CMAQ MultiPollutant Response Surface Modeling: Applications of an Innovative Policy Support Tool

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CMAQ Multi-Pollutant Response Surface Modeling
Applications of an Innovative Policy Support Tool

• CMAS Conference October 17, 2006
• Session 2 Analysis Methods and Tools
• USEPA/OAQPS - Sharon Phillips, Bryan Hubbell,
  Carey Jang, Pat Dolwick, Norm Possiel, Tyler Fox

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Outline

• Overview of Response Surface Model (RSM)
• Need for the RSM
• Define RSM
• Development of multi-pollutant RSM using CMAQ
• Steps in designing CMAQ RSM applications
• Experimental design
• CMAQ SMOKE interface development
• Evaluation Validation
• RSM outputs / Visual Policy Analyzer
• CMAQ RSM application results
• Next steps

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Need for Response Surface Model

• Growing importance of AQ models in guiding and
  supporting policy analysis and implementation for
  complex AQ issues such as PM, O3, and air toxics
• Enormous computational costs (time resources)
  always present a challenge for time pressing need
  of policy analysis
• Current model operation in comparing the efficacy
  of various control strategies and policy
  scenarios is typically inefficient, if not
  ineffective
• An innovative policy support tool to address
  these issues in an economical manner is needed

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What is a Response Surface Model?

• Response Surface Model (RSM) is a reduced form
  model of a complex air quality model (e.g. CMAQ)
  meta-model
• Based on a systematically selected set of model
  runs, statistical techniques can be used to
  represent the relationship between model inputs
  and outputs (e.g. emissions control and
  concentrations of PM ozone)
• Once the response surface has been generated,
  it can be used to simulate the functions of the
  more computationally expensive photochemical air
  quality model
• Cross-validation can then be conducted to examine
  the validity of RSM to represent model responses
• Can also be used to derive analytical
  representations of model sensitivities to changes
  in model inputs

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How the RSM can be used

• Strategy design and assessment screening tool
• Comparison of urban vs. regional controls
• Comparison across sectors
• Comparison across pollutants
• Optimization
• Can be used to develop optimal combinations of
  controls to attain standards at minimum cost
• What If? Analyses
• provide real-time predictions of model responses
  to model inputs
• Quickly provide insights into questions for
  policy design, e.g. does is it matter whether
  regional controls are put in place before local
  controls?
• Model sensitivity
• Can be used to systematically evaluate the
  relative sensitivity of modeled ozone and PM
  levels to changes in emissions/met inputs

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Elements in the Development of a RSM

• Policy objectives
• Model requirements
• Geographic scale
• Output metrics
• Experimental design
• Selection of policy factors
• Specification of air quality model simulations
• Air quality modeling simulations
• Statistical analysis and development of
  predictive model
• Visualization software tool

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Development Plan for Response Surface Model
Determine Relevant Policy Factors
Assess Policy Objectives
Determine Output Metrics
Develop Experimental Design
Run Modeling Experiments
Select Base Inventory
Process Model Outputs
Model Validation for PM Response Metrics
Build Predictive Model
Develop Emissions Summaries
Develop Response Surface Summary Metrics
Development of Visual Policy Analysis Tool
Evaluate Relative Effectiveness of Policy Factors
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RSM Pilot Applications History
RSM for PM2.5 using REMSAD
RSM for O3 using CAMx
(Strategy Comparison)
NOx controls
VOC controls
VOC controls vs. NOx controls
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An Initial RSM Application using CMAQ
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Development of CMAQ RSM Applications

• Determine policy objectives
• Experimental design
• Selection of policy factors
• Emission control factors
• Regional vs. urban control
• Selection of air quality model simulations
• Continental U.S. modeling, 2010 CAIR Base, 36-km
  grid resolution
• 210 runs (in 3 stages) for 4 months (Feb., April,
  July, Dec.)
• CMAQ SMOKE Interface Development
• Develop a module within CMAQ to read directly the
  pre-merged SMOKE sector files (e.g., 3-D point,
  2-D mobile, etc.)
• Allow RSM to directly control changes of (1)
  emissions and (2) specified areas
• Output Metrics
• Response Surface Variables
• Validation and Evaluation
• Cross-validation
• Out-of-sample validation

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Policy Objectives

• Provide a modeling surrogate tool that can
  quickly simulate the PM and ozone impacts for a
  variety of control strategies for use in
  Regulatory Impact Analyses (e.g., PM NAAQS, O3
  NAAQS)
• For screening level estimates of the impacts of
  control strategies on NAAQS design values
• For use in generating screening estimates of the
  health benefits of reductions in PM and ozone
  precursors
• Provide air quality simulation tool for use in
  the Air Strategy Assessment Program (ASAP), a
  screening tool under development that evaluates
  the relative air quality impacts, costs, and
  health benefits of controlling emissions from
  different sources

