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

ENVIRON International Corporation University of California at Riverside Overview 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

ENVIRON International Corporation University of California at Riverside 2002 Base A Modeling CMAQ emissions ready September 12, 2005 Start annual 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 km CAMx run October 10, 2005 Perform PSAT PM Source Apportionment using CAMx October 31, Emission Inventories October 31, 2005

ENVIRON International Corporation University of California at Riverside 2002 Base A Modeling 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

ENVIRON International Corporation University of California at Riverside Example UCR Tool MPE Plots, CMAQ vs. CAMx for January & July 2002 allsite_allday for WRAP States

ENVIRON International Corporation University of California at Riverside MPE Plots for SO4 at Canyonlands and July 2002  CMAQ vs. CAMx Scatter Plot and Stats Observed, CMAQ, and CAMx  Time Series Plot

ENVIRON International Corporation University of California at Riverside SO4 IMPROVE in WRAP States Monthly Fractional Bias  CAMx  CMAQ

ENVIRON International Corporation University of California at Riverside Observed vs. Modeled Daily Canyonlands Observed CMAQCAMx

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside 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)

ENVIRON International Corporation University of California at Riverside 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 –SO2 i, NO i, VOC i, primary PM (crustal, EC, etc.) When secondary species form, tag them according to their parent primary species –SO4 i, NO3 i, SOA i

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside Difference due to oxidant limitation PSAT Zero-Out MRPO Large Source: Episode Maximum SO4 PSAT versus “Zero Out”

ENVIRON International Corporation University of California at Riverside 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 PSAT Zero- Out Oxidant Limiting Sulfate Example

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside PSAT for SOA 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

ENVIRON International Corporation University of California at Riverside PSAT Evaluation for NO3 and SOA 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 – _09-05.SF_CA/Alternative_Model_Mar8- 9_2005_MF_Meeting.ppt

ENVIRON International Corporation University of California at Riverside CAMx/PSAT and CMAQ/TSSA Comparisons Feb/Jul 2002 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

ENVIRON International Corporation University of California at Riverside PSAT/TSSA Source Region Map CA, NV, OR, WA, ID, UT, AZ, NM, CO, WY, MT, ND, SD, Eastern States and Mex/Can/Ocean

ENVIRON International Corporation University of California at Riverside Grand Canyon, Arizona Day 182 (07/01/02) [2 nd Worst Visibility Day in 2002] NV Points Highest AZ Points (5xsmall) “Mex” Points TSSA Units??? TSSA Other???

ENVIRON International Corporation University of California at Riverside Grand Canyon, Arizona Day 188 (07/07/02) [15 th Worst Visibility Day in 2002] Some differences TSSA and PSAT Pts_Mex, Other, BC

ENVIRON International Corporation University of California at Riverside Grand Canyon, Arizona Day 32 (02/01/02) [8 th 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

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside PSAT Plans for WRAP 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

ENVIRON International Corporation University of California at Riverside 22 Pre-Merged Emission Files 1.Argts: Area sources except dust sources 2.Arfgts: Area fires from CENRAP 3.Awfgts3d: WRAP wild, prescribed and agricultural fires 4.Bsfgts3d: Canadian Wild fires/Blue Sky algorithm 5.fdgts_RPO: Fugitive dust (Ag & construction) for entire domain 6.mbgts_WRAP: On road mobile sources for WRAP RPO 7.mbgts_CANDA_MEX: On road mobile sources for Can/Mex 8.mbvgts_CENRAP36: On-road mobile sources for CENRAP states 9.mbvgts_RPO_US36: On road mobile sources for MW, VISTAS, & MAINE-VU 10.nh3gts_RPO36: Ammonia from agricultural sources for CENRAP/MW states 11.nh3gts_WRAP36: Ammonia emissions ag sources for WRAP GIS model 12.Nrygts: Off road mobile with annual IDA files 13.Nrmgts: Off road mobile with monthly or seasonal IDA files 14.Nwfgts3d: Point sources fires from non WRAP states (CENRAP and VESTAS)

