Sensitivity of AERMOD (V21112) RLINEXT dispersion model outputs by source type to variability in single noise barrier height and separation distance

Hongyu Lu, Daejin Kim, Haobing Liu, Tian Xia, William Reichard, Michael O. Rodgers, Randall Guensler

Research output: Contribution to journalArticlepeer-review

Abstract

The U.S. Environmental Protection Agency (USEPA) introduced the RLINEXT modeling feature in the latest versions of AERMOD (since version v21112) to predict traffic-induced pollutant concentration when noise barriers are present. The research presented in this paper explores the impacts of noise barrier characteristics on AERMOD-predicted concentrations. The research finds that because AERMOD can currently only attach one barrier to each road side, and because that barrier only impacts the source to which it is attached, it is also important to split links so that they properly pair with barriers. Given the sensitivity of AERMOD-predicted concentrations to barrier characteristics (i.e., barrier heights and distances to roadway), the research also concludes that barriers must be appropriately matched with input links. This study investigated the sensitivity of CO concentration predicted by the latest AERMOD under various noise barrier conditions (barrier heights and distances between road and barrier) and meteorological conditions (wind directions and wind speeds). The results indicate that ground-level concentration of downwind receptors decreases with increased barrier heights, and that distant barriers have less of an impact on predicted concentrations. This study also explored the impact of noise barrier on both horizontal and vertical concentration profiles, indicating that concentrations rise behind the barrier as plumes are predicted to loft over the barrier. The sensitivity analysis associated with splitting roadway links to match with barriers indicated an impact on predicted concentration for certain receptors of up to 10%, but the overall the impact on maximum concentrations was marginal.

Original languageEnglish
Article number102318
JournalAtmospheric Pollution Research
Volume15
Issue number12
DOIs
StatePublished - Dec 2024
Externally publishedYes

Funding

This work was sponsored by GDOT via the FHWA project of AERMOD, RLINE, and RLINEXT: Case Study Analyses in Atlanta, Georgia and was in part supported by National Center for Sustainable Transportation (NCST) (Grant No. 69A3551747114). This work was also supported in part by INHA UNIVERSITY Research Grant, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1G1A1003881). This research was in part funded by Young Scientist Fund of the National Natural Science Foundation of China (No. 52202420), as well as Special Project for Carbon Peak and Carbon Neutrality under the 2022 Shanghai Action Plan for Science, Technology and Innovation (No. 22dz1207403). Portions of this manuscript were presented at the 101th Transportation Research Board (TRB) Annual Meeting held in Washington D.C. January 2022. The open source modeling tools employed in this project were developed by the authors and other Georgia Institute of Technology researchers in National Center for Sustainable Transportation, a multi-university University Transportation Center, funded by the U.S. Department of Transportation. This work was sponsored by GDOT via the FHWA project of AERMOD, RLINE, and RLINEXT: Case Study Analyses in Atlanta, Georgia and was in part supported by National Center for Sustainable Transportation (NCST) (Grant No. 69A3551747114). This work was also supported in part by INHA UNIVERSITY Research Grant, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1G1A1003881). This research was in part funded by Young Scientist Fund of the National Natural Science Foundation of China (No. 52202420), as well as Special Project for Carbon Peak and Carbon Neutrality under the 2022 Shanghai Action Plan for Science, Technology and Innovation (No. 22dz1207403). Portions of this manuscript were presented at the 101th Transportation Research Board (TRB) Annual Meeting held in Washington D.C., January 2022. The open source modeling tools employed in this project were developed by the authors and other Georgia Institute of Technology researchers in National Center for Sustainable Transportation, a multi-university University Transportation Center, funded by the U.S. Department of Transportation.

FundersFunder number
National Research Foundation of Korea
U.S. Department of Transportation
Georgia Department of Transportation
Inha University
Georgia Institute of Technology
National Center for Sustainable Transportation69A3551747114
MSITNRF-2022R1G1A1003881
National Natural Science Foundation of China52202420
2022 Shanghai Action Plan for Science, Technology and Innovation22dz1207403

    Keywords

    • AERMOD
    • Noise barriers
    • Pollutant concentration sensitivity
    • RLINEXT
    • Vertical and horizontal dispersion impacts

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