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Yunhee Kim , Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville

Impacts of Meteorological Variations on RRFs (Relative Response Factors) in the Demonstration of Attainment of the National Ambient Air Quality for 8-hr O 3 and PM 2.5. Yunhee Kim , Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville

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Yunhee Kim , Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville

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  1. Impacts of Meteorological Variations on RRFs (Relative Response Factors) in the Demonstration of Attainment of the National Ambient Air Quality for 8-hr O3 and PM2.5 Yunhee Kim, Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville Department of Civil & Environmental Engineering

  2. Outline • Background and Objective • Model Configurations and Descriptions • Sensitivity to PBL (Planetary Boundary Layer) Schemes • Sensitivity to RRF (Relative Response Factors) • Conclusions

  3. 8-hr O3 and PM2.5 Nonattainment Areas • Demonstrate the ozone & PM2.5 attainment in these nonattainment areas by SIPs (State Implementation Plans). • SIPs are submitted using model.

  4. Background • New NAAQS for 8-hr O3 was revised from 85 ppb to 75 ppb as May 27, 20081 and EPA has strengthen the level of 24-hour PM2.5 standard from 1997 level of 65 g/m³to 35 g/m³ (effective December 17, 2006). • The RRFs (Relative Response Factors) at a site are calculated by modeling results as a ratio of future year predictions divided by base year predictions. • Some studies have examined the calculation for the RRFs on various photochemical grid models.² However, none has reported the sensitivity of the RRFs to the various PBL schemes in MM5 model. • Optimal PBL schemes in MM5 model will give higher confidence in control strategies designed based on the modeled response to emission change. • 1. US EPA, 2007 2. Pun et al 2008; Sistla et al., 2004; Jones et al., 2005; Arunachalam et al., 2006

  5. Objective • To examine the sensitivity of MM5 PBL Schemes with CMAQ simulations to quantify the effects of RRFs (Relative Response Factors)on O3, annual and 24-hour PM2.5 SIPs

  6. MM5 Configurations and Descriptions • Horizontal Grid Resolution: 36-km/12-km/4-km • Vertical Grid Resolution: 34 layers • Simulation Period: May 15– September 15, 2002 for base year and 2009 for future year • MM5 (v.3.7) Options: • PBL: PX, Eta M-Y (Mellor-Yamada), MRF (Medium Range Forecast), Blackadar (BK), Gayno-seaman (G-S) • LSM: PX, NOAH, 5-layer soil model • Cumulus: KF2 (Kain and Fritsch) • Moisture: Mixed phase • Radiation: RRTM (rapid radiative transfer model)

  7. CON US 36-km ETN 4-km VISTAS 12-km CMAQ Configurations and Descriptions • Model Domain Descriptions: • Nestdown from VISTAS’s 12km • 121 x 114 grids, 19 layers • CMAQ 4.5 with CBIV mechanism • Initial & Boundary Condition: VISTAS 12-km obtained from VISTAS

  8. Simulation Descriptions • Descriptions: • Emissions: Typical 2002 and 2009 BaseG Emissions obtained from VISTAS • SMOKE2.1 used • For Base case : Area, Nonroad, Mobile, Point, Fire and Biogenic emissions • For Sensitivity : Mobile, Point, and Biogenic emissions to rerun

  9. Valley Sites Mountain Sites O3 and PM2.5 Observation Sites for Valley and Mountain Areas • For Valley Sites; Mildred, Rutledge, Jefferson, and Anderson • For Mountain Sites; Clingmans Dome, Look Rock, and Cove Mtn • For PM2.5; Mildred and Look Rock sites are selected as valley and mountain sites, respectively

  10. Methodology • Step– 4-km PBL and LSM Schemes Sensitivity Simulations • Baseline: PX • PBL Sensitivity: Eta_N, MRF_N, Eta_5, MRF_5, BK, and G-S

