Three-Dimensional Modeling ofParticulate Matter

Current Performance & Future Prospects

Christian Seigneur

AER

San Ramon, CA

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

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)

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

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

Importance of boundary conditions Sulfate over the United States

(Source: REMSAD, Mike Barna, CIRA)

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

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?

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

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

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

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

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

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

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

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

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

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

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

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