A study of process contributions to PM2.5 formation during the 2004 ICARTT period using the Eta-CMAQ

forecast model over the eastern U.S.

Shaocai Yu$, Rohit Mathur+, Kenneth Schere+, Daiwen Kang$, Jonathan Pleim+, Jeffrey Young+, and Daniel Tong$

 

Atmospheric Sciences Modeling DivisionNERL, U.S. EPA, RTP, NC 27711.

$ On assignment from Science and Technology Corporation + On assignment from Air Resources Laboratory, NOAA

IntroductionIntroductionIntroduction

PMPM2.52.5 (d< 2.5 m) forecast

Necessary PM2.5: primary and secondary pollutant,

produced by natural and human activities and chemical reactions A complex mixture of >100 different compounds Adversely affects human health and visibility

Warn the public: unhealthy air voluntarily reduce emission-producing activities

Forecasting methods (EPA, 1999): Persistence, climatology, regression equation etc.

3-D air quality models: Spatial and temporal distributions of PM2.5 and its precursors

Understand chemical-physical processes controlling PM2.5 formation

CMAQCMAQ CCommunityommunity M Multiscaleultiscale A Airir Q Qualityuality MModelodel

Community Model

Multiscale

– consistent model structures for interaction of urban through Continental scales

Multi-pollutant

– ozone, speciated particulate matter, visibility, acid deposition

and air toxics

Objective

Evaluate the ability of the Eta-CMAQ air quality forecast models

in representing spatial and temporal variations of PM2.5 and its

related species through comparisons with 2004 ICARTT and

AIRNOW, IMPROVE, CASTNET and STN obs data over the

eastern US

Study the contributions of physical and chemical processes to

PM2.5 formation by applying process analysis along selected back

trajectories over the eastern US with Eta-CMAQ

Community Multiscale Air Quality (CMAQ) model

International Consortium for Atmospheric Research on Transport and Transformation (ICARTT)

Model DescriptionModel Description

Eta-CMAQ model: (Paula Davison’s Talk )

Eta forecast model provides meteorological fields for CMAQ (Otte et al., 2005) CB-4 (version 4.2): photochemical processes Emissions processed using Emissions processed using SMOKE processing systemSMOKE processing system 12 km12 km horizontal grid resolution horizontal grid resolution 22 Vertical layers between surface and 100 22 Vertical layers between surface and 100 mbmb PA (process analysis): PA (process analysis): CHEM, CLD, DRY, HADV, HDIFF, VDIFF, ZADVCHEM, CLD, DRY, HADV, HDIFF, VDIFF, ZADV

NOAA HYSPLIT_4 model (Draxler, 2004):(Hybrid Single Particle Lagrangian Integrated Trajectory)

Meteorological data: Meteorological data: Eta model data (same as Eta-CMAQ)

3-D wind fields

Back trajectory: air mass originBack trajectory: air mass origin

ICARTT Period: July 1 to August 15, 2004Using results: 12 UTC Eta simulation cycle run

Observations

EPA AIRNOW network:

Hourly PM2.5 at 614 sites in E US.

STN, IMPROVE, CASTNet: PM2.5, SO42-, NO3

-, NH4+, OC,

EC and TC

Observations

2004 ICARTT Data (aircraft (P-3 and DC-8)

Vertical profiles (O3, HNO3, SO2, H2O2, PM2.5 (SO42-, NO3

-, NH4

+))

Tracks of (a) P-3, (b) DC-8

P-3

DC-8

•P-3: Northeast; •DC-8: Eastern US

Results: Operational evaluation at AIRNOW sites

Significant underprediction (7/16-7/24)

due to inadequate representation of biomass burning effects from outside the domain (Alaskan fire)

Results (PM2.5 forecast)

7/19/047/18/047/17/04

July 16-22, 2004: Evidence of effects of long range transport (Alaskan fire)

(1) MODIS (satellite) observations for AOD

(2) TOMS (satellite) observations for absorbing aerosol index

Significant underpredictions of PM2.5 by the model during July 16 to 26 are mainly due to inadequate representation of biomass burning (carbonaceous aerosol) effects from outside the domain (Alaskan fire)

