Vallamsundar and Lin 1
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 = 5294
Tables (3), Figures(5) = 2000
Total =
7294
TRB 2012 Annual Meeting Paper revised from original submittal.
Vallamsundar and Lin 2
ABSTRACT
On March 10, 2006, the U.S. Environmental Protection Agency (USEPA) published a final rule 1
requiring project level particulate matter (PM) transportation conformity analysis in non-2
attainment and maintenance areas for “projects of air quality concern”. EPA has released a 3
public draft on “Transportation Conformity Guidance for Quantitative Hot-spot Analyses in 4
PM2.5 and PM10 Nonattainment and Maintenance Areas”, in which MOVES and EMFAC in 5
California are designated as the official mobile emission models. The official air quality models 6
are AERMOD and CAL3QHCR. The public draft released by EPA requires detailed handling of 7
emission and air quality data which are new for state DOTs and MPOs. This paper showcases the 8
use of MOVES and AERMOD for transportation conformity analysis with priority given to the 9
setup and running of the models with their respective data inputs in accordance with EPA’s 10
transportation conformity guidance. Details of the input data preparation for MOVES and 11
AERMOD, MOVES emission factor generation, sensitivity test results from MOVES, and 12
importance of interagency consultation process are presented. This showcase is an extended 13
effort for better understanding the conformity process and setting up the models. Results from a 14
real world case study are presented as an example of the conformity process. 15
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Vallamsundar and Lin 3
1. INTRODUCTION 46 Particulate matter (PM) is fine particles of solid matter suspended in liquid or gas. Based on the 47
size, PM can be broadly classified into two groups: (i) coarser particles with sizes ranging from 48
2.5 to 10 µm. (ii) finer particles with sizes up to 2.5 µm. There are many studies in literature 49
showing a strong association between PM2.5 and adverse health outcomes (1, 2). Finer particles 50
can have worse health effects because they are made of more toxic metals and cancer causing 51
organic compounds and can easily pass through the respiratory system due to their size (3). 52
Kappos et al. (4) found increased exposure to fine PM leads to cardiovascular, respiratory 53
problems, infant mortality and affects the human immune system. Transportation sources are one 54
of the major sources contributing to PM emissions. The latest national database summary 55
prepared by EPA for PM2.5 emissions by source sector shows that road dust accounts for about 56
21.5% and on-road vehicles account for 3% for calendar year 2005 (5). 57
In 2006, EPA published a final rule requiring project level hot spot PM transportation 58
conformity analysis for “projects of air quality concern” in non-attainment and maintenance 59
areas (6). According to EPA Guidance (7), “projects of air quality concern” are those projects 60
that involve significant levels of diesel traffic leading to high PM concentrations or any other 61
projects that are identified by state SIP as a localized air quality concern. Hot spot analysis is an 62
estimation and comparison of likely future localized PM pollutant concentration with the current 63
PM concentration and National Ambient Air Quality Standards (NAAQS). This is mainly to 64
ensure that current and future transportation projects meet the Clear Air Act conformity 65
requirements (6). The standards to be attained and maintained for PM2.5 for 24 hour period are 66
35µg/m3
and 15µg/m3
for annual period. 67
The new PM Hot Spot analyses requires detailed modeling of PM emissions and 68
concentration levels for transportation projects. These requirements are new for state DOTs and 69
Metropolitan Planning Organizations (MPOs) and there are not many studies in literature to help 70
them in this modeling process. The objective of this study is to provide insights into PM hot spot 71
modeling process with respect to input data preparation, model setup and performance, 72
importance of interagency consultation process, which in this case involves USEPA, Federal 73
Highway Administration (FHWA), Illinois Department of Transportation (IDOT), Illinois EPA 74
(IEPA) and Chicago Metropolitan Agency for Planning (CMAP). A real world case study of I-80 75
and I-55 interchange near Joliet, Illinois is presented for showcasing the proposed work. The 76
following section gives the background of MOVES and AERMOD models followed by a 77
description of relevant work in literature. The fourth and fifth sections describe the model setup 78
and MOVES sensitivity tests. Finally the sixth section describes the case study followed by 79
conclusion in the last section. 80
81
2. BACKGROUND 82
2.1 MOVES Emission Model 83
The Motor Vehicle Emission Simulator (MOVES) is the new generation EPA’s regulatory 84
mobile source emissions model. MOVES serves as a single comprehensive system for 85
estimating emissions from both on-road and non-road mobile sources, and replaces MOBILE as 86
the officially approved model for developing state implementation plans (SIPs) and regional or 87
