1. Use of Airmass History Models & Techniques for Source Attribution Bret A. Schichtel Washington University St. Louis, MO Presentation to EPA Source Attribution workshop July 16 - 18, 1997 http://capita.wustl.edu/neardat/CAPITA/CapitaReports/AirmassHist/EPASrcAtt_jul17/index.htm

2. Airmass History Estimation of the pathway of an airmass to a receptor (backward AMH) or from a source (forward AMH) and meteorological variables along the pathway. Airmass Back Trajectory Airmass Met. Variables Plumes

3. Source Receptor Relationship Receptor Concentration Dilution Chemistry/ Removal Emissions = * * Airmass history modeling and analysis aid in the understanding of the SRR processes and qualitatively and quantitatively establish source contributions to receptors.

6. Regional Airmass History Models - ATAD -Single 2-D back/forward trajectories from single site -Wind fields: Diagnostic from available measured data -No Mixing - HY-SPLIT -3-D back/forward trajectories and plumes from single site -Wind fields: NGM, ETA, RAMS, ……. -Mixing for Plumes; No Mixing for back trajectories -Pollutant simulation - CAPITA Monte Carlo Model -3-D back/forward airmass histories and plumes from multiple sites -Wind fields: NGM, RAMS,…... -Mixing for forward and backward airmass histories -Pollutant simulation

7. Airmass Histories - Model Outputs 2-D Back Trajectory Multiple 3-D Back Trajectories Airmass History Variables

8. CAPITA Monte Carlo Model Direct simulation of emissions, transport, transformation, and removal http://capita.wustl.edu/capita/CapitaReports/MonteCarlo/MonteCarlo.html

9. Vertical Dispersion: Below the mixing layer particles are uniformly distributed from ground to mixing height. No dispersion above mixing layer. Transport Advection: 3-D wind fields Horizontal Dispersion: Eddy diffusion; K x and K y vary depending on hour of day

10. Kinetics Chemistry: Pseudo first order transformation rates, function of meteorological variables, such as solar radiation, temperature, water vapor content Deposition dry and wet: Pseudo first order rates equations Dry deposition function of hour of solar radiation, Mixing Hgt Wet deposition function of precipitation rate

12. Primary Meteorological Input Data National Meteorological Centers Nested Grid Model (NGM) Time range: 1991 - Present Horizontal resolution: ~ 160 km Vertical resolution: 10 layers up to 7 km 3-D variables: u, v, w, temp., humidity Surface variables include: Precip, Mixing Hgt,…. Database size: 1 year - 250 megabytes

13. Airmass History Analysis Techniques Individual Airmass Histories Techniques: -Visually combine measured/modeled air quality data with airmass history and meteorological data Uses: -Pollution episode analysis. Brings meteorological context to air quality data. Goals of Workshop addressed: -Pollution episode selection and analysis -Evaluate air quality models

14. Animation of Grand Canyon Fine Particle Sulfur, Back Trajectories & Precipitation On February 7, the Grand Canyon has elevated sulfur concentrations. The back trajectory shows airmass stagnation in S. AZ prior to impacting the Grand Canyon. The following day the airmass transport is still from the south, but it encountered precipitation near the Grand Canyon. The sulfur concentrations dropped by a factor of 8.

15. Merging Air Quality & Meteorological Data for Episode Analysis OTAG 1991 modeling episode Animation

16. Anatomy of the July 1995 Regional Ozone Episode Regional scale ozone transport across state boundaries occurs when airmasses stagnate over multi-state areas of high emission regions creating ozone “blobs” which are subsequently transport to downwind states

18. Airmass History Analysis Techniques Ensemble Analysis Techniques: - Cluster analysis; forward and backward AMH - Residence time analysis; Backward AMH - Source Regions of Influence; Forward AMH Uses: - Qualitative source attribution - Transport climatology Goals of Workshop addressed: - Area of Influence - Pollution episode “representativeness” - Selecting control strategies

19. Residence Time Analysis W here is the airmass most likely to have previously resided Residence Time Probabilities Whiteface Mt. NY, June - August 1989 - 95 Back Trajectories Wishinski and Poirot, 1995 http://capita.wustl.edu/otag/Reports/Restime/Restime.html Airmass histories from HY-SPLIT model

25. Source Region of Influence - St. Louis, MO Quarter 3, 1992 Quarter 3, 1995 The shape and size of the region of influence is dependent upon the pollutant lifetime, wind speed and wind direction. The longer the lifetime, higher the wind speed the larger the region of influence. The elongation is primarily due to the persistence of the wind direction.

27. High ozone in the central OTAG domain occurs during slow transport winds. In the north and west, high ozone is associated with strong winds. Low ozone occurs on days with transport from outside the region. The regions of influence (yellow shaded areas) are also higher on low ozone days. Transport Climatology - Local Ozone Episodes

28. Transport winds during the ‘91,‘93,‘95 episodes are representative of regional episodes. OTAG episode transport winds differ from winds at high local O 3 levels. Comparison of transport winds during the ‘91, ‘93, ‘95 episodes with winds during regional episodes in general. Comparison of transport winds during the ‘91, ‘93, ‘95 episodes with winds during locally high O 3. OTAG Modeling Episodes Representativeness

32. Airmass History Model Comparison HY-SPLIT Vs. CAPITA Monte Carlo Model HY-SPLIT: NGM wind fields, no mixing Monte Carlo Model: NGM wind fields, mixing At times individual Airmass histories compared very well At times individual Airmass histories compared very poorly

33. The three month aggregate of airmass histories produced similar transport patterns.

34. Airmass History Analysis Techniques Pollutant Simulation and Transfer Matrices Technique: -Airmass Histories + Emissions + Kinetics Uses: - Quantitative source attribution (transfer matrix) - Long-term and episode pollutant simulation Goals of Workshop addressed: - Area of Influence - Selecting control strategies

35. http://capita.wustl.edu/capita/CapitaReports/MonteCarlo/MonteCarlo.html

36. St. Louis airmass history Variation of rate coefficients along trajectory, and corresponding sulfur budget. Kinetic Processes Applied to Single Airmass History

37. Comparison of simulated Sulfate to Measured

38. Comparison of simulated Wet Deposited Sulfate to Measured

39. Transfer Matrices - Massachusetts Receptor, Q3 1992 Transit Probability SO 2 Kinetic Probability SO 4 Kinetic Probability Likelihood an airmass from a source is transported to the receptor Likelihood SO 2 emissions into the airmass impact the receptor as SO 2 Likelihood SO 2 emissions into the airmass impact the receptor as SO 4

40. Quantitatively Define Source Receptor Relationship SO 2 and SO 4 Source Attribution to Massachusetts Receptor, Q3 1992 1985 NAPAP SO 2 Emissions