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Comprehensive Review of WRAP Regional Modeling Center Deliverables (2002-2018)

This detailed review covers modeling elements, source apportionment technology, emissions inventories, and model performance evaluation tools related to the WRAP Regional Modeling Center's deliverables from 2002 to 2018.

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Comprehensive Review of WRAP Regional Modeling Center Deliverables (2002-2018)

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  1. Review of WRAP Regional Modeling Center (RMC) Deliverables Related to the Technical Support System (TSS) Ralph Morris and Gerry Mansell ENVIRON Corporation Gail Tonnesen and Zion Wang University of California, Riverside September 14-15, 2005 Attribution of Haze Workgroup Meeting San Francisco, California

  2. 2002 Base A Base Case CMAQ/CAMx Modeling and Model Evaluation 2002 CAMx PSAT Source Apportionment Modeling PSAT/TSSA Comparisons RMC BART Modeling Plans 2018 Simulations and Visibility Projections Modeling Elements of the Visibility SIP Weight of Evidence (WOE) Reasonable Progress Goal (RPG) Demonstration Overview

  3. CMAQ emissions ready September 12, 2005 Start annual 2002 36 km CMAQ run September 19, 2005 CAMx emissions ready September 19, 2005 Compare Jan/Jul 2002 CMAQ/CAMx October 3, 2005 Make decisions on model for 12 km modeling and control strategy evaluation Finish annual 2002 36 km CAMx run October 10, 2005 Perform PSAT PM Source Apportionment using CAMx October 31, 2005 2018 Emission Inventories October 31, 2005 2002 Base A Modeling

  4. Example of Model Performance Evaluation (MPE) displays of use to the TSS UCR MPE Tool Scatter & Time Series Plots by subregion allsite_allday (SO4 example for WRAP States) allday_onesite (SO4 example for Canyonlands) onesite_allday Monthly Bias/Error plots By subregion (Bias example for SO4 in WRAP States) Stacked 24-hr average extinction plots Observed vs. Model (Canyonlands example) Comparisons for Worst/Best 20% Days 2002 Base A Modeling

  5. Example UCR Tool MPE Plots, CMAQ vs. CAMx for January & July 2002 allsite_allday for WRAP States

  6. MPE Plots for SO4 at Canyonlands and July 2002 CMAQ vs. CAMx Scatter Plot and Stats Observed, CMAQ, and CAMx Time Series Plot

  7. SO4 IMPROVE in WRAP States • Monthly Fractional Bias • CAMx • CMAQ

  8. Observed vs. Modeled Daily Extinction @ Canyonlands Observed Observed CMAQ CAMx

  9. Source Apportionment Approaches • CALPUFF: Lagrangian non-steady-state puff model • “Chemistry” highly simplified, incorrect and over 20 years old (1983) • Fails to adequately account for wind shear • SCICHEM: Lagrangian model with full chemistry • Needs 3-D concentrations fields • Currently computationally demanding • Photochemical Grid Models: CMAQ/CAMx • Zero-Out Runs (actually sensitivity approach) • Reactive Tracer PSAT/TSSA approaches

  10. PM Source Apportionment Technology (PSAT) in CAMx • Reactive tracer approach that operates in parallel to the host model to track PM precursor emissions and formation • Set up to operate with families of tracers that can operate separately or together • Sulfate (SO4) • Nitrate (NO3) • Ammonium (NH4) • Secondary Organic Aerosols (SOA) • Mercury (Hg) • Primary PM (EC, OC, Soil, CM)

  11. PSAT Conceptual Approach • Modify CAMx to include families of tracers (tagged species) for user selected source “groups” • Source group = source category and/or geographic area • Build on CAMx ozone apportionment schemes (OSAT, APCA) • Tag primary species as they enter the model • SO2i , NOi , VOCi , primary PM (crustal, EC, etc.) • When secondary species form, tag them according to their parent primary species • SO4i , NO3i , SOAi

