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The SO2 emission rates from three power plants in North Carolina are estimated using the HYSPLIT Lagrangian dispersion model and aircraft measurements made on 26 March 2019. To quantify the underlying modeling uncertainties in the plume rise calculation, dispersion simulations are carried out in an ensemble using a total of 15 heat release parameters. For each heat release, the SO2 emission rates are estimated using a transfer coefficient matrix (TCM) approach and compared with the Continuous Emissions Monitoring Systems (CEMS) data. An "optimal" member is first selected based on the correlation coefficient calculated for each of the six segments that delineate the plumes from the three power plants during the morning and afternoon flights. The segment influenced by the afternoon operations of Belews Creek power plant has negative correlation coefficients for all the plume rise options and is first excluded from the emission estimate here. 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 ratios inside each of the five segments agree well with the CEMS data, with relative errors of 18 %, - 12 %, 3 %, 93.5 %, and - 4 %. After emission estimations are performed for all the plume rise runs, the lowest root mean square errors (RMSEs) between the predicted and observed mixing ratios are calculated to select a different set of optimal plume rise runs which have the lowest 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 a negative correlation between predictions and observations. The RMSE-based optimal runs result in much better agreement with the CEMS data for the previously severely overestimated segment and do not deteriorate much for the other segments, with relative errors of 18 %, - 18 %, 3 %, - 9 %, and 27 % for the five segments and 2 % for the Belews Creek afternoon segment. In addition, the RMSE-based optimal heat emissions appear to be more reasonable than the correlation-based values when they are significantly different for CPI Roxboro power plant. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from the model, with strict mass conservation at each satellite footprint pixel level. With the downscaling approach, NO2 column densities from the Ozone Monitoring Instrument (OMI; 13 × 24 km nadir footprint resolution) and the Global Ozone Monitoring Experiment-2 (GOME-2; 40 × 80 km) show excellent agreement with the Community Multiscale Air Quality (CMAQ; 4 × 4 km) NO2 column densities, with R = 0.96 for OMI and R = 0.97 for GOME-2. We further introduce an approach to reconstruct surface NO2 concentrations by combining satellite column densities and simulated surface-to-column ratios from the model. Compared with the Environmental Protection Agency's (EPA) Air Quality System (AQS) surface observations, the reconstructed surface concentrations show a good agreement; R = 0.86 for both OMI and GOME-2. This study demonstrates that the conservative downscaling approach is a useful tool to compare coarse-scale satellites with fine-scale models or observations in urban areas for air quality and emissions studies. The reconstructed fine-scale surface concentration field could be used for future epidemiology and urbanization studies. [ABSTRACT FROM AUTHOR]