air quality project - ncrst-sepp home page

Air Quality Project

AIR QUALTY MODELING AND ANALYSIS OF GROUND-LEVEL OZONE AND NITROGEN

DIOXIDE, VALIDATION, AND IMPLMENTATION IN NORTHERN MISSISSIPPI

FINAL REPORT UM-CAIT/2004-01

June 2004

AIR QUALITY MODELING AND ANALYSIS CONSIDERING CLIMATOLOGICAL DATA,

VEHICLE EMISSIONS, POINT SOURCE EMISSIONS, AND AVIATION SOURCES

US DOT Research and Special Program Administration

NO2Ozone NO2Ozone

Center for Advanced Infrastructure Technology

The University of MississippiCarrier 203, University, MS 38677-1848, USA

Voice: 662-915-5366 Fax: 662-915-5523http://www.olemiss.edu/projects/cait/Index.html

[email protected]

Center for Advanced Infrastructure Technology

The University of MississippiCarrier 203, University, MS 38677-1848, USA

Voice: 662-915-5366 Fax: 662-915-5523http://www.olemiss.edu/projects/cait/Index.html

[email protected]

Air Quality Project Director: Waheed Uddin, Ph.D., P.E. Associate Professor Department of Civil Engineering Director, CAIT (662) 915-5363 [email protected]

Air Quality Project Final Report/ Uddin UM-CAIT/2004-01 June 2004

2 2

EXECUTIVE SUMMARY This air quality analysis project, a part of the NCRST-E overall effort, was conducted by the Center for Advanced Infrastructure Technology at the University of Mississippi, a consortium partner, and is focused on the adverse effects of air pollutants generated by transportation systems on the quality of life. The primary objectives of this final project report is to document air quality models, developed and implemented in this study, for the prediction of air pollution; considering the impact of highway traffic, land use, terrain type, and climatic factors. To safeguard the public health, the Environmental Protection Agency has enforced the 1970 Clean Act requirements and 1990 amendments by developing threshold standard concentration levels of: tropospheric or ground- level Ozone, Nitrogen Oxides, Carbon Dioxide, Carbon Monoxide, Sulfur Dioxide, and particulate matter. Significant air pollution is generated from coal-fired power generating plants and vehicle emissions . Other sources of pollution include industries, railroad, aviation, fires, and natural emitters. Some of these pollutants may be linked to respiratory problems and lung diseases, especially in the high-risk groups of young children and senior citizens. Ozone, a major air pollutant, is formed by a chemical reaction involving volatile organic compounds, Nitrogen Dioxide, and sunlight. Ozone and Nitrogen Dioxide create smog that negatively affects health. Ozone and smog are particularly high during hot summer days, especially in urban and suburban areas plagued by high traffic volumes and gridlocks. Despite considerable regulatory and pollution control efforts over the last three decades, high Ozone concentrations in urban, suburban, and rural areas continue to be a major environmental and health concern. Numerous cities and urban areas are listed as nonattainment areas for Ozone. A database of air pollution, traffic and climatic parameters was created from 1996 to 2001 for the selected cities of Hernando and Tupelo in rural northern Mississippi, both of which have air quality monitoring stations. The air quality database was used to develop an air quality model, which considered the impact of highway corridors on air pollution concentration and dispersion. The developed models not only contain key climatological parameters affecting Ozone and Nitrogen Dioxide but also include emissions of Ozone precursors from highway motor vehicles and point sources, as well as traffic data, aircraft operation data, and day of the year. The Ozone model has a multiple correlation R of +0.74 and standard error of estimate of 0.012 parts per million (ppm). The Ozone predictions for the hottest day in Tupelo and Hernando in 2001 differed by 12% and 2%, respectively, from the measured values. Similarly, good results were also shown for Nitrogen Dioxide; however, measured values were available for Hernando only. The Ozone model was implemented for Oxford, with predictions for selected days in 2001 in the range of 0.016-0.048 ppm, which are reasonably accurate considering the expected background level of Ozone. The model also shows the significant impact of heavy truck traffic on Ozone levels at the NCAT accelerated highway test track site at Auburn University. The developed Ozone model is superior to other reviewed models, which generally require the prior day’s Ozone concentration. Reasonable results were also obtained for the Nitrogen Dioxide model. This validated Ozone model can be used to assist in air quality management programs, such as air pollution control programs, and/or to determine air quality trend analysis for locations where there is no air quality monitoring station. It can also serve as a decision-making tool for better air quality management of sustainable landuse and transportation development programs.


3 3

LIST OF COOPERATING AGENCIES AND ORGANIZATIONS

US DOT Research and Special Program Administration (RSPA) - (Study Sponsor) * Mississippi State University (MSU) Remote Sensing Technologies Center (NCRST-E Lead University)* NCRST-E => National Consortia for on Remote Sensing in Transportation- Environmental Assessment The University of Mississippi (UM) Center for Advanced Infrastructure Technology (CAIT) (NCRST-E Air Quality Project)* Mississippi Department of Environment Quality (DEQ) Office of Pollution Control, Air Division (Observations and DEQ monitoring data ) Mississippi Department of Transportation (MDOT) (Traffic volume data) City of Oxford, Mississippi (Traffic volume data by automatic counters; coordination with Oxford ITS project) City of Tupelo, Mississippi (Logistic support) Tupelo Regional Airport Authority, Mississippi (Coordination for field visit to DEQ monitoring station) Space Imaging, Inc, NASA Stennis Space Center, Mississippi (IKONOS Imagery of Oxford area) Skyborne, Inc. (Service provider for laser measurement of air pollution)

* Study partners and their roles

The author appreciates the support of Roger King, the NCRST-E consortium project director, Charles O’Hara, the consortium coordinator, all pertinent individuals from the agencies and organizations listed above, and the comments of the NCRST-E advisory committee during the periodic study meetings. Thanks are due to Lucy Phillips, Julia Phillips, Van Gilbert, James Caughorn, and other UM undergraduate senior students who assisted in traffic data collection, field work, and weather data collection and analysis. The authors appreciate the efforts of Ja-Wan Carter and Levell Spencer, Jr., seniors in industrial technology at Alcorn State University, who assisted in meteorological data collection and processing as a part of their internship at CAIT through the UM Student Research Institute for Undergraduates (SRIU) program during summer 2002 and summer 2003. Thanks are also due to Scott Dooley in the preparation of this report and contributions of Celina Sumrall and several other graduate students. The final air quality project report is authored by Waheed Uddin, the project director at CAIT University of Mississippi, with major contributions from UM doctoral dissertations of Kanok Boriboonsomsin and Sergio Garza in the department of civil engineering.


4 4

TABLE OF CONTENTS Page TITLE PAGE EXECUTIVE SUMMARY ............................................................................................. 2 LIST OF COOPERATING AGENCIES AND ORGANIZATIONS.............................. 3 TABLE OF CONTENTS ................................................................................................ 4 1. AIR QUALITY MODELING AND ANALYSIS ............................................... 5 1.1 Overview of NCRST-E Air Quality Project ........................................... 5 1.2 Air Pollutant Monitoring ........................................................................ 5

1.3 Air Quality Management Issues .............................................................. 6 1.4 Objectives and Scope ............................................................................... 9

2. AIR POLLUTION DISPERSION MODELING............................................... 10

2.1 CALINE4 Dispersion Model ................................................................ 10 2.2 ALOHA Dispersion Model.................................................................... 17 2.3 DISPER2D Dispersion Model ............................................................... 20 2.4 DISPER3D Dispersion Model ............................................................... 40 2.5 Comparison of Dispersion Models ........................................................ 46 2.6 Urban Airshed Model for Ground-Level Ozone ................................... 49

3. ROADWAY VEHICULAR EMISSION MODEL .......................................... 53 3.1 EPA’s MOBILE6 Emission Model for On-Road Vehicles ................... 53 3.2 Case Study of MS Highway 6 near Oxford .......................................... 55 3.3 Development of Simplified Vehicular Emission Models ..................... 58 3.4 Emission Rates Considering Traffic Mix and Model Years ................. 67

4. AIR POLLUTION MODELING AND ANALYSIS USING EPA DATA ..... 70

4.1 Formation and Transport of Ground-Level Ozone .................................. 70 4.2 Overview of Ozone Prediction Methodologies......................................... 74 4.3 CAIT Air Quality Modeling Database .................................................... 76 4.4 Vehicle Emission Data from MOBILE6-Estimated Emission Rates ...... 90 4.5 Point Source Emission Data from EPA Emission Inventory .................. 94 4.6 Aviation Operations Data from FAA ...................................................... 99

5. DEVELOPMENT AND IMPLEMENTATION OF O3 AND NO2 MODELS 101

5.1 Ozone Modeling .................................................................................. 101 5.2 Nitrogen Dioxide Modeling .................................................................. 114 5.3 Air Quality Modeling and Analysis (AQMAN) Program ................... 122 5.4 Application of Air Quality Models for Future Air Quality Predictions 129 5.5 GIS-Based Air Quality Visualization .................................................. 137 5.5 Evaluation of Benefits and Costs ........................................................ 141

6. CONCLUSIONS AND RECOMMENDATIONS ........................................... 149 7. REFERENCES………………………………………………………………… 151 APPENDIX .............................................................................................................. 157


5 5

1. AIR QUALITY MODELING AND ANALYSIS

1.1 Overview of NCRST-E Air Quality Project The National Consortia on Remote Sensing in Transportation (NCRST) were established by the U.S. Department of Transportation Research and Special Program Administration (RSPA) in collaboration with NASA, using funding from the Transportation Equity Act for the 21st Century (TEA-21), Section 5113 to lead in the application of remote sensing and spatial information technologies in the transportation industry [1]. The primary mission of the university consortium for Environmental Assessment (NCRST-E), one of four consortia is to develop and promote the use of remote sensing and geospatial technologies and analysis products by transportation decision-makers and environmental assessment specialists to measure, monitor, and assess environmental conditions in relation to transportation infrastructure. This four-year study was begun in March 2000. One of the NCRST-E project tasks was the development of an enhanced model to predict air pollutant concentrations, considering their transport and dispersion [2, 3].

The NCRST-E air quality project was focused on remote sensing laser measurements of air pollutants, air quality modeling, and the air quality impact of transportation systems. This approach can quantify the contribution of mobile sources of air pollution. The project involved: (a) comprehensive literature search and review of air pollutant databases, transportation related air pollution data, air pollutant concentration and dispersion modeling, and available remote sensing technologies, (b) selection of study sites, (c) deployment of selected remote sensing technology for measuring air pollution at selected sites, interpretation and assessment of data, and the study of adverse effects due to weather and urban sprawl on air quality, and (d) modeling of air pollution considering traffic, weather, and land use. 1.2 Air Pollutant Monitoring Increased human activities in the last four decades have disturbed the natural cycles of key elements of life. These concerns led to the creation of a national environmental policy nearly three decades ago. In the United States, the U.S. Environmental Protection Agency (EPA) was created to develop specific regulations and monitoring methods to improve air and water quality. The EPA also ensures that environmental quality is not degraded by urban growth, industrial development, transportation growth, and higher traffic volume. The 1970 Clean Air Act, Section 309 Public Law 91-604 § 12(a), 42 U.S.C. § 7609, was passed by the U. S. Congress in 1970 [4]. This law established the first specific responsibilities for government and private industry to reduce emissions from vehicles, factories, and other pollution sources. In response, the U.S. EPA established National Ambient Air Quality Standards (NAAQS) for various “criteria” pollutants that adversely affect human health. These pollutants are Carbon Dioxide (CO2), Carbon Monoxide (CO), Volatile Organic Compound (VOC), hydrocarbons (HC), Oxides of Nitrogen (NOx), particulate matter (PM), and Sulfur Dioxide (SO2). Most of these pollutants are principally emitted to the atmosphere by the transportation industry and fossil fuel consumption in industry. In the past, motor vehicles were a source of lead (Pb) emissions, but are no longer a major contributor because leaded gasoline is no longer


6 6

available for transportation usage. Compliance with national (and state standards, if more stringent) ambient air quality standards is an important consideration under the Clean Air Act. Table 1 shows the six principal pollutant emissions by source category for 1997, reported by the Environmental Protection Agency (EPA) [6]. This table shows that CO, NOx, and VOC are primarily emitted to the atmosphere by transportation sources.

Table 1. Air pollutant emissions by source category, 1997 [6]

Principal Pollutant Source Category CO Pb NOx VOC PM10 SO2

Transportation 76.6 % 13.3 % 49.2 % 39.9 % 23.0 % 6.8 % Industrial Processes 6.9 % 74.2 % 3.9 % 51.2 % 42.0 % 8.4 % Fuel Combustion 5.5 % 12.6 % 45.4 % 4.5 % 34.9 % 84.7 % Miscellaneous 10.9 % - 1.5 % 4.5 % - 0.1 % Source: National Air Quality and Emissions Trends Report, 1997 Ground- level Ozone (O3), another major air pollutant, is produced in the atmosphere by a photochemical reaction involving Nitrogen Dioxide (NO2) and VOC in the presence of sunlight. Higher concentrations of O3 are expected during daylight hours on hot summer days, rather than at nighttime. The Clean Air Act of 1970 established specific responsibilities for government and private industry to control and reduce pollution from these emissions sources. To enforce 1990 amendments to the Clean Air Act, the EPA has developed the Air Quality Index (AQI) for: tropospheric or ground-level O3, NOx, CO2, CO, PM, and SO2. Above certain concentration levels, O3, CO, and PM can cause or exacerbate health problems and/or increase mortality rates, making their control an important goal. Figure 1 shows the AQI scale used for this purpose. The EPA national strategy for air pollution abatement relies on fixed point monitoring of air pollutants within the troposhere. Figure 2 shows the Ozone and PM data collected in 1999 at the Tupelo Airport air pollution station, established by the Mississippi Department of Environmental Quality (DEQ) for monitoring EPA standards of air pollutants. This is the standard EPA method of air pollution monitoring. 1.3 Air Quality Management Issues

Reductions in transportation emissions and subsequent improvements in air quality have been made through federal regulations, state enforcement, and participation by industry and the public. However, the transportation sector is still a major emitter of key pollutants. For instance, the distribution of NOx emissions in the U.S. by source in 2002 was 56% from transportation, 37% from fuel combustion, 5% from industrial processes, and 2% from miscellaneous [10]. In the U.S., over 25 regional episodic O3 control programs have been established nationwide in response to the persistence of the O3 air pollution problem [11]. A key component of these programs is forecasting probable high O3 days, and subsequent promulgation of an Ozone action day in the community. An air pollution control policy is stated either in terms of the maximum concentration of the pollutant that it is permitted in the air (ambient standard) or in terms of the amount of pollutant released by a source (emission standard) [12]. Table 2 shows the NAAQS standards for the six criteria pollutants in U.S. [13].


7 7

Source: http://www.epa.gov/airnow/publications.html Air Quality Index (AQI), EPA-454/R-99-010

Figure 1. The Air Quality Index scale

Source: http://www.epa.gov/air/data/index.html U.S. Environmental Protection Agency AIRData: Access to Air Pollution Data

Figure 2. Ozone and PM 10 data at Tupelo Airport DEQ monitoring station in northern Mississippi

301+

AQI

201-300

151-200

101-15051-100

0-51

AIR QUALITY

Good

Moderate

Unhealthy(for sensitive groups)

Unhealthy

Hazardous

Very Unhealthy

301+

AQI

201-300

151-200

101-15051-100

0-51

AIR QUALITY

Good

Moderate

Unhealthy(for sensitive groups)

Unhealthy

Hazardous

Very Unhealthy

Map of Mississippi Showing locations of DEQ Monitoring Stations

Tupelo, Lee County

Oxford, Lafayette County

Southaven, DeSoto County


8 8

Table 2. National Ambient Air Quality Standards (NAAQS) [13]

Pollutant Standard Value CARBON MONOXIDE (CO)

8-hour Average 1-hour Average

9 ppm (10 mg/m3) 35 ppm (40 mg/m3)

Nitrogen Dioxide (NO2) Annual Arithmetic Mean

0.053 ppm (100 µg/m3)

OZONE (O3) 1-hour Average 8-hour Average

0.12 ppm (235 µg/m3) 0.08 ppm (157 µg/m3)

LEAD (PB) QUARTERLY AVERAGE

1.5 µg/m3

PARTICULATE PM10

ANNUAL ARITHMETIC MEAN 24-HOUR AVERAGE

50 µg/m3 150 µg/m3

PARTICULATE PM2.5 ANNUAL ARITHMETIC MEAN

24-HOUR AVERAGE

15 µg/m3 65 µg/m3

SULFUR DIOXIDE (SO2) ANNUAL ARITHMETIC MEAN

24-HOUR AVERAGE 3-HOUR AVERAGE

0.030 ppm (80 µg/m3) 0.14 ppm (365 µg/m3) 0.50 ppm(1300 µg/m3)

Source: U.S. Environmental Protection Agency, http://www.epa.gov/airs/criteria.html

The use of dispersion models to predict concentration levels has been useful in establishing emission limits for the transportation industry in order to maintain acceptable concentration levels. In order to quantify the benefits from the reduction of a pollutant emission, or to quantify the costs due to an increment in emission levels, the expected change in concentration is used. Tsohos [14] describes two different theories to evaluate the environmental impacts and associated societal costs due to air pollution; The Damage Value Method (DVM) and the Control Cost Method (CCM).

• The DVM directly estimates emissions, physical effects on humans and the environment, and the monetary value of damages.

• On the other hand, the CCM is based on the assumption that ideal emission or air quality standards have established that the marginal damage of pollution is equal to the marginal cost of controlling pollution. This method involves the following two major steps: (a) determination of marginal measures and (b) estimation of monetary values of control measures.

A comparison of both methods shows that the DVM estimated values are lower than the CCM estimated values [14]. The simulation of pollutant concentration and dispersion is an important step in the evaluation of environmental impact and quantification of the societal costs. Considerable efforts have been made by the US DOT and EPA to quantify the costs associated with motor vehicle emissions. Chapter 5 includes further discussion on societal costs for a case study evaluated in the air quality project.


9 9

1.4 Objectives and Scope The U.S. EPA’s air pollution monitoring stations are located in cities around the U.S.,

however, many cities do not have EPA monitoring stations. Therefore, reliable air quality models are needed to serve as a decision-making tool in city planning and air quality management programs in these cities. Simulation models are the primary tool used by several states in their State Implementation Plans for attainment demonstration. For other air quality management routines, such as O3 forecasting programs, statistical models are often used due to the ease of use and generally good forecasts.

The objectives of this study are: (1) to develop a daily maximum 8-hour average O3 concentration prediction model considering O3 precursor emissions, based on data from rural areas of Northern Mississippi, (2) to validate the model using historical measured O3 concentration data from EPA monitoring stations, and (3) to implement the model in a location where there is no monitoring station.


10 10

2. AIR POLLUTION DISPERSION MODELING

A critical step in determining the benefits of air pollution control is to determine how

emissions changes impact air quality. This determination is traditionally done using emission modeling and air pollutant dispersion models. Air pollutant dispersion models can be characterized by the manner in which the dispersion of pollutants within the air is simulated. Models of physical processes whose equations are stated relative to a volume of air that follows the dispersing material during transport downwind can be referred as Langragian models. Models of physical processes whose equations are stated relative to a volume of air fixed in space, through which the air moves are referred as Eulerian models [15].

Many different dispersion models, recommended by the U.S. EPA, are available to the public [16]. Two different dispersion models are explained in this research, CALINE4 and ALOHA. CALINE4 was developed by the California Department of Transportation to assess air quality near transportation facilities [17]. On the other hand, ALOHA (Areal Locations of Hazardous Atmosphere) was developed by EPA and the National Oceanic and Atmosphere Administration (NOAA) for chemical emergencies, planning and training purposes [18]. The Urban Airshed Model simulates Ozone pollutant and dispersion [19]. An explanation for each model is presented in the following sections. 2.1 CALINE4 Dispersion Model

The CALINE4 dispersion model was developed by the California Department of Transportation (Caltrans) for predicting air pollutant concentration near roadways and is based on vehicular traffic emissions, site geometry, and meteorology data. It uses a line source simulation model to predict pollutant concentration for receptors located near transportation corridors. Examples of input data are traffic volume, emission factors, roadway geometry, wind speed and direction, ambient air temperature, mixing height, atmospheric stability class, and coordinate of receptors. Predictions can be made for CO, NO2, and PM for receptors located within 500 meters of the roadway. It is based on the Gaussian diffusion equation and employs a mixing zone concept to characterize pollutant dispersion over the roadway [17]. Equation 1 shows the Gaussian diffusion equation for line sources, including reflection, for a differential highway length with uniform source strength [17].

+−+

−−

−⋅= 2

2

2

2

2

2

2)(

exp2

)(exp

2exp

2 zzyzy

HzHzyu

dyqdC

σσσσσπ (1)

where: dC = Incremental concentration (µg/m3) q = Uniform lineal source strength (µg/m/sec) dy = Differential highway length with uniform source strength (m) u = Wind speed (m/s) H = Source height (m) σy, σz = Horizontal and vertical dispersion parameters (m)


11 11

Dispersion of pollutants computed with Equation 1 presents a bell-shaped normal distribution. The shape of this normal distribution is a function of the horizontal and vertical dispersion parameters (σy, σz). These horizontal and vertical dispersion parameters are function of the downwind distance (x) and the atmospheric stability.

The incremental concentration, calculated with Equation 1, has units of µg/m3. This concentration can be converted to part per million (ppm) using the expression in Equation 2 [20]. This equation is for an atmospheric pressure of 1.0 atm. For a different atmospheric pressure, the concentration needs to be modified dividing by the actual atmospheric pressure in atmospheres (atm).

+

⋅⋅=273

27302241.0)/()( 3 T

MWTmgCppmC µ (2)

where: T = Air temperature (oC) MWT = Molecular Weight (kg/kmol)

CALINE4 divides the highway length in different elements as presented in Figure 3 [17].

The element length is a function of the highway width, the wind direction with respect to the highway, and the element number. A finite line source is assumed perpendicular to the wind. The length of the finite line source is a function of the wind direction and the element length. The finite line source is divided in three different zones, where the central zone has a uniform emission source and the peripheral zones have an emission decreasing linearly to zero at the ends of the finite line source. The concentration at the receptor is computed as a series of incremental contributions from each element integrating Equation 1 along the finite line source.

Figure 3. Element series represented by series of equivalent finite line sources [17]

Wind Direction

φ

Receptor

Plume Centerline

GaussianPlume

Highway

x

y

Wind Direction

φ

Receptor

Plume Centerline

GaussianPlume

Highway

x

y


12 12

Mixing Zone Model CALINE4 considers the region directly over the highway as a zone of uniform emissions and turbulence called the mixing zone. Within this region, the mechanical turbulence created by moving vehicles and the thermal turbulence created by hot vehicle exhaust are assumed to be the dominant dispersive mechanism. The initial vertical dispersion parameter (SGZI) is modeled as a function of pollutant residence time (TR) within the mixing zone. The expression to calculate SGZI is as follow [17]:

)10(5.1 TRSGZI += (3)

)sin(2

φ⋅=

uW

TR (4)

where: SGZI = Initial vertical dispersion parameter (m) TR = Pollutant residence time (sec) W2 = Half highway width (m) u = Wind speed (m/s) φ = Highway – wind angle (degrees), ≥ 45o

Vertical Dispersion Parameter, σz

CALINE4 uses a modified version of the Pisquill-Smith vertical dispersion curves to

describe the Gaussian vertical dispersion parameter, σz, downwind from highways [17]. The vertical dispersion curves are constructed using SGZI from the mixing zone model, a modified value of σz at 10 kilometers incorporating thermal effects (SGZM), and a final value of σz at 10 kilometers for a passive release under ambient stability conditions (SGZF). Figure 4 shows the composite vertical dispersion curve that CALINE4 uses for the vertical dispersion parameter. Horizontal Dispersion Parameter, σy

This method used by CALINE4 states that the horizontal dispersion parameter is a

function of the horizontal wind angle dispersion parameter, the downwind distance, and the diffusion time. The expression to compute the horizontal dispersion parameter is [17]:

1fxy ⋅⋅= θσσ (5) where:

σθ = Horizontal wind angle standard deviation (rad) x = Downwind distance (m) u = wind speed (m/s) f1 = Universal function of the diffusion time =

TITT9.01

1

+

TT = Diffusion time = ux

TI =

≥⋅

<

550for 001.0

550for 3002 TTTT

TT


13 13

Figure 4. Composite vertical dispersion curve (σz) used by CALINE4 [17] CALINE4 Input Values

The input values required by CALINE4 to compute the pollutant dispersion include: link geometry (highway), link activity (traffic, emission), run conditions, and receptor locations. Table 3 shows a complete list of all the inputs required by CALINE4 to predict CO concentration. For the dispersion of NO2, ambient concentration values for NO, NO2, and O3 are additional inputs. Emission Dispersion Example Using CALINE4

The CALINE4 dispersion model was used to predict NOx concentration values close to MS Highway 6 in Oxford for the traffic data collected on during April and May of 2001. The NOx emission rate generated by the vehicular traffic was calculated from the MOBILE6 program [21]. Two different cases were studied, one for May 24, 2001 at nighttime, and the other one for May 25, 2001 at daytime. Table 4 shows the input values used for these two cases. The two numerical examples were conducted using the total traffic volume, including cars and trucks, and without considering chemical reaction [22]. The pollutant concentration was calculated at different receptor locations along the center of the plume (y = 0.0 m), which presents the maximum concentration. The receptor distances from the highway were 1, 2, 5, 10, 20, 50, 100, 150, 200, and 250 meters. Table 5 shows the results from CALINE4 for the two different cases. These values were calculated with a receptor height of 0.30 meters (1 ft). Figures 5 and 6 show the concentration values calculated for the daytime and nighttime cases, respectively, at different receptor distances and at different receptor heights.

ln (x)

ln σz

SGZI

SGZM

SGZF

WMIX DMIX DREF(10 km)ln (x)

ln σz

SGZI

SGZM

SGZF

WMIX DMIX DREF(10 km)

WMIX = Distance over the mixing zone (constant SGZI) DMIX = The distance traversed by the finite line source plume centerline over the mixing zone DREF = Reference distance of 10 km


14 14

Table 3. CALINE4 input data

Information Input Data and Units

Run Type Standard Multi-Run Worst-Case Wind Angle Multi-Run / Worst Case Hybrid

Aerodynamic Roughness Coefficient

Rural (10 cms) Suburban (100 cms) Central Business District (400 cms), and Other

Model Information

Link geometry units (m, ft) Altitude above sea level (m)

Link Geometry 1 – 20 links (highway) X1, Y1, X2, Y2 – Link coordinates (m) Height (m) Mixing Zone Width = Highway width plus 3 m each side

Link Activity Traffic, VPH Emission factor, (grams/veh-mi)

Run Conditions Wind speed, (m/s) Wind direction, (degrees) Wind direction standard deviation (degrees) Atmospheric stability class (1 – 7) Mixing height (m) Ambient temperature (oC) Ambient pollutant concentration (ppm)

Receptor Location

1 – 20 Receptors X, Y, Z coordinates per each receptor


15 15

Table 4. Input values for MS Highway 6 example using CALINE4

Air Temperature

(oC)

Wind Speed (m/s)

Stability Class

* Traffic Volume (VPH)

NOx Emission

Rate (g/veh/mi)

May 24, 2001

11 – 12 pm Nighttime

11.8 0.6 F Car: 358 Truck: 29 Total : 382

2.57

May 25, 2001

10 – 11 am Daytime

17.3 2.1 A Car: 1,198 Truck: 99

Total : 1,297 2.51

Run Type: Standard Aerodynamic Roughness Coefficient: 100.0 cm Altitude Above Sea Level = 120 m Link Height = 0.30 m Mixing Zone Width = 30.0 m Wind Direction Standard Deviation = 44o (Nighttime), 15º (Daytime) Mixing Height = 1000 m

Other Inputs

Ambient Pollutant Concentration = 0 ppm * Average vehicle speed = 76 km/h (40 mi/h)

Table 5. CALINE4 results for MS Highway 6 example

NOx Concentration, ppm

Receptor Distance, m May 24, 2001 11 – 12 pm Nighttime

May 25, 2001


1 0.015 0.026 2 0.016 0.028 5 0.019 0.033 10 0.024 0.041 20 0.024 0.038 50 0.015 0.015 100 0.010 0.008 150 0.008 0.005 200 0.007 0.004 250 0.007 0.003

* EPA Standard for NOx = 0.053 ppm (Annual average)


16 16

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0 50 100 150 200 250

Receptor Distance (x), m

NO

x Con

cent

ratio

n, p

pm

Receptor Height = 0.30 mReceptor Height = 1.0 mReceptor Height = 3.0 mReceptor Height = 10.0 m

May 24, 2002 - 11-12 pm (Nighttime)150 ppb

0 ppb

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0 50 100 150 200 250


NO

x C

once

ntra

tion,

ppm


May 25, 2002 - 10-11 am (Daytime)150 ppb

0 ppb

Figure 5. CALINE4 dispersion model results for MS Highway 6 example, daytime

Figure 6. CALINE4 dispersion model results for MS Highway 6 example, nighttime


17 17

x

y

C

2.2 ALOHA Dispersion Model ALOHA (Areal Locations of Hazardous Atmospheres) is a computer program developed

by EPA and NOAA especially for use by people responding to hazardous chemical release accidents, as well as for emergency planning and training. ALOHA can predict the rates at which chemical vapors may escape into the atmosphere from broken gas pipes, leaking tanks, and evaporating puddles [18]. ALOHA was not designed to predict concentration values from transportation vehicle sources, however, it was used in this study for the purpose of comparisons.

ALOHA uses two different dispersion models depending on the gas type: Gaussian and heavy gas models. The Gaussian model is used when the pollutant of concern has the same density as air. In addition, the heavy gas model is for pollutants with a density greater than the density of the air.

According to the Gaussian model wind and atmospheric turbulence are the forces that move the molecules of a released gas through the air, so as an escaped cloud is blown downwind (x), “turbulent mixing” causes it to spread out in the crosswind (y) and upward (z) directions. The gas concentration at any crosswind slice of a moving pollutant cloud looks like a bell-shaped curve, high in the center and lower on the sides. Figure 7 shows the Gaussian spread.

Figure 7. Pollutant concentration for downwind and crosswind locations using Gaussian formulation [18]

ALOHA Procedure

The first step in the ALOHA dispersion model is to select the location of study. Predefined locations are available; however, new locations can be defined using the new location coordinates (latitude, longitude). Figure 8 shows the screen for the location information. In this study, a new location for Oxford, Mississippi was created. The next step is to choose the chemical of concern. A chemical library is available in ALOHA. Nitrogen dioxide was chosen for this study. The next step is to describe the current weather conditions. For this purpose, ALOHA uses two windows, as shown in Figure 9. Wind speed, wind direction, wind measurement height, ground roughness, and cloud cover are the inputs required for this step. The wind measurement height is used to account for the wind changes above ground level. On the second window for atmospheric data, the air temperature, the stability class, the inversion height, and the humidity are the rest of the input values required.


18 18

Figure 8. ALOHA location information

Figure 9. ALOHA atmospheric input data

Figure 10. User input source strength for a direct release in the atmosphere


19 19

The next step is to describe the source strength. ALOHA allows different options for the pollutant release. These options are: direct release, puddle, tank, or pipe. For the case of a transportation source, the release can be considered as a direct source. Figure 10 shows the window to enter the release information for the case of direct source. If the release is from a continuous source, the amount of pollutant released in grams per unit time is required. For the case of an instantaneous source, the amount of pollutant released in grams is required.

The amount of pollutant emitted by the vehicle can be calculated using the information of traffic volume and the emission factor. The source strength (grams/sec) is equal to the emission factor (grams/veh/h) multiplied by the traffic volume (VPH) and divided by 3,600. The following expression shows a conversion example for the emission strength (daytime case of Mississippi Highway 6 in Oxford).

h-secg

57.4sec600,3

hr 1h

veh297,1

h-vehg

69.12 =××

Figure 11. Concentration versus time at a location away from the source

With this information, ALOHA can compute the pollutant dispersion at different receptor locations. Figure 11 shows an example of the concentration plot calculated by ALOHA. Only one location at a time can be calculated in ALOHA. If more locations are required, the new coordinates must be re-entered and the concentration is calculated again for the new location. Numerical Example Using ALOHA

The ALOHA dispersion model was used to predict NOx concentrations for MS Highway 6 during April and May of 2001. The emission rate generated by the traffic was calculated from the MOBILE6 program [21]. The two cases are studied, nighttime and daytime. The pollutant concentration was calculated at different receptor locations along the center of the plume (y = 0.0 m), which presents the maximum concentration. The results from ALOHA for the two cases are very large. These values cannot be compared with CALINE4 results because ALOHA used a very large concentration for one-hour continuous source.


20 20

ALOHA has many strengths on the chemical source term side. It includes various source types and an large array of pure chemicals for users to choose. However, the dispersion model used in ALOHA may give unreliable results under some particular conditions: for example, very low wind speeds, very stable atmospheric conditions, wind shifts, and terrain steering effects [18]. It is imperative to note that the dispersion model in ALOHA is based on the point source emissions, which is not the case for roadway traffic. However, it can be adopted for use in transportation applications in some situations, such as when there is a leak of chemical gas from a truck accident. One example of the use of ALOHA in transportation accident applications was done by Sumrall [23]. In his study, the impact of a Chlorine release on a highway in a hypothetical situation was assessed. Note that selecting Chlorine required the use of the Heavy Gas model in the study. The effects of wind speed (0.63, 5.6, and 20.0 m/s, source strength (1,000, 5,000, and 10,000 g/min), and duration of release (varied from 15 to 60 minutes) were analyzed. The analysis results show that wind speed has the greatest impact on plume dispersion. The greatest release is dispersed quicker in high wind than even the smallest release in moderate wind. The presence of a strong wind creates a turbulent atmosphere where the plume is torn apart and dispersed rapidly into surroundings. This effect acts to narrow the plume as the wind speed increases. Wind also plays a role in determining the atmospheric stability class. The more unstable the atmosphere is, the better the gas disperses. 2.3 DISPER2D Dispersion Model A two-dimensional (2D) dispersion model was developed in the research study of Garza [22]. The following section presents the different approaches conducted for the development of the 2D dispersion model. In the first part, the solution for the 2D dispersion equation in cylindrical coordinates for an instantaneous point source was obtained. Then, the solution for the 2D dispersion equation in rectangular coordinates for a continuous vehicle point source was obtained. Two-Dimensional Model for Instantaneous Point Source

In this section the following Partial Differential Equation (PDE) is solved:

∂∂

+∂∂

=∂∂

ru

rru

Ktu

r

12

2

(6)

where:

u = average concentration (g / m3) r = radius distance from the source at the middle of the highway (m) Kr = radial eddy diffusion coefficient, also known as eddy diffusivity (m2 / s) The PDE in Equation 6 is the governing equation for the dispersion of pollutants in

cylindrical coordinates without wind. Figure 12 shows the dispersion problem represented by this diffusion equation.

The following boundary and initial conditions are used to solve Equation 6:


21 21

( ) 0, =∞± tu (7) ( ) )(0, rSru δ= (8)

The initial condition presented in Equation 7.8 represents an instantaneous source of

pollutants where S has units of mass per area and per time (g/m2/s).

Figure 12. Problem statement in cylindrical coordinates, pollutant dispersion model

For the solution of Equations 6, 7, and 8, the method of Fourier Transform is used, as presented by Seinfeld and Pandis (page 893) [24]. The detailed derivation is presented by Garza [22]. Then, the final solution for Equation 6 is as follows:

+−=

trKtr

trKS

trurr 4)(

exp4

),(2

π (9)

where:

u = average concentration (g / m3) r = radius distance (m)

Kr = radial eddy diffusion coefficient, also known as eddy diffusivity (m2 / s) S = source strength (g / m2 / s) t = time (s)

Two-Dimensional Dispersion Model with Continuous Vehicle Source

This section shows the solution of the dispersion equation with a continuous source in rectangular coordinates for one vehicle point on the highway, as shown in Figure 13. The atmospheric diffusion equation taken from Seinfeld and Pandis (page 886) [24] for a generalize case considering x, y, and z coordinates, is shown below:

r = x 2 +y2

x

y

r = x 2 +y2

x

y

Roadway

x

c

y

w

Roadway

x

c

y

w


22 22

),(2

2

tSx

uK

xu

wtu

jj

jj x+

∂∂

=∂∂

+∂∂

(10)

where: u = mean concentration (g / m3) wj = velocity vector (j = x, y, c) (m / s) xj = x, y, and c coordinates (m) Kj = eddy diffusion coefficient (j = x, y, c) (m2 / s) S(x, t) = Source strength at position x, and time t (g / m3 / t)

To derive a simplified 2D diffusion equation, the following assumptions and

simplifications are applied to Equation 10: 1. The process is steady state, ∂u/∂t = 0. 2. The diffusion is considered only in the x, y coordinate system (2D) as shown in Figures

13 and 14. 3. The major transport direction due to the wind is chosen to lie along the x-axis

(perpendicular to the road), and the vertical diffusion is chosen to lie along the y-axis as shown in Figures 13 and 14.

4. The wind speed w is chosen to be constant at any point in the x, y coordinate system and perpendicular to the highway (in the x direction).

5. No wind is considered in the y direction. 6. The transport of pollutant due to the wind in the x-direction is dominant over the

downwind diffusion, wx(∂u/∂x)>>Kx(∂2u/∂x2). Therefore the term Kx(∂2u/∂x2) is dropped from the differential equation.

7. The source term S is defined using the delta Dirac function as follows: S(x, y) = qδ(x)δ(y), where q is the source strength with units of mass per unit length per unit time (g / m / s).

8. The boundary at y = 0 (ground level) is not considered in this solution. Therefore, the 2D diffusion equation is as follows:

)()(2

2

yxqyu

Kxu

w y δδ+∂∂

=∂∂

(11)

where:

w = wind speed (m / s) u = average concentration (g / m3)

Ky = vertical eddy diffusion coefficient, also known as eddy diffusivity (m2 / s) q = source strength in mass per length per time (g / m / s) x = downwind distance from the source at the middle of the highway (m) y = elevation for receptor from ground level (m) The following boundary conditions are used to solve Equation 11:

( ) 0,0 =yu (12)

( ) 0, =±∞xu (13)


23 23

Roadway

x

c

y

w

C

C

Roadway

x

c

y

w

C

C

The source term included in Equation 11 can be handled in a different way. Seinfeld showed a procedure to eliminate this term from the differential equation [24].

