Using MOVES and AERMOD models for PM2.5 Conformity …docs.trb.org/prp/12-4503.pdf · 74 Highway...

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.


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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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



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



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

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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.



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



– 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



� 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



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



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



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



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



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



342

343 FIGURE 5 (a) PM2.5 concentrations without background concentration (b) Location of 344

highest top ten concentrations 345 346



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

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