1. Georgia Institute of Technology Georgia Air Quality: A Tale of Four Cities Armistead (Ted) Russell Georgia Power Professor of Environmental Engineering Georgia Institute of Technology

2. Context Every one knows about Atlanta’s air quality: –Lots of people + –Lots of driving + –Lots of trees = –Lots of ozone and other stuff (more in just a second, but it is what you see) But it does not stop there! –Macon, Augusta and Columbus (the Fall line cities) also have poor air quality –Even the middle of nowhere (Leslie) exceeds the standards! Pollutants of concern –Ozone: Respiratory implications –Particulate Matter (this is what you see): Respiratory and cardiopulmonary implications –Joint Emory-GIT study identifying associations.

3. Georgia Institute of Technology Ozone Levels

4. Georgia Institute of Technology Particulate Matter Annual Standard

5. Georgia Institute of Technology So, where does this come from?

6. Georgia Institute of Technology Ozone Formation h (sunlight) O3O3 NO x oxides of nitrogen (NO + NO 2 ) VOCs Volatile organic compounds

7. Georgia Institute of Technology Chemical Regimes Volatile Organic Compounds (VOCs) Nitrogen Oxides (NO x ) Low O 3 High O 3 Transport Radical/VOC Limited: Abundant NO 2 removes OH, Inhibitting oxidation of VOCs and HO 2 /RO 2 formation: Low utilization of NO x emissions NO x Limited: Lack of NO x limits ozone formation High utilization of NO x emiss. Often in areas of high biogenics

8. Georgia Institute of Technology PM Formation h (sunlight) PM NO x VOCs SO 2 Sulfur dioxide

9. Georgia Institute of Technology PM Air Quality: Regional Context

10. Georgia Institute of Technology F ALL LINE A IR Q UALITY S TUDY © Augusta Chronicle

11. Georgia Institute of Technology FAQS Four year study to understand the sources of elevated ozone (and PM) in the three fall line cities –Augusta, Macon and Columbus Impending non-attainment –Need to understand Atlanta’s impacts as well Measurement –Detailed in addition to routine Modeling –Emissions –Air quality Assessment of control strategies –What will work best and what is needed to reach attainment

12. Georgia Institute of Technology FAQS Project Team Michael Chang Ted Russell Karsten Baumann Rodney Weber Michael Bergin Carlos Cardelino Talat Odman Don Blake Doug Worsnop Jing Zhao Doug Orsini Kip Carrico Yong-Tau Hu Frank Ift Roby Greenwald Jin Xu Yilin Ma Amy Sullivan Wes Younger Danny Dipasquale Sergey Napelenok Di Tian Dan Cohan Kari Meier Jaemeen Baek Alper Unal C.S. Kiang William Chameides

13. 13 Georgia’s historical AQ problem: Atlanta GA EPD ozone monitoring site

14. Georgia Institute of Technology Georgia’s air quality problem now: Atlanta, Augusta, Macon, Columbus, Leslie. Home grown pollution?

15. Georgia Institute of Technology Georgia’s air quality problem now: Atlanta, Augusta, Macon, Columbus, Leslie. Or Big City Transport?

16. Georgia Institute of Technology Georgia’s air quality problem now: Atlanta, Augusta, Macon, Columbus, Leslie. Or Regional Scale?

17. Georgia Institute of Technology Georgia’s air quality problem now: Atlanta, Augusta, Macon, Columbus, Leslie. Or Combination?

18. Georgia Institute of Technology Our working hypothesis: Atlanta and Macon share an airshed that is capable of generating significant ozone independent of the region. Columbus and Augusta, are more closely related to the larger regional airshed, with some impacts from Atlanta and other cities, but also may be responsible for a small but significant amount of local ozone production. Leslie??

19. Georgia Institute of Technology Correlations with Wind Direction: O 3 Period 2001+ 02 MAY-OCT NOV-APR

20. Georgia Institute of Technology Correlations with Wind Direction: PM 2.5 Period 2001+ 02 MAY-OCT NOV-APR

21. Georgia Institute of Technology Air Quality Model Representation of physical and chemical processes –Numerical integration routines Scientifically most sound method to link future emissions changes to air quality Computational Planes 5-20 50-200 Air Quality Model 200 species x 5000 hor. grids x 20 layers= 20 million coupled, stiff non-linear differential equations Atmospheric Diffusion Equation Discretize Operator splitting 50-100 Emissions Chemistry Meteorology Numerics C=AxB+E

