Implementation and Testing of EQUISOLV II in the CMAQ Modeling System

Similar documents
Source apportionment of fine particulate matter over the Eastern U.S. Part I. Source sensitivity simulations using CMAQ with the Brute Force method

The Effect of Future Climate Change on Aerosols: Biogenic SOA and Inorganics

EVALUATION OF ORIGINAL AND IMPROVED VERSIONS OF CALPUFF USING THE 1995 SWWYTAF DATA BASE. Technical Report. Prepared by

Atmospheric Pollution Research

Comparing Modal and Sectional Approaches in Modeling Particulate Matter in Northern California

An assessment of the ability of three-dimensional air quality models with current thermodynamic equilibrium models to predict aerosol NO 3

Response to Referee 2

A comparative review of inorganic aerosol thermodynamic equilibrium modules: similarities, di!erences, and their likely causes

J3.3 IMPLEMENTATION AND TESTING OF A NEW AEROSOL MODULE IN WRF/CHEM

SUPPLEMENTARY MATERIAL

model to data comparison over Europe for year 2001

Review of the IMPROVE Equation for Estimating Ambient Light Extinction

Gas/aerosol partitioning: 1. A computationally efficient model

Thermodynamic characterization of Mexico City aerosol during MILAGRO 2006

Wet plus Dry Deposition of Atmospheric Hg in the SE U.S. NADP Technical Meeting Sept , 2007 Boulder, CO, USA

COPYRIGHT 2018 BY QI WANG

PRELIMINARY EXPERIENCES WITH THE MULTI-MODEL AIR QUALITY FORECASTING SYSTEM FOR NEW YORK STATE

Atmospheric Environment

A.Nenes 1,2,3,4, R.J. Weber 1, H.Guo 1, A.Russell 5 with contributions from A.Bougiatioti 1,3 and N.Mihalopoulos 3

Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory

1.7 APPLICATION AND EVALUATION OF MM5 FOR NORTH CAROLINA WITH A 4-KM HORIZONTAL GRID SPACING

Online Coupled Meteorology and Chemistry Models in the U.S.

Aerosol modeling with WRF/Chem

The Importance of Ammonia in Modeling Atmospheric Transport and Deposition of Air Pollution. Organization of Talk:

Progress on Application of Modal Aerosol Dynamics to CAM

Direct radiative forcing due to aerosols in Asia during March 2002

Change of aerosol and precipitation in the mid troposphere over central Japan caused by Miyake volcano effluents

Preliminary Experiences with the Multi Model Air Quality Forecasting System for New York State

Abstract. 1 Introduction

Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model

Ongoing EPA efforts to evaluate modeled NO y budgets. Heather Simon, Barron Henderson, Deborah Luecken, Kristen Foley

Determination of Gas/Particle Partitioning of Glyoxal and Other Bifunctional Species using the Annular Denuder-Filter Sampling Technique

A new inorganic atmospheric aerosol phase equilibrium model (UHAERO)

Statistical integration of disparate information for spatially-resolved PM exposure estimation

Continuous measurement of airborne particles and gases

Modelling of Saharan dust transport to the Southern Italy

Modeling atmospheric transport and fate of ammonia in North Carolina Part I: Evaluation of meteorological and chemical predictions

Plume-in-grid Modeling of Atmospheric Mercury Deposition in the United States

ISORROPIA v1.7 REFERENCE MANUAL

Know and Respond AQ Alert Service. Paul Willis SCOTTISH AIR QUALITY DATABASE AND WEBSITE ANNUAL SEMINAR Stirling 30 th March 2011

The aerosol distribution in Europe derived with CMAQ: comparison to near surface in situ and sunphotometer t measurements

Fine Particles: Why We Care

The Atmospheric Chemistry and Physics of Ammonia

Parameterization of the nitric acid effect on CCN activation

Testing MOSAIC aerosol scheme implemented in CESM and evaluation with observations

High aerosol acidity despite declining atmospheric sulfate concentrations:

Gas Particle Partitioning to OA

A new inorganic atmospheric aerosol phase equilibrium model (UHAERO)

A cloud microphysics model including trace gas condensation and sulfate chemistry

CALPUFF CHEMISTRY UPGRADE. Prepared by. Prakash Karamchandani, Shu-Yun Chen and Christian Seigneur

Characterization of atmospheric ammonia near Fort Worth, TX Part II. Impact of

Incorporating Space-borne Observations to Improve Biogenic Emission Estimates in Texas (Project )

