synergisms in the development of the cmaq and camx pm/ozone models ralph e. morris, greg yarwood...
TRANSCRIPT
Synergisms in the Development of the CMAQ and CAMx PM/Ozone Models
Ralph E. Morris, Greg Yarwood
Chris Emery, Bonyoung Koo
ENVIRON International Corporation
101 Rowland Way
Novato, CA
Presented at
CMAS Models-3 User’s Workshop
October 27-29, 2003
Research Triangle Park, NCPresents:slides/
Introduction
Numerous challenges in particulate matter modeling:> Multiple Components
• SO4, NO3, SOA, POC, EC, Crustal, Coarse, Other
> Multiple Processes• Gas-, Aqueous-. Heterogeneous-, Aerosol-Phase Chemistry• Rainout/washout, dry deposition of Gases and Particles• Advections and Diffusion• Clouds, Canopy, Terrain, etc.
> Numerous Uncertainties• Chemistry (e.g., nitrate, SOA, aromatic, etc.), PM Size
Distribution, Meteorology, Emissions, Measurements
Introduction
> CMAS Workshop Good Forum to Discuss Challenges, Approaches and Potential Solutions for Improving PM Modeling
> CMAS Workshop Theme Emphasizes the Common Challenges of PM Modeling
• One Atmosphere• One Community• One Model
One Atmosphere
One Community
One Model
CMAQ
One Model?
CMAQ
MM5 RAMS WRF
One Model??
CMAQ
MM5 RAMS WRF
SMOKE EMS EPS OPEM
One Model???
CMAQ
MM5 RAMS WRF
SMOKE EMS EPS OPEM
MOBILE NONROAD EDMS EMFAC AP42
One Model????
CMAQ
MM5 RAMS WRF
SMOKE EMS EPS OPEM
MOBILE NONROAD EDMS EMFAC AP42
IMPROVE CASTNET STN AQS/AIRS NADP SuperSites
Multi-Model Intercomparisons> Intercomparing models and alternative formulations is an integral
part of model development> Photochemical grid model development has taught us that much
more can be learned from comparing different models with different formulations – this is even more true for PM models due to more uncertainties in processes
Early 1980s UAM vs. CIT
~ 1990 UAM vs. CALGRID
Early 1990s UAM-V vs. UAM vs. SAQM
Mid 1990s UAM-V vs. CAMx vs. MAQSIP
Early 2000s CMAQ vs. CAMx
Early CMAQ vs. CAMx Comparisons for Ozone
• 1991 Lake Michigan Ozone Study (LMOS) Databases> Tesche ands co-workers (2001) (available at www.crcao.com as
CRC Project A-25)> MM5 and RAMS Meteorology> No one model performing sufficiently better than another> CMAQ and CAMx using MM5 more similar than CAMx using
RAMS> Similar ozone responses to VOC/NOx controls> CMAQ using QSSA and SMVGEAR chemistry solvers takes ~5
and ~8 times longer to run than CAMx
EPA implements faster Hertel/MEBI chemistry solver in CMAQ
Early CMAQ vs. CAMx Comparisons for Ozone
• July 1995 NARSTO-Northeast Ozone Episode> Morris and co-workers (available at www.crcao.com as CRC
Project A-24)> MM5 and RAMS Meteorology
> Layer 1 KV mixing issues
EPA implements 1.0 m2/s minimum KV in MCIP, land use specific lower layers minimum KV used with CAMx
> QSSA chemistry solver accuracy and stability issues
Hertel/MEBI solver implemented in CMAQ> Smolarkiewicz advection solver is overly diffusive.
Smolarkiewicz removed from CAMx (not in CMAQ)
Early CMAQ vs. CAMx Comparisons for Ozone
• July 1995 NARSTO-Northeast Ozone Episode> SAPRC97 chemistry more reactive than CB-IV
Both CMAQ and CAMx implement SAPRC99 chemistry > Different horizontal diffusion (KH) formulations in CMAQ and
CAMx• CMAQ inversely and CAMx proportional to grid spacing
Area of future research and sensitivity tests (e.g., spawned BRAVO sensitivity test)
> MM5 convective activity potentially can produce modeling artifacts MM5 interface an area of continued research for CMAQ and
CAMx
Emerging PM Model Development Issues
• Aqueous-Phase Chemistry> High pH dependency of aqueous-phase O3+SO2 reaction
> Coarse and fine droplets may have different buffering and different pH effects on aqueous-phase sulfate formation
> Test this effect using PMCAMx sectional PM model that incorporates CMU VSRM aqueous-phase chemistry module
• October 17-19, 1995 Southern California PM episode• Two aqueous-phase chemistry modules used
– CMU 1-section bulk module– CMU 2-section VSRM module
Southern California Modeling Domain
VSRM (Multi-Section) vs. Bulk Aqueous ChemistryPercent Increase in Sulfate (%)
By second day, VRSM estimates ~15-30% more sulfate across the SoCAB with > 50% increase offshore and around Long Beach
VSRM (Multi-Section) vs. Bulk Aqueous Chemistry
PM10 Sulfate - Long Beach - Oct. 18, 1995
0
5
10
15
20
25
30
1 6 11 16 21
Time (hr)
Sulfa
te (
m g/m
3) VSRM
Bulk
No Aqueous-PhaseChem.
