Chapter 11 Air Quality Oct 15 - Purdue Universityce561/classnotes/Chapter 11.pdfAlthough the figures...

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Chapter 11 Air Quality Impacts of Transportation Improvements

Module 4: ENVIRONMENTAL IMPACTS

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Topics: Air Quality Sources and Trends, Adverse Impacts of Air Pollution, Factors Affecting Vehicle Pollutant Emissions and Concentrations, Estimation of Pollutant Emissions and Concentrations, Methodology for Estimating Air Quality Impacts of Transportation Investments, Mitigation of Air Pollution, Air Quality Standards, Air Quality Legislation and Regulations.

11.1 INTRODUCTION

Air pollution constitutes a serious threat to the welfare of humans and the integrity of the earth’s

environment. Air pollution is not confined to local effects (urban and industrial centers) or regional effects

(trans-boundary transport of pollutants) but also has global repercussions in the form of greenhouse effects and

ozone layer depletion. Mobile sources, particularly motor vehicles, are a major cause of air pollution. In the new

millennium, the 500-million global automobile population affords increased mobility and enhanced quality of

life, but at significant cost in air pollution. Motor vehicles emit carbon monoxide, nitrogen oxides, small

particulate matter and other toxic substances cause health problems when inhaled. The adverse effects of

increasing vehicle use on the environment had initially been manifest mostly in the urban areas, but have

subsequently spread to forests, lake and rivers. The contribution of motor vehicle use to global warming has

generated much concern as anthropogenic impacts on the upper atmosphere become more and more evident. In

an effort to reduce air pollution from mobile sources, emission rates from the automobiles have been regulated

by legislation in some industrialized countries and also in some developing countries. Long term measures to

reduce and maintain vehicle pollution at acceptable levels will require a set of strategies including legislation and

enforcement, vehicle engine standards, promotion of less polluting modes of transportation, improved fuel

quality, non-hydrocarbon fuels, and transportation planning and traffic management.

This chapter discusses the sources of air pollution and the distribution of such sources, the adverse

impacts of air pollution, factors that affect pollutant emissions and concentrations, and describes how to estimate

pollutant emissions and concentrations using various models. A methodology is presented to estimating the air

quality impacts of transportation investments, and possible measures to mitigate air pollution impacts are

discussed. Finally, air quality standards in various countries are presented, and the various legislations and

regulation that have been passed to control transportation related air pollution, are discussed.

11.2 AIR QUALITY SOURCES AND TRENDS

11.2.1 Types and Sources of Air Pollutants

An air pollutant is described as primary if it is emitted directly into the atmosphere by a stationary or

mobile source, such as carbon monoxide, hydrocarbons, and sulfur oxides. If the pollutant is formed in the

atmosphere as a result of physical and chemical processes such as hydrolysis, oxidation and photochemistry

(such as photochemical oxidant including ozone, and acidic depositions, it is described as a secondary air

pollutant. Carbon dioxide has no direct adverse impact on human health or public welfare, but its build-up

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contributes to the greenhouse effect. Other greenhouse gases such as nitrous oxides, methane,

chlorofluorocarbons (CFC) and ozone also trap heat and thereby contribute to global warming. Natural sources

of air pollution include forest fires and volcanoes, while anthropogenic sources include power generation, fuel

use, incineration, slash-and-burn agricultural practices and transportation.

Spatial Dimensions of Air Quality Problem: (1) Local – Urban Smog (fog with dust & smoke – fine

particles, ozone – photochemical oxidants), (2) Regional – Acid Rain (NOx, SOx in combination with moisture –

acid deposits), and (3) Global – Greenhouse Gases.

Analysis Ranges (scale):

• Micro – Immediate vicinity – CO

• Meso – Entire urban area or air basins – smog/HC

• Macro – Beyond air basins with long-range effects like SOx

• Meta – Greenhouse gases

Effects: human beings, plants and animals, property damage. According to the U.S. EPA, mobile

sources may be responsible for about 650-1900 cancer deaths/year.

Table 11-1 presents a description of the various types of pollutants, their anthropogenic sources, effects

and current typical scale.

Table 11-1: Details of Major Vehicle Pollutants [USEPA, 1999]

Emission Description Source Harmful Effects Scale

Carbon monoxide (CO)

A toxic gas which undermines blood’s ability to carry oxygen.

Engine Human health, Climate change

Very local

Fine particulates (PM10; PM2.5)

Inhaleable particles consisting of bits of fuel and carbon.

Diesel engines and other sources.

Human health, aesthetics.

Local and Regional

Road dust Dust particles created by vehicle movement.

Vehicle use. Human health, aesthetics.

Local

Nitrogen oxides (NOx)

Various compounds. Some are toxic, all contribute to ozone.

Engine Human health, ozone precursor.

Regional

Hydrocarbons (HC) Unburned fuel. Forms ozone. Fuel production and engines.

Human health, ozone precursor.

Regional

Volatile organic hydrocarbons (VOCs).

A variety of organic compounds that form aerosols.

Fuel production and engines.

Human health, ozone precursor.

Local and Regional

Toxics (e.g. ,benzene)

VOCs that are toxic and carcinogenic. Fuel production and engines.

Human health risks Very local

Ozone (O3) Major urban air pollutant resulting from combination of NOx and VOCs

NOx and VOC Human health, plants, aesthetics.

Regional

Sulfur oxides (SOx) Lung irritant, and causes acid rain. Diesel engines Human health risks, acid rain

Regional

Carbon dioxide (CO2)

A byproduct of combustion Fuel production and engines.

Climate change Global

Methane (CH4) A gas with significant greenhouse gas properties.

Fuel production and engines.

Climate change Global

CFCs Durable chemical harmful to the ozone layer and climate.

Older air conditioners.

Ozone depletion Global

Note: Particulate matters include dust & smoke (diesel vehicles emit more than gasoline vehicles), Pd, Cd, Zn, and Cu

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Total air pollution increased from 1960 to 1970, but has been on the decline since, generally reaching

1960 levels in 1995, in spite of a great increase in vehicular travel within this period (Figure 11-1). The emission

of volatile organic compounds and particulate matter have steadily declined over the years, while there has been

a slight increase in nitrogen oxide and sulfur dioxide emissions. Also, lead emissions have dropped sharply

following the development of lead-free gasoline. The drop in emissions over the years is largely due to

governmental intervention through the establishment of increasingly restrictive federal emission standard

standards. For example, between 1980 and 1995, the allowable level of carbon monoxide emissions from a

passenger car has fallen from 7.0 grams per mile to 3.4 grams per mile. Also, vehicles being manufactured today

typically emit 90 percent less carbon monoxide, 70 percent less oxides of nitrogen and 80 to 90 percent less

hydrocarbons over their lifetimes than their vehicles manufactured in the of the 1960s [USEPA, 1993]. Table

11-2 shows the change in the emissions for selected air pollutant, and their concentrations, between 1981 and

2000, for all sources [USEPA, 2000].

While other transport modes contribute to air pollution, automobile travel is the most important source

of such damage to the environment, accounting for 9855 of CO, 83% of NO, 90% of VOCs, 67% of fuel-related

particulate matter, 50% of sulfur dioxides, 885 of lead emissions.

Figure 11-1: Trends in Pollutant Emissions, 1960-1995.

Table 11-2: Changes in Emissions, 1981 – 2000 [USEPA, 2000]

Pollutant % Change in Emissions % Change in Air Quality Pollutant Concentration

-32% (VOC) Ozone1-Hour

+4% (NOX) -21%

PM10 -19% -19%

Carbon Monoxide -18% -61%

Lead -94% -93%

0 2 4 6 8

10 12 14

1955 1965 1975 1985 1995

Year

Emis

sion

(mill

ions

of s

hort

tons

)

Carbon Monoxide (*10)

Nitrogen Oxides

Volatile Organic Compounds Particulate Matter

Sulfur Dioxide

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The Bureau of Transportation Statistics (BTS) estimates that in 1999, transportation contributed about

56% of the carbon monoxide (CO), 37% of the volatile organic carbons (VOC) and 44% of the nitrogen oxides

(NOx) emissions in the United States [BTS, 2001]which is illustrated in the Figure 11-2 below.

Figure 11-2: Transportation Contribution to Air Pollution [BTS, 2001].

Although the figures indicate that tailpipe emission rates measured have declined significantly over the

past few decades, in reality the actual reductions are smaller [Homburger et al., 2001], since the standard tests do

not reflect real driving conditions, and vehicles producing harmful emissions are not measured in these tests

[BTS, 1997]. Also increased vehicle mileage has offset much of the reduction in per-mile emissions, so vehicle

emissions continue to be a major problem. The magnitude of these emissions is heavily dependent on various

traffic flow characteristics such as the average flow speed, frequency and intensity of vehicle acceleration and

deceleration, number of stops and the vehicle operating mode. There are generally two categories of air

pollutants: Criteria air pollutant and greenhouse gases.

11.2.2 Criteria Air Pollution

Criteria Air Pollutants consist of carbon monoxide (CO), nitrogen oxide (NOx), volatile organic

compounds (VOC), particulate matter of size 10 microns or less, particulate matter of size 2.5 microns or less,

sulfur dioxide (SO2), and ammonia (NH3).

Table 11-3 presents the total national emissions of the criteria air pollutants by end-use sector in 1999.

It is seen that transportation accounts for the largest share of CO and NOx emissions, and highway vehicles are

responsible for the largest share of the transportation emission sources for these pollutants.

CO

Transportation56%

Non Transpor

tation44%

VOCs

Transportation37%

Non Transpor

tation63%

NOx

Transportation44%Non

Transportation56%

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Table 11-3: Total National Emissions of the Criteria Air Pollutants by Sector, 1999 (millions of short tons/percentage)

Source: U. S. Environmental Protection Agency, National Air Pollutant Emission Trends 1999

Tables 11-4 and 11-5 present the trends in total emissions of CO from various transportation and

highway sources, respectively. Gasoline powered light vehicles continue to be responsible for the majority of

carbon monoxide emissions from highway vehicles. However, the total pollution from light vehicles in 1999 is

less than half of its 1970 level, even though there were far less such vehicles in 1970.

Table 11-4: Total National Emissions of Carbon Monoxide, 1970–99a, (million short tons)

Source: USDOE, 2002 Transportation CO emission estimation methodology changed in 1970, while all others changed in 1990.

