the vehicle activity.
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schedule, or operating mode
distribution should be
incorporated.
Speed values are decided based on
sensitivity test results (Section 4.2)
Link Source Type Fraction
Vehicle fleet composition
All 13 source types are used.
Source Type Age
Distribution
Vehicle age distribution
Meteorology
Temperature and humidity
values
Fuel Supply
Fuel supply parameters and
associated market share for
each fuel
I/M Program
Inspection-maintenance
program parameters for nonattainment areas
Separate age distribution data for
Chicago and MetroEast were
obtained in MOBILE format from
IEPA and converted into MOVES
format using EPA converters (29).
Hourly temperature and relative
humidity values were obtained
from IEPA in AERMET format
and was extracted to be used for
MOVES.
MOVES default fuel data was used
with changes made to Reid Vapor
Pressure, Sulfur content based on
local data.
Default MOVES database. To
note, there is no PM benefit from
I/M
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4.2 Sensitivity Tests
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The first sensitivity test was performed to test the effect of using the same meteorological data
for future years due to the lack of future meteorological data. The second test was performed to
decide the average speed values to be used for EF lookup table.
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4.2.1 Effect of Temperature
Through interagency consultation process, it was decided to use the same meteorological data for
both MOVES and AERMOD for maintaining consistency. Meteorological data was obtained
from IEPA for the latest available calendar years 2005 to 2009 in AERMET format and average
of the 5 years data was used in MOVES. Sensitivity test was performed for analyzing the effect
of using this average meteorological data for future years. Historic trend for temperature
difference over the past 30 years from year 1980 to 2010 in Chicago (30) was found to vary
between 0.2 and 3. Based on the temperature differences, sensitivity test were performed for 0.5
o
F and 3 oF increases in temperature and EFs are found to increase by 2% and 9% respectively.
Further EFs increased by the same percentage for all vehicle types and speed values. However
the temperature increase had no effect on the following MOVES vehicle types: single unit and
combination short-haul and long-haul trucks and intercity bus. Based on these results, it was
decided to use the average of 5 year meteorological data for future years.
4.2.2 Effect of Average Speed
Initially the EFs were estimated for the speed range from 0mph to 70mph at every 5mph
intervals. Sensitivity test was performed by comparing EFs calculated by MOVES and those
obtained by interpolation between the speed intervals for all vehicle types. Fig 1 shows the
sensitivity test results. The results show that for speed range of 10 – 15 mph, 30 – 35 mph and 45
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– 50 mph the difference between MOVES and interpolation are the highest especially for trucks.
The reason for the highest speed difference observed for trucks requires further investigation in
the future. Based on sensitivity test results, the above speed ranges were fine tuned to every
1mph interval and rest at 5mph interval. This results in a total of 21 average speed values.
FIGURE 1 Sensitivity test for all vehicle types and average speed values
4.3 PM2.5 Emission Factor Generation
The range of scenarios considered for generating MOVES EFs is shown in Fig.2. The time span
covered is for 4 months (January, April, July, October) that are representative of the seasons and
4 distinct time periods (morning peak, midday, evening peak, and overnight) in accordance with
(7). EFs calculated for a typical weekday are for calendar years 2011 to 2040. The speed range is
from 0mph to 70mph and intervals between them are chosen based on the sensitivity test results.
The EFs obtained from MOVES are in terms of grams/mile/veh/hr.
AERMOD requires a composite EF (in grams/sec/m2 in the area source approach) based on
traffic volume and EF corresponding to each vehicle type in the fleet mix. MOVES was executed
for the range of scenarios as shown in Fig.2 for a generic roadway link of length 1mile and
traffic volume of 13 (1 for each vehicle type). The EFs obtained from MOVES for this generic
roadway link can be used to calculate the EFs off model for any real world roadway link for the
same scenario (same area, facility, year, season, time period, vehicle type, average speed). The
following steps are proven, after numerous model experiments and consultation with the US
EPA, to be able to convert the EFs generated for a generic roadway link to any real world
roadway link in terms of grams/sec/m2 for AERMOD area source modeling.
