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

http://capita.wustl.edu/neardat/CAPITA/CapitaReports/AirmassHist/EPASrcAtt_jul17/index.htm

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

Prim ary Pollutant

Secondary Pollutant

The Monte Carlo Approach

Quantum

Deposited Prim ary

Deposited Secondary

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.

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.

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

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.

SO2 ExampleRate Coefficients Influencing Parameters

SO2 oxidation; kt Humidity, Solar Radiation, Precipitation

SO2 dry deposition; kd2 Scale height, Solar Radiation

SO42- dry deposition; kd4 Scale height, Solar Radiation

SO2 wet deposition; kw2 Precipitation rate, SO2 concentration

SO42- wet deposition; kw4 Precipitation rate

Quanta mass conservation equations:

1) d(SO2) /dt = -(kt + kd2 + kw2) SO2

2) d(SO42-) /dt = kt SO2 -(kd4 + kw4) SO4

2-

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

0

10

20

30

40

0 1 2 3

Quantum Age (days)

Rat

e C

oef

fici

ents

(%

/ h

r)

SO4 Wet Deposition

SO2 Wet Deposition

SO2 Transformation

SO2 Dry Deposition

0

0.5

1

0 1 2 3Quantum Age, Days

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ctio

nal

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lfu

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ass

SO2

Deposited SO2

Deposited SO42-SO4

2-

Sulfur Budget

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0 1 2 3Quantum Age (Days)

Heig

ht

(m)

Mixing Hgt Quantum Hgt

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0 1 2 3Quantum Age (Days)

Scale

d S

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0.1

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Quantum Age (Days)

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No Clouds With Clouds

St. Louis airmass history

Variation of rate coefficients along trajectory, and corresponding sulfur

budget.

Kinetic Processes Applied to Single Airmass History

New England Daily SO42- Concentrations, g/m3

Q1y = 1.61x - 1.69

R2 = 0.80

0

3

6

9

12

15

18

0 3 6 9 12 15 18Observed

Sim

ula

tio

nQ2

y = 0.88x - 0.13

R2 = 0.83

0

3

6

9

12

15

18

0 3 6 9 12 15 18Observed

Sim

ula

tio

n

Q3y = 0.93x + 0.63

R2 = 0.56

0

3

6

9

12

15

18

0 3 6 9 12 15 18Observed

Sim

ula

tio

n

Q3y = 1.18x - 0.48

R2 = 0.72

0

3

6

9

12

15

18

0 3 6 9 12 15 18Observed

Sim

ula

tio

n

YEARy = 1.03x - 0.18

R2 = 0.64

0

3

6

9

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15

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0 3 6 9 12 15 18Observed

Sim

ula

tio

n

Comparison of simulated Sulfate to Measured

Comparison of simulated Wet Deposited Sulfate to Measured

New EnglandWeekly Total SO42- Wet Deposition Rates, g/m2/yr

Q1

y = 0.61x + 0.21

R2 = 0.50

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7Observation

Sim

ula

tio

n

Q2

y = 0.80x - 0.07

R2 = 0.94

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7Observation

Sim

ula

tio

n

Q3y = 0.68x + 0.28

R2 = 0.68

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7Observation

Sim

ula

tio

n

Q4y = 0.82x + 0.17

R2 = 0.82

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7Observation

Sim

ula

tio

n

YEAR

y = 0.72x + 0.16

R2 = 0.79

0

1

2

3

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6

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0 1 2 3 4 5 6 7Observation

Sim

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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

Quantitatively Define Source Receptor Relationship

SO2 and SO4 Source Attribution to Massachusetts Receptor, Q3 1992

1985 NAPAP SO2

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

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.