Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and

Regional Air Quality Models

Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and

Regional Air Quality Models

Institute for Multidimensional Air Quality StudiesInstitute for Multidimensional Air Quality Studies(IMAQS)(IMAQS)

University University ofof Houston Houston

Institute for Multidimensional Air Quality StudiesInstitute for Multidimensional Air Quality Studies(IMAQS)(IMAQS)

University University ofof Houston Houston

Daewon W. Byun, Nankyoung Moon, Heejin InDaewon W. Byun, Nankyoung Moon, Heejin InDaewon W. Byun, Nankyoung Moon, Heejin InDaewon W. Byun, Nankyoung Moon, Heejin In

Harvard UniversityHarvard UniversityHarvard UniversityHarvard University

Daniel Jacob, Rokjin ParkDaniel Jacob, Rokjin ParkDaniel Jacob, Rokjin ParkDaniel Jacob, Rokjin Park

IntroductionIntroduction

It could be different at each side of domain reflecting certain regional differences.

One of key problems of regional air quality models is finding accurate initial and boundary conditions for the simulations. Distribution of surface air chemistry and PM monitoring sites is limited both in the spatial density and in the physical and chemical details.

The fixed profile BCs are never accurate and cannot account for changes due to air pollution long-range transport events.

Some US regional air quality problems may be originated from long-range transport processes(eg. Transport of EC/OC/CO/dust from Sahara & biomass burning from Central America)

Current method: Run a regional air quality model at a coarser resolution with seasonal profile data and use emissions input for a long period for the spin-up process.

What is the sensitivity of the simulations to the different IC/BCs?

Study ObjectivesStudy ObjectivesProvide tools/methods to link regional and global Provide tools/methods to link regional and global modeling systemsmodeling systems

- Dynamic Representations in Global and Regional Models- Chemical Representations in Global and Regional Models- Mechanics of Linkage

-Linkage of scales: grid structure and scales of data representation (generation of IC/BCs)-Linkage of chemical species-Linkage of dynamics

Three Areas of Inter-linkage Issues

(e.g.) Set the boundary of the domain that outside areas (e.g.) Set the boundary of the domain that outside areas do not have much direct emissions and no high do not have much direct emissions and no high concentration blobs already existingconcentration blobs already existing

LAT-LON 2 degree X 2.5 degree

20 layers in Sigma P

LAMBERT CONFORMAL

108 km X 108 km

23 layers in Sigma Po

Initial & Boundary Condition

IO/API Format in 108 km resolution

GEOS-CHEM (Goddard Earth Observing System-CHEMisrty)

MODEL3 CMAQ(Multi-pollutant Air Quality model)

Mechanics of Linkage

Linkage of scales: Currently, grid structures of the global and regional models are not “consistent”

•Requires less preferable horizontal & vertical interpolationImplementation Example

Future – requires “geocentric” coordinates(from a flat-earth to a spherical earth, if not spheroid)

Horizontal distribution of O3 concentration from GEOS-CHEM global output at Layer 1

108km resolution2 X 2.5 degree resolution

For 2000 August episode

O3-NOX-Hydrocarbon chemistry : 24 species24 species

CMAQ

MAPPING Table

CB4O3-NOx-Hydrocarbon

chemistry

[NO2 ] [NOx ]-[NO]

[O3 ] [Ox ] - [NOx ]

[N2O5] [N2O5]

[HNO3] [HNO3]

[PNA ] [HNO4]

[H2O2] [H2O2]

[CO ] [CO ]

[PAN ] [PAN ] + [PMN ] + [PPN ]

[MGLY] [MP ]

[ISPD] [MVK ] + [MACR]

[NTR ] [R4N2]

[FORM] [CH2O]

[ALD2] [ALD2] + [RCHO]

[PAR ] [ALK4] + [C3H8] + [C2H6]

[OLE ] [PRPE]

[ISOP] [ISOP]

GEOS-CHEM

CB4 : 16 species16 species

Un-used species : ACET, ALD2

Chemical species:Currently, chemical mechanisms in global and regional models are not “consistent”

Mechanics of Linkage

Mapping Table

SAPRACO3-NOx-Hydrocarbon

chemistry

[NO2 ] [NOx ] – [NO]

[PAN] [PAN]

[CO] [CO]

[ALK3] [ALK4]+[ALK5 [ALK4]

[ISOPRENE ] [ISOP]

[HNO3] [HNO3]

[H2O2] [H2O2]

[ACET ] [ACET]

[MEK] [MEK]

[CCHO] [ALD2]

[RCHO] [RCHO]

[MRTHACRO] [MACR]

[MA_PAN] [PMN]

[MVK] [MVK]

[PAN2] [PPN]