> 6 mg/m3 difference
> 16% difference in daily avg
PM10 Sulfate - (18,15) - Oct. 18, 1995
0
5
10
15
20
1 6 11 16 21
Time (hr)Su
lfate
(m g
/m3
) > 10 mg/m3 difference
> 31% difference in daily avg
VRSM can form significantly more sulfate than the bulk 1-section aqueous-phase chemistry module
Emerging PM Model Development Issues• Conclusions on Bulk vs. Multi-Section Aqueous-Phase
Chemistry Tests> Multi-section aqueous-phase chemistry module made significantly
more sulfate in the Southern California test case> Due to low sulfate in Southern California, differences were not
significant enough to appreciably affect sulfate model performance> Need further testing for eastern US where higher sulfate
concentrations occur> Merging of CAMx4 and PMCAMx models provides platform for
testing RADM and CMU 1-section bulk aqueous-phase chemistry modules against the CMU VSRM multi-section module
> CMU VSR multi-section module requires ~5 times more CPU time than CMU 1-section module (Further optimization warranted)
Emerging PM Model Development Issues• Aerosol Thermodynamics Gas/Particle Partitioning
> Gas/Particle equilibrium usually assumed> ISORROPIA equilibrium scheme widely used
• Fast and reliable• CMAQ, CAMx, URM, etc.
> Equilibrium assumption may not always be correct, especially for coarse particles
> PMCAMx sectional PM model includes three options for Gas/Particle partitioning:
• Equilibrium (ISORROPIA)• Dynamic (MADM)• Hybrid (equilibrium for fine/dynamic for coarse particles)
> Testing using October 1995 Southern California Database
Equilibrium vs. Dynamic vs. Hybrid
0 5 10 150
2
4
6
8
10
12
14
16
Measured concentration (ug/m)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
0 50 100 1500
50
100
150
Measured concentration (mg/m3)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
+30%
0 5 100
2
4
6
8
10
12
14
Measured concentration (ug/m)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
0 20 40 60 80 1000
10
20
30
40
50
60
70
80
90
100
Measured concentration (mg/m3)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
PM2.5 SO4 PM10 SO4
PM2.5 Mass PM10 Mass
EQUIHYBRMADM
Equilibrium vs. Dynamic vs. Hybrid
0 5 10 150
2
4
6
8
10
12
14
16
18
Measured concentration (mg/m3)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
0 10 20 30 40 500
10
20
30
40
50
Measured concentration (ug/m3)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
PM2.5 NO3
0 10 20 30 40 500
10
20
30
40
50
Pre
dict
ed c
once
ntra
tion
(mg/
m3)
0 5 10 150
2
4
6
8
10
12
14
16
Measured concentration (mg/m3)
Pre
dic
ted
con
cen
tra
tion
(mg
/m3)
PM10 NO3
PM2.5 NH4 PM10 NH4
+30%
EQUIHYBRMADM
Emerging PM Model Development Issues
• Conclusions on use of equilibrium approach for gas/particle partitioning> For Southern California application:
• dynamic and hybrid modules produce nearly identical results• most of the time equilibrium approach produces results very
close to dynamic and hybrid approaches, but differences as high as 30% did occur
• dynamic (MADM) approach requires approximately 10 times the CPU time as equilibrium approach
> Further tests of equilibrium assumption warranted> Given sufficient accuracy, uncertainties and computational
requirements, equilibrium approach appears adequate for annual modeling
Emerging PM Model Development Issues
• Particle Size Distribution> Different representations of particle size distribution in difference
models• CMAQ modal approach using 3 modes and assumes all
secondary PM is fine
• CAMx4, REMSAD and MADRID1 assume fine and coarse PM (all secondary PM is fine)
