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Lessons Learned: One-Atmosphere Photochemical Modeling in Southeastern U.S. Presentation from Southern Appalachian Mountains Initiative to Meeting of Regional.

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Presentation on theme: "Lessons Learned: One-Atmosphere Photochemical Modeling in Southeastern U.S. Presentation from Southern Appalachian Mountains Initiative to Meeting of Regional."— Presentation transcript:

1 Lessons Learned: One-Atmosphere Photochemical Modeling in Southeastern U.S. Presentation from Southern Appalachian Mountains Initiative to Meeting of Regional Planning Organizations December 3, 2002

2 SAMI Atmospheric Modeling zUnique Contributions: yDemonstrated fully-integrated one-atmosphere model xozone, aerosols, and deposition x“performance comparable to or better than recent applications of CMAQ or REMSAD”

3 zIn 1997 selected to use: yRAMS-3B meteorological model yEMS-95 emissions model yUrban to Regional Multi-scale (URM) air quality model xvariable grid (12-km over Southern Appalachian Mountains) xSAPRC chemical mechanism for gases xISORROPIA for aerosols xReactive Scavenging Module for deposition xDecoupled Direct Method for sensitivity to emissions changes SAMI Atmospheric Model

4 SAMI Atmospheric Modeling Domain Georgia Institute of Technology

5 SAMI Atmospheric Modeling zUnique Contributions: ySelected episodes to represent annual and seasonal air quality measures xbased on meteorology characterized for 5-year period x9 episodes in Feb, Mar, Apr, May, Jun, Jul, Aug yLesson learned: prioritize computational power: greater spatial resolution or longer time periods?

6 SAMI Atmospheric Model: Lessons Learned zEmissions Inventory uncertainties: yespecially NH3, primary OC; non-road, area sources zMeteorological Model performance: yclouds and precipitation affect chemistry and deposition ywind speed and direction, mixing heights affect transport

7 SAMI Atmospheric Model: Lessons Learned zAir Quality Observations limited spatially and temporally: yPM2.5 data, especially NH4 ywet and dry deposition data yvertical profiles for initial and boundary conditions

8 SAMI Atmospheric Model: Lessons Learned zPhotochemical Model Performance: ySO4 and OC best performance (+/- 50%), largest components of PM2.5 yoverpredict NO3, soil, and EC; small components ySO4 not fully neutralized by NH4, atmosphere NH4- limited yneed better measures: NH3, NH4, primary vs secondary OC

9 URM Model Performance - Fine Particle Mass Great Smoky Mtns 2/01/01 SO4NO3NH4ORGECSOIL 0 10.0 20.0 30.0 Concentration (  g/m3) Class 5Class 4Class 3Class 2Class 1 2/09/94 3/24/93 4/26/95 8/04/93 8/07/93 8/11/93 7/12/95 7/31/91 7/15/95 Modeled (left) IMPROVE (right)

10 July 1995 May 1995 May 1993 March 1993 February 1994 July 1991 June 1992 August 1993 April 1995 URM Model Performance: Sulfate Fine Particle Mass + 50% - 50% (based on data from 3-10 IMPROVE sites in 12, 24, and 48 km grids)

11 July 1995 May 1995 May 1993 March 1993 February 1994 July 1991 June 1992 August 1993 April 1995 URM Model Performance: PM2.5 Mass URM Modeled Concentration (  g/m3) IMPROVE Measurements (  g/m3) + 50% - 50% (based on data from 3-10 IMPROVE sites in 12, 24, and 48 km grids)

12 Wet Ammonium Deposition Normalized Percent Bias 0 50 100 150 200 250 July 11-18, 1995 May 23-30, 1995 May 11-18, 1993 March 23-30, 1993 Feb 8-15, 1994 April 26 - May 3, 1995 August 3-11, 1993 June 24-29, 1992 July 23-30, 1991 Normalized Percent Bias (based on data from 9-14 NADP wet deposition sites in 12, 24, and 48 km grids)

13 SAMI Atmospheric Modeling zUnique Contributions: yTo assess effects, used modeled relative change in air quality to adjust measured air quality: xvisibility xozone effects to forests xacid deposition effects to streams and forests yLesson learned: bound relative reduction factor by model performance

14 SAMI Atmospheric Modeling zUnique Contributions: yUsed direct sensitivity analyses to evaluate state contributions to Class I areas xDecoupled Direct Method (DDM-3D) xevaluated responses to 10% change in emissions zLessons learned : ytrust relative contributions rather than absolute ydaily source contributions from DDM compare favorably to daily back trajectories

