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
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Use of Airmass History Models & Techniques for Source Attribution Bret A. Schichtel Washington University St. Louis, MO Presentation to EPA Source Attribution.
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Use of Airmass History Models &
Techniques for Source Attribution
Bret A. SchichtelWashington University
St. Louis, MO
Presentation to EPA Source Attribution workshopJuly 16 - 18, 1997
Airmass HistoryEstimation 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
Source Receptor Relationship
ReceptorConcentration
DilutionChemistry/Removal
Emissions= * *
Airmass history modeling and analysis aid in the understanding of the SRR processes and qualitatively and quantitatively establish source contributions to receptors.
C P P Et kSources * *
Transfer Matrix
Airmass History Analysis Techniques• Individual airmass histories
• Backward and forward airmass history ensemble analysis
• Air quality simulation
• Transfer matrices
• Emission Retrieval
• Area of Influence
• Selecting and analyzing pollution episodes
• Selecting control strategies
• Evaluate air quality models
Goals of Workshop addressed:
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
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
Airmass Histories - Model Outputs
2-D Back TrajectoryMultiple 3-D Back
TrajectoriesAirmass History
Variables
CAPITA Monte Carlo Model
Direct simulation of emissions, transport, transformation, and removal
Below the mixing layer particles are uniformly distributed from ground to mixing height. No dispersion above mixing layer.
TransportAdvection:
3-D wind fields
Horizontal Dispersion:Eddy diffusion; Kx and Ky vary depending on hour of day
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
Model Output:
• Database of airmass histories• Pollutant concentrations and deposition fields• Transfer matrices
Computation Requirements:
Low: 3 months of back airmass histories for 500 sites ~1 day3 months of sulfate simulations over North America ~2 days
Computer Platform
IBM-PC
User expertise:
Airmass history server- Low Pollutant simulation - High
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
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
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.
Merging Air Quality & Meteorological Data for Episode Analysis
OTAG 1991 modeling episode Animation
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
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.
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
Residence Time AnalysisWhere is the airmass most likely to have previously resided
Residence Time ProbabilitiesWhiteface Mt. NY, June - August 1989 - 95
Back Trajectories
Wishinski and Poirot, 1995 http://capita.wustl.edu/otag/Reports/Restime/Restime.htmlAirmass histories from HY-SPLIT model
Whiteface Mt. NY- Residence Time Probabilities
Low ozone concentrations are associated with airflow from the northeast
High ozone concentrations are associated with airflow from the east to southeast
Airmass History Stratification
Ozone > 51 ppb
June - August 1989 - 95
Ozone < 51 ppb
June - August 1989 - 95
• Technique identifies airmass pathways not the source areas along the pathway• Central bias - all airmass histories must pass through receptor grid cell
Removing the Central Bias Incremental Probability Analysis
Incremental Probability
Stratified Probability
Everyday Probability= -
Upper 50% Ozone Vs. Everyday
• High ozone is associated with airflow from the central east• Regions implicated increase from south to north
Identifying Unique Source RegionsIncremental Probabilities from 23 Combined Receptor Sites
• 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.
Upper 50% OzoneLower 50% Ozone
June - August 1989 - 95June - August 1989 - 95
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
Source Region of InfluenceThe most likely region that a source will impact
Transfer MatrixForward Airmass Histories
• 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.
St. Louis Source
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.
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
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
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.
OTAG Modeling Episodes Representativeness
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
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.
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 errorSeveral 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
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
The three month aggregate of airmass histories produced similar transport patterns.
- Area of Influence - Selecting control strategies
Kinetics-Forward airmass histories calculated from each source-Pseudo first order rate equations applied to each airmass history-Rate coefficients depend on meteorological and chemical environment.-Rate Coefficient relationships determined through “tuning” procedure.
• Kinetics most appropriate for time periods used for tuning
• Low spatial resolution - not suitable for evaluation of near field influences
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.