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Ph.D. Capstone Seminar

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Ph.D. Capstone Seminar

  1. 1. Distributed Dry Deposition Modeling: Sensitivity Analyses and GIS Coupling with A Component-Based Approach Satoshi Hirabayashi College of Environmental Science and Forestry State University of New York Satoshi Hirabayashi College of Environmental Science and Forestry State University of New York
  2. 2. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  3. 3. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  4. 4. Overview Dissertation Overview Coupling of Environmental Models and Geographic Information Systems Model Spatial Scheme GIS Coupling Component Technology Development Platform Analyses Hydrologic Lumped Loose ArcGIS 9.0 COM Regression Resolution Impacts Dry Deposition Tight Distributed Lumped VB EXE COM ArcGIS 9.2 - Dry Deposition Area Identification Sensitivity COM Dry Deposition
  5. 5. Overview Dissertation Overview Coupling of Environmental Models and Geographic Information Systems Model Spatial Scheme GIS Coupling Component Technology Development Platform Analyses Hydrologic Lumped Loose ArcGIS 9.0 COM Regression Resolution Impacts Dry Deposition Tight Distributed Lumped VB EXE COM ArcGIS 9.2 - Dry Deposition Area Identification Sensitivity COM Dry Deposition
  6. 6. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  7. 7. Urban Air Quality <ul><li>Big issue in cities worldwide </li></ul><ul><li>3,700 deaths annually in the United States </li></ul><ul><li>0.8 million deaths annually worldwide </li></ul><ul><li>Urban population: 3.3 billion (2008) - 5 billion (2030) </li></ul>Introduction
  8. 8. Dry Deposition to Trees Introduction Dry Deposition <ul><li>Air pollutant transport onto tree surfaces </li></ul><ul><li>Intake of air pollutants through leaf stomata </li></ul>
  9. 9. Dry Deposition to Trees Introduction Dry Deposition Urban Forest Effects – Deposition (UFORE-D) <ul><li>Estimates dry deposition by urban forests </li></ul><ul><li>CO, NO 2 , O 3 , PM10, and SO 2 </li></ul><ul><li>Lumped model: Estimations for entire city </li></ul><ul><li>Air pollutant transport onto tree surfaces </li></ul><ul><li>Intake of air pollutants through leaf stomata </li></ul>
  10. 10. Dry Deposition to Trees Introduction Dry Deposition Urban Forest Effects – Deposition (UFORE-D) <ul><li>Air pollutant transport onto tree surfaces </li></ul><ul><li>Intake of air pollutants through leaf stomata </li></ul><ul><li>Estimates dry deposition by urban forests </li></ul><ul><li>CO, NO 2 , O 3 , PM10, and SO 2 </li></ul><ul><li>Lumped model: Estimations for entire city </li></ul>Distributed UFORE-D for better urban forest management & planning
  11. 11. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  12. 12. Objectives Objectives <ul><li>Identify influential parameters via sensitivity analyses </li></ul>
  13. 13. Objectives Objectives <ul><li>Identify influential parameters via sensitivity analyses </li></ul><ul><li>Develop distributed dry deposition model GIS framework </li></ul>
  14. 14. Objectives Objectives <ul><li>Identify influential parameters via sensitivity analyses </li></ul><ul><li>Develop distributed dry deposition model GIS framework </li></ul><ul><li>Locate potential urban forest planting spots </li></ul>
  15. 15. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  16. 16. Method 1 Aerodynamic Resistance ( R a ) UFORE-D: Multiple-Resistance Analogy Model
  17. 17. Method 1 Aerodynamic Resistance ( R a ) Quasi-Laminar Boundary Layer Resistance ( R b ) UFORE-D: Multiple-Resistance Analogy Model
  18. 18. Method 1 Aerodynamic Resistance ( R a ) Quasi-Laminar Boundary Layer Resistance ( R b ) Canopy Resistance ( R c ) UFORE-D: Multiple-Resistance Analogy Model C uticular Resistance (r t ) Mesophyll Resistance (r m ) Stomatal Resistance (r s )