Accuracy and Cost Considerations in Choosing a Chemical Mechanism for Operational Use in AQ Models

MULTI-MODEL AIR QUALITY FORECASTING OVER NEW YORK STATE FOR SUMMER 2008

Global Change and Air Pollution (EPA-STAR GCAP) Daniel J. Jacob

Putting Secondary Organic Aerosol in Global Models. Issues and Approaches

Chemistry 201. Working with K. NC State University. Lecture 11

Inconsistency of ammonium-sulfate aerosol ratios with thermodynamic models in the eastern US: a possible role of organic aerosol

NO X emissions, isoprene oxidation pathways, and implications for surface ozone in the Southeast United States

Environmental impact of atmospheric NH 3 emissions under present and future conditions in the eastern United States

FINAL REPORT An Assessment of Nitryl Chloride Formation Chemistry and its Importance in Ozone Non-attainment areas in Texas. AQRP Project

AIRQUEST Annual Report and State of the Model

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, D07S14, doi: /2004jd005038, 2005

The effects of dust emission on the trans- Pacific transport of Asian dust in the CESM

Implementation and evaluation of PM2.5 source contribution analysis in a photochemical model

Ammonia Emissions and Nitrogen Deposition in the United States and China

AQRP Monthly Technical Report

NOAA s Air Quality Forecasting Activities. Steve Fine NOAA Air Quality Program

Recent Ozone Modeling Results

3) Big Bend s Aerosol and Extinction Budgets during BRAVO

Comprehensive Analysis of Annual 2005/2008 Simulation of WRF/CMAQ over Southeast of England

WRAP 2002 Visibility Modeling: Emission, Meteorology Inputs and CMAQ Performance Evaluation

Determination of Water Activity in Ammonium Sulfate and Sulfuric Acid Mixtures Using Levitated Single Particles

SCICHEM: A Puff Model with Chemistry. Part 2: Ozone and Particulate Matter

INSITU project within AeroCom Phase III: Description and Model Output Request

Surface-Atmosphere Exchange of Ammonia in a Non-fertilized Grassland and its Implications for PM 2.5

Improving the understanding of the secondary inorganic aerosol distribution over the Netherlands

Ambient Ammonium Contribution to total Nitrogen Deposition

J4.2 ASSESSMENT OF PM TRANSPORT PATTERNS USING ADVANCED CLUSTERING METHODS AND SIMULATIONS AROUND THE SAN FRANCISCO BAY AREA, CA 3.

Particulate Matter Modeling: Including Nanoparticles

Rapid formation and evolution of an extreme haze episode in

THESIS AN INVESTIGATION OF AMMONIA AND INORGANIC PARTICULATE MATTER IN CALIFORNIA DURING THE CALNEX CAMPAIGN. Submitted by. Luke D.

A Solution to the Problem of Nonequilibrium Acid/Base Gas-Particle Transfer at Long Time Step

6 th INTERNATIONAL WORKSHOP ON SAND/DUSTSTORMS AND ASSOCIATED DUSTFALL 7-9 September 2011, Athens, Greece

Responsibilities of Harvard Atmospheric Chemistry Modeling Group

Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States

Lab 4 Major Anions In Atmospheric Aerosol Particles

Presented at the NADP Technical Meeting and Scientific Symposium October 16, 2008

ACID, BASE, AND ph STUDYGUIDE

Dominant aerosol processes during high-pollution episodes over Greater Tokyo

CHAPTER 8. AEROSOLS 8.1 SOURCES AND SINKS OF AEROSOLS

Current and Future Impacts of Wildfires on PM 2.5, Health, and Policy in the Rocky Mountains

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D7, 8414, doi: /2001jd001592, 2003

Reactive Nitrogen Monitoring

Recent Ozone Modeling Results

Examining the impact of heterogeneous nitryl chloride production on air quality across the United States

1 Executive summary. 2 Research activities and results

UNIT IV PPT #3 Ka and Kb KEY.notebook. November 23, WHAT IS Ka? UNIT IV. CALCULATIONS USING Ka. WHAT IS Ka? Nov 10 9:42 PM.

ISORROPIA v2.1 REFERENCE MANUAL

Impacts of Air-sea Interactions on Regional Air Quality Predictions Using a Coupled Atmosphere-ocean Model in Southeastern U.S.