Sector CO NOx VOC PM-10 PM-2.5 SO2 NH3 Highway vehicles 49.99 8.59 5.30 0.3 0.23 0.36 0.26

55.9% 35.1% 29.6% 0.8% 2.7% 1.9% 5.2% Aircraft 1.00 0.16 0.18 0.04 0.03 0.01 0.00

1.1% 0.7% 1.0% 0.1% 0.3% 0.1% 0.1% Railroads 0.12 0.95 0.05 0.03 0.03 0.11 0.00

0.1% 3.9% 0.3% 0.1% 0.4% 0.6% 0.0% Vessels 0.14 1.00 0.04 0.04 0.04 0.27 0.00

0.2% 4.1% 0.2% 0.1% 0.5% 1.4% 0.0% Other off-highway 18.71 3.17 2.19 0.35 0.31 0.54 0.00

20.9% 13.0% 12.2% 1.0% 3.7% 2.9% 0.1%Transportation total 70.3 13.05 7.79 0.72 0.64 1.30 0.27

78.6% 53.4% 43.5% 2.1% 7.6% 6.9% 5.4%Stationary source fuel combustion 5.37 10.19 0.89 1.09 0.78 16.09 0.05

6.0% 41.7% 5.0% 3.1% 9.3% 85.3% 1.0%Industrial processes 3.71 0.80 8.02 0.71 0.38 1.43 0.20

4.1% 3.3% 44.8% 2.0% 4.6% 7.6% 4.0%Waste disposal and recycling total 1.15 0.10 0.43 0.31 0.24 0.04 0.09

1.3% 0.4% 2.4% 0.9% 2.8% 0.2% 1.8%Miscellaneous 8.92 0.33 0.79 31.92 6.35 0.01 4.36

10.0% 1.3% 4.4% 91.9% 75.8% 0.1% 87.8%Total of all sources 89.45 24.45 17.92 34.74 8.38 18.87 4.96

100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%


55.9% 35.1% 29.6% 0.8% 2.7% 1.9% 5.2% Aircraft 1.00 0.16 0.18 0.04 0.03 0.01 0.00

1.1% 0.7% 1.0% 0.1% 0.3% 0.1% 0.1% Railroads 0.12 0.95 0.05 0.03 0.03 0.11 0.00

0.1% 3.9% 0.3% 0.1% 0.4% 0.6% 0.0% Vessels 0.14 1.00 0.04 0.04 0.04 0.27 0.00






1.3% 0.4% 2.4% 0.9% 2.8% 0.2% 1.8%Miscellaneous 8.92 0.33 0.79 31.92 6.35 0.01 4.36


100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%


55.9% 35.1% 29.6% 0.8% 2.7% 1.9% 5.2% Aircraft 1.00 0.16 0.18 0.04 0.03 0.01 0.00

1.1% 0.7% 1.0% 0.1% 0.3% 0.1% 0.1% Railroads 0.12 0.95 0.05 0.03 0.03 0.11 0.00

0.1% 3.9% 0.3% 0.1% 0.4% 0.6% 0.0% Vessels 0.14 1.00 0.04 0.04 0.04 0.27 0.00






1.3% 0.4% 2.4% 0.9% 2.8% 0.2% 1.8%Miscellaneous 8.92 0.33 0.79 31.92 6.35 0.01 4.36


100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Percentof total,

Source category 1970 1980 1990 1995 1998 1999 1999 Highway vehicles 88.03 78.05 58.44 54.81 52.36 49.99 51.3% Aircraft 0.51 0.74 0.90 0.94 1.00 1.00 1.0% Railroads 0.07 0.10 0.12 0.11 0.12 0.12 0.1% Vesselsb 0.02 0.06 0.13 0.13 0.14 0.14 0.1% Other off-highway 11.38 13.59 17.04 19.04 23.87 23.90 24.5%Transportation total 100.00 92.54 76.64 75.04 77.48 75.15 77.1%Stationary fuel combustion total 4.63 7.30 5.51 5.93 5.08 5.32 5.5%Industrial processes total 9.84 6.95 4.77 4.61 3.81 3.80 3.9%Waste disposal and recycling total 7.06 2.30 1.08 1.19 1.14 3.79 3.9%Miscellaneous total 7.91 8.34 11.21 7.30 9.36 9.38 9.6%Total of all sources 129.44 117.43 99.12 94.06 96.87 97.44 100.0%

Percentof total,

Source category 1970 1980 1990 1995 1998 1999 1999 Highway vehicles 88.03 78.05 58.44 54.81 52.36 49.99 51.3% Aircraft 0.51 0.74 0.90 0.94 1.00 1.00 1.0% Railroads 0.07 0.10 0.12 0.11 0.12 0.12 0.1% Vesselsb 0.02 0.06 0.13 0.13 0.14 0.14 0.1% Other off-highway 11.38 13.59 17.04 19.04 23.87 23.90 24.5%Transportation total 100.00 92.54 76.64 75.04 77.48 75.15 77.1%Stationary fuel combustion total 4.63 7.30 5.51 5.93 5.08 5.32 5.5%Industrial processes total 9.84 6.95 4.77 4.61 3.81 3.80 3.9%Waste disposal and recycling total 7.06 2.30 1.08 1.19 1.14 3.79 3.9%Miscellaneous total 7.91 8.34 11.21 7.30 9.36 9.38 9.6%Total of all sources 129.44 117.43 99.12 94.06 96.87 97.44 100.0%

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Table 11-5: Emissions of Carbon Monoxide from Highway Vehicles, 1970–99a, (million short tons)

bLess than 8,500 pounds. cData are not available.

The situation for nitrogen oxides is similar. The transportation sector accounted for over half of the

nation’s nitrogen oxide (NOx) emissions in 1999, with the highway sector responsible of a large share of these

emissions, as seen in Tables 11-6 and 11-7. Heavy diesel-powered vehicles accounted for one-third of NOx

emissions from highway vehicles, while light gasoline vehicles were responsible for nearly 67%.

Table 11-6: Total National Emissions of Nitrogen Oxides, 1970–99a, (million short tons)

Transportation NOX emission estimation methodology changed in 1970, while all others changed in 1990.

Percentof total,

Source category 1970 1980 1990 1995 1998 1999 1999 Highway vehicles 7.39 8.62 7.21 7.96 8.82 8.59 33.8% Railroads 0.50 0.73 0.93 0.99 1.22 1.20 4.7% Other off-highway 1.44 2.80 3.88 4.14 4.32 4.32 17.0%Transportation total 9.32 12.15 12.01 13.08 14.36 14.11 55.5%Stationary fuel combustion total 10.06 11.32 10.89 10.83 10.40 10.03 39.5%Industrial processes total 0.78 0.56 0.80 0.77 0.85 0.85 3.4%Waste disposal and recycling total 0.44 0.11 0.09 0.10 0.10 0.09 0.4%Miscellaneous total 0.33 0.25 0.37 0.27 0.32 0.32 1.3%Total of all sources 20.93 24.38 24.17 25.05 26.02 25.39 100.0%

Percentof total,


Percentof total,


Percentof total,


Percent ofSource category 1970 1975 1980 1985 1990 1995 1999 total, 1999

Light vehicles & motorcycles 64.03 59.28 53.56 49.45 35.00 29.79 27.38 54.8%Light trucksb 16.57 15.77 16.14 18.96 17.12 19.43 16.12 32.2%Heavy vehicles 6.71 7.14 7.19 7.72 5.03 4.10 4.26 8.5%Total 87.31 82.19 76.89 76.13 57.14 53.32 47.76 95.5%

Light vehicles c 0.03 0.02 0.02 0.02 0.03 0.01 0.0%Light trucksb c c 0.00 0.00 0.05 0.01 0.01 0.0%Heavy vehicles 0.72 0.92 1.14 1.24 1.22 1.45 2.22 4.4%Total 0.72 0.95 1.16 1.26 1.30 1.49 2.23 4.5%

Highway vehicle total 88.03 83.13 78.05 77.39 58.44 54.81 49.99 100.0%Percent diesel 0.8% 1.1% 1.5% 1.6% 2.2% 2.7% 4.5%

Diesel powered

Total

Gasoline powered





Diesel powered

Total

Gasoline powered





Diesel powered

Total

Gasoline powered

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Table 11-7: Emissions of Nitrogen Oxides from Highway Vehicles, 1970–99 (million short tons)

bLess than 8,500 pounds. cData are not available.

With regards to volatile organic compounds, the transportation sector accounted for over 45% of total

emissions in 1999, most of which were from highway vehicles, as seen in Tables 11-8 and 11-9. Highway VOC

emissions in 1999 were less than one-half of the 1970 levels. Gasoline-powered vehicles were responsible for

nearly 95% of highway vehicle VOC emissions.

Table 11-8: Total National Emissions of Volatile Organic Compounds, 1970–99 (million short tons)

The EPA's definition of volatile organic compounds excludes methane, ethane, and certain other non-photochemically reactive organic compounds. Transportation VOC emission estimation methodology changed in 1970, while all others changed in 1990.





Diesel powered

Total

Gasoline powered





Diesel powered

Total

Gasoline powered





Diesel powered

Total

Gasoline powered

Percent of total,

Source category 1970 1980 1990 1995 1998 1999 1999 Highway vehicles 12.97 8.98 6.44 5.82 5.44 5.30 29.2% Off-highway 1.88 2.31 2.55 2.70 3.30 3.23 17.8%Transportation total 14.85 11.29 8.99 8.52 8.74 8.53 47.0%Stationary fuel combustion total 0.72 1.05 1.01 1.07 0.86 0.90 5.0%Industrial processes total 12.33 12.10 9.01 9.71 7.88 7.41 40.8%Waste disposal and recycling total 1.98 0.76 0.99 1.07 0.43 0.59 3.2%Miscellaneous total 1.10 1.13 1.06 0.55 0.71 0.72 3.9%Total of all sources 30.98 26.34 21.05 20.92 18.61 18.15 100.0%

Percent of total,


Percent of total,


Percent of total,


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Table 11-9: Emissions of Volatile Organic Compounds from Highway Vehicles, 1970–99 (million short tons)

Compared to other pollutants, the share of particulate matter emitted by transportation is about the least.

In 1999, the transportation sector accounted for only 3% of the nations emissions of particulate matter of size 10

microns or less (Tables 11-10 and 11-11). For the past two decades, diesel-powered vehicles have been

responsible for over one-half of highway vehicle emissions of particulate matter of this size, and of these heavy

diesel-powered vehicles accounted for the major share. A similar distribution is seen for particulate matter of

smaller size (2.5 microns or less).

Table 11-10: Total National Emissions of Particulate Matter PM-10, 1970–99 (million short tons)

Transportation PM emission estimation methodology changed in 1970, while all others changed in 1990.

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999

Light vehicles & motorcycles 9,193 7,248 5,907 5,864 3,692 3,029 2,911 55.0%Light trucksb 2,770 2,289 2,059 2,425 2,016 2,135 1,722 32.5%Heavy vehicles 743 657 611 716 405 325 375 7.1%Total 12,706 10,194 8,577 9,005 6,113 5,489 5,008 94.5%

Light vehicles c 15 8 8 9 12 3 0.1%Light trucksb c c 2 2 24 5 2 0.1%Heavy vehicles 266 335 392 360 298 309 284 5.4%Total 266 350 402 370 331 326 289 5.5%

Highway vehicle total 12,972 10,545 8,979 9,376 6,443 5,816 5,297 100.0%Percent diesel 2.1% 3.3% 4.5% 3.9% 5.1% 5.6% 5.5%

Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,


Percentof total,


Percentof total,


Percentof total,


Percentof total,


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Table 11-11: Emissions of Particulate Matter PM-10 from Highway Vehicles, 1970–99 (million short tons)

Table 11-12: Total National Emissions of Lead, 1970–99 (million short tons)

Source: U. S. Environmental Protection Agency, National Air Pollutant Emission Trends, 1900-1998, 2000.

With regard to lead, the transportation sector (highway vehicles in particular) has long been identified

as a dominant source of lead emissions (Table 11-12). In 1978, regulatory action was taken to reduce the lead

content of all gasoline fuels, and very significant gains have been made in this direction. In 1999, transportation

accounted for 13% of lead emissions, and most of this is attributed to off-highway fuel use.

