Reducing Air Pollution In Los Angeles

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
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

3
2002 Base A Modeling

• 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

4
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

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 _at_
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)

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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
MRPO Large Source Episode Maximum SO4 PSAT
versus Zero Out
PSAT
Zero-Out
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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

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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

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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

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PSAT for SOA

• CAMx SOA scheme
• VOC -- OH, O3, NO3 --gt Condensable Gas (CG) ltgt
  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

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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
• http//pah.cert.ucr.edu/aqm/308/meetings/March_200
  5/03-08_09-05.SF_CA/Alternative_Model_Mar8-9_2005_
  MF_Meeting.ppt

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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)
  AreaNon-Road
• Initial and Boundary Concentrations
• 77 Source Groups (7715 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

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PSAT/TSSA Source Region Map CA, NV, OR, WA, ID,
UT, AZ, NM, CO, WY, MT, ND, SD, Eastern States
and Mex/Can/Ocean
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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???
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Grand Canyon, Arizona Day 188 (07/07/02) 15th
Worst Visibility Day in 2002 Some differences
TSSA and PSAT Pts_Mex, Other, BC
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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
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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
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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, doesnt suffer
  Other unexplained portion of PM like TSSA
• TSSA being implemented in latest versions of CMAQ

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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

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22 Pre-Merged Emission Files

• 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)

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22 Pre-Merged Emission Files

• 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

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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

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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)

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CALPUFF estimates higher visibility impacts than
CAMx/PSAT and consequently generally more days
and larger spatial extent of dV gt 0.5 deciview
PSAT
CALPUFF
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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
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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
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2018 Modeling/Visibility Projections

• 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)

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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
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Great Smoky Mountains Obs vs. Model Extinction
W20
gt 80 extinction due to SO4
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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
• Dont CAIR states
• Point source SO2 and NOx controls much less
  effective at reducing visibility in west compared
  to east

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Five examples of WRAP visibility
projections WHIT, NM GRCA, AZ CRLA, OR SAGO,
CA DENA, AK
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Dust ?
41
White Mountain, NM Worst 20 Days in 2002
Observations vs. Predictions
? Fires
Obs Dust ?
? Nitrate
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Grand Canyon, AZ Worst 20 Days in
2002 Observations vs. Predictions
? Fires in model
? Dust in obs
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Denali Glide Path to Natural Conditions, Baseline
for Current Worst Days (10 dv) gt 2064 Natural
Conditions for many eastern Class I areas (e.g.,
GRSM _at_ 11 dv) Denali 2018 RPG Reduction 0.61
dv
48
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)
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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

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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/internatio
  nal)
• 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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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???

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RMC 2018 Modeling Schedule

• 2018 SMOKE Emissions Modeling Oct05
• 2018 36 km CMAQ/CAMx Modeling Nov05
• Preliminary 2018 visibility projections Dec05
• 2018 12 km modeling Nov-Dec05
• 2018 Source Apportionment Modeling Jan06
• 2018 Control Strategy Modeling 2006