Transcription:

Implementation and Testing of EQULV II in the CMAQ Modeling System Yang Zhang North Carolina State University Mark Z. Jacobson Stanford University, Stanford, CA CMAQ, September 26-29, 25

Acknowledgments NOAA: Project Sponsor Ken Schere, Prakash Bhave, and Shaocai Yu, U.S. NOAA/EPA, for providing all input files for setting up the SOS episode and Fortran script for statistical calculations. Alice Gilliland and Steve Howard, U.S. NOAA/EPA, for providing Fortran code for extracting data from observational databases and CMAQ. Eric Edgerton and Rick Saylor, ARA, Inc., for providing the SEARCH database. Ping Liu, Kai Wang, and Jianlin Hu, NCSU, for help in post-processing model results.

Presentation Outline Background and Objectives Box Model Comparison -D Testing and Evaluation Episode, Model Configuration, Available Measurements Preliminary Results Summary and Future Work

Background and Objectives Background Thermodynamic equilibrium affects gas-particle partitioning and PM formation (e.g., nitrate, ammonium, chloride); Uncertainties in experimental data (e.g., activity coefficients, DRH); Numerical difficulties in simulating a mixed-phase system; Existing modules are computationally efficient but numerically less accurate and vice versa. Objectives Improve EQULV II in terms of numerical accuracy and computational speed; Incorporate EQULV II into CMAQ and CMAQ-MADRID as an alternative thermodynamic module to RRPIA; Apply and evaluate CMAQ-EQULV II s performance for the 12-28 June 1999 episode.

Main Features of RROPIA and EQULV II RROPIA EQULV II System solved -Na-NO -SO -Cl -Na-Ca-Mg-K-NO -SO -Cl-CO Equilibrium reaction/ chemical species Solution method 15 reactions 21 species ( gases, 9 liquids, 9 solids) Iterative bisection Iterative bisection-newton for H Chemical regime Subdomains (sulfate very rich/rich/poor) Full domain Activity coefficient Binary - Kusik and Meissner, 1978 Multi-component Bromley, 197 Precalculated lookup table Temperaturedependence Solid treatment Equilibrium constants, DRHs (1) Solid formed when RH < MDRH (metastable) (2) No solid formation for all RHs (liquid only) Size-resolved equilibrium No Yes 18 reactions 26 species ( gases, 1 liquids, 9 solids) Mass Flux Iteration (MFI) and Analytical Equilibrium Iteration (AEI) Binary - Hamer and Wu, 1972; Goldberg, 1981; Bassett and Seinfeld, 198; Filippov et al., 1985; and Pitzer, 1991 Multi-component - Bromley method, 197 Precalculated lookup tables Equilibrium constants, DRHs, activity coefficients, solute and water activity coefficients down to 19 K for several species (1) Solid formed when RH < CRH (meta-stable) (2) Solid formed when RH < DRH (stable) () No solid formation for all RHs (liquid only) -D implementation Single-cell only (1)Single-cell (2)Multiple-cell (vectorization) Reference Nenes et al., 1998, 1999 Jacobson et al., 1996; Jacobson, 1999 a & b

Box Model Test Conditions [H2SO ], μg m- 5 2 T/TSO 2-16.5 - TNO/TSO 1.2-12. - TNaCl/TSO, 2-8,.5-2 Temperature, K 278, 288, 298, 8 278, 288, 298, 8 RH, % 1-1 1-1 Solid treatment Iteration number used in EQULV II, N Solid formed when RH < CRH; No solid for all RHs 1, 1, 1, 1 Benchmark: 1 Total number of cases 112 112 Solid formed when RH < CRH; No solid for all RHs 1, 1, 1, 1 Benchmark: 1 Total number of simulations (112 112 N) 2 solid treatments = 11,2 (112 112 N) 2 solid treatments = 11,2

Box Model Results: HNO (g) RROPIA, V1.5 EQULV II (NROT = 1) TSO = 5 TSO = 2 2 2 1 2 2 RROPIA vs. Exact HNO (g) 1 2 2 vs. Exact HNO (g) RROPIA, V1.5 2 2 1 RROPIA vs. Exact HNO (g) 1 2 2 vs. Exact 1 1 N=1 N=1 1 2 2 1 2 2 EQULV II (NROT = 1) 2 2 HNO (g) 9 5 1 - -7-11 2 1 NMBs for HNO(g) at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 ug m - RROPIA, v1.5 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for HNO(g) at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 ug m - EQULV II (NROT = 1) vs. Exact HNO (g) 2 2 1 N=1E 1 2 2 EQULV II (NROT = 1) vs. Exact HNO (g) 2 2 1 N=1E 1 2 2-1 -2 - - 278 288 298 8 278-8 T, K RROPIA, v1.5

