Articles | Volume 23, issue 19
https://doi.org/10.5194/acp-23-12907-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-12907-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Oct 2023
Research article |
| 13 Oct 2023
| 13 Oct 2023
Estimation of power plant SO2 emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Satellites Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20740, USA
Xinrong Ren
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Fong Ngan
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Satellites Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20740, USA
Mark Cohen
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Alice Crawford
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
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Short summary
The SO2 emissions of three power plants are estimated using aircraft observations and an ensemble of HYSPLIT dispersion simulations with different plume rise parameters. The emission estimates using the runs with the lowest root mean square errors (RMSEs) and the runs with the best correlation coefficients between the predicted and observed mixing ratios both agree well with the Continuous Emissions Monitoring Systems (CEMS) data. The RMSE-based plume rise appears to be more reasonable.
The SO2 emissions of three power plants are estimated using aircraft observations and an...
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