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Characterizing the determinants of vehicle traffic emissions exposure: Measurements and modeling of land-use, traffic, emissions, transformation and transport

Principal Investigator: 

North Carolina State University

This study is exploring how traffic activity metrics, land-use parameters, and transport of pollutants influence the near-road concentrations measured through extensive sampling campaigns.

Funded under
In review

Abstract for the 2017 HEI Annual Conference

Characterizing the Determinants of Vehicle Traffic Emissions Exposure: Measurement and Modeling of Land-Use, Traffic, Transformation and Transport
H. Christopher Frey, Andrew Grieshop, Nagui Rouphail, Montse Fuentes, Andrey Khlystov, John Bangs, and Daniel Rodriquez
North Carolina State University, Raleigh, NC, USA; Desert Research Institute, Reno, NV, USA; North Carolina Central University, Durham, NC, USA; University of North Carolina–Chapel Hill, USA
Background The objectives are to:  (1) determine the most important variables that explain spatial and temporal variance of near road traffic-related pollutant concentrations; (2) demonstrate novel surrogates of near-road traffic-related pollution: and (3) improve inputs for exposure models for traffic-related health.  We focus on key factors that influence the source-to-exposure continuum: built environment; road infrastructure and traffic; transport and transformation of traffic generated pollutants from source to near road receptors; and concentrations in the near road environment.
Methods We leveraged an EPA near road air quality monitoring site along I-40 in Wake County, NC, and a newly installed urban monitoring site at North Carolina Central University in Durham, NC.  Land use metrics, and spatially and temporally resolved metrics for traffic activity, emissions source strength, meteorology, and measured near road air quality, were used to calibrate spatiotemporal models of near road air quality.  Traffic was monitored using an existing fixed site traffic detector at the I-40 site and temporary video-based traffic detection at the Durham site, supplemented with on-road measurements of vehicle trajectories.  We have conducted summer and winter field measurements at each of the I-40 freeway and Durham intersection sites.  At the I-40 site, measurements of aerosol size distributions, NO/NO2, black carbon (BC) and aerosol mass concentration and volatility were conducted.  Measurements of NO/NO2, BC and aerosol size distribution were collected at a background site.  Furthermore, transects perpendicular to I-40 in the downwind direction were conducted including measurements of aerosol size distributions, NO/NO2, black carbon (BC), and aerosol volatility at 4-5 locations (15, 50, 100, 150, 220 m from highway edge). Measurements at the urban site included walk-along air quality trajectories for UFP, PM2.5, and ozone, and daily PM2.5, NOx, and O3 measurements at the four quadrants surrounding the key intersection of interest.
Results At the freeway site, vehicle emission tracers (NOx, NO, BC) decay to background levels within 200-300 m of the highway edge.  Vehicle emission related pollutants (e.g., NOx, NO, BC, sub-micron particle number) concentrations were consistently higher during the morning compared to mid-day and afternoon measurements. This trend is likely linked with both a lower morning mixing-height and high traffic volume during morning rush hour.  Aerosol size distributions at the near-road site show the dominant contribution from the smaller, fresh vehicle emissions superimposed upon the regional aerosol measured at the background site.  We evaluated a hybrid statistical-mechanistic model formulation in which air pollutant concentration gradients predicted using R-LINE were incorporated into the statistical framework.  As an example, for NOx concentration at the freeway site, the key statistically significant predictors include season, wind direction, spatial concentration gradient predicted using R-LINE, and heavy duty vehicle count.  The freeway near-road NOx concentration model was validated with data collected at the same site but with a separate monitor not used for model calibration and for time periods other than those used to calibrate the model.  At the urban site, there is significant spatial and temporal variability in ultrafine particles and ozone.  For PM2.5, temporal variation was significant but spatial variability was not.  UFP concentrations were found to be significantly related to distance from the nearest bus stop, traffic flow on the main corridor, wind speed, and temperature.  The generalizability of the developed freeway site model is being assessed via application to a different study area.
Expected Results The long-term goal of this work is to enable improved quantification of human exposure to traffic generated pollution.  An example is improving the scientific basis for future risk and exposure assessments that support review of the National Ambient Air Quality Standards and other policy-relevant applications.