Box Model Results: NO - TSO = 5 TSO = 2 RROPIA, V1.5 2 2 1 RROPIA vs. Exact PNO 1 2 2 RROPIA, V1.5 2 2 1 RROPIA vs. Exact PNO 1 2 2 5 5 25 15 5-5 NMBs for NO at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - RROPIA, v1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 2 1 vs. Exact PNO 1 2 2 2 2 1 N=1 vs. Exact N=1E 1 2 2 PNO EQULV II (NROT = 1) EQULV II (NROT = 1) 2 2 2 2 1 vs. Exact N=1 1 PNO 1 2 2 vs. Exact N=1E PNO 1 2 2-15 25 2 15 1 5-5 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for NO at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 278 288 298 8 278-8 T, K RROPIA, v1.5

Box Model Results: (g) RROPIA, V1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 2 1 TSO = 5 TSO = 2 RROPIA vs. Exact (g) 1 2 vs. Exact (g) 1 2 2 1 vs. Exact (g) 1 2 RROPIA, V1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 RROPIA vs. Exact (g) 1 2 2 1 2 1 vs. Exact (g) N=1 N=1 N=1E 1 2 vs. Exact (g) N=1E 1 2 15 1 11 9 7 5 1-1 - 8 6 2-2 NMBs for (g) at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - RROPIA, v1.5 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for (g) at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 278 288 298 8 278-8 T, K RROPIA, v1.5

Box Model Results: RROPIA, V1.5 2 1 TSO = 5 TSO = 2 RROPIA vs. Exact P 1 2 RROPIA, V1.5 2 1 RROPIA vs. Exact P 1 2 5 1-1 - NMBs for at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - RROPIA, v1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 vs. Exact P 1 2 2 1 vs. Exact P 1 2 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 2 1 vs. Exact P N=1 N=1 N=1E 1 2 vs. Exact P N=1E 1 2-5 2-2 - 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for at T = 278-8 K, RH =1-5%, and [TSO]=5, 2 g m - 278 288 298 8 278-8 T, K RROPIA, v1.5

Box Model Results: HCl(g) RROPIA, V1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 2 1 TSO = 5 TSO = 2 RROPIA vs. Exact HCl(g) 1 2 vs. Exact HCl(g) 1 2 2 1 vs. Exact HCl(g) 1 2 RROPIA, V1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 2 1 2 1 RROPIA vs. Exact HCl(g) 1 2 vs. Exact HCl(g) 1 2 vs. Exact HCl(g) N=1 N=1 N=1E N=1E 1 2 21 16 11 6 1 - -9-1 12 8 - NMBs for HCl(g) at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % RROPIA, v1.5 NMBs for HCl(g) at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 278 288 298 8 278-8 T, K RROPIA, v1.5

Box Model Results: Cl - RROPIA, V1.5 2 1 TSO = 5 TSO = 2 RROPIA vs. Exact PCl 1 2 RROPIA, V1.5 2 1 RROPIA vs. Exact PCl 1 2 1 9 5 1 - NMBs for Cl at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - RROPIA, v1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 2 1 vs. Exact PCl 1 2 vs. Exact PCl 1 2 EQULV II (NROT = 1) EQULV II (NROT = 1) 2 1 2 1 vs. Exact PCl N=1 N=1 N=1E 1 2 vs. Exact PCl N=1E 1 2-7 6 - -6 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for Cl at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - RROPIA, v1.5 278 288 298 8 278-8 T, K

EQULV II Differences in Equilibrium Reactions 1. 2.. ( g) ( g) Cl( s) H HNO ( g) Cl NO Gives lower HNO (g) / ; higher NO - / (g)/cl -. NO ( s) NO 5. HNO ( g) HNO ( l) 6. H 2 SO ( l) RROPIA H HSO 7. 8. 9. ( g) H NO 2 Cl( s) O ( s) ( g) HCl( g) OH ( g) HNO ( g) Gives higher HNO (g)/ / HCl(g); lower NO - / (g)

