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Office of Planning, Environment, & Realty (HEP)

Toolbox for Regional Policy Analysis Report (2000)

Impact Methodologies - Environmental - Operating


Operating environmental impacts result from the operation of transportation vehicles. Operating impacts may include:

Key factors determining environmental impacts include:

Case Studies

Sacramento, California

An integrated transportation-land use model is used to incorporate feedback from transportation investments into land development, and subsequently into transportation performance and transportation-related emissions.

Salt Lake City, Utah

A simplified air quality model is developed that simulates the dispersion of emissions from mobile and point sources. Pollutants are then overlaid on population data to obtain metrics of the air quality impacts of alternative regional development scenarios.


Transportation-related emissions data are decomposed within a GIS raster (grid cell) environment to pinpoint emissions spatially. A dispersion model is then applied to track pollutants and to measure their coincidence with population. Exposure to emissions is measured by socioeconomic group to develop indicators of equity.

Waterloo, Iowa

GIS is used in conjunction with emissions and noise models to plot emissions and noise contours around transportation facilities. These are then overlaid on minority and low-income population data to develop measures of environmental justice.

Forecasting Methods

Emissions Air Quality Noise Energy Forecasting Methods


Key emissions include criteria pollutants and precursors (VOCs, CO, NOx, PM, and SOx); toxics; and greenhouse gases, especially CO2. Emissions are an intermediate impact. Impacts of ultimate concern include the level of population exposure to unhealthful levels of criteria pollutants, as well as contributions to acidic precipitation or greenhouse gas effects.

Method 1: Heuristics

Heuristic methods involve the multiplication of average emission rates by VMT, by vehicle type. This approach can be taken to estimate criteria pollutants or CO2 emissions from motor vehicles when a detailed analysis is not required. Emission rates can be obtained from the output of emission factor models applied for other purposes.

Method 2: Emission Factor Models

Emission factor models are used to develop emission rates (grams of pollutant per mile of travel or start) for a variety of pollutants. These are estimated for an average vehicle in the vehicle fleet, based on a variety of data on the vehicle fleet characteristics, temperatures, speeds, and other factors that influence emissions. Commonly used emission factor models the U.S. EPA's MOBILE and PART5 models and the California Air Resources Board's MVEI model. MOBILE and MVEI can estimate emissions of hydrocarbons, NOx, CO, and CO2. PART5 bears similarities to MOBILE and estimates PM emissions. A new version of MOBILE, MOBILE6, will be released in early 2001 to replace the existing MOBILE5 model. Outputs of emission factor models are then multiplied by VMT by speed, vehicle type, temperature, and/or other parameters to estimate overall vehicle fleet emissions. Emission factor models are widely applied in the U.S. for regulatory purposes.

If transportation inputs can be developed spatially (e.g., by traffic analysis zone), emission factors can be applied to estimate total emissions on a spatial basis. Emissions by location can then be used as inputs to regional airshed models that simulate the reaction of primary pollutants to form secondary pollutants (such as ozone), as well as the movement of these pollutants.

Method 3: Advanced Emission Models

Considerable research has been undertaken recently to improve the state of practice in emissions modeling. In addition to continuing advances by EPA and CARB to the MOBILE and MVEI models, notable developments include:

Air Quality

The Clean Air Act, which was last amended in 1990, requires EPA to set National Ambient Air Quality Standards for pollutants considered harmful to public health and the environment. The Clean Air Act established two types of national air quality standards. Primary standards set limits to protect public health, including the health of "sensitive" populations such as asthmatics, children, and the elderly. Secondary standards set limits to protect public welfare, including protection against decreased visibility, damage to animals, crops, vegetation, and buildings. EPA has established ambient standards for six "criteria" pollutants including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), lead (Pb), particulates less than 10 micrometers (PM10) and less than 2.5 micrometers (PM2.5), and sulfur dioxide (SO2).

Assessing the impacts of transportation activity on air quality requires translating emissions by location into criteria pollutants by location. Air quality models have been developed at the micro-scale, to examine air quality impacts adjacent to roadway segments or intersections, as well as at the regional scale, to examine the regionwide formation and transport of pollutants. Some emissions from vehicle exhaust, such as CO, are also criteria pollutants. Others, such as volatile organic compounds (VOC) and oxides of nitrogen (NOx), react to form criteria pollutants such as ozone.

For criteria pollutants from exhaust, it may be sufficient to model only the dispersion of pollutants from the source of emissions. For secondary pollutants such as ozone, it is also necessary to model reactions that take place in the atmosphere that lead to formation of the pollutant. In either case, modeling of air quality requires, at a minimum:

A final step, which is usually not performed in air quality modeling, is to overlay the distribution of air pollutant concentrations with the distribution of human activity, to estimate the actual exposure of people to pollution.