  19. 19. Method 1 Aerodynamic Resistance ( R a ) Quasi-Laminar Boundary Layer Resistance ( R b ) Canopy Resistance ( R c ) UFORE-D: Multiple-Resistance Analogy Model Pollutant flux, F Dry deposition velocity, V d C uticular Resistance (r t ) Mesophyll Resistance (r m ) Stomatal Resistance (r s )
  20. 20. Sensitivity Analysis Method 1 <ul><li>Examine how input variations impact model output </li></ul>
  21. 21. Sensitivity Analysis Method 1 <ul><li>Examine how input variations impact model output </li></ul><ul><li>Output: Dry deposition velocity ( V d ) </li></ul>
  22. 22. Sensitivity Analysis R a = ƒ ( Temperature , Wind speed ) R b = ƒ ( Temperature , Wind speed ) r t = ƒ ( LAI ) r m = ƒ ( LAI ) r s = ƒ ( LAI, PAR, Pressure, Relative humidity, Temperature ) Method 1 <ul><li>Examine how input variations impact model output </li></ul><ul><li>Output: Dry deposition velocity ( V d ) </li></ul><ul><li>Input: </li></ul>
  23. 23. Sensitivity Analysis R a = ƒ ( Temperature , Wind speed ) R b = ƒ ( Temperature , Wind speed ) r t = ƒ ( LAI ) r m = ƒ ( LAI ) r s = ƒ ( LAI, PAR, Pressure, Relative humidity, Temperature ) Method 1 <ul><li>Examine how input variations impact model output </li></ul><ul><li>Output: Dry deposition velocity ( V d ) </li></ul><ul><li>Input: </li></ul><ul><li>Methods: </li></ul>Monte Carlo with Latin Hypercube Sampling (LHS-MC) Morris One-At-a-Time (MOAT)
  24. 24. Study Area and Data Method 1 Baltimore, MD Hourly Meteorological Data Baltimore Washington International Airport Min, max, mean, standard deviation at noon in July, 2005
  25. 25. Monte Carlo with Latin Hypercube Sampling (LHS-MC) X 1,1 X 2,1 X 3,1 X 4,1 X 5,1 X 6,1 X 1,2 X 2,2 X 3,2 X 4,2 X 5,2 X 6,2 . . . X 1,n X 2,n X 3,n X 4,n X 5,n X 6,n Latin Hypercube Sampling Parameter Set Method 1 Parameter Value Min Max 1/8 n=8 X i,1 X i,2 X i,3 X i,4 X i,5 X i,6 X i,7 X i,8 pdf Monte Carlo Analysis Multiple model runs with model parameters randomly selected according to probability density function of the parameters
  26. 26. pdf The elementary effect, d i , for x i Morris One At a Time (MOAT) Methods 1 X 1 X 2 X 3 X 4 X 5 X 6 +/- Δ Morris One At a Time method Parameter Set Parameter Value High mean d i : Influential parameter High standard deviation d i : nonlinear or interaction X i,1 X i,4 X i,2 X i,3 Δ /2
  27. 27. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  28. 28. LHS-MC Results for NO 2 Results and Discussion 1 Temperature ( ℃ ) V d (cm/s) LAI V d (cm/s) PAR (W/m 2 ) V d (cm/s) LAI PAR Pressure (mBar) V d (cm/s) Pressure Relative humidity V d (cm/s) Relative humidity Wind speed (m/s) Wind speed V d (cm/s) Temperature
  29. 29. Result: MOAT <ul><li>Temperature: greatest impact and non-linear effect on V d </li></ul><ul><li>LAI: 2 nd largest impact on NO 2 V d </li></ul><ul><li>Wind speed: 2 nd largest impact on O 3 and SO 2 V d </li></ul>NO 2 O 3 SO 2 Results and Discussion 1 LAI PAR Pressure Relative humidity Temperature Wind speed 0.1 1 10 0.1 1 10 Mean Standard deviation 0.1 1 10 0.1 1 10 Mean Standard deviation 0.1 1 10 0.1 1 10 Mean Standard deviation
  30. 30. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  31. 31. Distributed UFORE-D Method 2 Distributed UFORE-D Output Input LAI Concentration Temperature Temperature Other Meteorological Data UFORE-D and ArcGIS coupled system Aerodynamic Resistance ( R a ) Quasi-Laminar Boundary Layer Resistance ( R b ) Canopy Resistance ( R c ) Pollutant flux, F Dry deposition velocity, V d
  32. 32. Component-Based Approach <ul><li>Separate codes into components that perform certain tasks </li></ul><ul><li>Components linked at run time to form complete model </li></ul><ul><li>Microsoft’s Component Object Model (COM) </li></ul>Method 2 Function 1 Function 2 Function 3 Function 4 Conventional Model Component-based Model Component1 Component2 Component3 Component4