Figure 13. Roadway plan view (Cross Section C-C is shown in Figure 14)

Equating material fluxes at x = 0, we have the following relation:

∫∞

∞−=⋅ dxyxqyuw )()(),0( δδ (14)

then,

)(),0( ywq

yu δ= (15)

Now the PDE to solve is:

2

2

yu

Kxu

w y ∂∂

=∂∂

(16)

with the new boundary conditions:

)(),0( ywq

yu δ= (17)

( ) 0, =±∞xu (18)

The set of equations 11, 12, and 13 are similar to the set of equations 16, 17, and 18. The last three equations are solved with the method of Fourier Transform [22]. The final solution for Equation 16 is as follows:

−=

xKwy

xwKq

yxuyy

4exp

4),(

2

π (19)


24 24

where: u = average concentration (g / m3) w = wind speed (m / s) Ky = vertical eddy diffusion coefficient, also known as eddy diffusivity (m2 / s) q = source strength in mass per length per time (g / m / s)

x = downwind distance for receptor from the middle of the highway (m) y = elevation for receptor from ground level (m) Analysis of Diffusion Coefficients

The criteria for the selection of one of six atmospheric stability classes are defined by

Turner [26] as a function of the wind speed, solar radiation, and cloud cover. These criteria are copied from Seinfeld and Pandis [24] and shown in Table 6. The expressions available for the vertical diffusion coefficient, Ky, as noted by Seinfeld and Pandis [24], are based on Monin-Obukhov similarity theory coupled with observational or computationally generated data. These expressions are organized according to the class of atmospheric stability. Three different classes of atmospheric stability are defined by Seinfeld: unstable, neutral, and stable. For each class of stability, there is an expression for Ky.

Table 6. Estimation of Pasquill stability classes [24, 26]

Solar Radiation Nighttime

Cloud Cover Fraction

Surface Wind Speed

at 10 m (m/s)

Strong (I > 1.0

Langley/min)

Moderate (0.5 = I = 1.0 Langley/min)

Slight (I < 0.5

Langley/min) = 4/8 = 3/8

< 2 A A-B B 2-3 A-B B C E F 3-5 B B-C C D E 5-6 C C-D D D D > 6 C D D D D

A: extremely unstable D: neutral B: moderately unstable E: slightly stable C: slightly unstable F: moderately stable

a) Unstable Conditions (A, B, or C): In unstable conditions there is usually an inversion base height at y = yi that defines the extent of the mixed layer. The two parameters that are key in determining Ky are the wind speed w and yi [24]. The following empirical expression for Ky under unstable conditions was derived by Lamb, et al. [27] and Lamb and Duran [28], under the condition that the emissions are at or near ground level, as noted by Seinfeld and Pandis (page 939) [24].


25 25

>

≤<

−

≤≤

+

−

+

+

≤≤

−

=⋅

10.100130

10.160.0106exp20.0

60.005.0560.2096.4351.1408.0021.0

05.001515.2

432

4134

i

ii

iiiii

ii

i

y

yy.

yy

yy

yy

yy

yy

yy

yy

yy

Ly

yy

yw

K

κ

(20) where:

Ky = vertical eddy diffusion coefficient, also known as eddy diffusivity (m2/ s) w = wind speed (m / s) y = elevation for receptor from pavement surface (m) yi = inversion layer height which defines the extent of the mixed layer (m) κ = 0.4 (integration constant) recommended by Seinfeld and Pandis [24] L = Monin-Obukhov length (m), which is the height above the ground at which the

production of turbulence by both mechanical and buoyancy forces is equal. These variables are shown in Figure 14. The value for the Monin-Obukhov length (L) is

calculated using the following expression defined by Golder [29] and copied from Seinfeld and Pandis (page 873) [24].

0log1

zbaL

+= (21)

where: z0 = roughness length (m), an integration constant defined by Seinfeld and Pandis [24] for

dispersion in the roadway and surrounding terrain type, as shown in Figure 14. a, b = coefficients as function of the stability class (from Table 7) Table 8 shows some common values for the roughness length depending on the

surrounding terrain type, defined by McRae, et al. [30] and noted by Seinfeld and Pandis [24].

Table 7. Coefficients for straight- line approximation of 1/L, by Golder [29] and noted by Seinfeld and Pandis [24]

Coefficients

Pasquill Stability Class a b

Extremely Unstable A -0.096 0.029 Moderately Unstable B -0.037 0.029 Slightly Unstable C -0.002 0.018 Neutral D 0.000 0.000 Slightly Stable E +0.004 -0.018 Moderately Stable F +0.035 -0.036


26 26

Cross Section C-C

Figure 14. Definition of variables for the 2D dispersion problem in rectangular coordinates

Table 8. Roughness lengths for various surfaces defined by McRae, et al. [30] and noted by Seinfeld and Pandis [24]

Surface z0 (m)

Very Smooth (ice, mud flats) 10-5 Snow 10-3 Smooth sea 10-3 Level desert 10-3 Lawn 10-2 Uncut grass 0.05 Fully grown root crops 0.1 Tree covered 1 Low-density residential 2 Central business district 5-10 Note: The terrain near MS Highway 6 site in Oxford is a combination of trees, grassy land, and buildings. Therefore, a value of 1.0 has been used for the analyses in this research

b) Neutral Conditions (D): For neutral conditions, Myrup and Ranzieri [31] proposed the

following empirical form for Ky, as noted by Seinfeld and Pandis (page 940) [24]:

>≤≤−

<

=1.10

1.11.0)1.1(

1.0

*

*

i

ii

i

y

yyforyyforyyyu

yyforyu

K κ

κ

(22)

x

y

w

Continuous Source at the Middle of the Roadway

Inversion Height, yi

Vertical Eddy Diffusion

Coefficient, Ky

Receptor Height, y

Roadway Cross Section

Roughness Length, z0

Downwind Distance, x

x

y

w

Continuous Source at the Middle of the Roadway

Inversion Height, yi

Vertical Eddy Diffusion

Coefficient, Ky

Receptor Height, y

Roadway Cross Section

Roughness Length, z0

Downwind Distance, x


27 27

where:

y = elevation for receptor from pavement surface (m) yi = inversion layer height which defines the extent of the mixed layer (m) u* = friction velocity = 1 m/s as recommended by Seinfeld and Pandis [24] κ = 0.4 (integration constant) recommended by Seinfeld and Pandis [24]

c) Stable Conditions (E, or F): Businger and Ayra [32] proposed the following

expression to calculate the vertical diffusion coefficient for stable conditions, as noted by Seinfeld and Pandis (page 942) [24]:

−

+=

*

* 8exp

)(7.474.0 ufy

Lyyu

K y

κ (23)

where: y = elevation for receptor from pavement surface (m) κ = 0.4 (integration constant) recommended by Seinfeld and Pandis [24] u* = friction velocity = 1 m/s as recommended by Seinfeld and Pandis [24] f = Coriolis Parameter = 2Ω sin φ Ω = Rotational velocity of the Earth = 7.2722 x 10-5 rad/s φ = Latitude of the receptor site (degrees) L = Monin-Obukhov Length (m) (Equation 21)

The Coriolis parameter was calculated for the rotational velocity of the Earth and the latitude of 34o, with a value equal to 0.6228 x 10-4 1/s [24]. Application of “DISPER2D” Dispersion Program

The implementation of the solution in Equation 194 and the evaluation of the vertical eddy diffusion coefficient become complex because the vertical eddy diffusion coefficient is a function of the vertical elevation, which gives a different coefficient for each receptor location. Therefore, a computer program was developed to calculate the vertical eddy diffusion coefficient and the dispersion of pollutants at different locations from the continuous source of a vehicle point on the roadway. This program was written in Fortran. The complete source code of “DISPER2D” is produced by Garza [22].

This computer program reads from an input file “conc.in” the emission factor of a given air pollutant in g/veh/mi, which is converted to µg/m/s, the traffic volume (veh/h), the stability class, the ambient temperature (oC), the inversion layer height (m), the roughness length of the roadway and the surrounding terrain type (m), and the receptor locations away from the center of the roadway (m). A list of (x, y) coordinates must be included in the input file. The program calculates the concentration values for each receptor coordinate provided in the input file.

The results of a numerical example are presented using the data collected on MS Highway 6 in Oxford, Mississippi. The data used in this example are presented in Table 9. These data are similar to the data used for the example conducted with CALINE4 (Table 4). The NOx emission rates presented in this table were calculated using the MOBILE6 program [21]. Two different cases are studied, one for May 24, 2001 at nighttime, and the other one for May 25,


28 28

2001 at daytime. The calculations were conducted using the total traffic volume, including cars and trucks, and without considering chemical reaction. All calculations were conducted at a receptor height of 0.30 m and an average vehicle speed of 76 km/h (40 mi/h).

The NOx air pollutant concentration was calculated at the following receptor distances from the center of the highway: 1, 2, 5, 10, 20, 50, 100, 150, 200, and 250 meters. Table 10 shows the results calculated from the “DISPER2D” computer program. These concentration values were calculated at an elevation of 0.30 m above the pavement surface. Figures 15(a) and 15(b) show the calculated concentration values for both cases.

Table 9. Input values for MS Highway 6 example using “DISPER2D”

Air

Temperature (oC)

Wind Speed (m/s)

Stability Class

* Traffic Volume (veh/h)

NOx Emission

Rate (g/veh/mi)

May 24, 2001

11 – 12 pm Nighttime

11.80 0.60

F

Car: 358 Truck: 29 Total : 382

2.57

May 25, 2001


17.30 2.10 A Car: 1,198 Truck: 99

Total : 1,297 2.51

Receptor Height: 0.30 m (above the pavement surface) + Roughness Length: 1.0 m Other Inputs Inversion Layer Height: 1,000 m

+ y = 0.30 m assuming all flat surface around the highway for the purpose of these concentration dispersion calculations * Average vehicle speed = 76 km/h (40 mi/h) Table 10 also shows the concentration values calculated from “DISPER2D” for the peak traffic volume measured on May 25, 2001, from 11 to 12 am. The emission rate for this peak traffic volume, assuming an average vehicle speed of 48 km/h (30 mi/h), is 2.54 g/veh/mi. For comparison purpose, the rest of the input values (air temperature, wind speed, stability class, inversion height, and roughness length) were unchanged. From these calculations, we can see an increase in the concentration values of 35%, while the traffic was increased 33%. Therefore, a higher traffic volume slows the traffic flow at a reduced speed and increases the emission rate, which leads to higher concentration values.


29 29

Table 10. NOx emission results for MS Highway 6 example from “DISPER2D”

NOx Concentration from DISPER2D, ppm

Receptor

Distance, m

May 24, 2001 11 – 12 pm Nighttime

May 25, 2001 10 – 11 am

Daytime

May 25, 2001 11 – 12 am

Peak Traffic+

1 0.105 0.077 0.103 2 0.115 0.109 0.146 5 0.095 0.104 0.141 10 0.074 0.085 0.114 20 0.054 0.064 0.087 50 0.035 0.042 0.057 100 0.025 0.030 0.041 150 0.021 0.025 0.034 200 0.018 0.022 0.029 250 0.016 0.019 0.026

* EPA Maximum Standard for NOx = 0.0530 ppm (Annual average) + These concentration values were calculated using the following peak traffic volume and the emission rate, assuming the same weather data as during 10-11 am Traffic Volume (Cars & Trucks): 1,730 veh/h

Emission Rate: 2.54 g/veh/mi Average Vehicle Speed: 48 km/h (30 mi/h) Figures 15(a) and 15(b) show that the pollutant dispersion attenuation is very similar to the dispersion calculated from CALINE4, the difference is in the magnitudes. Figures 16(a) and 16(b) show a comparison of the concentration values calculated from CALINE4 and DISPER2D, for daytime and nighttime respectively. These concentration values are plotted for a receptor height of 0.30 meters. Figures 16(a) and 16(b) show that reasonably good results are calculated from the DISPER2D dispersion program, compared to the CALINE4 calculated concentration values. The daytime concentration values of NOx calculated with this newly developed computer program were higher than those calculated by CALINE4 for the same case and using the same input values.

The CALINE4 concentration and the DISPER2D concentration values at 250 me ters from the center of the road are: 0.003 ppm and 0.019 ppm respectively. In reality, there should be higher NOx concentration near the source and away from the source because of the traffic on adjacent roads, as well as the emissions generated by five other stationary/point sources in Oxford [3]. The solution for the atmospheric diffusion equation (Equation 19) implemented in the “DISPER2D” program, apparently gives reasonable dispersion results. Differential Absorption LIDAR (DIAL) measurements of air pollution conducted in Oxford, MS during May, 2001 [3] show significantly higher concentration values than those calculated by CALINE4 and Equation 19 used in the “DISPER2D” program. These differences in the calculated and measured concentrations can be caused by other factors, such as emissions from adjacent roads and stationary/point sources, which are not considered in these calculations.


30 30

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0 50 100 150 200 250Receptor Distance (x), m

NO

x Con

cent

rati

on, p

pmReceptor Height = 0.30 mReceptor Height = 1.0 mReceptor Height = 3.0 mReceptor Height = 10.0 m


0 ppb

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0 50 100 150 200 250


NO

x Con

cent

rati

on, p

pm



0 ppb

(a) Daytime

(b) Nighttime

Figure 15. “DISPER2D” program results for MS Highway 6 example


31 31

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0 50 100 150 200 250


NO

x C

once

ntra

tion

, ppm

DISPER2D Dispersion Model

CALINE4 Dispersion Model


0 ppb

0.000

0.025

0.050

0.075

0.100

0.125

0.150


NO

x C

once

ntra

tion

, ppm




0 ppb

(a) Daytime

(b) Nighttime

Figure 16. Comparison of CALINE4 and DISPER2D results for MS Highway 6


32 32

Two-Dimensional Dispersion Model with Total Reflection The solution for the 2D atmospheric dispersion equation shown in Equation 19 does not consider the boundary at y = 0 (ground level). A simple procedure to account for this boundary at ground level is presented by Seinfeld and Pandis [24]. For total reflection of pollutants, an imaginary source at an elevation of –H must be added to the problem. Therefore, the total concentration at the receptor location is calculated by adding the concentration from a source at an elevation of H and the concentration from a source at an elevation of –H.

Adding these two concentration values, the final equation considering total reflection at ground level is as follows:

+−+

−−=

xKwHy

xKwHy

xwKq

yxuyyy

4)(

exp4

)(exp

4),(

22

π (24)

In Equation 24, the second exponential term inside the parenthesis is from the imaginary

source. This equation was implemented in a second version of the DISPER2D program. The results from this second version of the DISPER2D program are shown in Figures 17(a) and 17(b), for the daytime and nighttime cases respectively. A comparison with the CALINE4 results is shown in Figures 18(a) and 18(b). The results from this second version of DISPER2D are higher for receptors close to the highway. This is caused by the reflection of the pollutants from the ground. Sensitivity Analysis of “DISPER2D”

To gain more understanding of the dispersion behavior, a sensitivity analysis for different stability classes (for a range of wind speeds) was conducted. This analysis was conducted without considering reflection (first version of DISPER2D). The sensitivity analysis results for the dispersion of pollutants for different stability classes are shown in Figure 19.

The concentration values shown in Figure 19 were calculated for a downwind distance of 10 m from the middle of the roadway. This figure shows that the maximum concentration values are calculated for the stability class A at a low wind speed of 1 m/s. Minimum concentration values are calculated for stability class D at a higher wind speed of 10 m/s.


33 33

0.000

0.050

0.100

0.150

0.200

0.250

0.300


NO

x Con

cent

rati

on, p

pm



0 ppb

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0 50 100 150 200 250


NO

x C

once

ntra

tion

, ppm



0 ppb

(a) Daytime

(b) Nighttime

Figure 17. “DISPER2D” program results with total reflection for MS Highway 6


34 34

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0 50 100 150 200 250


NO

x C

once

ntra

tion,

ppm




0 ppb

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0 50 100 150 200 250


NO

x C

once

ntra

tion

, ppm




0 ppb

(a) Daytime

(b) Nighttime

Figure 18. Comparison of CALINE4 and DISPER2D results for MS Highway 6


35 35

Figure 19. Sensitivity analysis of DISPER2D for different stability class and wind speed

Another sensitivity analysis was conducted using different average vehicle speeds of 0, 10, 20, 30, 40, 50, 60, and 70 mi/h (0, 16, 32, 48, 64, 80, 96, 112 km/h) at a receptor distance of 10 meters. Figure 20 shows the sensitivity analysis results for different average vehicle speeds. This figure shows that for vehicle speeds less than 32 km/h (20 mi/h), the concentration values increase. In addition, for vehicle speeds above 64 km/h (40 mi/h), the concentration values also increase. These results show that the vehicle speed of 48 km/h (30 mi/h) gives the lowest concentration value.

The following sensitivity analysis was conducted to study the effect of the roughness length on the calculated concentration dispersion values. Figure 21 shows the results from this sensitivity analysis. The concentration values were calculated at a receptor distance of 10 meters from the middle of the roadway. This sensitivity analysis included roughness lengths from 0.001 m for uncut grass, through 10 m for a central business district (see Table 8 for roughness values for different terrain types). Relatively small differences are calculated for different roughness lengths, irrespective of y values.

Figures 22 and 23 show the calculated concentration values for the sensitivity analyses of wind speed and vehicle speed for different receptor distance in the x-direction. These values were calculated using a receptor height of 0.30 m. Note that higher wind speeds result in relatively lower concentration near the source and, therefore, will tend to dissipate at shorter distances compared to calm winds.

0.00

0.05

0.10

0.15

0.20

0.25

0 1 2 3 4 5 6 7 8 9 10 11

Wind Speed, m/s

NO

x Con

cent

rati

on, p

pm

y = 0.3 m

y = 2 m

y = 3 m

Stability Class A DB CB

Notes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Receptor distance, X = 10 m from middle of the roadway 3) Source at the middle of the roadway on the pavement surface 4) Average vehicle speed = 40 mph 5) NOx emission rate estimated from MOBILE6 = 2.51 g/veh/mi 6) Incoming solar radiation = moderate 7) y = receptor height

250 ppb

0 ppb


36 36

0.00

0.05

0.10

0.15

0.20

0.25

0 10 20 30 40 50 60 70

Average Vehicle Speed, mi/h

NO

x Con

cent

rati

on, p

pmy = 0.3 m

y = 2 my = 3 m

Notes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Receptor distance, X = 10 m from middle of the roadway 3) Source at the middle of the roadway on the pavement surface 4) y = receptor height

250 ppb

0 ppb

0.00

0.05

0.10

0.15

0.20

0.25

0 2 4 6 8 10

Roughness Length, m

NO

x C

once

ntra

tion

, ppm

y = 0.3 m

y = 2 m

y = 3 m

Notes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Receptor distance, X = 10 m from middle of the roadway 3) Source at the middle of the roadway on the pavement surface 4) Average vehicle speed = 40 mph 5) NOx emission rate estimated from MOBILE6 = 2.51 g/veh/mi 6) y = receptor height

250 ppb

0 ppb

Figure 20. Sensitivity analysis of DISPER2D for average vehicle speed

Figure 21. Sensitivity analysis of DISPER2D for different roughness length


37 37

0.00

0.05

0.10

0.15

0.20

0.25


NO

x C

once

ntra

tion,

ppm

Vehicle Speed = 10 mi/h



Receptor Height = 0.30 mNotes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Source at the middle of the roadway on the pavement surface 3) y = receptor height

250 ppb

0 ppb

0.00

0.05

0.10

0.15

0.20

0.25


NO

x C

once

ntra

tion,

ppm

Wind Speed = 1 m/s - Stability Class A

Wind Speed = 2 m/s - Stability Class B

Wind Speed = 6 m/s - Stability Class C

Receptor Height = 0.30 mNotes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Source at the middle of the roadway on the pavement surface 3) Average vehicle speed = 40 mph 4) NOx emission rate estimated from MOBILE6 = 2.51 g/veh/mi 5) y = receptor height

250 ppb

0 ppb

Figure 22. Sensitivity analysis of DISPER2D for different stability class and wind speed, concentration against receptor distance

Figure 23. Sensitivity analysis of DISPER2D for different vehicle speed, concentration against receptor distance


38 38

Comparison of CALINE4 and DISPER2D The main difference between CALINE4 and DISPER2D dispersion programs is the eddy diffusivity. CALINE4 uses the Gaussian line source formula, where the dispersion parameters, σy (in the horizontal direction and perpendicular to the receptor distance x) and σz (in the vertical direction), are introduced in the solution of the atmospheric diffusion equation. These diffusion parameters, which are function of wind speed, stability class, and receptor distance, simplify the evaluation of the dispersion equation. The DISPER2D program does not use these dispersion parameters; it uses directly the eddy diffusion coefficient. A relation between the dispersion parameter, σz (vertical direction), and the eddy diffusion coefficient, Ky (vertical direction), can be derived from the solution of the atmospheric diffusion equation. Thus, from Equation 19:

w

xK yz

2=σ (25)

A comparison between the dispersion parameter, σz, used in CALINE4 and the dispersion parameter calculated using Equation 25 with the values of the eddy diffusion coefficient, Ky, from DISPER2D is shown in Figure 24. This figure shows that the dispersion parameter, calculated using the values of the eddy diffusion coefficient, Ky, is smaller than the dispersion parameter, σz, from CALINE4 for receptor heights less than 3 m. This is the reason for higher concentration values calculated using DISPER2D (as shown in Figures 16(a) and 16(b)).

Figure 24 also shows that the dispersion parameter from CALINE4 is a function of the receptor distance (x) and it does not depend on the receptor height, y. The values for the dispersion parameters σz at different x values and the eddy diffusion coefficients Ky (function of vertical distance and constant with respect to x) were calculated for the MS Highway 6 example (daytime case) and considering the wind perpendicular to the roadway. For a more meaningful comparison between the two dispersion models, the values of the eddy diffusion coefficient, Ky, calculated using the dispersion parameters, σz, from CALINE4, were used in the DISPER2D program. Figure 25 shows a comparison between the Ky from the DISPER2D program with the eddy diffusion coefficient calculated using the σz values from CALINE4. Using these values, the results of NOx dispersion from CALINE4 and DISPER2D for the MS Highway 6 example (daytime case) are compared in Figure 26. These concentration values were calculated for a receptor height of 0.30 m. This figure shows a good agreement between CALINE4 and DISPER2D for a receptor distance greater than 15 m from the center of the roadway. Apparently the CALINE4 results are problematic within the first 15 m.


39 39

0.0

10.0

20.0

30.0

40.0

50.0

0 50 100 150 200 250


σz,

m

CALINE4

DISPER2D at y = 0.30 m




Receptor Height = 0.30 m




To calculate σz using Ky from DISPER2D:Example for Daytime Case

NOTE: y = vertical direction in DISPER2D

w

xK yz

2=σ

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0 50 100 150 200 250


Κy,

m2 /s

CALINE4





Receptor Height = 1.00 mReceptor Height = 3.00 m


To calculate Ky using σz from CALINE4:Example for Daytime Case

NOTE: y = vertical direction in DISPER2Dxw

K zy 2

2σ=

Figure 24. Comparison of σz from CALINE4 with σz calculated using Ky from DISPER2D

Figure 25. Comparison of Ky from DISPER2D with Ky calculated using σz from CALINE4


40 40

0.000

0.025

0.050

0.075

0.100

0.125

0.150


NO

x Con

cent

rati

on, p

pmDISPER2D with CALINE4 Dispersion Parameters



0 ppb


Figure 26. Comparison of CALINE4 with DISPER2D using eddy diffusion coefficient, Ky, calculated from CALINE4 dispersion parameter

2.4 DISPER3D Dispersion Model The dispersion model implemented in the DISPER2D program is the solution from the 2D dispersion equation shown in Equation 11. This solution represents a line source model, since the concentration in the crosswind direction remains constant. In addition, the solution of this dispersion equation was obtained assuming that the wind direction is perpendicular to the road; however, this is not always the case. In this section, an alternative analysis is presented to solve the dispersion problem when the wind is not perpendicular to the road. For a wind direction that is not perpendicular to the line source, Turner [26] suggests that the calculated concentration value be divided by (sin φ), where φ is the angle between the line source and the wind direction. However, this correction should not be used when φ is less than 45 degrees [20, 26]. When the wind is parallel to the roadway, a buildup of pollutants occurs in the downwind direction. CALINE2 dispersion model, an early version of CALINE4, used the concept of a virtual point source to account for a wind parallel to the roadway [33]. CALINE2 used the three-dimensional (3D) dispersion model for a continuous point source when the wind was parallel to the roadway. The highway is divided in different elements as in the procedure of CALINE4. Then, the area sources from these elements are approximated as a series of virtual point sources along the roadway [33]. CALINE2 used the 3D dispersion model for continuous point source to predict concentration values produced by these virtual point sources. This approach seems reasonable, however, in a later study, comparison of predicted and measured results showed that the predicted concentrations near the roadway were two to five


41 41

w x

c

y

φ(xR, cR, yR)

Receptor

w x

c

y

φ(xR, cR, yR)

Receptor

times greater than measured values for stable, parallel wind conditions [17]. In addition, another study concluded that CALINE2 overpredicted for parallel winds by an average of 66% for all stabilities [17. For this reason, this approach is no longer used in CALINE4.

Figure 27. Dispersion model for a wind direction not perpendicular to the roadway, the infinite line source is approximated as a series of point sources

A new three-dimensional dispersion model is developed to account for wind not perpendicular to the road. Instead of converting an area source to a virtual point source, the infinite line source is considered as a series of point sources. Figure 27 shows the approach implemented to calculate pollutant concentrations when the wind is not perpendicular to the roadway. The concentration value at the receptor location is calculated as an incremental concentration from each point source in the roadway. In order to solve this dispersion problem, the solution for the 3D dispersion equation for a continuous point source must be derived. The 3D dispersion equation, as presented by Seinfeld and Pandis (page 899) [24], is as follows:

)()()('2

2

2

2

cyxqcu

Kyu

Kxu

w cy δδδ+∂∂

+∂∂

=∂∂

(26)

and the boundary conditions are:

0),,0( =cyu and ±∞→= cy, 0),,( cyxu (27) where:

u = average concentration (g / m3) w = wind speed (m / s)

Ky = vertical eddy diffusion coefficient, also known as eddy diffusivity (m2 / s)


42 42

Kc = horizontal eddy diffusion coefficient (m2 / s) q’ = source strength in mass per time (g / s) x = downwind distance from the source at the middle of the highway (m) c = crosswind distance (m) y = elevation for receptor from ground level (m) The following assumptions are applied to the Equation 26:

1. The process is steady state, therefore ∂u/∂t = 0. 2. The major transport direction due to the wind is chosen to lie along the x-axis, the

vertical diffusion is chosen to lie along the y-axis, and the crosswind is chosen to lie along the c-axis, as shown in Figure 27.

3. The wind speed w is chosen to be constant at any point in the x, y, c coordinate system, with an angle φ with respect to the roadway centerline.

4. No wind is considered in the y- or c-direction. 5. The transport of pollutant due to the wind in the x-direction is dominant over the

downwind diffusion, wx(∂u/∂x)>>Kx(∂2u/∂x2). Therefore the term Kx(∂2u/∂x2) is dropped from the differential equation.

6. The source term S is defined using the delta Dirac function as follows: S(x, y, c) = q’ δ(x)δ(y) δ(c), where q’ is the source strength with units of mass per time (g / s).

7. The boundary at y = 0 (ground level) is not considered in this solution. The same procedure to solve the 2D dispersion equation was followed to solve Equations

26 and 27. First, the set of equations was modified equating material fluxes across the plane at x = 0. This was done following the procedure presented by Seinfeld and Pandis [24]. The new set of differential equations is as follows:

2

2

2

2

cu

Kyu

Kxu

w cy ∂∂

+∂∂

=∂∂

(28)

and the new boundary conditions are:

)()(),,0( cywq

cyu δδ′

= (29)

±∞→= cy, 0),,( cyxu (30) The set of equations 28, 29, and 30 are solved with the method of Fourier Transform, first with respect to the y direction and then with respect to the c direction. After following the same procedure, the final solution for the 3D dispersion equation is as follows:

+−=

cycyKc

Ky

xw

xKK

qcyxu

22

21 4

exp)(4

'),,(

π (31)


43 43

where: u = average concentration (g / m3) w = wind speed (m / s)

Ky = vertical eddy diffusion coefficient, also known as eddy diffusivity (m2 / s)

Kc = horizontal eddy diffusion coefficient (m2 / s) = 31

43

)4.0(10.0−

⋅−⋅⋅ Ly i from Seinfeld and Pandis [24]

q’ = source strength in mass per time (g / s) x = downwind distance from the source at the middle of the highway (m) c = crosswind distance (m)

y = elevation for receptor from ground level (m) Equation 31 is the solution for a 3D dispersion problem with a continuous point source (q’ in g/s). However, for the dispersion problem shown in Figure 27, the emission is from an infinite line source (q in g/m/s). Considering the infinite line source as a series of point sources, Equation 31 can be used to calculate the concentration values, at a particular receptor location, from these point sources. Equation 31 is modified as follows:

jj cycy

Kc

Ky

xw

xKK

qcyxu ∑

+−

⋅=

22

21 4

exp)(4

1),,(

π (32)

where q is the source strength per unit length per unit time (g / m / s). This equation was implemented in the DISPER3D computer program. This 3D dispersion program is able to calculate pollutant concentration and dispersion for a single roadway and with different wind direction. The input file for this dispersion program is similar to the one used for the DISPER2D program, but including the wind direction angle (phi) with respect to the North (0o – 180o) and the roadway length. The DISPER3D dispersion program was used to show the effect of the wind direction on the pollutant dispersion. Figures 28 and 29 show the concentration values calculated for different wind directions and at a receptor distance of 10 and 100 meters respectively. Figure 28 shows that the highest pollutant concentration value at a receptor distance of 10 m is calculated for a wind parallel to the roadway alignment (0o and 180o). The lowest concentration value at this distance is calculated for a wind direction of 90o (perpendicular to the roadway). Figure 29 shows that the lowest concentration value at a receptor distance of 100 meters is calculated for a wind parallel to the roadway and the highest concentration value is calculated for a wind with an angle of 20 degrees with respect to the roadway alignment.


44 44

0.000

0.100

0.200

0.300

0.400

0.500

0 20 40 60 80 100 120 140 160 180

Phi, Degrees

NO

x C

once

ntra

tion

, ppm

y = 0.30 m

y = 2.0 my = 3.0 m

500 ppb

0 ppb

Notes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Receptor distance, X = 10 m from middle of the roadway 3) Source at the middle of the roadway on the pavement surface 4) Average vehicle speed = 40 mph 5) NO x emission rate estimated from MOBILE6 = 2.51 g/veh/mi 6) y = receptor height 7) Phi = angle of wind with respect to roadway alignment

0.000

0.100

0.200

0.300

0.400

0.500

0 20 40 60 80 100 120 140 160 180

Phi, Degrees

NO

x C

once

ntra

tion

, ppm

y = 0.30 my = 2.0 my = 3.0 m

500 ppb

0 ppb

Notes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Receptor distance, X = 100 m from middle of the roadway 3) Source at the middle of the roadway on the pavement surface 4) Average vehicle speed = 40 mph 5) NO x emission rate estimated from MOBILE6 = 2.51 g/veh/mi 6) y = receptor height 7) Phi = angle of wind with respect to roadway alignment

Figure 28. Sensitivity analysis of DISPER3D for wind direction at a receptor location of 10 m

Figure 29. Sensitivity analysis of DISPER3D for wind direction at a receptor distance of 100 m


45 45

0.000

0.100

0.200

0.300

0.400

0.500

0 50 100 150 200 250


NO

x C

once

ntra

tion

, ppm Phi = 0.0 Degrees

Phi = 20.0 Degrees

Phi = 40.0 DegreesPhi = 60.0 Degrees

Phi = 90.0 Degrees

500 ppb

0 ppb

Notes: 1) May 25, 2001, 10-11 a.m., air temperature 17.3 °C, traffic volume (car and truck) = 1,297 veh 2) Source at the middle of the roadway on the pavement surface 3) Average vehicle speed = 40 mph 4) NOx emission rate estimated from MOBILE6 = 2.51 g/veh/mi 5) Phi = angle of wind with respect to roadway alignment

Figure 30. Effect of wind direction on pollutant dispersion at different receptor distance Figure 30 shows the concentration values calculated from DISPER3D against receptor distance for different wind directions. From figure 30 it can be seen that a wind parallel to the roadway alignment produces higher concentration values close to the source but lower concentration values away from the source. A wind direction of 40 degrees with respect to the roadway alignment produces the highest concentration values at a distance of 250 meters from the roadway. Concluding Remarks

The solution obtained for the two-dimensional atmospheric diffusion equation was implemented in the DISPER2D computer program to calculate the concentration dispersion values from transportation sources. This program was implemented using the emission rate, traffic volume, and meteorological data collected previously for MS Highway 6 near Oxford, Mississippi.

The results in this section show that the DISPER2D air quality dispersion model gives reasonable results for predicting pollutant concentration and dispersion produced by transportation mobile sources. The results compared well with the CALINE4 air quality dispersion model after the first 15 m. This was further validated using the dispersion parameters from CALINE4 in DISPER2D. The results of the sensitivity analysis using the DISPER2D program showed that: a) Concentration decreases as wind speed increases from 1 m/s to 10 m/s and stability class

changes from A to D. b) Significantly higher concentration is predicted for average vehicle speeds less than 32

km/h (20 mi/h) and more than 64 km (40 mi/h). c) There is no significant effect of roughness length on the calculated concentration values. d) These results indicate that emission dispersion is significantly influenced by slower


46 46

vehicle speed, as well as lower wind speed. The DISPER2D program was developed to calculated concentration dispersion produced by transportation sources assuming that the wind is perpendicular to the road. An alternative solution to account for wind that is not perpendicular to the roadway was also presented. This alternative was implemented in the DISPER3D program to account for different wind directions. The sensitivity analysis for different wind direction shows that wind parallel to the roadway alignment produces the highest concentration values close to the source and lower concentration values away from the source. A wind direction of 40 degrees with respect to the roadway alignment produces the highest concentration value at 250 m from the source. 2.5 Comparison of Dispersion Models

The dispersion models, reviewed in this chapter, are somewhat similar in certain aspects. On the other hand, they also have some different characteristics. The main difference between CALINE4 and Garza’s DISPER2D and DISPER3D dispersion models is the eddy diffusivity. CALINE4 uses the Gaussian line source formula, where the dispersion parameters, σy (in the horizontal direction and perpendicular to the receptor distance x) and σz (in the vertical direction), are introduced in the solution of the atmospheric diffusion equation. These diffusion parameters, which are function of wind speed, stability class, and receptor distance, simplify the evaluation of the dispersion equation. The DISPER2D and DISPER3D programs do not use these Gaussian dispersion parameters; the eddy diffusion coefficient is directly applied into the equations. This results in higher concentration values calculated by the DISPER2D and DISPER3D programs at different receptor distances away from the source. The comparison of dispersion results calculated by CALINE4, DISPER2D, and DISPER3D for MS Highway 6, Oxford, Mississippi, is shown in Figure 31. Figure 31(a) shows more reasonable dispersion results using DISPER2D and DISPER3D near the source using the dispersion parameters from CALINE4. After 15 m, the dispersion results are similar. Figure 31(b) shows the dispersion results using DISPER2D and DISPER3D directly derived using the eddy diffusion coefficient. The input data and results of this MS Highway 6 example are summarized in Table 11. A major difference of the ALOHA dispersion model from CALINE4, DISPER2D, and DISPER3Ds is the source type. The Gaussian equation implemented in ALOHA is based on the point source formulation, while the Gaussian equation in the other models is based upon the line source formulation. Garza [22] simulated a case study by using CALINE4 and ALOHA to estimate the NOx concentration dispersion nearby MS Highway 6, Oxford, Mississippi. It was found that significantly higher concentration values were calculated by the ALOHA dispersion model compared to the CALINE4 dispersion model. Therefore, the line source Gaussian model seems to be more reasonable in predicting air pollutant concentrations and dispersion from highway traffic. Dispersion modeling and analysis methodologies, reviewed in this chapter, are not pursued further in this study.


47 47

Table 11. Summary for dispersion analysis of the MS Highway 6 example [22]

Air Temperature

(oC)

Wind Speed (m/s)

Stability Class

++Traffic Volume (veh/h)

NOx Emission Rate (source) (g/veh/mi)**

May 25, 2001 10 – 11 am

Daytime 17.30 2.10 A

Car: 1,198 Truck: 99

Total : 1,297 2.51

Receptor Height: 0.30 m (above the pavement surface) + Roughness Length: 1.0 m Other Inputs Inversion Layer Height: 1,000 m

+ y = 0.30 m assuming all flat surface around the highway for the purpose of concentration dispersion calculations ++ Average vehicle speed = 64 km/h (40 mi/h) ** 1 g/veh/mi = 0.625 g/veh/km

NO2 Concentration, ppm Receptor Distance from Source, m 1 m 5 m 50 m 200 m

CALINE4 Dispersion Model 0.026 0.033 0.015 0.004 DISPER2D with CALINE4 Dispersion Parameters 0.050 0.050 0.015 0.004 DISPER3D* with CALINE4 Dispersion Parameters 0.078 0.049 0.015 0.004 DISPER2D with Eddy Diffusion Coefficient 0.309 0.183 0.082 0.043 DISPER3D* with Eddy Diffusion Coefficient 0.309 0.183 0.082 0.043

* Use Phi = 90 degrees (wind perpendicular to highway)


48 48

(a) DISPER2D and DISPER3D with CALINE4 dispersion parameters

(b) DISPER2D and DISPER3D with the eddy diffusion coefficient

Figure 31. Comparison of CALINE4, DISPER2D, and DISPER3D results for MS Highway 6 [22]

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0 50 100 150 200 250


NO

2 Con

cent

rati

on, p

pm CALINE4 Dispersion Model

DISPER2D with CALINE4 Dispersion Parameters

DISPER3D with CALINE4 Dispersion Parameters

500 ppb

0 ppb

Notes: 1) May 25, 2001; 10-11 am (Daytime)2) Receptor height = 0.30 m3) Use Phi = 90 degrees (wind perpendicular to highway) for DISPER3D

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0 50 100 150 200 250


NO

2 C

once

ntra

tion

, ppm CALINE4 Dispersion Model

DISPER2D with Eddy Diffusion Coefficient

DISPER3D with Eddy Diffusion Coefficient

500 ppb

0 ppb

Notes: 1) May 25, 2001; 10-11 am (Daytime)2) Receptor height = 0.30 m3) Use Phi = 90 degrees (wind perpendicular to highway) for DISPER3D


49 49

Mixed Layer

Stable Layer

2.6 Urban Airshed Model For Ground-Level Ozone

The Urban Airshed Model (UAM) is a three-dimensional photochemical grid model designed to calculate the concentrations of both inert and chemically reactive pollutants by simulating the physical and chemical processes in the atmosphere that affect pollutant concentrations [19]. It requires specifications of meteorological, emissions, landuse, and other geographic inputs. These inputs are required at each grid cell in the three-dimensional domain. Figure 32 shows a simple illustration of the three-dimensional grid concept of UAM [34].