• PMCAMx, CMAQ-AIM and MADRID2 are fully sectional models where PM10 is divided up into N sections (e.g., N=10)
Emerging PM Model Development Issues
• Particle Size Distribution> Testing of assumptions of particle size distribution using
new merged CAMx4/PMCAMX code• M4 = CAMx4 2 section plus RADM aqueous• EQUI = N sections equilibrium + VRSM aqueous• MADM = 10 sections dynamic + VRSM aqueous• RADM/EQ = 10 sections equil. + RADM aqueous• RADM/EQ4 = 4 sections equil. + RADM aqueous
> October 17-18, 1995 Southern California Episode
M4
EQUI
24-Hour Sulfate (g/m3)
October 18, 1995
• M4 peak SO4 39 g/m3
• EQUI peak SO4 51 g/m3
• ~ Long Beach Area
• Differences due to more sulfate production in CMU VRSM than RADM aqueous-phase chemistry
• Further downwind (Riverside) M4 produces more sulfate than EQUI
24-Hour Nitrate (g/m3)
October 18, 1995
• M4 peak NO3 83 g/m3
• EQUI peak NO3 54 g/m3
• Observed NO3 peak at Riverside ~40 g/m3
• Differences partly due to assuming all nitrate is fine vs. PM nitrate represented by 10 size sections (EQUI)
• Differences in M4 RADM and EQU VSRM also contribute
M4
EQUI
24-Hour Nitrate (g/m3)
October 18, 1995
• M4 peak NO3 83 g/m3
• EQUI peak NO3 54 g/m3
• EQUI 10-Section grows PM NO3 into coarser sections where it dry deposits faster than M4 NO3 that is assumed to be fine
• Result is less NO3 in downwind Riverside area that agrees better with observations
M4
M4 - EQUI
0
10
20
30
40
50
60
70
80
90
0.01 0.1 1 10
M4
EQUI
MADM
RADM/EQ
Diameter [mm]
dM
/dL
og
(D)
[mg
/m3]
Sensitivity to Number of Size Sections (10 vs. 4) @ (34,16)
0
20
40
60
80
100
0.01 0.1 1 10
0
2
4
6
8
10
0.01 0.1 1 10
0
10
20
30
40
50
0.01 0.1 1 10
0
5
10
15
20
0.01 0.1 1 10
RADM/EQ4
RADM/EQ
Diameter [mm]
dM/d
Log(
D)
[mg/
m3]
Computational Efficiency Model ConfigurationsCPU hours per simulation day
(based on Athlon 1600 CPU)
0.1
1
10
100
M4 RADM/EQ4 RADM/EQ EQUI MADM
0.42 0.52
1.2
5.8
63
Emerging PM Model Development Issues
• Nighttime Nitrate Chemistry> September 2003 CMAQ release
• Zero N2O5+H2O gas-phase reaction rate
• 0.02 and 0.002 probability for heterogeneous rate> April 2003 CAMx4 release
• Keep gas-phase N2O5+H2O reaction rate
– German smog tests provide upper bound rate, but is real gas-phase reaction
• Current research suggests part of overestimation tendency may be due in part to assuming all nitrate is fine
> More updates in future
Emerging PM Model Development Issues• Interface with Meteorological Model (MM5/RAMS)
> Mass Conservations and Mass Consistency> Clouds and Precipitation (resolved and unresolved)> Instantaneous meteorological data (convective down bursts)> MM5 PBL heights – what to do when collapsed from clouds/snow
Conclusions on Model Development Synergisms• CMAQ and CAMx offer two completely different
platforms to test alternative PM modules and formulations> provides an “independent” test of the assumptions> identifies potential for introducing compensatory errors
• Numerous common challenges in PM modeling, the more ways of looking at the problem the better> nitrate formation, size sections and deposition> aqueous-phase chemistry> PM size distribution> meteorology> computational efficiency
Toola to Facilitate Model Intercomparisons
• MM5 Interface Software> MCIP 2.2> MM5CAMx + kvpatch
• CMAQ-to-CAMx conversion software> Emissions> IC/BC
• CAMx-to-CMAQ conversion software> Emissions> IC/BC
Current CMAQ/CAMx Comparisons
• 1996 Western USA> WRAP and CRC
• Jan 2002, July 2001, July 1991Eastern USA> VISTAS
• August – September 1997 Southern CalEfornia> CRC
• Midwest US/Supersites> MRPO