  12. Zero-Out Comparisons for Sulfate • Use Eastern US/Canada modeling domain • Add four hypothetical point sources to base emissions • Test large and small emission rates to investigate signal/noise • Large: SOx = 850 TPD • Small: SOx = 0.85 TPD X X X X

  13. Difference due to oxidant limitation MRPO Large Source: Episode Maximum SO4 PSAT versus “Zero Out” PSAT Zero-Out

  14. Oxidant Limiting Sulfate Example PSAT Zero-Out • PSAT attributes 50% of SO4 to source A (and 50% to B) • Zero-out attributes zero SO4 to source A (no source is culpable) • Zero-out result (sensitivity) is not a reasonable apportionment for this example

  15. PSAT Sulfate Evaluation • Good agreement for extent and magnitude of sulfate impacts between PSAT and zero-out • Comparing the outer plume edge is a stringent test • Zero-out impacts can be smaller or larger due to oxidant limited sulfate formation and changes in oxidant levels. • Run times look very good • Two tracers per source group for sulfate • PSAT obtains 50+ SO4 source contributions in time needed for 1 zero-out assessment

  16. PSAT Chemical Scheme for NOy Gasses • PSAT tracks 4 groups of NOy gasses • RGN • TPN • HN3 • NTR • Conversion of RGN to HN3 and NTR is slowly reversible • Conversion of RGN to TPN is reversible – rapidly or slowly

  17. CAMx SOA scheme VOC -- OH, O3, NO3 --> Condensable Gas (CG) <==> SOA CGs partition to an SOA solution phase PSAT implementation straightforward, but many terms Three types of VOC precursor alkanes, aromatics, terpenes Five pairs of CG/SOA four anthropogenic, one biogenic low/high volatility products PSAT tracers for VOC, CG and SOA species 14 tracers per source group PSAT for SOA

  18. Independent check against SOME Source Oriented External Mixture (Kleiman et al at UC David) SOME uses explicit species for each source group that are integrated in the model Highly computationally demanding Zero-Out comparisons not appropriate for VOC/NOx due to nonlinear chemistry Good agreement between PSAT and SOEM for NO3 and SOA http://pah.cert.ucr.edu/aqm/308/meetings/March_2005/03-08_09-05.SF_CA/Alternative_Model_Mar8-9_2005_MF_Meeting.ppt PSAT Evaluation for NO3 and SOA

  19. PSAT Configuration 15 source regions 5 Source Categories: (1) Biogenic; (2) On-Road Mobile; (3) Points; (4) Fires and (5) Area+Non-Road Initial and Boundary Concentrations 77 Source Groups (77=15 x 5 + 2) SO4, NO3 and NH4 families of tracers Did not run SOA, Hg and Primary PM tracers TSSA Configuration Differences in source group source categories (e.g., mv = on-road + non-road, fires?, BC??) “Other” category in TSSA for unattributable PM CAMx/PSAT and CMAQ/TSSA Comparisons Feb/Jul 2002

  20. PSAT/TSSA Source Region Map CA, NV, OR, WA, ID, UT, AZ, NM, CO, WY, MT, ND, SD, Eastern States and Mex/Can/Ocean

  21. Grand Canyon, Arizona Day 182 (07/01/02) [2nd Worst Visibility Day in 2002] NV Points Highest AZ Points (5xsmall) “Mex” Points TSSA Units??? TSSA Other???