project-level transportation conformity analyses (8). 88
There are a number of key features which sets MOVES far superior compared to its 89
predecessor model namely MOBILE. These include modal based approach to estimate emissions, 90
availability of three scales of analyses, incorporation of MySQL relational database, ability to 91
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Vallamsundar and Lin 4
model alternative fuel and vehicle types, estimation of total emissions and emission factors, 92
sophisticated approach to estimate GHG and energy consumption, inclusion of a number of 93
pollutants and emission processes. MOVES follows a “modal approach” for emission factor 94
estimation and calculates emissions using a set of modal functions. MOVES applies a “binning” 95
approach wherein each vehicle activity is binned or distributed according to different factors 96
depending on the emission process and pollutant. After distribution of total activity into different 97
bins, MOVES assigns an emission rate for each unique combination of source and operating 98
mode bins and the emission rates are aggregated for each vehicle type. A few correction factors 99
are applied to the emission rates to adjust for the influence of temperature, air conditioning and 100
fuel effects to obtain the total emissions (8). 101
102
2.2 Air Dispersion Models 103
Air dispersion models are used to determine how air-borne pollutants disperse in the atmosphere 104
and how their concentration dilutes over distance and time. EPA recommends using either 105
AERMOD or CAL3QHCR for highway and intersection projects, but using only AERMOD for 106
transit, freight, terminal projects and projects that involve both highway/ intersection and 107
terminals and/ or nearby sources (7). Both AERMOD and CAL3QHCR are Gaussian based 108
models and are derived for steady state conditions. The dispersion in Gaussian models are 109
estimated with a Gaussian equation which incorporates factors that account for the rate the plume 110
disperses in each direction, reflection from the ground and plume rise (9). 111
AERMOD was developed as a replacement for EPA’s Industrial Source Complex Model 112
by incorporating the planetary boundary layer (PBL) (10). PBL is the turbulent air layer next to 113
the earth’s surface which is affected by the surface heating, drag, turbulence and friction due to 114
its contact with the planetary surface (11). There are two types of PBL, namely (1) Convective 115
boundary layer (CBL) driven by surface heating (2) Stable boundary layer (SBL) driven by 116
surface cooling. AERMOD utilizes a Gaussian distribution in both horizontal and vertical 117
direction in SBL similar to CAL3QHCR but uses a Gaussian distribution in the horizontal but bi-118
Gaussian in the vertical direction and the concentration is calculated as a weighted average of 119
two distributions in CBL (10). 120
121
3. RELEVANT WORK 122 With MOVES being a new model, there have been few studies in literature assessing MOVES 123
performance. Studies (12, 13) compared the macroscopic scale of MOVES and MOBILE 124
showed that the difference in emission estimates is attributed to inclusion of alternative fuel 125
types, newer technology vehicles in fleet mix by MOVES. Song et al. (14) compared 126
macroscopic scale of MOVES with EMFAC and showed that CO2, CH4 emission difference to 127
depend on vehicle activity and base emission rates respectively. Vallamsundar et al. (15) 128
compared mesoscopic scale of MOVES with MOBILE and found lower estimates from 129
MOBILE compared to MOVES which is attributed to underlying base emission rates. 130
There are a number of studies in literature mostly related to the sensitivity testing and 131
performance of AERMOD. Zou et al. (16) evaluated the sensitivity of AERMOD and found the 132
effect of urban/ rural dispersion coefficients, terrain conditions to have limited influence on 133
model’s performance. Studies (17, 18, 19) compared the effect of each surface characteristic on 134
AERMOD concentrations and found the Bowen ratio to have little effect and surface roughness 135
to have the greatest effect on model concentrations. Schroeder et al., (20) found out the location 136
and type of land use around meteorological data location to significantly affect surface roughness 137
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Vallamsundar and Lin 5
length. It is worth noting that most of these studies have focused mostly on industrial sources and 138
hence there is a gap in the current literature on roadway sources. With respect to model 139
comparison, a number of studies compare AERMOD with its predecessor ISC. Studies (21, 22) 140
found that compared to ISC, AERMOD generally tends to generate lower concentration results. 141
Chen et al. (23) compared CALINE4, CAL3QHCR and AERMOD for near road PM2.5 and 142
found CALINE, CAL3QHCR results matched the observed concentrations moderately well but 143
AERMOD under estimated PM2.5. Donaldson et al. (24) found that CALPUFF predictions of 144
fugitive PM lower than that of AERMOD using a combination of area and volume sources. 145
AERMOD can model roadway line source as a series of volume or area sources (25). 146