  33. 33. VB Executable (EXE) User interface (COM) Component-Based Lumped UFORE-D Lumped UFORE-D (SAS) Lumped UFORE-D (Visual Basic) Core function Data input/output Method 2 Windows Registry COM component (DLL) registered refers method calls registered COM component (DLL)
  34. 34. (COM) Component-based Distributed UFORE-D Distributed UFORE-D (ArcGIS) Method 2 refers imports Windows Registry registered registered registered method calls COM component (DLL) COM component (DLL) Menubar (DLL)
  35. 35. Menubar LAI Model Road Emission Model UFORE-D Method 2 Temperature Calculation LAI Calculation Road Emission Calculation Road Dispersion Calculation Facility Dispersion Calculation Concentration Adjustment Dry Deposition Calculation Create Average Raster Temperature Processing LAI Processing Concentration Processing Dry Deposition Processing Ap Utilities Δ T Regression Model Gaussian Line Source Model Gaussian Point Source Model
  36. 36. Gaussian Point Source Dispersion Model Plume dispersion in 3D Plume dispersion on a grid basis (2D) z Δ h y x Wind h s R (x r , y r , z r ) S (0, 0, 0) S R Source Receptor (Z r = 1.5 m) Gaussian point source dispersion model Method 2 R R R R R R R R R R R R R R Wind Buffer x y S
  37. 37. Emission and Gaussian Line Source Dispersion Models Emission model Grid-based implementation Gaussian line source dispersion model Method 2 Interstate Highway US Highway State Highway Local Road Wind Wind
  38. 38. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  39. 39. Results for July 1st, 2005 at 9:00 AM Results and Discussion 2 Canopy Resistance (s/m) Aerodynamic Resistance (s/m) Boundary Layer Resistance (s/m) Temperature NO 2 Concentration ( μ g/m 3 ) NO 2 Flux (mg/m 2 /h) Deposition Velocity (cm/s) LAI
  40. 40. Results: Averaged NO 2 Concentration Emission (tons/yr) Concentration ( μ g/m 3 / h) Averaged concentration over 160 hours Windrose for 160 hours Results and Discussion 2 Concentration near NO 2 emitting facility
  41. 41. Potential Urban Forest Planting Areas > 90 th percentile (25.6 μ g/m 3 ) < 30 th percentile (0.49 cm /s ) AND = Results and Discussion 2 False True False True False True NO 2 Concentration NO 2 Deposition Velocity Potential Planting Areas
  42. 42. Outline <ul><li>Overview </li></ul><ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>Methods 1 </li></ul><ul><li>Results and Discussions 1 </li></ul><ul><li>Methods 2 </li></ul><ul><li>Results and Discussions 2 </li></ul><ul><li>Conclusions </li></ul>Outline
  43. 43. Conclusions Conclusions <ul><li>Identify influential parameters via sensitivity analyses </li></ul>
  44. 44. Conclusions Conclusions <ul><li>Temperature: nonlinear effect </li></ul><ul><li>Leaf Area Index (LAI): near linear effect </li></ul><ul><li>Wind speed: effect limited to small value </li></ul><ul><li>Identify influential parameters via sensitivity analyses </li></ul>
  45. 45. Conclusions Conclusions <ul><li>Identify influential parameters via sensitivity analyses </li></ul><ul><li>Develop distributed dry deposition model GIS framework </li></ul><ul><li>Temperature: nonlinear effect </li></ul><ul><li>Leaf Area Index (LAI): near linear effect </li></ul><ul><li>Wind speed: effect limited to small value </li></ul>
  46. 46. Conclusions Conclusions <ul><li>Identify influential parameters via sensitivity analyses </li></ul><ul><li>Develop distributed dry deposition model GIS framework </li></ul><ul><li>Distributed temperature, LAI, and concentration </li></ul><ul><li>Lumped UFORE-D developed with COM technology </li></ul><ul><li>Distributed UFORE-D coupled with ArcGIS </li></ul><ul><li>Temperature: nonlinear effect </li></ul><ul><li>Leaf Area Index (LAI): near linear effect </li></ul><ul><li>Wind speed: effect limited to small value </li></ul>
