1. Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Mercury, Ozone, and Particulate Matter US EPA / OAQPS / AQAD / AQMG Sharon Phillips, Kai Wang, Carey Jang, Norm Possiel, Madeleine Strum, and Tyler Fox 7 th Annual CMAS Conference – October 6-8, 2008

2. 2 Outline 2002 modeling platform and components Criteria Air Pollutants Only “CAP-only” Criteria Air Pollutants Only “CAP-only” Criteria Air Pollutants + Hazardous Air Pollutants “CAP+HAP” Criteria Air Pollutants + Hazardous Air Pollutants “CAP+HAP” Model Evaluation of 2002 CMAQ CAP & CAP+HAP predictions with ambient data Possible causes for model performance issues and analyses to further explore

3. 3 2002 Modeling Platform Developed jointly between OAQPS & ORD Promotes multi-pollutant assessments Integrated inventory (criteria and air toxics) Integrated inventory (criteria and air toxics) “One-atmosphere” CMAQ with AERMOD / Fine scale grid modeling for selected urban areas (e.g. Detroit MP Study) “One-atmosphere” CMAQ with AERMOD / Fine scale grid modeling for selected urban areas (e.g. Detroit MP Study) Currently moving towards 2005 MP modeling platform (CMAQv4.7) Currently moving towards 2005 MP modeling platform (CMAQv4.7) Provides consistency, transparency, and efficient development of baselines for: OAR regulatory assessments OAR regulatory assessments CMAQ evaluations & research efforts by ORD CMAQ evaluations & research efforts by ORD Accountability efforts across EPA Accountability efforts across EPA Public health & exposure assessments Public health & exposure assessments Provides data and examples for others (e.g., State/local agencies) Emissions data: http://www.epa.gov/ttn/chief/emch/index.html#2002 Emissions data: http://www.epa.gov/ttn/chief/emch/index.html#2002

4. 4 Components of 2002 MP Modeling Platform 2002 National Emissions Inventory (NEI) v3 Criteria (CAPs) and HAPs Criteria (CAPs) and HAPs 2002 Meteorological Data MM-5 and MCIP v3.1 MM-5 and MCIP v3.1 36km US, 12 km EUS, 12km WUS (from WRAP) 36km US, 12 km EUS, 12km WUS (from WRAP) Emissions Models, Tools and Ancillary Data Emissions Modeling Framework (EMF) Emissions Modeling Framework (EMF) Emissions processing: SMOKE version 2.3.2 Emissions processing: SMOKE version 2.3.2 Biogenics: BEIS 3.13 Biogenics: BEIS 3.13 Onroad/nonroad emissions: NMIM (w/ MOBILE6 & NONROAD 2005) Onroad/nonroad emissions: NMIM (w/ MOBILE6 & NONROAD 2005) EGU projections: IPM 3.0 EGU projections: IPM 3.0 Ancillary data: speciation, temporal, spatial allocation Ancillary data: speciation, temporal, spatial allocation Boundary Condition Concentrations 2002 simulations of GEOS-Chem: 2° x 2° grids & 30 layers up to stratosphere 2002 simulations of GEOS-Chem: 2° x 2° grids & 30 layers up to stratosphere 36-km US domain for CAPS, mercury, and some HAPS (e.g. formaldehyde) 36-km US domain for CAPS, mercury, and some HAPS (e.g. formaldehyde) For toxics not simulated by GEOS-Chem we used concs based on remote measurements and literature values (joint effort b/n AQAG & ORD) For toxics not simulated by GEOS-Chem we used concs based on remote measurements and literature values (joint effort b/n AQAG & ORD) Air Quality Model CMAQ v4.6 (base CAP only & “proto-type” multi-pollutant version) CMAQ v4.6 (base CAP only & “proto-type” multi-pollutant version) CB-05 chemical mechanism with mercury and chlorine chemistry CB-05 chemical mechanism with mercury and chlorine chemistry Ozone, PM, and additional 38 HAPs Ozone, PM, and additional 38 HAPs

5. 5 36km Domain Boundary 12km East Domain Boundary 12km West Domain Boundary Model Domains