Box Model Results: H RROPIA, V1.5.8.6..2 TSO = 5 TSO = 2 RROPIA vs. Exact H.2..6.8 RROPIA, V1.5.8.6..2 RROPIA vs. Exact H.2..6.8 NMBs for H at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 99 99 299 199 99 2.6E16 RROPIA, v1.5 EQULV II (NROT = 1) vs. Exact.8 N=1.6..2 H.2..6.8 EQULV II (NROT = 1).8.6..2 vs. Exact N=1 H.2..6.8-1 2 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for H at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 1 EQULV II (NROT = 1) vs. Exact.8 N=1E.6..2 H.2..6.8 EQULV II (NROT = 1).8.6..2 vs. Exact N=1E.2..6.8 H -1-2 2.2E1 278 288 298 8 278-8 T, K RROPIA, v1.5 7.E1

Predicted [H ] at T = 278 K, TSO = 2 μg m - ug m - (k) T/TSO=1., TNO/TSO=1, TNaCl/TSO=.5 2.E 1.5E 1.E E E EQULV II, NROT1 EQULV II, NROT1 RROPIA_v1.5 1 2 5 6 7 8 9 1 Relative Humidity, % ug m - (l)t/tso=1.5, TNO/TSO=1, TNaCl/TSO=.5 1.E1 8.E12 6.E12.E12 2.E12.E EQULV II, NROT1 EQULV II, NROT1 RROPIA_v1.5 1 2 5 6 7 8 9 1 Relative Humidity, % ug m - 1.E1 8.E12 6.E12.E12 2.E12.E (o) T/TSO=1.5, TNO/TSO=., TNaCl/TSO=.5 EQULV II, NROT1 EQULV II, NROT1 RROPIA_v1.5 2 6 8 1 Relative Humidity, % ug m - 1.E1 8.E12 6.E12.E12 2.E12.E (q)t/tso=1.5, TNO/TSO=, TNaCl/TSO=.5 EQULV II, NROT1 EQULV II, NROT1 RROPIA_v1.5 2 6 8 1 Relative Humidity, %

Box Model Results: H 2 O TSO = 5 TSO = 2 RROPIA, V1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 8.E5 6.E5.E5 2.E5 RROPIA vs. Exact.E.E 2.E5.E5 6.E5 8.E5 8.E5 6.E5.E5 2.E5 8.E5 6.E5.E5 2.E5 vs. Exact N=1 vs. Exact PH2O PH2O.E.E 2.E5.E5 6.E5 8.E5 N=1E.E.E 2.E5.E5 6.E5 8.E5 PH2O RROPIA, V1.5 EQULV II (NROT = 1) EQULV II (NROT = 1) 8.E5 6.E5.E5 2.E5 RROPIA vs. Exact PH2O.E.E 2.E5.E5 6.E5 8.E5 8.E5 6.E5.E5 2.E5 vs. Exact PH2O.E.E 2.E5.E5 6.E5 8.E5 8.E5 6.E5.E5 2.E5 N=1 vs. Exact N=1E PH2O.E.E 2.E5.E5 6.E5 8.E5 6 5 2 1-1 598 98 98 298 198 98-2 NMBs for H2O at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - RROPIA, v1.5 1_5 1_2 5 6 7 8_95 99 1 1_1 RH, % NMBs for H2O at T = 278-8 K, RH =1-1%, and [TSO]=5, 2 g m - 278 288 298 8 278-8 T, K RROPIA, v1.5

Box Model Results: Predictions of NO - Solid Liquid vs. Liquid only SolidLiquid TSO = 5 TSO = 2 Liquid SolidLiquid Liquid RROPIA vs. Exact RROPIA vs. Exact RROPIA vs. Exact RROPIA vs. Exact RROPIA, V1.5 2 2 1 PNO 1 2 2 RROPIA, V1.5 2 2 1 PNO 1 2 2 RROPIA, V1.5 2 2 1 PNO 1 2 2 RROPIA, V1.5 2 2 1 PNO 1 15. 2 2 EQULV II (NROT = 1) vs. Exact 2 2 1 PNO 1 2 2 EQULV II (NROT = 1) vs. Exact PNO 2 2 1 1 2 2 EQULV II (NROT = 1) vs. Exact 2 2 1 PNO 1 2 2 Top: RROPIA vs. Exact Bottom: EQULV II, NROT = 1 vs. Exact EQULV II (NROT = 1) vs. Exact PNO 2 2 1 1 15. 2 2

