Uncertainty in Project-level Emissions and Air Quality Analysis
When a transportation project is undertaken, an air quality analysis may be required to meet federal and/or state requirements related to U.S. EPA transportation conformity regulations, NEPA, associated FHWA guidance, and/or state environmental policy. The nature (qualitative or quantitative) and scope/depth of the analysis varies according to project criteria and is specified in FHWA and EPA guidance. Major projects often require quantitative analysis that includes forecasting of mobile source emissions under each project alternative based on traffic projections, and in some case dispersion modeling to estimate changes in localized air pollutant concentrations. Changes in emissions may be compared with budgets set for conformity purposes, and changes in ambient air quality may be compared with National Ambient Air Quality Standards. The air quality analysis is generally reported and evaluated as a single point estimate for each project alternative and year. This does not communicate the uncertainties that may exist in the data and models that underlie the analysis. Uncertainties may arise as a result of key data inputs and drivers (e.g., traffic growth, traffic mix, speeds, fuel characteristics, future meteorology) as well as uncertainties introduced within models (e.g., MOVES for emission rates, AERMOD or CAL3QHC for dispersion and concentration values). Some uncertainties may affect mainly the baseline, leaving the comparison of relative air quality impacts under each alternative robust; but others may affect the reported differences among alternatives, possibly leading to different rankings of alternatives (including the No Build) under a range of plausible assumptions. If this were the case, the value of the air quality analysis at informing the decision would be called into question. However, the impact of uncertainties on project evaluation outcomes is rarely evaluated in practice, so the potential effects of uncertainty on decision-making are unknown. This research would consider the following questions: 1. What are the limitations of existing methods for modeling project-level air quality impacts? 2. How can we properly estimate and validate the lower and upper bounds of the modeling uncertainty based on the state-of-the-practice methods? 3. How does uncertainty at each step of the modeling process, or with each key input, vary by order of magnitude? What are the key sources of uncertainty? 4. What uncertainty factors (alone or interacting with others) could affect the directionality, not just the magnitude of emissions and pollutant concentration change estimates? 5. Are there systematic biases in these estimates? 6. How can uncertainty be considered in the various processes in which emissions and air quality analysis are conducted? 7. What is best method or appropriate tool(s) in quantitative treatment of uncertainty in emissions and air quality analysis? 8. How can methods for quantifying variability and uncertainty in emission inventories, and identifying key sources, be made flexible enough to accommodate context-specific assessment objectives? 9. What steps can we take to reduce the uncertainties in project-level air quality analysis? It is expected that a study relying primarily on existing data/analysis could be funded in the upper half of the $100-249k range. A higher level of funding could support more new analysis/modeling to support uncertainty quantification.
Uncertainty in the modeling chain was identified as a key under-studied area in transportation and air quality by a group of practitioners and researchers convening for the TRB Air Quality Committee 2018 Summer Meeting. Quantitative air quality analysis can add a significant level of effort to environmental reviews. It is hoped that expending this level of effort would lead to a corresponding value added to the information used to support decision-making. In addition, the analysis should provide information on environmental effects, as required by Federal and state laws, with a reasonable level of confidence. This work will investigate the reliability of current air quality analysis methods regarding project-level air quality impacts. If the reliability of these estimates are questionable, the research will propose methods for considering and communicating uncertainty, potential changes to procedural approaches that acknowledge and consider uncertainty, and additional research or methodological development that may be needed to improve estimates.
Cambridge Systematics, Inc.
June 3, 2019
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