6. 6 Model Performance Analysis PM 2.5 Species: SO 4, NO 3, TNO 3, OC, EC, SO 2 STN, CASTNet, IMPROVE, NADP STN, CASTNet, IMPROVE, NADP Ozone: 1 hr-max & 8 hr-max AQS, SEARCH AQS, SEARCH HAPs: Mercury, Formaldehyde, Acetaldehyde, Benzene, etc., + metals MDN, NATTS, STN, IMPROVE MDN, NATTS, STN, IMPROVE Graphics and Statistics: AMET 1.1 (www.cmascenter.org) hourly, daily, monthly, seasonal & annual hourly, daily, monthly, seasonal & annual Spatial tile maps comparing observed and predicted species concentrations/deposition Spatial tile maps comparing observed and predicted species concentrations/deposition Scatter plots of observations vs predictions Scatter plots of observations vs predictions Time-series plots of observations vs predictions Time-series plots of observations vs predictions Statistics: Statistics: Normalized Mean Bias (NMB) / Fractional Bias (FB) Normalized Mean Error (NME) / Fractional Error (FE) Root Mean Square Error (RMSE)

7. 7 Examples of Model Performance For Selected CAPs Based on 2002 CAP-only CMAQ Modeling at 12 km EUS & WUS

8. 8 Highlights of 2002 Model Evaluation for CAPs Ozone Under predicted for 1-hr & 8-hr daily max. especially O 3 > 60 ppb Under predicted for 1-hr & 8-hr daily max. especially O 3 > 60 ppb Sulfate PM Under predicted (~ up to 20%) in all seasons in the East & West Under predicted (~ up to 20%) in all seasons in the East & West Sulfur Dioxide Over predicted (~ 15 to 65%) in all seasons in the East & West Over predicted (~ 15 to 65%) in all seasons in the East & West Nitrate PM Over predicted (~ 5 to 40%) in Fall, Winter, and in northern areas of the East in the Spring Over predicted (~ 5 to 40%) in Fall, Winter, and in northern areas of the East in the Spring Organic Carbon Over predicted in the North and under predicted in South and West in the Winter Over predicted in the North and under predicted in South and West in the Winter Under predicted in all areas (~ 25 to 65%) in Fall, Spring & Summer Under predicted in all areas (~ 25 to 65%) in Fall, Spring & Summer Elemental Carbon Mostly over predicted in urban areas (~ 40%) in all seasons in the East and West Mostly over predicted in urban areas (~ 40%) in all seasons in the East and West Mostly under predicted in rural areas (10 to > 40%) in all seasons in the East and West Mostly under predicted in rural areas (10 to > 40%) in all seasons in the East and West

9. 9 SO 4 Wet Deposition 2002 Total NADP 12-km EUS 12-km WUS Summer Mean NMB (%)NME (%) EUS14.838.1 WUS-35.250.1

10. 10 NO 3 Wet Deposition 2002 Total 12-km EUS 12-km WUS Winter Mean NMB (%)NME (%) EUS8.837.9 WUS-18.659.7 NADP

11. 11 CAP+HAP vs CAP-only differences Emission Differences Use HAPs for speciation for select sources Use HAPs for speciation for select sources Use formaldehyde, acetaldehyde and methanol for all sources Use formaldehyde, acetaldehyde and methanol for all sources Very small spatial/temporal profile differences in some geographic areas Very small spatial/temporal profile differences in some geographic areas Model Differences Chlorine chemistry Chlorine chemistry Added air toxics Added air toxics Differences in Predictions Slight differences in Summer & Winter Ozone (Northeast, CA, UT) Slight differences in Summer & Winter Ozone (Northeast, CA, UT) Slight differences in Winter NO 3 (Northeast, GA, UT) Slight differences in Winter NO 3 (Northeast, GA, UT) Negligible differences in Summer SO 4 Negligible differences in Summer SO 4 Some differences in Winter & Summer Formaldehyde & Acetaldehyde Some differences in Winter & Summer Formaldehyde & Acetaldehyde

12. 12 2002 July Ozone: CAP+HAP – CAP-only July: CAP+HAP slightly less ozone in Northeast, more ozone in Utah in vicinity of large source of chlorine emissions.

13. 13 2002 January Formaldehyde: CAP+HAP – CAP-only Difference in CMAQ FORM Difference in FORM Emissions CMAQ differences in winter appear to be due to differences in speciation of residential wood combustion CAP VOC vs formaldehyde in the HAP inventory (CAP << HAP).