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Step 1: EFs for a generic roadway link of 1mile length, traffic volume of 13, gives EFs in
terms of grams/mile/vehicle/hr which is assigned A
Step 2: Multiply A with actual traffic volume in the real world roadway link gives B in
terms of grams/ mile/ hour
Step 3: Multiply B with actual link/ source length in miles gives C in terms of grams/hour
Step 4: Divide C by 3600 to obtain D in terms of grams/second
Step 5: Divide D by source area to obtain E in grams/ sec/ m2
Note that an alternative approach is to run MOVES each time for each project of interest
and obtain the EFs specific to the project. This requires running MOVES each time for a
different project. Using our approach described above (i.e., a generic EF database + off model
adjustment) requires running MOVES limited number of times, which saves computational time.
5. AIR DISPERSION MODELING SETUP
The two regulatory components for AERMOD are (1) Meteorological preprocessor (AERMET)
(2) Terrain data preprocessor (AERMAP). According to the EPA guidelines (7), meteorological
data for PM hot spot analyses could be site specific data which requires one year of
meteorological data. If using off-site data, five consecutive years of meteorological data is
required. For this study, meteorological data was obtained from IEPA for calendar years 2005 to
2009 in AERMET format. The total percentage of missing data for the 5years meteorological
data was found to be 2.13%. Only if the number of hours of missing meteorological data exceeds
10% of the total number of hours for a given model run, user should refer to (31) for ways to
process the missing data. The averaging period is annual as both Chicago and MetroEast are
designated as non-attainment areas for annual PM2.5.
AERMOD can model roadway line source as a series of volume or area sources (25). For
this study AREA and AREPOLYGON sources are used. Parameters required for area source
modeling are listed below:
(a) Source dimensions - Length of the sides in meters. Sources are defined based on (1)
travel activity which corresponds to volume and speed, (2) physical dimensions and (3)
orientation. All three affect the EF in each source. For example, a single source can be
used for a roadway link if they have the same travel activity and no change in geometry.
However for a curved link with same travel activity, more than one AERMOD source is
required to be used to preserve the geometry.
(b) Area source emission factor in grams/ sec/ m2
(c) The initial vertical dispersion height is assumed to be about 1.7 times the average vehicle
height, to account for the effects of vehicle induced turbulence. The source release height
is the height at which wind effectively begins to affect the plume and is estimated from
the midpoint of the initial vertical dimension. For a combination of vehicles with
different heights, these dimensions are computed using a traffic volume/ emissions
weighted average (7).
(d) Receptor characterization – receptors are placed at a height of 1.8m above the ground.
Around the sources, receptors are placed with finer spacing (e.g., 10-25 meters) and with
wider spacing (e.g., 50-100 meters) farther from a source.
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FIGURE 2 Scenarios considered in MOVES EF Generation
Background concentration includes emissions from all sources other than project which affects
concentrations in the project area. The concentration obtained from AERMOD should be added
with the background concentration to get the total representative concentration called the design
value which describes the future air quality concentration in a project area that can be compared
to a NAAQS. There are several options for obtaining the background concentration and they can
be found in (7).
6. CASE STUDY: DESCRIPTION AND RESULTS
6.1 Description
The case study consists of I-80 and I-55 interchange near Joliet, Illinois (Fig. 3). Both highways
extending 0.5 mi (804.7m) from center of the interchange, 4 inclined and circular ramps
connecting the highways are considered to be emission sources. The length of the inclined ramps
is 0.5 mi (804.7m) and circumference of the circular ramps is 0.4 mi (643.7 m). The distance
from intersection of interchange to inclined ramps is 0.35 mi (563.3 m). It is assumed that all
inclined ramps are of the same dimensions and all circular ramps are of same dimensions.
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The design speeds for highways, inclined and circular ramps are 60mph (26.82m/s),
45mph (20.12m/s) and 40mph (17.88m/s) respectively. The pollutant estimated is PM2.5 for
annual averaging period for calendar year 2011. The traffic volume data was obtained from
IDOT. The fleet composition data from traffic counters consists of vehicle split in 3 broad
categories namely 4tire, single unit and multiple unit. Based on the association between HPMS
and MOVES vehicle types, these 3 categories were mapped into MOVES vehicle types. MOVES
vehicle type split under each category was obtained from local data from CMAP. Table 2 shows
the overall traffic volume corresponding to each time period.
