Use of Airmass History Models & Techniques for Source Attribution

1. Use of Airmass History Models & Techniques for Source Attribution Bret A. SchichtelWashington UniversitySt. Louis, MO Presentation to EPA Source Attribution workshopJuly 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 ReceptorConcentration 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.

4. Airmass History Analysis Techniques • Individual airmass histories • Backward and forward airmass history ensemble analysis • Air quality simulation • Transfer matrices • Emission Retrieval Goals of Workshop addressed: • Area of Influence • Selecting and analyzing pollution episodes • Selecting control strategies • Evaluate air quality models

5. Characteristics of Airmass History Analyses to be presented • Regional Pollutants • Ozone • Fine particulates • visibility • Climatological analysis • Proposed year fine particle standard • Source attribution for typical conditions • Source attribution for typical episodes

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 Multiple 3-D Back Trajectories Airmass History Variables 2-D Back Trajectory

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

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

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 equationsDry deposition function of hour of solar radiation, Mixing Hgt Wet deposition function of precipitation rate

11. Model Output: • Database of airmass histories • Pollutant concentrations and deposition fields • Transfer matrices Computer Platform IBM-PC Computation Requirements: Low: 3 months of back airmass histories for 500 sites ~1 day3 months of sulfate simulations over North America ~2 days User expertise: Airmass history server- Low Pollutant simulation - High

12. Primary Meteorological Input Data National Meteorological Centers Nested Grid Model (NGM) Time range: 1991 - Present Horizontal resolution: ~ 160 kmVertical resolution: 10 layers up to 7 km3-D variables: u, v, w, temp., humiditySurface 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 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. 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.

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

17. Strengths • Applicable to particulates, ozone and visibility • Informed decision - Brings multiple variables and views of data for selection and analysis of episodes • High user efficiency - Visualize large quantities of data quickly • Low computer resources Weaknesses • Single trajectories prone to large errors. • Potential for information overload.

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

20. Ozone > 51 ppb June - August 1989 - 95 Airmass History Stratification Whiteface Mt. NY- Residence Time Probabilities Ozone < 51 ppb June - August 1989 - 95 High ozone concentrations are associated with airflow from the east to southeast Low ozone concentrations are associated with airflow from the northeast • Technique identifies airmass pathways not the source areas along the pathway • Central bias - all airmass histories must pass through receptor grid cell

21. Upper 50% Ozone Vs. Everyday Removing the Central Bias Incremental Probability Analysis Incremental Probability Stratified Probability Everyday Probability = - • High ozone is associated with airflow from the central east • Regions implicated increase from south to north

22. Identifying Unique Source RegionsIncremental Probabilities from 23 Combined Receptor Sites Lower 50% Ozone Upper 50% Ozone June - August 1989 - 95 June - August 1989 - 95 • High ozone is associated with airflow from the Midwest • Implies that Midwest is “source” of high ozone to many receptors. This region would be good source area to focus control strategies on.

23. Strengths • Applicable to particulates, ozone, visibility • Ensemble analysis reduces trajectory error • Does not include a prior knowledge of emissions and kinetics • Receptor viewpoint: Which sources contribute to favorite receptor region • Regional scale analysis and climatology Weaknesses • Qualitative • Not suitable to evaluate local scale influences • Does not implicate specific sources or source types

24. Source Region of InfluenceThe most likely region that a source will impact St. Louis Source Forward Airmass Histories Transfer Matrix • St. Louis emissions can impact anywhere in the Eastern US. The impact tends to decrease with increasing transport distances. • The source region of influence is defined as the smallest area encompassing the source that contains ~63% of ambient mass. Note, this is a relative measure.

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.

26. Transport Climatology - Summer • Resultant transport from Texas around Southeast and eastward. • Region of influence is ~40% smaller in Southeast compared to rest of Eastern US. Schichtel and Husar, 1996 http://capita.wustl.edu/otag/reports/sri/sri_hlo3.htm

27. Transport Climatology - Local Ozone Episodes 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.

28. OTAG Modeling Episodes Representativeness Transport winds during the ‘91,‘93,‘95 episodes are representative of regional episodes.OTAG episode transport winds differ from winds at high local O3 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 O3.

29. Strengths • Source viewpoint: Which receptors are impacted by favorite source region • Applicable to particulates, ozone, and visibility • Applicable to climatology and episode analysis • Direct measure of a source’s region of influence if pollutant lifetime is known Weaknesses • Pollutant lifetime varies with time & space - often ill-defined • Simplified kinetics - can only define a boundary, not a source contribution field • Does not account for vertical distribution of pollutants Future Development • Include vertical distribution of pollutants • Enhance kinetics - add removal and transformation processes • define contribution field within the region of influence

30. Complementary Analyses • Forward and backward airmass history analysis techniques • Analyses incorporating measured meteorology and receptor data Ozone roses for selected 100 mile size sub-regions. Calculated from measured surface winds and ozone data. At many sites, the avg. O3 is higher when the wind blows from the center of the domain. Same conclusion drawn from forward and backward airmass history analyses.

31. Airmass History Uncertainty • Sources of uncertainty: • Meteorological data • Physical assumptions of airmass history model • Horizontal and vertical transport & dispersion • Airmass starting elevations • Inclusion of surface affects • Uncertainty Quantification: • 20 - 30 %/day trajectory error.HY-SPLIT model and NGM winds evaluated during the ANATEX tracer experiments (Draxler (1991) J. Appl. Meterol. 30:1446-1467). • 30 - 50 %/day trajectory error • Several models and wind fields evaluated during the ANATEX tracer experiments (Haagenson et al., (1990) J. Appl. Meterol. 29:1268-1283) • Uncertainties can be reduced by considering ensembles of airmass histories, assuming errors are stochastic and not biased

32. Airmass History Model ComparisonHY-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 + KineticsUses: - 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.htmlhttp://capita.wustl.edu/capita/CapitaReports/MonteCarlo/MonteCarlo.html

36. Kinetic Processes Applied to Single Airmass History Variation of rate coefficients along trajectory, and corresponding sulfur budget. St. Louis 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 SO2 Kinetic Probability SO4 Kinetic Probability Likelihood an airmass from a source is transported to the receptor Likelihood SO2 emissions into the airmass impact the receptor as SO2 Likelihood SO2 emissions into the airmass impact the receptor as SO4

40. Quantitatively Define Source Receptor Relationship 1985 NAPAP SO2 Emissions SO2 and SO4 Source Attribution to Massachusetts Receptor, Q3 1992

41. Strengths • Applicable to particulates and visibility • Applicable to climatology and episode analysis • Regional scale analysis • Quantitative • Applicable to “what if” analyses Weaknesses • Cannot simulate coupled non-linear chemistry • Kinetics most appropriate for time periods used for tuning • Low spatial resolution - not suitable for evaluation of near field influences

42. Summary • Airmass history models and analysis can and have been be used to qualitatively and quantitatively perform source attribution. • Airmass history models and analysis are suitable for addressing regional air quality issues, such as ozone, fine particulates and visibility degradation. • Airmass history models and analysis are applicable to long term analysis, so can be used for source attribution for the proposed year fine particle standard. • Many of these analyses are qualitative in nature and are appropriate as support for other analysis procedures.