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Experimental Design (1) Selection of control
factors

• 12 emission control factors selected based on
  precursor emissions source category relevant to
  policy analysis of interest

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Factors Provide Reasonable Aggregation
Omitted
Combined
Combined
Combined
Omitted

• Source groupings with smaller contributions to
  emissions are grouped with similar larger source
  groupings for efficiency
• NonEGU Area NOx and SO2 sources are primarily
  smaller industrial combustions sources such as
  coal, oil, and natural gas powered boilers and
  internal combustion engines
• Agricultural area sources are only significant
  contributors to ammonia emissions
• VOC sources are lumped together because VOCs are
  not expected to influence PM levels significantly

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Experimental Design (1) Selection of control
factors

• Covers from zero to 120 percent of baseline
  emissions
• Staged Latin Hypercube (space filling design)
• 210 total runs, 120 runs in first stage, 60 runs
  in stage two and 30 boundary condition runs
• Will allow testing of additional predictive power
  of additional model runs
• 30 additional model validation runs

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Experimental Design (1) Selection of control
factors

• Regional vs. Urban control independent response
  surfaces for 9 urban areas, as well as a
  generalized response surface for the rest of
  model domain

• Nine urban areas include NY/Philadelphia,
  Chicago, Atlanta, Dallas, San Joaquin, Salt Lake
  City, Phoenix, Seattle, and Denver
• Selected so that ambient PM2.5 in each urban area
  is largely independent of the precursor emissions
  in all other included urban areas

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CMAQ SMOKE Interface Timely Efficient
Development of Model-ready Emissions
Region A (9 urban areas)
Region B (rest of domain)
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Experimental Design (2) Selection of Model
Simulations

• CMAQ model simulations
• CMAQ v4.4 14 vertical layers
• Domain Continental U.S. 36-km CAIR modeling
  domain
• 4 months, one from each season, February, April,
  July, October (months selected to provide best
  prediction of quarterly mean)
• Baseline Emissions Data
• CAIR 2010 Base Case
• Includes Tier 2, Heavy Duty Diesel Engines, and
  Nonroad Diesel standards, as well as the NOx SIP
  Call and MACT standards

CMAQ Modeling Domain
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Output Metrics (Response Variables)

• Quarterly mean and annual 98th percentile daily
  average sulfate, nitrate, crustal, elemental
  carbon, organic carbon, ammonium
• PM2.5 annual and daily design values (at
  monitored locations)
• Annual/Seasonal nitrogen and sulfate deposition
• Visibility (light extinction) annual mean,
  average of 20 worst days, average of 20 best
  days
• Ozone summer averages for
• 8hr max, 1hr max, 5hr avg, 8hr avg, 12hr avg,
  24hr avg
• Ozone 8-Hour design values (at monitored
  locations)

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RSM Validation and Evaluation

• Cross-validation
• for each RSM iteration, one of the model runs was
  left out, the RSM is computed and used to predict
  the omitted run
• RSM predicted changes in AQ are compared with
  CMAQ predictions and the mean square error (MSE)
  over all grid cells was computed for the run
• Out-of-sample validation
• 30 additional CMAQ runs were conducted (not part
  of the experimental design and were not used in
  developing RSM)
• RSM predictions for these model runs were
  compared with the CMAQ predictions and the MSE
  over all grid cells was computed for each run

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Cross-Validation Comparison of RSM Predicted to
CMAQ true Values for July PM2.5 mass
based on an evenly geographically distributed
sub-sample of 700 grid cells, out of 6,300 in
the continental U.S.
July total PM2.5 mass (sample of 700 grid cells)
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Cross-Validation Comparison of RSM Predicted to
CMAQ true Values for October PM2.5 mass
based on an evenly geographically distributed
sub-sample of 700 grid cells, out of 6,300 in
the continental U.S.
October total PM2.5 mass (sample of 700 grid
cells)
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Similarity of Geographic Patterns of Predicted
PM2.5 (mean total) changes for October based on
Run 120
CMAQ
RSM
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RSM Graphical Tool Visual Policy Analyzer

• Graphical analysis tool to allow for real-time
  RSM predictions of ozone, PM, visibility, and
  deposition
• Continuous improvements are implemented to the
  user interface and functions

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2-Way Response Surfaces for Chicago
NR VOC
NR NOx
Onroad VOC
Onroad NOx
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RSM Visualization Tool 3-D View Mode
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Quick re-cap

