Mobile Air Quality Monitoring for Local High-Resolution Characterization of Vehicle-Sourced Criteria Pollutants

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

  • Record URL:
  • Supplemental Notes:
    • This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Ohio State University, Columbus

    Department of Civil, Environmental, and Geodetic Engineering
    2070 Neil Avenue
    Columbus, OH  United States  43210-1275

    NEXTRANS

    Purdue University
    3000 Kent Avenue
    Lafayette, IN  United States  47906-1075

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • May, Andrew A
    • Mishalani, Rabi G
    • McCord, Mark R
    • Sivandran, Gajan
    • Zou, Yangyang
  • Publication Date: 2017-6-19

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; Photos; References;
  • Pagination: 20p

Subject/Index Terms

Filing Info

  • Accession Number: 01653364
  • Record Type: Publication
  • Report/Paper Numbers: NEXTRANS Project No. 179OSUY2.2
  • Contract Numbers: DTRT12-G-UTC05
  • Files: UTC, TRIS, RITA, ATRI, USDOT
  • Created Date: Nov 30 2017 11:01AM