Table 2 Traffic Volumes
Description
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Morning
Midday
Evening
Overnight
I55 NB On Ramp from I80 EB
637
581
557
173
I55 NB On Ramp from I80 WB
382
787
913
165
I55 North of I80 – N Leg
2591
2829
2847
694
I55 North of I80 – S Leg
2323
2889
2881
740
I55 SB On Ramp from I80 EB
124
103
105
30
I55 SB On Ramp from I80 WB
447
649
737
160
I55 South of I80 – N leg
1930
2466
2486
547
I55 South of I80 – S leg
2273
2229
2121
608
I80 East of I55- E leg
1485
2547
2912
587
I80 East of I55- W leg
2945
1893
1963
619
I80 EB On Ramp from I55 NB
841
598
615
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I80 EB On Ramp from I55 SB
1016
486
474
177
I80 WB On Ramp from I55 NB
105
110
108
30
I80 WB On Ramp from I55 SB
441
618
729
159
I80 West of I55- E leg
1209
1839
2086
449
I80 West of I55- W leg
1817
1482
1536
465
MOVES default split of fuel types for each vehicle type was used except for transit buses where
the fuel type was changed to 100% diesel based on local data. Composite EF was computed from
MOVES EF lookup table and off model adjustments as discussed in section 4.3.
AREA sources are used for the highways and AREAPOLYGON sources for circular and
inclined ramps. In accordance with (7), receptors are placed at a finer resolution of 25m near all
the sources and spacing is increased to 50m and 100m as the distance from the source increases.
The first line of receptors is placed at a distance of 50 ft from the edge of the roadway to allow
for the right of way distance. Receptor placement for annual PM2.5 is in accordance with the
requirement (7) of being population oriented and representing community wide air quality effect.
A total of 36 sources and 1168 receptors are used for the case study. Table 3 gives the source and
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receptor characterization for the case study. Case study location and AERMOD setup of sources
and receptors are shown in Fig. 3.
TABLE 3 Source and Receptor Characterization for I80 & I55 interchange near Joliet
Highway I80
Total length = 1649.34m
(2 lanes of traffic in each direction)
Width in each direction = 7.3m
Total no of sources for I80 = 4
The two ways of traffic are physically separated
from each other and have been incorporated in
the area source modeling
Highway I55
Total length = 1649.94m
(3 lanes of traffic in each direction)
Width in each direction = 11m
Total no of sources for I55 = 4
No median between the lanes
Inclined Ramps
Total length = 800m
(Same dimensions for all 4 ramps)
Width = 5m
Total no of sources for all ramps = 4
Circular Ramps
Total length = 946m
(Same dimensions for all 4 ramps)
Width = 5m
Total no of sources for all ramps = 24
Receptor Setting
− First set of receptors are placed with a
spacing of 25m for 100m
− Second set of receptors are placed with a
spacing of 50m for next 200m
− Third set of receptors are placed with a
spacing of 100m for the next 500m
Receptor Height = 1.8m
Total no of receptors = 1168
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FIGURE 3 Location and AERMOD setup of case study
6.2 Results
The most recent monitoring data for Chicago and Metro-East for calendar years 2008 to 2010
was obtained from IEPA. The background concentration values range from 9-10 ug/m3 in the
rural and far suburban portions of the nonattainment area, to 12-13 ug/m3 in the peak areas. After
interagency consultation, it was decided that Elgin, Aurora and Braidwood sites in the Chicago
metropolitan area be used to spatially interpolate (using the distance weighted approach) the
background values for the case study region. This approach results in the background
concentration of 10.41 ug/m3 for case study.
