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Simulating diurnal changes of speciated particulate matter in Atlanta, Georgia using CMAQ

This study simulates diurnal changes of speciated particulate matter in Atlanta, Georgia using the CMAQ model. It compares PM2.5 compositions at different sites and times, evaluates EC/OC concentration gradients, and assesses model performance against observations. The research aims to improve emission characterization and reconcile discrepancies between model simulations and measurements. Results suggest encouraging performance of the 1.3-km grid resolution with some limitations on capturing diurnal variations of OC and SOA. Further enhancements in emission modeling are needed.

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Simulating diurnal changes of speciated particulate matter in Atlanta, Georgia using CMAQ

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  1. Simulating diurnal changes of speciated particulate matter in Atlanta, Georgia using CMAQ Yongtao Hu, Jaemeen Baek, Bo Yan, Rodney Weber, Sangil Lee, Evan Cobb, Amy Sullivan, Armistead G. Russell School of Civil and Environmental Engineering and School of Earth and Atmospheric Sciences, Georgia Institute of Technology CMAS conference, October 18th, 2006 Acknowledgements: Eric S. Edgerton and John Jansen ARA and Southern Company

  2. Background

  3. Speciated particulate matter monitored at two sites in Georgia Tech's campus, 500m away from each other

  4. Measurements at neighboring sitesHYW and ROF • Frequency: twice per day of 12-hr average compositions of PM2.5 for daytime (10am~10pm) and nighttime (10pm~10am). • Items: ions, EC/OC, organic compounds and metals. • Periods: Jun. 15~18, 2006 and Jan. 19~26, 2006. • Findings: • Compare two sites: ROF is cleaner; SO4 and NH4: no significant difference;NO3: ROF is higher, but both very low;EC and OC: HYW is significantly higher. • Compare day and night: Higher percentage of OC at night; Higher percentage of SO4 during day.

  5. - 56 km- 1.3-km Grid - 56 km - Other PM2.5 composition monitors in Atlanta Met

  6. Sampling frequency • SEARCH stations: JST and YRK, hourly composition of PM2.5, as well as daily 24-hr averages • ASACA stations: FTM, TUC, SDK, YGP, daily 24-hr average composition of PM2.5 • STN site: South De Kalb (same location as SDK), every third day 24-hr average composition of PM2.5

  7. Can CMAQ capture the observed gradient of the EC/OC concentration at the two closely neighboring sites?Can CMAQ capture the observed diurnal changes of PM2.5 and its components? Questions:

  8. Objectives of this work • Simulating PM2.5 speciation using CMAQ at very fine scale. • Characterize emissions from freeway. • Compare fine scale CMAQ results to observations using detailed speciation of organics and metals (just have EC/OC and ions for now). Next to freeway, nearby (500m), 2km away, within the region. • Mutual calibration with receptor modeling results. • Reconcile differences: Improve emission characterization, emissions distributions, dispersion, etc.

  9. CMAQ v4.5 simulation • Four nesting domains down to 1.3-km resolution. • Thirteen vertical layers, first layer ~18 meters. • Simulating summer episode currently: June 12-20, 2005. • SAPRC99 mechanism plus aero4 module. • MM5 and SMOKE provide meteorology and emission rate fields. • OSU land surface model plus 4DDA (only for 36-km and 12-km grids) used in MM5. • VISTAS 2002 emissions inventory projected to 2005, CEM data used for EGU sources.

  10. Brute force sensitivity simulations 20 sensitivity runs sensitivity fields = air quality fields basecase - air quality fields reduced case

  11. 1.3-km 4-km 12-km 36-km Modeling Domains

  12. Basecase 1.3-km Grid Emissions NOx CO PEC POA

  13. Simulated Spatial Distributions on 1.3-km Grid (basecase) O3 SO4 NH4 NO3 OC EC

  14. First Concern: Is 1.3-km grid performance worse than coarse grid?

  15. MM5 Performance: 1.3-km grid vs. other resolutions Compare with TDL hourly surface observations

  16. CMAQ Performance: 1.3-km grid vs. other resolution Compare with Network measurements from: AIRNOW, STN, CASTNet (O3 only), IMPROVE, SEARCH and ASACA

  17. Further Concern: Is PM2.5 performance becoming worse when compared to measurements in higher temporal resolution?

  18. 1.3-km grid PM2.5 performanceCompare with 24-, 12- and 1-hr measurements, respectively

  19. Limited EC/OC gradient was captured between HYW and ROF HIGHWAY ROOF

  20. Non-road EC Non-road EC Mobile EC Mobile EC EC Sensitivity results show a higher contribution from traffic emissions at HIWAY HIGHWAY ROOF

  21. Diurnal Changes: captured OK for SO4, NH4 and EC, not OK for OC Jefferson Street (urban) Yorkville (rural)

  22. OC performance: diurnal change Jefferson Street (urban) Yorkville (rural)

  23. OC Sensitivity: does it make sense? BVOC DPOA BVOC OAPOA NPOA MPOA FPOA DPOA OAVOC OAVOC OAPOA NPOA MPOA MNOX FPOA Jefferson Street (urban) Yorkville (rural)

  24. Estimate Secondary OC from OC measurements ROOF When EC was well reproducedAssume Pri OCobs = Pri OCsim,then, we have SOAobs = OCobs - PriOCsim Yorkville Secondary OC was not captured by CMAQ, both mechanism and precursor emissions need improvements.

  25. Summary • Performance of 1.3-km grid is as good as other resolutions. This is encouraging. • Limited EC/OC gradient was captured at neighboring sites. Link-base VMT is necessary to allocate the mobile emissions more accurately. • Utilize modeled primary OC to split SOA from observed OC. With uncertainty. • OC diurnal change was not captured. SOA prediction needs to be improved. Problems are from both mechanism and precursor emissions.

  26. 4-km grid PM2.5 performanceCompare with 24-, 12- and 1-hr measurements, respectively

  27. EC performance

  28. SO4 performance

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