1. Regional Air Quality Modeling: From Source Identification to Health Impacts Amit Marmur, …, many great students and senior researchers, and Armistead (Ted) Russell Georgia Institute of Technology Atlanta, Georgia USA

2. With Special Thanks to: Paige Tolbert and the Emory crew –As part of ARIES, SOPHIA, and follow on studies NIEHS, US EPA, FHWA, Southern Company, SAMI –Financial assistance JGSEE of Thailand And more…

3. Issues Approximately 799,000 excess deaths per year occur per year due to air pollution –487,000 in Asia (S, SE and W. Pacific) Variety of health impacts in Thailand tied to air pollutants –Primarily due to: PM2.5: small particles, Range of health impacts, visibility impairment, … Ozone PAPA Studies show strong associations with PM and ozone in Asia Most of PM 2.5 burden comes from combustion to transform energy –Primary and secondary emissions Need reliable approaches to identify how energy sources impact air quality: Source Apportionment –Air quality management –Health impact assessment

4. Ozone Formation h (sunlight) O3O3 NO x oxides of nitrogen (NO + NO 2 ) VOCs Volatile organic compounds E VOC E NOx Low O 3 High O 3 Ozone Isopleth

5. PM Formation h (sunlight) PM NO x VOCs, OC & EC SO 2 Sulfur dioxide

6. Particulate Matter Complex mixture of solid and liquid particles suspended in the ambient air Size classifications –“super-coarse” > 10μm –“coarse” (PM 10 ) < 10μm –“fine” (PM 2.5 ) < 2.5μm –“ultrafine” < 0.1μm Many sources Many chemical species:

7. Fine Particles: Why should we care ? Airway Inflammation Effects on Lung Function RESPIRATORY EFFECTS CARDIOVASCULAR EFFECTS Effects on Cardiovascular Function Vascular Inflammation Image courtesy of the U.S. EPA

8. Outline Atmospheric modeling –Types –Basics –Approaches Advanced approaches Applications –Source impacts –health impact assessment

9. PM (Source Apportionment) Models (those capable of providing some type of information as to how specific sources impact air quality) PM Models Emissions- Based Receptor Lag.Eulerian (grid) CMBFA PMF UNMIX Molec. Mark.Norm. “Mixed PM” Source Specific* Hybrid First-principle Statistical

10. Role of Atmospheric Modeling In Air Quality Assessment Emissions Air Quality/Health Impacts Control s Pollutant Distributions Air Quality Model Air Quality Goals or

11. Receptor Models Obsserved Air Quality C i (t) Source Impacts S j (t) C i - ambient concentration of specie i (  g/m 3 ) f i,j - fraction of specie i in emissions from source j S j - contribution (source-strength) of source j (  g/m 3 )

12. Receptor Models Strengths –Results tied to observed air quality –Less resource intensive (provided data is available) Weaknesses –Data dependent (accuracy, availability, quantity, etc.) Monitor Source characteristics –Not apparent how to calculate uncertainties –Do not add “coverage” directly

13. Emissions-based 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 10000 hor. grids x 20 layers= 40 million coupled, stiff non-linear differential equations Atmospheric Diffusion Equation Discretize Operator splitting 50-100 Emissions Chemistry Meteorology Numerics C=AxB+E

14. Air Quality Model Air Quality Temperature Radiation Cloud Cover Wind Emissions 1. Anthropogenic 2. Geogenic 3. Biogenic Sources Transported Pollutants Sources (E,S, BCs) Geographical Features Transport (U, K, V d ) Turbulence Numerical Solution Techniques Surface Deposition Sink Processes Topography & Land use Photochemical Reactions Thermochemical Reactions Homogeneous Processes Heterogeneous Processes Computed Concentrations Meteorology Aerosol Dynamics Chemical Processes (R) Chemistry and Aerosol Dynamics

15. Atmospheric Modeling Process Foundation 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

16. Grids Nested Multiscale (Odman et al.) Adaptive (Odman et al.) About 15 vertical Layers up to 15 km (many in first 1 km)

17. How well do they work?* *Performance relies on quality of inputs. US has spent decades on emissions inventory development. Meteorological modeling also contributes significantly to errors

18. Source-based Models Strengths –Direct link between sources and air quality –Provides spatial, temporal and chemical coverage Weaknesses –Result accuracy limited by input data accuracy (meteorology, emissions…) –Resource intensive