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The prevailing wind rose diagram for the case study region is shown in Fig. 4. The
average wind speed is 8.66 knots and dominant wind direction is from SW to NE. The composite
EFs for case study vary between [5.7E-08 to 6.87E-07] for circular ramps, [7.2E-08 to 9.9E-07]
for inclined ramps, [1.5E-07 to 8.1E-07] for I55 and [2.2E-07 to 1.22E-06] for I80. The annual
PM2.5 concentration results from AERMOD without the background concentration is shown in
Fig. 5a. The location of the highest top ten concentrations in red circles is shown in Fig. 5b.
The concentrations are found to be higher near the sources and the concentration
gradually decreases as the distance from the source increases. The highest top ten concentrations
are obtained at locations where the traffic volumes are the highest. In addition, these
concentrations are located in the NE quadrant which matches with the direction of the prevailing
winds from SW to NE for case study location. The highest concentration obtained without the
background concentration is 0.45ug/m3 in the NE quadrant. This highest annual average
concentration combined with background concentration is 10.85ug/m3. This is well below the
conformity standards for annual PM2.5.
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FIGURE 4 Wind rose diagram using AERMET data for case study
(Source: WRPLOT, Lakes Environmental Software)
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FIGURE 5 (a) PM2.5 concentrations without background concentration (b) Location of
highest top ten concentrations
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7. CONCLUSION
This study is a first undertaking by a state DOT to implement the PM hotspot analyses in
accordance with the EPA guidance. Based on the literature review, it is clear that careful
selection of input parameters for both MOVES and AERMOD is required to avoid possible
variation in the concentration results. All input parameters for MOVES and AERMOD models
are decided through interagency consultation process as recommended by EPA (7).
The objective of this study is to provide insights into PM hot spot modeling process with
respect to input data preparation for emission and air quality models, sensitivity testing of
MOVES and model set up. Detailed explanation of each step is provided to help MPO’s and
practitioners to better understand the entire conformity process. PM2.5 conformity process is
conducted for a real world case study near Joliet, Illinois. The highest concentrations are
obtained at locations where the traffic volume are the highest and in the direction of prevailing
winds. Future steps include performing sensitivity tests on AERMOD performance with respect
to (1) number of sources to strike a balance between accuracy and computation time, (2) other
project types, (3) comparison between AREA and VOLUME sources in AERMOD.
The PM Hot-Spot Modeling was a steep learning curve and many challenges were
encountered during the process. Some of the important challenges encountered in air quality
modeling include (1) choosing between CAL3QHCR and AERMOD models as both are
recommended by EPA for highway projects (2) choosing between AREA and VOLUME sources
for modeling roadway line segments (3) placement of receptors (4) boundary of the urban area
required for calculating the urban population to account for urban heat island effect. The urban
population of Chicago and default surface roughness length of 1m was used for case study. The
sensitivity of urban population was tested by changing it to population of Chicago-NapervilleJoliet Metropolitan Statistical Area (MSA) and the difference in concentration was found to be
negligible. Challenges in emission modeling include obtaining the fleet composition for all 13
MOVES vehicle types as most of traffic counters give data on a broad classification of vehicles.
The above challenges and other issues involved with the input data preparation were
solved through the interagency consultation process. The interagency consultation process is an
important tool for performing any project-level conformity determinations and hot-spot analyses.
Technical review panel (TRP) for this study consists of representations from IDOT, FHWA,
EPA, IEPA, CMAP. The different agencies were helpful in solving technical issues and
evaluating the appropriate methods and assumptions to be used in the hot-spot analyses. Project
meetings were held monthly with the TRP and various technical and regulatory issues were
discussed at the meetings.
ACKNOWLEDGEMENTS
This research is funded by IDOT through the Illinois Center for Transportation. We thank our
technical review panel members for their valuable inputs and comments: Michael Claggett,
Cecilia Ho and Matt Fuller of FHWA, Walt Zyznieuski of IDOT, Michael Leslie of USEPA
Region V, Mike Rogers, Sam Long, and Rob Kaleel of IEPA, and Ross Patronsky of CMAP.
We have received generous technical support from Chris Dresser of USEPA, Matt Will of IEPA,
Song Bai of Sonoma Tech, Inc.
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