22. Georgia Institute of Technology Modeling Process Pollutant Distributions Evolving: Sensitivities Uncertainties Emissions Model Meteorological Inputs Historical 2- or 3-D winds; Ground level T, RH; Mixing height, Land use Evolving: 3-D Winds, Diffusivities, Temp., RH, D, Solar Insolation (UV & total solar)... Chemical Mechanism Historical: Specified Evolving: Compiler Numerical Routines Historical: Advection Chem. Kinet. Evolving Sens. Anal. Proc. Integ. Unc. Anal. Air Quality Model Emissions Inputs Historical: NO, NO 2, HONO Lumped VOCs CO, SO 2 Evolving: PM, NH 3, Detailed VOCs, Adv. Biogenics Inputs: Emissions Inventory Population Roads Land Use Industry Meteorology Model Parameter Calculation Temperature, Solar Insolation Chemical Mechanism Specification Chemical Mechanism Specification Air Quality Data Analysis and Processing Meteorological Model (Diagnostic or Prognostic) Meteorological Model (Diagnostic or Prognostic) Model Evaluation Air Quality Observations Air Quality Observations Meteorological Observations Meteorological Observations Emissions, Industry and Human Activity Data Emissions, Industry and Human Activity Data Topographical Data Topographical Data Emissions Inventory Development Emissions Inventory Development Foundation Weak link Atmospheric Modeling System (e.g., Models 3)

23. Georgia Institute of Technology History, Present, Future Species –50  150  250 –Gas  Gas + PM + Deposition + toxics  …+local climatic impacts Spatial Domain –Urban or regional: 200x200 km or 1500 x 1500 km  –Urban AND regional/continental or global  … AND global Temporal Period –1-3 days  1-2 weeks  1-2 years Grid structure –Monoscale  nested/multiscale  adaptive Level of knowledge of users, time to simulation, systemization, ubiquity, standardization, computer platform, use for forecasting, …

24. Georgia Institute of Technology Grids Nested Multiscale Adaptive

25. Georgia Institute of Technology Sensitivity Analysis Calculate sensitivity of gas and aerosol phase concentrations and wet deposition fluxes to input and system parameters – s ij (t)=  c i (t)/  p j –Provides knowledge as to system response to perturbations Brute-Force method –must run the model a number of different times –inaccurate sensitivities may result due to numerical noise propagating in the model DDM - Decoupled Direct Method (Dunker, 1982; Yang et al., 1997) –Use direct derivatives of governing equations –Assess impacts of emissions, model parameters, IC/BCs… –Advantages: fast and accurate for typical emissions changes Multiple regions, multiple pollutants at one time

26. Georgia Institute of Technology Air quality model uses –Assess response of species concentrations to controls –Understand role of specific physical and chemical processes in species dynamics –Uncertainty analysis –Inverse modeling Powerful and useful if implemented efficiently –Readily (?) implemented in other atmospheric chemistry models Role of Sensitivity Analysis

27. Georgia Institute of Technology Brute-Force Sensitivity Analysis Air Quality Model base scenario O 3 (t,x,y,z) NO(t,x,y,z) NO 2 (t,x,y,z) VOC i (t,x,y,z)... Air Quality Model Air Quality Model base scenario + p j, O 3, (t,x,y,z) NO, (t,x,y,z) NO 2, (t,x,y,z) VOC i, (t,x,y,z)...

28. Georgia Institute of Technology Brute Force Strengths –Easily implemented –Efficient for few parameters –Captures non-linearities Tests suggest these are small for moderate perturbations Weaknesses –Inefficient for many parameters –Inaccurate for small responses Typical incremental emissions perturbations Assessing the impact of one source

29. Georgia Institute of Technology 1st Order Direct Sensitivity Analysis 3-D Air Quality Model NO o NO 2 o VOC i o... T K u, v, w E i k i BC i... O 3 (t,x,y,z) NO(t,x,y,z) NO 2 (t,x,y,z) VOC i (t,x,y,z)... DDM-3D Sensitivity Analysis J decoupled Concentrations Response to emissions and parameter perturbations Simultaneously

30. Georgia Institute of Technology Modeling Approach Apply MM5, SMOKE, CMAQ to three episodes –Check episodes for representativeness –Develop emissions –Apply MM5/SMOKE/CMAQ system –Evaluate –Conduct diagnostic and sensitivity analysis runs –Strategy assessment Stakeholders involved in identifying choices

31. Georgia Institute of Technology 36-km 4-km 12-km

32. Georgia Institute of Technology Performance 35% Ozone Sulfate

33. Georgia Institute of Technology Source Impacts

34. Georgia Institute of Technology Macon

35. Georgia Institute of Technology

38. Correlations with Wind Direction: O 3 Period 2001+ 02 MAY-OCT NOV-APR

39. Georgia Institute of Technology What Is Going To Happen? Impact of Planned Controls

40. Georgia Institute of Technology Example Results from FAQS: Impact of Planned Controls: 2000 vs. 2007 Emissions reductions lead to about a 12 ppb ozone reduction *nb: these results are preliminary and need to be verified

41. Georgia Institute of Technology Ozone Reduction in Georgia Cities Region AtlantaAugustaColumbusMaconBirmingham Observed173129 154142 12-km grid150→135110→95123→108127→111164→127 4-km grid152→137108→96125→113152→135-

42. Georgia Institute of Technology Summary Georgia air quality is some of the worst in the nation –Ozone and PM Atlanta has highest levels –Dense urban emissions built upon a high regional background Fall line cities also experience high levels from a variety of sources –Air quality getting better –Source impacts depend upon city –Each city does contribute to its own problems to some degree