Transcript Use of Airmass History Models & Techniques for Source Attribution

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
C
=

Dilution
Sources
Chemistry/
* Removal
*
Emissions
Pt * Pk * E

Transfer Matrix
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
Goals of Workshop addressed:
• 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 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
The Monte Carlo Approach
Quantum
Secondary Pollutant
Primary Pollutant
Deposited Primary
Deposited Secondary
Direct simulation of emissions, transport, transformation,
and removal
http://capita.wustl.edu/capita/CapitaReports/MonteCarlo/MonteCarlo.html
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.
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
Computer Platform
IBM-PC
Computation Requirements:
Low:
3 months of back airmass histories for 500 sites ~1 day
3 months of sulfate simulations over North America ~2 days
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 Analysis
Where is the airmass most likely to have previously resided
Whiteface Mt. NY, June - August 1989 - 95
Back Trajectories
Residence Time Probabilities
Wishinski and Poirot, 1995 http://capita.wustl.edu/otag/Reports/Restime/Restime.html
Airmass histories from HY-SPLIT model
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
Ozone < 51 ppb
June - August 1989 - 95
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
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 Regions
Incremental Probabilities from 23 Combined Receptor Sites
Lower 50% Ozone
June - August 1989 - 95
Upper 50% Ozone
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
Source Region of Influence
The 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.
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
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.
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.
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 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:
Monte Carlo Model:
At times individual Airmass histories
compared very well
NGM wind fields, no mixing
NGM wind fields, mixing
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
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 Example
Rate 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) SO42http://capita.wustl.edu/capita/CapitaReports/MonteCarlo/MonteCarlo.html
Rate Coefficients (% / hr)
Kinetic Processes Applied to Single Airmass History
40
30
SO4 Wet Deposition
SO2 Wet Deposition
20
SO2 Dry Deposition
10
SO2 Transformation
0
0
1
2
3
Height (m)
Mixing Hgt
Quantum Hgt
1000
0
0.5
0
0
1.
With Clouds
No Clouds
1
2
3
Quantum Age (Days)
1
2
3
Quantum Age (Days)
0.2
Precipitation Rate
(cm/hr)
Scaled Specific Humidity
0
0.5
0
1
2
3
Quantum Age (Days)
SO42-
Deposited SO42-
0.5
Deposited SO2
SO2
0
0
1
2
3
Quantum Age, Days
0.1
0.
0.
Sulfur Budget
1
1
Fractional Sulfur Mass
2000
Ground Level
Solar Radiation (KW/m2)
Quantum Age (days)
0
1
2
3
Quantum Age (Days)
St. Louis airmass history
Variation of rate coefficients along
trajectory, and corresponding sulfur
budget.
Comparison of simulated Sulfate to Measured
New England
Daily SO42- Concentrations, g/m3
12
9
6
9
6
3
0
0
3
6
9 12 15 18
Observed
Q3
9
6
3
3
6
9 12 15
Observed
18
Q3
y = 1.18x - 0.48
R2 = 0.72
15
12
9
6
3
0
0
0
3
6
9 12 15 18
Observed
9
6
0
18
Simulation
12
12
3
0
18 y = 0.93x + 0.63
R2 = 0.56
15
Simulation
12
3
0
YEAR
18 y = 1.03x - 0.18
R2 = 0.64
15
y = 0.88x - 0.13
R2 = 0.83
15
Simulation
Simulation
15
Q2
18
y = 1.61x - 1.69
R2 = 0.80
Simulation
Q1
18
0
3
6
9 12
Observed
15
18
0
3
6
9 12 15 18
Observed
Comparison of simulated Wet Deposited Sulfate to Measured
New England
Weekly Total SO42- Wet Deposition Rates, g/m2/yr
Q1
Q2
7
6
5
4
3
2
1
0
y = 0.80x - 0.07
R2 = 0.94
Simulation
Simulation
y = 0.61x + 0.21
R2 = 0.50
0
1
2 3 4 5
Observation
6
7
Q3
7
6
5
4
3
2
1
0
0
2 3 4 5
Observation
6
7
6
7
y = 0.82x + 0.17
R2 = 0.82
Simulation
Simulation
1
Q4
7
6
5
4
3
2
1
0
y = 0.68x + 0.28
R2 = 0.68
0
1
2 3 4 5
Observation
6
7
YEAR
7
6
5
4
3
2
1
0
y = 0.72x + 0.16
R2 = 0.79
Simulation
7
6
5
4
3
2
1
0
0
1
2 3 4 5
Observation
0
1
2 3 4 5
Observation
6
7
Transfer Matrices - Massachusetts Receptor, Q3 1992
Transit Probability SO2 Kinetic Probability SO4 Kinetic Probability
Likelihood an airmass
Likelihood SO2 emissions
from a source is
into the airmass impact the
transported to the receptor receptor as SO2
Likelihood SO2 emissions
into the airmass impact
the receptor as SO4
Quantitatively Define Source Receptor Relationship
1985 NAPAP SO2
Emissions
SO2 and SO4 Source Attribution to
Massachusetts Receptor, Q3 1992
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.