11.2.3 Air Pollutants Associated with Greenhouse Effects and Global Warming

In the past two decades, it has been proven that air pollution is associated with global warming, with

consequent damage to global ecology and extreme weather patterns. The impact of air pollution has therefore

taken a new and more urgent perspective. Global warming is caused by the emission of greenhouse gases such as

carbon dioxide, methane, nitrous oxides, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Not all

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999

Light vehicles & motorcycles 225 207 120 77 57 55 59 20.0%Light trucksb 70 72 55 43 37 41 36 12.2%Heavy vehicles 13 15 15 14 10 9 12 4.1%Total 308 294 190 134 104 105 107 36.3%


Highway vehicle total 443 471 397 363 349 300 295 100.0%Percent diesel 30.7% 37.4% 52.4% 62.8% 70.2% 64.7% 63.7%

Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999




Diesel powered

Total

Gasoline powered

Percentof total,

Source category 1970 1975 1980 1985 1990 1995 1999 1999 Highway vehicles 171.96 130.21 60.50 18.05 0.42 0.02 0.02 0.5% Off-highway 9.74 6.13 4.21 0.92 0.78 0.54 0.52 12.3%Transportation total 181.70 136.34 64.71 18.97 1.20 0.56 0.54 12.8%Stationary source fuel combustion 10.62 10.35 4.30 0.52 0.50 0.49 0.50 11.9%Industrial processes 26.36 11.38 3.94 2.53 2.48 2.27 2.35 55.9%Waste disposal and recycling total 2.20 1.60 1.21 0.87 0.80 0.60 0.81 19.4%

Total of all sources 220.87 159.66 74.15 22.89 4.98 3.93 4.20 100.0%

Percentof total,



Percentof total,



Percentof total,



Percentof total,



Percentof total,



387

air pollutants are greenhouse gases. Table 11-13 shows the estimated emissions of each greenhouse gas. An

emission of carbon dioxide, which is the dominant green house gas, was 17% higher in 2000 relative to 1990.

Table 11-13 shows the estimated levels of emissions of greenhouse gases between 1990 and 2000. In the United

States, 98% of carbon dioxide emissions are from petroleum fuels, particularly, motor gasoline.

Table 11-13: Estimated U.S. Emissions of Greenhouse Gases, 1990–2000

U.S. Department of Energy, Energy Information Administration, Emissions of Greenhouse Gases in the United States, 2000, Washington, DC, November 2001, Tables ES1 and ES2. aGases that contain carbon can be measured either in terms of the full molecular weight of the gasor just in terms of their carbon content. bBased on global warming potential. cHCFC = hydrochlorofluorocarbons. PFC-perfluorocarbons. SF6=sulfur hexafluoride.

Table 11-14 presents the carbon dioxide emissions from fossil fuel consumption in the U.S., by end-use

sector. The transportation sector accounts for approximately one-third of carbon dioxide emissions.

Table 11-14: U.S. Carbon Dioxide Emissions from Fossil Energy Consumption, by End-Use Sector, 1985-2000 (million metric tons of carbon)

Greenhouse gas Unit of measurea 1990 1995 1999 2000Carbon dioxide million metric tons of gas 4,969.4 5,273.5 5,630.7 5,805.5

million metric tons of carbon 1,355.0 1,438.0 1,536.0 1,583.0Methane million metric tons of gas 31.7 31.1 28.7 28.2

million metric tons of carbon (gwp)b 199.0 195.0 180.0 177.0Nitrous oxide million metric tons of gas 1.2 1.3 1.2 1.2

million metric tons of carbon (gwp)b 94.0 101.0 100.0 99.0HFCs, PFCs, and SF6

c million metric tons of carbon (gwp)b30.0 35.0 45.0 47.0

End use sector 1985 1990 1995 1996 1997 1998 1999 2000

Residential 245.8 257 277.9 229.9 292.8 293.7 298.8 313.4Commercial 189.6 210.3 224.6 233.1 245.4 250.4 253.1 267.8Industrial 424.1 452.7 461.1 476.7 481.5 469.5 465.8 465.7Transportation 384.4 431.8 457.8 468.9 473.6 481.5 499.4 514.8 Percentage 30.9% 32.0% 32.2% 31.9% 31.7% 32.2% 32.9% 33.0%Total energy 1,243.9 1,351.7 1,421.3 1,471.9 1,493.3 1,495.2 1,517.1 1,561.7

End use sector 1985 1990 1995 1996 1997 1998 1999 2000

Residential 245.8 257 277.9 229.9 292.8 293.7 298.8 313.4Commercial 189.6 210.3 224.6 233.1 245.4 250.4 253.1 267.8Industrial 424.1 452.7 461.1 476.7 481.5 469.5 465.8 465.7Transportation 384.4 431.8 457.8 468.9 473.6 481.5 499.4 514.8 Percentage 30.9% 32.0% 32.2% 31.9% 31.7% 32.2% 32.9% 33.0%Total energy 1,243.9 1,351.7 1,421.3 1,471.9 1,493.3 1,495.2 1,517.1 1,561.7

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Table 11-15: U.S. Carbon Dioxide Emissions from Energy Use in the Transportation Sector, 1980-2000(million metric tons of carbon)

U.S. Department of Energy, Energy Information Administration, Emissions of Greenhouse Gases, in the United States, 2000, Washington, DC, November 2001, Table 8, aLiquified petroleum gas. bShare of total electric utility carbon dioxide emissions weighted by sales to thetransportation sector.

Global Warming Potentials (GWP) have been developed to allow comparison of the ability of each

greenhouse gas to trap heat in the atmosphere relative to carbon dioxide. After extensive research, it has been

concluded that the individual contribution of various gases on global warming are too complex to be precisely

summarized by a single number. Also, as further understanding is gained through continual research, estimates

of such contributions are revised frequently. In spite of these obstacles, approximations have been developed for

such relative contributions, through research, as shown in Table 11-16.

What Causes Transportation Air Pollution (Greenhouse gases)

• CO & HC – incomplete combustion

• HC – also from evaporation

• NOx – oxygen and nitrogen in the air under combustion process

• Cold Start – “quenching” – HC

• Hot Soak – at the end of a trip

• Diurnal Breathing – HC only

• Running Exhaust – speed and trip length sensitive

– role of running exhaust emissions will continue to decrease for CO, but particularly

for HC

Fuel Emissions Percentage Emissions Percentage Emissions Percentage

Motor gasoline 238.1 62.9% 260.5 60.3% 301.5 58.6%LPGa 0.3 0.1% 0.4 0.1% 0.2 0.1%Jet fuel 42.0 11.1% 60.1 13.9% 68.5 13.3%Distillate fuel 55.3 14.6% 75.7 17.5% 106.6 20.7%Residual fuel 30.0 7.9% 21.9 5.1% 23.1 4.5%Lubricants 1.8 0.5% 1.8 0.4% 1.8 0.3%Aviation gas 1.2 0.3% 0.8 0.2% 0.7 0.1%Total 368.7 97.4% 421.2 97.5% 502.5 97.6%

Natural gas 94 2.5% 9.8 2.3% 11.4 2.2%Electricityb 0.3 0.1% 0.7 0.2% 0.9 0.2%Total 378.4 100.0% 432.8 100.0% 514.8 100.0%

Other energy

1980 1990 2000

PetroleumFuel Emissions Percentage Emissions Percentage Emissions Percentage

Motor gasoline 238.1 62.9% 260.5 60.3% 301.5 58.6%LPGa 0.3 0.1% 0.4 0.1% 0.2 0.1%Jet fuel 42.0 11.1% 60.1 13.9% 68.5 13.3%Distillate fuel 55.3 14.6% 75.7 17.5% 106.6 20.7%Residual fuel 30.0 7.9% 21.9 5.1% 23.1 4.5%Lubricants 1.8 0.5% 1.8 0.4% 1.8 0.3%Aviation gas 1.2 0.3% 0.8 0.2% 0.7 0.1%Total 368.7 97.4% 421.2 97.5% 502.5 97.6%

Natural gas 94 2.5% 9.8 2.3% 11.4 2.2%Electricityb 0.3 0.1% 0.7 0.2% 0.9 0.2%Total 378.4 100.0% 432.8 100.0% 514.8 100.0%

Other energy

1980 1990 2000

Petroleum

389

Table 11-16: Numerical Estimates of Global Warming Potentials Compared With Carbon Dioxide (kilogram of gas per kilogram of carbon dioxide)

U.S. Department of Energy, Energy Information Administration, Emissions of Greenhouse Gases in the United States 2000, Washington, DC, November 2001, Table 3. Original source: Intergovernmental Panel on Climate Change.

The typical uncertainty for global warming potentials is estimated by the Intergovernmental Panel on Climate Change + 35%.

aNo single lifetime can be defined for carbon dioxide due to different rates of uptake by different removal processes. bHydrofluorocarbons. cPerfluorocarbons

11.3 AN ANALYSIS OF AIR QUALITY EMISSIONS

Some Definitions

Emissions: Emission rate is defined as the amount of a particular pollutant type that is discharged into the

atmosphere. The magnitude of emissions depends on the number of emission sources, the diversity of source

types, the nature and scale of activity at the polluting source and emission characteristics. For instance, the

emission characteristics of motor vehicles worsen with altitude due to thinness of the air and subsequently,

inefficient combustion.

Mobile Emissions: A source of air pollution can be defined as one that is capable of moving from one place to

another under its own power. From this definition, a motorized vehicle is a mobile source, and the emissions

from it are described as mobile emissions. Mobile emissions typically consist of a variety of pollutants each of

which have various local, regional or global impacts on air quality.

LifetimeGas (years) 20 years 100 years 500 years

Carbon Dioxide 5-200a 1 1 1Methane 12 62 23 7Nitrous Oxide 114 275 296 156HFCsb, PFCsc, and Sulfur HFC-23 260 9,400 12,000 10,000 HFC-125 29 5,900 3,400 1,100 HFC-134a 14 3,300 1,300 400 HFC-152a 1 410 120 37 HFC-227ea 33 5,600 3,500 1,100 Perfluoromethane (CF4) 50,000 3,900 5,700 8,900 Perfluoroethane (C2F6) 10,000 8,000 11,900 18,000

Sulfur hexafluoride (SF6) 3,200 15,100 22,200 32,400

Global warming potentialdirect effect for time horizons ofLifetime

Gas (years) 20 years 100 years 500 yearsCarbon Dioxide 5-200a 1 1 1Methane 12 62 23 7Nitrous Oxide 114 275 296 156HFCsb, PFCsc, and Sulfur HFC-23 260 9,400 12,000 10,000 HFC-125 29 5,900 3,400 1,100 HFC-134a 14 3,300 1,300 400 HFC-152a 1 410 120 37 HFC-227ea 33 5,600 3,500 1,100 Perfluoromethane (CF4) 50,000 3,900 5,700 8,900 Perfluoroethane (C2F6) 10,000 8,000 11,900 18,000

Sulfur hexafluoride (SF6) 3,200 15,100 22,200 32,400

Global warming potentialdirect effect for time horizons of

390

Emission Factors: As there are a large number of emission sources as well as a large diversity of each source

type, it is considered impractical to determine the level of emissions on a source-by-source basis using field

measurements. It is therefore common practice to determine pollutant emissions in an area by making

generalized estimates of emissions from each of the source types and for each pollutant type. An emission factor

is an average estimate of the rate at which a pollutant is released into the atmosphere as a result of some activity

(such as motor vehicle operation) divided by the level of the activity (such as VMT for motor vehicles).

Concentration: Pollutants emitted from their sources disperse into the atmosphere, where they are transformed or

diluted. The resultant amount (mass or volume) of a pollutant per unit volume of air is described as the

concentration of the pollutant in the air. The atmospheric concentration of a pollutant is affected by the

magnitude of emissions, topographical features, altitude, meteorological conditions, and physical mixing and

chemical reactions in the atmosphere. Concentration levels are typically associated with harmful effects of the

air pollutants.

11.4 FACTORS AFFECTING POLLUTANT EMISSION FROM MOTOR VEHICLES

The magnitude of emissions depends on the number of sources and emitting characteristics. The major

factors that affect the level of vehicle emissions can be generally classified into four categories: which are travel-

related factors, driver-related factors, highway-related factors, and vehicle related factors (Figure 11-3).

