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A series of meteorological measurements with a small Uncrewed Aircraft System (sUAS) was collected at Oliver Springs Airport, Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary due to inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just a sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.

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.