Chapter 55
Assessment of Fine-Scale Dispersion
Modelling for Near-Road Exposure
Applications
Jennifer L. Moutinho, Donghai Liang, Jeremy Sarnat
and Armistead G. Russell
Abstract Detailed measurements and dispersion modeling were conducted to
develop more accurate integrated metrics to assess exposure to potentially high pollutant levels of primary traffic emissions. A 13-week intensive sampling campaign
was conducted at six monitoring sites surrounding one of the busiest highway segment in the US with the study area focusing on the Georgia Institute of Technology
campus to capture the heterogeneity in pollutant concentrations related to primary
traffic emissions. A dispersion model (RLINE) was used to develop spatial concentration fields at a fine-spatial resolution over the area of primary exposures. Initial
RLINE results were highly biased, due either to errors in the emissions or the model.
Analysis suggests that both may be important, depending upon species, though the
largest errors were due to how the model represents near-source dispersion, especially when the wind aligns with the road segments. To correct for high near-road
bias, the RLINE results were calibrated using measurement observations after the
urban background was removed. Performing the calibration hourly also reduced the
bias observed in the diurnal profile. Both the measurement observations and dispersion modeling results show that the highway has a substantial impact on primary
traffic pollutant (particularly elemental carbon and carbon monoxide) concentrations
and captures the prominent spatial gradients across the campus domain, though the
gradients were highly species dependent. These improved concentration fields were
used to enhance the characterization of pollutant spatial distribution around a traffic
hotspot and to quantify personal exposure to primary traffic emissions.
J. L. Moutinho (B) · A. G. Russell
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA,
USA
e-mail: jennifer.moutinho@gatech.edu
A. G. Russell
e-mail: ted.russell@gatech.edu
D. Liang · J. Sarnat
Rollins School of Public Health, Emory University, Atlanta, GA, USA
e-mail: donghai.liang@emory.edu
J. Sarnat
e-mail: jsarnat@emory.edu
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_55
347
Assessment of Fine-Scale Dispersion
Modelling for Near-Road Exposure
Applications
Jennifer L. Moutinho, Donghai Liang, Jeremy Sarnat
and Armistead G. Russell
Abstract Detailed measurements and dispersion modeling were conducted to
develop more accurate integrated metrics to assess exposure to potentially high pollutant levels of primary traffic emissions. A 13-week intensive sampling campaign
was conducted at six monitoring sites surrounding one of the busiest highway segment in the US with the study area focusing on the Georgia Institute of Technology
campus to capture the heterogeneity in pollutant concentrations related to primary
traffic emissions. A dispersion model (RLINE) was used to develop spatial concentration fields at a fine-spatial resolution over the area of primary exposures. Initial
RLINE results were highly biased, due either to errors in the emissions or the model.
Analysis suggests that both may be important, depending upon species, though the
largest errors were due to how the model represents near-source dispersion, especially when the wind aligns with the road segments. To correct for high near-road
bias, the RLINE results were calibrated using measurement observations after the
urban background was removed. Performing the calibration hourly also reduced the
bias observed in the diurnal profile. Both the measurement observations and dispersion modeling results show that the highway has a substantial impact on primary
traffic pollutant (particularly elemental carbon and carbon monoxide) concentrations
and captures the prominent spatial gradients across the campus domain, though the
gradients were highly species dependent. These improved concentration fields were
used to enhance the characterization of pollutant spatial distribution around a traffic
hotspot and to quantify personal exposure to primary traffic emissions.
J. L. Moutinho (B) · A. G. Russell
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA,
USA
e-mail: jennifer.moutinho@gatech.edu
A. G. Russell
e-mail: ted.russell@gatech.edu
D. Liang · J. Sarnat
Rollins School of Public Health, Emory University, Atlanta, GA, USA
e-mail: donghai.liang@emory.edu
J. Sarnat
e-mail: jsarnat@emory.edu
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_55
347