Results(PM2.5 Composition at IMPROVE, STN, CASTNET)

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

SO4 (IMPROVE)SO4 (STN)SO4 (CASTNet)

( g m-3)

SO4

2-

0

10

20

30

40

50

60

0 10 20 30 40 50 60

PM2.5 (IMPROVE)PM2.5 (STN)

( g C m-3)

PM2.5

0

5

10

15

0 5 10 15

NH4 (STN)NH4 (CASTNet)NH4 (IMPROVE)

( g m-3)

NH4

+

Mo

de

l

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8

NO3 (STN)NO3 (CASTNet)NO3 (IMPROVE)

( g m-3)

NO3

-

0

2

4

6

8

10

0 2 4 6 8 10

EC (IMPROVE)

( g C m-3)

EC

0

5

10

15

20

0 5 10 15 20

TC (IMPROVE)OC (IMPROVE)

( g C m-3)

OC and TC

Observation

PM2.5 under prediction

Overpredicted SO42-

Scatter for NO3-

The model under predicted OC by more than a factor of 2

Cause underprediction of PM2.5

PM2.5 SO42-

NH4+ NO3

-

ECOC, TC

Observation

Mod

el

Results (PM2.5 composition)

The model overpredicted SO42-

by 20%

The model under predicted OC by more than a factor of 2

Cause under prediction of PM2.5

0

5

10

15

20

Obs Model

SO4

NO3

NH4

OCM

EC

Other

Conc

entra

tion

( g

m-3

)

IMPROVE sites

46%

1%

11%

21%

3%

19%

65%

1%12%

8%2%12%

(a) (b)

Obs Model

SO4

NO3

NH4

TCM

Other

32%

4%

9%

33%

21%

42%

4%

13%

18%

22%

STN sites

IMPROVE STN

Obs Model Obs Model

Results (PM2.5): vertical profiles

Over predicted SO42- aloft

under predicted NH4+ and NO3

-

Results (Vertical profiles for SO2 and H2O2)

The model overpredicted SO42-

both at the surface and aloft,

in part, possibly reflecting the too much SO2 cloud oxidation because of overpredictions of both SO2 and H2O2 in the model.

0 5 10 15101

102

103

104 SO2 (Obs)SO2 (Model)

SO2 conc. (ppb)

Alti

tude

(m

)0 2 4 6 8 10 12 14

101

102

103

104

105

SO2 conc. (ppb)

Daily Layer Means

(1) P-3 (2) DC-8

SO2:Close to obs at high altitudeHigher than obs at low altitude relative to P3 obs

(3) DC-8

Results: HNO3, and O3 Vertical profiles (7/1-8/15)

HNO3:good at high altitude

0 2 4 6 8 10 12101

102

103

104 HNO3 (Obs)HNO3 (Model)

HNO3 conc. (ppb)

Alti

tude

(m

)

(1) P-3

Daily Layer Means

0 1 2 3 4 5 6 7101

102

103

104

105

HNO3

(ppb)

(2) DC-8

0 20 40 60 80 100120101

102

103

104O3 (Obs)O3 (Model)

O3 conc. (ppb)

Alti

tude

(m

)

0 50 100 150 200 250101

102

103

104

105

O3 conc. (ppb)

O3: good at low altitudeOverprediction

at high altitudes

(3) P-3 (4) DC-8

Preliminary results:PA along the back trajectories

Primary PM2.5 and SO2 sources: Washington, DC/NY/Boston urban corridor, Ohio River valley, Chicago

PM2.5>38 g m-3: two sites in PA (8/17)

South Allegheny High SchoolJohn

two sites in GA (8/18)South DekalbNewnan

SAHSJohn

SDNewnan

PA Results for PM2.5

Column mean: layers 1-14 (typical daytime boundary layer)

At SAHS: CLD and AERO productions as airmass travels over Ohio valley contribute to higher PM2.5 on 8/17

24-hr back trajectories ending at 11 UTC 8/17

PA Results for PM2.5

Column mean: layers 1-14

At John site: CLD and AERO productions as airmass travels over Ohio valley contribute to higher PM2.5 on 8/17

