Three-Dimensional Modeling of Particulate Matter Current Performance & Future Prospects Christian...
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![Page 1: Three-Dimensional Modeling of Particulate Matter Current Performance & Future Prospects Christian Seigneur AER San Ramon, CA.](https://reader030.fdocuments.us/reader030/viewer/2022032800/56649d255503460f949fc3af/html5/thumbnails/1.jpg)
Three-Dimensional Modeling ofParticulate Matter
Current Performance & Future Prospects
Christian Seigneur
AER
San Ramon, CA
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Schematic representationof a PM Eulerian model
Initial and Boundary Conditions
MeteorologicalModel Emissions
Concentrations of gases and PM
DropletChemistry
WetDeposition
Dry Deposition
Gas-phasechemistry
PM Chemistry and Physics
TransportAir Quality PM Model
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Current model performance for three typical regional PM studies
• Southern Appalachian Mountains Initiative (SAMI): URM for 9 episodes over the southeastern U.S. (Georgia Tech & TVA)
• EPA/NOAA: CMAQ for 2001 annual simulation (Eder et al., this workshop)
• Big Bend Regional Aerosol & Visibility Observational Study (BRAVO): MADRID 1 & REMSAD for 4 months over Texas and Mexico (AER, EPRI & CIRA)
• Southern Oxidants Study of 1999 (SOS 99): CMAQ, MADRID 1, MADRID 2 & CAMx for 1 episode over the southeastern U.S. (AER)
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Some model performancestatisticsa for PM2.5 components
Component SAMINMEb
EPANMEc
BRAVOMNEd
SOS 99MNEe
Sulfate 38% 24-43% 54 % 45-52%
Nitrate 98% 76-102% 194% 90-138%
Organics 38% 83% 63% 56-100%
Black carbon 44% 62% 89% 67-88%
(a) Note that the normalized mean error (NME) gives lower values than the mean normalized error (MNE)because it avoids dividing absolute errors by small observations(b) URM at IMPROVE sites, (c) CMAQ at MPROVE, CASTNet and STN sites, (d) MADRID 1 at Big BendNational Park, (d) CMAQ, MADRID 1, MADRID 2 & CAMx at IMPROVE, SEARCH & SOS sites
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Diagnostic performance evaluation
• There are many possible causes for model error
– model inputs (boundary conditions, meteorology, emissions)
– model formulation (transport, transformation, deposition)
• Diagnostic analyses provide insights into those causes
– sensitivity analyses
– specific performance evaluations
– spatial and temporal displays
– model intercomparisons
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Importance of boundary conditions Sulfate over the United States
(Source: REMSAD, Mike Barna, CIRA)
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Model performance for transport BRAVO tracer released from a point source
750 km northeast from the receptor
Regional models cannot reliably predict the impact of individual sources at long distances
0.00
0.05
0.10
0.15
0.20
0.25
0.30
815 817 819 821 823 825
36km
12km
Obs
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Spatial display of model error can provide insights into possible causes
< 1515 to 3030 to 4545 to 6060 to 7575 to 9090 to 105105 to 120> 120
%58.6
76.6
54.5
69.7
63
69.4
97.4
52.1
80.6
70
65.7 58.854.6
32.6
40.5
78.5
49
158.7
54
78.6
63.240.351.1
36.4
140
40.8
72.7
55.9
48.6
7749.8
90.369.2
49.7
67
36.7
66.2
CMAQ Sulfate StatisticsNormalized Error (% )
< 1515 to 3030 to 4545 to 6060 to 7575 to 9090 to 105105 to 120
< 1515 to 3030 to 4545 to 6060 to 7575 to 9090 to 105105 to 120> 120
%58.6
76.6
54.5
69.7
63
69.4
97.4
52.1
80.6
70
65.7 58.854.6
32.6
40.5
78.5
49
158.7
54
78.6
63.240.351.1
36.4
140
40.8
72.7
55.9
48.6
7749.8
90.369.2
49.7
67
36.7
66.2
CMAQ Sulfate StatisticsNormalized Error (% )
Sulfate error for
CMAQ-MADRID 1
in BRAVO:
Emissions?