Figure 11-3: Factors Affecting Vehicle Emissions.

Factors Affecting Emission of Vehicle Pollutants

Travel Related

Driver Related

Highway Related

Vehicle Related

Environmental

Vehicle Operating

Speed

Speed Variation

Hot Start

Cold Start

Hot Stabilized

Geometric/Traffic

Pavement Roughness

Age and Mileage

Maintenance Condition

Weight and Size

Engine Power

Fuel Type

Ambient Temperature

391

0 5

10 15 20 25 30 35 40

CO HC NOx

Bas

ic E

mis

sion

Rat

es (g

ram

s/m

ile)

Cold Starts

Hot Starts

Stabilized

Travel Related Factors

Travel-related factors include vehicle operating modes, speeds, and accelerations and decelerations.

Operating Mode:

Three operating modes are considered in estimating exhaust emissions: cold start, hot start, and hot

stabilized modes. Emission rates differ significantly from one mode to the other. The Environmental Protection

Agency defines a cold start as any start that occurs four hours or later following the end of the preceding trip for

non-catalyst-equipped vehicles, and one hour or later following the end of the preceding trip for catalyst-

equipped vehicles. Hot starts are those that occur less than four hours after the end of the preceding trips for non-

catalyst-equipped vehicles, and less than one hour after the end of the preceding trip for catalyst equipped

vehicles. The time between the start and the end of the trip is called the hot-stabilized period. The difference

between a cold start and a hot start mode is the duration of time that the engine is turned off before being

restarted. Emission rates of HC and CO are higher during cold start than during hot start and are lowest during

hot stabilized operation. The differences in vehicle emissions between operating modes are caused primarily by

two factors, namely: air to fuel ratio and the catalytic converters. In cold start mode, the catalytic emission

control system is not fully functional and the low air to fuel ratio gives the highest HC and CO emissions during

this mode of operation. The emission of NOx is however low during cold start modes. Figure 11-4 illustrates the

basic emission rates for different operating modes for the MOBILE 5A emission model [USDOT, 1994].

Figure 11-4: Basic Emission Rates for Vehicle Operating Modes [USDOT, 1994].

Average Speed:

Vehicle speed, acceleration, and load on the engine have a significant impact on the level of emissions.

Estimates from the state-of-practice emission models indicate that HC and CO emissions are highest at low-

speeds (Figure 11-5). The emissions of these pollutants are low at low speeds, decrease with increasing speed to

their minimum rates at intermediate speeds, and then rise again with increasing speed at high speeds, albeit to

levels lower than emissions at low speeds. NOx emissions however follow a different trend; they rise from the

392

low emission levels at a low-speed to the highest levels at high speeds [USEPA, 2001]. The smoothness and

consistency of vehicle speed, which can be explained by acceleration and heavily affected by traffic conditions

and driving behavior, contribute to more emissions Sharp acceleration at a high speed and heavy load on the

engine will require more fuel to feed into the engine, thus, generate more HC and CO emissions; there is little

effect on NOx emissions.

Figure 11-5 (a): Emission Rates at Various Speeds [USEPA, 1996].

Figure 11-5 (b): EPA Mobile.

Driver-Related Factors:

Driver behavior varies greatly by individual and by traffic conditions. For example, aggressive drivers

may exert sharp accelerations more frequently than their less aggressive counterparts in congested conditions.

Abrupt sharp accelerations (change of speed in a straight line, or same speed during curve negotiation) impose

heavy loads on the engine and thus result in high emission levels.

Emissions Rates for CO

0 5

10

15 20 25 30

35 40 45

0 10 20 30 40 50 60 70Vehicle Speed (mph)

Em

issi

ons (

g/m

ile)

CO

Emissions Rates for VOC and NOx

0

0.5

1

1.5

2

2.5

3

3.5

4

0 10 20 30 40 50 60 70Vehicle Speed (mph)

Em

issi

ons (

g/m

ile)

VOC NOx

393

Highway-Related Factors:

Highway-related factors are concerned with the physical characteristics of highways. These factors

include geometric features of a highway (such as the grade, the existence of ramps and signals) and the

roughness of a pavement surface. It has shown that the level of traffic signal coordination can result in up to 50

percent reduction in emissions when only through-traffic along a single direction was modeled [Rakha et al.,

1999].

Vehicle-Related and Other Factors:

Vehicle-related and other factors also influence vehicle emission levels. The emissions levels of a

vehicle relates to characteristics such as its age, mileage, maintenance condition, weight, size, and engine power.

Older model vehicles usually produce more emissions than a newer vehicle fleet and generally heavier and larger

vehicles emit more pollutants than lighter and smaller vehicles [Ding, 2000]. The type of fuel used also has a

significant impact on emission levels. Gasoline engines operate at different temperatures and pressures differ

from those of diesel engines. Also combustion methods for the two engine types differs, therefore pollutant

emissions rates from gasoline and diesel operated vehicles differ significantly.

Environmental Factors:

Environmental factors such as ambient temperature also influence the emission rates of motor vehicles.

At colder temperatures, a longer time is required to warm up the engine and the emission control systems, thus

increasing the level of cold start emissions. On the other hand, evaporative emissions increase at higher

temperatures, due to the increased fuel evaporation rate. Figure 11-6 below illustrates the effect of temperature

on the fleet-average emission rate for exhaust HC, CO, and NOx using the MOBILE 5A emission model

[USDOT, 1994].

Figure 11-6: Basic Emission Rates at Various Temperatures [USDOT, 1994].

Emissions Rates for CO

20 25 30 35 40 45 50

0 20 40 60 80 100

Temperature (Degrees F)

Em

issi

ons (

g/m

i)

CO

Emissions Rates for HC and NOx

1.5

2

2.5

3

3.5

4

4.5

0 20 40 60 80 100

Temperature (Degrees F)

Em

issi

ons (

g/m

i)

HC NOx

394

11.5 FACTORS AFFECTING POLLUTANT TRANSPORT AND CONCENTRATION

Meteorological Factors: The typical medium of transfer of pollutants from their emission sources to receptors

(humans, vegetation, etc.) is the atmosphere. Atmospheric conditions, expressed in terms of temperature,

atmospheric stability precipitation, wind speed and direction, humidity, and intensity of solar radiation [Bellomo

and Liff, 1984], therefore govern the temporal (hourly, daily and seasonal) and spatial variation of the

transmission and concentration of such pollutants.

Atmospheric Stability: The atmosphere tends to either suppress or enhance vertical motion within its domain: a

stable atmosphere (associated with motion suppression) generally increases pollutant concentrations while an

unstable atmosphere tends to minimize the concentration of pollutants. Atmospheric stability is related to the

change of temperature or wind speed or direction with height (also referred to as temperature gradient and wind

shear, respectively). Thermal inversion is a phenomenon characterized by increase of temperature with height (a

reversal of the normal condition) leading to the entrapment of cold air layers by a higher layer of warm air. Such

conditions lead to the accumulation of pollutants in the cold air layer. Also, the movement of air near the earth's

surface is resisted by frictional effects proportional to the surface roughness. Hence wind speeds are greater

farther from the ground surface [Wark et al., 1981]. Greater the wind speeds will result in a higher dispersion of

air pollutants

Intensity of Solar Radiation: Ceiling height, which is defined as the height above which relatively rigorous

vertical mixing occurs, varies by day and also by season. During summer daylight hours, the ceiling height may

reach several thousand feet. On the other hand, on a winter night, the ceiling height is low, reaching a few

hundred feet. Night-time and winter conditions are therefore associated with a relatively small volume of air

available for dispersion and are subsequently characterized by a much higher concentration of pollutants.

Topography and Urban Spatial Form: The topography of a region affects the wind speed and direction, and

atmospheric temperature, through air drainage and radiation, and therefore affect the dispersion (and

concentration) of pollutants. Air pollution problems are aggravated in some metropolitan areas by the street

“canyon” effect created by tall buildings.

For any city, an evaluation of the magnitude and causes of air pollution made complex by the diversity

of polluting activities, meteorological conditions, topographic features, and urban spatial forms.

11.6 ESTIMATION OF POLLUTANT EMISSIONS AND CONCENTRATIONS

A necessary prelude to the evaluation of the impacts of transportation improvements on air quality is

the estimation of the change in emissions and the resulting change in pollutant concentrations as a result of

increases in the average speed of vehicles, increases in motor vehicle trips and increases in vehicle miles of

395

travel (VMT) due to these improvements. Predicting the levels of vehicle emissions is considered a rather

difficult task as relevant factors such as ambient air temperature and the journey length (proportion of the trip

with hot engines) are beyond the control of vehicle manufacturers and drivers [Bennett et al., 2001]. However,

researchers have proposed several models that are used to estimate vehicle emissions. These emission models

can be grouped into four categories namely, speed based, modal, microscopic and fuel based emission models

[Ding, 2000]. Speed based models predict mobile emissions using the average speed of vehicles in the traffic

stream. The EPA MOBILE6.0 model is one such model.

11.6.1 The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model

The energy in greenhouse of the most recent version (Beta Version 1.6) of the GREET model are

shown in Table 11-17. The GREET model estimates the full fuel cycle emissions and energy use associated with

selected transportation fuels and advanced transportation technologies for light duty vehicles. It calculates fuel-

cycle emissions of the three dominant greenhouse gases: carbon dioxide, methane, and nitrous oxide, and five

criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, sulfur dioxides, and

particulate matter of less 10 microns or less). The model also calculates the total fuel cycle energy consumption,

fossil fuel consumption, and petroleum consumption associated with various transportation fuels. The GREET

model includes the fuel cycles illustrated as Figure 11-7.

396

Figure 11-7: Fuel Cycles Considered in GREET Model [USDOE, 2002]

Petroleum

Natural Gas

Coal

Uranium

Renewable Energy Sources (Hydropower, solar Energy, Wind)

Agricultural Sources (excluding Soybeans) (Corn, Woody Biomass, herbaceous biomass)

Soybeans

Landfill Gases

Conventional Gasoline

Reformulated Gasoline

Conventional Diesel

Reformulated Diesel

Liquefied Petroleum Gas

Electricity

Compressed Natural Gas

Methanol

Fischer-Tropisch Diesel

Dimethyl Ether

Hydrogen

Ethanol

Biodiesel

Liquefied Natural Gas

397

Table 11-17: Fuel-Cycle Energy and Greenhouse Gas Emission Changes of Alternative and Advanced Vehicle/Fuel Systems

MPG - GGE Total energy Fossil fuels Petroleum CO2 CH4 N2O GHGsGV: FRFG (btu/mile or grams/mile) 2410.0% 589100.0% 587200.0% 466500.0% 44600.0% 68.4% 3.0% 46900.0%

CNGV: NA NG 2410.0% -9.5% -9.7% -99.5% -26.8% 111.0% -49.6% -23.1%CNGV: NNA NG 2410.0% 1.2% 1.0% -99.5% -18.5% 216.8% -46.4% -13.1%

Propane vehicle 2530.0% -16.2% -16.0% -59.1% -20.1% -21.9% -3.1% -19.8%M90 MeOHV: NA NG 2530.0% 14.6% 14.9% -79.1% -5.7% -9.5% 0.5% -5.7%M90 MeOHV: NNA NG 2530.0% 16.3% 16.6% -79.9% -4.3% 8.5% 1.3% -3.9%E90 EtOHV: corn 2530.0% 10.4% -45.3% -75.0% -41.0% -27.6% 448.3% -31.0%