According to (26), volume source are more appropriate for line sources, which have some initial 147
plume depth (rail lines, conveyor belts) and area sources are more appropriate for near ground 148
level sources with no plume rise (viaduct, storage piles). Schewe et al. (27) performed a 149
comparison between area and volume source types for fugitive PM concentrations for a 150
hypothetical study location in Evansville, Indiana .The authors found higher concentrations from 151
volume source characterization compared to area sources which they attributed to the way each 152
source characterization calculates the initial plume dispersion and transport. EPA study (28) 153
found that modeling roadway line sources as volume sources is indistinguishable from modeling 154
them as area sources with an initial vertical dispersion parameter. 155
This study is motivated to provide an overview of the PM hot spot process with detailed 156
explanation of each step in the process. The scope of this study is restricted to modeling annual 157
PM2.5 for highway and arterial projects in the two non-attainment areas for annual PM2.5 in 158
Illinois namely Chicago and Metro-East. MOVES emission factors are developed for a range of 159
scenarios which are discussed in section 4. The roadway sources are modeled using AERMOD 160
Area source approach. The EFs obtained from MOVES are converted into a format compatible 161
for AERMOD’s area source characterization. Using the traffic activity, local specific data and 162
emission factors from MOVES, AERMOD computes the pollutant concentration. Details on 163
AERMOD model set up are discussed in section 5. 164
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4. EMISSION MODELING 166 MOVES emission factors are developed for a range of scenarios in Chicago and Metro East 167
areas based on interagency consultation process. The first subsection describes the input data; 168
second subsection presents the sensitivity tests; the third subsection presents the details of the EF 169
generation. 170
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4.1 MOVES Input Data 172 Most of the MOVES input data for the project scale was obtained from IEPA and IDOT. Table 1 173
lists the input data utilized for MOVES Project scale. 174
175
TABLE1 Inputs data for MOVES Project scale 176
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/
Opmode Distribution
Vehicle Activity. Either of
average speed, link drive
Average speed is used for
describing the vehicle activity.
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Vallamsundar and Lin 6
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 Separate age distribution data for
Chicago and MetroEast were
obtained in MOBILE format from
IEPA and converted into MOVES
format using EPA converters (29).
Meteorology Temperature and humidity
values
Hourly temperature and relative
humidity values were obtained
from IEPA in AERMET format
and was extracted to be used for
MOVES.
Fuel Supply
Fuel supply parameters and
associated market share for
each fuel
MOVES default fuel data was used
with changes made to Reid Vapor
Pressure, Sulfur content based on
local data.
I/M Program Inspection-maintenance
program parameters for non-
attainment areas
Default MOVES database. To
note, there is no PM benefit from
I/M
4.2 Sensitivity Tests 177
The first sensitivity test was performed to test the effect of using the same meteorological data 178
for future years due to the lack of future meteorological data. The second test was performed to 179
decide the average speed values to be used for EF lookup table. 180
4.2.1 Effect of Temperature 181 Through interagency consultation process, it was decided to use the same meteorological data for 182
both MOVES and AERMOD for maintaining consistency. Meteorological data was obtained 183
from IEPA for the latest available calendar years 2005 to 2009 in AERMET format and average 184
of the 5 years data was used in MOVES. Sensitivity test was performed for analyzing the effect 185
of using this average meteorological data for future years. Historic trend for temperature 186
difference over the past 30 years from year 1980 to 2010 in Chicago (30) was found to vary 187
between 0.2 and 3. Based on the temperature differences, sensitivity test were performed for 0.5
188 oF and 3
oF increases in temperature and EFs are found to increase by 2% and 9% respectively. 189
Further EFs increased by the same percentage for all vehicle types and speed values. However 190
the temperature increase had no effect on the following MOVES vehicle types: single unit and 191
combination short-haul and long-haul trucks and intercity bus. Based on these results, it was 192
decided to use the average of 5 year meteorological data for future years. 193
194
4.2.2 Effect of Average Speed 195 Initially the EFs were estimated for the speed range from 0mph to 70mph at every 5mph 196
intervals. Sensitivity test was performed by comparing EFs calculated by MOVES and those 197
obtained by interpolation between the speed intervals for all vehicle types. Fig 1 shows the 198
sensitivity test results. The results show that for speed range of 10 – 15 mph, 30 – 35 mph and 45 199