ENVIRON International Corporation University of California at Riverside 22 Pre-Merged Emission Files 15.Ofsgts3d: Off shore point sources in the Gulf of Mexico 16.Ofsmagts: Off shore Marines shipping in the Pacific Ocean 17.Ofsargts: Off shore area sources in the Gulf of Mexico 18.ptgts3d_RPO_US36: Point sources emissions for all RPOs, Can & Mex 19.rdgts_RPO: Road dust for the entire domain 20.B3gts_RPO: Biogenc emissions from BIES3 for the entire domain 21.wb_dus: Wind blown dust for entire domain 22.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

ENVIRON International Corporation University of California at Riverside WRAP RMC “BART” Modeling 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

ENVIRON International Corporation University of California at Riverside 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) Example of BART Modeling using Grid Models

ENVIRON International Corporation University of California at Riverside CALPUFF estimates higher visibility impacts than CAMx/PSAT and consequently generally more days and larger spatial extent of dV > 0.5 deciview CALPUFF PSAT

ENVIRON International Corporation University of California at Riverside CAMx PSAT CALPUFF July 19, Hour SO4 Concentrations IN Source (isgburn) CALPUFF much higher concentrations away from source. Why secondary CALPUFF SO4 peak over Cape Cod?

ENVIRON International Corporation University of California at Riverside 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 km When encountering stagnant conditions, puffs pile up on each other and stop dispersing –Violates 2 nd Law of Thermodynamics

ENVIRON International Corporation University of California at Riverside Surface Winds 0600 Surface Winds AGL Winds 0600 CALPUFF puff column advected north by winds at 300 m AGL even though surface winds from east and north

ENVIRON International Corporation University of California at Riverside 2018 Modeling/Visibility Projections Visibility projections use 2018 and 2002 modeling results in relative sense to scale observed visibility to 2018 –Draft EPA Guidance (2001) 2018 Visibility Goal based on Glide Path from current ( ) observed visibility to Natural Conditions in 2064 –EPA Guidance for default Natural Conditions (2003)

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside Great Smoky Mountains Obs vs. Model Extinction W20% > 80% extinction due to SO4

ENVIRON International Corporation University of California at Riverside Modeled Visibility Goal Test will be Difficult for WRAP Class I Areas 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

ENVIRON International Corporation University of California at Riverside Five examples of WRAP visibility projections: WHIT, NM GRCA, AZ CRLA, OR SAGO, CA DENA, AK

ENVIRON International Corporation University of California at Riverside Dust 

ENVIRON International Corporation University of California at Riverside White Mountain, NM – Worst 20% Days in 2002 Observations vs. Predictions Obs Dust   Fires  Nitrate

ENVIRON International Corporation University of California at Riverside

ENVIRON International Corporation University of California at Riverside Grand Canyon, AZ – Worst 20% Days in 2002 Observations vs. Predictions  Fires in model  Dust in obs

ENVIRON International Corporation University of California at Riverside

ENVIRON International Corporation University of California at Riverside

ENVIRON International Corporation University of California at Riverside

ENVIRON International Corporation University of California at Riverside Denali Glide Path to Natural Conditions, Baseline for Current Worst Days (10 dv) > 2064 Natural Conditions for many eastern Class I areas (e.g., 11 dv) Denali 2018 RPG Reduction = 0.61 dv

ENVIRON International Corporation University of California at Riverside 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)

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside 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

ENVIRON International Corporation University of California at Riverside Modeled WOE RPG Elements Glide paths and modeled RPG test (EPA) Eliminate days dominated by “natural” events in modeled RPG test (e.g., fires, dust) 2018 projections for species dominated by anthropogenic emissions (e.g., SO4, NO3) 2018 projections for modeled worst visibility days, worst sulfate days, etc. Other???

ENVIRON International Corporation University of California at Riverside RMC 2018 Modeling Schedule 2018 SMOKE Emissions Modeling Oct’ km CMAQ/CAMx Modeling Nov’05 –Preliminary 2018 visibility projections Dec’ km modeling Nov-Dec’ Source Apportionment Modeling Jan+’ Control Strategy Modeling 2006