E90 EtOHV: cellulosic biomass 2530.0% 53.8% -79.5% -74.9% -88.9% -63.3% 474.8% -77.1%GI SI HEV: FRFG 3380.0% -28.6% -28.6% -28.6% -28.6% -25.9% -1.6% -28.0%GC SI HEV: FRFG 5410.0% -40.7% -43.1% -57.7% -40.1% -39.4% -29.2% -39.9%

CIDIV: LS diesel 2960.0% -21.7% -21.7% -10.4% -17.1% -40.4% -42.3% -18.3%CIDIV: FTD, NA NG 2960.0% 8.7% 9.0% -99.0% -13.4% -40.3% -44.9% -14.8%CIDIV: FTD, NNA NG 2960.0% 10.4% 10.8% -98.5% -12.1% -24.9% -30.0% -12.7%

CIDIV: BD20 2960.0% -19.0% -19.1% -25.5% -28.4% -44.2% -34.1% -29.0%GI CIDI HEV: LS diesel 4100.0% -43.6% -43.6% -35.4% -40.2% -56.6% -43.3% -40.8%GC CIDI HEV: LS diesel 5770.0% -47.2% -49.6% -59.7% -44.6% -56.3% -57.0% -45.2%

EV: US mix 8440.0% -45.1% -52.5% -98.4% -43.5% -48.8% -84.1% -44.5%EV: NE US mix 8440.0% -46.2% -55.6% -97.5% -53.4% -36.3% -87.1% -53.5%EV: CA mix 8440.0% -50.6% -61.9% -99.7% -61.5% -43.2% -88.6% -61.5%

FCV: G.H2, central plant, NA NG 5070.0% -35.6% -36.6% -99.2% -47.7% -50.1% -94.9% -48.7%FCV: G.H2, central plant, NNA NG 5070.0% -30.0% -31.0% -99.3% -42.7% -4.3% -93.2% -42.6%

FCV: G.H2, refueling station, NA NG 5070.0% -32.9% -33.2% -99.7% -46.9% -36.2% -94.8% -47.5%FCV: G.H2, refueling station, NNA NG 5070.0% -28.4% -28.6% -99.6% -43.3% -3.3% -93.3% -43.2%FCV: G.H2, central electrolysis, renewables 5070.0% -37.6% -91.9% -99.5% -90.6% -89.5% -97.7% -90.7%

FCV: G.H2, station electrolysis, US generation mix 5070.0% 40.5% 22.4% -96.3% 44.7% 62.6% -64.9% 43.3%FCV: L.H2, central plant, NA NG 5070.0% -11.6% -11.4% -99.3% -28.8% -25.1% -86.2% -29.7%FCV: L.H2, central plant, NNA NG 5070.0% -8.5% -8.4% -99.0% -25.4% -21.6% -85.5% -26.4%

FCV: L.H2, refueling station, NA NG 5070.0% 12.4% 6.0% -98.4% -1.3% 6.5% -84.3% -2.5%FCV: L.H2, refueling station, NNA NG 5070.0% 19.5% 12.9% -98.4% 2.4% 81.3% -82.7% 2.9%

FCV: L.H2, central electrolysis, renewables 5070.0% -44.0% -98.7% -99.4% -98.8% -98.8% -99.6% -98.8%FCV: L.H2, station electrolysis, US generation mix 5070.0% 105.3% 61.7% -95.2% 91.1% 114.7% -53.7% 89.2%

FCV: MeOH, NA NG 42.2 -28.70% -28.50% -98.50% -43.50% -46.70% -77.40% -44.30%FCV: MeOH, NNA NG 42.2 -27.40% -27.20% -98.10% -42.50% -33.50% -76.70% -42.90%

FCV: gasoline 37.4 -35.50% -35.50% -35.50% -35.50% -39.30% -77.40% -36.30%FCV: cellulosic EtOH 39.3 19.90% -96.90% -94.40% -105.10% -91.80% 338.70% -96.00%FCV: CNG, NA NG 37.4 -41.60% -41.70% -99.70% -52.70% 15.00% -79.10% -51.10%FCV: CNG, NNA NG 37.4 -34.70% -34.80% -99.70% -47.40% 85.20% -77.00% -44.60%FCV: FT naphtha, NNA NG 37.4 -10.30% -10.00% -98.70% -32.70% -38.80% -79.90% -33.70%FCV: crude naphtha 37.4 -38.60% -38.60% -36.40% -41.30% -41.80% -78.60% -41.90%

MPG - GGE Total energy Fossil fuels Petroleum CO2 CH4 N2O GHGsGV: FRFG (btu/mile or grams/mile) 2410.0% 589100.0% 587200.0% 466500.0% 44600.0% 68.4% 3.0% 46900.0%

CNGV: NA NG 2410.0% -9.5% -9.7% -99.5% -26.8% 111.0% -49.6% -23.1%CNGV: NNA NG 2410.0% 1.2% 1.0% -99.5% -18.5% 216.8% -46.4% -13.1%

Propane vehicle 2530.0% -16.2% -16.0% -59.1% -20.1% -21.9% -3.1% -19.8%M90 MeOHV: NA NG 2530.0% 14.6% 14.9% -79.1% -5.7% -9.5% 0.5% -5.7%M90 MeOHV: NNA NG 2530.0% 16.3% 16.6% -79.9% -4.3% 8.5% 1.3% -3.9%E90 EtOHV: corn 2530.0% 10.4% -45.3% -75.0% -41.0% -27.6% 448.3% -31.0%

E90 EtOHV: cellulosic biomass 2530.0% 53.8% -79.5% -74.9% -88.9% -63.3% 474.8% -77.1%GI SI HEV: FRFG 3380.0% -28.6% -28.6% -28.6% -28.6% -25.9% -1.6% -28.0%GC SI HEV: FRFG 5410.0% -40.7% -43.1% -57.7% -40.1% -39.4% -29.2% -39.9%

CIDIV: LS diesel 2960.0% -21.7% -21.7% -10.4% -17.1% -40.4% -42.3% -18.3%CIDIV: FTD, NA NG 2960.0% 8.7% 9.0% -99.0% -13.4% -40.3% -44.9% -14.8%CIDIV: FTD, NNA NG 2960.0% 10.4% 10.8% -98.5% -12.1% -24.9% -30.0% -12.7%

CIDIV: BD20 2960.0% -19.0% -19.1% -25.5% -28.4% -44.2% -34.1% -29.0%GI CIDI HEV: LS diesel 4100.0% -43.6% -43.6% -35.4% -40.2% -56.6% -43.3% -40.8%GC CIDI HEV: LS diesel 5770.0% -47.2% -49.6% -59.7% -44.6% -56.3% -57.0% -45.2%

EV: US mix 8440.0% -45.1% -52.5% -98.4% -43.5% -48.8% -84.1% -44.5%EV: NE US mix 8440.0% -46.2% -55.6% -97.5% -53.4% -36.3% -87.1% -53.5%EV: CA mix 8440.0% -50.6% -61.9% -99.7% -61.5% -43.2% -88.6% -61.5%

FCV: G.H2, central plant, NA NG 5070.0% -35.6% -36.6% -99.2% -47.7% -50.1% -94.9% -48.7%FCV: G.H2, central plant, NNA NG 5070.0% -30.0% -31.0% -99.3% -42.7% -4.3% -93.2% -42.6%

FCV: G.H2, refueling station, NA NG 5070.0% -32.9% -33.2% -99.7% -46.9% -36.2% -94.8% -47.5%FCV: G.H2, refueling station, NNA NG 5070.0% -28.4% -28.6% -99.6% -43.3% -3.3% -93.3% -43.2%FCV: G.H2, central electrolysis, renewables 5070.0% -37.6% -91.9% -99.5% -90.6% -89.5% -97.7% -90.7%

FCV: G.H2, station electrolysis, US generation mix 5070.0% 40.5% 22.4% -96.3% 44.7% 62.6% -64.9% 43.3%FCV: L.H2, central plant, NA NG 5070.0% -11.6% -11.4% -99.3% -28.8% -25.1% -86.2% -29.7%FCV: L.H2, central plant, NNA NG 5070.0% -8.5% -8.4% -99.0% -25.4% -21.6% -85.5% -26.4%

FCV: L.H2, refueling station, NA NG 5070.0% 12.4% 6.0% -98.4% -1.3% 6.5% -84.3% -2.5%FCV: L.H2, refueling station, NNA NG 5070.0% 19.5% 12.9% -98.4% 2.4% 81.3% -82.7% 2.9%

FCV: L.H2, central electrolysis, renewables 5070.0% -44.0% -98.7% -99.4% -98.8% -98.8% -99.6% -98.8%FCV: L.H2, station electrolysis, US generation mix 5070.0% 105.3% 61.7% -95.2% 91.1% 114.7% -53.7% 89.2%

FCV: MeOH, NA NG 42.2 -28.70% -28.50% -98.50% -43.50% -46.70% -77.40% -44.30%FCV: MeOH, NNA NG 42.2 -27.40% -27.20% -98.10% -42.50% -33.50% -76.70% -42.90%

FCV: gasoline 37.4 -35.50% -35.50% -35.50% -35.50% -39.30% -77.40% -36.30%FCV: cellulosic EtOH 39.3 19.90% -96.90% -94.40% -105.10% -91.80% 338.70% -96.00%FCV: CNG, NA NG 37.4 -41.60% -41.70% -99.70% -52.70% 15.00% -79.10% -51.10%FCV: CNG, NNA NG 37.4 -34.70% -34.80% -99.70% -47.40% 85.20% -77.00% -44.60%FCV: FT naphtha, NNA NG 37.4 -10.30% -10.00% -98.70% -32.70% -38.80% -79.90% -33.70%FCV: crude naphtha 37.4 -38.60% -38.60% -36.40% -41.30% -41.80% -78.60% -41.90%

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Summary of Emissions Models using Mobile Emissions inventories are required of all ozone and CO non-attainment areas under CAAA of 90. 7 vehicle types and 2 regions: LDGV LDGT HDGV LDDV LDDT HDDV MC Model Estimates Emission Factors for Any Calendar Year Between 1960-2020

• Other inputs - Air temperature - Control device tampering - Gasoline volatility class - P.C. hot starts vs. cold starts - Vehicle age distribution - I/M program data - Catalyst vs. non-catalyst vehicles - AC use - Extra loads on vehicles

• Output – CO, HC, NOx - Exhaust - Refueling - Evaporative emissions for each type of vehicle - Composite emission factor weighted average based on VMT mix input

11.6.2 MOBILE 6.0 Mobile Source Emission Factor Model

The emissions model has two sources: mobile source (traffic) and indirect source (parking) which is not

included in Mobile 6.0. There are some factors related to this model such as traffic volume, density, vehicle type,

speed, and mode (idle, acceleration, cruise, deceleration). The EPA MOBILE6.0 model estimates emission

factors for three pollutants: hydrocarbons (HC), carbon monoxide (CO), and oxides of nitrogen (NOx) for

gasoline-fueled and diesel highway motor vehicles, and for certain specialized vehicles such as natural-gas-

fueled or electric vehicles. MOBILE6 calculates emission factors for 28 individual vehicles which depend on

various conditions, such as ambient temperature, travel speed, operating mode, fuel volatility, and mileage

accrual rates. In addition, MOBILE6 is capable of estimating emission factors for any calendar year between

1952 and 2050 [USEPA, 2002]. The MOBILE6 model also considers four vehicle roadway facilities namely:

freeways, arterial/collectors, local roadways, and freeway ramps.

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Modeling in MOBILE6

The MOBILE6 emissions model consists of five primary components: base emission rates, fleet

characteristics, local conditions (e.g., correction factors considering the impact of temperature, operating mode

and speed), fuel characteristics, and the inspection and maintenance program. The fleet average emission factor

for a vehicle class, calendar year, pollutant, and emission producing process is given as follows [Koupal et al.,

1999].