Figure 32. UAM grid model concept [34] The basis for the UAM model is the atmospheric diffusion equation, which represents a

mass balance in which all of the relevant emissions, transport, diffusion, chemical reactions, and removal processes are involved [19]. The mathematical terms of these processes are expressed in Equation 33. Note that the removal processes involve both dry and wet depositions.

UAM is mainly used to study the photochemical air quality, especially the effect of O3 pollution on a regional level. Because UAM accounts for spatial and temporal variations as well as differences in the reactivity of emissions, it is ideally suited for evaluating the effects of emission control scenarios on regional- level urban air quality. The evaluation starts by first replicating a historical O3 episode to establish a base case simulation. Model inputs are prepared from observed meteorological, emission, and air quality data for a particular day or days. The model is then applied with these inputs, and the results are evaluated to determine its performance. Once the model results have been evaluated and determined to perform within


50 50

( ) ( ) ( )

++−=

zwc

yvc

xuc

tc iiii

δδ

δδ

δδ

δδ

Change in Concentration

= Advection by Winds

Turbulent Diffusion

+ Ri + Si + Di + Wi Chemical Reaction

Emissions Dry Deposition

+

+

+

zc

Kzy

cK

yxc

Kx

iV

iH

iH δ

δδδ

δδ

δδ

δδ

δδ

Wet Deposition

prescribed levels, the same meteorological inputs and a projected emission inventory can be used to simulate possible future emission scenarios; that is, the model will calculate hourly O3 patterns likely to occur under the same meteorological conditions as the base case [19].

(33) where:

ci = concentration of pollutant i, a function of space (x,y,z) and time (t) u,v,w = horizontal and vertical wind speed components KH = horizontal turbulent diffusion coefficients KV = vertical turbulent exchange coefficients Ri = net rate of production of pollutant i by chemical reactions Si = emission rate of pollutant i Di = net rate of change of pollutant i due to surface uptake processes Wi = net rate of change of pollutant i due to wet deposition processes The variable-grid Urban Airshed Model version V (UAM-V) system is the latest

operational version of the UAM model. It incorporates multiple two-way grid nesting, allowing regional-scale O3 and precursor pollutant transport and several urban areas to be treated within a single modeling domain. In addition, the UAM-V program allows variability in the number and spacing of vertical layers, specification of three-dimensional meteorological variables, and explicit treatment of subgrid-scale photochemical plumes [35].

The UAM-V model calculates pollutant concentrations from the emissions, wind mechanism, dispersion of precursors, and the formation and deposition of pollutants within every grid cell of the modeling domain. To adequately replicate the full three-dimensional structure of the atmosphere during an O3 episode, the UAM-V program requires an hourly and day-specific database for input preparation. Several preprocessing steps to translate raw emissions, meteorological, air quality, and grid specific data are required to develop final UAM-V input files [35].

The new features of the UAM-V model necessitate the provision of more extensive input data. The input data include cell aggregation and grid nesting, layer heights and pressure, landuse type and surface albedo (reflectivity), terrain, wind components, temperature, vertical exchange coefficients, water vapor, cloud cover, rainfall rates, initial conditions, boundary conditions, area source emissions, elevated point source emissions, chemistry parameters, photolysis rates,


51 51

45x42 of 36sq km cells

(1620x1512 sq km)


(1212x816 sq km)


(872x404 sq km)


(1620x1512 sq km)


(1212x816 sq km)


(872x404 sq km)

turbidity and O3 column, aerosol mass distribution, and process analysis and integrated reaction rates. For landuse type, UAM-V recognizes 11 landuse categories. Surface roughness and UV albedo values are also given for each category.

Observed air quality data are used to evaluate model predictions. These data may also be used to estimate the initial concentrations and boundary conditions for O3, NOx, and VOC. The UAM-V output provides gridded hour-averaged and instantaneous concentration output for all simulated species and grids [35]. Applications of UAM-V in Mississippi Currently, there are two studies of ground- level Ozone underway in Mississippi. The first study is called the Arkansas-Tennessee-Mississippi Ozone Study (ATMOS). It provides technical information relevant to attainment of an 8-hour NAAQS for ozone in the Memphis, Nashville, and Knoxville areas. In addition, the study also provides information for addressing the emerging 8-hour ozone issues in the Hamilton County (Chattanooga), Tennessee; Lee County (Tupelo), Mississippi; and Little Rock, Arkansas areas [36].

The other study is called the Gulf Coast Ozone Study (GCOS). This study is aimed to provide technical information related to 8-hour ozone issues in the Gulf Coast area, and specifically to provide a basis for meeting regulatory modeling requirements and for longer-term decision-making for the states of Florida, Alabama, Mississippi, and Louisiana [37]. In both studies UAM-V is employed to assess ground- level Ozone formation, and to determine the most effective way to attain the ground-level Ozone standard in the study area. An example of the UAM-V modeling domain of ATMOS is given in Figure 33.

Figure 33. ATMOS modeling domain [36]


52 52

MM5 Community Model for Meteorological Prediction

UAM-V requires extensive three-dimensional gridded input data of meteorological parameters. These data are usually prepared using meteorological models such as MM5 [37]. MM5 is the mesoscale model developed at the Pennsylvania State/Nation Center for Atmospheric Research (PSU/NCAR). It is a limited-area, nonhydrostatic or hydrostatic, terrain-following sigma-coordinate model designed to simulate or predict mesoscale and regional-scale atmospheric circulation. The meteorological mesoscale ranges from several kilometers to around 100 kilometers above the earth’s surface. The model is supported by several auxiliary programs, including TERRAIN, REGRID, RAWINS, INTERPF, NESTDOWN, and GRAPH, which are referred to collectively as the MM5 modeling system [38]. The outputs from MM5 are the simulated atmospheric conditions such as sea level pressure, wind speed, precipitation, and air temperature.

Since MM5 is a regional model, it requires an initial condition as well as lateral boundary condition to run. To produce lateral boundary condition for a model run, one needs gridded data to cover the entire time period that the model is integrated [38]. Based on the extensive input data, MM5 performs the numerical weather prediction for each grid at every time step. The predicted atmospheric conditions include sea level pressure, wind speed, precipitation, and air temperature. Since it is a mesoscale model, the resolution of simulations is high enough to produce accurate forecasts for small regions. Therefore, it can be adopted to use with the air quality model for predicting the meteorological conditions in the study areas. The MM5 model was not pursued in this research because the short term predictions of meteorological condition for the northern Mississippi are generally available.


53 53

3. ROADWAY VEHICULAR EMISSION MODEL

3.1 EPA’s MOBILE6 Emission Model for On-Road Vehicles MOBILE6 is an application program developed by EPA for vehicle emissions. It is used by highway agencies to estimate air pollutant emission rates from highway motor vehicles. MOBILE6 is the latest version (2002) in a series of MOBILE models dating back to 1978 and is the first update in MOBILE since the release on MOBILE5b in 1996, and the first major update since the release of MOBILE5 in 1992 [39]. One of the primary uses of the MOBILE model is to develop emission inventories for State Implementation Plans and for conformity determinations. MOBILE is also used increasingly for other kinds of analysis ranging from estimating the impacts of motor vehicle emissions control strategies to estimating human exposure to pollutants at a specific intersection.

Three air pollutants estimated are Hydrocarbon (HC), CO, and NOx. The model inputs are grouped into six categories, which are external conditions, vehicle fleet characteristic, travel activity, state programs, fuel, and others. These inputs may be recalled from default values contained in the model, if not supplied by users. These defaults are designed to represent “national average” input values. Users who desire a more precise estimate of local emissions can substitute information that more specifically reflects local conditions. The core analysis model also contains adjustment factors that are used to adjust the emission rates when inputs are different from the default values.

In MOBILE6 several parameters are finely classified in order to make the model more realistic. There are eight types of emissions, 28 classifications of vehicle, five classifications of roadway, and five categories of Hydrocarbon [40]. MOBILE6 receives inputs from users through the use of commands in the command input file. Seven commands are required in every command input file. The MOBILE6 program is executed on DOS environment. The computer screen after accomplishing a MOBILE6 run is shown in Figure 34. The default output from MOBILE6 is reported as a text file giving the aggregated emission rates in the unit of g/veh/mi. Users can request detailed output, which is given as a database file. This database output provides emission rates in both g/veh/mi and g/veh/hr. The aggregation of the detailed output emission rates requires the use of appropriate weighting factors. MOBILE6 provides an option to estimate emission rates for the winter season (January 1) and/or the summer season (July 1). The emission rates on any particular day of a year are obtained by linearly interpolating between the emission rates on those two days [41].

According to 14 limitations of MOBILE5a addressed by the U.S. General Accounting Office, 1997 [42], many of them have been overcome in MOBILE6. However, a few limitations still remain as follows: 1. Emission estimates for higher speeds, especially speeds in excess of 104 km/h (65 mi/h). 2. Emissions estimates and assumptions for the inspection and maintenance (I&M) of heavy

duty vehicles (gross vehicle weight of 3,856 kg or 8,501 lbs or more). 3. Quantifying the uncertainty of the model’s estimates.

Giannelli et al, 2002 [43], conducted the sensitivity analysis of MOBILE6. As a result of the analysis, input parameters are categorized into three groups based on the level of their effects on emission rates. These three groups are: (1) parameters with major effects on emissions


54 54

(greater than 20% change), (2) parameters with intermediate effects on emission (5-20% change), and (3) parameter with minor effects on emissions (less than 5% change). The parameters that have major effects on emissions of three pollutants estimated by MOBILE6 are listed in Table 12.

Figure 34. The computer screen of a MOBILE6 run

Table 12. List of input parameters having major effects on emissions

HC CO NOx

• Average speed • Min/Max air temperatures • Registration distribution • Fuel Reid vapor pressure • Speed VMT command (for

arterial roadways)

• Average speed • Min/Max air temperatures • Registration distribution • Fuel Reid vapor pressure

• Average speed • Min/Max air temperatures • Registration distribution

It is noticed in Table 12 that three input parameters, which are average speed, minimum

and maximum air temperature, and registration distribution, all have major affects on emissions of all three pollutants. Therefore, it is necessary to enter local data for these three parameters in place of the default national data in order to obtain the more accurate, local-specific emission results.

It is known that vehicle speed is highly related to exhaust emissions. Vehicle speed is always included as one of several variables in emission estimation models [44, 45, 46, 47, and 48]. Air temperatures affect both exhaust and evaporative emissions where higher air temperature induces an increasing emission rate. Registration distribution represents the fleet mix on the road. Fleets with a higher percentage of older vehicles will have higher emissions for two reasons. Older vehicles have typically been driven more miles and have experienced more deterioration in emission control systems. A higher percentage of older vehicles also means that there are more vehicles in the fleet that do not meet newer, more stringent emissions standards [39].


55 55

Aggregated Air Pollutant Emission RatesMS Highway 6, Oxford - Year 2001

1.3430.600

1.285

17.075

2.856

15.980

1.188

21.870

2.781

0

4

8

12

16

20

24

Car only Truck only Car and TruckTraffic Combination

Em

issi

on R

ates

, g/v

eh/m

i

VOC

CO

NOx

% Car : % Truck 92.3:7.70 : 100100 : 0

Notes:1) Consider only exhaust running emissions2) Consider only emissions from vehicle on highway3) Assume average speed 40 mi/h

VOC NOx

CO

g/veh/km

15

0

3.2 Case Study of MS Highway 6 Near Oxford

This study estimated air pollutant emission rates on MS Highway 6 West in Oxford, Mississippi, from 6 a.m. on May 24, 2001, unt il 5 a.m. on May 26, 2001. The actual hourly air temperature and hourly traffic volume distributions were used. The air temperature data were obtained from Goodwin Creek Weather Station near Batesville, Mississippi, which is the closest meteorological station to Oxford. The average speed of 40 mi/h (64 km/h) was assumed for both vehicle classes. The emission models were run for three cases: (1) only car traffic, (2) only truck traffic, and (3) combination of car and truck traffic with the percentage proportions of 92.3 and 7.7. In this study, the HC emission rates are reported as VOC. Among 28 classes, Light-Duty Gasoline Vehicles (LDGV) are selected to represent car traffic whereas truck traffic is assigned to Class 8A Heavy-Duty Diesel Vehicles (HDDV8A) with 33,001-60,000 lbs as the Gross Vehicle Weight Rating. It should be noted that to coincide with the traffic characteristic on MS Highway 6, only the exhaust running emissions on the roadway type of freeway are considered.

The aggregated emission results in g/veh/mi are shown in Figure 35. There is a significant difference between the major pollutants emitted by different types of vehicle. Trucks emit almost 20 times more NOx than cars do. On the other hand, the major air pollutant produced by cars is CO. Cars emit about six times more CO than trucks do under the same conditions. For VOC, the difference is not as large as the previous two pollutants. It is emitted by cars two times more than by trucks.

Figure 35. Aggregated air pollutant emission rates


56 56

The sensitivity analysis of average vehicle speed on air pollutant emission rates was conducted by modeling the case study (car and truck) of MS Highway 6 at different average vehicle speeds. In MOBILE6 the average speed can be modeled from 2.5 to 65 mi/h (4 to 104 km/h) where the average speed of 2.5 mi/h (4 km/h) represents a near- idle traffic congestion condition and the average speed of 65 mi/h (104 km/h) represents a free-flow condition. Therefore, one MOBILE6 run was made for the average speed of 2.5 mi/h (4 km/h). Another run was made for the average speed of 65 mi/h (104 km/h). Additional six runs were made for the average speeds of 10, 20, 30, 40, 50, and 60 mi/h (16, 32, 48, 64, 80, and 96 km/h).

For each average speed, the estimated emission rates were calculated for both summer and winter seasons. Then they were algebraically averaged. Figure 36 shows the results of the daily average emission rates at different average speeds and the corresponding level of service (LOS). The relationship between emission rates and average speed is nonlinear. The emission rates of VOC diminish as average speed increases. However, the emission rates of CO and NOx are higher at low and high average speeds compared to mid-range speeds where the emission rates are less sensitive. The emission rates of these two pollutants are lowest at the average speed of about 30 mi/h (48 km/h). At a very low average speed or 2.5 mi/h or 4 km/h (near- idle congestion condition), the emission rates of all three pollutants are considerably high. The emission rates of VOC, CO, and NOx at this condition are 7.2, 3.3, and 1.7 times higher than the emission rates at average speed of 40 mi/h (64 km/h).

Figure 36 also shows the ranges of the LOS for 70-mi/h (112 km/h) design speed according to the Highway Capacity Guide [49]. The guide relates the LOS to the average speed, the volume per capacity ratio, and the maximum service flow rate per lane under ideal conditions. The average speed of 40 mi/h or 64 km/h (base case) assumed for this study is in the LOS range of E. The improvement in traffic flow, which increases the average speed to 50 mi/h (80 km/h) and improves the LOS of the highway from E to D, leads to a 4% decrease in VOC emission rates. However, it causes the increase in CO and NOx emission rates by 8 % and 7 %, respectively. On the other hand, the worse traffic flow condition, which has the average speed dropped from 40 mi/h to 20 mi/h (from LOS E to F), raises the VOC and NOx emission rates by 21% and 3% while it slightly reduces the CO emission rates by only 1 %. It is imperative to note that the user specified average speed applies to all vehicle classes in the model for the entire day. Thus, the resulting air pollutant emission rates may be either over-estimated or under-estimated, depending on the actual average speed of each vehicle class on the highway.

In this study, simplified procedures were developed to specifically prepare the vehicular emissions of the precursors of Ozone—VOC and NOx—for entering into the air quality database. They are intended to provide a sufficiently rational methodology to estimate reasonably accurate vehicular emissions based on the available data in a time-effective manner.

The MOBILE6 software [39, 43], the current version of MOBILE, was used to estimate emission rates of VOC and NOx from different classes of vehicles. In MOBILE6, 28 vehicle classes are defined, but only five of them were selected to represent the simplified vehicle classification used in this study. The selected MOBILE6 vehicle classes are the typical vehicle classes generally observed in Northern Mississippi. The relationship between the MOBILE6 vehicle classification and the simplified version used in this study is given in Table 13.


57 57

Air Pollutant Emission Rates at Different Average Vehicle SpeedsMS Highway 6, Oxford, MS - Year 2001

0

8

16

24

32

40

48

56

64

0 10 20 30 40 50 60 70Avergage Speed, mi/h

Em

issi

on R

ate,

g/v

eh/m

iVOC

CO

NOx

Notes:1) Consider only emissions from Light-Duty Gasoline Vehicles (LDGV) and Heavy-Duty Diesel Vehicle 8A (HDDV8A) with percentage proportion = 92.3:7.72) Consider only exhaust running emissions3) Consider only emissions from vehicle on highway

2.5 65

LOS F B ACE D(for 70 mi/h

design speed)

(Base Case)

0

g/veh/km40

Figure 36. Air pollutant emission rates at different average vehicle speeds

Table 13. Relationship between MOBILE6 and simplified vehicle classifications

Simplified Classification MOBILE6 Classification

Car LDGV: Light-Duty Gasoline Vehicles (Passenger Cars) Light Gasoline Truck LDGT4: Light-Duty Gasoline Trucks 4 Heavy Gasoline Truck HDGV7: Class 7 Heavy-Duty Gasoline Vehicles, and

HDGB: Gasoline Buses Diesel Truck HDDV8b: Class8b Heavy-Duty Diesel Vehicles

To develop the vehicular emissions inventory for use in this study, the following steps were implemented: 1. Compile a comprehensive pollutant emission rates database using MOBILE6

computations. 2. Develop the multiple linear regression models for the emission rates based on the

database. 3. Prepare necessary distribution factors:

- Fleet distribution by model year - Truck proportion by class

4. Estimate average travel distances. 5. Calculate vehicular emissions.


58 58

3.3 Development of Vehicular Emission Rates Database and Regression Models The emission rates database was compiled for 2001 meteorological data for Oxford. Note

that Oxford was selected because extensive traffic data collection was done in Oxford as a part of the air quality and ITS projects. Therefore, detailed data (hourly, daily, and weekly) were available in hand. The developed database contains the emission rates of highway motor vehicles, which cover a selected range of important parameters as follows: • Two types of pollutants: VOC and NOx • Five MOBILE6 vehicle classes, as defined in Table 13 • Three environmental (air temperature) conditions:

- Hottest day (min/max air temperatures = 21.2/34.8 °C = 70.2/94.6 °F) - Normal day (min/max air temperatures = 8.8/22.1 °C = 47.8/71.8 °F) - Coldest day (min/max air temperatures = -13.7/0.0 °C = 7.3/32.0 °F)

• Seven vehicle model years: 1977, 1980, 1985, 1990, 1995, 2000, and 2001 • Seven average vehicle speeds: 20, 25, 30, 35, 40, 45, and 50 mi/h

The MOBILE6 input files were created, and runs were made for the full factorial. The

estimated emission rates were extracted from output files and entered into the emission rates database. The estimated emission rates for the hottest day are shown as examples in Figures 37(a) and 37(b) for VOC and NOx, respectively. Examples of MOBILE6 input and output files for this analysis are presented by Boriboonsomsin [50]. It is important to note that these estimated emission rates are based upon the following assumptions: • Assume all vehicle miles traveled (VMT) occur on arterial/collector roadways. This was

done by selecting the arterial roadway type in the AVERAGE SPEED command of the MOBILE6 input files.

• The estimated emission rates are the results for July 1, 2001. This was done by entering a “7” value in the EVALUATION MONTH command. The emission rates for this date are preferred over the emission rates for January 1 because July 7 is in the high Ozone season.

• Only the running exhaust emission results are extracted. Under similar environmental and average vehicle speed conditions, the estimated

emission results for Oxford are valid also for Tupelo and Hernando. This is because the Reid Vapor Pressure (RVP), one of MOBILE6’s required inputs, for Mississippi in the month of July is equal to 7.8 pounds per square inch (psi) throughout the State [51].


59 59

Figure 37(a). MOBILE6-estimated VOC emission rates of highway motor vehicles in Oxford, Mississippi, for the hottest day in 2001

2) Light-Duty Gasoline Trucks Class 4 (LDGT4) 1) Light-Duty Gasoline Vehicles (LDGV)

4) Gasoline Buses (HDGB) 3) Class 7 Heavy-Duty Gasoline Vehicles (HDGV7)

5) Class 8b Heavy-Duty Diesel Vehicles (HDDV8b)

0

6

12

18

24

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

15.0

7.5

0

6

12

18

24

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

15.0

7.5

0

6

12

18

24

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

15.0

7.5

0

6

12

18

24

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

15.0

7.5

0

6

12

18

24

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

15.0

7.5

• Actual air temperature inputs used for the hottest day in 2001 (July 11); min/max air temperatures = 21.2/34.8 °C = 70.2/94.6 °F

• Emission results shown are estimated for July 1, 2001 (during Ozone season).

• Emission results shown are running exhaust emission only.

• Assume all vehicle miles traveled (VMT) occur on arterial/collector roadways.


60 60

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

40

20

Figure 37(b). MOBILE6-estimated NOx emission rates of highway motor vehicles in Oxford, Mississippi, for the hottest day in 2001

2) Light-Duty Gasoline Trucks Class 4 (LDGT4) 1) Light-Duty Gasoline Vehicles (LDGV)

4) Gasoline Buses (HDGB) 3) Class 7 Heavy-Duty Gasoline Vehicles (HDGV7)

5) Class 8b Heavy-Duty Diesel Vehic les (HDDV8b)

• Actual air temperature inputs used for the hottest day in 2001 (July 11); min/max air temperatures = 21.2/34.8 °C = 70.2/94.6 °F

• Emission results shown are estimated for July 1, 2001 (during Ozone season).

• Emission results shown are running exhaust emission only.

• Assume all vehicle miles traveled (VMT) occur on arterial/collector roadways.

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

40

20

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

40

20

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

40

20

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

20 mi/h

25 mi/h

30 mi/h

35 mi/h

40 mi/h

45 mi/h

50 mi/h

Average Speed

0

g/ve

h/km

40

20


61 61

From the estimated emission rates in the database, several plots were made to understand the correlation of the calculated emission rates with other input variables. For example, Figure 38(a) shows the plots of the estimated VOC emission rates versus the vehicle model year for each class of vehicles. It is intuitive that the emission rates produced by newer vehicles are less than the rates produced by older vehicles; and thus, the relationship is an inverse function. However, this relationship is not simply linear. It is found that the relationship follows the power decay function, where the logarithmic form of the emission rates gives the highest Pearson correlation (R) with the vehicle model year. The similar trends were found for the NOx emission rates. Figure 38(b) show the plots of the estimated NOx emission rates versus the vehicle model year for each class of vehicles.

In Figure 38(a), it is noticeable that the VOC emission rates from 1990 and onward are not significantly different from one vehicle class to another. However, for the vehicle model years 1985 and older, trucks mostly emit more VOC than cars, especial heavy gasoline trucks. According to Figure 38(b), it is obvious that diesel trucks have about two times higher NOx emission rates than heavy gasoline trucks and several times higher NOx emission rates than cars and light gasoline trucks for the same model year. These results emphasize an importance of developing the local vehicular emission inventory based on the local traffic mix and air temperature data [50].

The MOBILE6 emission rates for each class of vehicle were estimated for model years 1977, 1980, 1985, 1990, 1995, 2000, and 2001. To develop the multiple linear regression models, the emission rates for the model years 1980 to 2001 were used as a dependent variable. Independent predictor variables include maximum temperature, minimum air temperature, vehicle model year, and average vehicle speed. Note that the emission rates of the model year 1977 were excluded due to their irrelevant trends compared to other model years and because of the practically non-existence of so old model vehicles. In addition, the emission rates were linearly interpolated for the missing years between two milestone years. For example, the emission rates of the model years 1980 and 1985 were used for interpolation of the emission rates of the model years 1981, 1982, 1983, and 1984. These interpolations were aimed to increase the number of data sets for regression [50].

The regression models were developed using both sets of data—with and without the interpolated emission rates. The models developed from the data with interpolation points provided better results and, therefore, were selected for use in the emission inventory calculation for each day from 1996 through 2000 for Tupelo and Hernando. The final regression models estimate the pollutant emission rates of each class of vehicle as a function of maximum air temperature, minimum air temperature, vehicle model year, and average vehicle speed, as summarized in Table 14 for VOC and Table 15 for NOx. It should be noted that the constant values and the coefficients of each independent variable are statistically significant at 5% level (α) for every model.


62 62

• Actual air temperature inputs used for each environmental condition. - Hottest day (min/max air temperatures = 21.2/34.8 °C = 70.2/94.6 °F) - Normal day (min/max air temperatures = 8.8/22.1 °C = 47.8/71.8 °F) - Coldest day (min/max air temperatures = -13.7/0.0 °C = 7.3/32.0 °F)

• Emission results shown are estimated for July 1, 2001 (during Ozone season). • Emission results shown are running exhaust emission only. • Assume all vehicle miles traveled (VMT) occur on arterial/collector roadways.

Figure 38(a). MOBILE6-estimated VOC emission rates for Oxford, Mississippi

2) Light Gasoline Truck = Light-Duty Gasoline Trucks Class 4 (LDGT4)

1) Car = Light-Duty Gasoline Vehicles (LDGV)

4) Diesel Truck = Class 8b Heavy-Duty Diesel Vehicles (HDDV8b)

3) Heavy Gasoline Truck = Class 7 Heavy-Duty Gasoline Vehicles (HDGV7) and

Gasoline Buses (HDGB)

0

8

16

24

32

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

Hottest Day

Normal Day

Coldest Day

0

g/ve

h/km

20

10

Mean CV, %2.906 137.02.923 133.43.660 127.8Coldest Day

StatisticsHottest DayNormal Day

0

8

16

24

32

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

iHottest Day

Normal Day

Coldest Day

0

g/ve

h/km

20

10



0

8

16

24

32

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

Hottest Day

Normal Day

Coldest Day

0

g/ve

h/km

20

10

Mean CV, %2.715 123.82.476 115.72.950 110.1

StatisticsHottest DayNormal DayColdest Day

0

8

16

24

32

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

Hottest Day

Normal Day

Coldest Day

0

g/ve

h/km

20

10

Mean CV, %2.365 124.92.365 124.92.365 124.9

Normal DayColdest Day

StatisticsHottest Day


63 63

• Actual air temperature inputs used for each environmental condition.

- Hottest day (min/max air temperatures = 21.2/34.8 °C = 70.2/94.6 °F) - Normal day (min/max air temperatures = 8.8/22.1 °C = 47.8/71.8 °F) - Coldest day (min/max air temperatures = -13.7/0.0 °C = 7.3/32.0 °F)

• Emission results shown are estimated for July 1, 2001 (during Ozone season). • Emission results shown are running exhaust emission only. • Assume all vehicle miles traveled (VMT) occur on arterial/collector roadways.

Figure 38(b). MOBILE6-estimated NOx emission rates for Oxford, Mississippi






0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

iHottest Day

Normal Day

Coldest Day

0

g/ve

h/km

40

20


Hottest DayNormal Day

Statistics

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

Hottest Day

Normal Day

Coldest Day

0

g/ve

h/km

40

20



Statistics

0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

Hottest Day

Normal Day

Coldest Day

0

g/ve

h/km

40

20



0

16

32

48

64

1975 1980 1985 1990 1995 2000 2005

Vehicle Model Year

Em

issi

on R

ate,

g/v

eh/m

i

Hottest Day

Normal Day

Coldest Day

0

g/ve

h/km

40

20



Statistics


64 64

Table 14. Summary of VOC regression equations developed from MOBILE6-estimated emission rates for Mississippi

Vehicle Type N Regression Equation R2 Value

Car 462 y1 = 10(134.276 - 0.06826x1 + 0.119x2 - 0.122x3 - 0.005x4) 0.92 Light Gasoline Truck 462 y1 = 10(132.523 - 0.06737x1 + 0.139x2 - 0.143x3 - 0.006x4) 0.94 Heavy Gasoline Truck 924 y1 = 10(126.121 - 0.06359x1 + 0.085x2 - 0.090x3 - 0.017x4) 0.80

Diesel Truck 462 y1 = 10(89.594 - 0.04499x1 - 0.011x4) 0.85 Note: y1 = predicted VOC emission rates, g/veh/mi x1 = vehicle model year x2 = daily maximum air temperature, °C x3 = daily minimum air temperature, °C

x4 = average vehicle speed, mi/h

Table 15. Summary of NOx regression equations developed from

MOBILE6-estimated emission rates for Mississippi

Vehicle Type N Regression Equation R2 Value Car 462 y2 = 10(102.806 - 0.05258x1 + 0.154x2 - 0.156x3 - 0.001x4) 0.99

Light Gasoline Truck 462 y2 = 10(57.613 - 0.02935x1 + 0.084x2 - 0.087x3 - 0.001x4) 0.92 Heavy Gasoline Truck 924 y2 = 10(35.831 - 0.01778x1 + 0.024x2 - 0.025x3 + 0.003x4) 0.75

Diesel Truck 462 y2 = 10(27.994 - 0.01343x1 + 0.001x4) 0.90 Note: y2 = predicted NOx emission rates, g/veh/mi x1 = vehicle model year x2 = daily maximum air temperature, °C x3 = daily minimum air temperature, °C x4 = average vehicle speed, mi/h

According to Tables 14 and 15, the coefficient of determination (R2) of every model is reasonably high, especially for cars and light gasoline trucks. The regression-predicted emission rates are plotted against the MOBILE6-estimated emission rates in Figures 39 and 40 showing the goodness-of-fit of the developed models. The closer the points are to the equality line, the better the prediction results are and the more accurate the model is.


65 65

• MOBILE6-estimated emission data used for vehicle model years 1980, 1985, 1990, 1995, 2000, and 2001, and the interpolated data among these years used to develop the regression model.

• 2001 air temperature inputs used for estimating MOBILE6-emission rates. • Average vehicle speed range from 20 to 50 mi/h for MOBILE6-estimated emission rates.

Figure 39. MOBILE6-estimated VOC emission rates versus regression-predicted emission rates for motor vehicles in Oxford, Mississippi






0

2

4

6

8

0 2 4 6 8MOBILE6-Estimated Emission Rate, g/veh/mi

Line of Equality

y1 = 10(134.276 - 0.06826x1 + 0.119x2 - 0.122x3 - 0.005x4)

R2 = 0.92where: y1 = VOC emission rate, g/veh/mi x1 = Vehicle model year x2 = Maximum air temperature, °C x3 = Minimum air temperature, °C x4 = Average vehicle speed, mi/h

0

5.00

g/ve

h/km

5.02.5

2.5

g/veh/km

Reg

ress

ion-

Pre

dict

ed E

mis

sion

Rat

e, g

/veh

/mi

0

2

4

6

8


Line of Equality

y1 = 10(89.594 - 0.04499x1 - 0.011x4)

R2 = 0.96where: y1 = VOC emission rate, g/veh/mi x1 = Vehicle model year x4 = Average vehicle speed, mi/h

0

5.00

g/ve

h/km

5.02.5

2.5

g/veh/km

Reg

ress

ion-

Pre

dict

ed E

mis

sion

Rat

e, g

/veh

/mi

0

4

8

12

16


Line of Equality

y1 = 10(132.523 - 0.06737x1 + 0.139x2 - 0.143x3 - 0.006x4)


0

100

g/ve

h/km

105

5

g/veh/km

Reg

ress

ion-

Pre

dict

ed E

mis

sion

Rat

e, g

/veh

/mi

0

4

8

12

16


Line of Equality

y1 = 10(126.121 - 0.06359x1 + 0.085x2 - 0.090x3 - 0.017x4)


0

100

g/ve

h/km

105

5

g/veh/km

Reg

ress

ion-

Pre

dict

ed E

mis

sion

Rat

e, g

/veh

/mi


66 66

• MOBILE6-estimated emission data used for vehicle model years 1980, 1985, 1990, 1995, 2000, and 2001, and the interpolated data among these years used to develop the regression model.

• 2001 air temperature inputs used for estimating MOBILE6-emission rates. • Average vehicle speed range from 20 to 50 mi/h for MOBILE6-estimated emission rates.

Figure 40. MOBILE6-estimated NOx emission rates versus regression-predicted emission rates for motor vehicles in Oxford, Mississippi






0

2

4

6

8


Line of Equality

y2 = 10(102.806 - 0.05258x1 + 0.154x2 - 0.156x3 - 0.001x4)

R2 = 0.99where: y2 = NO x emission rate, g/veh/mi x1 = Vehicle model year x2 = Maximum air temperature, °C x3 = Minimum air temperature, °C x4 = Average vehicle speed, mi/h

0

5.00

g/ve

h/km

5.02.5

2.5

g/veh/km

Reg

ress

ion-

Pred

icte

d E

mis

sion

Rat

e, g

/veh

/mi

0

8

16

24

32


Line of Equality

y2 = 10(27.994 - 0.01343x1 + 0.001x4)

R2 = 0.91where: y2 = NO x emission rate, g/veh/mi x1 = Vehicle model year x4 = Average vehicle speed, mi/h

0

200

g/ve

h/km

2010

10

g/veh/km

Reg

ress

ion-

Pre

dict

ed E

mis

sion

Rat

e, g

/veh

/mi

0

2

4

6

8


Line of Equality

y2 = 10(57.613 - 0.02935x1 + 0.084x2 - 0.087x3 - 0.001x4)


0

5.00

g/ve

h/km

5.02.5

2.5

g/veh/km

Reg

ress

ion-

Pred

icte

d E

mis

sion

Rat

e, g

/veh

/mi

0

4

8

12

16


Line of Equality

y2 = 10(35.831 - 0.01778x1 + 0.024x2 - 0.025x3 + 0.003x4)


0

100

g/ve

h/km

105

5

g/veh/km

Reg

ress

ion-

Pred

icte

d E

mis

sion

Rat

e, g

/veh

/mi


67 67

In Figure 39, the plots show that the regression models for every vehicle class give very good prediction results for the low VOC emission rates. The plotted points are more scattered at the high end of the emission rates, especially for heavy gasoline trucks. For light gasoline trucks, the regression model underpredicts results compared to the MOBILE6-estimated emission rates higher than 4 g/veh/mi (2.5 g/veh/km).

In Figure 40, the similar trends are found for the NOx regression models. In the plot of the heavy gasoline trucks, there is an obvious desegregation of the plotted points when the MOBILE6-estimated emission rates are larger than about 8 g/veh/mi (5 g/veh/km). Recall from Table 6 that the heavy gasoline truck of the simplified classification consists of two different vehicle classes of the MOBILE6 classification (HDGV7 and HDGB). The NOx emission rates produced by these two classes increase until 8 g/veh/mi (5 g/veh/km).

One weakness of these regression models is that the emission results are unreasonably high for the day having a very wide range of air temperature. However, these regression models provide reasonably accurate results for the majority of the inference space. 3.4 Compiling Vehicle Emission Rate Considering Traffic Mix and Model Year

The weighted average vehicular VOC and NOx emission rates by model year were computed. Examples of weighted average emission rates for the hottest day in Tupelo, 2001, are shown in Figure 41. The emission rates of VOC and NOx are higher for congested traffic conditions (average speed of 16 km/h or 10 mi/h) than those for normal traffic conditions (average speed of 64 mi/h or 40 mi/h) for all types of vehicles except heavy gasoline trucks [50].

The daily vehicular emissions were calculated by multiplying the weighted average emission rates by the daily traffic vo lume for each vehicle type. Then, the total daily vehicular emissions were obtained by aggregating the emissions of each vehicle type in the traffic mix for each radial zone. Although cars have the lowest emission rates among the four defined vehicle types, they contribute the most emissions, as shown in Figure 42 for normal traffic condition. This is because they represent the largest proportion (> 90%) in the total traffic mix of vehicles registered in Mississippi. According to Figures 41 and 42, it is important that the correct proportion of traffic mix by vehicle type and model year is obtained for accurate estimation of vehicular emissions.


68 68

Weighted Average Vehicular Emission Rates by Vehicle Model Years Estimated for the Hottest Day in Tupelo, 2001 - Average Speed 40 mi/h

0.1 0.2 0.3 0.10.2 0.4

2.6

6.8

0

1

2

3

4

5

6

7

8

9

10

Cars Light Gasoline Trucks Heavy Gasoline Trucks Diesel Trucks

Vehicle Type

Em

issi

on R

ate,

g/v

eh/m

i

VOC NOx

NotesDate: July 25, 2001Daily Maximum Air Temperature: 35.0 °C (95.0 °F)Daily Minimum Air Temperature: 23.3 °C (73.9 °F)

16

0

8

Em

issi

on R

ate,

g/v

eh/k

m

Weighted Average Vehicular Emission Rates by Vehicle Model Years Estimated for the Hottest Day in Tupelo, 2001 - Average Speed 10 mi/h

0.40.7

1.6

0.70.2

0.7

2.2

8.6

0

1

2

3

4

5

6

7

8

9

10


Vehicle Type

Em

issi

on R

ate,

g/v

eh/m

i

VOC NOx

NotesDate: July 25, 2001Daily Maximum Air Temperature: 35.0 °C (95.0 °F)Daily Minimum Air Temperature: 23.3 °C (73.9 °F)

16

0

8

Em

issi

on R

ate,

g/v

eh/k

m

(a) Normal traffic condition, average speed of 64 km/h (40 mi/h)

(b) Congested traffic condition, average speed of 16 km/h (10 mi/h)

Figure 41. Estimated vehicular emission rates for the hottest day in Tupelo, 2001


69 69

Estimated Vehicular Emissions from Traffic within 16 km Radial Distancefor the Hottest Day in Tupelo, 2001 - Average Speed 40 mi/h

746

167

4 3

1,539

292

30

171

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800


Vehicle Type

Em

issi

ons,

kg/

day VOC NOx

NotesDate: July 25, 2001Daily Maximum Air Temperature: 35.0 °C (95.0 °F)Daily Minimum Air Temperature: 23.3 °C (73.9 °F)Total Traffic: 496,380 veh/dayAverage Travel Distance: 9.3 km (5.8 mi)

Figure 42. Estimated vehicular emissions from traffic within 16 km radial distance for the hottest day in Tupelo, 2001.