15 DDM Sensitivity Performance Gaseous Species Aerosol Species Wet Deposition Species SO2NOxNH3VOCs Ozone Good SO2 Good NH3 Good SO4 Good NO3 GoodPoorGood NH4 Good FairGood OC Good EC SOIL PM2.5 Good PoorGood SO4 Good NO3 PoorGoodPoorGood NH4 Poor Fair

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18 Annual SO4 Fine Particles Response to 10% Reduction in SO2 Emissions from 2010 A2 strategy SO4 Fine Particle Response (%) -8.0 -6.0 -4.0 -2.0 0.0 Sipsey, AL Cohutta, GA Joyce Kilmer, NC Great Smoky Mtn, TN Shining Rock, NC Linville Gorge, NC James River Face, VA Shenanhoah, VA Otter Creek, WV Dolly Sods, WV Non-SAMI states SAMI states

19 Appendix

20 Sulfate Aerosol Normalized Percent Bias -100 -50 0 50 100 150 7/12/19957/15/19957/19/19995/24/19955/27/19955/12/19935/15/19933/24/19933/27/19933/31/19932/9/19942/12/19944/26/19954/29/19955/3/19956/24/19926/27/19928/4/19938/7/19938/11/19937/24/19917/27/19917/31/1991 Normalized Percent Bias (based on data from 3-10 IMPROVE sites in 12, 24, and 48 km grids)

21 Ammonium Aerosol Normalized Percent Bias -60 -40 -20 0 20 40 60 80 100 120 7/12/1995 7/15/19957/19/19995/24/19955/27/19955/12/19935/15/19933/24/19933/27/19933/31/19932/9/19942/12/19944/26/19954/29/19955/3/19956/24/19926/27/19928/4/19938/7/19938/11/19937/24/19917/27/19917/31/1991 Normalized Mean Bias (based on data from 3-10 IMPROVE sites in 12, 24, and 48 km grids)

22 Organic Aerosol Normalized Percent Bias -60 -40 -20 0 20 40 60 80 100 120 7/12/19957/15/1995 7/19/19995/24/19955/27/19955/12/19935/15/19933/24/19933/27/19933/31/19932/9/19942/12/19944/26/19954/29/19955/3/19956/24/19926/27/19928/4/19938/7/19938/11/19937/24/19917/27/19917/31/1991 Normalized Percent Bias (based on data from 3-10 IMPROVE sites in 12, 24, and 48 km grids)

23 Nitrate Aerosol Normalized Percent Bias -50 0 50 100 150 200 250 7/12/1995 7/15/19957/19/19995/24/19955/27/19955/12/19935/15/19933/24/19933/27/19933/31/1993 2/9/1994 2/12/19944/26/19954/29/1995 5/3/1995 6/24/19926/27/1992 8/4/19938/7/1993 8/11/19937/24/19917/27/19917/31/1991 Normalized Percent Bias (based on data from 3-10 IMPROVE sites in 12, 24, and 48 km grids)

24 PM 2.5 Normalized Percent Bias -100 -50 0 50 100 150 200 2/9/94 3/24/933/31/93 4/29/95 5/15/935/24/956/24/92 7/24/91 7/31/91 7/15/95 8/4/93 8/11/93 Normalized Bias (%) Normalized Bias of +/- 50% is potential criteria for aerosol model performance (based on data from 3-10 IMPROVE aerosol sites in 12, 24, and 48 km grids) 2/12/943/27/93 4/26/95 5/12/935/21/95 5/27/956/27/92 7/27/91 7/12/957/19/95 8/7/93

25 Modeled on Left, IMPROVE On Right URM Model Results vs. Observations for July 15, 1995 PM 2.5 (  g/m3 ) 0.0 10.0 20.0 30.0 40.0 Aerosol Model Performance Sipsey, AL Great Smoky Mtns.,TN Shining Rock, NC James River Face, VA Shenandoah, VA Dolly Sods, WV 2/01/01 SO4NO3NH4ORGECSOIL

26 Wet Sulfate Deposition Normalized Percent Bias -30 -25 -20 -15 -10 -5 0 5 10 July 11-18, 1995 May 23-30, 1995 May 11-18, 1993 March 23- 30, 1993 Feb 8-15, 1994 April 26 - May 3, 1995 August 3- 11, 1993 June 24-29, 1992 July 23-30, 1991 Normalized Percent Bias (based on data from 9-14 NADP wet deposition sites in 12, 24, and 48 km grids)

27 Confidence Levels in SAMI 1990 Base Year inventory


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