Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs
<p>The <span class="inline-formula">SO<sub>2</sub></span> 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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-10-01
|
| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/23/12907/2023/acp-23-12907-2023.pdf |
| _version_ | 1797660592874455040 |
|---|---|
| author | T. Chai T. Chai T. Chai X. Ren F. Ngan F. Ngan F. Ngan M. Cohen A. Crawford |
| author_facet | T. Chai T. Chai T. Chai X. Ren F. Ngan F. Ngan F. Ngan M. Cohen A. Crawford |
| author_sort | T. Chai |
| collection | DOAJ |
| description | <p>The <span class="inline-formula">SO<sub>2</sub></span> 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 <span class="inline-formula">SO<sub>2</sub></span> 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 <span class="inline-formula">SO<sub>2</sub></span> 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
<span class="inline-formula">SO<sub>2</sub></span> mixing ratios inside each of the five segments agree well with the CEMS data, with relative errors of 18 %, <span class="inline-formula">−</span>12 %, 3 %,
93.5 %, and <span class="inline-formula">−</span>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 %,
<span class="inline-formula">−</span>18 %, 3 %, <span class="inline-formula">−</span>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.</p> |
| first_indexed | 2024-03-11T18:33:07Z |
| format | Article |
| id | doaj.art-9adc4b9a50934d218d8e867696b2a864 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-03-11T18:33:07Z |
| publishDate | 2023-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-9adc4b9a50934d218d8e867696b2a8642023-10-13T10:05:11ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242023-10-0123129071293310.5194/acp-23-12907-2023Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runsT. Chai0T. Chai1T. Chai2X. Ren3F. Ngan4F. Ngan5F. Ngan6M. Cohen7A. Crawford8NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USACooperative Institute for Satellites Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USADepartment of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USACooperative Institute for Satellites Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USADepartment of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USANOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA<p>The <span class="inline-formula">SO<sub>2</sub></span> 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 <span class="inline-formula">SO<sub>2</sub></span> 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 <span class="inline-formula">SO<sub>2</sub></span> 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 <span class="inline-formula">SO<sub>2</sub></span> mixing ratios inside each of the five segments agree well with the CEMS data, with relative errors of 18 %, <span class="inline-formula">−</span>12 %, 3 %, 93.5 %, and <span class="inline-formula">−</span>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 %, <span class="inline-formula">−</span>18 %, 3 %, <span class="inline-formula">−</span>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.</p>https://acp.copernicus.org/articles/23/12907/2023/acp-23-12907-2023.pdf |
| spellingShingle | T. Chai T. Chai T. Chai X. Ren F. Ngan F. Ngan F. Ngan M. Cohen A. Crawford Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs Atmospheric Chemistry and Physics |
| title | Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs |
| title_full | Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs |
| title_fullStr | Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs |
| title_full_unstemmed | Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs |
| title_short | Estimation of power plant SO<sub>2</sub> emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs |
| title_sort | estimation of power plant so sub 2 sub emissions using the hysplit dispersion model and airborne observations with plume rise ensemble runs |
| url | https://acp.copernicus.org/articles/23/12907/2023/acp-23-12907-2023.pdf |
| work_keys_str_mv | AT tchai estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT tchai estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT tchai estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT xren estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT fngan estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT fngan estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT fngan estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT mcohen estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns AT acrawford estimationofpowerplantsosub2subemissionsusingthehysplitdispersionmodelandairborneobservationswithplumeriseensembleruns |