PA Results for PM2.5

Column mean: layers 1-14

At NN site: AERO and EMIS production as airmass travels over AL, MS, LA contributes to high PM2.5

24 hr back trajectories ending at 11 UTC 8/19

PA Results for PM2.5

Column mean: layers 1-14

AT SD site: AERO and EMIS contribute to high PM2.5 as airmass travels over AL and MS, while HADV is the dominant sink

Contacts:

Brian K. Eder

email: eder@hpcc.epa.gov

www.arl.noaa.gov/

www.epa.gov/asmdnerl

Conclusions Model was able to reproduce the day to day variations in PM2.5 level

in the absence of long-range transport effects. Model slightly overpredicts sulfate due to overprediction of H2O2

CB-4 chemical mechanism produces too much H2O2

Model underpredict TC seriously, especially when Alaska fire plume affected the eastern U.S.

Process analysis for the mixing layer (layer 1 to 14) along the trajectories shows:

dominant processes for PM2.5 formation vary from the site to site

The integrated process budgets along the trajectories at the PA sites indicate large contributions from cloud processing to PM2.5

the trajectories reaching the sites in GA are characterized by negligible contributions by the cloud process but large contributions by the AERO and EMIS processes

Disclaimer

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 DW13921548. 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.

Results Layer conc. Of PM2.5 at NN, SD (GA)

Results: SO2 and HNO3 Vertical profiles

101

102

103

104

7/9

7/11

7/15 7/20 7/21 7/227/25 7/27

0 5 10101

102

103

104

7/28 7/318/3

8/68/7 8/9 8/11 8/14 8/15

HNO3 (Obs)HNO3 (3x)

5 105 105 105 105 105 105 105 10

HNO3 (ppbv)

Hei

gh

t (m

)

HNO3 (ppbv)

Hei

gh

t (m

)

101

102

103

104

105

7/157/18 7/20

7/22

7/25 7/28

HNO3 (Obs)HNO3 (3x)

0 5 10101

102

103

104

105

7/31 8/2 8/6 8/7 8/11 8/13 8/14

5 105 105 105 105 105 10

(3) P-3 (HNO3)

(4) DC-8 (HNO3)

HNO3:Good performanceExcept

P3: 7/9, 8/11DC-8: 7/18

101

102

103

104

7/9

7/11

7/15

7/20

7/217/22

7/25

7/27

0 7 14101

102

103

104

7/28 7/318/3

8/6

8/7

8/9 8/11 8/14 8/15

SO2 (ppbv)

SO2 (Obs)SO2 (3x)

7 147 147 147 147 147 147 147 14

Hei

ght (

m)

7/28

101

102

103

104

105

7/31 8/2 8/6 8/7

8/11

8/13 8/14

SO2 (Obs)SO2 (3x)

5 105 105 105 105 105 105 100SO

2 (ppbv)

Hei

ght

(m

)

101

102

103

104

105

7/157/18

7/20 7/22 7/25

(1) P-3 (SO2)

(2) DC-8 (SO2)

SO2:Close to obs at high altitudeHigher than obs at low altitude

most of time.

Results: O3 Vertical profiles

•Model reproducedobs at low altitudeand more uniform• Except: DC-8: 7/28, 8/11

P-3: 7/9, 7/15, 7/20-22, 7/28, 8/14

7/18 7/20

7/22

7/257/28

0 60 120 180101

102

103

104

105

7/31 8/2 8/6 8/7 8/11 8/13 8/14

60 120 180 60 120 18060 120 18060 120 180 60 120 180 60 120 180

O3 (Obs)O3 (3x)

Hei

gh

t (m

)

O3 concentration (ppb)

101

102

103

104

105

7/15

(1) DC-8

(2) P-3

Results: O3 Vertical profilesResults: O3 Vertical profiles

Lidar: Model reproduced obs magnitude at low altitude but smoother distributionOzonesonde: Over predictions above 6 km:

• Impact from GFS derived LBC and coarse model resolution in FT

Obs

6km

(2) July-August Median Profiles (Ozonesonde)

(1) Lidar on Ship

Model