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Three-Dimensional Modeling of Particulate Matter Current Performance & Future Prospects

Three-Dimensional Modeling of Particulate Matter Current Performance & Future Prospects. Christian Seigneur AER San Ramon, CA. Schematic representation of a PM Eulerian model. Meteorological Model. Initial and Boundary Conditions. Emissions. Air Quality PM Model. Transport.

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Three-Dimensional Modeling of Particulate Matter Current Performance & Future Prospects

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  1. Three-Dimensional Modeling ofParticulate MatterCurrent Performance & Future Prospects Christian Seigneur AER San Ramon, CA

  2. Schematic representation of a PM Eulerian model Meteorological Model Initial and Boundary Conditions Emissions Air Quality PM Model Transport PM Chemistry and Physics Droplet Chemistry Dry Deposition Wet Deposition Gas-phase chemistry Concentrations of gases and PM

  3. 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)

  4. Some model performancestatisticsa for PM2.5 components

  5. 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

  6. Importance of boundary conditions Sulfate over the United States(Source: REMSAD, Mike Barna, CIRA)

  7. Model performance for transportBRAVO 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

  8. Spatial display of model error can provide insights into possible causes Sulfate error for CMAQ-MADRID 1 in BRAVO: Emissions? Coastal meteorology?

  9. Diagnostic analysis using fine temporal resolution Observed and simulated (CMAQ-MADRID 2) organic mass in SOS 99, Cornelia Fort, July 1999

  10. 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

  11. Comparison of two PM modelsImportance of vertical mixing CMAQ CAMx PM2.5 concentrations over the U.S. on 6 July 1999 differ primarily because of different algorithms for vertical mixing

  12. Comparison of three SOA modules(Pun et al., ES&T, 37, 3647, 2003) • 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

  13. 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

  14. Response of PM to changes in precursors(adapted from NARSTO, 2003)

  15. 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

  16. Example of indicator speciesSensitivity of O3 formation to VOC & NOx • H2O2 / (HNO3 + Nitrate) as an indicator High values: NOx sensitive Low values: VOC sensitive O3 NO NO2 HNO3 H2O2 HO2 OH VOC

  17. Example of indicator speciesSensitivity of nitrate formation to NH3 & HNO3 • Excess NH3 as an indicator High values: HNO3 sensitive Low values: NH3 sensitive • Ammonium sulfate • Ammonium nitrate • HNO3 • NH3

  18. Qualitative estimates of uncertainties(adapted from NARSTO, 2003)

  19. 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

  20. 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

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