  22. Grand Canyon, Arizona Day 188 (07/07/02) [15th Worst Visibility Day in 2002] Some differences TSSA and PSAT Pts_Mex, Other, BC

  23. Grand Canyon, Arizona Day 32 (02/01/02) [8th Best Visibility Day in 2002] PSAT: UT_Points; BC; AZ_Points; UT_NonRoad; NM_Points TSSA: UT_Points; Other; OR_Points; WA_Points; ID_Points

  24. Rocky Mtn. NP, Colorado Day 182 (07/01/05) Worst Day of 2002 PSAT: UT_Fires; CO_Pts; NV_Pts; CO_Fires; UT_Pts. TSSA: Other; CO_Pts; UT_Pts; NV_Pts; If Fires in “Other” then fairly good agreement

  25. Conclusions – PM Source Apportionment • PSAT results mostly consistent with TSSA • Some differences, TSSA “Other” category makes it hard to interpret • Version of CMAQ with TSSA has known mass conservation problems • Powerful diagnostic tool that can be used for source culpability (e.g., BART) and to design optimally effective control PM/visibility control strategies • PSAT explains 100% of the PM, doesn’t suffer “Other” unexplained portion of PM like TSSA • TSSA being implemented in latest versions of CMAQ

  26. 2002 Base A Emissions Source Regions WRAP States plus others and IC/BC Source Categories Anthropogenic versus “Natural” emissions SO4, NO3 and NH4 initially, test SOA and primary PM 2018 Base Case emissions Source regions and categories TBD PSAT Plans for WRAP

  27. Argts: Area sources except dust sources Arfgts: Area fires from CENRAP Awfgts3d: WRAP wild, prescribed and agricultural fires Bsfgts3d: Canadian Wild fires/Blue Sky algorithm fdgts_RPO: Fugitive dust (Ag & construction) for entire domain mbgts_WRAP: On road mobile sources for WRAP RPO mbgts_CANDA_MEX: On road mobile sources for Can/Mex mbvgts_CENRAP36: On-road mobile sources for CENRAP states mbvgts_RPO_US36: On road mobile sources for MW, VISTAS, & MAINE-VU nh3gts_RPO36: Ammonia from agricultural sources for CENRAP/MW states nh3gts_WRAP36: Ammonia emissions ag sources for WRAP GIS model Nrygts: Off road mobile with annual IDA files Nrmgts: Off road mobile with monthly or seasonal IDA files Nwfgts3d: Point sources fires from non WRAP states (CENRAP and VESTAS) 22 Pre-Merged Emission Files

  28. Ofsgts3d: Off shore point sources in the Gulf of Mexico Ofsmagts: Off shore Marines shipping in the Pacific Ocean Ofsargts: Off shore area sources in the Gulf of Mexico ptgts3d_RPO_US36: Point sources emissions for all RPOs, Can & Mex rdgts_RPO: Road dust for the entire domain B3gts_RPO: Biogenc emissions from BIES3 for the entire domain wb_dus: Wind blown dust for entire domain Oggts3d: Oil and gas for WRAP states (except CA) 2002 PSAT run need to define “natural” emissions Arfgts: Area fires from CENRAP Awfgts3d: WRAP wild, prescribed and agricultural fires (will need to process wildfires separately) Bsfgts3d: Canadian Wild fires/Blue Sky algorithm Nwfgts3d: Point sources fires from non WRAP states (CENRAP and VESTAS)? B3gts_RPO: Biogenc emissions from BIES3 for the entire domain wb_dus: Wind blown dust for entire domain 22 Pre-Merged Emission Files

  29. RMC will perform regional photochemical grid model of alternative regional strategies using CMAQ and/or CAMx with PSAT RMC will assist States who desire to perform source-specific CALPUFF modeling Provide States with 3-tears of CALMET ready MM5 fields (2001, 2002 and 2003) May perform source-specific modeling using PSAT for 2002 WRAP RMC “BART” Modeling

  30. Example of BART Modeling using Grid Models • Midwest RPO (MRPO) • Use combination of photochemical grid and CALPUFF modeling in the BART analysis • Comprehensive Air-quality Model with extensions (CAMx) PM Source Apportionment Technology (PSAT)

  31. CALPUFF estimates higher visibility impacts than CAMx/PSAT and consequently generally more days and larger spatial extent of dV > 0.5 deciview PSAT CALPUFF

