Diagnostic Study on Fine Particulate Matter Predictions of CMAQ in the Southeastern U.S. Ping Liu and Yang Zhang North Carolina State University, Raleigh, NC Shaocai Yu, Prakash V. Bhave, Robert W. Pinder, and Kenneth L. Schere Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC

Outline Background Background  Model configurations and operational evaluation  Objectives and approaches Diagnostic study Diagnostic study  Integrated process rate (IPR) analysis  Sensitivity simulations Summary Summary

Model Configurations & Objective Configurations Configurations  Model: CMAQ v4.4  Episode: Contiguous United States, June 12-28, 1999  Domain: 32 km, 178 × 124 horizontal grid cells 21 layers from surface (first layer top, ~35 m) to the tropopause (~16 km) 21 layers from surface (first layer top, ~35 m) to the tropopause (~16 km)  Meteorology: MM5 v3.4  Emissions: EPA’s 1999 NEI v3, SMOKE v1.4  Initial and boundary conditions (ICs/BCs): EPA default files  Gas-phase mechanism: SAPRC99  Aerosol module: AERO3  Spin-up: June Objectives and Approaches Objectives and Approaches  Identify large contributors to the model biases in CMAQ aerosol predictions  Process analysis: Integrated process rate (IPR) analysis Integrated reaction rate (IRR) analysis Integrated reaction rate (IRR) analysis  Correlation analysis and sensitivity simulations for influential processes

Spatial Distribution of Simulated and Observed O 3 and PM 2.5 O 3 at AIRS-AQS sites (15-day mean of max. 8hr-average) PM 2.5 at IMPROVE sites (15-day mean of 24hr-average) NMB (%) AIRS-AQSCASTNet max. 1-hr O max. 8-hr O NMB (%) IMPROVESEARCHSTN PM SO NO NH

Integrated Process Rate (IPR) Analysis Column IPR analysis Column IPR analysis  Average export in the PBL in urban and rural areas IPR correlation analysis IPR correlation analysis  Process contribution versus large model bias at urban and rural sites  Large model bias: 2.6 km 0 km Block: 9 grid cells Column: layers 1-14

Column IPR Analysis at SEARCH Sites PM 2.5 SO 4 2- NO 3 - NH 4 +

IPR Correlation Analysis: SO 4 2- All Sites Urban Rural ○: dry deposition □: aerosol processes ∆: cloud processes Aerosol and cloud processes are slightly correlated Aerosol and cloud processes are slightly correlated Dry deposition is largely correlated Dry deposition is largely correlated

IPR Correlation Analysis: NO 3 - Aerosol and cloud processes are large contributors; dry deposition is a small contributor Aerosol and cloud processes are large contributors; dry deposition is a small contributor No obvious correlations are found for all processes No obvious correlations are found for all processes All Sites Rural Urban ○: dry deposition □: aerosol processes ∆: cloud processes

IPR Correlation Analysis: NH 4 + Aerosol processes are a large contributor and correlated Aerosol processes are a large contributor and correlated Cloud processes and dry deposition are small contributors and weakly correlated Cloud processes and dry deposition are small contributors and weakly correlated All Sites Rural Urban ○: dry deposition □: aerosol processes ∆: cloud processes

IPR Correlation Analysis: PM Precursors Cloud processes and dry deposition of SO 2 are sometimes correlated with SO 4 2- biases Cloud processes and dry deposition of SO 2 are sometimes correlated with SO 4 2- biases Dry deposition of HNO 3 is a large contributor and sometimes correlated with NO 3 - biases Dry deposition of HNO 3 is a large contributor and sometimes correlated with NO 3 - biases Aerosol processes of NH 3 are correlated with NH 4 + biases Aerosol processes of NH 3 are correlated with NH 4 + biases HNO 3 vs. NO 3 - NH 3 vs. NH 4 + ○: dry deposition □: aerosol processes ∆: cloud processes SO 2 vs. SO 4 2-

IPR Correlation Analysis: PM Precursors SO 2 emissions are largely correlated with SO 4 2- biases SO 2 emissions are largely correlated with SO 4 2- biases NO x and NH 3 emissions are sometimes correlated with NO 3 - and NH 4 + biases, respectively NO x and NH 3 emissions are sometimes correlated with NO 3 - and NH 4 + biases, respectively E SO2 vs. SO 4 2- E NH3 vs. NH 4 + E NOx vs. NO 3 -