19. Hybrid: Inverse Model Approach* Emissions (E ij (x,t)) C i (x,t), F ij(x,t), & S j (x,t) Air Quality Model + DDM-3D Receptor Model Observations taken from routine measurement networks or special field studies New emissions: E ij (x,t) Other Inputs INPUTS Main assumption in the formulation: A major source for the discrepancy between predictions and observations are the emission estimates

20. What’s next? Emissions-based air quality models work pretty well, how might we use them: –Identify, quantitatively, how specific sources impact air quality. –Develop and test control strategies Decoupled direct method (implemented in CIT, URM, MAQSIP, CMAQ, CAMX) –Dunker: initial applications –Yang et al.: large scale application, comp. efficient (CIT, URM) –Hakami et al.,Cohan et al: Higher order, with applications (MAQSIP, CMAQ) –Napelenok eet al., : PM Control strategy assessment –Least cost approach to attainment for Macon, GA (Cohan et al.) Assessing impacts of individual sources Area of Influence analysis (AOI) (similar information as developing the adjoint) –Or AOPI (potential influence) Application to health assessment

21. Emissions reductions lead to about a 12 ppb ozone reduction: Atlanta and Macon do not attain ozone standard (Macon by 6ppb) Example Results : Impact of Planned Controls: 2000 vs. 2007

22. Sensitivity analysis Given a system, find how the state (concentrations) responds to incremental changes in the input and model parameters: Inputs (P) Model Parameters (P) Model Sensitivity Parameters: State Variables: If P j are emissions, S ij are the sensitivities/responses to emission changes, e.g.., the sensitivity of ozone to Atlanta NOx emissions

23. Define first order sensitivities as Take derivatives of Solve sensitivity equations simultaneously Sensitivity Analysis with Decoupled Direct Method (DDM): The Power of the Derivative AdvectionDiffusionChemistryEmissions

24. 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 DDM-3D J decoupled

25. DDM compared to Brute Force Emissions of SO 2 Sulfate EBEB EAEA CBCB CACA

26. Consistency of first-order sensitivities Brute Force (20% change) DDM-3D R 2 > 0.99 Low bias & error

27. Advantages of DDM-3D Computes sensitivities of all modeled species to many different parameters in one simulation –Can “tell” model to give sensitivities to 10s of parameters in the same run Captures small perturbations in input parameters –Strangely wonderful Avoids numerical errors sometimes present in sensitivities calculated with Brute Force Lowers the requirement for computational resources

28. Evidence of Numerical Errors in BF NH 4 sensitivity to domain-wide SO 2 reductions NOx reductions at a point

29. Efficiency of DDM-3D

30. Control Strategy Development Macon out of attainment by 6 ppb in 2007 Want to identify least cost control strategy Process: –Identify possible controls and costs ($/ton of VOC or NOx) –Simulate response to controls (  [O 3 ]/ton VOC or NOx) –Calculate control effectiveness(  [O 3 ]/$) –Choose most effective controls until get 6 ppb –Test strategy

31. Sources of Macon’s ozone Macon Scherer Atlanta Branch 8-hr ozone, Aug. 17, 2000 (2007 emissions) M A S B

32. Sensitivity of 8-hr Ozone in Macon

33. NO x emission rates (tpd) Macon ozone sensitivity (ppt/tpd) 2007 Emissions and Sensitivities

34. Source-Receptor Response Marginal Abatement Costs by Region Cost-optimization Choose options with least marginal $/impact until: (1) attain a.q. goal, or (2) reach budget constraint Cost Impact

35. Strategies for Macon attainment (need 6.5 ppb) Key Measures Zero-cost options (PRB coal, burning ban,...): 1.72 ppb, $0 Bibb industrial NO x : 0.82 ppb, $2.6 million Locomotive controls: 0.77 ppb, $7.3 million SCRs at Scherer: 1.63 ppb, $20.9 million Vehicle I&M in Bibb: 0.25 ppb, $4.9 million

36. Provide a technique to evaluate the impacts from a single large emissions source on regional air quality, incorporating non-linear processes and multi-day effects in estimating pollutant responses to relatively small emissions perturbations. Single-Source Impact Analysis (Bergin et al.)