SAPRACO3-NOx-Hydrocarbon

chemistry

[RNO3] [R4N2]

[OLE1] + [OLE2] [PRPE]

[ALK2] [C3H8]

[HCHO] [CH2O]

[ALK1] [C2H6]

[N2O5] [N2O5]

[HNO4] [HNO4]

[COOH ] [MP]

CMAQ

GEOS-CHEM

SAPRAC-99

Linkage of Chemistry

Emission comparison

VOC = C2H6 + C3H8 + ALK4 + PRPE + ISOP + CH2O + ALD2 + RCHOGEOS-CHEM

VOC = PAR + 2OLE + 2ETH + 2ALD2 + 7TOL + 8XYL + 5ISOP + FORMCB4

VOC DefinitionChemical Mechanism

The emissions inputs used for the GEOS-CHEM and CMAQ for the NOx and VOC species were compared.

Table presents how total VOC in mechanisms are calculated and the values of NOx and VOC for GEIA represent smaller than NEI99-SMOKE in maximum values.

GEOS-CHEM OUTPUT, Layer-1, Ox

Summer

GEOS-CHEM OUTPUT, Top layer in CMAQ, Ox

Summer

GEOS-CHEM OUTPUT, Layer-1, OxWinter

GEOS-CHEM OUTPUT, Top layer in CMAQ, Ox

Winter

(Col:1,Row:88)

(Col:56,Row:114)

(Col:73,Row:1)

O3 and SO4 seasonal boundary condition time series

O3 time series at different vertical layers : Western Boundary

Summer Winter

O3 time series at different vertical layers : Southern Boundary

Summer Winter

O3 time series at different vertical layers : Northern Boundary

Summer Winter

SO

4

m

icro

gra

m/m

3

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Western BCSouthern BC Northern BC

June July August

SO4 seasonal boundary condition time series

Summer Winter

SO

4

m

icro

gra

m/m

3

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Western BCSouthern BC Northern BC

January February

CMAQ simulation CMAQ simulation

Emission

NEI99 ( SMOKE )

Emission

NEI99 ( SMOKE )

MET. DATA

MCIP (MM5)

MET. DATA

MCIP (MM5)

Chemical mechanism

CB4 / SAPRC99

Chemical mechanism

CB4 / SAPRC99

Domain

CONUS 36-km

Domain

CONUS 36-km

Simulation

IC & BC with Original profile dataIC & BC with GEOS-CHEM output

Simulation

IC & BC with Original profile dataIC & BC with GEOS-CHEM output

Comparison of simulated O3 concentration with AIRS

Profile Data Case GEOS-CHEM Data Case

Comparison of CMAQ results in different IC and BC (2000.08.25. 09, 21UTC)

03AM CST

03PM CST

In CMAQ simulations, the results using GEOS-CHEM output for boundary condition have smaller value from 16 ppb to 20 ppb than the results using profile data around western and northeast boundary area. On the other hand, there is opposite results at south boundary area, which is related with positive bios of GEOS-CHEM over the GULF of Mexico.It is necessary to investigate the chemical mechanism differences in CMAQ simulation with GEOS-CHEM boundary condition .

Comparison of O3 production rate

NOz (ppm)

0.000 0.005 0.010 0.015 0.020

O3

(p

pm

)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

CMAQ_SAPRC99GEOS-CHEM

NOz (ppm)

0.000 0.005 0.010 0.015 0.020

O3

(ppm

)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

PROFILE BCGEOS-CHEM

NOz (ppm)

0.000 0.005 0.010 0.015 0.020

O3

(p

pm

)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

GEOS-CHEM BCGEOS-CHEM

profile BC GEOS-CHEM BC

CMAQ/CB4 CMAQ/CB4

CMAQ/SAPRC

GEOS-CHEM BC

Comparison of wind field

This difference can be cause the uncertainty to regional air quality simulations.

MM5 NASA-GMAOGeneral patterns of wind fields are well

Some difference shows in circled area. - CMAQ/MM5 shows parallel to the grid - GEOS-CHEM/NASA-GMAO shows inflow

Let’s see how big the problem is:

GEOSCHEM : Easterly and northerlyMM5 : Clock wise rotation motion

MM5 GMAO

Study importance of the dynamic consistency Comparison of the first guess field used in MM5:

between ETA and GMAO

PREGRID MYPREGRID

REGRIDDER

EDAS DAO

Comparison of wind fields among three different MM5 results.