∑=

××=n

mmkjimkjimikji CFBERVMTFEF

1,,,,,,,,, )(_

where:

EFi,j,k = Fleet-average emission factor for calendar year i, pollutant j, and emission producing

process k (e.g., exhaust, evaporative)

F_VMTi,m = Fractional VMT attributed to model year m for a calendar year i (n = 28 in MOBILE6,

the sum of over all model years m is unity);

BERi,j,k,m = Basic emission rate for a calendar year i, pollutant j, process k, and model year m;

CFi,j,k,m = Correction factor(s) (e.g., for temperature, speed) for a calendar year i, pollutant j, process

k, and model year m, etc.

Unlike earlier versions of the model which give single average emission factors for a trip, the

MOBILE6 model produces separate emission factors for the start and running modes. The running mode

emission factors are based only on hot stabilized operating conditions while the start emissions represent the

additional emissions that result from a vehicle start. The base emission rates are derived from the Federal Test

Procedure (FTP) drive cycle and are calculated as follows [Glover et al., 1999]:

7.5 0.16)*0.57*BER Start0.43*BER Start 7.5*BER (Running BER ++

=

where:

BER = Basic emission rate

Running BER = Basic emission rate under hot stabilized operating conditions

Start BER = Basic emission rate under start conditions

MOBILE6 provides both daily average emission factors and hourly emission factors for each hour of

the day. In addition it incorporates other enhancements, including the following: an update of fuel effects on

emissions, a use of diurnal evaporative emissions based on real-time diurnal testing, an update of hot soak

evaporative emission factors, an update of heavy-duty engine emission conversion factors, an update fleet

400

characterization data, and a provision of distinct emission factor calculations for a wider range of vehicle

categories.

Shortcomings of Mobile 6

At the present time, the MOBILE6 model still uses the average speed as the sole explanatory variable

for estimating vehicle emissions. However, studies have shown that two vehicle trips with the same average

speed can have different speed profiles consisting of dramatically different modal characteristics (speed-

acceleration combinations), and thus different emissions results [Ding, 2000].

11.6.3 Emission Models based on Operating Modes

These are models that estimate vehicle emission based on the vehicle operating modes (cruise,

acceleration, deceleration and idling). Transportation improvements result in a change in the modal variables

such as vehicle speeds, acceleration profiles, idle times and power demand. Research by An et al., [1997] and

Barth et al., [1996] developed modal emission models for light duty cars and trucks. The model predicts the

engine power, engine speed, air-to-fuel ratio, fuel use, engine-out emissions, and catalyst pass fraction which

outputs tailpipe emissions and fuel consumption. The vehicle power demand is modeled as a function of

operating variables (i.e., vehicle acceleration and speed), specific vehicle parameters (e.g., vehicle mass,

transmission efficiency, effects of accessories), and road conditions. The fuel rate is calculated as a function of

the power demand, engine speed, and air/fuel ratio, which is used solely the computation of the engine-generated

emissions as expressed below:

CPFgFREmission tou ngineetailpipe ××=

where:

Emissiontailpipe = Tailpipe emission in grams/sec

FR = fuel-use rate in grams/sec

gengine out = grams of engine-out emissions per gram of fuel consumed

CPF = the catalyst pass fraction, (the ratio of tailpipe emissions to engine-out emissions)

The model does not inherently determine when a cold start occurs, however, it is able to determine

when the operating condition switches from cold start to stochiometric operation. Another modal emission model

is the MEASURE (Mobile Emissions Assessment System for Urban and Regional Evaluation) model developed

at Georgia Institute of Technology [Bachman et al., 1996; Wolf et al., 1998; Fomunung et al., 1999]. The

emission rates estimated by MEASURE are dependent on both modal variables (vehicle speed, acceleration

profile, idle times and power demand) and vehicle technology variables (fuel metering system, the catalytic

converter type, the availability of supplemental air injection, and the transmission speed). Models for estimation

of emission rates for each pollutant are provided in the next section.

401

The CO Emission Model [Fomunung et al., 1999]:

This model is given as follows:

LogRCO = 0.0809 + 0.002 × AVGSPD + 0.0461 × ACC.3 + 0.0165 × IPS.60 - 0.0283 × IPS45sar2 + 0.3778

× IPS90tran1 - 0.0055 × tran3idle + 0.1345 × tran5mi1 + 0.3966 × inj3sar3 - 0.0887

× cat3tran1- 0.2636 sar3tran4 - 0.481 × flagco

where:

AVGSPD = average speed of the driving cycle in mph;

ACC.3 = proportion of the driving cycle on acceleration greater than (3mph/sec);

IPS.60 = proportion of the driving cycle on inertial power surrogate (IPS) (speed x acceleration) greater

than X mph2/sec [Washington et al., 1994]. Thus IPS.60 implies IPS > 60 mph2/sec;

IPS45sar2 = interaction between IPS.45 (IPS ≥ 45 mph2/sec) and a vehicle with no air injection;

IPS90tran1 = interaction variable for a vehicle with automatic transmission on IPS.90 IPS ≥ 90 mph2/sec;

cat3idle = interaction variable for a 3-speed manual transmission at idle;

tran5mi1 = interaction variable for a 5-speed manual transmission vehicle with mileage ≤ 25k miles;

finj3sar3 = interaction variable for a vehicle that has throttle body fuel injection and pump air injection;

cat3tran1 = interaction variable for a vehicle with automatic transmission and TWC;

sar3tran4 = interaction variable for a vehicle with 4-speed manual transmission and pump air injection;

flagco = flag used to tag a high emitting vehicle under CO emissions.

The HC Emission Model: was derived similar to the CO model. The final emission rate model for HC is

[Fomunung et al., 1999]:

LogRHC = 0.0451 - 0.6707 × my79 - 0.1356 × my82 + 0.019 × AVGSPD + 0.2021 × finj2tran4 + 0.1795

× cat2sar1 + 0.1651 × cat3sar1 + 0.0318 × cat3sar2 - 0.1189 × sar3tran1 + 0.5646 × sar1tran5

+ 0.0004 × cid - 0.2581 × sar3kml - 0.0169 × finj2km3 - 0.5144 × flaghc - 0.0129 × acc1finj2

- 0.1626 × acc3cat2 - 0.3891 × IPS90sar3 + 0.0307 × dps8finj2

where:

my79 = model year < 79;

my83 = 79 < model year < 83;

AVGSPD = average vehicle speed (mph);

finj2tran4 = interaction variable for a 4-speed manual transmission vehicle with a carburetor;

cat2sar1 = pre 1981 model year vehicle with "oxidation only" catalyst and unknown air injection type;

cat3sar1 = pre 1981 model year vehicle with a TWC and unknown air injection type;

cat3sar2 = vehicle with TWC and no air injection;

sar3tran1 = automatic transmission vehicle with pump air injection;

402

sar1tran5 = pre-1981 model year, 5-speed manual transmission vehicle of unknown air injection type;

cid = cubic inches displacement;

sar3km1 = vehicle with pump air injection and mileage ≤ 25k miles;

finj2km3 = vehicle with pump air injection and 50k < mileage ≤ 100k miles;

flaghc = high emitting vehicle flag under HC emissions;

acc1finj2 = carburetor-equipped vehicle operating with acceleration greater than 1 mph/s;

acc3cat2 = oxidation only catalyst vehicle with acceleration greater than equal to 3.0 mph/s;

IPS90sar3 = vehicle with air pump and inertial power surrogate greater than or equal to 90 mph/s; and

dps8finj2 = proportion of drag power surrogate (DPS speed x speed x acceleration) greater than 8 mph/s.

The NOx Emission Model: was also derived similar to the CO and HC models and the final emission

rate model for NOx is [Fomunung et al., 1999]:

LogRNOx = -0.5864 + 0.0225 × AVGSPD + 0.3424 × IPS.120 + 0.6329 × ACC.6 + 0.0247 × DEC.2

+ 0.0083 × finj2km1 + 0.0028 × finj2km2 - 0.0021 × cat2km3 + 0.0026 × cat3km2 + 0.0003

cat3km3 - 0.0085 × finj1km3flagnox - 0.0068 × finj3km3flagnox

where:

IPS.120 = proportion of activity where IPS ≥ 120 mph2/sec;

ACC.6 = proportion of activity where acceleration ≥ 6.0 mph/s;

DEC.2 = proportion of deceleration ≤ -2.0 mph/s;

finj2km1 = carburetor equipped vehicle with mileage < 25k miles;

finj2km2 = carburetor equipped vehicle with 25K, mileage ≤ 50k miles;

cat2km3 = "oxidation only" catalyst vehicle with 50k < mileage ≤ 100k miles;

cat3km2 = TWC vehicle with 25K mileage ≤ 50k miles;

cat3km3 = TWC vehicle with 50K < mileage ≤ 100k miles;

finj1km3flagnox = second order interaction variable for a high emitting vehicle with port fuel injection and

50k < mileage ≤ 100k miles; and

finj3km3flagnox = second order interaction variable for a high emitting vehicle with throttle body fuel injection

and 50K < mileage ≤ 100k miles.

11.6.4 Microscopic Emission Models

Microscopic emission models predict vehicle emissions on a second-by-second basis as a function of

vehicle type, speed, and acceleration. Microscopic emission models are used in most traffic operations software

packages to estimate the vehicle emissions on highways particularly at intersections where the rate of emission

depend on the instantaneous characteristics of the vehicles such as the fuel consumption, vehicle speed,

acceleration, and engine power.

403

TRANSIMS Transportation Analysis and Simulation System

One such example of a microscopic model is the Transportation Analysis and Simulation System

(TRANSIMS). The model estimates the emissions on a second by second basis by multiplying the fractional

power change at a given time and the emission difference for the given speed and power and adding the result to

the emissions at constant power. The change in power as follows [Williams et al, 2002]:

−×−

∆−

××=− −−−

x

xxi

xxxiii V

VVpowppVpowpowpow 111

where:

powi = power of vehicle at time i

px = probability of a high power event at position x

Vx = speed of vehicle at position x

i = refers to time

x = refers to position

The emissions are sensitive to the change in power which depends on the acceleration profile of the vehicle.

INTEGRATION (v. 20) Traffic Simulation Model

Another microscopic model is INTEGRATION (v. 2.0) Traffic Simulation Model and dynamic

assessment model. The model covers effects of vehicle stops, accelerations, and decelerations both on freeways

and arterials The INTEGRATION emission model estimates emissions by computing the fuel consumption for

each vehicle on a second-by-second basis for three modes of vehicle operation: constant speed cruise, velocity

change, and idle. For a given vehicle, the fuel consumption rate (in liters/hour) is modeled as [USEPA, 1998]:

• a function of travel speed for the constant speed cruise vehicle operation mode

• a function of initial and final speed for the velocity change operation mode

• a constant during the idle operation mode.

The vehicle emissions are then estimated as a function of fuel consumption, ambient air temperature,

and the extent to which a particular vehicle’s catalytic converter has already been warmed up during an earlier

portion of the trip [Rouphail, et al., 2001]. The model also has the ability to capture the effects of congestion on

mobile emissions [Sinha et al., 1998].

FHWA’s TRAF-NETSIM Traffic Simulation Model

TRAF-NETSIM is a microscopic traffic simulation model that tracks the movements of individual

vehicles on a second-by-second basis at single intersections and on freeway segments and ramps. The model

however does not cover entire freeways or corridors. The model estimates hot stabilized emissions of CO, HC

and NOx as a function of a vehicle travel speed and the level of acceleration.