Sensitivity Simulations Dry deposition of NO 3 - and NH 4 + precursors Dry deposition of NO 3 - and NH 4 + precursors  Reduce dry deposition velocity of HNO 3 and NH 3 by 50% (based on literature review and field study) (based on literature review and field study) Emissions of NH 3 and SO 2 Emissions of NH 3 and SO 2  Increase NH 3 emissions by 25.5% (based on the CMU Ammonia Model)  Reduce SO 2 emissions by 20% (CMAQ overpredicts SO 4 2- by 20%) Cloud processes of SO 4 2- Cloud processes of SO 4 2-  Adjust [H 2 O 2 ] aq using a dissolution efficiency factor of 0.9 (MM5 overestimates observed cloud fractions by roughly 10%) (MM5 overestimates observed cloud fractions by roughly 10%) Rate constant of the gas-phase SO 2 oxidation reaction by OH Rate constant of the gas-phase SO 2 oxidation reaction by OH  Replace k r, SAPRC99 by k r, CB05 (k r, SAPRC99 > k r, CB05, e.g., T = 288 K, P = 1013 Pa, k r, SAPRC99 = 1.12 k r, CB05 ) (k r, SAPRC99 > k r, CB05, e.g., T = 288 K, P = 1013 Pa, k r, SAPRC99 = 1.12 k r, CB05 )

Dry Deposition of PM Precursors Reducing v d, HNO3 can increase NO 3 - by 27-29%, and reduce | NMB | by 8- 94% Reducing v d, HNO3 can increase NO 3 - by 27-29%, and reduce | NMB | by 8- 94% Reducing v d, NH3 can increase NO 3 - by 18-21%, and reduce | NMB | by 7-65% Reducing v d, NH3 can increase NO 3 - by 18-21%, and reduce | NMB | by 7-65% Increasing v d, SO2 can decrease SO 4 2- by 2%, and reduce NMB by 13-16% Increasing v d, SO2 can decrease SO 4 2- by 2%, and reduce NMB by 13-16% CASTNet IMPROVESEARCH

NH 3 and SO 2 Emissions Increasing E NH3 can increase NO 3 - by 43-73%, and reduce | NMB | by % Increasing E NH3 can increase NO 3 - by 43-73%, and reduce | NMB | by % Reducing E SO2 can reduce SO 4 2- by 15-16%, and reduce | NMB | by % Reducing E SO2 can reduce SO 4 2- by 15-16%, and reduce | NMB | by % CASTNet IMPROVESEARCH

Gas & Aqueous-Phase Oxidation of SO 2 Using k r, CB05 for SO 2 (g) + OH can reduce SO 4 2- by 3% and reduce NMB by 15-85% Using k r, CB05 for SO 2 (g) + OH can reduce SO 4 2- by 3% and reduce NMB by 15-85% Reducing dissolved [H 2 O 2 ] by 10% has negligible effects on SO 4 2- formation for this episode Reducing dissolved [H 2 O 2 ] by 10% has negligible effects on SO 4 2- formation for this episode SEARCHIMPROVE CASTNet

Effects of Multiple Adjustments IMPROVE CASTNet SEARCH SO 4 2- is reduced by 16-19% and |NMB| is reduced by 79-97% SO 4 2- is reduced by 16-19% and |NMB| is reduced by 79-97% NO 3 - is increased by %; |NMB| is reduced by 44% and 73% at IMPROVE and SEARCH sites but increased by 50% at CASTNet sites NO 3 - is increased by %; |NMB| is reduced by 44% and 73% at IMPROVE and SEARCH sites but increased by 50% at CASTNet sites NH 4 + is increased by 1-8%, |NMB| is reduced by 16% at SEARCH sites but increased by 19% and 6% at IMPROVE and CASTNet sites NH 4 + is increased by 1-8%, |NMB| is reduced by 16% at SEARCH sites but increased by 19% and 6% at IMPROVE and CASTNet sites Multiple adjustments: × E NH3 ; 0.8 × E SO2 ; k r, CB05 ; 0.84 × v d, HNO3

Summary Aerosol processes are large contributors to the formation of SO 4 2-, NO 3 -, and NH 4 +, and correlated with the biases in SO 4 2- and NH 4 + ; cloud processes contribute largely to rural NH 4 +, but weakly correlated with secondary PM; dry deposition is a large contributor and correlated with SO 4 2- biases Aerosol processes are large contributors to the formation of SO 4 2-, NO 3 -, and NH 4 +, and correlated with the biases in SO 4 2- and NH 4 + ; cloud processes contribute largely to rural NH 4 +, but weakly correlated with secondary PM; dry deposition is a large contributor and correlated with SO 4 2- biases Emissions, aerosol/cloud processes, and dry deposition of PM precursors may contribute to the model biases of the secondary PM Emissions, aerosol/cloud processes, and dry deposition of PM precursors may contribute to the model biases of the secondary PM Adjusting the most influential processes/factors (i.e., E NH3, E SO2, v d, HNO3, and k SO2+OH ) improves the model overall performance in terms of SO 4 2-, NO 3 -, and NH 4 + Adjusting the most influential processes/factors (i.e., E NH3, E SO2, v d, HNO3, and k SO2+OH ) improves the model overall performance in terms of SO 4 2-, NO 3 -, and NH 4 +

Acknowledgments & Disclaimer NSF Award No. Atm Steve Howard and Alice Gilliland (NOAA/EPA), for providing the Fortran code for extracting data from CMAQ and the CASTNet, IMPROVE, and AIRS-AQS observational databases Members of Air Quality Forecasting Lab at NCSU The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.