70 70

4. AIR POLLUTION MODELING AND ANALYSIS USING EPA DATA 4.1 Formation and Transport of Ground-level Ozone The U.S. EPA, 1999 [53], provides a discussion of the formation processes of ground-level O3, which is summarized here. In the presence of ultraviolet radiation (hV), Oxygen (O2) and Nitrogen Dioxide react in the atmosphere to form Ozone and Nitric Oxide through the reactions given in Equations 34 and 35.

NO2 + hV à NO + O (34)

O + O2 à O3 (35)

However, the resultant O3 is quickly reacted away to form NO2 by the process given in Equation (9). This conversion of O3 by NO is referred to as ‘titration’. In the absence of other species, a steady state is achieved through the reactions shown by Equations 34 through 36.

O3 + NO à NO2 + O2 (36)

Ozone cannot accumulate further unless Volatile Organic Compounds (VOCs), which include hydrocarbons, are present to consume or convert NO back to NO2 as shown by Equation 37.

VOC + NO à NO2 + other products (37)

Note that this equation is a simplified version of many complex chemical reactions. As NO is consumed by this process, it is no longer available to titrate O3. When additional VOC is added to the atmosphere, a greater proportion of the NO is oxidized to NO2, resulting in greater O3 formation. Additionally, anthropogenic sources of NO result in greater levels of NO2 in the atmosphere. This NO2 is then available for photolysis to NO and O (Equation 34) and, ultimately, for conversion to NO2 (Equation 37) and O3 (Equation 35). The formation and increase in O3 concentrations occur over a period of a few hours. Shortly after sunrise, NO and VOCs react in sunlight to form O3. Throughout the morning, O3 concentrations increase while NO and VOCs are depleted. Eventually, either lack of sunlight, NO, or VOCs limit the production of O3. This diurnal cycle varies greatly depending on site location, emission sources, and weather condition. After the O3 is formed, it may be transported to another location by upper-air wind over a region. A rural area may be subject to high levels of O3 concentration. This is not because of the precursor emissions in its locality, but due to the O3 and its precursors that are carried from upwind industrial or urban areas. The level of O3 concentration in industrial/urban area can be lower than the level of O3 concentration in the downwind rural area where there is no significant amount of precursor emissions produced [54]. An example of the area that is subject to the exceedance of O3 standard due to the transport phenomenon of O3 is DeSoto County, Mississippi. On the days in which the O3 levels were measured in DeSoto County above the standard, the polluted air had been transported into DeSoto County from the Memphis area in Shelby County, Tennessee [55].


71 71

Yearly Maximum 8-hour Average Ozone Concentration andAnnual Daily Traffic* - DeSoto, Mississippi

0.096

0.108

0.099

0.1080.119

48,000

39,000

47,000

38,000

48,000

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1996 1997 1998 1999 2000Year

Ozo

ne c

once

ntra

tion,

ppm

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000A

nnua

l Dai

ly T

raff

ic, v

eh/d

ay

Yearly maximum 8-hour averageOzone concentration

Annual Daily Traffic

*Total annual daily traffic from I-55 and US 51 (two major highways that pass through DeSoto County)

Ground- level O3 is the result of the complex photochemical reactions among emissions of VOCs and NOx in the troposphere that is influenced by meteorological conditions. Therefore, the levels of O3 primarily depend on the amount of precursor emissions released to the air and the meteorological conditions that are involved the formation, transport, and removal processes of O3.

Emission Sources

Sources of O3 precursor emissions are both anthropogenic (man-made) and biogenic (natural). Examples of the anthropogenic emissions include emissions from fuel combustion processes (both electrical and industrial), chemical processes, and waste disposal and recycling processes. These emissions are generally high near urban areas where there are heavy transportation activities and industries. Precursor biogenic emissions occur mostly in the forested regions with certain tree types, e.g. in the Southeastern states certain Oak trees emit VOC emissions [56]. Usually, the levels of biogenic emissions are typically lower than the levels of anthropogenic emissions. However, even without anthropogenic emissions, the photochemical reactions normally result in a natural background O3 concentration of 0.025 to 0.045 parts per million (ppm) [53]. Emission sources considered in this study consist of motor highway vehicle (on-road), aviation and other nonroad sources, and point sources. Figures 43 to 45 show the historical trends of these emission contributors along with the trend of yearly maximum 8-hour average O3 concentration in Hernando, DeSoto County, Mississippi. Note that in Figure 43 the on-road emissions are represented by the annual daily traffic, and in Figure 44 the aviation emissions are represented by the aircraft operations. Figure 45 presents the point sources emissions in terms of NOx emissions (tons per year).

Figure 43. Historical trend of Ozone and annual daily traffic


72 72

Yearly Maximum 8-hour Average Ozone Concentration and Total Yearly NOx Emissions from Point Sources* - DeSoto, Mississippi

0.096

0.108

0.099

0.1080.119

24,419

32,00128,210

35,792

39,582

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1996 1997 1998 1999 2000Year

Ozo

ne c

once

ntra

tion

, ppm

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

NO

x E

mis

sion

, ton

s/yr


Total yearly NOx emissions

*Total number of sources that produce NOx = 24

Note: Data of years 1996&1999 are from EPA website. Data of years 1997-1998 are from interpolation. Data of year 2000 are from extrapolation.

Yearly Maximum 8-hour Average Ozone Concentration andTotal Yearly Aircraft Operations* - DeSoto, Mississippi

0.096

0.108

0.099

0.1080.119

502,220

475,973491,921

476,847473,409

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1996 1997 1998 1999 2000Year

Ozo

ne c

once

ntra

tion

, ppm

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

Num

ber

of a

ircr

aft

oper

atio

ns


Total yearly aircraft operations

* Total operations from 3 airports in the vicinity of 20 miles from EPA monitoring station in Hernando1. Memphis International Airport2. Olive Branch Airport3. Hernando Airport

Figure 44. Historical trend of Ozone and aircraft operations

Figure 45. Historical trend of Ozone and point sources emissions


73 73

Meteorological Condition Air Temperature

The rate of photochemical reactions increases as air temperature rises. In addition, air temperature can affect emissions. For instance, vehicular emission rates of VOC and NOx increase with higher air temperatures [43]. In many O3 prediction studies, air temperature was found to be the strongest single predictor of O3 concentration [57].

The atmospheric lapse rate or stability (temperature change with height) controls the amount of vertical mixing that takes place [53]. A strong stability tends to reduce the mixing height and to confine emissions and O3 closer to the ground [58].

Wind

Surface wind speed controls the degree of ventilation. Calm or light winds allow more emissions to accumulate over large area, which result in higher precursor concentrations. The fundamental concept of air pollutant dispersion indicates that the air pollutant concentration is roughly inversely related to wind speed [58]. However, O3 formation and transport is a complex phenomenon, and the effect of surface wind speed on its concentration depends on the terrain characteristics of a particular location [53]. Wind direction is also associated with O3 levels. An area located downwind of precursor emissions sources is greatly inclined to high levels of O3.

Upper-air wind is involved in the transport process of O3. It transports O3 and precursors into a region during the nighttime and in the early morning, or transports locally formed O3 out of a region during the afternoon hours [53]. Precipitation

Precipitation is associated with reduced O3 levels primarily due to a wet deposition process. A study of O3 concentrations in the Lake Michigan area reported that a rain shower could reduce the O3 concentration to levels of 0.01-0.02 ppm within a few minutes [59]. In real-time measurements of NO2 and O3 concentrations in Tupelo, Mississippi, negligible NO2 and O3 concentrations were measured following a sudden rainfall in the area [4]. Solar radiation

Ultraviolet rays in solar radiation are needed for O3 photochemical reactions. These rays also influence maximum temperatures.

Urban-Heat Island Effect

In urban and metropolitan areas, paved surfaces, rooftops, and other constructed surfaces

cause air temperatures to be higher due to the heat transfer of these surfaces. On warm summer days the air in a city can be 3-4 °C (6-8 °F) hotter than its surrounding areas. These cities are called “Urban Heat-Islands” [60]. The increase in air temperature results in higher O3 concentrations. In many areas of the nation, a warming of 2.2 °C (4 °F) could increase O3 concentrations by about 5% [61]. A preliminary analysis of this heat island effect was conducted for Oxford in Mississippi, and the results are discussed later in Chapter 5.


74 74

4.2 Overview of Previous Ozone Prediction Methodologies

There are many methods for predicting concentrations of Ozone and other air pollutants. Some methods are simple to develop and easy to operate. However, they are not very accurate. Other methods are more difficult to develop but produce more accurate predictions. Some of these methods are objective while others are subjective. Using several methods in combination can balance one method’s strengths with another method’s limitations to produce a better prediction.

Many efforts have been put into developing Ozone prediction models in the last few decades. Wolf and Lioy, 1978 [62], developed a multiple regression model to forecast the next-day Ozone concentration for northern New Jersey, using only four regression variables: (1) peak upwind temperature on the previous day, (2) today’s peak temperature, (3) the reciprocal of mean wind speed from the surface to 1000 m, and (4) peak upwind Ozone concentration on the previous day. The model achieved a remarkably high explained variance (R2) of 0.92 and a low mean absolute error of 8.9%. This short-term model is of limited use because one must know the Ozone concentration on the previous day to forecast Ozone concentration for the next day.

Of nearly 100 potentially important meteorological predictors of daily Ozone levels that were examined by Cox and Chu, 1993 [63] and Chu, 1995 [64], six variables common to most metropolitan areas were found to strongly correlate with the daily maximum 1-hour Ozone concentrations. These variables include: (1) the daily maximum 1-hour surface temperature (positive correlation), (2) the morning average (7-10 a.m.) wind speed (negative correlation), (3) the afternoon average (1-4 p.m.) wind speed (negative correlation), (4) the midday (10 a.m.-4 p.m.) relative humidity (usually negative correlation), (5) the opaque cloud cover (negative correlation), and (6) the morning mixing height (negative correlation). For some urban areas in the western states that had facilities to monitor upper air conditions, the air temperature at 850 millibar (mb; 1 mb = 100 Pascal) at 12Z universal time (previously called Greenwich Mean Time) and the afternoon mixing height were found to be better predictors of the ground- level Ozone concentration than surface temperature and morning mixing height.

Ryan and Luebehusen, 1996 [65], developed the Ozone prediction models for the Baltimore/Washington area, incorporating ten predictor variables. These variables were: (1) the previous-day peak Ozone concentration, (2) the peak air temperature, (3) the minimum air temperature, (4) the sky cover, (5) the relative humidity, (6) the air temperature at 850 mb, (7) the morning wind speed, (8) the afternoon wind speed, (9) the wind speed at 850 mb, and (10) the length of daylight. The model gave the R2 value of 0.68.

Hubbard and Cobourn, 1998 [66] used the multiple linear regression technique to develop a daily domain-peak ground- level Ozone concentration prediction model for Louisville, Kentucky. The predictor variables include: (1) the daily 24-hour peak temperature, (2) the average wind speed (9 a.m.-3 p.m.), (3) the atmospheric transmittance, (4) the average cloud cover (10 a.m.-2 p.m.), (5) the daily 24-hour minimum temperature, (6) the average dew point (10 a.m.-2 p.m.), (7) the relative humidity at noon, (8) the day of week, (9) the daily total rainfall, and (10) the nighttime calms (12 midnight-4 a.m.). Parametric transformations were applied to the variables to improve accuracy. The resulting model uses a square root transformation for the peak Ozone concentration, the fourth-order polynomial terms for the peak air temperature, and an exponential decay function for the average wind speed. The overall coefficient of determination (R2) of the model was 0.70. Supplementary meteorological criteria were developed to better identify the days that Ozone levels exceed NAAQS. Another multiple


75 75

regression model was developed based on both extremes of the Ozone data set, i.e., events in which Ozone concentration was either above 100 ppb or below 50 ppb. The resulting regression model, called the “hi- lo” (HL) regression, better predicts the extreme Ozone events than the baseline model. Based upon these results, a hybrid model was constructed, which improves the R2 value to 0.82. It was suggested that the accuracy of the model should also be evaluated in terms of the percentage of correct air quality category forecasts. As long as the model predicts the correct air quality category, then it is accurate in terms of providing the public the information that it needs [66].

Based on several Ozone prediction models in various areas throughout the states [62, 65, 66, and 67], the typical explained variances (R2) are in the range of 60-80%, and the typical mean absolute errors are in the range 10-20 ppb. However, a common problem with these models is that on the very high Ozone days, the errors tend to be much larger, and the Ozone concentrations are systematically underpredicted. It is these extreme events that are most important from a health perspective.

Cobourn and Hubbard, 1999 [68] incorporated an air mass trajectory parameter into the nonlinear regression model to enhance the accuracy of predicting the high Ozone events. Note that the air mass trajectory analysis is a useful tool for identifying the source region and transport corridors of various air pollutants including ground- level Ozone. The resulting model performed significantly better in predicting the daily maximum 1-hour Ozone concentrations than did an earlier regression model [66]. In the work of Ibarra et al, 2001 [69], not only were short-term variations of meteorological variables found to be associated with the daily Ozone levels in Bilbao, Spain, but long-term changes of traffic in the area were also found to be highly correlated with the Ozone trend. Meteorological variables used in their study include average air temperature, maximum thermal gradient, average wind speed, absolute humidity, pressure, dew-point temperature, and solar radiation. Traffic variables include the average number of vehicles circulating above the sensor every 10 minutes and occupation percentage (the fraction of time the area of the road above the sensor is being occupied by a vehicle) of traffic flow in the area. By using the low-pass KZ filter technique, developed by Kolmogorov and Zurbenko, the time series of the logarithm of the daily maximum Ozone concentrations were split into long-term, seasonal, and short-term effects. Meteorological effects then were removed from the filtered Ozone series using multiple linear regression. Finally, the long-term evolution of the Ozone-forming capability due to changes in precursor emissions was obtained by applying the KZ filter to the residuals of the regression. The results show that the long-term trend of daily mean traffic can explain between 81% and 99.6% of the total variance of long-term Ozone changes at the three locations of Bilbao in Spain.

In the USA, over 25 regional episodic control programs have been established nationwide in response to the persistence of the Ozone air pollution problem [70]. Many urban areas have enacted voluntary Ozone control programs, aimed at increasing public awareness and participation in local clean air efforts. A key component of these programs is the forecasting of probable high Ozone days, and subsequent promulgation of an “Ozone action day” in the community. People who suffer Ozone-related respiratory problems would benefit from access to accurate forecasts of high Ozone days, so that they could reduce their health risk on those days by staying indoors or reducing outdoor activities.

Many different techniques have been used for developing Ozone prediction programs, for example, multiple regression (both linear and nonlinear), classification and regression tree


76 76

analysis, generalized additive models, and artificial neural network (ANN) modeling. The accuracy of the forecasts can vary among these techniques. Comrie, 1997 [71] and Cobourn et al, 2000 [72] conducted a comparison of neural networks and regression models for forecasting ground- level Ozone. It was found that these two techniques are very comparable in terms of accuracy (mean absolute errors of predictions in the range of 11.1 to 12.9 ppb). The choice of which of these techniques to use for Ozone forecasting should probably be based on other factors, such as familiarity with the technique, software availability, or availability of reasonable future estimates of predictor variables. The daily seasonal Ozone data can be modeled using AutoRegressive Integrated Moving Average (ARIMA) time series model as used successfully for other seasonal data like aviation traffic forecasting [73]. These models need recalibration for applications in other areas and do not include “casual” variables, which limits their use for air pollution modeling.

Ozone prediction can be made through various methods. Regression is a method that generally produces good results although it does not accurately predict extreme O3 concentrations. Several O3 prediction models, developed by the regression technique, that were reviewed are of limited use because they require the input of O3 concentration on the previous day to forecast Ozone concentration for the next day. In addition, none of the reviewed O3 regression models includes the emission variables although it has been found that the long-term change in traffic is highly associated with the change in the trend of O3 concentration. Therefore, in this study the effects of daily variation of VOC and NOx emissions, both from highway motor vehicles and point sources are considered on the development of an enhanced O3 concentration model using the multiple linear regression technique.

It has been investigated that the long-term change in traffic is highly associated with the change in the trend of O3 concentration. However, none of previously developed multiple regression and ANN models for predicting O3 concentration incorporates precursor emissions into the model. Therefore, in this study the effects of daily variation of VOC and NOx emissions, both from highway motor vehicles and point sources are considered in the development of enhanced O3 and NO2 concentration models. 4.3 CAIT Air Quality Modeling Database The air quality modeling databases were compiled in Microsoft Excel [4, 22]. As discussed in earlier sections, air quality is affected by many different factors presented in the atmosphere, for example, pollutant emissions, meteorological or climatological conditions, and location geography. The pollutant emissions come from many different sources such as mobile sources and point sources. Climatological conditions influence the formation of air pollutants as well as their dispersion in the atmosphere. Examples of important climatological parameters include air temperature, wind speed and direction, solar radiation, precipitation, and cloud cover. Some of them, especially wind speed and wind direction, are strongly dependent on the terrain and geographic location of the area. In addition to the terrain characteristic, the landuse and land surface type also has influence on air quality, e.g., the results of heatisland effect. Based on complete data history for all these predictor (independent) variables in conjunction with the actual measured pollutant concentrations (predicted or dependent variables), a statistical model can be developed to predict air pollutant concentrations.

As reviewed earlier, statistical regression models have been developed to predict the daily maximum 1-hour average O3 concentrations at several locations in the U.S. [63, 64, 65, and


77 77

66]. These models consider only the effects of climatological conditions and prior-day O3 concentration. As the new 8-hour O3 standard is taking action, new models for predicting the daily maximum 8-hour average O3 concentrations are needed. In addition, it has been determined that the long-term change in traffic is highly associated with the change in the trend of O3 concentration [69]. Therefore, in this study the effects of the daily variation of VOC and NOx emissions, both from highway motor vehicles and point sources, are considered in the development of an enhanced O3 concentration model without using the prior-day O3 predictor variable.

The air quality databases for model development were compiled for two locations in northern Mississippi. One is Tupelo in Lee County and the other one is Hernando in DeSoto County. Another database was compiled for Oxford, Mississippi, for model validation purpose. Tupelo and Oxford were selected for this study because the real-time measurements of air quality using DIAL technology were performed in these two locations and several traffic data samples were collected as parts of the air quality project at CAIT in the previous phase [3, 4]. Hernando was selected because there was an issue concerning the exceedance of the new standard of 8-hour O3 concentration in DeSoto County. Both Tupelo and Hernando have the EPA air quality monitoring stations, which are operated and maintained by the Mississippi Department of Environmental Quality (DEQ). The air pollution data were obtained from the EPA monitoring stations in these two locations through the EPA’s AQS database [74]. The air pollution data included in the database are O3 and NO2 concentrations. They were acquired for the years 1996 through 2001. Although there is no EPA monitoring station located in Oxford, the limited DIAL measurement results are available for comparison.

More than 30 different predictor variables are included in these databases. The predictor variables were assigned the name xi (i = 1, 2, 3, …) and the dependent variables were given the name yj (j = 1, 2, 3, …). The data in the database were collected from several different sources. Historical traffic data were obtained from the Mississippi Department of Transportation (MDOT) for the years 1990 through 2001 [75]. All climatological data, except solar radiation data, were obtained from the NOAA weather stations nearest to the study locations [76]. Solar radiation data were obtained from the Cooperative Networks For Renewable Resource Measurements (CONFRRM) for the years 1997 to 2001 [77]. The raw data files were processed to extract the needed data, which were stored into the air quality database.

The compiled air quality database, used for developing the air pollutant concentration models, contains daily data records of Tupelo and Hernando from July 1996 to December 2000. In addition, the air quality databases of 2001 for Oxford and Jackson, Mississippi [78], as well as for the asphalt highway test track at National Center for Asphalt Technology (NCAT) near Auburn, Alabama, were also compiled for model validation and implementation purposes.

Air Pollution Data from EPA Monitoring Stations

The group of dependent variables in the CAIT air quality database consists of all values related to the concentration levels of O3 and NO2. They are expressed in two ways: (1) as concentration, in part per million (ppm), and (2) as AQI value. The O3 data were downloaded from the EPA’s AirData Web sit for the historical years 1996 to 2001 [74]. The EPA data are presented as the AQI value of the days. These AQI values were used to calculate the O3 concentration (in ppm) before entering into the database. The NO2 data for Hernando were obtained from the Mississippi DEQ staff for the year 1998 to 2001 [78]. These data are in the


78 78

hourly concentration format. The daily summary of these data was calculated before storing into the database. The processed air pollution data were assigned as variables y1 to y15.

For developing the O3 model, the variable y3 was used. It is the maximum value of 8-hour average O3 concentration of the day. Note that the 8-hour average value means a moving average of eight 1-hour values. Therefore, there are 24 values of the 8-hour average O3 concentration in a day, and y3 is the maximum value among these 24 values. For the NO2 model, the variable used is y7, which is the daily average NO2 concentration. Unlike y3, this value is the arithmetic average of the 24 values of hourly NO2 concentrations in a day.

It should be noted that in June 2003, the EPA uploaded a new report and chart of AQI data at the AirData Web site, replacing the former report and chart of Pollutant Standard Index (PSI) [74]. Therefore, after the development of preliminary models, the O3 data in the air quality databases of this study were later revised, updated based on these new AQI data, and used to develop the revised models.

Figure 46 shows the measured values of daily maximum 8-hour average O3 concentrations monitored in Tupelo, 2001. Note that the measurement of O3 concentration was done only during O3 season (March to October). In November, December, January, and February, which are winter months, the O3 concentration values are usually not significant and not recorded at the monitoring stations.

Figure 46. Daily maximum 8-hour average O3 concentrations in Tupelo, 2001

Geographic Data from Topo USA Software

Because the CAIT air quality database contains air pollution data elements from two different locations, Tupelo and Hernando, geographic data are also considered. These data elements are location, latitude, longitude, and elevation. They are assigned the name x1 to x4. The variable x1 is a dummy variable, which is equal to 0 for Tupelo and 1 for Hernando. Latitude (x2) and longitude (x3) are the global geographic coordinates of the EPA air pollutant monitoring

Daily Maximum 8-hour Average Ozone ConcentrationTupelo, Mississippi - 2001

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1 2 3 4 5 6 7 8 9 10 11 12

Month

Ozo

ne C

once

ntra

tion

, ppm EPA 8-hour average Ozone National Ambient Air Quality Standard

EPA 1-hour average Ozone National Ambient Air Quality Standard

0.084

0.124


79 79

station in each city. They are included along with the air pollution data from each monitoring station. Elevation above mean sea level (x4) of each monitoring station was obtained using the USGS quad maps through the Topo USA 2.0 software [79].

Time Series Air Pollution Data

The daily data of air pollution concentration across a year has a seasonal pattern as illustrated in Figure 47. It shows the daily maximum values of measured O3 concentrations in Tupelo and Hernando from 1996 to 2000. During summer months, higher O3 concentrations are found. On the other hand, during the winter months, O3 concentrations reduce considerably. In addition, Figure 47 also indicates a large variation of O3 concentration in short term, as seen from the jumping peaks up and down along the long-term trend. Figures 48 to 50 present this variation of daily maximum O3 concentrations on a yearly, monthly, and weekly basis.

According to these figures, the variation has a cyclic pattern, which makes it difficult to capture in the simple linear regression statistical model. In addition, it was reported that regional-scale O3 levels in the mid-western and northeastern states had been observed to correlate with the days of the week, which may be associated with some patterns of human activities in the area [66]. Therefore, time-related variables were included in the air quality database. These variables are: (1) day of the year (from 1 to 365), (2) month (from 1 to 12), and (3) day of the week (1 = Monday to 7 = Sunday). These variables are represented by x5, x6, and x7, respectively.

Figure 47. Seasonal pattern of O3 concentration for Tupelo and Hernando, 1996-2000

Historical Trend of Daily Maximum 8-hour Average Ozone Concentration - Tupelo and Hernando, Mississippi

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1996 1997 1998 1999 2000Year

Ozo

ne C

once

ntra

tion

, ppm

Tupelo Hernando

EPA 8-hour average Ozone National Ambient Air Quality Standard

0.084

0.124EPA 1-hour average Ozone National Ambient Air Quality Standard


80 80

Air Pollutant N Mean, ppm SD, ppm CV, % Ozone 246 0.048 0.015 30.2

Nitrogen Dioxide 240 0.010 0.006 64.1

Figure 48. Cyclic pattern of air pollutant concentrations in a year



Figure 49. Cyclic pattern of air pollutant concentrations in a month

Daily Air Pollutant Concentration in Hernando, Year 2001

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0 50 100 150 200 250 300 350Day of Year

Con

cent

rati

on, p

pmDaily Maximum 8-hr Average Ozone Concentration

Daily Average Nitrogen Dioxide Concentration

National Ambient Air Quality Standard for ground-level Ozone (8-hr average)

National Ambient Air Quality Standard for

Nitrogen Dioxide (annual average)

0.053

Note:Annual average Nitrogen Dioxide concentration of year 2001 = 0.010 ppm

365

0.084

Daily Air Pollutant Concentration in Hernando, August 2001

0.000

0.020

0.040

0.060

0.080

0.100

0.120

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31Day of Month

Con

cent

rati

on, p

pm

Daily Maximum 8-hr Average Ozone Concentration





0.053


0.084


81 81

Daily Air Pollutant Concentration in Hernando, August 20-26, 2001

0.000

0.020

0.040

0.060

0.080

0.100

0.120

Monday Tuesday Wednesday Thursday Friday Saturday SundayDay of Week

Con

cent

rati

on, p

pm






0.053


0.084

08/20/2001 08/26/2001



Figure 50. Cyclic pattern of air pollutant concentrations in a day

Climatological Data from NOAA Stations

The climatological data is an important group of the independent variables in the database since the daily variation of O3 levels is greatly influenced by the climatological conditions. The climatological data, except for solar radiation, were obtained from the selected NOAA weather stations nearest to the DEQ air quality monitoring station. The unedited local climatological data hourly observations were downloaded from the Internet site of the National Climatic Data Center (NCDC) as ASCII text files [76]. Examples of the climatological data contained in the files include hourly air temperature, wind speed and direction, precipitation, and sky conditions. The pre-processing on these data was done to extract only necessary data, and then to calculate the extracted daily data summary for entering in the air quality database.

Note that the downloaded climatological data do not provide information about solar radiation. Thus, in the preliminary compilation of the air quality database, the solar radiation data of Memphis in 1990 was used for both Tupelo and Hernando, and for every year from 1996 to 2000. Afterward, the daily solar radiation data in Mississippi were found to be available from the CONFRRM network for the data years 1997 and onward [77], entered in the database to replace 1990 Memphis data, and used for revised air quality models.

The variables x11 through x20 were assigned to each climatological data in the database. The variables x11, x12, and x13 represent the daily average, maximum, and minimum air temperatures, respectively. Note that the air temperature data provided in the files include dry bulb temperature, wet bulb temperature, and dew point temperature. The dry bulb temperature was used for the calculation of x11, x12, and x13. The unit used for air temperature is degrees Celsius (oC). Figure 51 shows the seasonal variations of air temperature in Tupelo for the year


82 82

Daily Wind Speed and Direction - Tupelo, Mississippi, Year 2000

0

3

6

9

12

15

18

21

24

27

30

33

36

1 2 3 4 5 6 7 8 9 10 11 12Month

Win

d Sp

eed,

m/s

0

30

60

90

120

150

180

210

240

270

300

330

360

Win

d D

irec

tion

, deg

ree

Average Wind Speed Maximum Wind Speed Minimum Wind Speed Average Wind Direction

Daily Air Temperature - Tupelo, Mississippi, Year 2000

-20

-10

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10 11 12Month

Air

Tem

pera

ture

, °C

Average Air Temperature Maximum Air Temperature Minimum Air Temperature

122 °F

32 °F

-4 °F

2000. Recall that the seasonal variation of daily maximum O3 concentration in Tupelo is shown in Figure 46.

Other climatological variables are wind speed and wind direction. Three different variables for wind speed are the daily average, maximum, and minimum wind speeds, which are represented by x14, x15, and x16, respectively. The unit used for the wind speed variables is meters per second (m/s). The average wind direction (in degrees) is assigned to the variable x17. Figure 52 shows the variation of wind speed and direction in Tupelo in the year 2000. The data of both wind speed and direction show large variations.

Figure 51. Daily air temperature variation in Tupelo, 2000

Figure 52. Daily wind speed and direction variation in Tupelo, 2000


83 83

The last three independent variables in this group are precipitation, cloud cover, and solar radiation, which are represented by x18, x19, and x20 respectively. The variable x18 denotes the total daily precipitation in inches. The variable x19, daily cloud cover, is a dummy variable. It is given the value of 0 for completely cloudy day, 1 for partially cloudy day, and 2 for clear sky day. The interpretation of the cloud cover code compared with the NOAA’s sky condition data is given in Table 16. To determine the daily cloud cover code, first, the hourly sky condition data are assigned the value of 0, 1, or 2 based on Table 16 [76]. Then, the number of occurrences of each cloud cover code in a day is counted. Finally, the code that has the most number of occurrences is considered as the daily cloud cover code. In case there exists a situation where two or more codes have an equal number of occurrences, the code with higher value is designated as the daily cloud cover code; in other words, the designation tends toward a clear sky condition.

The variable x20 denotes the total daily solar radiation, which refers to global horizontal irradiance (direct normal irradiance plus diffuse horizontal irradiance) in Langleys/day. The original data, downloaded from the Internet site of the CONFRRM solar radiation monitoring network [77], were converted from Watt-hrs/sq m/day by the conversion factor 0.086 (1 Watt-hrs/sq m/day = 0.086 Langleys/day). The solar radiation data downloaded for this study were collected at the Mississippi Valley State University, Itta Bena [77], the only site in the network located in Mississippi. It was used for every city in the air quality database. Note that the data were available for the data years 1997 and onward. Therefore, the data of the year 1997 were also used for the year 1996.

Table 16. Category of cloud cover based on NOAA’s sky conditions code

Cloud Cover (x18) NOAA’s Sky Conditions

Code Description Code Description 0 – Yes Completely cloudy OVC Overcast: 8/8 sky cover

1 – Partial Partially cloudy BKN Broken: 5/8 – 7/8 sky cover SCT Scattered: 3/8 – 4/8 sky cover FEW Few: 0/8 – 2/8 sky cover

2 – No Clear sky CLR Clear below 12,000 ft

Figures 53 and 54 show the variations of the total daily precipitation and total daily solar radiation in Tupelo for the year 2000. According to Figure 53, there were more raining days from the beginning of November until the end of April compared to the other half of the year. Figure 48 implies a large variation in the total daily solar radiation across the year.


84 84

Total Daily Solar Radiation - Tupelo, Mississippi, Year 2000

0

2,000

4,000

6,000

8,000

10,000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Sola

r R

adia

tion

, Wat

t-H

rs/s

q m

0

860

Lan

gley

s/da

y

Total Daily Precipitation - Tupelo, Mississippi, Year 2000

0

10

20

30

40

50

60

70

80

90

1 2 3 4 5 6 7 8 9 10 11 12

Month

Tot

al D

aily

Pre

cipi

tatio

n, m

m

3.5 in

0 in

Figure 53. Total daily precipitation variation in Tupelo, 2000

Figure 54. Total daily solar radiation variation in Mississippi, 2000 Traffic Data from MDOT

Historical average annual daily traffic (ADT) data were obtained from the Mississippi DOT for Oxford, Tupelo, and Hernando for the years 1990 to 2001. The ADT on roads, highways, and freeways are different from one section to another. Also, the average vehicle speeds on different roadway types are different. The average vehicle speed on highways is higher than the average vehicle speed on arterial or local roads. To account for this variation in traffic characteristics, the areas of Tupelo and Hernando are divided into 3 radial zones with the radius


85 85

Radius less than 5 km (<3 mi)

Radius 5-8 km (3-5 mi)


Tupelo, Mississippi

Zone 1

Zone 3

Zone 2




Hernando, Mississippi

Zone 1

Zone 3

Zone 2

of 5, 8, and 16 kilometers (3, 5, and 8 miles) originating from the EPA air quality monitoring station in each city. Figures 55, 56, and 57 illustrate these radial zones for Tupelo, Hernando, and Oxford, respectively.

Figure 55. Radial zones of daily traffic volumes for Tupelo

Figure 56. Radial zones of daily traffic volumes for Hernando


86 86

Figure 57. Radial zones of daily traffic volumes for Oxford The ADTs on every road section within each radial zone were extracted from the ADT

maps provided by the Mississippi DOT. Then, the ADTs in each zone were summed together. Note that for zones 2 and 3, only the road sections in the marginal area (ring area) of each zone were accounted for. Table 17 provides an example of the ADT calculation sheet for Tupelo, 2000.

After the total ADT in each zone were obtained, they were distributed for a typical week using the daily distribution factors derived from actual traffic data collection on MS Highway 6 in Oxford, Mississippi [80]. This results in the approximate daily traffic volume in each radial zone for any day of week, as shown in Figure 58. It shows that the traffic volume on weekdays is higher than the traffic volume on weekends. For a typical week, Friday has the most traffic volume while Sunday has the least. These daily distribution factors were used to calculate typical week traffic volumes for all three locations (Tupelo, Hernando, and Oxford) for the data years 1996-2000. The final daily traffic distributions in each radial zone are provided in Table 18. A similar procedure was used to calculate daily traffic volumes for years 2001 and onwards for air quality model verification purpose.

In the air quality database, the independent variable x8 represents the total ADT on the roads in zone 1 (radius less than 5 km from the DEQ monitoring station). The variable x9 represents total ADT on the roads in zone 2 (radius 5 to 8 km). The variable x10 represents total ADT on the roads in zone 3 (radius 8 to 16 km).



Radius 8-16 km (5 -10 mi)

Oxford, Mississippi

Zone 1

Zone 3

Zone 2


87 87

Road < 5 km 5-8 km 8-16 km

A Natchez Trace 12,200 6,000B Gloster St. (Hwy 145) 20,000 19,000

C I-45 31,000 24,000D US-78 17,000 18,000 13,700E McCullough Blvd. (Hwy 145) 18,000 14,000 8,600

F Main St. (Hwy 6) 33,000 19,000 13,000G Coley Rd. 11,000

H Gookin Blvd. 11,000 10,000I Eason Blvd. 22,000 24,000J Green St. 12,000 2,800

K Franklin St. 3,200 2,400L Front St. 6,700 2,500

M Lawndale Dr. 5,600 5,600 1,200N Thomas St. 9,700O Lumpkin Ave. 6,100

P Mt. Vernon Rd. 1,600Q Industrial Rd. 4,300

R Blair St. 2,500 2,500 350S Veterans Blvd. 4,100 720T Jackson St. 13,800 6,900

U Canal St. 7,900 2,600V Bissell Rd. 1,200 1,200 510

W Purnell Rd. 3,000X CR 1600 3,300 830Y Countrywood Rd. 980 980 250

Total 170,980 193,380 97,460Combined Total --------------------------------------------------------------------------> 461,820

Table 17. ADT in traffic radial zones, Tupelo, 2000

Figure 58. Daily traffic variation in a typical week for each radial zone of Tupelo, Mississippi, 2000

Typical Week Traffic Distribution - Tupelo, Mississippi, 2000

0

100,000

200,000

300,000

400,000

500,000

600,000

Mon Tue Wed Thu Fri Sat Sun

Day of Week

Tra

ffic

Vol

ume,

veh

/day

Radius less than 5 km

Radius 5-8 km

Radius 8-16 km

Total

Tota

Zone

Zone

Zone 3


88 88

DailyRadius Zone 1 (< 5 km) Zone 2 (5-8 km) Zone 3 (8-16 km) Zone 1 (< 5 km) Zone 2 (5-8 km) Zone 3 (8-16 km) Distribution

Typical ADT 170,980 193,380 97,460 137,900 57,940 139,330 Factor

Mon 176,603 199,739 100,665 142,435 59,845 143,912 0.1476Tue 177,681 200,958 101,279 143,304 60,211 144,790 0.1485Wed 180,412 204,047 102,836 145,507 61,136 147,016 0.1507Thu 189,611 214,452 108,080 152,926 64,253 154,512 0.1584Fri 204,093 230,831 116,335 164,606 69,161 166,313 0.1705Sat 146,103 165,244 83,280 117,836 49,510 119,058 0.1221Sun 122,359 138,389 69,745 98,686 41,464 99,709 0.1022

Total 1,196,860 1,353,660 682,220 965,300 405,580 975,310 1.0000


Typical ADT 170,925 190,306 88,279 136,659 57,419 130,830 FactorMon 176,546 196,564 91,182 141,153 59,307 135,132 0.1476Tue 177,623 197,764 91,739 142,015 59,669 135,957 0.1485Wed 180,354 200,804 93,149 144,197 60,586 138,047 0.1507Thu 189,550 211,043 97,899 151,550 63,675 145,086 0.1584Fri 204,027 227,162 105,376 163,125 68,538 156,167 0.1705Sat 146,056 162,617 75,435 116,775 49,064 111,794 0.1221Sun 122,319 136,189 63,175 97,797 41,091 93,626 0.1022

Total 1,196,475 1,332,142 617,954 956,613 401,930 915,810 1.0000




Total 1,142,519 1,308,909 587,779 946,960 397,874 885,635 1.0000




Total 1,126,734 1,276,891 557,604 938,273 394,224 855,460 1.0000




Total 1,117,249 1,225,973 527,429 929,579 390,574 825,285 1.0000

Tupelo, Lee County, 1998 Hernando, DeSoto County, 1998


Hernando, DeSoto County, 2000

Hernando, DeSoto County, 1999


Tupelo, Lee County, 1999

Tupelo, Lee County, 2000

Table 18. Weekly ADT variation in each radial zone, 1996-2000


89 89

In addition to the traffic volume, the average vehicle speed also differs from one road to another. Different average vehicle speeds result in different emission rates of air pollutants emitted from motor vehicles. Generally, the average vehicle speed of traffic on a highway or a freeway is higher than the average vehicle speed of traffic on arterial and local roads. Since the inner zones (closer to the center of the city) tend to have more arterial and local roads than the outer zones (far away from the center of the city), the average vehicle speed in the inner zones is likely to be less. In this study, the average vehicle speed in each radial zone is assigned based on the amount of each roadway type in that particular zone and sample estimates collected during the DIAL study [3, 80]. It is separately considered for cars and trucks because these two types of vehicles generally have different average speeds. The average truck speeds are assigned the variables x8a, x9a, and x10a for zone 1, 2, and 3, respectively. For cars the average speeds are given the names x8b, x9b, and x10b. This naming style is intended to follow the variable names of the traffic volume in each radial zone. Note that the unit of the average vehicle speeds used in the air quality database is miles per hour (mi/h).

