Identification of the Models, Linkage and Variables Used in Highway Air Quality Analysis
Research Idea Scope
Background: There are several types of models used in analyzing highway emission impacts to air quality for both regional analysis and project or hotspot analysis and for both for NEPA and conformity analyses. These models are generally classified as traffic operations/transportation demand models, emission factor models, and dispersion models for most applications. Another type of model, although not typically used in the transportation analysis process are exposure models. Generally, the output of one model is used as input to another model, and thus the linkage of these models. Traffic operation and transportation demand models generate the volume of traffic and characteristics of that traffic such as speed. Emission factor models generate the emission rates in mass/distance or mass/time. Traffic characteristic and emission factors from these two types of models can be used together to develop emission inventories or can serve as inputs to dispersion models to yield concentrations in the ambient air. Concentrations outputs from dispersion models can then be used for exposure models to potentially provide some indication of the health effects and may provide risk assessment related to those emissions. This study will examine the models, the linkage of the models, the variables and the range of variable values used in the models to determine an area’s or a project’s contribution to regional and project emissions and concentrations. To start the evaluation, the project will examine traffic and transportation demand models which provide information about the sources of emissions. Although there are a number of these that are used, the analyst should identify one or two of the more commonly used traffic and travel demand models used as the source of traffic and travel activity information. Some models can be quite specific to as to the location of their use but the inputs, outputs and assumptions that are included or generated by the models are what is relevant to this project since this project is focused on identifying the variables and outputs and how they are passed from one model to the next model. The next model type that will be investigated is the emission factor model. For this step, the MOVES2014a model will be examined (since it is the model used in all States except California) to determine the variables used by the model including the variables that have values generated by the traffic/travel models in the previous step. The examination of this model would include the variables unique to the emission factor model itself and the variables satisfied by the output from the traffic/travel model examined in the first step. The output from the emission factor model can be used in conjunction with the traffic/travel demand models to develop a mobile source emission inventory or to develop emission factors for use in the last of the highway analysis models – the dispersion model. The final step would be to examine the variables required to run the dispersion model including those which were generated by the traffic/transportation model and the MOVES2014a emission factor model. For this investigation, ideally both commonly used models – EPA’s AERMOD and the CAL3QHCR models – would be investigated since both are currently used. However, if only one of the two models will be investigated, the AERMOD model should probably be selected since this is the model that is more likely to be used in the future due to EPA support for the model. An examination of a commonly used exposure model and risk assessment activities could also be considered as an optional although this topic isn’t generally dealt with in highway air quality impact assessment. Objective: The objective of the project is to identify the models and the variables in the models that are used in conducting highway-related air quality analysis. The reason for this is for the analyst to understand the data that is passed from one model to another, from the traffic and travel generating model to its use by the emission factor model; from the traffic and travel activity model and the emission factor models to the dispersion mode. Understanding the linkage between the individual models through the variables common to models will enhance the user’s ability to perform required air quality impact analysis. It will enable the analyst to consider appropriate variable ranges within the traffic/travel, emission factor, and dispersion model framework as the models are integrated sequentially into this framework – traffic data into emission factor model, traffic and emission factor data into dispersions model. These individual models were not designed to be integrated into a sequential chain which can lead to errors and accuracy issues with the final output data. Understanding the individual use of the models and integrating their outputs in the modeling chain process can improve the result calculated by the model and reduce improper modeling assumptions and practices. Two considerations when undertaking this project include: 1) Efficiency (or “parsimony”): In keeping with US DOT and state DOT initiatives to streamline the project-development process, and to the extent appropriate for the application, limit the required number of modeling inputs and their respective level of detail and precision to just those that contribute the most cost-effectively to model accuracy and reductions in uncertainty, which may be identified in a study conducted as part of the MIP process considering costs (for preparing input data and running the model) and sensitivity assessments of the contribution of each input to the accuracy and uncertainty of the output from each model and the overall modeling chain (traffic, emissions and dispersion) 2) Proportionality: In keeping with US DOT initiatives to streamline the project-development process, assess how accurate and precise the models “need” to be in order to meet specific regulatory requirements and, conversely, not require more accuracy, level of detail and precision of the models and inputs than are needed to meet the regulatory objectives for the specific regulatory application. Note careful consideration of uncertainties in modeling inputs, the model itself, and in background concentrations relative to the applicable NAAQS must be given Tasks (including deliverables): In general, panel review and approval is needed with each task. 1. Select the models to be evaluated – one commonly used traffic activity model (used in project level analyses); one commonly used travel demand model (used in regional analyses); the MOVES2014a emission factor model; the CAL3QHC/R and or the AERMOD dispersion model. Provide list of all the variables in each model. Although not all variables in each model may be pertinent to the project, identifying them may have usefulness in considering uncertainty issues that may arise in using the model. 2. Determine a subset of the variables that are used by more than one model. For instance, the emission factor model will require vehicle types or classes, speeds of the vehicles, meteorological data as well as other data. The dispersion model will require these same variables so these are common to both modes. 3. Define a range of values for the variables. In some models, certain values are not appropriate or will lead to inaccurate projected outputs. Defining this range will reduce errors and uncertainty with the model results. 4. Execute the traffic model (for project analyses) and the travel model (for regional analyses) for several scenarios to generate the vehicle activity that will be used for the emission factor model. In summary of results, list data used for all variables. 5. Using the traffic data and the travel data in Step 4, execute the emission factor model for the several traffic/travel scenarios initiated in Step 4 to generate emissions factors. Calculate the total emissions using the travel data from Step 4 and the emission factors using the traffic data from Step 4. In summary of results, list data used for all variables including the data used to generate the traffic/travel inputs used in the emission factor model from Step 4. 6. Using the traffic and travel data in Step 4 and the emission factor data from Step 5, execute the dispersion model using the traffic data from Step 4 and the emission factor data from Step 5. 7. Summarize the results in a final report of the results of running the various scenarios with the purpose of illustrating possible locations where inappropriate or inaccurate data may be used inadvertently or unknowingly by an analyst unfamiliar with the modeling chain process.
Urgency and Payoff
DOTs and MPOs continue to “wrestle” with the use of the various air quality models to meet their mobile source emission requirements. Key to reducing the uncertainties in the use of the various model types is a better understanding of the variables and data sources required by the model especially any variable used in one model that is dependent on data generated by another mode. Understanding the variables and data sources used by the models individually and within the modeling chain will provide greater confidence in using the models, reduce uncertainties in any particular model result, and lead to improved accuracies in the data generated by the models.
Kevin Black FHWA 410-962-2177