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

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

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.

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:

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

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 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 Trajectory Multiple 3-D Back Trajectories Airmass History Variables

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

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

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

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 day 3 months of sulfate simulations over North America ~2 days Computer Platform IBM-PC User expertise: Airmass history server- Low Pollutant simulation - High

Database of airmass histories

Pollutant concentrations and deposition fields

Transfer matrices

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.

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

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 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

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 Technique identifies airmass pathways not the source areas along the pathway Central bias - all airmass histories must pass through receptor grid cell 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 = - High ozone is associated with airflow from the central east Regions implicated increase from south to north 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 Regions Incremental 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% Ozone Lower 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.

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

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

Qualitative

Not suitable to evaluate local scale influences

Does not implicate specific sources or source types

Source Region of Influence The most likely region that a source will impact Transfer Matrix Forward 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

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.

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

Resultant transport from Texas around Southeast and eastward.

Region of influence is ~40% smaller in Southeast compared to rest of Eastern US.

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 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

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

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

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

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. O 3 is higher when the wind blows from the center of the domain. Same conclusion drawn from forward and backward airmass history analyses.

Forward and backward airmass history analysis techniques

Analyses incorporating measured meteorology and receptor data

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

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

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

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

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

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

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

Comparison of simulated Sulfate to Measured

Comparison of simulated Wet Deposited Sulfate to Measured

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

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

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

Applicable to particulates and visibility

Applicable to climatology and episode analysis

Regional scale analysis

Quantitative

Applicable to “what if” analyses

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

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.

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.