Coastal meteorology?
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Diagnostic analysis using fine temporal resolution
Observed and simulated (CMAQ-MADRID 2) organic mass
in SOS 99, Cornelia Fort, July 1999
0
5
10
15
20
6/30/99 0:00 7/2/99 0:00 7/4/99 0:00 7/6/99 0:00 7/8/99 0:00 7/10/99 0:00
Local Time
Org
an
ic M
ass
(
g/m
3 )
OCx1.4 8km
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Examples of diagnostic analyses using seasonal or monthly statistics
• CMAQ 2001 annual simulation (Eder et al., this workshop)
– PM2.5 performance is lowest in winter, possibly because PM2.5 is dominated by nitrate and carbonaceous components in winter
• BRAVO 4-month simulation (Pun et al., 2004)
– Sulfate is underestimated in July when Mexican contribution is highest and is overestimated in October when U.S. contribution is highest
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Comparison of two PM modelsImportance of vertical mixing
PM2.5 concentrations over the U.S. on 6 July 1999 differ
primarily because of different algorithms for vertical mixing
CMAQ CAMx
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Comparison of three SOA modules(Pun et al., ES&T, 37, 3647, 2003)
Odum/Griffin
0
0.05
0.1
0.15
0.2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Time (hr)
SOA (
g/m
3 )
BiogenicAnthropogenic
The three SOA modules differ in:•the total amounts of SVOC and SOA•the gas/particle partitioning •the relative amounts of anthropogenic and biogenic SOA
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Evaluating model response
• A satisfactory operational evaluation does not imply that a model will predict the correct response to changes in precursors emissions
• There is a need to conduct a diagnostic/mechanistic evaluation to ensure that the model predicts the correct chemical regimes
• Indicator species can be used to evaluate the model’s ability to predict chemical regimes
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Response of PM to changes in precursors(adapted from NARSTO, 2003)
Change in PM compositionReduction inemissions Sulfate Nitrate Organics
SO2
NOx
VOC
NH3
Black carbon
Primary OC
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Major chemical regimes
• Sulfate
– SO2 vs. oxidant-limited
• Ammonium nitrate
– NH3 vs. HNO3-limited
• Organics
– Primary vs. secondary
– Biogenic vs. anthropogenic
• Oxidants (O3 & H2O2)
– NOx vs. VOC-limited
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Example of indicator speciesSensitivity of O3 formation to VOC & NOx
• H2O2 / (HNO3 + Nitrate) as an indicator
Low values: VOC sensitive
High values:NOx sensitive O3
NO NO2
HNO3
OHHO2H2O2
VOC
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Example of indicator speciesSensitivity of nitrate formation to
NH3 & HNO3
• Excess NH3 as an indicator
Low values: NH3 sensitive
High values:HNO3 sensitive
Ammonium nitrate
HNO3 NH3
Ammonium sulfate
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Qualitative estimates of uncertainties(adapted from NARSTO, 2003)
Component Confidencea Major uncertainties
Sulfate M-H Clouds & precipitation
Nitrate L-M Emissions & partitioning
Ammonium L-M Emissions
Primary OC L Emissions
Secondary OC VL VOC emissions & formation
BC L Emissions
Crustal & seasalt L Emissions
Others VL Emissions
(a) H: high, M: medium, L: low, VL: very low
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Possible topics for improvingPM model performance
• Emission inventories (ammonia, primary PM, etc.)
• Transport processes (e.g., vertical mixing, plume-in-grid)
• Assimilation of cloud and precipitation data
• SOA formation
• Deposition velocities
• Heterogeneous chemistry
• Boundary conditions from global models
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Acknowledgments
• Funding for the BRAVO simulations was provided by EPRI and EPA
• Funding for the SOS 99 simulations was provided by Southern Company, EPRI, MOG and CRC
• Betty Pun, AER, Prakash Karamchandani, AER, Mike Barna, CIRA, Robert Griffin, University of New Hampshire, Brian Eder, EPA and Robin Dennis, EPA provided valuable inputs