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Vallamsundar and Lin 7
– 50 mph the difference between MOVES and interpolation are the highest especially for trucks. 200
The reason for the highest speed difference observed for trucks requires further investigation in 201
the future. Based on sensitivity test results, the above speed ranges were fine tuned to every 202
1mph interval and rest at 5mph interval. This results in a total of 21 average speed values. 203
204
205 FIGURE 1 Sensitivity test for all vehicle types and average speed values 206
207
4.3 PM2.5 Emission Factor Generation 208 The range of scenarios considered for generating MOVES EFs is shown in Fig.2. The time span 209
covered is for 4 months (January, April, July, October) that are representative of the seasons and 210
4 distinct time periods (morning peak, midday, evening peak, and overnight) in accordance with 211
(7). EFs calculated for a typical weekday are for calendar years 2011 to 2040. The speed range is 212
from 0mph to 70mph and intervals between them are chosen based on the sensitivity test results. 213
The EFs obtained from MOVES are in terms of grams/mile/veh/hr. 214
AERMOD requires a composite EF (in grams/sec/m2 in the area source approach) based on 215
traffic volume and EF corresponding to each vehicle type in the fleet mix. MOVES was executed 216
for the range of scenarios as shown in Fig.2 for a generic roadway link of length 1mile and 217
traffic volume of 13 (1 for each vehicle type). The EFs obtained from MOVES for this generic 218
roadway link can be used to calculate the EFs off model for any real world roadway link for the 219
same scenario (same area, facility, year, season, time period, vehicle type, average speed). The 220
following steps are proven, after numerous model experiments and consultation with the US 221
EPA, to be able to convert the EFs generated for a generic roadway link to any real world 222
roadway link in terms of grams/sec/m2 for AERMOD area source modeling. 223
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� Step 1: EFs for a generic roadway link of 1mile length, traffic volume of 13, gives EFs in 224
terms of grams/mile/vehicle/hr which is assigned A 225
� Step 2: Multiply A with actual traffic volume in the real world roadway link gives B in 226
terms of grams/ mile/ hour 227
� Step 3: Multiply B with actual link/ source length in miles gives C in terms of grams/hour 228
� Step 4: Divide C by 3600 to obtain D in terms of grams/second 229
� Step 5: Divide D by source area to obtain E in grams/ sec/ m2 230
Note that an alternative approach is to run MOVES each time for each project of interest 231
and obtain the EFs specific to the project. This requires running MOVES each time for a 232
different project. Using our approach described above (i.e., a generic EF database + off model 233
adjustment) requires running MOVES limited number of times, which saves computational time. 234
235
5. AIR DISPERSION MODELING SETUP 236 The two regulatory components for AERMOD are (1) Meteorological preprocessor (AERMET) 237
(2) Terrain data preprocessor (AERMAP). According to the EPA guidelines (7), meteorological 238
data for PM hot spot analyses could be site specific data which requires one year of 239
meteorological data. If using off-site data, five consecutive years of meteorological data is 240
required. For this study, meteorological data was obtained from IEPA for calendar years 2005 to 241
2009 in AERMET format. The total percentage of missing data for the 5years meteorological 242
data was found to be 2.13%. Only if the number of hours of missing meteorological data exceeds 243
10% of the total number of hours for a given model run, user should refer to (31) for ways to 244
process the missing data. The averaging period is annual as both Chicago and MetroEast are 245
designated as non-attainment areas for annual PM2.5. 246
AERMOD can model roadway line source as a series of volume or area sources (25). For 247
this study AREA and AREPOLYGON sources are used. Parameters required for area source 248
modeling are listed below: 249
(a) Source dimensions - Length of the sides in meters. Sources are defined based on (1) 250
travel activity which corresponds to volume and speed, (2) physical dimensions and (3) 251
orientation. All three affect the EF in each source. For example, a single source can be 252
used for a roadway link if they have the same travel activity and no change in geometry. 253
However for a curved link with same travel activity, more than one AERMOD source is 254
required to be used to preserve the geometry. 255
(b) Area source emission factor in grams/ sec/ m2 256
(c) The initial vertical dispersion height is assumed to be about 1.7 times the average vehicle 257
height, to account for the effects of vehicle induced turbulence. The source release height 258
is the height at which wind effectively begins to affect the plume and is estimated from 259
the midpoint of the initial vertical dimension. For a combination of vehicles with 260
different heights, these dimensions are computed using a traffic volume/ emissions 261
weighted average (7). 262
