United States Department of Transportation
i
Mobile Air Quality Monitoring for Local High-Resolution Characterization of Vehicle-Sourced Criteria Pollutant
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2017-06-19
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[PDF-1.71 MB]
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NTL Classification:NTL-ENERGY AND ENVIRONMENT-ENERGY AND ENVIRONMENT;NTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATION;
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Abstract:Transportation-related emissions are a major source of air pollution in many urban areas. Human exposure to this pollution is related to their proximity to major roadways, yet federal and state Environmental Protection Agencies (EPAs) conduct regulatory air quality monitoring at sparsely-distributed, fixed-site stations. While these distributed stations are useful for inferring the general hourly air pollution exposure of the public located within a broad geographic region, they cannot accurately capture exposure at a finer spatial resolution and adequately capture the spatiotemporal variability in transportation-related air pollution (TRAP). For example, there may be large differences in TRAP near urban highways and suburban collector roads within a geographic region. Furthermore, the temporal variations at each of those locations are likely to be substantially different in light of the differences in the factors that contribute to such variations. For examples, in addition to traffic activity, the built environment and terrain can also play a role in this variability. Thus, the use of data from the EPA monitoring sites may result in large uncertainties when estimating human exposure in different micro-environments within an urban area. In lieu of empirical data, models can be used to estimate air quality at locations away from monitoring sites. Dispersion models are used routinely by the EPA, but these models require some knowledge of the pollutant source. For TRAP, US EPA’s Motor Vehicle Emission Simulator is used to calculate vehicle emissions in a manner sensitive to roadway traffic conditions. However, the software is designed for spatiotemporal predictions of average conditions resulting in model uncertainties. For example, there is uncertainty in exposure assessments due to the transient nature of traffic activity throughout time (e.g., morning and afternoon commutes, weekday and weekend effects). Alternatively, statistical forecasting models can be used to predict air quality, but these models may reflect even more uncertainty due to the scarcity of data that are available to inform the estimates. In short, model outputs cannot substitute for the high-resolution spatiotemporal detail of air quality or the in-depth understanding of local conditions, which are the primary foci of this research effort
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