14. 14 Examples of Model Performance For Selected HAPs Based on 2002 CAP+HAP CMAQ Modeling for the 12 km EUS

15. 2002 Formaldehyde – EUS 12km Winter – Monthly Mean Summer – Monthly Mean High observations – may be due to short-term releases, near-source monitors, or instrument issues NMB = - 51.3% NME = 55.2% NMB = - 31.5% NME = 64.1%

16. 2002 Formaldehyde – EUS 12km Same data, but without high observed values Summer – Monthly Mean Winter – Monthly Mean NMB = - 35.6% NME = 42.1% NMB = - 15.7% NME = 56.9%

17. 2002 Acetaldehyde – EUS 12km Winter – Monthly Mean Summer – Monthly Mean Slightly higher concs. observed at sites in MA & GA NMB = 54.9% NME = 77.9% NMB = - 27.8% NME = 44.2%

18. 2002 Benzene – EUS 12km Winter – Monthly Mean Summer – Monthly Mean Slightly higher concs. observed at 1 site in TX & IN NMB = 41.0% NME = 55.0% NMB = - 27.3% NME = 58.3% Slightly higher concs. observed at 3 sites in TX & 1 site IN

19. 2002 Lead PM 2.5 – EUS 12km Winter – Monthly Mean Summer – Monthly Mean NMB = - 53.8% NME = 62.6% NMB = - 33.8% NME = 54.6% Slightly higher concentrations observed at Birmingham, AL

20. 2002 Chromium PM 2.5 – EUS 12km Winter – Monthly Mean Summer – Monthly Mean NMB = - 47.3% NME = 72.3% NMB = - 14.0% NME = 82.6% Slightly higher concentrations observed at same site in Birmingham, AL

21. 2002 Carbon Tetrachloride – EUS 12km Winter – Monthly Mean Summer – Monthly Mean NMB = - 73.0% NME = 73.0% NMB = - 59.7% NME = 60.6% Influence of detection level in observations

22. 22 Examples of Model Performance For Mercury Wet Deposition Based on 2002 CAP+HAP CMAQ Modeling at 12 km EUS

23. 23 2002 Annual Mercury Wet Deposition 12 km EUS NMB = - 10.1% NME = 23.2%

24. 2002 Seasonal Hg Wet Deposition – EUS 12-km Summer SpringWinter Fall Over-prediction in Winter & Spring Under-prediction in Fall & Summer NMB = 30.7% NME = 43.4% NMB = 15.1% NME = 32.7% NMB = -15.5% NME = 39.9% NMB = -34.2% NME = 39.8%

25. 25 Identified Technical Issues with Tools and Data for MP Analyses Model performance for some non-ubiquitous HAPs is not as “good” as that for ozone and PM2.5 Uncertainties in monitoring methods Uncertainties in monitoring methods Limited measurements in time/space to characterize ambient concentrations (“local in nature”) Limited measurements in time/space to characterize ambient concentrations (“local in nature”) Given local nature of some toxics, fine scale modeling may be needed Commensurability issues between measurements and model predictions Commensurability issues between measurements and model predictions Emissions and science uncertainty issues may also affect model performance Emissions and science uncertainty issues may also affect model performance Limited data for estimating intercontinental transport (i.e., Boundary Conditions) Limited data for estimating intercontinental transport (i.e., Boundary Conditions) Boundary estimates for some species are much higher than predicted values inside the domain

26. 26 Identified Technical Issues with Tools and Data for MP Analyses Emissions-related issues No available HAP inventories for Canada (except Hg) & Mexico No available HAP inventories for Canada (except Hg) & Mexico Inconsistencies between emissions factors for CAPs and HAPs Inconsistencies between emissions factors for CAPs and HAPs Criteria/HAP emissions are not easily integrated Criteria/HAP emissions are not easily integrated Inconsistencies in CAP/HAP emissions reported by States Inconsistencies between VOC speciation and HAP inventories – need for coordinated research effort Periodic nature of some toxic releases that are not well characterized in our inventories Periodic nature of some toxic releases that are not well characterized in our inventories Uncertainties in science (e.g., mercury chemistry and re-emissions) and evolving science for various components of the modeling system

27. 27 Future Efforts Continue model evaluation efforts and investigate performance issues in conjunction with ORD Prepare 2002 Multi-Pollutant Platform Report Complete 2005 Multi-Pollutant Modeling Platform Provide feedback to the 2008 NEI integrated inventory process and future modeling platforms