(d) Receptor characterization – receptors are placed at a height of 1.8m above the ground. 263
Around the sources, receptors are placed with finer spacing (e.g., 10-25 meters) and with 264
wider spacing (e.g., 50-100 meters) farther from a source. 265
266
267
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Vallamsundar and Lin 9
268 FIGURE 2 Scenarios considered in MOVES EF Generation 269
270
Background concentration includes emissions from all sources other than project which affects 271
concentrations in the project area. The concentration obtained from AERMOD should be added 272
with the background concentration to get the total representative concentration called the design 273
value which describes the future air quality concentration in a project area that can be compared 274
to a NAAQS. There are several options for obtaining the background concentration and they can 275
be found in (7). 276
277
6. CASE STUDY: DESCRIPTION AND RESULTS 278
6.1 Description 279
The case study consists of I-80 and I-55 interchange near Joliet, Illinois (Fig. 3). Both highways 280
extending 0.5 mi (804.7m) from center of the interchange, 4 inclined and circular ramps 281
connecting the highways are considered to be emission sources. The length of the inclined ramps 282
is 0.5 mi (804.7m) and circumference of the circular ramps is 0.4 mi (643.7 m). The distance 283
from intersection of interchange to inclined ramps is 0.35 mi (563.3 m). It is assumed that all 284
inclined ramps are of the same dimensions and all circular ramps are of same dimensions. 285
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Vallamsundar and Lin 10
The design speeds for highways, inclined and circular ramps are 60mph (26.82m/s), 286
45mph (20.12m/s) and 40mph (17.88m/s) respectively. The pollutant estimated is PM2.5 for 287
annual averaging period for calendar year 2011. The traffic volume data was obtained from 288
IDOT. The fleet composition data from traffic counters consists of vehicle split in 3 broad 289
categories namely 4tire, single unit and multiple unit. Based on the association between HPMS 290
and MOVES vehicle types, these 3 categories were mapped into MOVES vehicle types. MOVES 291
vehicle type split under each category was obtained from local data from CMAP. Table 2 shows 292
the overall traffic volume corresponding to each time period. 293
294
Table 2 Traffic Volumes 295
Description 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 841 598 615 177
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
296
MOVES default split of fuel types for each vehicle type was used except for transit buses where 297
the fuel type was changed to 100% diesel based on local data. Composite EF was computed from 298
MOVES EF lookup table and off model adjustments as discussed in section 4.3. 299
AREA sources are used for the highways and AREAPOLYGON sources for circular and 300
inclined ramps. In accordance with (7), receptors are placed at a finer resolution of 25m near all 301
the sources and spacing is increased to 50m and 100m as the distance from the source increases. 302
The first line of receptors is placed at a distance of 50 ft from the edge of the roadway to allow 303
for the right of way distance. Receptor placement for annual PM2.5 is in accordance with the 304
requirement (7) of being population oriented and representing community wide air quality effect. 305
A total of 36 sources and 1168 receptors are used for the case study. Table 3 gives the source and 306
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Vallamsundar and Lin 11
receptor characterization for the case study. Case study location and AERMOD setup of sources 307
and receptors are shown in Fig. 3. 308
309
TABLE 3 Source and Receptor Characterization for I80 & I55 interchange near Joliet 310
Highway I80
(2 lanes of traffic in each direction)
Total length = 1649.34m
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
(3 lanes of traffic in each direction)
Total length = 1649.94m
Width in each direction = 11m
Total no of sources for I55 = 4
No median between the lanes
Inclined Ramps
(Same dimensions for all 4 ramps)
Total length = 800m
Width = 5m
Total no of sources for all ramps = 4
Circular Ramps
(Same dimensions for all 4 ramps)
Total length = 946m
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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311 FIGURE 3 Location and AERMOD setup of case study 312
313
6.2 Results 314 The most recent monitoring data for Chicago and Metro-East for calendar years 2008 to 2010 315
was obtained from IEPA. The background concentration values range from 9-10 ug/m3 in the 316
rural and far suburban portions of the nonattainment area, to 12-13 ug/m3 in the peak areas. After 317
interagency consultation, it was decided that Elgin, Aurora and Braidwood sites in the Chicago 318
metropolitan area be used to spatially interpolate (using the distance weighted approach) the 319
background values for the case study region. This approach results in the background 320
concentration of 10.41 ug/m3 for case study. 321
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Vallamsundar and Lin 13
The prevailing wind rose diagram for the case study region is shown in Fig. 4. The 322
