13 Mar 2023
 | 13 Mar 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Estimation of power plant SO2 emissions using HYSPLIT dispersion model and airborne observations with plume rise ensemble runs

Tianfeng Chai, Xinrong Ren, Fong Ngan, Mark Cohen, and Alice Crawford

Abstract. SO2 mixing ratio measurements were made by a research aircraft close to three power plants in North Carolina on March 26, 2019. An ensemble of dispersion simulations with HYSPLIT model v5.2.0 are carried out using a total of 15 heat release parameters ranging from 10 MW to 150 MW for the Briggs plume rise calculation to quantify some of the modeling uncertainties related to this phenomena. For each heat release value, a total of 72 independent HYSPLIT Lagrangian model runs with unit hourly emissions from the three point sources are made to form a transfer coefficient matrix (TCM) with the airborne observations. The TCMs can be decoupled into six segments where the observations of each segment are only influenced by a single power plant in its morning or afternoon operation. Prior to estimating the power plant emissions, the simulation performance is first evaluated with the correlation coefficients between the observations and the model prediction with constant unit-emission in its morning or afternoon operations. The segment influenced by the afternoon operations of Belews Creek power plant has negative correlation coefficients for all the plume rise options and is excluded from the emission estimate when the “optimal” member is selected based on the correlation coefficient. For the other segments, the plume rise runs with the highest correlation coefficients are selected for the emission estimates using the HYSPLIT inverse modeling system. In the TCM-based inverse modeling, the emission estimates are obtained by minimizing a cost function which measures the difference between logarithmic predicted and observed mixing ratios but also takes model uncertainties into account. A cost function normalization scheme is adopted to avoid spurious emission solutions when using logarithmic concentration differences following Chai et al. (2018). The source estimation results of the three power plants with the morning and afternoon flight segments are compared with the Continuous Emissions Monitoring Systems (CEMS) data. Overestimations are found for all the segments before considering the background SO2 mixing ratios. Both constant background mixing ratios and several segment-specific background values are tested in the HYSPLIT inverse modeling. The estimation results by assuming the 25th percentile observed SO2 mixing ratio inside each of the five segments agree well with the CEMS data, with relative errors as 18 %, -12 %, 3 %, 93.5 %, and -4 %. After emission estimations are performed for all the plume rise runs, least root mean square error (RMSE) between the predicted and observed mixing ratios are calculated to select a different set of “optimal” plume rise runs which have the least RMSEs. Identical plume rise runs are chosen as the “optimal” members for Roxboro and Belews Creek morning segments, but different members for the other segments yield smaller RMSEs than the previous correlation-based “optimal” members. It is also no longer necessary to exclude the Belews Creek afternoon segment that has negative correlation between predictions and observations. The RMSE-based “optimal” runs result in a much better agreement with the CEMS data for the previous severely overestimated segment and do not deteriorate much for the other segments, with relative errors as 18 %, -18 %, 3 %, -9 %, and 27 % for the five segments, and 2 % for Belews Creek afternoon segment. While the RMSE-based “optimal” plume rise runs appear to agree better with the observations than the correlation-based “optimal” runs when they are different, significant differences exist in the area where observations are missing.

Tianfeng Chai et al.

Status: open (until 26 Apr 2023)

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Tianfeng Chai et al.

Tianfeng Chai et al.


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Short summary
An ensemble of HYSPLIT dispersion simulations are carried out with 15 different heat release parameters in plume rise calculation. The SO2 emissions of the three power plant are estimated by minimizing a cost function which measures the difference between logarithmic predicted and observed mixing ratios. The emission estimates based on the highest correlation coefficient or the lowest RMSE between the predicted and observed mixing ratios are presented and they agree well with the CEMS data.