Case 1; MM5 results with EDAS first guess

Case 2; MM5 results with ETA first guess and GMAO objective analysis

Case 3; MM5 results with GMAO first guess

~ trying to get closer wind fields to GMAO

CASE 1 CASE 2

CASE 3 GMAO

August 25. 00 UTCMM5 Results

According to the evaluation result of numerical models, RMSE was 1.63, 1.57 and 1.41 for wind speed and 68.37, 66.66 and 69.49 for wind direction for RAMS, MM5 and Meso-Eta respectively (Zhong and Fast, 2003). In that evaluation, RMSE was for observation data and simulation results for different meteorological model outputs.

Region Simulation CaseRMSE

Wind Speed Wind Direction

Whole Domain

Case 1 1.80 60.13

Case 2 1.63 54.34

Case 3 1.99 76.62

Western BC

Case 1 2.06 41.99

Case 2 1.54 37.59

Case 3 1.39 40.94

Eastern BC

Case 1 2.03 52.74

Case 2 1.73 47.82

Case 3 1.55 41.56

Northern BC

Case 1 1.64 55.35

Case 2 1.56 47.90

Case 3 1.57 57.31

Southern BC

Case 1 1.89 52.16

Case 2 1.68 44.02

Case 3 2.54 75.12

Comparison of Root Mean Square Error (RMSE)

RMSE is for MM5 result of each case and GMAO

• Case 2 (ETA first guess and objective analysis with GMAO)

shows the most closest results to GMAO filed in three cases

from RMSE analysis.

• Even if MM5 use GMAO data for the first guess in case3,

MM5 can not simulate closer values to initial filed (GMAO)

with the lower resolution of GMAO in time(6 hourly) and

space(2X2.5).

• The best case is use of ETA data of high resolution in time and space for the first guess and use of objective analysis with GMAO data in INTERPF.

CMAQ Simulation

Emission ; EPA-NEI99

Chemical Mechanism ; CB4

Meteorology ; MM5 results of three cases

(Each case has corresponding case of MM5)Comparison of Ozone difference

CASE2 – CASE1

; (ETA_first guess & GMAO_objective analysis) – (ETA_first guess)

CASE3 – CASE1

; (GMAO_first guess) – (ETA_first guess)

CMAQ Simulation Results: Ozone Concentration DifferencesMM5 with DAO - MM5 with EDAS for August 25, 2000

Case2 – Case1Comparison of O3 difference

Biomass burning due to ENSO-related drought in Mexico and Central America during April ~ June 1998

May 13 1998

May 14 1998

May 15 1998

May 16 1998

TOMS Aerosol Index

Aerosol species Mapping

36 km × 36 kmRambert Conformal

GEOS-CHEM CMAQ

AORGI+AORGJ+AORGPAI+AORGPAJAORGBI+AORGBJ

Coordinate transformation

OC_hydrophilic + OC_hydrophobic

EC_hydrophilic + EC_hydrophobic AECI+AECJ

OC

EC

23 Sigma vertical layer(Ptop= 50 mb)

30 vertical layer(Ptop= 10 mb)

2.5°× 2°Simple

Interpolation

GEOS2CMAQ Interface

GEOS2CMAQ InterfaceICON

BCON

GEOS-CHEM Global simulation ( 2.5°× 2° )

EC OC

DATA SOURCE : US EPA NEI 99Processed with SMOKE OCEC

EMISSION

Spatial Evolutional Feature of OC

CMAQ ver 4.3

Grids : 133 × 91 × 23 Resolution : 36 km × 36 km Science Process : CB4-AERO3- EBI solver

Meteorogical data from MM5 ver3.6

A

CB

B

EC OC

SimulatedMonthly CON

Evaluation byIMPROVEMonitoring

Difference

W/ GEOS-CHEM BC

W/ fixed profile BC

Daily concentrations of Simulated vs. Observed OC

WA OR

CA NV

CO

VTFL

UT

Region A

Region B

IMPROVE Network

Improving of OC concentration

AZ TX

TNAR

Region C

A

B

C

Daily Mean CON

OCEC

P

G

Observation

Sim

ula

tio

n

R= 0.59

R= 0.33

G

P

R= 0.60

R= 0.35

EC OC

Sim

ula

tio

n

Observation

G

G

P P

R= 0.75

R= 0.33

R= 0.79

R= 0.68

Monthly concentrations of Simulated vs. Observed

ConclusionLinkage issues between global tropospheric chemistry model and regional air quality model has been studied.

We observe significant differences between profile vs. GEOSCHEM IC/BC.

To investigate the effects of using GEOS-CHEM output as initial and boundary conditions instead of the profile data on regional simulations, we have conducted 4 sensitivity CMAQ simulations with the CB4 and SAPRC99 as the chemical mechanisms.

Global-regional scale linking is the best when direct emission source is little outside the regional domain boundary; e.g., US-continental domain.

It is necessary to quantify and minimize the effects of different dynamics between the global and regional meteorological data used and to study the issues of consistency in chemical mechanisms.