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Georgia Air Quality: A Tale of Four Cities

Georgia Air Quality: A Tale of Four Cities. Armistead (Ted) Russell Georgia Power Professor of Environmental Engineering Georgia Institute of Technology. Context. Every one knows about Atlanta’s air quality: Lots of people + Lots of driving + Lots of trees =

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Georgia Air Quality: A Tale of Four Cities

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

  3. Ozone Levels Georgia Institute of Technology

  4. Particulate Matter Annual Standard Georgia Institute of Technology

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

  6. Ozone Formation h (sunlight) NOx oxides of nitrogen (NO + NO2) O3 VOCs Volatile organic compounds Georgia Institute of Technology

  7. Chemical Regimes Radical/VOC Limited: Abundant NO2 removes OH, Inhibitting oxidation of VOCs and HO2/RO2 formation: Low utilization of NOx emissions NOx Limited: Lack of NOx limits ozone formation High utilization of NOx emiss. Often in areas of high biogenics Transport NitrogenOxides(NOx) High O3 Low O3 Volatile Organic Compounds (VOCs) Georgia Institute of Technology

  8. PM Formation h (sunlight) SO2 Sulfur dioxide PM NOx VOCs Georgia Institute of Technology

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

  10. FALL LINE AIR QUALITY STUDY © Augusta Chronicle Georgia Institute of Technology

  11. 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 Georgia Institute of Technology

  12. FAQS Project Team • 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 • 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 Georgia Institute of Technology

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

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

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

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

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

  18. Our working hypothesis: 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. Atlanta and Macon share an airshed that is capable of generating significant ozone independent of the region. Leslie?? Georgia Institute of Technology

  19. Correlations with Wind Direction: O3 Period 2001+ 02 MAY-OCT NOV-APR Georgia Institute of Technology

  20. Correlations with Wind Direction: PM2.5 Period 2001+ 02 MAY-OCT NOV-APR Georgia Institute of Technology

  21. Chemistry 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 50-100 50-200 Air Quality Model Atmospheric Diffusion Equation Numerics C=AxB+E Discretize Emissions Meteorology Operator splitting 200 species x 5000 hor. grids x 20 layers= 20 million coupled, stiff non-linear differential equations Georgia Institute of Technology

  22. Numerical Routines Historical: Advection Chem. Kinet. Evolving Sens. Anal. Proc. Integ. Unc. Anal. Model Parameter Calculation Emissions Model Modeling Process Atmospheric Modeling System (e.g., Models 3) Chemical Mechanism Specification Air Quality Model Chemical Mechanism Historical: Specified Evolving: Compiler Model Evaluation Emissions Inputs Historical: NO, NO2, HONO Lumped VOCs CO, SO2 Evolving: PM, NH3, Detailed VOCs, Adv. Biogenics Pollutant Distributions Evolving: Sensitivities Uncertainties Inputs: Emissions Inventory Population Roads Land Use Industry Meteorology 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)... Temperature, Solar Insolation Weak link Emissions Inventory Development Air Quality Data Analysis and Processing Meteorological Model (Diagnostic or Prognostic) Topographical Data Meteorological Observations Emissions, Industry and Human Activity Data Air Quality Observations Foundation Georgia Institute of Technology

  23. 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, … Georgia Institute of Technology

  24. Grids Nested Adaptive Multiscale Georgia Institute of Technology

  25. Sensitivity Analysis • Calculate sensitivity of gas and aerosol phase concentrations and wet deposition fluxes to input and system parameters • sij(t)=ci(t)/pj • 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 Georgia Institute of Technology

  26. Role of Sensitivity Analysis • 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 Georgia Institute of Technology

  27. Brute-Force Sensitivity Analysis O3(t,x,y,z) NO(t,x,y,z) NO2(t,x,y,z) VOCi(t,x,y,z) ... Air Quality Model base scenario base scenario + pj, O3,(t,x,y,z) NO ,(t,x,y,z) NO2,(t,x,y,z) VOCi,(t,x,y,z) ... Air Quality Model Air Quality Model Georgia Institute of Technology

  28. 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 Georgia Institute of Technology

  29. NOo NO2o VOCio ... T K u, v, w Ei ki BCi ... 3-D Air Quality Model O3(t,x,y,z) NO(t,x,y,z) NO2(t,x,y,z) VOCi(t,x,y,z) ... decoupled DDM-3D Sensitivity Analysis J 1st Order Direct Sensitivity Analysis Concentrations Simultaneously Response to emissions and parameter perturbations Georgia Institute of Technology

  30. 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 Georgia Institute of Technology

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

  32. 35% Performance Ozone Sulfate Georgia Institute of Technology

  33. Source Impacts Georgia Institute of Technology

  34. Macon Georgia Institute of Technology

  35. Georgia Institute of Technology

  36. Georgia Institute of Technology

  37. Georgia Institute of Technology

  38. Correlations with Wind Direction: O3 Period 2001+ 02 MAY-OCT NOV-APR Georgia Institute of Technology

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

  40. 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 Georgia Institute of Technology

  41. Ozone Reduction in Georgia Cities Georgia Institute of Technology

  42. 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 Georgia Institute of Technology

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