Other traffic data included in the air quality database are percentages of car and truck in traffic mix. They are important because the air pollutant emission rates produced by cars and trucks are significantly different. In the air quality database, the percentages of car and truck traffic are assigned different values for each radial zone. The percentages of truck traffic are named x8c, x9c, and x10c, and the percentages of car traffic are named x8d, x9d, and x10d, respectively. Because only two vehicle types are considered, the percentages of car are equal to 100 minus the percentages of truck. Determination of the percentages of truck traffic is based on the amount of different roadway types in the radial zones and sample data collected in Oxford and Tupelo during the DIAL study [80]. Typically, highways and freeways carry more truck traffic than arterial and local roads do. A summary of the average vehicle speeds and percentage of traffic mix in Tupelo and Hernando is given in Table 19. Note that these values were used in the air quality database for every data year from 1996 to 2000, as well as for future prediction years.

Table 19. Average vehicle speed and percentage of traffic mix in Tupelo and Hernando

Tupelo Zone 1 (<5 km) Zone 2 (5-8 km) Zone 3 (8-16 km)

Average car speed, km/h (mi/h) 48 (30) 56 (35) 80 (50) Average truck speed, km/h (mi/h) 40 (25) 48 (30) 80 (50)

Percentage of car 94.4 92.0 80.0 Percentage of truck 5.6 8.0 20.0

Hernando Zone 1 (<5 km) Zone 2 (5-8 km) Zone 3 (8-16 km) Average car speed, km/h (mi/h) 56 (35) 64 (40) 88 (55)

Average truck speed, km/h (mi/h) 48 (30) 56 (35) 88 (55) Percentage of car 93.0 90.0 75.0

Percentage of truck 7.0 10.0 25.0


90 90

4.4 Vehicular Emission Data from MOBILE6-Estimated Emission Rates

As discussed in chapter 3, MOBILE is the emission factor model used by the EPA and state agencies, when preparing emission inventories and SIPs, to estimate the emission rates of pollutants emitted from on-road vehicles. Procedures to prepare the emission inventory for mobile sources are described in detail by EPA [81]. These procedures require extensive traffic and meteorological data for input into the MOBILE model. In this study, simplified procedures were developed to specifically prepare the vehicular emissions of the precursors of Ozone—VOC and NOx—for entering into the air quality database. They are intended to provide a sufficiently rational methodology to estimate reasonably accurate vehicular emissions based on the available data in a time-effective manner.

The MOBILE6 software [39, 43], the current version of MOBILE, was used to estimate emission rates of VOC and NOx from different classes of vehicles. In MOBILE6, 28 vehicle classes are defined, but only five of them were selected to represent the simplified vehicle classification used in this study. The selected MOBILE6 vehicle classes are the typical vehicle classes generally observed in northern Mississippi. The relationship between the MOBILE6 vehicle classification and the simplified version used in this study is given in Table 20.

Table 20. Relationship between MOBILE6 and simplified vehicle classifications

Simplified Classification MOBILE6 Classification

Car LDGV: Light-Duty Gasoline Vehicles (Passenger Cars) Light Gasoline Truck LDGT4: Light-Duty Gasoline Trucks 4 Heavy Gasoline Truck HDGV7: Class 7 Heavy-Duty Gasoline Vehicles, and

HDGB: Gasoline Buses Diesel Truck HDDV8b: Class8b Heavy-Duty Diesel Vehicles

To develop the vehicular emissions inventory for use in this study, the following steps

were implemented: 1. Compile a comprehensive pollutant emission rates database using MOBILE6

computations. 2. Develop the multiple linear regression models for the emission rates based on the

database. 3. Prepare necessary distribution factors:

- Fleet distribution by model year - Truck proportion by class

4. Estimate average travel distances. 5. Calculate vehicular emissions. Vehicle Mix Distribution Factors Since the VOC and NOx emission rates are different depending on the vehicle classes and the vehicle model year, two distribution factors are needed when calculating vehicle emissions. These two distribution factors are: (1) fleet distribution of model year, and (2) truck proportion by vehicle class.


91 91

Fleet Distribution by Model Year

Fleet distribution has a vital effect on the calculation of vehicular emission inventory. Fleets with a higher percentage of older vehicles will have higher emissions for two reasons. Older vehicles have typ ically been driven more miles and have experienced more deterioration in emission control systems. A higher percentage of older vehicles also means that there are more vehicles in the fleet that do not meet newer, more stringent emissions standards.

The fleet distribution factors by model year for Mississippi were derived from the database of the National Personal Transportation Survey (NPTS), 1995 [82]. The data field “Vehicleyear”, which refers to the model year of the surveyed vehicles, was extracted for the census division 6. The census division 6 consists of states in the East South Central region including Kentucky, Tennessee, Alabama, and Mississippi. The extracted data were sorted into two groups, cars and trucks. For each group the percentages of vehicle model year are calculated for the model years 1980 or older, 1981, 1982, ..., 1995. These percentages are the distribution of vehicle model years in the analysis year of 1995. For other analysis years, e.g., 1996 and onward, the distribution is shifted ahead assuming that the distribution in that particular analysis year has the same shape as the distribution in 1995. It is also assumed that in any analysis year there are no vehicles 20 years or older. For instance, in the analysis year 2000, the percentage of vehicle model years 1980 or older is 0. This methodology is shown in Figure 59 for cars, and in Figure 60 for trucks.

Figure 59. Fleet distribution by car model year in Mississippi

Percentage of Mix of Different Model Year Car in MississippiAnalyzed from Nationwide Personal Transportation Survey (NPTS) 1995 Data; http://www.transtats.bts.gov

0

5

10

15

20

25

30

35

40

1975 1980 1985 1990 1995 2000 2005 2010

Model Year

% M

ix

1995 Data2000 Data2005 DataAssume 0% for 20 years model or olderAssume 0% for 20 years model or older

Assume 0% for 20 years model or older

Note: For 2000 and 2005 data assume the same percentage of mix as of 1995 data.

Extrapolation

Extrapolation

Extrapolation


92 92

Figure 60. Fleet distribution by truck model year in Mississippi Truck Proportion by Class To calculate the amount of traffic volume for each class of vehicles, the percentages of car and truck traffic mix are needed. Table 19 provides the percentages of traffic mix between car and truck in each radial zone. Therefore, the amount of car volume in any radial zone is simply equal to traffic volume in that zone multiplied by the percentage of car in that zone. In the same way, the amount of truck volume in any radial zone is equal to traffic volume in that zone multiplied by the percentage of trucks in that zone. To further calculate the amount of light gasoline trucks, heavy gasoline trucks, and diesel trucks in any particular zone, the proportions of these three classes of truck are required. This data for Mississippi was not readily available. It was derived from the title summary totals (as of October 11, 2003), of the motor vehicle licensing and title statistics reported by Mississippi State Tax Commission [83]. The results give the proportion of light gasoline trucks/heavy gasoline trucks/diesel trucks as 94.9/1.6/3.5. This proportion was used for the analysis years 1998 and onward. For the analysis years 1996 and 1997 the truck proportions of 96.0/ 3.0/ 1.0 were used. The latter proportion was estimated based on the experience that the diesel truck became more popular just only in later years. In addition to distributing truck traffic volume in the analysis year for three truck types, the truck proportions are also needed for distributing the truck traffic for each truck model year. In this study, the truck proportion was assumed to be 94.9/ 1.6/ 3.5 for the truck model years 1991 and onward. It was assumed to be 96.0/ 3.0/ 1.0 for the truck model years 1990 and older. Estimation of Average Travel Distance per Day Since the emission rates are predicted on a per unit distance basis, the local information regarding the average travel distance made by vehicles in each city is needed. This information is not readily available from several sources that were investigated. Therefore, a methodology for approximating the average travel distance was designed. The average travel distance per day for

Percentage of Mix of Different Model Year Truck in MississippiAnalyzed from Nationwide Personal Transportation Survey (NPTS) 1995 Data; http://www.transtats.bts.gov

0

5

10

15

20

25

30

35

40

1975 1980 1985 1990 1995 2000 2005 2010

Model Year

% M

ix

1995 Data

2000 Data

2005 Data




Note: For 2000 and 2005 data assume the same percentage of mix as of 1995 data.

Extrapolation

Extrapolation

Extrapolation


93 93

vehicles in any city is estimated by averaging the radial distance from the center of the city in north, south, east, and west directions, and multiplying the average value by two. Multiplying the average value by two is based on the assumption that commuters travel from an origin to a destination and from the destination back to the origin. For an estimation of the radial distances, the digital aerial photography of the cities of Tupelo and Hernando were downloaded from the Internet site of Mississippi Automated Resource Information System (MARIS). They were taken during the winters of 1995 and 1996 under the U.S. Geological Survey’s (USGS) National Aerial Photography Program (NAPP) [84]. These digital aerial photographs are provided in a MrSID (*.sid) file format. The MrSID file can be read by several imaging programs, such as GeoExpress View, which is used in this study. The estimated average travel distances for Tupelo, Hernando, and Oxford are 9.2, 8.0, and 7.6 km (5.8, 5.0, 4.8 mi) per day, respectively. Calculation of Vehicular Emissions After all necessary information is acquired, the calculation for the daily vehicular VOC and NOx emissions can be made. The calculation is based on aggregating the emission rates and traffic volume for each vehicle class and model year in each zone, using the appropriate distribution factors, as shown in Equations38 and 39. These equations are also implemented in the AQMAN program to calculate vehicular VOC and NOx emissions for future air quality prediction. The calculated daily vehicular VOC emissions are assigned to the variable x34, and the calculated daily vehicular NOx emissions are assigned to the variable x35 in the air quality database.

(38)

(39) where: eijk = emission rate (g/veh/mi) of vehicle class i in zone j for model year k pik = fleet distribution factor by model year of vehicle class i for model year k

qij = traffic volume (veh/day) of vehicle class i in zone j qj = traffic volume (veh/day) in zone j

sjm = fraction of traffic mix m in zone j tn = proportion of truck type n d = average travel distance (mi)

i = car, light gasoline truck, heavy gasoline truck, diesel truck j = 1, 2, 3 k = 1980 or older, 1981, 1982, ..., analysis year m = car, truck n = light gasoline truck, heavy gasoline truck, diesel truck Examples: Tupelo, July 25, 2001 qcar,zone1 = qzone1⋅szone1,car = 184,938 x 0.944 = 174,581 veh/day qLGT,zone1 = qzone1⋅szone1,truck⋅tLGT = 184,938 x 0.056 x 0.949 = 9,828 veh/day

( )∑ ⋅∑

⋅

∑

∑ ⋅=

i jij

kik

kikijk

dqp

pe (g)emission Daily

njmjij tsqq ⋅⋅=


94 94

4.5 Point Source Emissions Data from EPA Emission Inventory

The methodology to account for the point emission sources is similar to the methodology

for roadway traffic emissions. Three radial zones are defined in the areas of Tupelo and Hernando. The first zone covers the area that has the radial distance from the EPA monitoring station less than 8 km (<5 mi). The second zone covers the area that has the radial distance from 8 to 16 km (5-10 mi). The last zone covers the area that has the radial distance from 16 to 32 km (10-20 mi). Figures 61, 62, and 63 show the radial zones for point sources in Tupelo, Hernando, and Oxford, respectively. Note that these radial zones were used for aviation emission sources as well.

Figure 61. Radial zones for point and aviation emission sources in Tupelo

Radius less than 8 km (5 mi)


Radius 16-32 km (10-20 mi)

Point source AIRPOR

TUPELO, MISSISSIPPI

Zone 1

Zone 3

Zone 2


95 95



Radius 16-32 km (10-20 mi)

Point source AIRPORT

OXFORD, MISSISSIPPI

Zone 1

Zone 3

Zone 2



Radius 16-32 km (10-20 mi)

AIRPOR

HERNANDO, Point source

Memphis, Tennessee

DeSoto, Mississippi

Zone 1

Zone 3

Zone 2

Figure 62. Radial zones for point and aviation emission sources in Hernando

Figure 63. Radial zones for point and aviation emission sources in Oxford


96 96

The point source emissions data were acquired from the NEI database provided on the EPA AirData Web site [74]. The NEI database contains the emission data of every county in the U.S. for the years 1996 and 1999. Table 21 shows the point source emission inventories for Oxford and Tupelo. Figure 64 compares emission inventories for point sources for Oxford, Tupelo, and Hernando in northern Mississippi. Figure 65 depicts the historical trend of the total VOC and NOx emissions from point sources located within a 32 km (20 mi) radius of the EPA monitoring station in Tupelo. The total VOC and NOx emissions were calculated for each radial zone. Note that the data of 1997 and 1998 were linearly interpolated from the actual data of 1996 and 1999. The data of 2000 and 2001 were obtained by linear extrapolation. Figure 66 illustrates the trend of the O3 concentration along with the trend of the total VOC and NOx emissions for point sources in each year.

Table 21. Point Sources Emissions in a Vicinity of 32-km (20-mi) Radial Distance of

(a) Tupelo, Lee County, Mississippi, 1999

ID County Name VOC, U.S. tons/yr

NOx, U.S. tons/yr

28-081-0008 Lee Cooper Tire & Rubber Company 214.1 39.6 28-081-0013 Lee Bond Paving Materials Inc. 112.0 - 28-081-0024 Lee Carpenter Company 20.4 0.2 28-081-0025 Lee Stanley Works 125.8 - 28-081-0029 Lee Krueger International 274.1 - 28-081-0041 Lee Crain Industries Inc. 17.7 - 28-081-0049 Lee Tuscarora Plastics Inc. 96.8 - 28-081-0053 Lee Tupelo MFG Company Inc. 21.5 - 28-081-0058 Lee Norbord Industries Inc. 288.3 261.2 28-081-0071 Lee Confortaire, Inc. 1.5 - 28-081-0072 Lee Foamcraft Inc. 1.1 - 28-081-0099 Lee FMC Corporation 46.4 - 28-081-0107 Lee Tupelo Foam Sales 0.1 - 28-081-0112 Lee Bassco Foam, Inc. 1.9 - 28-115-0024 Pontotoc Premiere Plastics Inc. 195.4 - 28-115-0035 Pontotoc Best Foam, Inc. 2.8 - 28-145-0008 Union Master Bilt Products 54.2 2.3 28-145-0034 Union Hickory Springs MFR 189.5 -

Note: - no emission Total 1,663.8 303.4

(b) Oxford, Lafayette County, Mississippi, 1999

ID County Name VOC, U.S. tons/yr

NOx, U.S. tons/yr

28-071-00009 Lafayette Oxford Asphalt Company 3.6 10.9 28-071-00013 Lafayette Georgia Pacific Corporation - Oxford Plant 861.8 274.0 28-071-00014 Lafayette Jack King Asphalt Company 6.6 32.0 28-071-00021 Lafayette University of Mississippi 15.6 49.0 28-071-00023 Lafayette Emerson Electric Motor Company 7.2 17.7 28-013-00005 Calhoun Columbia Gulf Transmission Company 357.1 1,525.6 28-161-00010 Yalobusha Borg-Warner Automotive 0.0 0.1


97 97

Point Source VOC Emissions in a Vicinity of 32-km (20-mi) Radial Distance for Rural Cities in Mississippi

1,998

1,252

755

1,525 1,664 1,756

8,181 8,045 7,955

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

1996 1999 2001

Year

VO

C E

mis

sion

s, U

.S. t

ons/

yrOxford Tupelo Hernando

Metric tons/yr

9,072

4,536

0

Point Source NOx Emissions in a Vicinity of 32-km (20-mi) Radial Distance for Rural Cities in Mississippi

1,665 1,909 2,072274 303 323

39,582

28,210

20,628

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

1996 1999 2001

Year

NO

x Em

issi

ons,

U.S

. ton

s/yr

Oxford Tupelo Hernando

Metric tons/yr

45,359

22,680

0

Total 1,251.9 1,909.1

NOTE: DATA OF YEARS 1996 AND 1999 ARE MEASURED DATA . DATA OF YEAR 2001 ARE EXTRAPOLATED BASED ON 1996 AND 1999 DATA .

Note: Data of years 1996 and 1999 are measured data. Data of year 2001 are extrapolate based on 1996 and 1999 data.

Figure 64. Emission Inventories for Point Sources for northern Mississippi Cities


98 98

Yearly Ozone Concentration andTotal Yearly NOx Emission from Point Sources - Tupelo, Mississippi

0.0790.0780.082

0.080.078

0.075

0.0580.06

0.0610.061

0.050.05

322.92313.14

303.36293.58

283.80274.02

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

1996 1997 1998 1999 2000 2001Year

Ozo

ne c

once

ntra

tion

, ppm

0

50

100

150

200

250

300

350

400

450

500

Em

issi

on, t

ons/

yr

Yearly maximum Ozone concentration

Yearly average Ozone concentration

Total NOx emission within a 20 mile radius of the EPA monitoring station

Total NOx Emission from Point Sources Located within a 20 mile Radius of the EPA Monitoring Station in Tupelo, Mississippi

0 0

39.8349.11

263.53

224.91

303.36

274.02

0

50

100

150

200

250

300

350

400

1996 1997 1998 1999 2000 2001

Year

Em

issi

on, t

ons/

yr

Within radius less than 5 miles

Within radius 5 to 10 miles

Within radius 10 to 20 miles

Total

Notes:1) Total number of point sources = 19 sources2) The data of years 1996 and 1999 are actual data. The data of the other years are interpolated and extrapolated from the actual data.

Total

Zone

Zone 1

Zone 3

Figure 65. Historical trend of point source emissions in Tupelo

Figure 66. Historical trend of O3 levels and point source emissions in Tupelo In the air quality database, the independent variable x24 represents the total NOx

emissions from point sources in zone 1 (radius less than 8 km from the DEQ monitoring station). The variable x25 represents the total NOx emissions from point sources in zone 2 (radius 8 to 16 km). The variable x26 represents the total NOx emissions from point sources in zone 3 (radius 16 to 32 km). Similarly, the variables x27, x28, and x29 represent the total VOC emissions from point sources in zones 1, 2, and 3, respectively.


99 99

Aircraft Operations at Tupelo Regional Airport, Mississippi

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

1996 1997 1998 1999 2000 2001Year

Num

ber

of O

pera

tions

Air Carriers (Started from 1993) Local General AviationAir Taxi and Commuters Local MilitaryGeneral Aviation Subtotal Local OperationsMilitary Total OperationsSubtotal Operations

4.6 Aviation Operations Data from FAA

The emissions from airports and aviation sources can be estimated using the EDMS program [85] developed by the Federal Aviation Administration (FAA), which was reviewed in section 2.4. However, this is not included in the scope of the study. In this study, the contribution of aviation source emissions is taken into account by considering the number of aircraft operations per day.

The methodology to account for the total aircraft operations is similar to the methodology for point source emissions. Three radial zones with the same radial distances as the zones for point source emissions were defined in the areas of Tupelo and Hernando. Each airport in the vicinity of 32-km radial distance is assigned to one of the three zones according to their location.

Historical aircraft operation data at any airport and airfield were obtained from the inventory of the Federal Aviation Administration (FAA) [86]. The inventory contains the historical data of annual aircraft operations for different types of aircraft. It also provides the data of the aircraft operations in future years predicted by the Terminal Area Forecast (TAF) system developed by the FAA [86]. Figure 67 (a) shows the historical data of aircraft operations at the Tupelo Regional Airport. It should be noted that the EPA monitoring station in Tupelo is located at the airport. Therefore, the O3 concentrations measured at this monitoring station are directly affected by the emissions from aircraft at the airport and helicopter operations at the adjacent National Guard Air station. Figure 67 (b) illustrates the trend of the O3 concentration along with the trend of the total aircraft operations in past years in Tupelo [87].

In the air quality database, the independent variable x21 represents the total daily aircraft operations at airport(s) located in zone 1 (radius less than 8 km from the DEQ monitoring station). The variable x22 the total daily aircraft operations at airport(s) located in zone 2 (radius 8 to 16 km). The variable x23 represents the total daily aircraft operations at airport(s) located in zone 3 (radius 16 to 32 km).

Figure 67 (a). Historical trend of aircraft operations at Tupelo Regional Airport


100 100

Yearly Ozone Concentration andTotal Yearly Aircraft Operations - Tupelo, Mississippi

0.0750.078 0.080

0.0820.078 0.079

0.050 0.050

0.061

0.061 0.0600.05857,620

63,936 63,937

40,680

46,02848,997

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

1996 1997 1998 1999 2000 2001Year

Ozo

ne c

once

ntra

tion

, ppm

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

Num

ber

of a

ircr

aft

oper

atio

ns

Yearly maximum Ozone concentration

Yearly average Ozone concentration

Total yearly aircraft operations (at Tupelo Regional Airport)

Figure 67 (b). Historical trend of O3 levels and total aircraft operations in Tupelo

Air Quality Project Final Report/ Uddin 101 UM-CAIT/2004-01

June 2004

5. DEVELOPMENT AND IMPLEMENTATION OF O3 AND NO2 MODELS

5.1 Ozone Modeling

After the compilation of the air quality databases, the model for predicting daily maximum 8-hour average O3 concentrations (y3) was developed using the historical daily data records of Tupelo and Hernando from 1996 to 2000. The total number of 78 independent variables, plus 8 interaction terms, were defined. The Pearson correlation (R) values of these explanatory variables with the dependent y3 variable were calculated to study the degree of linear relationship between each explanatory and dependent variables. Its values range from –1 to 1. The positive Pearson correlation means the two variables are positively related; if one variable increases, the other also increases. On the other hand, the negative Pearson correlation means that if one variable increases, the other decreases. The Pearson correlation R close to –1 or 1 implies a perfectly strong linear relationship between the two variables.

The SPSS statistical software package [88] was used to develop the model by the multiple linear regression method. Several transformations, such as log, square root, square, and cubic, were applied to some variables in order to change the nonlinear relationship between them and y3 to be linear. These models were evaluated by considering R and the coefficient of determination (R2), the plot showing a goodness-of-fit (measured values versus predicted values), and the accuracy of predictions for the years 2001, 2002, and 2003. Revised Final Ozone Model with Vehicular and Point Sources Emissions

The revised final O3 model was developed using the updated air quality database for 1996-2000. In this extensively revised new database, there were several changes as described below: 1. The radial zones of traffic volume were re-defined. The three radial zones have radius of

5, 8, and 16 kilometers (3, 5, and 10 miles), respectively. 2. The vehicular emissions of VOC and NOx for each day were calculated and added. The

methodology for estimating these emissions is described earlier. 3. The VOC emissions from point sources were added. 4. The cloud cover code was re-defined as in Table 16; ‘0’ for completely cloudy day, ‘1’

for partially cloudy day, and ‘2’ for clear sky day. 5. The solar radiation data for Memphis, 1990, were replaced by the actual solar radiation

data from 1997 to 2000 collected at the Mississippi State Valley University, one of the sites in the CONFRRM network [77]. For the year 1996, the data of 1997 were used.

6. The daily maximum 8-hour average O3 concentration (y3) was recalculated from the AQI values after the EPA uploaded them onto the AirData Web site in June 2003. Using the updated data in the new air quality database, the correlation plots between each

predictor variables and y3 were made. This is to identify the type of the relationship between the predictor and dependent variables so that an appropriate transformation of the predictor variables can be made. For example, Figure 68 shows that the plot between day of year (x5) and y3 is a parabolic curve. Thus, including the polynomial term of x5 in the regression model will better explain the variance of y3 than using just x5 alone.


June 2004

Daily Maximum 8-hour Average Ozone Concentrations and Total Daily Vehicular Emissions during July 23-29, 2001 - Tupelo

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Day of Week

Em

issi

ons,

tons

/day

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Ozo

ne C

once

ntra

tion

, ppm

Total Vehicular VOC Emissions

Total Vehicular NOx Emissions

Daily Maximum 8-hr AverageOzone Concentration

07/23/2001 07/29/2001

R = +0.926

R = +0.922

Day of Year versus Daily Maximum 8-hour Average Ozone Concentration - Tupelo and Hernando, 1996-2000

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0 50 100 150 200 250 300 350

Day of Year

Ozo

ne C

once

ntra

tion

, ppm

365

Generally, the variations of traffic volume show a cyclic shape on a weekly basis due to the pattern of human activities. Figures 69 and 70 show the variations of O3 concentrations and traffic-related variables, including vehicular emissions, during the hottest week in 2001. Note that vehicular emission is better indicator of air pollutants than simply the use of traffic vo lume because traffic volume generally increase with time but the newer vehicle models are more fuel efficient and produce significantly less emissions than the older vehicle models. All these variables are highly correlated with the O3 concentrations implying that they should be included in the O3 model.

Figure 68. Day of year (x5) versus the dependent variable (y3)

Figure 69. Weekly variation of O3 concentrations and vehicular emissions


June 2004

Air Pollutant Concentrations and Traffic VariablesAugust 20-26, 2001 - Hernando

0

4

8

12

16

20

24


Day of Week

xtru

ck o

r xc

ar ( x

106 )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Air

Pol

luta

nt C

once

ntra

tion

, ppm

xtruck

xcar



08/20/2001 08/26/2001

R(O3) = +0.686R(NO2) = +0.516

R(O3) = +0.686R(NO2) = +0.516

Figure 70. Weekly variation of O3 concentrations and traffic variables Based on the information extracted from the correlation plots, a number of multiple linear

regression models containing several different combinations of predictor variables were developed and eva luated. The final revised model was selected based on the prediction accuracy. This selected model (model 3L), containing 19 predictor variables, is given in Equation 40. The statistical attributes of the coefficients in the regression model are shown in Table 22. The model has a standard error of prediction of 0.012 ppm. The R and R2 values of this model are equal to +0.740 and 0.548, respectively.

(40)

The predicted daily maximum 8-hour average O3 concentrations by the revised improved model 3L were plotted in Figure 71against the measured values to determine a goodness-of-fit of the model. The regression line and the lines showing 95% confidence interval were drawn. Most of the points fall in the range of 95% confidence interval, except some points at the bottom of the plot. According to the statistics shown in Figure 71, the sample mean of both measured and predicted O3 concentrations is equal to 0.051 ppm. The measured concentrations are more dispersed as their coefficient of variation (CV) is higher.

251951820171916

181517141613151214111310129118

762295234213342351033

ˆ

xbxbxbxb

xbxbxbxbxbxbxbxb

xbxbxbxbxbxbxbby

a

cartruckdistallL

++++

++++++++

+++++++=−


June 2004

Measured Concentration, ppm

.12.10.08.06.04.020.00

Pred

icte

d C

once

ntra

tion,

ppm

.12

.10

.08

.06

.04

.02

0.00

Daily Maximum 8-hour Average Ozone Concentration Tupelo and Hernando, 1996-2000

+95% confidence interval

-95% confidence interval

Regression line R = +0.740

Table 22. Statistical attributes of the regression coefficients in the O3 model 3L

Constant and Variables Regression Coefficient

Standard Error

Constant b0 -3.046E-02 9.43E-03 srtx35=sqrt(x35) b1 9.561E-03 2.31E-03 srtx34=sqrt(x34) b2 -2.338E-02 4.86E-03 x21=Daily Aircraft Operations (< 8 km radius) b3 7.780E-05 2.51E-05 x23all=Daily Aircraft Operations (Total) b4 -2.532E-05 1.11E-05 x29dist2=[(x27/8)+(x28/16)+(x29/32)] b5 8.269E-02 2.65E-02 xtruck=[(x8*x8a*x8c)+(x9*x9a*x9c)+(x10*x10a*x10c)] b6 -2.068E-09 3.30E-09 xcar=[(x8*x8b*x8d)+(x9*x9b*x9d)+(x10*x10b *x10d)] b7 9.721E-10 5.00E-10 x11=Average Air Temperature, °C b8 3.107E-03 3.82E-04 x12=Maximum Air Temperature, °C b9 1.777E-03 3.14E-04 x13=Minimum Air Temperature, °C b10 -4.157E-03 3.07E-04 x14=Average Wind Speed, m/s b11 -2.533E-03 4.36E-04 x15=Maximum Wind Speed, m/s b12 5.589E-05 2.11E-04 x16=Minimum Wind Speed, m/s b13 8.808E-04 3.47E-04 x17=Average Wind Direction, degree b14 -1.317E-05 4.21E-06 x18=Total Precipitation, inches b15 -2.585E-03 9.34E-04 x19a=Cloud Cover (0=Yes, 1=Partial, 2=No) b16 4.197E-03 6.41E-04 x20=Solar Radiation, Langley/day b17 -4.801E-07 1.09E-06 x5 =Day of Year b18 1.568E-04 2.82E-05 sqx5=x5^2 b19 -4.945E-07 7.16E-08

Ozone N Mean, ppm SD, ppm CV, % Measured 2,197 0.051 0.018 35.3 Predicted 2,197 0.051 0.013 25.5

Figure 71. Measured versus predicted O3 concentrations


June 2004

Revised Ozone Model Validation In the process of model validation, this revised model was used to predict the O3 concentrations in 2001 for Tupelo, Hernando, Oxford, and Jackson. The prediction results were compared to the measured concentrations for Tupelo, Hernando, and Jackson. For Oxford, the prediction results were assessed based on their reasonableness. Predictions for the Selected Days in 2001 Three days in 2001 were selected for evaluation of the accuracy of the model prediction. They are: (1) the hottest day in summer 2001, which was different from one city to another, (2) March 7, 2001, in spring, and (3) October 23, 2001, in fall. These three days represent a normal day in fall and the two extremes of the O3 season. It is known that the O3 level tends to be high in a hot, dry, stagnant day during summer time. Selecting the hottest day is done to evaluate the predictions for the high O3 days. Recall that the O3 regression models reviewed earlier did not generally perform well during the extreme hot days. The other two days were selected to evaluate the predictions for the low O3 days. These two days are basically one week after the O3 season started, and one week before the O3 season ended. Table 23 summarizes the data of all independent variables as well as the air pollutant concentrations for the hottest day in 2001 for the selected cities in Mississippi and the NCAT test track site near Auburn in Alabama. The NCAT site had predominantly heavy truck traffic at accelerated scale (5 years truck traffic in one year) [89].

The prediction results and the input values of selected variables of Tupelo, Hernando, and Oxford for the selected days in 2001 are shown in Figures 72, 73, and 74. The measured O3 concentrations are also presented for Tupelo and Hernando, which are the correct values in the revised air quality database. The model results are fairly reasonable for all the days. For the hottest day in 2001, the predicted O3 concentration for Tupelo (0.064 ppm) was 12% lower than the measured concentration (0.073 ppm). For Hernando the predicted O3 concentration of August 22 (0.059 ppm) was very close (within 2%) to the measured concentration (0.058 ppm) and the prediction of August 23 (0.057 ppm) was 10% lower than the measured concentration (0.063 ppm). Note that there were three days in 2001 (August 22, 23, 24) that the maximum air temperature in Hernando appeared to be equal. The prediction for the other hottest day (August 24) was 8% lower than the measured value (0.061 versus 0.066 ppm). On the other hand, the predictions for the selected low O3 days in March and October were higher than the measured values for both cities. These results indicate that the revised improved model slightly underpredicts the high peak O3 concentrations but overpredicts the low peak O3 concentrations. The model results appear to be more reasonable than other reviewed models considering that one does not need to know prior day O3 value. A typical limitation of other reviewed regression models for predicting O3 concentrations is that they may not accurately predict extreme concentrations. The revised O3 model developed in this study seems to provide better results for extreme O3 event days.

Although there are no measured values for Oxford, the prediction results seem to be reasonable for two reasons. One reason is that Oxford is a smaller city with less traffic volume than Tupelo and Hernando where there are more anthropogenic emissions of the O3 precursors. The point source emission in Oxford is also lower than Hernando. Therefore, the O3 concentrations in Oxford are expected to be lower than in Tupelo and Hernando. The second reason is the natural background level of O3 concentration, reported to be 25 to 45 ppb [53], which is about the range of the predicted values in Oxford by the revised model for the selected


June 2004

days. Figure 75 presents the predicted O3 concentrations by the revised model for the selected days in 2001 for Oxford. Note that for May 24, 2001, the concentration is predicted for nighttime assuming no point source emissions and 1% of total traffic volume. The prediction results show that the nighttime O3 concentration is approximately three times less than the daytime concentrations. The model results for these rural regions are, therefore, reasonable and acceptable.

Table 24 summarizes the O3 concentrations predicted by the revised model. The prediction results for Jackson are also included. By comparing the predicted values with the measured values, the revised model shows reasonable results. It gives better predictions (closer to the measured values) in most of the cases. It is important to note that the predicted O3 concentrations for Jackson (metropolitan urban city) are unreasonably higher than the measured concentrations. This indicates that the model needs calibration for urban metropolitan areas because their traffic volume is significantly high beyond the model inference limit.

The revised model was also used to predict the level of O3 as a result of the truck operations at the NCAT test track site on the hottest day of Summer, 2001. This prediction considered only the effects of vehicular emissions in the NCAT test track area where the truck traffic variables (volume and speed) were derived from the actual truck operation data. The volume of car was assumed to be 100 cars/day representing the cars of visitors, students, faculty, and staff. The aircraft operations and point source emissions were assumed to be zero. The climatological inputs were obtained from the weather monitoring station at the track. The O3 prediction result based on the NCAT site inputs shown in Table 23 is 0.058 ppm (AQI = 45; AQI category = Good), which seems reasonable considering round-the-clock test track truck traffic.

Predictions of O3 for the 15-day Interval along the Year 2001 In order to evaluate the prediction accuracy of the revised model (model 3L) for the entire O3 season, the predicted O3 concentrations of every 15-day interval from March to October, 2001, were plotted along with the measured O3 concentrations, as shown in Figures 76 and 77 for Tupelo and Hernando. The summary statistics are also provided. The statistics for both Tupelo and Hernando indicate that the model overpredicts the O3 concentrations, as the mean values of the predicted concentrations are 8% (both Tupelo and Hernando) higher than the measured values. Also, the variation in the predicted values is less than the variation in the measured values as their CV values are less. In both Figures, the prediction results agree reasonably with the measured values for most of the days. For Tupelo there are 12 days out of 16 (75%) and 62.5% (10 days out of 16) for Hernando that the differences between the predicted and measured concentrations are less than 0.01 ppm.


