NCHRP 25-07 [Completed]
Improving Transportation Data for Mobile Source Emissions Estimates
| Project Data
||The University of Tennessee, Transportation Research Corp.|
The 1990 Clean Air Act Amendments (CAAA) require states to attain and maintain ambient air quality standards. Geographic areas not meeting air quality standards are designated as nonattainment areas and must satisfy certain requirements and deadlines depending on the severity of the air quality problem. Mobile sources, like automobiles and other vehicles, are considered to be a significant component of the nonattainment problem, and consequently, the transportation sector is expected to provide appropriate emissions reductions. Decisions for achieving reductions are based on different levels and types of analyses appropriate to local conditions, such as attainment status, the size and complexity of the area, and the type of pollutants. Also, there are a variety of analysis needs, including, but not limited to, emission-inventory development, transportation-control management (TCM) strategies, conformity assessments, and state implementation plan (SIP) development.
To meet these requirements, transportation data are being and will be used, but frequently in ways not originally intended. This usage results in a lack of fit in transportation data inputs into the air quality modeling process. For example, the speed outputs from travel models are subject to significant error, yet they are among the most crucial inputs into the mobile source emissions models, such as the U.S. Environmental Protection Agency's MOBILE 5 model or the California Air Resources Board's EMFAC model.
In addition, air quality modeling often requires transportation data that are difficult to obtain or may not exist. To overcome this problem, default values for transportation data, such as cold starts and vehicle mix and age characteristics, are used. Also, the CAAA identifies estimates of vehicle miles traveled (VMT) as an important component in the attainment of ambient air quality standards, but no accepted technique exists for estimating VMT on local roads. Yet, local roads are assumed to carry one-third of the VMT in an urbanized area, under high emission conditions of low speeds and high idle times. The impacts of these problems on emissions estimates for nonattainment areas with differing characteristics are not well understood.
Because transportation modeling and air quality modeling are dependent on each other, a better understanding of this dependence and the underlying assumptions used in individual modeling processes is needed. Recognizing the prescriptive nature of the types of air quality models that must be used, an initial priority is to examine the impact of transportation data on the estimates from these air quality models and on air quality planning.
The objective of this research is to improve the integration of transportation data with emissions estimation procedures and air quality planning. Key elements of this integration process include: (1) transportation variables that are available or necessary for developing emissions burdens and other air quality projections, (2) techniques for developing values for these variables, and (3) interrelationships between transportation data and emission rates. The research will critically evaluate these elements, and then identify and prioritize improvements to existing procedures for calculating or estimating transportation data, given existing transportation, emissions, and air quality models.
Accomplishment of this objective will require at least the following tasks. Tasks 1 through 3 need not be done sequentially. (1) Identify Key Variables by Level and Type of Transportation/Air Quality Analysis. Identify the factors that dictate how a nonattainment area must structure its level of effort with respect to collecting data and estimating mobile source emissions. Given the requirements of the CAAA, describe the process by which monitoring tools such as the Federal Highway Administration's Highway Performance Monitoring System (HPMS), travel demand models, emissions models, and air quality models are integrated for air quality purposes. Identify the transportation variables of significance in emissions modeling and air quality planning. Expected variables include speeds, trip ends, cold starts, vehicle class and age distribution, percent of high emitters and temporal and spatial patterns of VMT; however, additional variables are expected to be identified based on inputs to the MOBILE and EMFAC emission models, as well as other air quality analysis needs. (2) Evaluate Current Practice and Alternative Approaches. (a) Describe and evaluate the current state of practice in transportation data development for air quality analyses. Alternative approaches not in practice, but available, shall be included. Information sources may include literature searches, surveys, interviews, existing SIPs, and the National Association of Regional Councils' (NARC) manual for modeling practices for air quality analyses. (b) Investigate the relationships among different model sets and monitoring tools and assess the consistency of data, the approach, and the management of information. Existing databases, models, theories, and methodologies will be evaluated to determine which produce the data and information most suited for use in air quality estimation.
Evaluations will include assumptions made, known correlations of variables and estimates based on currently used data, ease of implementation, and suitability of data format for air quality estimate input. Strengths and weaknesses of each method will be identified. (c) Assess the effect of local, functionally classified road information and varying geographic boundaries on VMT estimates. (d) Assess the use of travel behavior information taken from travel surveys in making air quality estimations. (3) Conduct Sensitivity and Uncertainty Analyses. Estimate the sensitivity of emissions models to key transportation variables. Uncertainties for each variable will be quantified for the various levels of spatial and temporal aggregation required for various regulatory analyses. A qualitative evaluation of the influence of these uncertainties on other air quality models will also be included. (4) Prepare Final Report and Establish Priorities for Developing Improvements. The contractor will prepare a report documenting Tasks 1 through 3. The report will include the following: (a) An introductory overview of how the travel, emissions, and air quality models relate to one another, and the most important assumptions and variables. (b) Recommendations of the most appropriate method(s) to be used for the various levels and types of analyses examined. Recommendations will build on and not duplicate the NARC modeling manual. (c) Priorities for improvements to estimation procedures and, if appropriate, the collection methods of needed transportation data. These recommendations shall reflect the expected degree of improvement in emissions estimates and the cost and level of effort required to achieve the improvements. (d) Protocols for implementing and testing high priority improvements. As appropriate, software documentation and various databases created throughout the research effort will be submitted with the report. All computer programs and databases will be in the public domain.
Status: The project has been completed. NCHRP Report 394, "Improving Transportation Data for Mobile Source Emission Estimates," documents the findings of this project.