No research was found that looks at uncertainty for the whole transportation air quality modeling chain. The National
Research Council (NRC) highlighted the need to address uncertainty in regulatory modeling for environmental
applications in a landmark 2007 report, "Models in Environmental Regulatory Decision Making”. The report assessed
EPA’s use of computer models in developing regulations and recommended a series of guidelines and principles for
improving agency models and decision-making processes for considering uncertainty. Given the long duration in
service (decades) of EPA models, the NRC report identified a critical need for a periodic and transparent review and
update process for each model. This process would ensure that the models continue to meet regulatory needs with
requisite accuracy (for which uncertainty must be assessed and appropriately communicated) and are efficient and
cost-effective to apply. The findings and recommendations of the 2007 NRC study though critical to good modeling
practices have not yet been implemented for the specific application of transportation air quality analyses. This
proposed study would address that gap for the transportation air quality modeling chain.
EPA addressed uncertainty in a 1998 report on “Quantification of Uncertainty in Air Quality Models Used for Analysis
of Ozone Control Strategies”, which demonstrated the use of systematic methods to quantify uncertainty (Bayesian
Monte Carlo) and identified influential sources of uncertainty in photochemical air quality models. The EPA report
emphasized the importance of utilizing the results to help decision makers gauge the reliability of models used for
assessing the effect of proposed control measures and set priorities for further research to improve predictive
capabilities. Early studies on estimating uncertainties of dispersion models include Lamb and Hati (1987), Venkatram
(1988) Lewellen and Sykes (1989), and Weil et al. (1992). Pielke (1998) points out the need to assess the
uncertainties of linked meteorological and dispersion models. A study by Hanna (2007) provided a framework for
uncertainty analysis of atmospheric models used to estimate transport and dispersion, and to tie this framework to
what can be used in other environmental and risk assessment disciplines. A study by Borrego et al. (2008) presented
a review of the existing methodologies to estimate air quality modeling uncertainty and produced guidelines for
modeling uncertainty estimation, which can be used by local and regional authorities for air quality management.
They suggested three types of uncertainty analysis depending on the level of complexity: qualitative analysis
(analysis of model results against measured), total model uncertainty (quantitative analysis of results using a set of
statistical parameters) and total model uncertainty by components (sensitivity analysis and/or model inter-comparison
to evaluate different model modules). It is important to note that most of these studies have focused on a specific
modeling component. However, it is essential to look at uncertainty analysis for the entire modeling chain, as
uncertainties propagate through the entire chain.
The literature is limited in terms of quantifying the range or degree of uncertainty introduced by the dispersion
modeling step (e.g., using AERMOD or CAL3QHCR). While travel demand and emission modeling are well
established and widely used analytical methods, air dispersion modeling is only now emerging as a method for
mobile source air quality analysis. This is an important gap since U.S. EPA conformity requirements have recently
been extended to cover modeling near-road particulate matter concentrations using dispersion models. Streamlining
compliance with these complicated and challenging new modeling requirements would be of significant benefit to
State DOTs and other project sponsors.
A number of studies have shown that there is a critical need to address uncertainties in health risk assessments
(HRA) due to incomplete and unavailable information (OEHHA, 2003; Claggett and Houk 2006, Claggett and Miller
2005, FAA 2001, FAA 2003, FAA2005a, and FHWA 2006). A U.S. Environmental Protection Agency guidance
document indicates that health risk assessments of national and state policies should include sensitivity and
uncertainty analyses (EPA, 2001). Nonetheless, current HRA methodologies do not report variability and uncertainty
in their health impact estimates. Variability and uncertainty in estimated health risks of traffic-related air pollution
arises from multiple sources that includes population, weather patterns, traffic, geographic features, accuracy of
models involved in the modeling chain, etc. (Frey and Burmaster, 1999). For example, EPA conducted risk
assessments near 63 schools throughout the U.S. as part of their study “Assessing Outdoor Air Near Schools”. While
the risk assessment indicated 14 schools were above short or long-term health thresholds for mobile source related
pollutants, the actual monitoring data showed all 14 schools were well below any risk thresholds. After this study was
completed, EPA requested several state DOTs to conduct HRAs under NEPA for large transportation projects.
However, FHWA indicated the outcome of such an assessment, adverse or not, would be influenced more by the
uncertainty introduced into the process through assumption and speculation rather than any genuine insight into the
actual health impacts directly attributable to emission exposure associated with a proposed action. Documenting HRA
uncertainties can be used, as needed, in NEPA administrative records.
Beckx, C., L. Int Panis, K. Van De Vel, T. Arentze, W. Lefebvre, D. Janssens, and G. Wets. 2009. The contribution of
activity-based transport models to air quality modelling: A validation of the ALBATROSS–AURORA model chain.
Science of The Total Environment, Vol. 407, No. 12, pp. 3814–3822.
Borrego, C., Monteiro, A., Ferreira, J., Miranda, A.M., Costa, A.M., Carvalho, A.C., Lopes, M. 2008. Procedures for
Estimation of Modelling Uncertainty in Air Quality Assessment. Environment International, Vol 34(5), pp. 613–620.
California Office of Environmental Health Hazard Assessment (OEHHA). 2003. Air Toxics Hot Spots Program Risk
Assessment Guidelines. The Air Toxics Hot Spots Program Guidance Manual for Preparation of Health Risk
Claggett and Miller 2005. A Method for Evaluating Mobile Source Air Toxic Emissions Among Transportation Project
Alternatives. Paper presented at Air and Waste Management Association 98th Annual Conference and Exhibition.
Minneapolis, MN. June 2005.
Claggett and Houk 2006. Claggett, Michael, Ph.D., and Jeffrey Houk. FHWA Workshop on Project-Level Mobile
Source Air Toxics. Phoenix, AZ. March 7, 2006.
Chen, H., S. Bai, D. Eisinger, D. Niemeier, and M. Claggett. 2009. Predicting Near-Road PM2.5 Concentrations.
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D.C. March 2013. http://archive.epa.gov/schoolair/web/html/index.html
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With Aircraft, Airports, and Aviation. Technical Directive Memorandum D01-010. Washington, DC. July 1, 2003
FAA 2005a. U.S. Department of Transportation, Federal Aviation Administration. O’Hare Modernization Final
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Frey, H.C and Burmaster, D.E. 1999. Methods for characterizing variability and uncertainty: comparison of bootstrap
simulation and likelihood-based approaches. Risk Analysis 19 (1), 109–130.
Hanna, S.R. 2007. A Review of Uncertainty and Sensitivity Analyses of Atmospheric Transport and Dispersion
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Lefebvre, W., J. Vercauteren, L. Schrooten, S. Janssen, B. Degraeuwe, W. Maenhaut, I. de Vlieger, J. Vankerkom, G.
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MIMOSA-AURORA-IFDM model chain for policy support: Modeling concentrations of elemental carbon in Flanders.
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