June 2004

Table 23. Data summary of the hottest day in 2001 for selected locations

7/25/2001 8/22/2001 8/23/2001 7/11/2001 7/6/2001 7/19/2001Variable Description Tupelo Hernando Hernando Oxford Jackson NCAT*

x1 Location (0=Tupelo, 1=Hernando, 2=Oxford, 3=Jackson)) 0 1 1 2 3 4x2 Latitude 34.26 34.82 34.82 34.36 32.39 32.60x3 Longitude -88.77 -89.98 -89.98 -89.56 -90.14 -85.30x4 Elevation, m 101.8 127.4 127.4 115.5 92.4 196.6x5 Day of Year 206 234 235 192 187 200x6 Month 7 8 8 7 7 7x7 Day of Week (1=Monday, …, 7=Sunday) 3 3 4 3 5 3x8 Traffic Volume (<5 km radius), veh/day 184,938 164,447 172,833 159,646 480,211 1,519x9 Traffic Volume (5-8 km radius), veh/day 208,996 73,323 77,062 91,535 563,171 0x10 Traffic Volume (8-16 km radius), veh/day 102,446 155,985 163,938 38,503 541,840 0

x10all Traffic Volume (Total), veh/day 496,380 393,755 413,833 289,684 1,585,222 1,519x8a Average Truck Speed (< 5 km radius), mi/h 25 30 30 30 20 45x9a Average Truck Speed (5-8 km radius), mi/h 30 35 35 35 25 0x10a Average Truck Speed (8-16 km radius), mi/h 50 55 55 55 45 0x8b Average Car Speed (<5 km radius), mi/h 30 35 35 35 25 25x9b Average Car Speed (5-8 km radius), mi/h 35 40 40 40 30 0x10b Average Car Speed (8-16 km radius), mi/h 50 55 55 55 45 0x8c % Truck (<5 km radius) in decimal 0.056 0.070 0.070 0.010 0.065 0.934x9c % Truck (5-8 km radius) in decimal 0.080 0.100 0.100 0.077 0.100 0.000x10c % Truck (8-16 km radius) in decimal 0.200 0.250 0.250 0.077 0.200 0.000x8d % Car (<5 km radius) in decimal 0.944 0.930 0.930 0.990 0.935 0.066x9d % Car (5-8 km radius) in decimal 0.920 0.900 0.900 0.923 0.900 0.000x10d % Car (8-16 km radius) in decimal 0.800 0.750 0.750 0.923 0.800 0.000

xtruck [(x8*x8a*x8c)+(x9*x9a*x9c)+(x10*x10a*x10c)] 1,784,964 2,746,763 2,886,814 457,641 6,908,762 63,844xcar [(x8*x8b*x8d)+(x9*x9b*x9d)+(x10*x10b*x10d)] 16,064,955 14,426,759 15,162,389 10,865,811 45,936,789 2,506x11 Average Air Temperature, °C 28.2 29.7 29.8 27.2 29.1 26.2x12 Maximum Air Temperature, °C 35.0 35.0 35.0 34.8 35.0 34.6x13 Minimum Air Temperature, °C 23.3 24.4 25.6 21.2 22.2 16.9x13a Range of Air Temperature (Max-Min), °C 11.7 10.6 9.4 13.6 12.8 17.7x14 Average Wind Speed, m/s 3.4 3.2 3.6 0.9 0.8 0.8x15 Maximum Wind Speed, m/s 6.7 4.6 4.6 5.6 2.1 5.5x16 Minimum Wind Speed, m/s 1.5 0.0 1.5 0.0 0.0 0.0x17 Average Wind Direction, degree 160.0 215.0 208.3 194.9 191.4 243.0x18 Total Precipitation, inches 0.00 0.00 0.00 0.69 0.01 0.00x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 2 1 1 1 2 2x20 Solar Radiation, Langley/day 596 564 538 628 555 589x21 Daily Aircraft Operations (< 8 km radius) 134 12 12 38 148 0x22 Daily Aircraft Operations (8-16 km radius) 0 0 0 0 392 0x23 Daily Aircraft Operations (16-32 km radius) 99 1,416 1,416 168 68 0

x23all Daily Aircraft Operations (Total) 233 1,428 1,428 206 607 0x24 Point Sources NOx Emissions (< 8 km radius), tons/day 0.000 0.220 0.220 0.325 12.503 0.000x25 Point Sources NOx Emissions (8-16 km radius), tons/day 0.092 0.039 0.039 0.933 3.961 0.000x26 Point Sources NOx Emissions (16-32 km radius), tons/day 0.793 56.256 56.256 4.418 2.261 0.000

x26all Point Sources NOx Emissions (Total), tons/day 0.885 56.515 56.515 5.676 18.725 0.000x26dist [(x24*8)+(x25*16)+(x26*32)] 26.8 1802.6 1802.6 158.9 235.7 0.000x26dist2 [(x24/8)+(x25/16)+(x26/32)] 0.031 1.788 1.788 0.237 1.881 0.000

x27 Point Sources VOC Emissions (< 8 km radius), tons/day 1.128 1.330 1.330 0.000 7.518 0.000x28 Point Sources VOC Emissions (8-16 km radius), tons/day 1.450 0.061 0.061 1.097 0.655 0.000x29 Point Sources VOC Emissions (16-32 km radius), tons/day 2.233 20.403 20.403 0.978 1.450 0.000

x29all Point Sources VOC Emissions (Total), tons/day 4.811 21.794 21.794 2.075 9.623 0.000x29dist [(x27*8)+(x28*16)+(x29*32)] 103.7 664.5 664.5 48.8 117.0 0.000x29dist2 [(x27/8)+(x28/16)+(x29/32)] 0.301 0.808 0.808 0.099 1.026 0.000

x30 City Population 34,564 7,181 7,181 11,933 183,018 -x31 City % Population Change with respect to 1990 12.6 129.8 129.8 19.5 0.0 -x32 County Population 76,773 111,128 111,128 39,436 250,436 -x33 County % Population Change with respect to 1990 17.1 63.6 63.6 23.9 0.0 -x34 Total Vehicular VOC Emissions, tons/day 1.065 0.505 0.383 0.841 4.309 0.035x35 Total Vehicular NOx Emissions, tons/day 2.260 1.240 0.992 1.959 9.089 4.016y3 Daily Maximum 8-hour Average Ozone Concentration, ppm 0.073 0.058 0.063 - 0.059 -y7 Daily Average Nitrogen Dioxide Concentration, ppm - 0.003 0.005 - - -

* Simulation case - considering only effects of vehicular emissons in the NCAT test track area, especially from trucks runing on the test track- not available


June 2004

Figure 72. Predicted O3 concentrations by the revised model for the hottest day, 2001

Figure 73. Predicted O3 concentrations by the revised model for March 7, 2001


June 2004

Figure 74. Predicted O3 concentrations by the revised model for October 23, 2001

Figure 75. Predicted O3 concentrations by the revised model for selected days in 2001, Oxford


June 2004

Table 24. Summary of the prediction results of O3 concentration

a) Summer – hottest day, 2001 Measured Predicted

City Date y3 y3-3L Tupelo 7/25/2001 0.073 0.064

Hernando 8/22/2001 0.058 0.059 Hernando 8/23/2001 0.063 0.057

Oxford 7/11/2001 - 0.045 NCAT 7/19/2001 - 0.019 Jackson 7/6/2001 0.059 0.137

b) Spring – March 7, 2001

Measured Predicted City Date y3 y3-3L

Tupelo 3/7/2001 0.044 0.051 Hernando 3/7/2001 0.028 0.049

Oxford 3/7/2001 - 0.033 NCAT 3/7/2001 - 0.058 Jackson 3/7/2001 0.042 0.109

c) Fall – October 23, 2001

Measured Predicted City Date y3 y3-3L

Tupelo 10/23/2001 0.031 0.048 Hernando 10/23/2001 0.029 0.031

Oxford 10/23/2001 - 0.016 NCAT 10/23/2001 - 0.034

Jackson 10/23/2001 0.020 0.112


June 2004

Ozone N Mean, ppm SD, ppm CV, % Measured 16 0.052 0.012 22.5 Predicted 16 0.056 0.012 21.5

Figure 76. Predicted O3 concentrations for the 15-day interval in 2001, Tupelo

Ozone N Mean, ppm SD, ppm CV, % Measured 16 0.048 0.015 30.7 Predicted 16 0.052 0.011 20.3

Figure 77. Predicted O3 concentrations for the 15-day interval in 2001, Hernando

Measured and Predicted Daily Maximum 8-hour AverageOzone Concentrations - Tupelo, 2001

0.000

0.050

0.100

0.150

3/1/01

3/15/0

14/1

/014/1

5/01

5/1/01

5/15/0

16/1

/016/1

5/01

7/1/01

7/15/0

18/1

/018/1

5/01

9/1/01

9/15/0

110/

1/01

10/15/

01

Date

Ozo

ne C

once

ntra

tion

, ppm

Measured Ozone Concentration

Predicted Ozone Concentration

EPA 8-hour average Ozone National Ambient Air Quality Standard0.084


Measured and Predicted Daily Maximum 8-hour AverageOzone Concentrations - Hernando, 2001

0.000

0.050

0.100

0.150

3/1/01

3/15/0

14/1

/014/1

5/01

5/1/01

5/15/0

16/1

/01

6/15/0

17/1

/017/1

5/01

8/1/01

8/15/0

19/1

/019/1

5/01

10/1/0

1

10/15/

01

Date

Ozo

ne C

once

ntra

tion

, ppm

Measured Ozone Concentration

Predicted Ozone Concentration




June 2004

Measured and Predicted Daily Maximum 8-hour AverageOzone Concentrations - Oxford, 2001

0.000

0.050

0.100

0.150

3/1/01

3/15/0

14/1

/014/1

5/01

5/1/01

5/15/0

16/1

/016/1

5/01

7/1/01

7/15/0

18/1/

018/1

5/01

9/1/01

9/15/0

110/

1/01

10/15/

01

Date

Ozo

ne C

once

ntra

tion,

ppm



Measured and Predicted Daily Maximum 8-hour AverageOzone Concentrations - NCAT Test Track, Alabama, 2001

0.000

0.050

0.100

0.150

3/1/01

3/15/0

14/1

/014/1

5/01

5/1/01

5/15/0

16/1

/016/1

5/01

7/1/01

7/15/0

18/1/

018/1

5/01

9/1/01

9/15/0

110/

1/01

10/15/

01

Date

Ozo

ne C

once

ntra

tion,

ppm



Ozone N Mean, ppm SD, ppm CV, % Predicted 16 0.034 0.014 40.5

Figure 78. Predicted O3 concentrations for the 15-day interval in 2001, Oxford

Ozone N Mean, ppm SD, ppm CV, %

Predicted 16 0.026 0.012 46.9

Figure 79. Predicted O3 concentrations for the 15-day interval in 2001, NCAT


June 2004

Accuracy of Predicting AQI Category Since the levels of O3 concentration are reported to public in terms of the AQI value and category, an evaluation of how accurate the revised model predicts the O3 concentration in the correct AQI category has been made. Table 25 presents the number of days that the measured and predicted O3 concentrations are designated to each AQI category. From 246 O3 season days in 2001, more than 85% had the measured O3 concentrations in ‘good’ category. There was only one day in Tupelo that the AQI value exceeded 100 and was considered as unhealthy for sensitive groups. For Tupelo, the model correctly predicted 88% and 67% of occurrences of the ‘good’ and ‘moderate’ categories, respectively. The model did not predict the one exceedance of the NAAQS. For Hernando, the prediction accuracy was 95% and 40% for the ‘good’ and ‘moderate’ categories, respectively. There was no exceedance of the O3 standard in Hernando, 2001.

Table 25. Number of days of O3 in each AQI category (measured and predicted)

Tupelo, 2001 Hernando, 2001 AQI Category Measured Predicted Measured Predicted

Good (% Days)

212 (86.2%)

197 (88.1%)

211 (85.8%)

222 (90.2%)

Moderate (% Days)

33 (13.4%)

49 (19.9%)

35 (14.2%)

24 (9.8%)

Unhealthy (for sensitive groups)

1 (0.4%)

0 (0%)

0 (0%)

0 (0%)

Unhealthy (% Days)

0 (0%)

0 (0%)

0 (0%)

0 (0%)

Very unhealthy (% Days)

0 (0%)

0 (0%)

0 (0%)

0 (0%)

Hazardous (% Days)

0 (0%)

0 (0%)

0 (0%)

0 (0%)

Total (% Days)

246 (100%)

246 (100%)

246 (100%)

246 (100%)

Notes: 1) The accuracy of predicting “Good” and “Moderate” categories for Tupelo is 88% and 67%, respectively. 2) The accuracy of predicting “Good” and “Moderate” categories for Hernando is 95% and 40%, respectively. Sensitivity Analysis with Regards to Changes in Traffic Volume

In addition to the use of the O3 model to predict a future O3 concentration for reporting to public, the O3 prediction model can be used for evaluating the changes in O3 level due to changes of key parameters affecting O3. For example, it can be used in transportation planning to determine the effects of a new highway or congestion on air quality. It can also used to determine the degradation of air quality due to a new industrial plant close by a city. In this study, the sensitivity analysis of the O3 levels due to the changes in traffic volume was conducted. The selected hottest day in 2001 (August 22) for Hernando was used as a base case. The predicted O3 concentration for that day was 0.059 ppm and the measured concentration was 0.058 ppm.

Two scenarios were investigated: (1) 50% reduction in traffic volume (half traffic); and (2) two times increase in traffic volume (double traffic). The change in traffic volume also results in changes in vehicular emissions, as shown in Table 26. The values of affected input variables


June 2004

change in the same proportion as the change in traffic volume. Based on these changes, the predicted O3 concentration by the revised O3 model reduces to 0.057 ppm in the case of half traffic volume, and it increases to 0.065 ppm in the case of double traffic volume.

Table 26. Sensitivity analysis of the predicted O3 concentration in Hernando, 2001

Variable Base Case* Half Traffic Double Traffic Predicted O3 concentration, ppm 0.059 0.057 0.065 Total traffic volume, veh/day 393,755 196,878 787,510 xtruck 2,746,763 1,373,381 5,493,526 xcar 14,426,759 7,213,380 28,853,518 Total vehicular VOC emissions, tons/day 0.505 0.252 1.009 Total vehicular NOx emissions, tons/day 1.240 0.620 2.480 * Selected hottest day (8/22/2001); measured O3 concentration = 0.058 ppm 5.2 Nitrogen Dioxide Modeling

The data of Nitrogen Dioxide (NO2) concentrations are available only for Hernando, 1998-2001. Therefore, the model for predicting a daily average NO2 concentration was developed using the daily data records of Hernando from 1998 to 2000. The data records for 2001 were used for validation. The total numbers of 78 independent variables plus 8 interaction terms, as well as their Pearson correlation with the daily average NO2 concentration (y7) are listed in Table 27. The modeling results using the revised updated air quality database only are presented here for brevity. The development of the multiple linear regression model for predicting a daily average NO2 concentration is similar to the development of the O3 model. The independent variables were selected based on the O3 models with the exception of the VOC emissions from vehicular and point sources, which were excluded. Several models were developed and evaluated. Model 4D was first selected as the best preliminary model due to better prediction results for Hernando, compared to other models. However, the model gives negative values for Tupelo, Oxford, and Jackson, which are unreasonable. It was investigated later that the variables that cause these unreasonable results are the total aircraft operations and the point source NOx emission. When substituting these variables by zero values for Hernando, it turns out that the predicted NO2 concentration for Hernando became negative as well. It was observed that the values of these variables are very different from one city to another. For example, the point source NOx emissions in zone 3 (16-32 km radius) of Hernando are 14 and 8 times higher than those of Tupelo and Oxford, respectively. Most of these emissions are produced in Memphis, Tennessee (about 30 km or 19 mi North of Hernando), not in Hernando itself. Since the air quality database used for developing the NO2 concentration regression models has only the historical data for Hernando, the regression coefficient do not properly reflect effects of the values of predictor variables that are significantly different from the inference limits. Therefore, the application of the developed NO2 models to predict NO2 concentration for other two locations in northern Mississippi produced unreasonably negative results. Because of the reasons explained, the aviation operations and point source NOx emission variables were excluded from the revised NO2 modeling. The revised model did not produce


June 2004

negative NO2 concentrations for Tupelo, Oxford, and Jackson, which are more reasonable than the results from the preliminary model 4D.

The final revised model (model 4J), containing 15 predictor variables, is given in Equation 41. The statistical attributes of the coefficients in the regression model are shown in Table 28, and the plot of predicted concentrations versus the measured concentrations is shown in Figure 80 along with a regression line and the range of 95% confidence interval. The model has a standard error of prediction of 0.005 ppm. The R and R2 values of this model are equal to +0.761 and 0.579, respectively.

(41)

25155142013191218111710169158

1471361251143352351047

ˆ

xbxbxbxbxbxbxbxb

xbxbxbxbxbxbxbby

a

truckJ

++++++++

+++++++=−


June 2004

Table 27. List of variables in the updated air quality database and their correlation with y7

Variable Name and Description Rx1=Location n/ax2=Latitude n/ax3=Longitude n/ax4=Elevation, m n/ax5=Day of Year 0.346sqx5=x5^2 0.384sinx5=sin(x5) 0.030cosx5=cosine(x5) -0.041x6=Month 0.341sqx6=x6^2 0.372x7=Day of Week -0.143sinx7=sin(x7) -0.033cosx7=cos(x7) -0.157x8=Traffic Volume (<5 km radius), veh/day 0.165x9=Traffic Volume (5-8 km radius), veh/day 0.165x10=Traffic Volume (8-16 km radius), veh/day 0.145x10all=Traffic Volume (Total), veh/day 0.158x8a=Average Truck Speed (< 5 km radius), mi/h n/ax9a=Average Truck Speed (5-8 km radius), mi/h n/ax10a=Average Truck Speed (8-16 km radius), mi/h n/ax8b=Average Car Speed (<5 km radius), mi/h n/ax9b=Average Car Speed (5-8 km radius), mi/h n/ax10b=Average Car Speed (8-16 km radius), mi/h n/ax8c=% Truck (<5 km radius) in decimal n/ax9c=% Truck (5-8 km radius) in decimal n/ax10c=% Truck (8-16 km radius) in decimal n/ax8d=% Car (<5 km radius) in decimal n/ax9d=% Car (5-8 km radius) in decimal n/ax10d=% Car (8-16 km radius) in decimal n/axtruck=[(x8* x8a *x8c)+(x9* x9a *x9c)+(x10* x10a *x10c)] 0.149xcar=[(x8* x8b *x8d)+(x9* x9b *x9d)+(x10* x10b *x10d)] 0.157x11=Average Air Temperature, °C -0.060x12=Maximum Air Temperature, °C 0.007x13=Minimum Air Temperature, °C -0.155x13a=Range of Air Temperature (Max-Min), °C 0.316x14=Average Wind Speed, m/s -0.530x15=Maximum Wind Speed, m/s -0.420x16=Minimum Wind Speed, m/s -0.413x17=Average Wind Direction, degree -0.337x18=Total Precipitation, inches -0.140pwx18=10^(-x18) 0.171x19a=Cloud Cover (0=Yes, 1=Partial, 2=No) 0.243x20=Solar Radiation, Langley/day -0.082


June 2004

Table 27. List of variables in the updated air quality database and their

correlation with y7 (continued)

Variable Name and Description Rx21=Daily Aircraft Operations (< 8 km radius) n/ax22=Daily Aircraft Operations (8-16 km radius) n/ax23=Daily Aircraft Operations (16-32 km radius) -0.026x23all=Daily Aircraft Operations (Total) -0.026x23dist2=(x21/8)+(x22/16)+(x23/32) -0.026x24=Point Sources NOx Emissions (< 8 km radius), tons/day 0.048x25=Point Sources NOx Emissions (8-16 km radius), tons/day 0.031x26=Point Sources NOx Emissions (16-32 km radius), tons/day 0.049x26all=Point Sources NOx Emissions (Total), tons/day 0.049x26dist=[(x24*8)+(x25*16)+(x26*32)] 0.049x26dist2=[(x24/8)+(x25/16)+(x26/32)] 0.049x27=Point Sources VOC Emissions (< 8 km radius), tons/day -0.049x28=Point Sources VOC Emissions (8-16 km radius), tons/day -0.048x29=Point Sources VOC Emissions (16-32 km radius), tons/day 0.048x29all=Point Sources VOC Emissions (Total), tons/day 0.048x29dist=[(x27*8)+(x28*16)+(x29*32)] 0.048x29dist2=[(x27/8)+(x28/16)+(x29/32)] 0.034x30=City Population -0.048x31=City % Change with respect to 1990 -0.048x32=County Population -0.048x33=County % Change with respect to 1990 -0.048x34z1=Vehicular VOC Emissions (<5 km radius), tons/day -0.039x34z2=Vehicular VOC Emissions (5-8 km radius), tons/day -0.041x34z3=Vehicular VOC Emissions (8-16 km radius), tons/day -0.046x34=Total Vehicular VOC Emissions, tons/day -0.042x34dist2=(x34z1/5)+(x34z2/8)+(x34z3/16) -0.041srtx34=sqrt(x34) 0.128logx34=log(x34) 0.342sqx34=x34^2 -0.082x35z1=Vehicular NOx Emissions (<5 km radius), tons/day -0.068x35z2=Vehicular NOx Emissions (5-8 km radius), tons/day -0.067x35z3=Vehicular NOx Emissions (8-16 km radius), tons/day -0.062x35=Total Vehicular NOx Emissions, tons/day -0.066x35dist2=(x35z1/5)+(x35z2/8)+(x35z3/16) -0.067srtx35=sqrt(x35) 0.073logx35=log(x35) 0.318sqx35=x35^2 -0.076vocdist2=x29dist2+x34dist2 -0.041srtvoc=sqrt(vocdist2) 0.017logvoc=log(vocdist2) 0.097noxdist2=x26dist2+x35dist2 -0.063srtnox=sqrt(noxdist2) -0.015lognox=log(noxdist2) 0.063n/a = not available


June 2004

Table 28. Statistical attributes of the regression coefficients in the final NO2 model 4J

Constant and Variables Regression Coefficient

Standard Error

Constant b0 1.967E-02 2.46E-03 x35=Total Vehicular NOx Emissions, tons/day b1 8.974E-06 2.94E-05 srtx35=sqrt(x35) b2 -9.262E-04 6.36E-04 xtruck=[(x8*x8a*x8c)+(x9*x9a*x9c)+(x10*x10a*x10c)] b3 3.932E-09 5.00E-10 x11=Average Air Temperature, °C b4 5.977E-04 2.82E-04 x12=Maximum Air Temperature, °C b5 7.620E-05 2.04E-04 x13=Minimum Air Temperature, °C b6 -8.908E-04 1.70E-04 x14=Average Wind Speed, m/s b7 -2.550E-03 3.24E-04 x15=Maximum Wind Speed, m/s b8 2.188E-04 1.56E-04 x16=Minimum Wind Speed, m/s b9 -4.605E-04 2.11E-04 x17=Average Wind Direction, degree b10 -3.192E-05 2.84E-06 x18=Total Precipitation, inches b11 -1.570E-03 7.24E-04 x19a=Cloud Cover (0=Yes, 1=Partial, 2=No) b12 4.549E-04 5.20E-04 x20=Solar Radiation, Langley/day b13 7.443E-07 6.52E-07 x5=Day of Year b14 -7.340E-05 2.70E-05 sqx5=x5^2 b15 2.383E-07 6.88E-08

'

Nitrogen Dioxide N Mean, ppm SD, ppm CV, % Measured 709 0.010 0.007 69.2 Predicted 709 0.010 0.005 50.8

Figure 80. Measured versus predicted NO2 concentrations

Measured Concentration, ppm

.04.03.02.010.00

Pred

icte

d C

once

ntra

tion,

ppm

.04

.03

.02

.01

0.00

Daily Average Nitrogen Dioxide Concentration Hernando, 1998-2000

+95% confidence interval

-95% confidence interval

Regression line

R = +0.761


June 2004

Nitrogen Dioxide Model Validation The predicted NO2 concentrations by the selected revised model were validated by comparing with the measured concentrations for Hernando in 2001. The NO2 concentrations were also predicted for Tupelo, Oxford, and Jackson to assess their reasonableness. Predictions for the Selected Days in 2001 For the same days used in the O3 model validation, the NO2 concentrations were predicted for Hernando as shown in Figure 81. For the hottest days in 2001, the predictions on August 22 and 23 were 0.011 and 0.009 ppm while the measured concentrations were 0.003 and 0.005 ppm. Note that the predicted and measured values of August 24 were 0.014 and 0.003 ppm. The prediction for March 7 (0.020 ppm) was 29% lower than the measured concentration of 0.028 ppm. The prediction for October 23 (0.005 ppm) was 2.5 times higher than the measured value of 0.002 ppm, with both values in the third decimal.

By comparing the predicted values with the measured values for Hernando, model 4J was selected as the best model. It gives better predictions (closer to the measured values) in most of the cases. Table 29 summarizes the NO2 concentrations predicted by models 4D and 4J. The predicted concentrations for Tupelo, Oxford, and Jackson by the preliminary model 4D have negative values, which indicate that the NO2 level is zero in the area. These results show that the model is underpredicting NO2 levels in other cities and needs calibration using NO2 measured data from more locations. Although the revised model 4J does not include the aviation operations and point source NOx emission variables and still gives reasonable results for Hernando, it gives reasonably acceptable prediction results for Tupelo, Oxford, and Jackson. For the hottest day in 2001, the predicted NO2 concentrations in Tupelo and Oxford are close to the measured and predicted values in Hernando, as shown in Figure 82. The predicted value for Jackson is about 6 times higher than the measured value in Hernando. Note that the NAAQS for NO2 is based on the annual arithmetic mean, which must not exceed 0.053 ppm. For the year 2001, the annual mean of the predicted NO2 concentrations was 0.010 ppm for Hernando, equal to the annual mean of the measured concentrations. The model provides excellent results for Hernando in terms of predicting the tendency to conform to the NAAQS.


June 2004

Figure 81. Predicted NO2 concentrations for selected days in 2001, Hernando

Figure 82. Predicted NO2 concentrations by the revised model for the hottest day, 2001


June 2004

Table 29. Summary of the prediction results of NO2 concentration

a) Summer – hottest day, 2001

Measured Predicted City Date Y7 y7-4J

Tupelo 7/25/2001 - 0.007 Hernando 8/22/2001 0.003 0.011 Hernando 8/23/2001 0.005 0.009

Oxford 7/11/2001 - 0.007 NCAT 7/19/2001 - 0.011 Jackson 7/6/2001 - 0.032

b) Spring – March 7, 2001

Measured Predicted City Date Y7 y7-4J

Tupelo 3/7/2001 - 0.015 Hernando 3/7/2001 0.028 0.020


c) Fall – October 23, 2001

Measured Predicted City Date y7 y7-4J

Tupelo 10/23/2001 - 0.011 Hernando 10/23/2001 0.002 0.005


Sensitivity Analysis with Regards to Traffic Change

The sensitivity analysis of the NO2 levels due to the changes in traffic volume was conducted using the selected hottest day in 2001 (August 23) for Hernando as a base case. The predicted NO2 concentration for that day was 0.009 ppm while the measured concentration was 0.005 ppm. The predic ted NO2 concentrations as well as several traffic-related variables for each scenario are presented in Table 30. The predicted NO2 concentrations change in direct proportion to the change in traffic, indicating a linear relationship between the two variables.

Table 30. Sensitivity analysis of the predicted NO2 concentration in Hernando, 2001

Variable Base Case* Half Traffic Double Traffic Predicted NO2 concentration, ppm 0.009 0.004 0.020 Total traffic volume, veh/day 413,833 206,917 827,666 xtruck 2,886,814 1,443,407 5,773,628 xcar 15,162,389 7,581,194 30,314,777 Total vehicular NOx emissions, tons/day 0.992 0.496 1.984 * Selected hottest day (8/23/2001); measured NO2 concentration = 0.005 ppm


June 2004

5.3 Air Quality Modeling and ANalysis (AQMAN) Program

The current version of AQMAN is a user-friendly computer program written in Delphi, which is the object-oriented Pascal language. The selected revised O3 and NO2 pollutant models have been incorporated in the AQMAN program for air quality analysis. Figure 83 shows an example of a summary climatological data report of air temperature, wind speed, and precipitation. This report was generated by an early version of AQMAN. Besides the graphical outputs, AQMAN also produces tabular outputs on both daily and hourly basis. These outputs are printed to text files, which can be easily imported into the air quality database.

Figure 83. Example of summary report of AQMAN climatological data analysis option


June 2004

The analysis results of air pollution are reported both as concentration in ppm and as AQI value. An example of a summary report from an earlier version of AQMAN is shown in Figure 84. In addition, input data of point emission sources and aviation data are required for air quality prediction. Air Quality Prediction

After all necessary inputs are prepared and analyzed, they can be used to predict the air quality. The air quality prediction option gets the required inputs from users through user-friendly interfaces, and predicts the air pollutant concentrations on daily, monthly, and yearly basis, using the regression coefficients of the latest regression equations for O3 and NO2. The prediction results are displayed on the screens. Also, a graphical report and summary text output files are produced. Figure 85 shows the main screen and Figure 86 shows the traffic data screen. A sample report is shown in Figure 87.

Figure 84. Example of summary report of AQMAN air pollution data analysis option


June 2004

Figure 85. AQMAN main screen


June 2004

Figure 86. Air Quality Prediction screen (AQPredictDaily) – Traffic Data Step-by-Step Procedures of Air Quality Prediction 1. Identify location (geographic data) and day of prediction (March to October only) 2. Get climatological data (air temperature, wind speed and direction, precipitation, cloud

cover, and solar radiation) 3. Collect traffic volume, average speed, traffic mix (cars and trucks type), and vehicle

model year 4. Calculate vehicular emissions (VOC and NOx) 5. Collect point source emissions (VOC and NOx) 6. Collect aviation source data (total operations) 7. Calculate O3 and NO2 concentrations from AQMAN analysis option. AQMAN screens and examples of analysis result text files and reports are given in Appendix.


June 2004

Figure 87. Report screen (Daily Report)


June 2004

Enhancement of AQMAN Air Quality Models

The currently selected AQMAN air quality models for O3 and NO2 provide reasonable prediction results when compared to the measured values. However, they can be further enhanced to improve the accuracy of the prediction, especially for the extreme concentration days. Air temperature is strongly correlated with the O3 concentration since it directly induces the photolysis rate as well as increases the precursor emissions. In areas covered by constructed surfaces, such as pavement and concrete, air temperature can be higher due to the heatisland effect, which results in higher O3 concentration. Hence, considering this effect may help improve the prediction of O3 concentration. This can be done by incorporating the surface temperature as another predictor variable in the enhanced O3 prediction model.

Figure 88 shows the weekly variations of O3 concentration, air temperatures, and surface temperature during the hottest week in 2001 for Tupelo. Figure 88 shows the variations of O3 concentration and surface temperature during the O3 season in 2001 for Tupelo. In Figure 88, the Pearson correlation R values of maximum, minimum, and weighted average surface temperatures with the O3 concentration are given above each plot. These high R values imply that during the hottest week in 2001 for Tupelo the weighted average surface temperature is more strongly correlated with the O3 concentration than the maximum and minimum air temperatures. This implication is also true for the entire O3 season (Figure 89) where the R value of the weighted average surface temperature (+0.606) is higher than the R values of the maximum and minimum air temperatures (+0.439 and +0.127).

Figure 88. Weekly variations of O3 concentrations with air and surface temperatures

Daily Maximum 8-hour Average Ozone Concentrations andTemperature during July 23-29, 2001 - Tupelo

0

10

20

30

40

50

60


Day of Week

Tem

pera

ture

, °C

0.000

0.020

0.040

0.060

0.080

0.100

0.120

Ozo

ne C

once

ntra

tion

, ppm

Maximum Air Temperature

Minimum Air Temperature

Weighted Average Surface Temperature


07/23/2001 07/29/2001

R = +0.911

R = -0.841

R = +0.716


June 2004

Figure 89. Variations of O3 concentration and surface temperature during O3 season

The multiple linear regression models developed for predicting the ground- level O3 concentrations were evaluated by comparing the model predictions with the measured daily maximum 8-hour average O3 concentrations. The selected revised O3 model produces adequately accurate prediction results when compared to the measured concentrations for Tupelo, and Hernando. It also provides reasonable prediction results for Oxford and NCAT test track in Alabama. However, this revised model has some limitations: 1. It does not provide adequately accurate results when predicting the extreme values of O3

concentrations for both high O3 and low O3 days. This limitation is expected to be overcome by taking into consideration the new variable—surface temperature—in the enhanced model.

2. It gives unreasonably high prediction results for Jackson. Note that Jackson is an urban metropolitan city, which is different from the rural cities like Tupelo, Hernando, and Oxford in many aspects including the proportions of traffic volume and built-up areas. The regression coefficients derived from the database of the rural cities may not appropriately reflect the O3 trend and processes in such highly urbanized areas with higher traffic volumes and daily traffic congestion during peak hours. Therefore, the calibration of the regression coefficients is necessary for improving the regression model so that it provides reasonably accurate prediction results for metropolitan and urban areas. This can be done by deriving the regression coefficients after including the local historical data of O3 concentrations and predictor variables of the urban area in the enhanced air quality database.

Daily Maximum 8-hour Average Ozone Concentration and Surface Temperature - Tupelo, 2001

0

10

20

30

40

50

60

Mar Apr May Jun Jul Aug Sep Oct

Month

Tem

pera

ture

, °C

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Ozo

ne C

once

ntra

tion

, ppm

Weighted Average Surface Temperature


R = +0.606


June 2004

The selected revised model for predicting the daily average NO2 concentrations for Hernando produced not accurate results when considered on a day-by-day basis. It cannot explain well the variance of the measured NO2 concentrations in each day. However, it provides an excellent result in terms of the annual average concentration, which is used to determine the conformity of Hernando to the NAAQS for NO2. Without aviation operations and point source NOx emissions variables, the revised model also gives reasonable prediction results for Tupelo, Oxford, and Jackson. In order to incorporate these variables in the NO2 modeling, the historical measured NO2 concentration data from other locations should be included in the enhanced air quality database.

The selected revised models, incorporated in the user- friendly AQMAN program, can be used as decision-making tools for predicting O3 and NO2 concentrations in rural areas. 5.4 Application of Air Quality Models for Future Air Quality Predictions This section demonstrates the use of the air quality models developed in this study to predict future air pollutant concentrations in Tupelo, Hernando, and Oxford. The hottest day in 2001 for each city is used for comparison. The predictions are made for the same day of years 2002, 2003, and 2004. Model 3L is used to make predictions of O3. Model 4J is used to make predictions of NO2.

Several categories of input data are required for the prediction. Some of them, such as climatological data of years 2002 and 2003, are actual data. In many cases, they are simulated based on several assumptions. These assumptions are discussed below: • Climatological Data: The climatological data of years 2002-2003 were obtained from

NOAA weather stations. The data used for each city are the data collected at the nearest weather station to those cities. The climatological data from Tupelo Regional Airport were used for Tupelo. The climatological data from Memphis International Airport in Memphis, Tennessee, were used for Hernando. The data of these two weather stations were downloaded from the NCDC Internet site [76]. The climatological data from Goodwin Creek Station in Batesville, Mississippi, were used for Oxford. The data of this weather station were downloaded from the Internet site of the SURFRAD network [90]. The solar radiation data for all three locations were obtained from the CONFRMM network monitoring station at the Mississippi Valley State University [77]. The climatological data for these three locations were then analyzed using the climatological data analysis option in the AQMAN program.

The climatological data of the selected days of July and August in 2004 are not available from NOAA at the time of this report. The forecasts provided online by weather Internet sites are generally made for only next 5-10 days. One alternative to obtain the needed climate forecast is to use the MM5 model [38]. However, it is not available for this study. Therefore, the climatological data forecast based on an extrapolation technique was used instead. This technique was applied to every climatological variable in the air quality database from x11 (average air temperature) to x20 (solar radiation). The climatological data of year 2004 for Tupelo and Hernando are predicted based on the historical trends of years 1996-2003. Although the actual climatological data of year 2003 for Oxford are obtained but the data of the selected day are not valid. Therefore, the data of both 2003 and 2004 were predicted based on the trends of years 1996-2002. The


June 2004

predictions were made using linear extrapolation. The summary of the historical and extrapolated climatological data for every city is given in Table 31.

• Traffic Data: The traffic data of years 2002-2004 are linearly extrapolated from the data of years 1996-2001. The extrapolation is done separately for each radial zone. Figures 90 and 91 shows the historical trends of annual daily traffic in each radial zone for Tupelo and Hernando, respectively. The line of best fit is drawn, and the simple linear regression equation of the line is derived. These equations are used to estimate the traffic volume in each zone for years 2002-2004. Note that the slope coefficient of these equations is referred to the traffic growth rate for that zone. For example, the ADT of zone 1 in Tupelo has a traffic growth rate of 3,317 veh/year. For Oxford, the same traffic growth rates as for Tupelo are assumed. Average speed and percentages of cars and trucks are assumed to remain same for 2002 to 2004 as for 2001 in every radial zone.

• Aircraft Operations Data: The aircraft operations of years 2002 and 2003 data at each airport in each radial zone are directly obtained from the inventory of the FAA. The aircraft operations data of year 2004 are obtained from the prediction by the TAF system of FAA [86].

• Point Source Emissions Data: Both VOC and NOx emissions from point sources of years 2002-2004 are obtained by further extrapolating the previous extrapolated trends, which are based on the actual data of years 1996 and 1999.

• Percentages of the Areas of Surface Class: The percentages of the areas of surface class, which are required input for weighted average surface temperature calculation, are assumed to be the same through all prediction years for every city. They are equal to the percentages of the areas, which are used for 1996-2001. Tables 32 and 33 summarize all input data used for air quality prediction for all three

cities from years 2001 to 2004. Note that the forecasts of the minimum wind speed of years 2003 and 2004 for Oxford return negative values, which are not reasonable. Therefore, they are adjusted to be 0 before being used for air quality prediction, as shown in Table 32.


June 2004

Figure 90. Trend of traffic data in Tupelo for future traffic prediction

Figure 91. Trend of traffic data in Hernando for future traffic prediction

Annual Daily Traffic Volume in 3 Radial Zones forTupelo, Mississippi

y = 3,317(x - 1996) + 158,536R

2 = 0.94

y = 4,311(x - 1996) + 176,939R

2 = 0.98

0

50,000

100,000

150,000

200,000

250,000

1995 1996 1997 1998 1999 2000 2001 2002

Year

Tra

ffic

Vol

ume,

veh

/day

Zone 1 (< 5 km)

Zone 2 (5-8 km)

Zone 3 (8-16 km)

y = 4,311(x - 1996) + 75,347

Note: For zone 3 assume the same traffic growth as for zone 2.