37. Motivation and Application The ability to evaluate regional secondary pollution impacts from large single sources would provide a valuable tool for more effective air quality management practices, such as refining programs (e.g. emissions trading, regional planning), and supporting more effective compliance enforcement. Typical modeling approach (removing the emissions from a single source) has numerical errors. Court case led to need to assess impact of a single power plant (Sammis) in Ohio on downwind areas (a distance of up to about 1000 km)

38. Average Day Elevated NO x Emissions W. H. Sammis Power Plant (court estimated emissions) 0 500 1000 1500 2000 2500 May-95Jul-95Aug-00 Ohio Elevated EGU Jul-95 Model Inventory NOx Emissions (avg tons/day) excess allowable Court Estimated from W.H. Sammis Plant

39. Approach Two air quality models and grids, three ozone episodes, and three sensitivity techniques (brute-force, DDM, higher order DDM) CMAQ, 36x36 km Aug. 12-20, 2000 2-ord. DDM URM, multiscale from 24x24 km 2 July 11-19 and May 24-29, 1995 DDM

40. Maximum increase in 1-hr avg O 3 Comparison of the maximum increase in hourly-averaged ozone concentrations due to excess NO x emissions from the Sammis plant. (a)July 11-19, 1995(b) May 24-29, 1995(c) August 12-20, 2000 URM with DDM CMAQ with 2 nd order DDM

41. When O 3 > 0.060 ppm 1-hr O 3 cell responses to excess emissions All hours Max. increases Max. decreases maximum = 2.2 minimum = -3.6 minimum = -1.2 CMAQ, 2 nd ord DDM, August

42. Conclusions Single-source simulation results agree with past field experiments, indicating that appropriate modeling techniques are available for quantifying single-source regional air quality impacts.

43. Air Quality Models and Health Impact Assessment (How) Can we use “air quality models” to help identify associations between ozone PM sources and health impacts? –Species vs. sources –Very different than for traditional air quality management

44. Epidemiology Identify associations between air quality metrics and health endpoints: Sulfate Health endpoints Statistical Analysis Association

45. Epidemiologic Analysis log{E(CVD)} =  +  [PM 2.5 ] + covariate terms Covariates: time trend (mo. knots), day-of-week, holidays, hospital entry/exit, temperature, dew point 70 35 0 140 70 0 199820012004 1998 2001 2004 Exposure: daily PM 2.5 (  g/m 3 ); lag 0, 1, 2 Outcome: daily ED visit counts for CVD

46. Association between CVD Visits and Air Quality (Tolbert et al., 2004)

47. Issues May not be measuring the species primarily impacting health –Observations limited to subset of compounds present Many species are correlated – Inhibits correctly isolating impacts of a species/primary actors Inhibits identifying the important source(s) Observations have errors –Traditional: Measurement is not perfect –Representativeness (is this an error? Yes, in an epi-sense) Observations are sparse –Limited spatially and temporally Multiple pollutants may combine to impact health –Statistical models can have trouble identifying such phenomena Ultimately want how a source impacts health –We control sources

48. Use AQ Models to Address Issues: Link Sources to Impacts Data Air Quality Model Source Impacts S(x,t) Health Endpoints Statistical Analysis Association between Source Impact and Health Endpoints

49. Source Impacts on Air Quality (Nov 1998 – Aug 2000) CMB Source Impacts

50. Power-plant derived SO 4 -2

51. Diesel Elemental Carbon Particulate Matter

52. Source-specific RRs: Wood burning RR 95% CI All respiratory (263 daily ED visits)All cardiovascular (86 daily ED visits) RR significant if CI does not cross unity (RR=1.0)

53. Source-specific RRs: Wood burning RR All respiratory (263 daily ED visits)All cardiovascular (86 daily ED visits) Wood- PMF K Wood- CMB-LGO OC PM 2.5

54. Source-specific RRs: Mobile sources All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits) Diesel- PMF,CMB-LGO Mobile- PMF PM 2.5 CO EC Fe Gas- PMF CO

55. Source-specific RRs: Soil dust Soil- CMB-LGO All resp. (263 ED visits) All CVD (86 ED visits) Asthma (54 ED visits) Soil- PMF Si Soil- CMB-LGO PM 2.5

56. Source-specific RRs: “Other” OC “Other” OC OC PM 2.5

57. Summary Air quality models provide powerful tools to link how energy conversion and utilization impact air quality, health and the environment. –Emissions-based (First principles) –Receptor (statistical) models Advanced techniques provide means to efficiently assess impacts from individual sources and non-linear interactions –DDM Application of PM Source apportionment models in health studies more demanding than traditional modeling –Provide additional power and insight to identifying which sources impact health, not just which species Particularly important for organic carbon that comes from many sources

58. Thanks… Questions?