1 Projects:/WRAP_RMC/Presents/ADEQ_Feb062003.ppt Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) Preliminary Fire Modeling Results.

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Presentation transcript:

1 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) Preliminary Fire Modeling Results Presented by: Ralph Morris WRAP Regional Modeling Center (RMC ) Presented at: Fire Emissions Joint Forum Meeting San Francisco, California June 3, 2003

2 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP Regional Modeling Center (RMC) University of California at Riverside (UCR) – Gail Tonnesen, Zion Wang, Jung Chien, etc. – Host RMC, CMAQ Modeling, Analysis ENVIRON International Corporation – Ralph Morris, Gerry Mansell, Steve Lau, etc. – Interpretation of Results, MM5 & REMSAD Modeling UNC Carolina Environmental Program (MCNC) – SMOKE Emissions Modeling WRAP Modeling Forum Co-Chairs – John Vimont (NPS), Mary Uhl (NM), Kevin Briggs (CO) WRAP Technical Coordinators – Tom Moore and Lee Alter

3 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Content of Today’s Talk Overview of WRAP Objectives Overview of Visibility Calculations WRAP §309 SIP/TIP Modeling Approach CMAQ Model Performance Evaluation Use of Modeling Results to Project Future-Year Visibility Fire Management Practice Modeling Glide Path Slopes toward Natural Visibility Conditions Estimated 2018 Visibility Progress for §309 Scenarios – Scenario #1: P2 + Annex + BSM – Scenario #2: P2 + Annex + OSM

4 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP Visibility Objectives §309 SIP/TIP due 2003 – 9 “Grand Canyon” states may opt-in (AZ, CA, CO, ID, NV, NM. OR, UT, and WY). – Focus on 16 Class I Areas on the Colorado Plateau §308 SIP/TIP due 2008 – visibility baseline – 2018 end of first planning period – Show progress toward natural visibility conditions by 2064

5 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Section 309 SIP/TIP Modeling Requirements Demonstrate that SO 2 Annex Milestone control strategy is better than BART with Uncertainty Analyze “significance” of Mobile Source and Road Dust at 16 Class I Areas Estimate visibility improvements in 2018 due to §309 All Control Strategy Evaluate PM/NOx point source controls Evaluate alternative fire management practices

6 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP §309 Modeling Approach 1996 Baseline Modeling Period 36-km Grid Covering Western US SMOKE emissions modeling system using emissions provided by WRAP and EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system REMSAD model dropped from §309 modeling due to time/resource constraints

7 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP CMAQ and REMSAD Modeling Domains

8 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Components of Light Extinction Light scattering and absorption – SO4 sulfate, ammonium sulfate SO 4 (NH 4 ) 2 – NO3nitrate, ammonium nitrate NO 3 NH 4 – OC organic compound/organic matter OC, OM, SOA – EC elemental carbon Soot – PMFother fine particulates (<2.5  )Soil – PMCcoarse PM (  )PM NO2 absorption considered a plume blight issue and not typically accounted for in regional haze assessments

9 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Components of Light Extinction (continued) Associated with each species is an “extinction coefficient” that converts concentration (  g/m 3 ) to light extinction (Mm -1 ) Total visibility impairment is obtained as the sum of extinction due to each species:

10 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP Visibility Modeling (continued) CMAQ 1996 Annual Runs – ~ 110 Gb of emission inputs – ~ 130 GB of other inputs – ~ 365 Gb of output Initially annual simulations required 2 weeks – Multiprocessing allows runs to be completed in as little as 3 days Challenge is processing 365 Gb of output into regulatory relevant results

11 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP Visibility Modeling (continued) SMOKE emissions modeling becomes bottleneck – SMOKE QA/QC did not catch all errors in processing Errors in treating holidays as weekdays Many 2018 scenarios errors in allocating elevated sources dropped emissions OSM vs BSM errors not caught – Interpretation of results requires matching runs in a consistent fashion (i.e., with common errors)

12 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt WRAP CMAQ Model Performance Evaluation ~30 IMPROVE sites in western US Issues in matching monitored species with modeled species – Reconstructed Mass Equations – Actual Species How to display results to convey performance WRAP RMC website has 100s of scatterplots and time series plots by site, by day, by month:

13 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt 1996 CMAQ Model Performance Issues Nitrate overprediction bias especially in Winter and Spring/Fall – Ammonia emissions overstated under cold conditions 2003 project to improve ammonia emissions – Deposition of ammonia and nitrate underestimated – June 2002 CMAQ release new heterogeneous nitrate formation Exacerbated nitrate overprediction bias

14 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt 1996 CMAQ Model Performance Issues Some skill in sulfate estimates EC, OC, and especially Soil highly scattered Coarse Matter (CM) greatly underestimated – Missing local (subgrid-scale) impacts – Missing wind blown fugitive dust – 2003 project to develop wind blown dust inventory Relatively better model performance is exhibited at sites on the Colorado Plateau and in the summer months when the Worst 20% days occur

15 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Projecting Future-Year Visibility Follow EPA draft guidance for projecting future- year visibility (EPA, 2001a,b,c) Use model in a relative fashion to scale the current (1996) observed visibility for the Best 20% and Worst 20% days based on the ratio of the 2018 to 1996 modeling results – Relative Reductions Factors (RRFs) – Class I Area specific – Specific for each component of light extinction (SO4, NO3, EC, OC, Soil, and CM)

16 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Projecting Future-Year Visibility Accounting for missing fugitive dust emissions – No wind blown fugitive dust in inventory – Major component of observed Soil and CM – Model estimated RRFs for Soil and CM are in error Set RRFs for Soil and CM to unity RRF(Soil) = RRF(CM) = 1.0 Assumes 2018 Soil and CM concentrations are the same as 1996

17 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Glide Path Slope Values to Natural Visibility Conditions (NVC) Observed Baseline Visibility Conditions (Anchors Glide Path Slope) – Worst 20% Days: Progress toward Natural Visibility Conditions in 2064 with Planning Periods ending at 2018, 2028, 2038, 2048, 2058, and 2064 – Best 20% Days: No Degradation in Visibility Glide Path Slope Values assumes linear progress to Natural Visibility Conditions in 2064

18 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Preliminary Glide Path Slope Values to NVC Use most current five-years of observed visibility to anchor Glide Path 2004 starting point for Worst 20% average visibility – used in preliminary analysis – Soon to be updated with data Map Observed Visibility Conditions from Class I Areas with IMPROVE Monitoring to Nearby Similar Unmonitored Class I Areas Use current EPA draft guidance for natural visibility conditions (NVC) for worst days (EPA, 2001)

19 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Mapping of IMPROVE Data to Class I Areas

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21 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Preliminary Glide Path Estimates Using Preliminary Observed Data – Will soon update to observations Based on Current EPA Draft Guidance for Natural Visibility Conditions and f(RH) Values (EPA, 2001) – Revised Draft EPA Guidance expected soon New f(RH) values are generally slightly lower Have updated Glide Path Slope Value plots with new (2001) information

22 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Projecting 2018 Visibility Improvements Use relative changes in modeling results between 1996 and 2018 for average of Worst 20% (Best 20%) days to scale visibility baseline ( observed visibility) – Effects of changes in Soil and CM not accounted for [RRF(Soil) = RRF(CM) = 1.0] 2018 Projections for 2018 §309 All Control Strategies Scenario

23 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt 2018 §309 All Control Strategy Scenarios#1&#2 Area, Road Dust, Off-Road, On-Road Emissions – 2018 Base Case Conditions Biogenic Emissions – 1996 Base Case Conditions “Typical year” Wildfires Base Case Point Sources – SO 2 Annex Milestones + Pollution Prevention) Agricultural and Forest/Range Prescribed Fires – Scenario#1: Base Smoke Management (BSM) – Scenario#2: Optimal Smoke Management (OSM) Example Emission Difference Plots for EC – Scenario#1 – Scenario#2 (BSM-OSM)

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25 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt (BSM-OSM)

26 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt 2018 Reasonable Progress Plots 2018 Reasonable Progress Target Based on Preliminary Information – Observed Visibility – Preliminary f(RH) and Natural Conditions – Straight Line Projection from 2004 to 2064 BSM Versus OSM Scenarios – Potential error in OSM scenario with daily emissions sometimes higher than BSM

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36 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt BSM Versus OSM Results OSM Emissions Sometimes Higher Than BSM – Results in worsening in visibility if occurs during a day from the Worst 20% days Need to Develop New OSM Emissions Inventory? – UNC/CEP emissions development delayed by lack of 2003 contract Additional Fire Management Scenarios to be Modeled?