Annual Daily Traffic Volume in 3 Radial Zones forHernando, Mississippi

y = 2,112(x - 1996) + 53,687R

2 = 0.57

y = 3,664(x - 1996) + 129,595R

2 = 0.64

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

1995 1996 1997 1998 1999 2000 2001 2002

Year

Tra

ffic

Vol

ume,

veh

/day

Zone 1 (< 5 km)

Zone 2 (5-8 km)

Zone 3 (8-16 km)

Extrapolation

Assume the same traffic growth as for zone 2 of Tupelo.

y = 8,500(x - 1996) + 105,330R

2 = 1

y = 4,311(x - 1996) + 113,587


June 2004

Note: Tupelo and Hernando data extrapolated for 2004; Oxford data extrapolated for 2003 and 2004

1996 1997 1998 1999 2000 2001 2002 2003 2004x11 Average Air Temperature, °C 23.9 28.1 23.6 28.4 24.5 28.2 26.0 23.9 25.8x12 Maximum Air Temperature, °C 28.9 33.3 27.8 35.0 31.1 35.0 31.7 30.0 32.5x13 Minimum Air Temperature, °C 20.6 22.8 22.2 24.4 17.2 23.3 22.8 17.8 20.1x14 Average Wind Speed, m/s 2.0 1.5 2.0 1.6 2.5 3.4 2.1 1.2 2.2x15 Maximum Wind Speed, m/s 10.3 4.1 5.7 7.2 5.1 6.7 5.7 3.6 4.0x16 Minimum Wind Speed, m/s 0.0 0.0 0.0 0.0 0.0 1.5 0.0 0.0 0.4x17 Average Wind Direction, degree 228.0 162.2 218.5 210.5 29.5 160.0 184.8 89.2 95.2x18 Total Precipitation, inches 0.57 0.01 0.38 0.12 0.01 0.00 0.27 0.00 -0.04x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 1 1 1 1 2 2 1 2 2x20 Solar Radiation, Langley/day 55 55 233 569 663 596 508 580 543

1996 1997 1998 1999 2000 2001 2002 2003 2004x11 Average Air Temperature, °C 27.9 22.6 28.0 28.1 29.8 29.7 28.3 28.5 30.0x12 Maximum Air Temperature, °C 33.9 28.3 32.2 35.0 36.1 35.0 33.3 33.3 35.0x13 Minimum Air Temperature, °C 21.7 16.1 23.3 20.6 25.0 24.4 25.0 20.0 24.2x14 Average Wind Speed, m/s 2.3 2.5 2.5 2.5 3.2 3.2 2.2 3.5 3.3x15 Maximum Wind Speed, m/s 4.1 4.6 3.6 5.1 4.6 4.6 4.1 14.4 9.5x16 Minimum Wind Speed, m/s 0.0 0.0 0.0 1.5 1.5 0.0 0.0 0.0 0.4x17 Average Wind Direction, degree 157.1 158.0 164.0 104.8 165.4 215.0 195.0 176.7 195.7x18 Total Precipitation, inches 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.35 0.22x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 1 1 1 1 1 1 1 1 1x20 Solar Radiation, Langley/day 474 474 10 519 260 564 564 514 572

1996 1997 1998 1999 2000 2001 2002 2003 2004x11 Average Air Temperature, °C 28.8 22.6 27.4 26.1 30.4 26.9 27.4 28.2 28.4x12 Maximum Air Temperature, °C 34.4 27.8 33.3 28.9 36.1 31.7 32.8 33.5 33.4x13 Minimum Air Temperature, °C 23.9 17.2 20.6 22.2 24.4 24.4 20.6 23.1 23.4x14 Average Wind Speed, m/s 2.3 2.6 2.4 2.3 3.4 3.4 3.2 3.3 3.7x15 Maximum Wind Speed, m/s 4.1 5.1 3.6 5.7 4.6 9.8 9.3 7.9 9.7x16 Minimum Wind Speed, m/s 0.0 0.0 0.0 0.0 1.5 1.5 0.0 1.0 1.2x17 Average Wind Direction, degree 170.0 78.9 173.9 137.9 219.1 220.0 86.3 175.1 173.3x18 Total Precipitation, inches 0.00 0.00 0.00 0.00 0.00 0.25 0.13 0.13 0.19x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 1 1 1 1 1 1 1 1 1x20 Solar Radiation, Langley/day 577 577 69 468 368 538 528 478 530

1996 1997 1998 1999 2000 2001 2002 2003 2004x11 Average Air Temperature, °C 24.3 26.2 24.7 23.0 29.2 27.2 26.6 27.8 28.3x12 Maximum Air Temperature, °C 30.0 32.9 30.4 29.3 34.5 34.8 32.8 34.4 35.0x13 Minimum Air Temperature, °C 19.4 21.2 22.3 21.4 23.8 21.2 22.5 23.2 23.6x14 Average Wind Speed, m/s 1.6 1.0 1.5 1.9 2.2 0.9 1.3 1.5 1.4x15 Maximum Wind Speed, m/s 3.2 6.9 4.0 6.0 5.8 5.6 6.0 6.4 6.7x16 Minimum Wind Speed, m/s 0.3 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.1x17 Average Wind Direction, degree 67.9 196.3 133.6 124.8 220.3 194.9 137.2 195.2 205.7x18 Total Precipitation, inches 0.00 0.00 0.00 0.00 0.00 0.69 0.00 0.30 0.34x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 1 1 1 1 1 1 1 1 1x20 Solar Radiation, Langley/day 375 375 434 372 556 628 428 556 580

Tupelo (July 25)

Hernando (August 22)

Hernando (August 23)

Oxford (July 11)

Table 31. Summary of extrapolated climatological data based on historical data

Table 32. Data summary of the selected days, 2001-2004, for Tupelo and Oxford


June 2004

7/25/01 7/25/02 7/25/03 7/25/04 7/11/01 7/11/02 7/11/03 7/11/04Variable Description Tupelo Tupelo Tupelo Tupelo Oxford Oxford Oxford Oxford

x1 Location (0=Tupelo, 1=Hernando, 2=Oxford, 3=Jackson)) 0 0 0 0 2 2 2 2x2 Latitude 34.26 34.26 34.26 34.26 34.36 34.36 34.36 34.36x3 Longitude -88.77 -88.77 -88.77 -88.77 -89.56 -89.56 -89.56 -89.56x4 Elevation, m 101.8 101.8 101.8 101.8 115.5 115.5 115.5 115.5x5 Day of Year 206 206 206 206 192 192 192 192x8 Traffic Volume (<5 km radius), veh/day 184,938 197,878 216,950 132,440 159,646 171,465 188,519 115,396x9 Traffic Volume (5-8 km radius), veh/day 208,996 224,902 247,224 151,302 91,535 100,984 113,842 71,336x10 Traffic Volume (8-16 km radius), veh/day 102,446 112,240 125,957 78,599 38,503 45,247 53,849 35,369

x10all Traffic Volume (Total), veh/day 496,380 535,020 590,130 362,341 289,684 317,695 356,210 222,101x8a Average Truck Speed (< 5 km radius), mi/h 25 25 25 25 30 30 30 30x9a Average Truck Speed (5-8 km radius), mi/h 30 30 30 30 35 35 35 35x10a Average Truck Speed (8-16 km radius), mi/h 50 50 50 50 55 55 55 55x8b Average Car Speed (<5 km radius), mi/h 30 30 30 30 35 35 35 35x9b Average Car Speed (5-8 km radius), mi/h 35 35 35 35 40 40 40 40x10b Average Car Speed (8-16 km radius), mi/h 50 50 50 50 55 55 55 55x8c % Truck (<5 km radius) in decimal 0.056 0.056 0.056 0.056 0.010 0.010 0.010 0.010x9c % Truck (5-8 km radius) in decimal 0.080 0.080 0.080 0.080 0.077 0.077 0.077 0.077x10c % Truck (8-16 km radius) in decimal 0.200 0.200 0.200 0.200 0.077 0.077 0.077 0.077x8d % Car (<5 km radius) in decimal 0.944 0.944 0.944 0.944 0.990 0.990 0.990 0.990x9d % Car (5-8 km radius) in decimal 0.920 0.920 0.920 0.920 0.923 0.923 0.923 0.923x10d % Car (8-16 km radius) in decimal 0.800 0.800 0.800 0.800 0.923 0.923 0.923 0.923

xtruck [(x8*x8a*x8c)+(x9*x9a*x9c)+(x10*x10a*x10c)] 1,784,964 1,939,189 2,156,633 1,334,533 457,641 515,211 591,409 376,656xcar [(x8*x8b*x8d)+(x9*x9b*x9d)+(x10*x10b*x10d)] 16,064,955 17,335,330 19,142,883 11,766,597 10,865,811 11,966,535 13,468,865 8,427,682x11 Average Air Temperature, °C 28.2 26.0 23.9 25.8 27.2 26.6 27.8 28.3x12 Maximum Air Temperature, °C 35.0 31.7 30.0 32.5 34.8 32.8 34.4 35.0x13 Minimum Air Temperature, °C 23.3 22.8 17.8 20.1 21.2 22.5 23.2 23.6x14 Average Wind Speed, m/s 3.4 2.1 1.2 2.2 0.9 1.3 1.5 1.4x15 Maximum Wind Speed, m/s 6.7 5.7 3.6 4.0 5.6 6.0 6.4 6.7x16 Minimum Wind Speed, m/s 1.5 0.0 0.0 0.4 0.0 0.0 0.0 0.0x17 Average Wind Direction, degree 160.0 184.8 89.2 95.2 194.9 137.2 195.2 205.7x18 Total Precipitation, inches 0.00 0.27 0.00 0.00 0.69 0.00 0.30 0.34x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 2 1 2 2 1 1 1 1x20 Solar Radiation, Langley/day 596 508 580 543 628 428 556 580x21 Daily Aircraft Operations (< 8 km radius) 134 135 137 138 38 38 38 38x22 Daily Aircraft Operations (8-16 km radius) 0 0 0 0 0 0 0 0x23 Daily Aircraft Operations (16-32 km radius) 99 99 99 99 168 168 168 168

x23all Daily Aircraft Operations (Total) 233 234 236 237 206 206 206 206x24 Point Sources NOx Emissions (< 8 km radius), tons/day 0.000 0.000 0.000 0.000 0.325 0.338 0.350 0.363x25 Point Sources NOx Emissions (8-16 km radius), tons/day 0.092 0.084 0.075 0.067 0.933 1.025 1.116 1.207x26 Point Sources NOx Emissions (16-32 km radius), tons/day 0.793 0.828 0.863 0.898 4.418 4.537 4.656 4.775

x26all Point Sources NOx Emissions (Total), tons/day 0.885 0.912 0.938 0.965 5.676 5.899 6.122 6.345x26dist2 [(x24/8)+(x25/16)+(x26/32)] 0.031 0.031 0.032 0.032 0.237 0.248 0.259 0.270

x27 Point Sources VOC Emissions (< 8 km radius), tons/day 1.128 1.159 1.191 1.222 0.000 0.000 0.000 0.000x28 Point Sources VOC Emissions (8-16 km radius), tons/day 1.450 1.493 1.535 1.577 1.097 0.464 0.000 0.000x29 Point Sources VOC Emissions (16-32 km radius), tons/day 2.233 2.285 2.338 2.390 0.978 0.978 0.978 0.978

x29all Point Sources VOC Emissions (Total), tons/day 4.811 4.937 5.063 5.190 2.075 1.442 0.978 0.978x29dist2 [(x27/8)+(x28/16)+(x29/32)] 0.301 0.310 0.318 0.326 0.099 0.060 0.031 0.031

x34 Total Vehicular VOC Emissions, tons/day 1.065 0.415 1.113 0.608 0.841 0.311 0.379 0.212x35 Total Vehicular NOx Emissions, tons/day 2.260 1.029 2.604 1.514 1.959 0.646 0.860 0.511x36 Weighted Average Surface Temperature, °C 45.6 42.4 46.0 44.2 50.1 41.6 45.9 47.4y3 Daily Maximum 8-hour Average Ozone Concentration, ppm 0.073 0.046 0.066 - - - - -y7 Daily Average Nitrogen Dioxide Concentration, ppm - - - - - - - -

Notes: 1) - not available 2) % Built-up area (in 8 km x 8 km): Oxford, 15.2%; Tupelo, 19.1%; Hernando, 5.7%


June 2004

Table 33. Data summary of the selected days, 2001-2004, for Hernando Notes: 1) - not available 2) % Built-up area (in 8 km x 8 km): Oxford, 15.2%; Tupelo, 19.1%; Hernando, 5.7%

The data of Tupelo (July 25, 2002-2004), Hernando (August 22, 2002-2004), and Oxford (July 11, 2002-2004) were used to predict the O3 concentrations. The data of Hernando (August 23, 2002-2004) were used to predict the NO2 concentrations. The predictions were made through the AQMAN program. The prediction results of O3 for Tupelo, Hernando, and Oxford are plotted in Figures 92, 93, and 94, respectively. The results are reasonable. Except for August 22, 2003, in Hernando and July 25, 2002 in Tupelo, the estimation results for other days are adequately accurate. The offsets from the measured values are within

8/22/01 8/22/02 8/22/03 8/22/04 8/23/01 8/23/02 8/23/03 8/23/04Variable Description Hernando Hernando Hernando Hernando Hernando Hernando Hernando Hernando

x1 Location (0=Tupelo, 1=Hernando, 2=Oxford, 3=Jackson)) 1 1 1 1 1 1 1 1x2 Latitude 34.82 34.82 34.82 34.82 34.82 34.82 34.82 34.82x3 Longitude -89.98 -89.98 -89.98 -89.98 -89.98 -89.98 -89.98 -89.98x4 Elevation, m 127.4 127.4 127.4 127.4 127.4 127.4 127.4 127.4x5 Day of Year 234 234 234 234 235 234 234 234x8 Traffic Volume (<5 km radius), veh/day 164,447 168,094 185,305 113,716 172,833 180,932 132,653 164,128x9 Traffic Volume (5-8 km radius), veh/day 73,323 73,589 81,730 50,511 77,062 79,209 58,508 72,903x10 Traffic Volume (8-16 km radius), veh/day 155,985 173,365 196,752 124,019 163,938 186,606 140,848 178,999

x10all Traffic Volume (Total), veh/day 393,755 415,047 463,787 288,246 413,833 446,747 332,008 416,030x8a Average Truck Speed (< 5 km radius), mi/h 30 30 30 30 30 30 30 30x9a Average Truck Speed (5-8 km radius), mi/h 35 35 35 35 35 35 35 35x10a Average Truck Speed (8-16 km radius), mi/h 55 55 55 55 55 55 55 55x8b Average Car Speed (<5 km radius), mi/h 35 35 35 35 35 35 35 35x9b Average Car Speed (5-8 km radius), mi/h 40 40 40 40 40 40 40 40

x10b Average Car Speed (8-16 km radius), mi/h 55 55 55 55 55 55 55 55x8c % Truck (<5 km radius) in decimal 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070x9c % Truck (5-8 km radius) in decimal 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100x10c % Truck (8-16 km radius) in decimal 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250x8d % Car (<5 km radius) in decimal 0.930 0.930 0.930 0.930 0.930 0.930 0.930 0.930x9d % Car (5-8 km radius) in decimal 0.900 0.900 0.900 0.900 0.900 0.900 0.900 0.900

x10d % Car (8-16 km radius) in decimal 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750xtruck [(x8*x8a*x8c)+(x9*x9a*x9c)+(x10*x10a*x10c)] 2,746,763 2,994,322 3,380,532 2,120,851 2,886,814 3,223,017 2,420,002 3,061,064xcar [(x8*x8b*x8d)+(x9*x9b*x9d)+(x10*x10b*x10d)] 14,426,759 15,271,939 17,089,962 10,635,621 15,162,389 16,438,355 12,234,090 15,350,591x11 Average Air Temperature, °C 29.7 28.3 28.5 30.0 29.8 26.9 27.4 28.4x12 Maximum Air Temperature, °C 35.0 33.3 33.3 35.0 35.0 31.7 32.8 33.4x13 Minimum Air Temperature, °C 24.4 25.0 20.0 24.2 25.6 24.4 20.6 23.4x14 Average Wind Speed, m/s 3.2 2.2 3.5 3.3 3.6 3.4 3.2 3.7x15 Maximum Wind Speed, m/s 4.6 4.1 14.4 9.5 4.6 9.8 9.3 9.7x16 Minimum Wind Speed, m/s 0.0 0.0 0.0 0.4 1.5 1.5 0.0 1.2x17 Average Wind Direction, degree 215.0 195.0 176.7 195.7 208.3 220.0 86.3 173.3x18 Total Precipitation, inches 0.00 0.11 0.35 0.22 0.00 0.25 0.13 0.19x19a Cloud Cover (0=Yes, 1=Partial, 2=No) 1 1 1 1 1 1 1 1x20 Solar Radiation, Langley/day 564 564 514 572 538 538 528 530x21 Daily Aircraft Operations (< 8 km radius) 12 12 12 12 12 12 12 12x22 Daily Aircraft Operations (8-16 km radius) 0 0 0 0 0 0 0 0x23 Daily Aircraft Operations (16-32 km radius) 1,416 1,407 1,469 1,513 1,416 1,407 1,469 1,513

x23all Daily Aircraft Operations (Total) 1,428 1,419 1,481 1,525 1,428 1,419 1,481 1,525x24 Point Sources NOx Emissions (< 8 km radius), tons/day 0.220 0.204 0.187 0.171 0.220 0.204 0.187 0.171x25 Point Sources NOx Emissions (8-16 km radius), tons/day 0.039 0.040 0.040 0.041 0.039 0.040 0.040 0.041x26 Point Sources NOx Emissions (16-32 km radius), tons/day 56.256 45.886 35.516 25.146 56.256 45.886 35.516 25.146

x26all Point Sources NOx Emissions (Total), tons/day 56.515 46.129 35.744 25.358 56.515 46.129 35.744 25.358x26dist2 [(x24/8)+(x25/16)+(x26/32)] 1.788 1.462 1.136 0.810 1.788 1.462 1.136 0.810

x27 Point Sources VOC Emissions (< 8 km radius), tons/day 1.330 1.366 1.402 1.439 1.330 1.366 1.402 1.439x28 Point Sources VOC Emissions (8-16 km radius), tons/day 0.061 0.063 0.065 0.067 0.061 0.063 0.065 0.067x29 Point Sources VOC Emissions (16-32 km radius), tons/day 20.403 20.241 20.078 19.916 20.403 20.241 20.078 19.916

x29all Point Sources VOC Emissions (Total), tons/day 21.794 21.670 21.546 21.421 21.794 21.670 21.546 21.421x29dist2 [(x27/8)+(x28/16)+(x29/32)] 0.808 0.807 0.807 0.806 0.808 0.807 0.807 0.806

x34 Total Vehicular VOC Emissions, tons/day 0.505 0.237 0.971 0.246 0.383 0.194 0.506 0.284x35 Total Vehicular NOx Emissions, tons/day 1.240 0.719 2.565 0.738 0.992 0.642 1.348 0.888x36 Weighted Average Surface Temperature, °C 44.6 43.9 43.2 45.0 43.2 39.8 42.2 41.9y3 Daily Maximum 8-hour Average Ozone Concentration, ppm 0.058 0.054 0.052 - 0.063 0.049 0.059 -y7 Daily Average Nitrogen Dioxide Concentration, ppm 0.003 - - - 0.005 - - -


June 2004

Measured and Predicted Ozone Concentrations for the Selected Day Tupelo, Mississippi

0.073

n/a

0.066

0.046

0.074 0.071

0.0570.064

0.000

0.050

0.100

0.150

7/25/2001 7/25/2002 7/25/2003 7/25/2004Year

O3 C

once

ntra

tion

, ppm

Measured Predicted



Measured and Predicted Ozone Concentrations for the Selected Day Hernando, Mississippi

0.054 0.052

n/a

0.058 0.0590.055 0.059

0.069

0.000

0.050

0.100

0.150

8/22/2001 8/22/2002 8/22/2003 8/22/2004Year

O3

Con

cent

rati

on, p

pm

Measured Predicted



the range of ± 0.01 ppm. The percentage errors of estimation lie within the range of 2-15%. For Oxford, there is no measured O3 concentration available. However, the prediction results by both models indicate that the air quality in Oxford, in terms of O3 contamination, is good. This is because the predicted O3 concentrations are low even in the hottest day when the O3 levels are expected to be high, compared to the days in other seasons of a year. Figure 95 shows the prediction results of NO2 concentration in August 23, 2002-2004 for Hernando, which are compared with the measured concentration of August 23, 2001.

Figure 92. Measured and predicted O3 concentrations for the selected day, Tupelo

Figure 93. Measured and predicted O3 concentrations for the selected day, Hernando


June 2004

Measured and Predicted Ozone Concentrations for the Selected Day Oxford, Mississippi

n/a n/a n/an/a

0.0450.036 0.0340.036

0.000

0.050

0.100

0.150

7/11/2001 7/11/2002 7/11/2003 7/11/2004Year

O3

Con

cent

rati

on, p

pm

Measured Predicted



Measured and Predicted Nitrogen Dioxide Concentrations for the Selected Day, Hernando, Mississippi

n/a n/a n/a0.005

0.009 0.010 0.0130.016

0.000

0.020

0.040

0.060

0.080

0.100

8/23/2001 8/23/2002 8/23/2003 8/23/2004Year

NO

2 Con

cent

ratio

n, p

pm

Measured Predicted

EPA's National Ambient Air Quality Standard for Annual Average Nitrogen Dioxide 0.053

Figure 94. Measured and predicted O3 concentrations for the selected day, Oxford

Figure 95. Measured and predicted NO2 concentrations for the selected days, Hernando


June 2004

5.5 GIS-Based Air Quality Visualization The Geographic Information System for Infrastructure Asset Management (GISAM) program has been developed at CAIT as a dedicated GIS program for transportation infrastructure management [91]. It is coded in the Delphi computer language using commercial graphical function components. The GISAM program allows users to work with either a raster layer or a vector layer and various types of imagery such as USGS maps, aerial photos, and satellite images. It also allows the users to create a new vector layer of selected planimetric entities. The attributes of each entity are stored in the database.

The GISAM program was enhanced in this study by adding new functions for GIS-based predictions and visualization of vehicular emissions on roadways. Selected vehicular emission calculation functions from AQMAN were incorporated into the GISAM program. The results are displayed as vector polygon (or ‘planimetric’) maps, with an appropriate color scheme.

The sample image of Highway 6-Jackson Avenue intersection area in Oxford demonstrated in the previous section is also used for the visualization of estimated vehicular emissions of VOC and NOx air pollutants. The new planimetric layer of selected roadway sections in the study area is created. These roadway sections are Highway 6 (referred to both East and West directions), Jackson Avenue, Highway 6 – Jackson Avenue intersection, and West Oxford Loop. The vehicular emissions are estimated for two cases: (1) Non-peak hour (6 - 7 a.m.), and (2) Peak hour (12 noon – 1 p.m.). The traffic data on May 25, 2001 on each of them are listed in Table 34. Note that this day is chosen for this analysis because the real-time measurement of NO2 concentration was conducted in the field nearby MS Highway 6 on this day as a part of the air quality project at CAIT [81]. These traffic data include: • Traffic Volume: The actual hourly traffic volume on MS Highway 6 on May 25, 2001, is

available from the traffic count conducted as a part of the air quality project. The traffic count was also made for the Highway 6 – Jackson Avenue intersection on May 1, 2001 [26]. The results from the traffic count at the intersection show that the traffic volume on Jackson Avenue was 44% of the traffic volume on both directions of Highway 6. This percentage value was assumed for estimating the traffic volume on Jackson Avenue on May 25, 2001. The traffic volume at the intersection is simply a summation of the traffic volume from Highway 6 and Jackson Avenue. Finally, the traffic volume on West Oxford Loop is assumed to be 10% of the traffic volume on Jackson Avenue.

• Average Speed: Average speeds on each of the roadway sections are assumed based on the speed limits of the roadways. The average speed at the Highway 6 – Jackson Avenue intersection is assumed to be 4 km/h (2.5 mi/h), as referred to an idle condition. The assumed average speeds are used for both cars and trucks in this analysis. They are also used for both non-peak and peak hours.

The climatological data of the selected day was previously obtained from Goodwin Creek

weather station in Batesville, a city nearby Oxford (for the air quality database). The vehicular emission rates of VOC and NOx then were estimated using the regression equations presented in section 3.3.6. Note that these equations cover the inference space of the average speed only from 32 to 80 km/h (20 to 50 mi/h). Therefore, additional runs of MOBILE6 were made for the idle speed of 4 km/h (2.5 mi/h) at the intersection. The VOC emission rate calculated by MOBILE6 at this speed is three to six times higher than the VOC emission rates at 32 km/h (20 mi/h) estimated using the regression equations with the exception of cars where both cases give about the same VOC emission rates. The NOx emission rate results from MOBILE6 at this speed is


June 2004

approximately two times higher than the NOx emission rates at 32 km/h (20 mi/h) estimated using the regression equations with the exception of heavy gasoline trucks where both cases give similar NOx emission rates.

Table 34. Traffic data of selected roadway sections around the Highway 6-Jackson Avenue intersection area, Oxford, Mississippi, May 25, 2001

Road-ways

Name Average Speed1, km/h

(mi/h)

Non-peak2 Traffic Volume,

veh/hr

Peak3 Traffic Volume, veh/hr

1 Highway 6 80 (50) 800 1,730 2 Jackson Avenue 48 (30) 352 761 3 Highway 6-Jackson

Avenue Intersection 4 (2.5) 1,152 2,491

4 West Oxford Loop 32 (20) 35 76 1 Used for both cars and trucks 2 6 – 7 a.m. 312 noon – 1 p.m. Assumptions: 1) Traffic volume on Jackson Avenue = 44% of traffic volume on Highway 6 2) Traffic volume at the Highway 6 - Jackson Avenue intersection = traffic volume on Highway 6 + traffic volume on Jackson Avenue 3) Traffic volume on West Oxford Loop = 10% of traffic volume on Jackson Avenue

The aggregated VOC and NOx emissions were then calculated following the procedures discussed in section 3.3.6 for both non-peak and peak hours. The results are presented in Table 35. The emissions during the non-peak hour are approximately 46% of the emissions during the peak hour, which is the same percentage as the percentage of the traffic volumes between those two hours. The regression equations for estimating vehicular emission rates and the aggregated emission calculation procedures were implemented in the GISAM program. The estimated emission results are presented on the GIS planimetric map layer by using different colors for different levels of emissions, as shown in Figure 96 for VOC and Figure 97 for NOx.

Table 35. Estimated vehicular emissions from roadways in the Highway 6-Jackson Avenue intersection area, Oxford, Mississippi, May 25, 2001

Road-ways

Name Average Speed1,

Non-peak Hour2 Emissions, g

Peak Hour3 Emissions, g

km/h (mi/h) VOC NOx VOC NOx 1 Highway 6 80 (50) 1,867 4,586 4,037 9,917 2 Jackson Avenue 48 (30) 1,045 2,101 2,259 4,544 3 Highway 6-Jackson

Avenue Intersection 4 (2.5) 23,849 13,708 51,573 29,643

4 West Oxford Loop 32 (20) 118 215 255 464 1 Used for both cars and trucks 2 6 – 7 a.m. 312 noon – 1 p.m.


June 2004

(a) Non-peak hour (6 – 7 a.m.) Note: Background layer shows IKONOS 1-m spatial resolution satellite imagery (acquired on March 27, 2000, courtesy of Space Imaging, Inc.)

(b) Peak hour (12 noon – 1 p.m.)

Figure 96. Vehicular VOC emissions from roadways around the Highway 6-Jackson Avenue

intersection area, Oxford, Mississippi, May 25, 2001

≤ 500 g 501 – 5,000 g 5,001 – 10,000 g

10,001 – 20,000 g 20,001 – 30,000 g 30,001 – 40,000 g > 40,000 g

≤ 500 g 501 – 5,000 g 5,001 – 10,000 g

10,001 – 20,000 g 20,001 – 30,000 g 30,001 – 40,000 g > 40,000 g


June 2004

(a) Non-peak hour (6 – 7 a.m.)

(b) Peak hour (12 noon – 1 p.m.)

Figure 97. Vehicular NOx emissions from roadways around the Highway 6-Jackson Avenue intersection area, Oxford, Mississippi, May 25, 2001

≤ 500 g 501 – 5,000 g 5,001 – 10,000 g

10,001 – 20,000 g 20,001 – 30,000 g 30,001 – 40,000 g > 40,000 g

≤ 500 g 501 – 5,000 g 5,001 – 10,000 g

10,001 – 20,000 g 20,001 – 30,000 g 30,001 – 40,000 g > 40,000 g


June 2004

Based on the estimated on-road emissions presented in Figure 97, coupled with the prevailing atmospheric conditions, it is possible to estimate the dispersion of NO2 air pollutant from the motor vehicles on the roadways. This can be done on a pixel basis by setting the receptor coordinates relevant to the coordinates of a pixel. The final results of the air pollutant dispersed concentration for each pixel can be displayed on a GIS grid layer. Figure 98 shows the GIS visualization of O3 concentration for different traffic radial zones.

An additional key factor affecting O3 concentrations in urban areas with high traffic volumes is the “heatisland” effect, which can be represented by the surface temperature of an area. A preliminary study of the surface temperature in Oxford, Mississippi, shows that the “heatisland” effect is present even for small rural towns (Figure 99). Figure 99 shows that during the hottest hour in 2001, the weighted average surface temperature in the city area was about 5 °C (9 °F) higher than that of surrounding areas, and about 15 °C (27 °F) higher than the air temperature. 5.6 Evaluation of Benefits and Costs The AQMAN air quality analysis methodology, developed in this study for estimating O3 and NO2 pollutants, presents a valuable decision-making tool for enhanced air quality management. It can be used to assess air quality where there is no EPA monitoring station and impacts of congestion of traffic flow on air quality degradation. It can also be used to evaluate benefits and costs associated with public health and motorists. This section reviews the methodology to identify and quantify benefits and costs associated with air quality. Also, the cost-effectiveness of the AQMAN model for evaluating air pollution mitigation measures is discussed. Benefits

Benefits of improved traffic flow are traditionally measured in terms of travel time saving, reduction of accident rate, reduction in fuel consumption and other vehicle operating costs, and reduction of air pollution from vehicular sources. The reduction of air pollution has been generally measured on a qualitative base. The benefits of air pollution reduction are primarily the reduction of public health risks from air pollution. Although these are considered as indirect benefits, they can contribute to a large portion of the total dollar benefit. Keeping in mind that the human being is the most valuable resource and that public health is always the top priority for every society, the reduction of risk to public health can be the equivalent of a tremendous amount of dollar saving.

It is known that air pollution adversely affects public health. In addition, it is harmful to agricultural products and ecosystems, and even deteriorates constructed materials and structures. The decrease in these undesirable consequences, therefore, can be considered as the benefits to the society [92]. In order to quantify the benefits due to air pollution reduction by any pollution control measure or air quality improvement program, one may alternatively determine the amount of adverse effects the pollution can have on human and human environment.


June 2004

Figure 98. O3 concentration visualization through GIS layers, Tupelo


June 2004

Figure 99. Surface temperature profile along E-W cross section of Oxford, Mississippi

34.8 °C

W E

34.8 °C

W E


June 2004

The AQMAN air quality analysis methodology assists in the benefit/cost analysis and DVM analysis. For example, the developed air quality models can be used to predict the air pollution concentrations in a particular area after a transportation project or a congestion mitigation strategy is implemented. Then, the change in the level of air pollution concentrations can be converted into monetary terms. In addition to health benefits, Halvorsen and Ruby [93] discuss methodologies for estimating air pollution control benefits on the societal welfare. These benefits are divided into three benefit groups, which are vegetation and ecosystem benefits, materials benefits, and aesthetic benefits.

The EPA studied the benefits and costs of the Clean Air Act (CAA) from 1970 to 2001 [5, 94]. Several benefits are identified, which can be grouped into two major groups: health benefits and welfare benefits. Health benefits are defined as an avoidance of air pollution-related health effects, such as premature mortality, respiratory illness, and heart disease. Welfare benefits are accounted for when improved air quality reduces damage to measurable resources, including agriculture production and visibility. The analysis of physical effects due to air pollution requires a combination of three components: air quality, population, and health or welfare effects. A three-step analysis process used in their study involves: (1) estimating changes in air quality between the control and no-control scenarios, (2) estimating the human populations and natural resources exposed to these changed air quality conditions, and (3) applying a series of concentration-response equations which translates changes in air quality to changes in physical health and welfare outcomes for the affected populations. Table 36 presents the mean estimates of an economic value (in 1990 dollars) per unit of avoided effect for both health and welfare benefit groups. For instance, the benefit of having one person avoiding from chronic asthma, which is caused by O3, is $25,000 per year. Based on such data, one can calculate the total benefits and costs due to the change in air quality as a result of transportation improvement projects and pollution control measures.

The studies by the EPA were reviewed by the National Academy of Sciences on the methodology to estimate regulatory health benefits. The critical steps of a health benefit analysis recommended by the National Academy of Sciences are [95]: • Defining the proposed regulation • Establishing the boundaries of the analysis • Defining the regulatory baseline • Estimating changes in pollutant emissions • Estimating changes in ambient air pollutant concentrations • Estimating changes in human health outcomes

Another example is increased risk for crash accidents due to adverse congestion

conditions. The study of the economic impact of motor vehicle crashes [96] is a good source of congestion unit cost data. The costs due to motor vehicle crashes are separated into injury components and non- injury components. The injury components include medical, emergency services, loss in market productivity, loss in household productivity, insurance administrative cost, workplace cost, and legal costs. The non- injury components are travel delay and property damage. Costs

In order to quantify an economic value of transportation control measures (TCMs) and air quality mitigation programs, the change in the level of air pollution needs to be evaluated. This


June 2004

evaluation can be done through a specialized monitoring program set up for a particular evaluation site or the use of reliable air quality modeling. Both of them are subjected to certain amounts of implementing, operating, and other associated costs. Therefore, the appropriate air quality evaluation method needs to be carefully selected for being both technically and economically sound. The study on the feasibility of using advanced air quality monitoring systems to evaluate TCMs [97] reports the following findings:

• Air quality monitoring is technically feasible for primary pollutants, such as CO, VOCs, and NOx.

• TCM effects on secondary pollutants, such as O3, require a combination of monitoring and modeling approaches.

• It is feasible to observe TCM effects only if they exceed a certain threshold value. • Monitoring over a large geographic area to determine the effects of areawide TCM programs

is inherently more difficult than monitoring on a location- or facility-specific basis. • Specialized TCM air quality monitoring programs are costly.

Table 36. Mean estimates of economic value per unit of avoided effect (in 1990 dollars) [5, 94]

Endpoint Pollutant Valuation (mean estimate)

Mortality PM & Lead $4,800,000 per case/1 Chronic Bronchitis PM $260,000 per case Chronic Asthma Ozone $25,000 per case IQ Changes Lost IQ Points Lead $3,000 per IQ point IQ less than 70 Lead $42,000 per case Hypertension Lead $680 per case Strokes /2 Lead $200,000 per case-males /3 $150,000 per case-females /3 Coronary Heart Disease Lead $52,000 per case Hospital Admissions All Respiratory PM, Ozone, NO2, SO2 $6,900 per case All Cardiovascular PM, Ozone, NO2, SO2, CO $9,500 per case Emergency Room vis its for Asthma PM & Ozone $ 194 per case Respiratory Illness and Symptoms Acute Bronchitis PM $45 per case Acute Asthma PM & Ozone $32 per case Acute Respiratory Symptoms PM, Ozone, NO2, SO2 $18 per case Upper Respiratory Symptoms PM $19 per case Lower Respiratory Symptoms PM $12 per case Shortness of Breath, Chest Tightness, or Wheeze

PM & SO2 $5.30 per day

Work Loss Days PM $83 per day Mild Restricted Activity Days PM & Ozone $38 per day Welfare Benefits Visibility DeciView $14 per unit change in DeciView Household Soiling PM $2.50 per household per PM10 change Decreased Worker Productivity Ozone $1 /4

Agriculture (Net Surplus) Ozone Change in Economic Surplus /1


June 2004

Alternatively, equal to $293,000 for each life-year lost /2 Strokes are comprised of atherothrombotic brain infarctions and cerebrovascular accidents; both are estimated to have the same monetary

value. /3 The different valuations for stroke cases reflect differences in lost earnings between males and females. /4 Decreased productivity valued as change in daily wages; $1 per worker per 10% increase in Ozone.

The AQMAN model presents an alternative simulation approach to estimate the impacts of TCM strategies and identify sites and locations for air quality monitoring. Regarding the TCM, an example is given that the cost of two 3-month monitoring programs (one before and the other after implementation) to study effects on O3 formation would cost on the order of $1,300,000 [97]. Because of this high cost, it is probably not practical for local transportation or air quality agencies to undertake such specialized monitoring programs. Therefore, the AQMAN air quality models would be an attractive alternative for assessing the effects of TCMs on air quality, due to its reasonable calibration and operation costs, which are less than $50,000 for 3-4 months implementation time.

One alternative approach to evaluate the air pollution control measures is to use a cost-effectiveness ratio—cost per ton of pollutant emission reduced. For example, Figures 100 and 101 presents the vehicular emissions per unit area of VOC and NOx for rural cities in Mississippi in 2001. Note that these vehicular emissions are estimated based on the total traffic volume in the vicinity of 16-km radial distance from the city. When calculating the emissions per unit area, the square area of 32x32 sq km was used. This square area is an approximation of the circle area with 16-km radius used in the traffic volume estimation. The estimated vehicular emissions per unit area in Oxford are higher than the ones in Hernando for every case despite the lower total traffic volume. This is due to the wider daily air temperature range measured at the Goodwin Creek weather station. The daily air temperature ranges for all three days in Oxford were about 14 °C (57 °F) while the daily air temperature ranges for all three days in Tupelo and Hernando were about 11 °C (52 °F). The wider daily air temperature range results in higher estimated vehicular emission rates by the developed regression equations for both VOC and NOx. These emissions can be used as a base line for evaluating the effectiveness of several alternatives of air pollution control measures.

One example of the study on the cost of emission reduction was done by The Energy Information Administration (EIA) within the U.S. Department of Energy. EIA estimated the incremental cost to control CO emissions and its economic impacts in several scenarios. For example, the costs to reduce the CO emissions in 2010 to be 3% less than the CO emissions in 1990 are $294 (1996 dollars) per metric ton. However, this accomplishment would decrease the economic value of the nation by $327 billion (1992 dollars), and decrease the Gross Domestic Product (GDP) by 3.5% [98]. Note that CO is well known as one of the greenhouse gases, which are the major reason for the global climate change. Analysis of Air Quality Impacts of Abnormally High Traffic Volume and Congestion Due to Football Event in Oxford Oxford, Mississippi, is a small rural city, which rarely has traffic congestion in typical days. However, in the days of Football events, the traffic condition in Oxford is abnormally congested, especially during the hour after the game end. Boriboonsomsin [50] evaluated the incremental cost due to the abnormally high traffic volume and congestion in the football game days in Oxford. Step-by-step calculations of societal costs are shown below due to: (1) traffic congestion (due to 90,000 additional traffic volume), (2) about 2.5 times increase in CO emissions, and (3)


June 2004

4.8% increase in O3 concentration in Oxford for the game day, September 13, 2003. Constant interest rate of 5% was assumed to calculate 2003 costs. The total societal costs due to game day traffic on September 13, 2003 (2003 dollars) are $368,743 considering Ozone-related morbidity cost, decrease in worker productivity, and differential vehicle operating costs. This amounts to $6.14 per person present in Oxford on the game day event. This analysis shows an alternative approach to quantify societal costs from public health costs related to the impact of air quality degradation and increased vehicle operating costs due to abnormally high traffic volume and congestion conditions.

Figure 100. Vehicular VOC emissions per unit area of rural cities in Mississippi, 2001

(+ 12,000 university students)

1,027

685

982944

447

745

998

555

847

0

1,000

2,000

3,000

4,000

Tupelo Hernando Oxford

Veh

icul

ar V

OC

Em

issi

on, g

/sq

km Coldest Day Hottest Day Normal Day

Total Traffic Volume, veh/day Coldest Day Hottest Day Normal DayCity Population in 2001*City Land Area†, sq km

488,866496,380561,53534,564132.3

393,755393,755445,440

7,18129.3

289,684289,684327,70811,93325.9

* Data extrapolated from 1990 and 2000 data obtained from http://www.olemiss.edu/depts/sdc/† Data from http://www.city-data.com


June 2004

Figure 101. Vehicular NOx emissions per unit area of rural cities in Mississippi, 2001

(+ 12,000 university students)

1,959

1,483

2,1102,003

1,099

1,736

2,031

1,309

1,879

0

1,000

2,000

3,000

4,000

Tupelo Hernando Oxford

Veh

icul

ar N

Ox E

mis

sion

, g/s

q km

Coldest Day Hottest Day Normal Day

Total Traffic Volume, veh/day Coldest Day Hottest Day Normal DayCity Population in 2001*City Land Area†, sq km

488,866496,380561,53534,564132.3

393,755393,755445,440

7,18129.3

289,684289,684327,70811,93325.9

* Data extrapolated from 1990 and 2000 data obtained from http://www.olemiss.edu/depts/sdc/† Data from http://www.city-data.com


June 2004

6. CONCLUSIONS AND RECOMMENDATIONS

The simplified vehicular emission models and O3 and NO2 pollutant models developed in this study show reasonably accurate results. The primary conclusions are: • The vehicular emission factors extracted from the MOBILE6 computations show the

form of power decay function with respect to vehicle model year. The R2 values of the developed regression equations for estimating VOC, NOx, and CO emission rates range from 0.75 to 0.99, which are reasonably high. The plots between the MOBILE6 emission rates and the regression-predicted emission rates show excellent goodness-of-fit for later model years of cars and trucks with smaller emission rates. The emission rates of diesel trucks do no vary by air temperatures. The emission rates of all three pollutants in the coldest day are 6-40% higher than the emission rates in the hottest day, with the exception of the CO emission rates for heavy gasoline trucks. Considering vehicle model years 1980 to 2001, cars emit two to three times more CO than diesel trucks do while diesel trucks produce 10 times more NOx than cars do.

• The vehicular emission (of VOC and NOx) is a better indicator of air pollutants than simply using traffic volume because traffic volume generally increases with time, but the newer vehicle models are more fuel efficient and produce significantly less emissions than the older vehicle models.

• The O3 concentration model is a function of air temperature, wind speed and direction, precipitation, cloud cover, solar radiation, vehicular traffic and their emissions, aircraft operations, point source emissions, and day of year. The selected model has the R2 values of 0.55 and the standard error of the estimate 0.012 ppm.

• The O3 models give adequately accurate prediction results for Tupelo and Hernando. The prediction for Tupelo on the hottest day of July 25, 2001, is 0.064 ppm (12% less than the measured value of 0.073 ppm). The prediction for Hernando on the hottest day of August 22, 2001, is 0.059 ppm (2% higher than the measured value of 0.058 ppm).