404

11.6.5 Fuel Based Emission Models

Fuel based vehicle emission models estimate vehicle emissions based on fuel consumption as vehicles

operate in the various modes of travel. An example of a fuel based emission model is the SYNCHRO traffic

model which contains a simplified emissions model. The model predicts vehicle emissions by first predicting

fuel consumption, which is calculated as a function of vehicle-miles, total delay in veh-hr/hr, and total stops in

stops per hour. Then, the fuel consumption is multiplied by an adjustment factor depending on the type of

emissions to estimate vehicle emissions [Rouphail et al., 2001].

11.6.6 Dispersion

11.6.6.1 Dispersion Factors: wind speed, wind direction, mixing height

Models:

Gaussian – widely used

Numerical – diffusion and advection in a series of boxes

Box – uniform dispersion throughout a single box

11.6.6.2 Example

3 × 3 km city

1000 m average mixing height

5 m/sec. average wind speed

Time taken for pollutant particle emitted at one edge to be blown across the city and out of the box = 600

seconds

Max = 510003× =600 seconds

Minimum = 0

Average = 300

On the average, the box will contain the pollutants emitted during 300 seconds.

405

If 90 kg of pollutant were emitted every 300 seconds and dispersed evenly up to the mixing height:

The average concentration of pollutant = 10001000310003

gms 100090××××

×

= 10 / µg / cumeter

CALINE 4 – Caltrans model

Gaussian plume line source emission factors and meteorological data are input

Output – concentration of CO, HC, NOx

11.6.6.3 State-of-the-Art Air Dispersion Models

Air dispersion models are used to evaluate air quality impacts by determining the levels or

concentrations of the various air pollutants in the atmosphere. Pollutants emitted into the atmosphere emission

are dispersed by molecular diffusion, eddy diffusion and random shifts [Wayson, 2002]. The level of air

pollutants at a give location depends on a number of factors which includes: meteorological conditions such as

the wind speed and temperature gradient, the number of emission sources and the emission rates of these

sources. Dispersion models usually utilize the Gaussian model to estimate the dispersion of a non-reactive

pollutant released steadily from a source. The equation is of the form [Wayson, 2002].

−×+

+××

××

=

222

5.05.05.02 zzyzy

HzHzyuQC

σσσσσπ

where:

C = concentration (mass/volume)

Q = emission rate (mass/time)

U = wind speed

σy, σz = standard deviation of dispersion in the y and z direction respectively

y = distance receiver is remove from the x axis

z = receptor height

H = source height

A number of air dispersion models have been developed for highway and transportation projects. These

include the California Line Source (CALINE 4), HIWAY, PAL, TEXIN 2 and CAL3QHC models. The HIWAY

and PAL models can only be used for free flow conditions [Wayson, 2002] however models such as the TEXIN

2, CALINE 4 and CAL3QHC that are recommended by the EPA account for queuing delays, excess emissions

due to modes and cruise.

406

11.7 METHODOLOGY FOR ESTIMATING AIR QUALITY IMPACTS OF TRANSPORTATION

IMPROVEMENTS

Most transportation improvements result in a change in road capacity and the average speed of vehicles,

and ultimately lead to change in emission-related air pollution. Figure 11-8 below gives the detailed

methodology for determining changes in average speed and capacity hence the change in vehicles emissions due

to physical or regulatory improvements that engender such changes in sped and capacity.

407

Figure 11-8: Steps Involved in Evaluating Air Quality Impacts of Transportation Investments

Physical Improvement

Policy Change

New Road

Select Intervention Type

Timing Improvementof Traffic Signal

Grade Improvement

Lane Widening

Curve Improvement

Pavement Resurfacing

Update Travel Demand (AADT), if applicable

Update Vehicle Stream Composition, if applicable

Update Travel Speeds, if applicable

Update Emission Rates, if applicable Changes in Speed

Limits

Changes in Travel Restrictions

Changes in Air Quality Standards

Determine Total Emissions (VOC, CO and NOX) using MOBILE 6

Repeat for Each Alternative Intervention Type or level of Physical or Policy Change

Changes in Travel Characteristics

408

11.7.2 Improvements That Involve a Change in Curvature

The effect of curvature can be determined as follows [FHWA, 2000]:

DCSPFRATIOVCURVE /)(5.292 +×=

where:

DC = degree of curvature in radians

FRATIO - maximum perceived friction ratio = 0.155

SP = superelevation which is given as

SP =

+−×+≥≤

)ln(007.00317.0)ln(0972.00318.0:otherwise 10 for 1.0

1 for 0

DCDCDCDCDCDC

11.7.3 Change in Free Flow Speed (FFS) due to Improvement

The free flow speed (FFS) stated above is determined as follows [FHWA, 2000]:

1.0101010 111−

+

+

=

VSPLIMVROUGHVCURVEFFS

where: VCURVE = effect of curvature on speed

VROUGH = effect of pavement roughness on speed

VSPLIM = effect of speed limits on speed which is given as

VSPLIM = Speed limit × S miles per hour

Where X = 9.323 for urban freeways by design and rural multilane roads with partial or

full access control and 6.215 for all other cases

11.7.4 Change in Peak Capacity due to Improvement

The peak capacity of a multilane highway is given as follows [HPMS, 2000]:

pHV f f N PHF BaseCapPeakCap ××××=

where:

PeakCap = Peak Capacity, vehicles per hour (all lanes, one direction)

BaseCap = Base capacity

PHF = Peak Hour Factor = 0.92 for urban facilities

N = Number of lanes in one direction

fHV = Adjustment factor for heavy vehicles

fp = Adjustment factor for driver population = 1.0

409

Base Capacity

The base capacity is given as [HPMS, 2000]:

BaseCap =

>≤×+

60 FFS for 2200 60 FFS for FFS20 1000

Adjustment factor for Heavy Vehicles

The adjustment factor for heavy vehicles is based on calculating passenger-car equivalents for trucks and buses

[HPMS, 2000]:

fHV = TP0.5 1

1×+

where:

PT = Proportion of trucks and buses in the traffic stream

11.7.5 Change in Traffic Signal Delay due to Improvements

The average delay due to congestion (D) for arterial roads is computed as follows [FHWA, 2000]:

>−×+−×

<<−×+−×+×−

<×+×−

=−

−

−

13.2 AADT/Cfor )7(16.0)1(3.23713.2 AADT/C 7for )7(16.0 ))7(7.177.68(1

7 AADT/Cfor 7.177.681

2)4.24/(

22)4.24/(

)4.24/(

ACReACRACRe

ACReD

N

N

N

where:

ACR = AADT/Capacity ratio for the section

C = capacity of the section

N = number of traffic signals on the section

11.7.6 Determination of Average Effective Speed (AES)

The Federal Highway Administration’s Highway Economic Requirements (HERS) Model estimates the average

speed as follows [FHWA, 2000]:

1

10001 −

+=

DFFS

AES

where:

AES = Average Effective speed

FFS = Free flow speed

D = Average delay due to congestion and/or traffic control devices in hours per 1000 vehicle miles

410

11.7.7 Determination of Vehicle Mix Fractions

The vehicle mix fractions used in this study was the default vehicle mix fractions used in the MOBILE6

model [USEPA, 2002]. The vehicle mix fractions are assumed to remain the same for the base case and the

different scenarios for curvature improvements.

11.7.8 Estimation of Emissions

The average speed computed can then be used in the MOBILE6 emissions model to obtain the emissions factors

for the VOC, CO and NOx.

11.8 CASE STUDIES USING HYPOTHETICAL PHYSICAL IMPROVEMENTS

11.8.1 Effect of Road Widening

A case study using hypothetical values was used to evaluate the effects of road widening on air quality.

For this case study, the capacity of a section of an urban arterial road (State Road 267 in Hendricks county of

Indiana) was increased by widening the road from four lanes to six lanes. It was assumed that the road widening

project will take two years to complete with an annual traffic growth rate of 1.02%. Also the speed limit will be

increased from 45 mph to 55 mph after the road widening project. The average effective speed was then

computed and then used in the MOBILE6 emissions model to obtain the emissions rates for the VOC, CO and

NOx. Table 11-18 below gives the characteristics of a section of State Road 267 that was used in the analysis.

Table 11- 18: Initial Characteristics of Road for Analysis

System and Operation

Characteristics Value Characteristics Value

Functional Class Urban Arterial Length 5.86 miles

Type of Facility 2-way Number of Lanes 4

Geometrics


Lane Width 12 feet Curvature (VCURVE) 137.832

Speed Limit 45 mph Roughness (VROUGH) 78.409

Intersections 1 Grade 2.142%

Traffic


AADT 12089 veh

Capacity 3624 % Trucks 15%

411

Table 11-19 below shows the summary of results before and after the road widening improvement.

Table 11-19: Summary of Results

Scenario Number of Lanes

Free Flow Speed (mph) AADT Capacity

Ave. Effective

Speed (mph)

VOC Emission (g/mile)

CO Emission (g/mile)

NOx Emission (g/mile)

Before Road Widening 4 54.875 6165 3623.8 45.063 1.601 18.699 2.493

After Road Widening 6 64.254 6297 7601.9 52.880 1.291 16.022 2.286

Figure 11-9 illustrates the effect of road widening on the VOC emissions, CO emissions and NOx emissions.

Figure 11-9: Effect of Road Widening on Emissions.

11.8.2 Effect of Curvature Improvement

A case study using hypothetical values was used to evaluate the effects of curvature improvements on

air quality. For this case study, the curvature of a section of a rural major arterial road (State Road 25 in

Tippecanoe county of Indiana) was improved by decreasing the degree of curvature from an initial value of 80

degrees to 30 degrees. It was assumed that the curvature improvement project will take one year to complete

with an annual traffic growth rate of 1.80%. Also the speed limit will be increased from 35 mph to 45 mph after

curvature improvement. The average effective speed was then computed and used in the MOBILE6 emissions

model to obtain the emissions rates for the VOC, CO and NOx. Table 11-20 gives the characteristics of a section

of State Road 25 that was used in the analysis. Table 11-21 shows the summary of results before and after the

curvature improvement, while Figure 11-10 illustrates the effect of curvature improvement on VOC emissions,

CO emissions and NOx emissions.

0

2.5

5

7.5

10

12.5

15

17.5

20

VOC CO NOx

Em

issi

ons (

g/m

ile)

Before Road Widening After Road Widening

412

Table 11-20: Initial Characteristics of Road for Analysis

System and Operation


Functional Class Rural Arterial Length 6.14 miles

Type of Facility 2-way Number of Lanes 2

Geometrics


Lane Width 10 feet Curvature 80 degrees

Speed Limit 35 mph Roughness (VROUGH) 78.409

Intersections 1 Grade 2.142%

Traffic


AADT 6165 veh

Capacity 3128 % Trucks 11%

Table 11-21: Summary of Results

Scenario Curvature (degrees) VCURVE

Free Flow Speed (mph)

AADT/ Capacity

Ave. Effective

Speed (mph)

VOC Emission (g/mile)

CO Emission (g/mile)

NOx Emission (g/mile)

Before Curvature Improvement 90 91.882 44.9826 1.7710 43.48599 1.615 18.456 2.47

After Curvature Improvement 30 159.145 54.87536 1.7892 52.66427 1.431 18.865 2.851

Figure 11-10: Effect of Curvature Improvements on Emissions.

0 2 4 6

8 10

12 14

16 18

20

VOC CO NOx

Em

issi

ons (

g/m

ile)

Before Curvature Improvement After Curvature Improvement

413

11.8.3 Discussion of Results

For road widening, the results showed a reduction in the vehicle emissions rates of VOC, CO, and NOx.