Method 1: Micro-scale Transport/Diffusion Models

Micro-scale transport/diffusion models, such as CALINE or CAL3QHC, are commonly used to assess the air quality impacts of actions such as intersection improvements or developments that increase traffic at particular intersections. These models require data on the traffic patterns on the roadway, wind speed and direction for typical meteorological conditions, and the geometry of the roadways and receptors. The models produce concentrations of pollutants by distance from the roadway. Micro-scale models are typically applied for CO, but may also be applied for NO2 and PM.

Method 2: Regional Transport/Diffusion Models

Regional Gaussian dispersion models simulate the transport of pollutants through a region, usually across a two-dimensional grid. As with micro-scale models, wind speed and direction for typical meteorological conditions are used in conjunction with emissions by spatial location (generally by grid cell) to estimate concentrations in each grid cell. These models can be overlaid with population data to estimate exposure to pollutants such as NO2 and PM. Guassian dispersion models are much simpler to apply than regional airshed models, which also model reactions and may be three-dimensional. However, they cannot be used to determine exposure to pollutants, especially ozone, that are formed mainly through chemical reactions.

The Envision Utah and SPARTACUS case studies both provide examples of the application of Gaussian dispersion models at the regional level:

Method 3: Regional Airshed Models

Regional airshed models also operate by taking inputs of typical meteorological conditions and emissions by grid cell, and simulating the movement of pollutants among grid cells. In addition, the models consider reactions between compounds that depend upon meteorological conditions. They are typically applied in the U.S. for regulatory modeling purposes, to simulate conditions under which ozone exceedances are likely to occur. The Urban Airshed Model is a commonly used model in the U.S.

Regional airshed models are data-intensive and time-consuming to develop and calibrate. Also, the relationships being modeled are quite complex and so current models have some limitations. As a result, regional airshed models are not normally used in conjunction with transportation models to model the impacts of alternative regional transportation investments or policies. With advances in computing power, scientific knowledge, and GIS data management techniques, the direct use of these models in transportation planning could become more common in the future. The MODELS-3 project being undertaken by the EPA is one effort to advance the state of practice in this area.


Noise can be defined as unwanted or detrimental sound. Traffic noise is a function of the volume, speed, and composition of traffic. The level of noise at any given point also depends on the distance from the source and physical objects that may absorb or reflect sound waves. Sound is measured in decibels (dB), which have a logarithmic scale so that an increase of 10 dB sounds twice as loud. When measuring noise, an adjustment factor is typically applied to weight high-pitched and low-pitched sounds to approximate human hearing of these sounds. The resulting measure is known as "A-weighted decibels" (dBA).

Noise from traffic can be disruptive to people both outdoors and indoors by causing sleep disturbance, communication interference, and general annoyance. Tolerance for noise can vary greatly from person to person. Communities often set acceptable noise thresholds based on the time of day and adjacent land uses. Common noise standards include L10, the noise level in dBA exceeded 10 percent of the time during specified hours; L50, the noise level exceeded 50 percent of the time; and Leq, a scale that converts a varying noise level to an equivalent constant noise level.

From a regional planning perspective, the exposure of population to traffic noise can be influenced by the location of major roadways; automobile and truck traffic volumes; the design of neighborhoods and buildings; and attenuation measures such as noise wall construction.

Method 1: Traffic Noise Models
Method 2: Noise Valuation


Energy consumption from transportation is primarily a function of vehicle-miles of travel and fuel efficiency by vehicle type. Other parameters, such as speed and acceleration, also influence energy consumption. However, their effect on regional transportation energy use is not usually considered because of the extra data requirements.

In addition to the transportation system, land use planning and urban design affect energy use. The density, mix, and arrangement of land uses in a community heavily influence the amount and mode of travel and, therefore, transportation energy use. These same urban characteristics also affect the amount of energy needed to heat and cool buildings and to build and operate community infrastructure. The relationship of energy consumption to land development patterns can be estimated by relating development by type and density to energy use. The more detail is available on the specific design characteristics of development (e.g., size of buildings, solar orientation, relative siting, topography), the more accurate the measurement of energy consumption.

Energy consumption can be important because of the negative environmental impacts associated with many types of energy use, as well as the economic and national security implications of dependence on foreign energy. In the case of transportation vehicles, energy consumption is almost entirely from petroleum-based fuels, of which a substantial proportion are from foreign oil reserves. Consumption of petroleum fuels is also directly related to emissions of carbon dioxide, a greenhouse gas.

Method 1: Heuristics
Method 2: Custom Models

A handful of custom models have been developed to assist state and local governments in identifying the energy impacts of different transportation and land use scenarios. These include:

General References

The EPA Office of Transportation and Air Quality provides information and software in support of emission factor models including MOBILE5, MOBILE6, and PART5.

The EPA Support Center for Regulatory Air Models provides information and software for the Urban Airshed Model, CAL3QHC, and other air pollutant dispersion models.

National Cooperative Research Program Project 8-33, Quantifying air quality and other benefits and costs of Transportation Control Measures, reviews the state of practice and discusses needed advances in modeling emissions and air quality from transportation sources.

Guidance on estimating noise impacts of highway projects can be obtained from the Federal Highway Administration Office of Environment and Planning.

Updated: 4/24/2012
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