• The predicted O3 concentrations in Oxford for the selected days in 2001 range from 0.016 ppm to 0.048 ppm, which agree with the level of natural background O3 concentration of 0.025 to 0.045 ppm reported in literature.

• The NO2 concentration model has the R2 values of 0.58 and the standard error of the estimate 0.005 ppm. The NO2 model is without aviation operations and point source emissions variables (necessary to obtain reasonable results for Hernando, Tupelo, and Oxford). They provide reasonable prediction results for Hernando, 0.009 ppm for the hottest day in 2001, when compared to the measured value of 0.005 ppm. Reasonable results for Tupelo and Oxford are also obtained, for example, ranging from 0.007 to 0.010 ppm for the hottest day in 2001.

• The sensitivity analysis results for Hernando show that the O3 and NO2 concentrations are sensitive to a double increase in traffic volume.

• The validated O3 and NO2 models have been implemented in the AQMAN air quality and analysis program. The program was applied for future air quality predictions. For example, on July 25, 2003, the O3 prediction for Tupelo is 0.074 ppm, compared to 0.066 ppm measurement. On August 22, 2003, the O3 prediction for Hernando is 0.069 ppm, compared to 0.052 ppm measurement. On July 11, 2003, the O3 prediction for Oxford is 0.036 ppm. These results show reasonableness of the models for predicting O3 and NO2


June 2004

concentrations in rural areas of Mississippi. This is especially useful for air quality management in locations where there is no monitoring station.

• The O3 prediction for Hernando on August 22, 2004 is 0.059 ppm. The NO2 prediction for Hernando on August 23, 2004 is 0.013 ppm.

• The developed O3 and NO2 models are not applicable for urban and metropolitan areas (like Jackson, Mississippi) without proper calibration.

• The GISAM program has been enhanced for GIS-based visualization of vehicular emissions and O3 results.

• The functions of vehicular emission estimation from AQMAN were implemented in the enhanced GISAM program. The results from each function are displayed as planimetric maps, with an appropriate color scheme.

The following recommendations are offered for implementation of the developed air

quality models in future research. • Use AQMAN as a decision-making tool in air quality management, sustainable

community development, and related transportation applications. For instance, it can be used in transportation planning to determine the impact of a new highway corridor on air quality, or to determine the air quality improvement due to transportation control measures. It can also be used to evaluate the degradation of air quality due to traffic congestion, new industrial plants, increased aircraft operations, or a new airport close by a city.

• Use AQMAN as an alternative simulation approach to estimate the impacts of traffic control strategies and identify sites and locations for air quality monitoring.

• Calibrate the coefficients of the O3 and NO2 concentration models for urban and metropolitan areas such as Jackson, Mississippi, using their historical data.

• Enhance the NO2 models considering the emissions of aviation and point sources. • Enhance the O3 and NO2 models considering built-up area estimates and surface

temperature effects. • Implement the alternative two-dimensional and three-dimensional dispersion models to

enhance GIS visualization of vehicle emissions. • Implement a methodology to account for the reaction of NOx and the deposition of

particulates for the emission dispersion model to assess air qua lity impact of transportation projects. Verify and calibrate the results of the dispersion models with field measurements.


June 2004

7. REFERENCES

[1] Transportation, Air Quality, and Remote Sensing Laser Measurements of Air Pollutants

Related to Highway Traffic in Mississippi – Oxford and Tupelo. Achievements of the DOT-NASA Joint Program on Remote Sensing and Spatial Information Technologies Application to Multimodal Transportation 2000-2002, April 2002.

[2] Uddin, W. Application of Remote Sensing Tunable Laser Technology for Measuring Transportation Related Air Pollution. Proceedings of the Seventh International Conference on Applications of Advanced Technology in Transportation, American Society of Civil Engineers, Boston, Massachusetts, August 5-7, 2002, pp. 257-265.

[3] CAIT, “Air Quality Project – Remote Sensing Tunable Laser Measurements of Air Pollution.” Technology Guide NCRSTE_TG004, Center for Advanced Infrastructure Technology, The University of Mississippi, October 2001. http://www.ncrste.msstate.edu/publications/ncrste_tg004.pdf

[4] Eccleston, Charles H. The NEPA Planning Process: A Comprehensive Guide with Emphasis on Efficiency. John Wiley & Sons, Inc. New York, 1999.

[5] U.S. Environmental Protection Agency. The Benefits and Costs of the Clean Air Act, 1970 to 1990. EPA 410-R-97-002, Final Report to U.S. Congress, October 1997, http://www.epa.gov/air/sect812/copy.html. Accessed on March 28, 2004.

[6] U.S. Environmental Protection Agency, “National Air Quality and Emissions Trends Report”, EPA-454/R-98-016, 1997, www.epa.gov/oar/aqtrnd97, Accessed on April 13, 2003.

[7] Environmental Protection Agency, “Motor Vehicles and the 1990 Clean Air Act.” EPA 400-F-92-013, August 1994. http://www.epa.gov/otaq/11-vehs.htm. Accessed April 2001.

[8] Transportation Research Board, National Research Council. The Congestion Mitigation and Air Quality Improvement Program, Assessing 10 Years of Experience. Special Report 264, Washington, D.C., 2002.

[9] Jensen, G. Clean Air Act Success Story: Continuing Reductions in Transportation Emissions. TR NEWS Number 227, Transportation Research Board, July – August 2003, pp. 4-9.

[10] U.S. Environmental Protection Agency. Latest Findings on National Air Quality: 2002 Status and Trends. EPA 454/K-03-001, August 2003. http://www.epa.gov/airtrends/2002_airtrends_final.pdf. Accessed January 10, 2004.

[11] S. Environmental Protection Agency. Survey of Episodic Control Programs. http://www.epa.gov/OMSWWW/reports/episodic/study.htm. Accessed November 28, 2003.

[12] “Estimating the Public Health Benefits of Proposed Air Pollution Regulations.” National Research Council, The National Academies Press, Washington, D.C., 2002.

[13] U.S. Environmental Protection Agency, “National Ambient Air Quality Standards (NAAQS)” www.epa.gov/airs/criteria.html Accessed on April 18, 2003.

[14] Tsohos, G.H., “Highway Environmental Engineering.” Thessaloniki, Greece, 2001. [15] Irwin, John S., “Modeling Air Quality Pollutant Impacts” Air Quality Management in

Urban Areas in the Light of EU Legislation, November 2000, www.meteo.bg/EURASAP/40/paper1.html.


June 2004

[16] U.S. Environmental Protection Agency, “Support Center for Regulatory Air Models.” U.S. EPA, www.epa.gov/scram001

[17] Benson, Paul E. CALINE4, a dispersion model for predicting air pollutant concentrations. Report No. FHWA/CA/TL-84/15. Sacramento, California, November 1984.

[18] ALOHA – Areal Locations of Hazardous Atmospheres, User’s Manual, U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, August 1999.

[19] Morris, R. E., and T. C. Myers. Users’s Guide for the Urban Airshed Model - Volume I: Users Manual for UAM (CB-IV). Systems Applications, Inc. EPA-450/4-90-007A, June 1990.

[20] Wark, K., Warner, C., Davis, W., “Air Pollution Its Origin and Control”, Third Edition, Addison-Wesley, 1998.

[21] Environmental Protection Agency, “User’s Guide to MOBILE6.0: Mobile Source Emission Factor Model.” http://www.epa.gov/otaq/models/mobile6/r02001.pdf. Accessed March 28, 2002.

[22] Garza, S. G. Integration of Pavement Nondestructive Evaluation, Finite Element Simulation, and Air Quality Modeling for Enhanced Transportation Corridor Assessment and Design. Ph.D. Dissertation, Department of Civil Engineering, The University of Mississippi, May 2003.

[23] Sumrall, R. J. Dispersion Effects of Accidental Hazardous Material Release. Master of Science Graduation Report, Department of Civil Engineering, The University of Mississippi, December 2003.

[24] Seinfeld, John H, and Pandis, Spyros, N., “Atmospheric Chemistry and Physics: from Air Pollution to Climate Change.” Wiley Inc., New York, 1998.

[25] Kreyszig, E., “Advanced Engineering Mathematics”, 8th Edition, John Wiley & Sons, Inc., 1999.

[26] Turner, D. B., “Workbook of Atmospheric Dispersion Estimates”, 2nd Edition, CRC Press, Inc, Boca Raton, Florida, 1994.

[27] Lamb, R.G., Chen, W.H., and Seinfeld, J.H., “Numerico-empirical Analyses of Atmospheric Diffusion Theories.” Journal of the Atmospheric Science, Vol. 32, 1975, pp. 1794-1807.

[28] Lamb, R.G., and Duran, D.R., “Eddy Diffusivities Derived from a Numerical Model of the Convective Boundary Layer.” Nuovo Cimento, Vol. 1C, 1977, pp. 1-17.

[29] Golder, D., “Relations among stability parameters in the surface layer.” Boundary Layer Meteorology, Vol 3., 1972, pp. 47-58.

[30] McRae, G.J., Goodin, W.R., and Seinfeld, J.H., “Development of a Second-Generation Mathematical Model for Urban Air Pollution 1. Model Formulation.” Atmospheric Environment, Vol. 16, 1982, pp. 679-696.

[31] Myrup, L.O., and Ranzieri, A.J., “A Consistent Scheme for Estimating Diffusivities to Be Used in Air Quality Models.” Report CA-DOT-TL-7169-3-76-32. California Department of Transportation, Sacramento, California, 1976.

[32] Businger, J.A., and Ayra, S.P.S., “Height of the Mixed Layer in the Stably Stratified Planetary Boundary Layer.” Advances in Geophysics, Vol. 18A, 1974, pp. 73-92.

[33] Ward, C.E. and Ranzieri A.J., “CALINE2: An Improved Microscale Model for the Diffusion of Air Pollutants from a Line Source.” Special Report 167, Transportation


June 2004

Research Board, Assessing Transportation Related Air Quality Impacts, TRB, National Academy of Sciences, 1976.

[34] Warden, D. Air Quality Issues in Tennessee. Presented at Air & Waste Management Association Southern Section Conference, August 1, 2003, http://www.gaawma.org/events/03conference/BarryStephens_TNAirUpdate.pdf. Accessed on October 24, 2003.

[35] Systems Applications International, Inc. User’s Guide to the Variable-Grid Urban Airshed Model (UAM-V). Revised October 1999, http://uamv.saintl.com/info.htm. Accessed on October 24, 2003.

[36] ATMOS. Arkansas-Tennessee-Mississippi Ozone Study, http://atmos.saintl.com/. Accessed on October 24, 2003.

[37] GCOS. Gulf Coast Ozone Study, http://www.gcos.saintl.com/. Accessed on October 24, 2003.

[38] MM5 Community Model. http://www.mmm.ucar.edu/mm5/mm5-home.html. Accessed on October 24, 2003.

[39] U.S. Environmental Protection Agency. Technical Guidance on the Use of MOBILE6 for Emission Inventory Preparation. http://www.epa.gov/otaq/models/mobile6/m6techgd.pdf. Accessed on September 5, 2002.

[40] U.S. Environmental Protection Agency. User’s Guide to MOBILE6.0: Mobile Source Emission Factor Model. EPA420-R-02-001, United States Environmental Protection Agency, January 2002.

[41] Heirigs, P., S. Delaney, and R. Dulla. MOBILE6 On-Road Motor Vehicle Emissions Model: 5-Day Training Course. http://www.epa.gov/otaq/m6.htm. Accessed September 5, 2002.

[42] U.S. General Accounting Office. Air Pollution: Limitations of EPA’s Motor Vehicle Emissions Model and Plans to Address Them. GAO/RCED-97-210, September 1997, http://www.conginst.org/resultsact/PDF/RC97210.PDF. Accessed on September 5, 2002.

[43] Giannelli, R. A., J. H. Gilmore, L. Landman, S. Srivastava, M. Beardsley, D. Brzezinski, G. Dolce, J. Koupal, J. Pedelty, and G. Shyu. Sensitivity Analysis of MOBILE6.0. EPA420-R-02-035, December 2002.

[44] Evans, L. Exhaust Emissions, Fuel Consumption and Traffic: Relations Derived from Urban Driving Schedule Data. Research Publication GMR-2599, General Motors Research Laboratories, Michigan, December 7, 1977.

[45] Guensler, R. Data Needs for Evolving Motor Vehicle Emission Modeling Approaches. Transportation Planning and Air Quality II, P. Benson, Ed., American Society of Civil Engineers, New York, 1993.

[46] Washington, S. Estimation of a Vehicular Carbon Monoxide Modal Emissions Model and Assessment of an Intelligent Transportation Technology. Dissertation Report. Institute of Transportation Studies, University of California at Davis, 1994.

[47] Fomunung, I., S. Washington, and R. Guensler. A Statistical Model for Estimating Oxides of Nitrogen Emissions from Light Duty Motor Vehicles. Transportation Research Part D: Transport and Environment, Vol. 4, Issue 5, July 1999, pp. 333-359.

[48] Dion, F., M. V. Aerde, and H. Rakha. Mesoscopic Fuel Consumption and Vehicle Emission Rate Estimation as a Function of Average Speed and Number of Stops. The 79th Annual Meeting of Transportation Research Board (CD-ROM), National Research Council, Washington, D.C., Jan. 2000.


June 2004

[49] Papacostas, C. S., and P. D. Prevedouros. “Transportation Engineering and Planning.” 2nd edition, Prentice Hall, Inc., 1993.

[50] Boriboonsomsin, K. Transportation-Related Air Quality Modeling and Analysis Based on Remote Sensing and Geospatial Data. Ph.D. Dissertation, Department of Civil Engineering, The University of Mississippi, May 2004.

[51] U.S. Environmental Protection Agency. Controls and Prohibitions on Gasoline Volatility. 40 CFR Part 80, Federal Register, Vol. 14, Revised as of July 1, 2003, pp. 586-590.

[52] U.S. Environmental Protection Agency. AIRData. http://www.epa.gov/air/data/index.html. Accessed on September 16, 2002.

[53] U.S. Environmental Protection Agency. Guideline for Developing an Ozone Forecasting Program. EPA-454/R-99-009, July 1999. http://www.epa.gov/ttn/oarpg/t1/memoranda/foreguid.pdf. Accessed May 19, 2002.

[54] U.S. Environmental Protection Agency. Ground- level Ozone: What is it? Where does it come from? http://www.epa.gov/air/urbanair/ozone/what.html. Accessed on September 16, 2002.

[55] U.S. Environmental Protection Agency. DeSoto County, Mississippi: Boundary Guidance Criteria in Proposing a Separate Nonattainment Area than the Memphis MSA. http://www.epa.gov/air/oaqps/glo/designations/documents/O3Recommendations/4/s/Mississippi.pdf. Accessed on November 25, 2003.

[56] Chameides, W. L., R. W. Lindsay, J. Richardson, and C. S. Kiang. The Role of Biogenic Hydrocarbons in Urban Photochemical Smog: Atlanta as a Case Study. Science 241, September 1988, pp. 1473-1475.

[57] Hubbard, M. C. and W. G. Cobourn. Development of a Regression Model to Forecast Ground-Level Ozone Concentration in Louisville, Kentucky. Atmospheric Environment, Volume 32, 1998, pp. 2637-2647.

[58] Wark, K., C. F., Warner, W. T. Davis. Air Pollution, Its Origin and Control. Addison-Wesley, 1998.

[59] Hanna, S. R., and J. C. Change. Relations between Meteorology and Ozone in the Lake Michigan Region. Journal of Applied Meteorology, Volume 34, 1995, pp. 670-678.

[60] Heat Island Group. http://eande.lbl.gov/HeatIsland/. Accessed April 2, 2003. [61] U.S. Environmental Protection Agency. Global Warming – Impacts: Health.

http://yosemite.epa.gov/oar/globalwarming.nsf/content/ImpactsHealth.html. Accessed September 16, 2002.

[62] Wolff, G. T. and P. J. Lioy. An Empirical Model for Forecasting Maximum Daily Ozone Levels in the Northeastern U.S. Journal of the Air Pollution Control Association. Volume 28, 1978, pp. 1035-1038.

[63] Cox, W. M. and S. Chu. Meteorologically Adjusted Trends in Urban Areas: A Probabilistic Approach. Atmospheric Environment, Volume 27B, 1993, pp. 425-434.

[64] Chu, S. Meteorological Considerations in Siting Photochemical Pollutant Monitors. Atmospheric Environment, Volume 29, 1995, pp. 2905-2913.

[65] Ryan, W. F. and E. Luebehusen. Accuracy of Ozone Air Quality Forecasts in the Baltimore Metropolitan Area. 96-TA45.03. Presentation at 89th Annual Meeting and Exhibition of the Air and Waste Management Association, Nashville, Tennessee, June 23-28, 1996.

[66] Hubbard, M. C. and W. G. Cobourn. Development of a Regression Model to Forecast Ground-Level Ozone Concentration in Louisville, Kentucky. Atmospheric Environment, Volume 32, 1998, pp. 2637-2647.


June 2004

[67] Comrie, A. C. Comparing Neural Networks and Regression Models for Ozone Forecasting. Journal of Air & Waste Management Association, Volume 47, June 1997, pp. 653-663.

[68] Cobourn, W. G. and M. C. Hubbard. An Enhanced Ozone Forecasting Model Using Air Mass Trajectory Analysis. Atmospheric Environment, Volume 33, 1999, pp. 4663-4674.

[69] Ibarra, G., I. Madariaga, A. Elías, E. Agirre, and J. Uria. Long-term Changes of Ozone and Traffic in Bilbao. Atmospheric Environment, Volume 35, 2001, pp. 5581-5592.

[70] U.S. Environmental Protection Agency. Survey of Episodic Control Programs. http://www.epa.gov/OMSWWW/reports/episodic/study.htm. Accessed on November 28, 2003.

[71] Comrie, A. C. Comparing Neural Networks and Regression Models for Ozone Forecasting. Journal of Air & Waste Management Association, Volume 47, June 1997, pp. 653-663.

[72] Cobourn, W. G., L. J. Dolcine, M. French, and M. C. Hubbard. A comparison of Nonlinear Regression and Neural Network Models for Ground- level Ozone Forecasting. Journal of the Air & Waste Management Association, Volume 50, November 2000, pp. 1999-2009.

[73] Uddin, W., B. F. McCullough, and M. B. Crawford. Methodology for Forecasting Air Travel and Airport Expansion Needs. in TRR 1025, Journa l of Transportation Research Board, National Research Council, Washington, D.C., 1990, pp. 91-100.

[74] U.S. Environmental Protection Agency. AIRData. http://www.epa.gov/air/data/index.html. Accessed on September 16, 2002.

[75] Mississippi Department of Transportation. http://www.mdot.state.ms.us. Accessed on January 16, 2002.

[76] National Climatic Data Center, National Oceanic and Atmospheric Administration. http://www.ncdc.noaa.gov. Accessed on January 16, 2002.

[77] Cooperative Networks For Renewable Resource Measurements. Solar Energy Resource Data. http://rredc.nrel.gov/solar/new_data/confrrm/. Accessed on September 26, 2003.

[78] Sumrall, C. A Study of Air Pollution Trends in Mississippi. Master of Science Graduation Report, Department of Civil Engineering, The University of Mississippi, May 2003.

[79] Topo USA 2.0 Southeast Region, DeLORME, 1999. http://www.delorme.com/topousa/default.asp. Accessed May 8, 2003.

[80] Uddin, W. Remote Sensing Laser Measurement of Air Pollution: Year 1 Study in North Mississippi. Year 1 Final Report UM-CAIT/2000-01, NCRST-E Air Quality Project, Center for Advanced Infrastructure Technology, the University of Mississippi, August 2001.

[81] U.S. Environmental Protection Agency. Procedures for Emission Inventory Preparation Volume IV: Mobile Sources. EPA420-R-92-009, December 1992. http://www.epa.gov/otaq/invntory/r92009.pdf. Accessed on June 7, 2003.

[82] Bureau of Transportation Statistics. TranStats: The Intermodal Transportation Database. http://www.transtats.gov. Accessed on October 22, 2003.

[83] Mississippi State Tax Commission. Motor Vehicle Licensing and Title Statistics: Title Summary Totals as of 10/11/2003, http://www.mstc.state.ms.us/mvl/stats/main.htm, Accessed on December 16, 2003.

[84] Mississippi Automated Resource Information System (MARIS). http://www.maris.state.ms.us/. Accessed on October 1, 2003.


June 2004

[85] Federal Aviation Administration. EDMS Background. http://www.aee.faa.gov/emissions/EDMS/Background.htm. Accessed on January 11, 2003.

[86] Federal Aviation Administration. The Terminal Area Forecast System. http://www.apo.data.faa.gov/faatafall.HTM. Accessed on April 10, 2003.

[87] Uddin W. Air Quality Analysis Considering Mobile and Aviation Sources and Monitoring Using Remote Sensing Tunable Laser Technologies. Proceeding of 2003 International Conference “Airport: Planning, Infrastructure & Environment,” Rio De Janeiro, Brazil, June 8-11, 2003.

[88] SPSS Inc. SPSS Base 10.0 Applications Guide. Chicago, 1999. [89] NCAT Pavement Test Track. http://www.pavetrack.com/. Accessed on July 27, 2002. [90] The SURFRAD Network, Surface Radiation Research Branch, National Oceanic and

Atmospheric Administration. http://www.srrb.noaa.gov/surfrad/index.html. Accessed on February 19, 2004.

[91] Yiqin, L. Evaluation of Advanced Innovative Technologies for Designing Modern Highways. Master of Science Graduation Report, Department of Civil Engineering, The University of Mississippi, December 2000.

[92] U.S. DOT. Indirect Benefits, Congestion Mitigation and Air Quality Improvement Program. Publication No. FHWA-PD-97-045, U.S. Department of Transportation, Federal Highway Administration, Federal Transit Administration.

[93] Halvorsen, R. and M. G. Ruby. Benefit-Cost Analysis of Air-Pollution Control. LexingtonBooks, D.C. Heath and Company, Massachusetts, 1981.

[94] U.S. Environmental Protection Agency. The Benefits and Costs of the Clean Air Act, 1990 to 2010. EPA 410-R-99-001, Final Report to U.S. Congress, November 1999, http://www.epa.gov/air/sect812/copy99.html. Accessed on March 28, 2004.

[95] National Research Council. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. The National Academies Press, Washington, D.C., 2002.

[96] Blincoe, L., A. Seay, E. Zaloshnja, T. Miller, E. Romano, S. Luchter, and R. Spicer. The Economic Impact of Motor Vehicle Crashes, 2000. NHTSA Technical Report DOT HS 809446, May 2002.

[97] Cambridge Systematics, Inc. Quantifying Air-Quality and Other Benefits and Costs of Transportation Control Measures. NCHRP Report 462, Transportation Research Board, National Research Council, Washington, D.C., 2001.

[98] Stephenson, J. B. Climate Change: Analysis of Two Studies of Estimated Costs of Implementing the Kyoto Protocol. GAO-04-144R, http://www.gao.gov/new.items/d04144r.pdf. Accessed on March 27, 2004.


June 2004

APPENDIX

Examples of AQMAN Reports and output files


June 2004

Tupelo, Hottest Day, 2001


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_O3_TUP_07-25-2001.txt Weather Station: Tupelo Regional Airport Analysis Date: 16 June 2004 Pollutant: O3, Daily maximum 8-hour average Ozone concentration Prediction Date: 25 July 2001 Location==> City: Tupelo County: Lee State: MS ******************************************Input Data********************************************* Climatological Data: Average Maximum Minimum Air Temperature, °C 28.2 35.0 23.3 (°F) (82.8) (95.0) (73.9) Wind Speed, m/s 3.4 6.7 1.5 (mi/h) (7.6) (15.0) (3.4) Wind Direction, degrees 160 ......................................................................... Total Precipitation, mm/day 0 (0.00 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 2 Solar Radiation, Watt-hours/sq m 6,930 (596 Langleys/day) --------------------------------------------------------------------------------------- Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 184,938 208,996 102,446 496,380 Average Car Speed, km/h 48 56 80 (mi/h) (30) (35) (50) Average Truck Speed, km/h 40 48 80 (mi/h) (25) (30) (50) Percentage of Car 94.4 92 80 Percentage of Truck 5.6 8 20 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.411 0.451 0.205 Total NOx Emissions, U.S. tons/day 0.796 0.929 0.538 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 1.128 1.450 2.233 Total NOx Emissions, U.S. tons/day 0 0.092 0.793 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 134 0 99 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_O3_TUP_07-25-2001.txt Pollutant: O3, Daily maximum 8-hour average Ozone concentration National Ambient Air Quality Standards (NAAQS): 0.084 ppm Predicted O3, Daily maximum 8-hour average Ozone concentration: 0.064 ppm Predicted Air Quality Index (AQI): 50 Predicted AQI category: Good ---------------------------------------------------------------------------------------


June 2004


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_NO2_TUP_07-25-2001.txt Weather Station: Tupelo Regional Airport Analysis Date: 16 June 2004 Pollutant: NO2, Daily average Nitrogen Dioxide concentration Prediction Date: 25 July 2001 Location==> City: Tupelo County: Lee State: MS ******************************************Input Data********************************************* Climatological Data: Average Maximum Minimum Air Temperature, °C 28.2 35.0 23.3 (°F) (82.8) (95.0) (73.9) Wind Speed, m/s 3.4 6.7 1.5 (mi/h) (7.6) (15.0) (3.4) Wind Direction, degrees 160 ......................................................................... Total Precipitation, mm/day 0 (0.00 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 2 Solar Radiation, Watt-hours/sq m 6,930 (596 Langleys/day) --------------------------------------------------------------------------------------- Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 184,938 208,996 102,446 496,380 Average Car Speed, km/h 48 56 80 (mi/h) (30) (35) (50) Average Truck Speed, km/h 40 48 80 (mi/h) (25) (30) (50) Percentage of Car 94.4 92 80 Percentage of Truck 5.6 8 20 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.411 0.451 0.205 Total NOx Emissions, U.S. tons/day 0.796 0.929 0.538 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 1.128 1.450 2.233 Total NOx Emissions, U.S. tons/day 0 0.092 0.793 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 134 0 99 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_NO2_TUP_07-25-2001.txt Pollutant: NO2, Daily average Nitrogen Dioxide concentration No National Ambient Air Quality Standards (NAAQS) for short-term NO2 concentration NAAQS for annual average NO2 concentration 0.053 ppm Predicted NO2, Daily average Nitrogen Dioxide concentration: 0.007 ppm No Air Quality Index (AQI) available. No AQI category available. ---------------------------------------------------------------------------------------


June 2004

****************************AQMAN - Air Quality Modeling and Analysis****************************


June 2004

Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_O3_HER_08-22-2001.txt Weather Station: Memphis International Airport Analysis Date: 21 June 2004 Pollutant: O3, Daily maximum 8-hour average Ozone concentration Prediction Date: 22 August 2001 Location==> City: Hernando County: DeSoto State: MS ******************************************Input Data********************************************* Climatological Data: Average Maximum Minimum Air Temperature, °C 29.7 35.0 24.4 (°F) (85.5) (95.0) (75.9) Wind Speed, m/s 3.2 4.6 0 (mi/h) (7.2) (10.3) (0.0) Wind Direction, degrees 215 ......................................................................... Total Precipitation, mm/day 0 (0.00 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 1 Solar Radiation, Watt-hours/sq m 6,558 (564 Langleys/day) --------------------------------------------------------------------------------------- Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 164,447 73,323 155,985 393,755 Average Car Speed, km/h 56 64 88 (mi/h) (35) (40) (55) Average Truck Speed, km/h 48 56 88 (mi/h) (30) (35) (55) Percentage of Car 93 90 75 Percentage of Truck 7 10 25 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.221 0.096 0.191 Total NOx Emissions, U.S. tons/day 0.440 0.209 0.588 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 1.33 0.061 20.403 Total NOx Emissions, U.S. tons/day 0.22 0.039 56.256 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 12 0 1,416 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_O3_HER_08-22-2001.txt Pollutant: O3, Daily maximum 8-hour average Ozone concentration National Ambient Air Quality Standards (NAAQS): 0.084 ppm Predicted O3, Daily maximum 8-hour average Ozone concentration: 0.059 ppm Predicted Air Quality Index (AQI): 46 Predicted AQI category: Good ---------------------------------------------------------------------------------------


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_NO2_HER_08-22-2001.txt Weather Station: Memphis International Airport Analysis Date: 21 June 2004 Pollutant: NO2, Daily average Nitrogen Dioxide concentration Prediction Date: 22 August 2001 Location==> City: Hernando County: DeSoto State: MS ******************************************Input Data********************************************* Climatological Data: Average Maximum Minimum Air Temperature, °C 29.7 35.0 24.4 (°F) (85.5) (95.0) (75.9) Wind Speed, m/s 3.2 4.6 0 (mi/h) (7.2) (10.3) (0.0) Wind Direction, degrees 215 ......................................................................... Total Precipitation, mm/day 0 (0.00 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 1 Solar Radiation, Watt-hours/sq m 6,558 (564 Langleys/day) --------------------------------------------------------------------------------------- Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 164,447 73,323 155,985 393,755 Average Car Speed, km/h 56 64 88 (mi/h) (35) (40) (55) Average Truck Speed, km/h 48 56 88 (mi/h) (30) (35) (55) Percentage of Car 93 90 75 Percentage of Truck 7 10 25 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.221 0.096 0.191 Total NOx Emissions, U.S. tons/day 0.440 0.209 0.588 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 1.33 0.061 20.403 Total NOx Emissions, U.S. tons/day 0.22 0.039 56.256 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) * NO2 models do not consider point sources and aviation sources. --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 12 0 1,416 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) * NO2 models do not consider point sources and aviation sources. --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_NO2_HER_08-22-2001.txt Pollutant: NO2, Daily average Nitrogen Dioxide concentration No National Ambient Air Quality Standards (NAAQS) for short-term NO2 concentration NAAQS for annual average NO2 concentration 0.053 ppm Predicted NO2, Daily average Nitrogen Dioxide concentration: 0.011 ppm No Air Quality Index (AQI) available. No AQI category available. ---------------------------------------------------------------------------------------


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_O3_OXF_07-11-2001.txt Weather Station: Goodwin Creek Analysis Date: 21 June 2004 Pollutant: O3, Daily maximum 8-hour average Ozone concentration Prediction Date: 11 July 2001 Location==> City: Oxford County: Lafayette State: MS ******************************************Input Data********************************************* Climatological Data: Average Maximum Minimum Air Temperature, °C 27.2 34.8 21.2 (°F) (81.0) (94.6) (70.2) Wind Speed, m/s 0.9 5.6 0 (mi/h) (2.0) (12.5) (0.0) Wind Direction, degrees 194.9 ......................................................................... Total Precipitation, mm/day 17.5 (0.69 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 1 Solar Radiation, Watt-hours/sq m 7,302 (628 Langleys/day) --------------------------------------------------------------------------------------- Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 159,646 91,535 38,503 289,684 Average Car Speed, km/h 56 64 88 (mi/h) (35) (40) (55) Average Truck Speed, km/h 48 56 88 (mi/h) (30) (35) (55) Percentage of Car 99 92.3 92.3 Percentage of Truck 1 7.7 7.7 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.464 0.277 0.096 Total NOx Emissions, U.S. tons/day 1.055 0.636 0.259 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 0 1.097 0.978 Total NOx Emissions, U.S. tons/day 0.325 0.933 4.418 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 38 0 168 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_O3_OXF_07-11-2001.txt Pollutant: O3, Daily maximum 8-hour average Ozone concentration National Ambient Air Quality Standards (NAAQS): 0.084 ppm Predicted O3, Daily maximum 8-hour average Ozone concentration: 0.045 ppm Predicted Air Quality Index (AQI): 35 Predicted AQI category: Good ---------------------------------------------------------------------------------------


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_NO2_OXF_07-11-2001.txt Weather Station: Goodwin Creek Analysis Date: 21 June 2004 Pollutant: NO2, Daily average Nitrogen Dioxide concentration Prediction Date: 11 July 2001 Location==> City: Oxford County: Lafayette State: MS ******************************************Input Data********************************************* Climatological Data: Average Maximum Minimum Air Temperature, °C 27.2 34.8 21.2 (°F) (81.0) (94.6) (70.2) Wind Speed, m/s 0.9 5.6 0 (mi/h) (2.0) (12.5) (0.0) Wind Direction, degrees 194.9 ......................................................................... Total Precipitation, mm/day 17.5 (0.69 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 1 Solar Radiation, Watt-hours/sq m 7,302 (628 Langleys/day) --------------------------------------------------------------------------------------- Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 159,646 91,535 38,503 289,684 Average Car Speed, km/h 56 64 88 (mi/h) (35) (40) (55) Average Truck Speed, km/h 48 56 88 (mi/h) (30) (35) (55) Percentage of Car 99 92.3 92.3 Percentage of Truck 1 7.7 7.7 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.464 0.277 0.096 Total NOx Emissions, U.S. tons/day 1.055 0.636 0.259 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 0 1.097 0.978 Total NOx Emissions, U.S. tons/day 0.325 0.933 4.418 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) * NO2 models do not consider point sources and aviation sources. --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 38 0 168 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) * NO2 models do not consider point sources and aviation sources. --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_NO2_OXF_07-11-2001.txt Pollutant: NO2, Daily average Nitrogen Dioxide concentration No National Ambient Air Quality Standards (NAAQS) for short-term NO2 concentration NAAQS for annual average NO2 concentration 0.053 ppm Predicted NO2, Daily average Nitrogen Dioxide concentration: 0.007 ppm No Air Quality Index (AQI) available. No AQI category available. ---------------------------------------------------------------------------------------


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_O3_NCAT_07-19-2001.txt Weather Station: NCAT Analysis Date: 22 June 2004 Pollutant: O3, Daily maximum 8-hour average Ozone concentration Prediction Date: 19 July 2001 Location==> City: Opelika-Auburn County: Lee State: AL ******************************************Input Data********************************************* Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 1,519 0 0 1,519 Average Car Speed, km/h 40 0 0 (mi/h) (25) (0) (0) Average Truck Speed, km/h 72 0 0 (mi/h) (45) (0) (0) Percentage of Car 6.6 100 100 Percentage of Truck 93.4 0 0 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Climatological Data: Average Maximum Minimum Air Temperature, °C 26.2 34.6 16.9 (°F) (79.2) (94.3) (62.4) Wind Speed, m/s 0.8 5.5 0 (mi/h) (2.0) (12.3) (0) Wind Direction, degrees 243 ......................................................................... Total Precipitation, mm/day 0 (0.00 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 2 Solar Radiation, Watt-hours/sq m 6,849 (589 Langleys/day) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.001 0.000 0.000 Total NOx Emissions, U.S. tons/day 0.041 0.000 0.000 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 0 0 0 Total NOx Emissions, U.S. tons/day 0 0 0 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 0 0 0 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_O3_NCAT_07-19-2001.txt Pollutant: O3, Daily maximum 8-hour average Ozone concentration National Ambient Air Quality Standards (NAAQS): 0.084 ppm Predicted O3, Daily maximum 8-hour average Ozone concentration: 0.058 ppm Predicted Air Quality Index (AQI): 45 Predicted AQI category: Good ---------------------------------------------------------------------------------------


June 2004

****************************AQMAN - Air Quality Modeling and Analysis**************************** Prediction of Ground-Level Ozone and Nitrogen Dioxide Considering Climatological Data, Vehicle Emissions, Point Source Emissions, and Aviation Sources March 2004, Revised June 2004 AQMAN predicts air quality in terms of air pollutant concentration and air quality index (AQI) using climatological data, traffic data, vehicular emissions, point source emissions, and aviation sources. ************************************************************************************************* Result output file: ResultAQ_NO2_NCAT_07-19-2001.txt Weather Station: NCAT Analysis Date: 22 June 2004 Pollutant: NO2, Daily average Nitrogen Dioxide concentration Prediction Date: 19 July 2001 Location==> City: Opelika-Auburn County: Lee State: AL ******************************************Input Data********************************************* Traffic Data: Zone 1 Zone 2 Zone 3 Total (<5 km) (5-8 km) (8-16 km) Traffic Volume, veh/day 1,519 0 0 1,519 Average Car Speed, km/h 40 0 0 (mi/h) (25) (0) (0) Average Truck Speed, km/h 72 0 0 (mi/h) (45) (0) (0) Percentage of Car 6.6 100 100 Percentage of Truck 93.4 0 0 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<5 km radius) Zone 2 (5-8 km radius) Zone 3 (8-16 km radius) (<3 mi) (3-5 mi) (5-10 mi) --------------------------------------------------------------------------------------- Climatological Data: Average Maximum Minimum Air Temperature, °C 26.2 34.6 16.9 (°F) (79.2) (94.3) (62.4) Wind Speed, m/s 0.8 5.5 0 (mi/h) (2.0) (12.3) (0) Wind Direction, degrees 243 ......................................................................... Total Precipitation, mm/day 0 (0.00 inches/day) Cloud Cover (0=Yes, 1=Partial, 2=No) 2 Solar Radiation, Watt-hours/sq m 6,849 (589 Langleys/day) --------------------------------------------------------------------------------------- Vehicular Emissions: Zone 1 Zone 2 Zone 3 (<5 km) (5-8 km) (8-16 km) Total VOC Emissions, U.S. tons/day 0.001 0.000 0.000 Total NOx Emissions, U.S. tons/day 0.041 0.000 0.000 --------------------------------------------------------------------------------------- Point Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Total VOC Emissions, U.S. tons/day 0 0 0 Total NOx Emissions, U.S. tons/day 0 0 0 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) * NO2 models do not consider point sources and aviation sources. --------------------------------------------------------------------------------------- Aviation Sources: Zone 1 Zone 2 Zone 3 (<8 km) (8-16 km) (16-32 km) Aircraft Operations/day 0 0 0 Radial zones are defined by the radial distance from the prediction point. Zone 1 (<8 km radius) Zone 2 (8-16 km radius) Zone 3 (16-32 km radius) (<5 mi) (5-10 mi) (10-20 mi) * NO2 models do not consider point sources and aviation sources. --------------------------------------------------------------------------------------- ************************************AQMAN Prediction Results************************************* Result output file: ResultAQ_NO2_NCAT_07-19-2001.txt Pollutant: NO2, Daily average Nitrogen Dioxide concentration No National Ambient Air Quality Standards (NAAQS) for short-term NO2 concentration NAAQS for annual average NO2 concentration 0.053 ppm Predicted NO2, Daily average Nitrogen Dioxide concentration: 0.011 ppm No Air Quality Index (AQI) available. No AQI category available.

---------------------------------------------------------------------------------------


June 2004

air quality project - ncrst-sepp home page

Documents