  47. 47. Conclusions Conclusions <ul><li>Identify influential parameters via sensitivity analyses </li></ul><ul><li>Develop distributed dry deposition model GIS framework </li></ul><ul><li>Distributed temperature, LAI, and concentration </li></ul><ul><li>Lumped UFORE-D developed with COM technology </li></ul><ul><li>Distributed UFORE-D coupled with ArcGIS </li></ul><ul><li>Locate potential urban forest planting spots </li></ul><ul><li>Temperature: nonlinear effect </li></ul><ul><li>Leaf Area Index (LAI): near linear effect </li></ul><ul><li>Wind speed: effect limited to small value </li></ul>
  48. 48. Conclusions Conclusions <ul><li>Identify influential parameters via sensitivity analyses </li></ul><ul><li>Develop distributed dry deposition model GIS framework </li></ul><ul><li>Distributed temperature, LAI, and concentration </li></ul><ul><li>Lumped UFORE-D developed with COM technology </li></ul><ul><li>Distributed UFORE-D coupled with ArcGIS </li></ul><ul><li>Locate potential urban forest planting spots </li></ul><ul><li>Areas along highways </li></ul><ul><li>Areas surrounding NO 2 emitting facilities </li></ul><ul><li>Temperature: nonlinear effect </li></ul><ul><li>Leaf Area Index (LAI): near linear effect </li></ul><ul><li>Wind speed: effect limited to small value </li></ul>
  49. 49. Conclusions Conclusions Final Thoughts <ul><li>One integrated system: easier to use than EPA models </li></ul>
  50. 50. Conclusions Conclusions Final Thoughts <ul><li>One integrated system: easier to use than EPA models </li></ul><ul><li>Model performance and validation limited </li></ul>
  51. 51. Conclusions Conclusions Final Thoughts <ul><li>One integrated system: easier to use than EPA models </li></ul><ul><li>Model performance and validation limited </li></ul><ul><li>COM compatible with MATLAB, Mathcad, Manifold GIS </li></ul>
  52. 52. Conclusions Conclusions Final Thoughts Future Studies <ul><li>Model improvement </li></ul><ul><li>More distributed parameters </li></ul><ul><li>One integrated system: easier to use than EPA models </li></ul><ul><li>Model performance and validation limited </li></ul><ul><li>COM compatible with MATLAB, Mathcad, Manifold GIS </li></ul>
  53. 53. Conclusions Conclusions Final Thoughts Future Studies <ul><li>Model improvement </li></ul><ul><li>More distributed parameters </li></ul><ul><li>Client-Server/Web based </li></ul><ul><li>One integrated system: easier to use than EPA models </li></ul><ul><li>Model performance and validation limited </li></ul><ul><li>COM compatible with MATLAB, Mathcad, Manifold GIS </li></ul>
  54. 54. Acknowledgement <ul><li>Advisor </li></ul><ul><ul><li>Chuck N. Kroll </li></ul></ul><ul><li>Committee </li></ul><ul><ul><li>Dave Nowak </li></ul></ul><ul><ul><li>Lee P. Herrington </li></ul></ul><ul><ul><li>Lindi J. Quackenbush </li></ul></ul><ul><ul><li>Theodore A. Endreny </li></ul></ul>
  55. 55. Outline Questions?
  56. 56. LHS-MC Results for NO 2 Results and Discussion 1 V d (cm/s) V d (cm/s) V d (cm/s) LAI V d (cm/s) PAR (W/m 2 ) V d (cm/s) LAI PAR Temperature ( ℃ ) V d (cm/s) Temperature LAI PAR (W/m 2 ) Temperature ( ℃ )
  57. 57. Model Run Operational Conditions: Method 2 <ul><li>Rain = 0 for dry deposition </li></ul><ul><li>Wind speed > 0 </li></ul><ul><li>Daytime with unstable atmosphere </li></ul>Receptor height =1.5 m Above Canopy Concentration 160 hours in July and August, 2005 met conditions in Baltimore
  58. 58. Averaged Concentration and Deposition Velocity Frequency NO 2 Concentration > 50 th (24.5 μ g/m 3 ) > 70 th (24.6 μ g/m 3 ) > 90 th (25.6 μ g/m 3 ) < 10 th (0.41 cm/s) < 30 th (0.49 cm/s) < 50 th (0.51 cm/s) NO 2 Deposition Velocity Results and Discussion 2 False True False True False True False True False True False True NO 2 Concentration ( μ g/m 3 ) 20 40 60 80 100 0 50,000 100,000 150,000 200,000 230,000 5,300 1,400 300 70 25 9 3 250,000 V d (cm/s) Frequency 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0 10,000 20,000 30,000 40,000 50,000 60,000 52,000 59,000 60,000 22,000 0 0 40,000

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