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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 2018 HEI Annual Conference

Characterizing the Determinants of Vehicle Traffic Emissions Exposure: Measurement and Modeling of Land-Use, Traffic, Transformation and Transport

H. Christopher Frey,1 Andrew Grieshop,1 Nagui Rouphail,1 Joe Guinness,1 Andrey Khlystov,2 John Bangs,3 Daniel Rodriquez4

1 North Carolina State University, Raleigh; 2 Desert Research Institute, Reno, NV; 3 North Carolina Central University, Durham; 4 University of California - Berkeley

Background. The objective is to determine key sources of spatial and temporal variance of near road traffic-related pollutant concentrations, taking into account built environment; road infrastructure and traffic; transport and transformation of traffic generated pollutants; and concentrations in the near road environment.

Methods. We conducted measurements at a freeway site and urban site. Metrics for land use, traffic activity, emissions source strength, meteorology, and near road air quality were used to calibrate spatiotemporal models of near road air quality for both sites. Traffic was monitored using a detector at the I-40 site and video at the Durham site, supplemented with on-road measurements of vehicle trajectories. Summer and winter field measurements were made at the freeway and urban sites. At the freeway site, aerosol size distributions, NO/NO2, black carbon (BC) and aerosol mass concentration and volatility were measured. Measurements of NO/NO2, BC and aerosol size distribution were collected at a background site. Aerosol size distributions, NO/NO2, black carbon (BC), and aerosol volatility were measured at locations perpendicular to the freeway. Measurements at the urban site included pedestrian transects for UFP, PM2.5, and ozone, and daily PM2.5, NOx, and O3 measurements at the four quadrants surrounding the intersection.

Results. For the freeway site, near road NOx concentrations were found to be significantly influenced by upwind background concentration, season, wind direction, wind speed, and the interaction between predicted dispersion from R-LINE and heavy duty vehicle index. For UFP at the freeway site, the significant predictors include background concentration, temperature, season, wind direction, and interaction between predicted dispersion and vehicle density index. At the urban site, daily average NOx concentrations were significantly influenced by distance-adjusted traffic, season, rain, temperature, and the interaction between distance adjusted traffic and season. The urban site UFP concentrations were significantly influenced by distance from the nearest bus stop, traffic counts, tailpipe CO emissions from the nearest 0.05 mile road segment, wind speed, temperature, and relative humidity. We applied the freeway site NOx concentration model to a different site to gain insight regarding model validity and generalizability.

Conclusions. In general, air pollutant concentrations decrease with distance from the roadway and are affected by atmospheric stability, mixing height, wind speed, wind direction, and physico-chemical interactions. Some species, such as NOx and BC, are relatively unreactive and are useful tracers of vehicle emission dispersion, with the latter being a marker for diesel emissions. UFP number concentrations are strongly influenced by ambient temperature/season. Indices based on traffic count and proximity to the receptor are useful predictors of road side concentrations. Spatial variability in near road concentrations is influenced by spatial variability in vehicle emissions, which are not uniformly distributed along a road. Predicted dispersion is helpful in predicting near road concentration gradients. Relatively simple statistical models can explain a substantial amount of variability in near road concentrations. Implications for future work include further assessment and selection of averaging times, spatial resolution, and study locations for further data collection and model development.



H. Christopher Frey, Maryam Delavarrafiee, and Sanjam Singh. Real-World Freeway and Ramp Activity and Emissions for Light-Duty Gasoline Vehicles. Transportation Research Record: Journal of the Transportation Research Board, No. 2627, 2017, pp. 17–25.

Provat Kumar Saha, Andrey Khlystov, and Andrew Patrick Grieshop. Downwind evolution of the volatility and mixing state of near-road aerosols near a US interstate highway. Atmos. Chem. Phys., 18, 2139-2154, 2018 

Saha, P. K., Khlystov, A., Snyder, M. G., and Grieshop, A. P. (2018). “Characterization of air pollutant concentrations, fleet emission factors, and dispersion near a North Carolina interstate freeway across two seasons.” Atmospheric Environment, 177, 143-153