  32. July 19, 2002 24-Hour SO4 Concentrations IN Source (isgburn) CALPUFF much higher concentrations away from source. Why secondary CALPUFF SO4 peak over Cape Cod? CAMx PSAT CALPUFF

  33. CALPUFF More Conservative than Grid Models • CALPUFF chemistry overstates NO3 and SO4 in winter • CALPUFF understates dispersion because it fails to adequately account for wind shear and wind variations across the puff • Uses just one wind to advect entire column of puff • IWAQM found CALPUFF overestimation bias of a factor of 3-4 at distances beyond 200-300 km • When encountering stagnant conditions, puffs pile up on each other and stop dispersing • Violates 2nd Law of Thermodynamics

  34. CALPUFF puff column advected north by winds at 300 m AGL even though surface winds from east and north Surface Winds 0600 Surface Winds 1200 300 AGL Winds 0600

  35. Visibility projections use 2018 and 2002 modeling results in relative sense to scale observed 2000-2004 visibility to 2018 Draft EPA Guidance (2001) 2018 Visibility Goal based on Glide Path from current (2000-2004) observed visibility to Natural Conditions in 2064 EPA Guidance for default Natural Conditions (2003) 2018 Modeling/Visibility Projections

  36. Baseline Conditions = 28.9 dv Natural Conditions = 11.4 dv 2018 Visibility Goal = 24.9 dv 2018 Reduction Goal = 4.1 dv 2018 Modeled Reduction = 5.2 dv GRSM achieves 2018 Vis Goal

  37. Great Smoky Mountains Obs vs. Model Extinction W20% > 80% extinction due to SO4

  38. Worst days not always dominated by SO4 -- OMC, NO3 and/or CM can be more important than SO4 at many sites California NO3 issue Southwestern Desert dust (CM) Fires, Fires, Fires, Fires Posses unique and special conditions for modeling visibility projections May be more difficult to model achievement of visibility goal Many sites dominated by fires for Worst 20% days and assumed to remain unchanged from 2002 to 2018 Don’t CAIR states Point source SO2 and NOx controls much less effective at reducing visibility in west compared to east Modeled Visibility Goal Test will be Difficult for WRAP Class I Areas

  39. Five examples of WRAP visibility projections: WHIT, NM GRCA, AZ CRLA, OR SAGO, CA DENA, AK

  40. Dust 

  41. White Mountain, NM – Worst 20% Days in 2002 Observations vs. Predictions  Fires Obs Dust   Nitrate

  42. Grand Canyon, AZ – Worst 20% Days in 2002 Observations vs. Predictions  Fires in model  Dust in obs

  43. Denali Glide Path to Natural Conditions, Baseline for Current Worst Days (10 dv) > 2064 Natural Conditions for many eastern Class I areas (e.g., GRSM @ 11 dv) Denali 2018 RPG Reduction = 0.61 dv

  44. Denali National Park Best 20% Days (B20) Current 5-Year Average for B20 Days (1.91 dv) lower than EPA default natural conditions for best days (2.30 dv)

  45. Conclusions – WRAP Vis Projections (1) • Much more diverse PM mixture in western US on Worst 20% days than in the east • Fires and wind blown dust much more important – little opportunity to control • Focus reasonable progress on days with high anthropogenic contributions? • Incorporate fires and dust in Natural Conditions endpoint? • Mexico, Canada and global transport can have large influence at some Class I areas • Modeled visibility goal test will likely not be achieved at many WRAP Class I areas

  46. Conclusions – WRAP Vis Projections (2) • Need to start developing strategy for demonstrating reasonable progress for WRAP • Weight of Evidence (WOE) RPG demo needed • Enforceable emission reductions • Treatment of extreme events (fires/dust/international) • Visibility improvements on days due to US anthro sources • Examine extinction improvements by species? • Smoke management plan • Modeled visibility changes are just one element of WOE RPG demonstration

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