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

Transcription

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 GLAC CABI FOPE MELALOST VOYA FLAT BOWA MONT ULBE ISLE GAMO THRO SULA SENE YELL NOAB CLPE THBA WICA BADL BLMO GRRI BRID BRLA CRESNEBR MOZI VILA ROMO LASU SAFO BOND WHRI CEBL GRSA ELDO MEVE WEMI HEGL MINGCADI MACA WHPE SAPE BAND ELLI UPBU GRSM WIMO CACR SIPS COHU BOAP GICL WHIT SACR GUMO SIKE BIBE BRET IMPROVE AND IMPROVE PROTOCOL MONITORING SITES Causes of Haze Phase I ( ) PM Cluster Analysis Ensemble Airshed Analysis Causes of Haze Phase II ( ) 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

7

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 countylevel 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 Mingo Wilderness Extinction Budget - 20% Worst Visual Air Quality Days (2002) NO3_bext 14% EC_bext 3% CM_bext 3% SOIL_bext 1% OMC_bext 7% SO4_bext 72% SO4_bext CM_bext EC_bext NO3_bext OMC_bext SOIL_bext Initial PMF Results - Mingo Wilderness 20% Worst Days (2002) 12% 7% Bio 9% Sec Carb Pb-Cu 10% 21% Soil 7% SO4 I 16% 18% NO3 SO4 II Mixed (Metals)

14 MING Probability Analysis

15 Boundary Waters Example Boundary Waters Canoe Area Extinction Budget - 20% Worst Visual Air Quality Days (2002) SOIL_bext 1% OMC_bext 14% NO3_bext 21% EC_bext 4% CM_bext 4% SO4_bext 56% SO4_bext CM_bext EC_bext NO3_bext OMC_bext SOIL_bext Initial PMF Results - Boundary Waters Canoe Area 20% Worst Visual Air Quality Days (2002) 11% 15% 11% 16% 11% 17% 13% 6% Bio NO3 Soil Fe-As SO4 Sec Carb Ca-Mn Zn-Pb-Cu

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 SO4 CAMx BaseA FB Bugle SO4 CMAQ BaseA FB Bugle Fractional Bias (% ) (+) Goal (-) Goal (+) Criteria (-) Criteria IMPROVE CASTNET STN NADP Fractional Bias (% ) (+) Goal (-) Goal (+) Criteria (-) Criteria IMPROVE CASTNET STN NADP Average Concentration (ug/m3) -200 Average Concentration (ug/m3)

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