• RSM can analyze air quality changes in 9 urban
  areas and associated counties independently of
  one another
• For each urban area
• Input to RSM
• local or regional reduction for one or more of
  the 12 factors
• Output from RSM
• Estimated changes in air quality at peak monitor
  in each county on an annual and daily basis
• Gridded air quality changes across urban area
• To estimate regional emission reductions reduces
  the regional emission reduction in the entire
  rest of US, which is outside of the 9 urban areas

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VPA example Monitors with annual average PM2.5
Post CAIR 2015 greater than 13 µg/m3
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VPA example Monitors with annual average PM2.5
Post CAIR 2015 greater than 13 µg/m3 after
applying 50 percent reduction in carbon
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Example of Air Quality Impacts Regionality of
SO2 vs. Locality of Carbon
SO2
Carbon
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Key Local Factors are Carbon, EGU SO2, NonEGU
SO2, VOC, and NH3 Key Regional Factors are EGU
SO2, NonEGU SO2, Area NH3, and Carbon
Availability of controls may limit the ability to
achieve the desired percent reductions in
specific sources and pollutants that would result
in reductions in ambient PM2.5 levels to meet the
attainment targets.
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Relative effectiveness per ton in reducing
ambient PM2.5 levels is only one factor in
determining the appropriateness of controls.
Cost effectiveness per microgram is the more
complete measure, and reflects both the
atmospheric response and costs of the controls.
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What types of reductions have the biggest local
effect on PM2.5 in the East?
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Next Steps

• Planning for 12km Local Scale RSM for selected
  areas of concern (FY06/FY07)
• Implementation of multi-pollutant ASAP version
  using CMAQ RSM
• Use RSM results to investigate/guide sector based
  O3/PM analyses
• Collaboration outreach to AQ community (RPOs,
  academic, international, etc.) to facilitate
  transfer of methods and development of RSM tools

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Appendix
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Development of CMAQ Response Surface Model
Determine Relevant Policy Factors
Universe of Potential Factors
Factor Elimination Process
Assess Policy Objectives
Time/Resources Tradeoff Matrix
CMAQ/CAMx Limits
Determine Output Metrics (Response Variables)
Emissions Inventories
Select Air Quality Model
Develop Experimental Design(Battelle)
Preliminary Modeling
Select Base Inventory (projection year base
control set)
Specify Modeling Domain (including nested
subgrids)
Select Model Grid Size (e.g. 12km or 36 km)
Run Modeling Experiments
Specify Modeling Periods (e.g. 4 months, one
from each season)
Develop Emissions Summaries (Annual or Daily
Emissions by Factor)
Evaluation of 2001 full year CMAQ results
Process Model Outputs (compute response
variables)
PM2.5 and Ozone Monitoring Data
Model Validation for Ozone and PM2.5 Response
Metrics
Build Predictive Model
FLOWCHART KEY
Develop Normalized Adjustment Ratios (SMAT
technique for PM2.5, BenMAP eVNA for Ozone)
PREPARATION
Develop Response Surface Summary Metrics
Development of Visualization Tool (Batelle) And
Integration into ASAP
DATA/INPUT
PROCESS
Evaluate Relative Effectiveness of Policy Factors
Evaluate Relative Cost-Effectiveness
And Calculate optimal factor levels
DECISION
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PM2.5 Areas of Influence for All 9 Urban
Locations
July 2001 (monthly avg.)
February 2001 (monthly avg.)
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Areas of Influence for Selected Urban Locations
New York/Philadelphia
Chicago
PM2.5 July monthly avg.
Atlanta
Small overlaps between Chicago and NY influences
in Ohio and Western NY. No overlap between
Atlanta and NY
Small overlaps between Atlanta and Chicago
influences in Western KY
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Extent of Air Quality Influence Region for 9
Selected Urban Areas
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CMAQ Application Analyzing Illustrative Control
Scenarios

• Analyze sequence of controls
• Demonstrates ways in which states might meet the
  standard
• Each bin contains multiple control options
• Iterate analysis to identify mix of local and
  regional controls
• Use RSM to help optimize for cost and monetized
  human health benefits

Baseline Modeling (e.g CAIR)
(1) Local Controls
Initial Iteratation?
?Subsequent Iteration
(2) Non-EGU Regional Controls
(3) Local-Scale Targeted EGU Controls as
Proposed by States
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Relative Impacts of 30 Percent Reductions in
Precursor Emissions Across Source Categories
Included in the Response Surface Model
Chicago 2015 Annual Design Value 16.9
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Relative Impacts of 30 Percent Reductions in
Precursor Emissions Across Source Categories
Included in the Response Surface Model
San Joaquin 2015 Annual Design Value 21.7
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2-Way Response Surfaces for NY
NR VOC
NR NOx
Onroad VOC
Onroad NOx