  11. Methodology 2.Step – Attainment Test with RRFs for O3, Annual PM2.5 and 24-hour PM2.5 RRFs = mean FVs / mean BVs Where FVs : Future Values modeled for 2009 BVs :Base year Values modeled for 2002 DVFs = DVCs x RRFs Where DVCs : Current Design Values DVFs : Future Design Values

  12. Methodology • To demonstrate future attainment of 8-hr O3 NAAQS and Annual and 24-hr NAAQS for PM2.5, - If DVFs  0.08 ppm, then it is set to be in attainment of 8-hr O3 - If DVFs  15 g/m³, then it is set to be in attainment of annual PM2.5 - If DVFs  65 g/m³, then it is set to be in attainment of 24-hr PM2.5

  13. Sensitivity to PBL Statistics for Meteorology

  14. Sensitivity to PBL Statistics of Max 8-hr O3

  15. RRFs for 8-hr Ozone • PX, MRF_5, and G-S showed the lower RRFs at valley sites while PX and MRF_N presented the highest RRFs at mountain sites. Overall, G-S showed the lowest RRFs at valley and mountain sites.

  16. DVFs for 8-hr Ozone • Eta_N and Eta_5 showed larger errors at valley site predicted higher DVFs whereas PX and MRF_N showed larger errors at mountain site presented higher DVFs. Overall, G-S showed the lowest DVFs at valley and mountain sites. • It appears that meteorological variations predicted from each PBL scheme has strong relationships with daily maximum 8-hr ozone formation and affects the RRFs.

  17. PM2.5 major Components Valley Mountain • Sulfate > OC > Ammonium > Crustal Material > Nitrate, EC Total PM2.5 = SO4 + NO3 + NH4 +OC + EC + Crustal Material

  18. Statistics of each component of PM2.5

  19. Statistics of each component of PM2.5

  20. RRFs for Annual PM2.5 Valley Mountain

  21. RRFs for 24-hour PM2.5 Valley Mountain

  22. Summary- PM2.5 Valley Annual 24-hour • OC, Nitrate, and Crustal material are greatly affected by meteorological variations.

  23. Summary- PM2.5 Mountain Annual 24-hour • The much larger difference in OC RRFs for annual and 24-hr PM2.5 is shown at mountain sites than at valley sites.

  24. Conclusion • The model performance of meteorology and air quality had impacts on the estimating RRFs. • The differences of 8-hr O3 in RRFs and DVFs are 1-7 % and 1-6 ppb from observed monitoring sites, computed from seven sensitivity tests. This result may lead to a significant outcome in determining attainment or nonattainment status. • The differences of PM2.5 in RRFs are larger at valley site than mountain site whereas the difference of 8-hr ozone is shown in larger RRFs at mountain site than valley site. • It appears that there is a relationship between model performance on PBL schemes associated with LSMs and RRFs uncertainties.

  25. Conclusion • Large uncertainties are seen for the OC, Nitrate, and Crustal material RRFs whereas smaller uncertainties are seen for the sulfate and ammonium RRFs, which is consistent with good model performance for sulfate and ammonium than OC, Nitrate, and Crustal material. • Larger uncertainties for 24-hr PM2.5 RRFs are seen than annual PM2.5 RRFs. • Based on our results, PX scheme is the optimal scheme for PM2.5 and G-S PBL scheme for 8-hr O3. • It may be desirable to apply more than one PBL schemes associated with LSMs to improve the confidence before making a decision for the design of effective emission control strategies for ozone and PM2.5 reductions

  26. Acknowledgements • Observed Data for Great Smoky Mountain National Park: Jim Renfro, Air Quality Program Manager Great Smoky Mountains National Park Resource Management & Science Division • Obtained Data for ICs and BCs and Meteorological Data for VISTAS 12-km: VISTAS (Visibility Improvement State and Tribal Association of the Southeast) • Funding: TDEC (Tennessee Department of Environment and Conservation)

  27. Thank you !

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