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Some Thoughts on the Current State of Emissions-Based PM Air Quality Modelling

Some Thoughts on the Current State of Emissions-Based PM Air Quality Modelling. Michael Moran Air Quality Research Branch Meteorological Service of Canada Toronto, Ontario, Canada EMEP Workshop on PM Measurement & Modeling

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Some Thoughts on the Current State of Emissions-Based PM Air Quality Modelling

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  1. Some Thoughts on the Current State of Emissions-Based PM Air Quality Modelling Michael Moran Air Quality Research Branch Meteorological Service of Canada Toronto, Ontario, Canada EMEP Workshop on PM Measurement & Modeling New Orleans, Louisiana, U.S.A. 20-23 April 2004

  2. Talk Outline Workshop objectives and emissions-based AQ modelling: • How well are we doing? • What new insights have we gained? • What do we need to do better? • What are some “low-hanging fruit”?

  3. How well are we doing? Encouragingly well. Models are improving, and current models have some predictive skill for episodic and seasonal simulations. AURAMS examples 

  4. AURAMS Overview • AUnified Regional Air-quality Modelling System • Episodic, Eulerian, regional, size-resolved, chemically-characterized particulate-matter (PM) modelling system intended initially for research and policy support • “unified’ in that it considers multiple air pollutants and can be applied to multiple AQ issues (PM, O3, acid deposition) for integrated AQ management • consists of three main components: • regional emissions processing system; • prognostic regional meteorological model; • regional sectional PM air-quality model • current PM resolution: 12 size bins (0.01-40.96 m) and 8 chemical components (SO4=, NO3-, NH4+, BC, OC, CM, SS, H2O)

  5. Time Series, Feb. 7–14, 1998 PM2.5 PM2.5 PM2.5 O3

  6. 24-Hour PM2.5 Species Scatterplots: Feb. 7-14, 1998, IMPROVE & GAViM PM2.5 SO4 NH4 NO3

  7. What are some new insights that we have gained to date? • Atmospheric responses to • emission reductions may vary • by season • These responses may also be • significantly nonlinear

  8. 2020P - 2020B Scenario “Deltas” for SO2 and NOx Emission Reductions, July 1995 & Feb. 1998 Cases PM2.5 SO4 PM2.5 NO3 PM2.5 NH4 July 8-18 Feb. 7-15

  9. Change in PM2.5 Nitrate Due to Reductions in SO2 and NOx Emissions July 8-18, 1995 Feb. 7-15, 1998

  10. What do we need to do better? • Everything. There is room for • improvements related to: • emissions • meteorology • PM process representations • ambient measurements • numerics

  11. Iterative Improvement Process emissions, meteorology air-quality models ambient AQ measurements

  12. Treatment of Emissions Treatment of primary PM emissions • need PM speciation profiles • need size disaggregation profiles for PM2.5 • need better transport factors (AQ model?) • need wind-blown dust & wildfires • Treatment of NH3 emissions • need monthly and diurnal variation • need subgrid-scale removal

  13. Some Less Certain AQ Process Representations • cloud and precipitation properties • condensable p-OC and SOA formation • PM dry deposition • PM cloud processing (how good is SO4?) • subgrid-scale vertical cloud transport • PM wet removal

  14. A Few Evaluation Issues • 1. Consideration of comprehensive data sets • meteorological measurements • PM mass, composition, size distribution • gaseous co-pollutants • deposition measurements • optical measurements (visibility, optical depth) • 2. Frequency (1/1 vs. 1/3 or 1/6 or 2/7)

  15. What are some “low-hanging fruit”?

  16. Inexpensive Non-Technical Actions to Improve Source- Based PM Modelling (1) • Include a reading list in report from this workshop (list some papers and reports that are important and recent) • Organize a focussed PM modelling workshop (current mtgs such as AMS-APM, AMS-AC, NATO-CCMS, AAAR, AGU, EGU are not focussed; NARSTO? EMEP? CMAS?)

  17. Inexpensive Non-Technical Actions to Improve Source- Based PM Modelling (2) • Expedite access to field study data (e.g., encourage early modelling participation) • Describe model evaluation“good practice”: identify useful data sets, techniques, metrics • Encourage model confidence-building initiatives, such as joint studies, detailed evaluations, model intercomparisons, collaborations with receptor models and data analysts, …

  18. Inexpensive Non-Technical Actions to Improve Source- Based PM Modelling (3) • Develop WMO GTS exchange code (BUFR?) for transmission of air concentration measurements • Rely on evidence-based research prioritization instead of intuition-based or interest-based (e.g., model performance for different PM components) • Build data “warehouses” for input data sets

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