1. CENRAP Modeling and Weight of Evidence Approaches
National RPO Meeting
June 9, 2005
Annette Sharp and Bret Anderson

2. Weight of Evidence Approach
Derived from PM2.5/Regional Haze Modeling Guidance
Similar to PM2.5 in many respects, but approach differs slightly…
Specific modeling analyses for Regional Haze
Nested grids (36 km and 12km episodic analysis)
Results of more than one model (CAMx v. CMAQ /RPO v. RPO)
PSAT (CAMx) - TSSR (CMAQ) - Model SA
Refinements to the reasonable progress test
Examine the RRF on individual days
Re-rank best and worst days based on future year model predictions
Use day specific relative humidity factors
Data analysis approach
Review of trends in visibility (although some Class I areas do not have a relatively long ambient data record)
Observational models
Hybrid source apportionment – observational and trajectory analyses

3. Data Analysis Activities
Causes of Haze Phase I (2002 – 2003)
PM Cluster Analysis
Ensemble Airshed Analysis
Causes of Haze Phase II (2004 – 2005)
Ensemble Airshed Analysis w/ Particle Trajectories (HYSPLIT)
Source Apportionment (Observational Modeling – Positive Matrix Factorization)
CENRAP Data Analysis (2005)
PM Regionalization
Meteorological Analysis
Hybrid Modeling (Source Apportionment (PMF) and Lagrangian Particle Modeling

4. Causes of Haze Phase II PM Regionalizaton Analysis
Air Quality and Emissions Trend Analysis
Observational or Hybrid Models (useful for tagging local contributions, identifying if control strategies are oriented appropriately towards observed pollutants and source categories

5. PM Regionalization
Spatiotemporal analysis using Singular Value Decomposition (SVD)
Provides indication of areas in which observed air quality behaves in a homogenous manner.
Provides understanding of relationship of CENRAP monitoring sites to surrounding areas.
Useful for selecting representative sites for source apportionment and meteorological analyses.

6. Tools used - PORSCH System
The PORSCH system is a suite of GIS tools that combines modeled backward wind trajectories, monitored concentrations, meteorological conditions, and EIs.
Written in VBA, SQL, and FORTRAN
Utilizes ESRI ArcInfo, ESRI Spatial Analyst, SQL Server, MS Excel, and the NOAA HYSPLIT trajectory model
Outputs formatted images and data

8. Spatial Probability Density (SPD) and Conditional Probability Impact Assessment (CoPIA)
SPD aggregates trajectory ensembles.
SPD illustrates the overall transport region for specific conditions (e.g., 20%-worst, 20%-best, or typical days).
CoPIA illustrates how transport on specific dates differs from typical conditions.

9. In this example, transport to the Guadalupe Mountains site is more likely to originate in the darkened areas on 20%-worst days than on other days.

10. Prevailing Transport and EI Tools
Prevailing Transport (PT) tool.
PT combines ensemble trajectories with speciation data and other meteorological parameters.
PT automatically generates images for cluster analyses of the 20%-best and 20%-worst days.
Emission Impact Potential (EIP) tool.
EIP combines ensemble trajectories with county-level EIs.
EIP calculates the trajectory-density-weighted emissions likely to impact selected receptor sites.

11. A Picture is Worth a Thousand Words

12. Hybrid Models – Combining PMF and Trajectory Analyses
Representative sites selected from IMPROVE SVD analysis utilized for Observational/Trajectory analysis.
Positive Matrix Factorization using IMPROVE data from VIEWS utilized.
Combined HYSPLIT/LPDM analysis for air mass history analysis.

13. Mingo Wilderness Example

14. MING Probability Analysis

15. Boundary Waters Example

16. PMF Profile Example - BOWA

17. BOWA Probability Analysis

18. WOE - Air Quality Modeling Approach
Corroborative analysis w/ CAMx (ENVIRON) and CMAQ (UC-R).
Variable grid resolution – 36 km v. 12 km (episodic analysis)
Process Analysis or other model diagnostic techniques such source apportionment or tagged species analysis (PSAT/TSSA)

19. Corroborative Analysis from Alternative Air Quality Models

20. Evaluation of Alternative Models Example

21. Air Quality Simulations – Nested Grids
Evaluation of model performance to determine if higher resolution meteorology and/or emissions improves simulation
CENRAP 12 km MM5 domain

22. Conclusions
CENRAP will utilize both data analysis and chemical transport modeling to help guide and develop possible control scenarios.
PMF and air mass analyses can be utilized to help refine control scenarios, in addition to its role in WOE. However, approach is limited because it cannot predict future air quality scenarios from OTB/OTW controls.
WOE demonstration will consist of a myriad of data analysis and model evaluation techniques to support ROP demonstration.
CMAQ/CAMx evaluations
36 km/12 km nested evaluations
Inter-RPO modeling comparison