average wind speed is 8.66 knots and dominant wind direction is from SW to NE. The composite 323
EFs for case study vary between [5.7E-08 to 6.87E-07] for circular ramps, [7.2E-08 to 9.9E-07] 324
for inclined ramps, [1.5E-07 to 8.1E-07] for I55 and [2.2E-07 to 1.22E-06] for I80. The annual 325
PM2.5 concentration results from AERMOD without the background concentration is shown in 326
Fig. 5a. The location of the highest top ten concentrations in red circles is shown in Fig. 5b. 327
The concentrations are found to be higher near the sources and the concentration 328
gradually decreases as the distance from the source increases. The highest top ten concentrations 329
are obtained at locations where the traffic volumes are the highest. In addition, these 330
concentrations are located in the NE quadrant which matches with the direction of the prevailing 331
winds from SW to NE for case study location. The highest concentration obtained without the 332
background concentration is 0.45ug/m3 in the NE quadrant. This highest annual average 333
concentration combined with background concentration is 10.85ug/m3. This is well below the 334
conformity standards for annual PM2.5. 335
336
337 FIGURE 4 Wind rose diagram using AERMET data for case study 338
(Source: WRPLOT, Lakes Environmental Software) 339
340
341
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342
343 FIGURE 5 (a) PM2.5 concentrations without background concentration (b) Location of 344
highest top ten concentrations 345 346
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7. CONCLUSION 347 This study is a first undertaking by a state DOT to implement the PM hotspot analyses in 348
accordance with the EPA guidance. Based on the literature review, it is clear that careful 349
selection of input parameters for both MOVES and AERMOD is required to avoid possible 350
variation in the concentration results. All input parameters for MOVES and AERMOD models 351
are decided through interagency consultation process as recommended by EPA (7). 352
The objective of this study is to provide insights into PM hot spot modeling process with 353
respect to input data preparation for emission and air quality models, sensitivity testing of 354
MOVES and model set up. Detailed explanation of each step is provided to help MPO’s and 355
practitioners to better understand the entire conformity process. PM2.5 conformity process is 356
conducted for a real world case study near Joliet, Illinois. The highest concentrations are 357
obtained at locations where the traffic volume are the highest and in the direction of prevailing 358
winds. Future steps include performing sensitivity tests on AERMOD performance with respect 359
to (1) number of sources to strike a balance between accuracy and computation time, (2) other 360
project types, (3) comparison between AREA and VOLUME sources in AERMOD. 361
The PM Hot-Spot Modeling was a steep learning curve and many challenges were 362
encountered during the process. Some of the important challenges encountered in air quality 363
modeling include (1) choosing between CAL3QHCR and AERMOD models as both are 364
recommended by EPA for highway projects (2) choosing between AREA and VOLUME sources 365
for modeling roadway line segments (3) placement of receptors (4) boundary of the urban area 366
required for calculating the urban population to account for urban heat island effect. The urban 367
population of Chicago and default surface roughness length of 1m was used for case study. The 368
sensitivity of urban population was tested by changing it to population of Chicago-Naperville-369
Joliet Metropolitan Statistical Area (MSA) and the difference in concentration was found to be 370
negligible. Challenges in emission modeling include obtaining the fleet composition for all 13 371
MOVES vehicle types as most of traffic counters give data on a broad classification of vehicles. 372
The above challenges and other issues involved with the input data preparation were 373
solved through the interagency consultation process. The interagency consultation process is an 374
important tool for performing any project-level conformity determinations and hot-spot analyses. 375
Technical review panel (TRP) for this study consists of representations from IDOT, FHWA, 376
EPA, IEPA, CMAP. The different agencies were helpful in solving technical issues and 377
evaluating the appropriate methods and assumptions to be used in the hot-spot analyses. Project 378
meetings were held monthly with the TRP and various technical and regulatory issues were 379
discussed at the meetings. 380
381
ACKNOWLEDGEMENTS 382
This research is funded by IDOT through the Illinois Center for Transportation. We thank our 383
technical review panel members for their valuable inputs and comments: Michael Claggett, 384
Cecilia Ho and Matt Fuller of FHWA, Walt Zyznieuski of IDOT, Michael Leslie of USEPA 385
Region V, Mike Rogers, Sam Long, and Rob Kaleel of IEPA, and Ross Patronsky of CMAP. 386
We have received generous technical support from Chris Dresser of USEPA, Matt Will of IEPA, 387
Song Bai of Sonoma Tech, Inc. 388
389
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