Box Model: Timing Test 28 Cases with T=298 K, RH=1-1%, TSO= 2 μg m - Iteration Numbers MAXIT=1 NSWEEPI= RROPIA EQULV II CPU Time, s NROT CPU Time, s (Single-cell) CPU Time, s (9-cell) 5.77 1-5 1 2.97 1-5 1. 1-5 5.6 1-5 1.5 1-5 5 6.9 1-5 2. 1-5 1 8.6 1-5. 1-5 2 1. 1 -.51 1-5 1.21 1 -.1 1-5 1.5 1 -.9 1-5 5 1.51 1-1 -5 1 2.2 1-6.9 1-5 1 6.7 1-2.5 1-1. 1-1.89 1 - CPU Time, sec 1.E-2 1.E- 1.E- 1.E-5 CPU time vs. Iteration number RROPIA EQULV II (1-cell) EQULV II (9-cell) 1 1 1 1 1 Total Number of Iterations used in EQULV II

-D Test with CMAQ for the June 1999 SOS Episode Simulation Period 12-28 June 1999 Grid Resolution 2 km, 178 12 grid cells, 19 layers Meteorology MM5/FDDA Emissions EPA s NEI 99 inventory Initial/Boundary cond. 2-day spinup, CMAQ default ICs/BCs CMAQ V., SAPRC99/EBI or ROS, modified to include ternary nucleation Datasets for Evaluation CASTNet: hourly O, weekly SO 2-, NO -, at 8 sites IMPROVE: -day SO 2-, NO -,, EC, OC; 2-hr PM 2.5 at 15 sites STN: -day SO 2-, NO -,, EC, OC, PM 2.5 at 19 sites AIRS: hourly O at 1161 sites SEARCH: Hourly O ; 2-hr PM 2.5 at JST, YRK, BHM, CTR, OLF, PNS, OAK, GFP

Observed vs. Predicted PM 2.5 at SEARCH Sites 6 Observation RROPIA JST -Jefferson Street, Atlanta, GA 6 Observation RROPIA YRK - Yorkville, GA PM 2.5 (ug m - ) 2 PM2.5 (ug m - ) 2 6/1/99 6/16/99 6/18/99 6/2/99 6/22/99 6/2/99 6/26/99 6/28/99 Local Time (EDT) 6/1/99 6/16/99 6/18/99 6/2/99 6/22/99 6/2/99 6/26/99 6/28/99 Local Time (EDT) 6 Observation RROPIA CTR - Centreville, AL 6 Observation RROPIA OAK - Oak Grove, MS PM 2.5 (ug m - ) 2 PM 2.5 (ug m - ) 2 6/1/99 6/16/99 6/18/99 6/2/99 6/22/99 6/2/99 6/26/99 6/28/99 Local Time (CDT) 6/1/99 6/16/99 6/18/99 6/2/99 6/22/99 6/2/99 6/26/99 6/28/99 Local Time (CDT)

Observed vs. Predicted PM 2.5 and Its Composition at JST and YRK, GA 2 hr-avg. PM2.5 at JST, June 17, 1999 2 hr-avg. PM2.5 at YRK, June 17, 1999 Mass conc8 ug m - 5 25 2 15 1 NO 1 observation RROPIA EQULV II, N=2 EC 1 Mass conc8 ug m - 5 25 2 15 1 NO 1 observation RROPIA EQULV II, N=2 EC 1 5 5 PM2.5 NO- SO= EC OM PM Species PM2.5 NO- SO= EC OM PM Species Mass conc8 ug m - 5 25 2 15 1 2 hr-avg. PM2.5 at JST,June 18, 1999 NO 1 observation RROPIA EQULV II, N=2 EC 1 Mass conc8 ug m - 5 25 2 15 1 2 hr-avg. PM2.5 at YRK, June 18, 1999 NO 1 observation RROPIA EQULV II, N=2 EC 1 5 5 PM2.5 NO- SO= EC OM PM Species PM2.5 NO- SO= EC OM PM Species