This reduction in emissions can be attributed to the fact that the road widening improvement increased in the

capacity of the road thus reducing the level of congestion and increasing the average speed of vehicles on the

road. For curvature improvement, the results showed an increase in the vehicle emissions rates of CO, and NOx,

however for VOC there was a reduction in the emission rate. This can be attributed to the increase in the average

speed of vehicles on the road as result of the curvature improvement. The results are consistent with the VOC,

CO and NOx emission trends shown in Figure 11-5, indicating that VOC emissions has an inverse relation with

the average speed, while NOx and CO emissions show a direct relation with the average speed of vehicles for the

range of speeds used in the case study.

The results obtained from the case study indicate that transportation improvements have considerable

impacts in air quality by increasing the average speed of vehicles and increasing the volume of travel. By

increasing the volume of travel the transportation improvement increases the volume of pollutant emissions. The

increase in the average speed of vehicles can increase or reduce emissions per vehicle-mile of some pollutants

thus adding or offsetting some or all of the effect of the higher volume. The total air quality impact of a

transportation improvement can therefore be either positive or negative depending on the type of improvement

that was undertaken.

11.9 ESTIMATION OF AIR POLLUTION COSTS

[Work on this aspect is still in progress. Students should contact Professor

Sinha for information on the unit and overall costs of air pollution]

414

11.10 AIR QUALITY STANDARDS

Table 11-22 shows the air quality standards that have been established buy the EPA for each pollutant.

Table 11-22: Air Quality Standards [EPA, 2002]

POLLUTANT MEASURE STANDARD VALUE STANDARD TYPE

8-hour Average 9 ppm (10 mg/m3) Primary Carbon Monoxide (CO) 1-hour Average 35 ppm (40 mg/m3) Primary

Nitrogen Dioxide (NO2) Annual Arithmetic Mean 0.053 ppm (100 µg/m3) Primary & Secondary

1-hour Average 0.12 ppm (235 µg/m3) Primary & Secondary Ozone (O3)

8-hour Average 0.08 ppm (157 µg/m3) Primary & Secondary

Lead (Pb) Quarterly Average 1.5 µg/m3 Primary & Secondary

Annual Arithmetic Mean 50 µg/m3 Primary & Secondary Particulate (PM 10) Particles with diameters of 10 micrometers or less 24-hour Average 150 µg/m3 Primary & Secondary

Annual Arithmetic Mean 15 µg/m3 Primary & Secondary Particulate (PM 2.5) Particles with diameters of 2.5 micrometers or less 24-hour Average 65 µg/m3 Primary & Secondary

Annual Arithmetic Mean 0.03 ppm (80 µg/m3) Primary

24-hour Average 0.14 ppm (365 µg/m3) Primary Sulfur Dioxide (SO2)

3-hour Average 0.50 ppm (1300 µg/m3) Secondary

11.11 MITIGATION OF AIR POLLUTION

The quest to reduce automotive air pollution has been spearheaded by industrialized countries through a

variety of measures including legislation and enforcement, vehicle engine standards, promotion of less polluting

modes of transportation, improved fuel quality, non-hydrocarbon fuels, and transportation planning and traffic

management.

11.11.1 Traffic Management and Policy Instruments

While the control of pollutant emissions at source should be given probably highest priority, it is

obvious that such measures alone will not suffice in eliminating the problem due to increasing vehicle ownership

and travel. Traffic management and policy instruments such as urban growth and transportation considerations,

traffic management, auto restrictions and vehicle free zones, and economic policies could be used to control the

amount of travel.

Urban Growth and Transportation:

Measures aimed at reducing the amount of trips and overall travel show tremendous potential in

reducing transportation related pollution. Such measures include transportation planning on an urban and

regional level, traffic control and management, and regulatory and pricing policies. The air pollution problem is

directly linked with land-use and transportation. Increased transport-related air pollution is typically associated

415

with changes in urban form in the direction of lower inner city densities combined with decentralization of

employment, a shift from manufacturing to services, and increased rates of work and leisure trips. However,

change in urban growth and land-use cannot be guided solely by air quality considerations, as the social and

economic impacts of such controls can be costly. Consistent with the goals of sustainable development, land-use

and urban planning policies should seek a commonality between the attainment of various goals including not

only air pollution abatement, but also congestion reduction, safety improvement, energy conservation,

transportation costs reduction, and economic sustainability. In the area of urban design, opportunities exist to

reduce the concentration of air pollutants. Provision of wide streets with free flowing traffic and landscaping

would help obviate high concentrations of carbon monoxide that typically occur in congested narrow streets,

garage forecourts, and other confined spaces within the metropolitan transportation infrastructure. Furthermore,

careful design and location planning of streets and highways, and the orientation of buildings with respect to

such facilities could reduce the exposure of people to vehicle emissions. Major road alignments may be

depressed or even tunneled through areas having large concentrations of people.

Encouragement of transit use, through facility improvement and pricing, could yield lower emissions

per trip. The use of clean fuels for buses and taxis, electrification of commuter rail lines could also be

encouraged to enable transportation of large numbers of people at reduced cost to the environment.

Encouragement of non-fossil modes such as bicycles could be encouraged through various policies and facilities

such as congestion pricing and provision of bike lanes.

Traffic Management:

Measures aimed at improving traffic circulation have been found to show much promise in enhancing

vehicle mobility and consequently reducing pollutant emissions. Such measures include the following:

• improved traffic signalization for traffic networks and corridors

• prohibition/restriction of turning movements

• use of one-way streets pairs and reversible lanes

• designation of exclusive facility privileges for high occupancy vehicles

• segregation of motorized and non-motorized traffic through provision exclusive facilities for

non-motorized modes of transportation

• improved commercial vehicle operations in urban areas such as designation of truck routes in

metropolitan areas, temporal restrictions on urban goods movement, and the use of

strategically located freight transfer centers

• use of staggered work hours and telecommuting

• congestion pricing, and parking controls and restrictions

The effectiveness (and even, appropriateness) of these interventions depends on the physical layout of

the metropolitan area and characteristics of its transportation system. It has been estimated that various forms of

bus priorities can reduce exhaust emissions by 7% if priority turns and other minor measures are used and as

416

much as 60% if exclusive bus streets and freeway privileges are implemented [ECMT, 1990]. In many

developing countries, lack of coordinated city-wide traffic signal systems exacerbates the traffic congestion

situation, leading to high emission levels. Centrally computerized control systems can greatly enhance the

efficiency and timing of traffic signals to help maintain uniform speed and stable traffic flows. It has been

established that progressive and simple linking of signals along arterial roads can minimize the number of

acceleration/deceleration operations, while maintaining the cruising traffic speed within a range at which

emission rates are lowest [UNEP, 1981; UNEP, 1986].

Auto Restrictions and Vehicle Free Zones

Air pollution typically encountered in certain parts of cities can be controlled by designating selected

streets or area as auto-free zones that allow only high occupancy vehicles and public buses, or allows only

pedestrians. Vehicle free zones typically result in considerable reduction of CO concentrations. The central

business districts of cities typically include many roadways characterized by high concentrations of carbon

monoxide. Such areas are typically too large to be converted into vehicle –free zones. However, if a bypass route

is available or can be established, then it may be possible to achieve significant reductions of CO concentrations

at the central area by diverting the through traffic to bypass the area. This can be achieved dividing the central

area into traffic cells, or zones that are accessible to vehicles only from the bypass route. Direct vehicular travel

between cells is discouraged or prevented by means of systems of one-ways streets, turn restrictions, or physical

barriers. However, the diversion of through traffic to bypass roads that occurs when a traffic cell system is

established is likely to increase the lengths of through trips. The lengths of local trips may also increase due to

the need to use bypass roads for travel between cells. Such increased circuitry of travel may cause aggregate HC

and NOx emissions to rise. Traffic cells systems have been implemented in several European cities and Japan

[Gakenheimer, 1978; OECD, 1988b]. Experience with these systems indicates that they can achieve substantial

reductions in central area CO concentrations. In Gothenburg, Sweden, implementation of a central area traffic

cell system is reported to have reduced half-hour average CO concentrations in the central area from 60-70 ppm

to 5 ppm [Horowitz, 1982].

Emergency traffic management measures to alleviate air pollution and traffic congestion in many cities

have often included a revolving ban on license plate numbers. Such bans are typically in effect on weekdays

during business hours. Any given day, only cars with license plates ending in an odd or even number are allowed

to circulate. Enforcement is carried out by imposition of large fines. This scheme has been used in Athens,

(Greece) and Lagos (Nigeria). In Santiago, a similar ban based on license plate numbers keeps one-fifth of all

automobiles off the streets each weekday, and the ban is extended to two days a week during periods of

excessive air pollution. The city of Florence (Italy) converts its downtown area into a pedestrian mall during

certain period of the day [French, 1990]. In Mexico City, private automobiles were banned one day a week

during eth winter of 1989-90.

417

11.11.2 Economic Instruments

Financial incentives and disincentives can be employed to ensure compliance with air pollution control

policies. Such measures are typically referred to as “economic levers” or market/price mechanisms and are

designed to induce a change in the behavior of producers and consumers. Commonly used measures include

subsidies, taxes/emission charges and fines, emission credits and quotas [ECE, 1987]. Such measures can be

used to promote the production and use of environmentally cleaner fuels. In the Netherlands, an environmental

tax on fuel was imposed to combat noise and air pollution. In Germany, Switzerland and the United Kingdom,

the tax on unleaded fuel is lower than that on leaded fuel. In Germany and Singapore, there are tax incentives for

the usage of vehicles that use non-fossil fuel. It is important that pollution reducing tax adjustments are carefully

planned over a period of time so that expected revenue from fuel sales and taxes are not jeopardized.

11.12 AIR QUALITY LEGISLATIONS AND REGULATIONS

The Clean Air Act (CAA), of 1963 which was subsequently amended in 1965 was one of the first of

several air quality legislations and regulations related to transportation. The Clear Air Act Amendment (CAAA)

of 1970 was the first air quality law that provided strong federal controls in individual states to regulate and

reduce motor vehicle and aircraft emissions. To achieve this goal the CAAA established the National Ambient

Air Quality Standards (NAAQS) for pollutants considered harmful to public health and the environment. The

1977 CAAA, was again amended in 1990 CAAA in an attempt to balance the nation's mobility and air quality

requirements. Under the 1990 CAAA the EPA Office of Air Quality Planning and Standards (OAQPS) has set

National Ambient Air Quality Standards for six principal pollutants, which are called criteria pollutants. Areas

that do not meet these standards are classified as non-attainment areas. Depending on the severity of the air

quality problem, these non-attainment areas are classified as marginal, moderate, serious, severe and/or extreme

non-attainment areas. To help ensure attainment of these areas, the 1990 CAAA strengthens existing conformity

requirements. Conformity regulations require that MPOs in non-attainment and maintenance areas use the most

recent mobile source emission estimate models to show that:

a) all federal funded and "regional significant projects" including non-federal projects in regional

Transportation Improvement Programs (TIPs) and plans, will not lead to emissions higher than

those in the 1990 baseline year and

b) by embarking on these projects, emissions will be lower than the no-build scenario.

If a transportation plan, program, or project does not meet conformity requirements, then it must be

modified to offset the negative emission impacts, or work with the appropriate state agency to modify the SIP to

offset it. If any of the above actions is not accomplished, the plan, program, or project cannot be implemented.

Following the CAAA of 1990, the Intermodal Surface Transportation Efficiency Act of 1991(ISTEA) gave state

and local governments the tools to adapt their plans to meet the requirements of the CAAA. The ISTEA

complements the CAAA by providing funding and flexibility to use it in ways that will help improve air quality

through development of a balanced, environmentally sound, intermodal transportation program.

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