- Observed vs. Predicted HNO, NO - and PM 2.5 Composition at YRK, GA HNO (ppb) 15 12 9 6 YRK - Yorkville, GA observation RROPIA Mass conc ug m - 5 2 1 2 hr-avg. PM2.5 at YRK, June 21, 1999 NO 1 observation RROPIA EQULV II, N=2 EC 1 6/1/99 6/16/99 6/18/99 6/2/99 6/22/99 6/2/99 6/26/99 6/28/99 Local Time (EDT) PM2.5 NO- SO= EC OM PM Species 6 YRK - Yorkville, GA 2 hr-avg. PM2.5 at YRK, June 22, 1999 (ug m - ) NO 5 2 1 RROPIA EQUSOLV II, N=1 Mass conc ug m - 5 2 1 NO 1 observation RROPIA EQULV II, N=2 EC 1 6/1/99 6/16/99 6/18/99 6/2/99 6/22/99 6/2/99 6/26/99 6/28/99 Local Time (EDT) PM2.5 NO- SO= EC OM PM Species

RROPIA vs. EQULV: Performance Statistics Normalized Mean Error and Bias (Fraction) Normalized Mean Error Factor and Bias Factor (Fraction) Module CASTNet IMPROVE SEARCH NME % NMB % NME % NMB % NME% NMB % PM 2.5 EQUI 8 9-26 -29 2 2 - -5 Sulfate 2.5 EQUI 29 1 15 9 9 18 19 5 5 22 2 Nitrate 2.5 EQUI 89 75-16 -2 96 89-2 -9 76 67-71 -51 Ammonium 2.5 EQUI 28 1 5-7 6 2-22 7 6-2 -6 BC 2.5 EQUI 7-55 -5 7-5 5-51 OC 2.5 EQUI -25-25 1 1 - -

Daily Average Changes in HNO /NO - and / Due to Major Processes 6 2-2 - -6 Daily-average change in HNO 6/12 6/1 6/1 6/15 6/16 6/17 6/18 6/19 6/2 6/21 6/22 6/2 6/2 6/25 6/26 6/27 6/28 Time 1..5 -.5 Daily-average change in NO- 1 Daily-average change in 2.5 2. 1.5 1..5 -.5-1. -1.5-2. -2.5 Daily-average change in Change in conc. (ug m - ) 5-5 Change in mixing ratios (ppm) Change in HNO mixing ratios (ppm) Change in NO- conc. (ug m - ) -1. 6/12 6/1 6/1 6/15 6/16 6/17 6/18 6/19 6/2 6/21 6/22 6/2 6/2 6/25 6/26 6/27 6/28 Time -1 6/12 6/1 6/1 6/15 6/16 6/17 6/18 6/19 6/2 6/21 6/22 6/2 6/2 6/25 6/26 6/27 6/28 6/12 6/1 6/1 6/15 6/16 6/17 6/18 6/19 6/2 6/21 6/22 6/2 6/2 6/25 6/26 6/27 6/28 Time Time Horizontal Transport Vertical Transport Emissions Dry Deposition Gas Chemistry PM Processes Aqueous Processes Mass Balance Adjustment Horizontal Transport Vertical Transport Emissions Dry Deposition PM Processes Aqueous Processes Mass Balance Adjustment

Summary EQULV II (N 1) gives lower biases (< 1%) for HNO, NO -, HCl, Cl -, SO 2-, H 2 O, H, and (except for RH 99) and moderate biases (~27%) for. RROPIA gives lower biases for HNO, NO -, SO 2-, H 2 O (except RH = 1), H (except RH 5), and ; moderate biases (~-6%) for HCl, Cl - and abnormally high H 2 O and H biases (> E-2.6E1%) under some conditions. Larger biases occur for RH or 99 for most species. A thermodynamically metastable system (solid liquid) High sulfate conditions: RROPIA overestimates HNO (g) and underestimates NO - ; EQULV II (N=1) gives opposite trends. A supersaturated system (no solid) RROPIA gives some abnormally high or low HNO (g) and NO - ; EQULV II with N 1 gives slightly worse results than the metastable system (solidliquid). CMAQ with EQULV II and RROPIA performs similarly except for NO - and. EQULV II (solid and liquid) gives better NO - at SEARCH sites, RROPIA (no solid) gives slightly better NO - for IMPROVE and CASTnet sites, but it gives [HNO ] much higher than the observed one at some sites. Differences in NO - and predictions can be explained by different reactions and the high NO - predicted by RROPIA with liquid only. Thermodynamics only may not explain the observed gas/particle partitioning. Future work include multiple-cell implementation, size-resolved equilibrium, and application of CMAQ-EQULV II for regions with crustal species and sea-salt.

2024 © sciencedocbox.com Privacy Policy | Terms of Service | Feedback