Sensitivity of top-down correction of 2004 black carbon emissions inventory

in the United States to rural-sites versus urban-sites observational networks

Yongtao Hu1, Sergey L. Napelenok2, M. Talat Odman1 and Armistead G. Russell1

1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA

2Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC

Presented at the 6th Annual CMAS Conference, October 2nd, 2007

Motivation Black carbon (BC) is considered a big contributor to global and regional climate forcing. One source of the large uncertainty in BC study is its emissions inventory.

Regional air quality models perform poorly in predicting surface BC concentrations (under-predicted) because of possible underestimation of BC emissions at regional level.

A better estimation of BC emissions can also help better understand primary organic carbon (OC) emissions.

Inverse modeling is a widely used tool to estimate emissions in a top-down way.

To what extent/level a regional model equipped with inverse modeling technique can correct current bottom-up BC emissions in the United States? What are the limits of the top-down method?

How sensitive the inverse modeling to the observational networks which employed to scale the emissions?

One-year CMAQ simulation in 2004 on a 36-km grid covering continental United States as well as portions of Canada and Mexico. The 2002 VISTAS emissions inventory was projected to 2004 and used as the a priori inventory. Note that BC from fires and CEM are typical year averages.

Utilizing surface black-carbon observations from networks of IMPROVE, STN, SEARCH and ASACA. TOT measurements from STN and ASACA converted to TOR.

The difference between the CMAQ simulations and the observations, along with the DDM-3D derived sensitivities of BC concentrations to each source group, are used to estimate how much BC emissions from a specific source should be adjusted to optimize the CMAQ BC performance through ridge regression. We calculate optimized scaling factors m which minimize the objective function Γ.

Sensitivity tests: use observations from three different networks (1) R+U (all networks) (2) Rural (IMPROVE) (3) Urban (STN & others)to scale the a priori emissions.

Approach

mWmeWe mT

eT dWGWGWGm e

Tme

T 1

Scale BC emissions by five source categories at five RPO regions as well as Canada and Mexico totals

On-road

Non-road

Fire

Wood-fuel

“Others”

RPO regions Source Categories

Canada

MANE-VUMidwest RPO

VISTAS

CENRAP

WRAP

Mexico

United States

Rural Sites (green dots) vs. Urban Sites (red and pink dots)

BC monitoring networks: IMPROVE, STN, SEARCH and ASACA.

The 36-km Modeling Domain

Results

BC emissions scaling factors obtained for five months (Jan, Mar, May, Aug and Oct) for which the DDM sensitivity coefficients have been calculated. Three sets of scaling factors obtained by using R+U, Rural and Urban sites, respectively.

The a posteriori inventory estimated by scaling the a priori inventory for each month of the year. For the months for which the DDM sensitivities haven’t been calculated, the scaling factors from a representing month adopted. Jan: Dec and Feb; Mar: Apr; May: Jun; Aug: Jul and Sep; Oct: Nov.

U.S. total BC emissions in 2004 estimated by this study: the a priori 0.36 Tg and the posteriori 0.44 Tg (using R+U), 0.36Tg (Rural), and 0.46Tg (Urban).

Other studies of U.S. totals: 0.4Tg for 1996 (Bond et. al. 2004) and 0.75 Tg for 1998 (Park et. al. 2003)

Annual totals: the a priori vs. the a posteriori obtained using different obs. networks

By CategoryBy Region

0

20

40

60

80

100

120

140

160

CENRAP MANE_VU MIDWEST VISTAS WRAP

BC

em

issi

ons

(Gg)

a priori

a posteriori R+U

a posteriori Rural

a posteriori Urban

0

20

40

60

80

100

120

140

160

180

fire on-road off-road "others" wood fuel

BC

em

issi

ons

(Gg)

a priori

a posteriori R+U

a posteriori Rural

a posteriori Urban

Seasonal Variation (1)

Total

Fire

Total

0

10

20

30

40

50

Jan Mar May Aug Oct

BC

em

issi

ons

(Gg)

Fire

0

3

6

9

12

15

Jan Mar May Aug Oct

BC

em

issi

ons

(Gg)

On-road

0

1

2

3

4

Jan Mar May Aug Oct

BC

em

issi

ons

(Gg)

Off-road

0

4

8

12

16

20

Jan Mar May Aug Oct

BC

em

issi

ons

(Gg)

"Others"

0

2

4

6

8

Jan Mar May Aug Oct

BC

em

issi

ons

(Gg)

Seasonal Variation (2) on-road off-road

wood fuel “others”Wood fuel

0

3

6

9

12

15

18

Jan Mar May Aug Oct

BC

em

issi

ons

(Gg)

Re-run the CMAQ using the scaled emissions ( the three a posteriori inventory) as inputs.

Fractional bias (FB) and fractional (FE) error are calculated for all the CMAQ simulations using the a priori and the a posterior inventories.

Examine the model performance improvement by compare FB and FE of the simulations before and after the inverse, for R+U, Rural and Urban tests, respectively .

Robustness of the inverse estimates: Model Performance comparison

N

i ii

ii

ObsSim

ObsSim

NFB

1

%10021

N

i ii

ii

ObsSim

ObsSim

NFE

1

%10021

FB

FE

Monthly Model Performance Comparison

-70

-60

-50

-40

-30

-20

-10

0

Jan Mar May Aug Oct

Fra

ctio

nal

a priori

a posteriori R+U

a posteriori Rural

a posteriori Urban

0

10

20

30

40

50

60

70

80

Jan Mar May Aug Oct

Fra

ctio

nal E

rror

a priori

a posteriori R+U

a posteriori Rural

a posteriori Urban

FB, May

FE, May

May

0

10

20

30

40

50

60

70

80

90

100

US CENRAP MANE_VU MIDWEST WRAP VISTAS

FE

%

May

-100

-80

-60

-40

-20

0

20

US CENRAP MANE_VU MIDWEST WRAP VISTAS

FB

%

Model Performance for RPO Regions

May (Urban)

-80-70-60-50-40-30-20-10

01020304050607080

0 10 20 30 40 50 60 70 80 90 100

percentage of total sites

Diff

ere

nce

of |

FB

| (a

po

ste

rio

ri -

a

pri

ori

)

May (R+U)

-80-70-60-50-40-30-20-10

01020304050607080

0 10 20 30 40 50 60 70 80 90 100

Percentage of total sites

Diff

ere

nce

of |

FB

| (a

po

ste

rio

ri -

a p

rio

ri)

May (Rural)

-80-70-60-50-40-30-20-10

01020304050607080

0 10 20 30 40 50 60 70 80 90 100

percentage of total sites

Diff

ere

nce

of |

FB

| (a

po

ste

rio

ri -

a

pri

ori

)Improvement at sites: |FBa posteriori| - |FBa priori|

Negative difference means improved

60% sites has been improved in May using R+U in the inverse

50%: using Rural sites 64%: using Urban sites

Spatial Pattern: |FBa posteriori| - |FBa priori|

using Rural sites

using R+U sites

using Urban sites

Summary

We have conducted inverse modeling on BC emissions and estimated US total BC emissions was 0.44, 0.36 and 0.46 Tg for year 2004, using observations from rural + urban, rural and urban sites respectively.

With scaled emissions inventory, CMAQ performance improved when the scaling factors calculated using rural+urban or urban sites only, but decreased when using rural sites only. The inverse estimation of US total BC emissions is more robust using all networks or urban networks only.

CMAQ performance improved significantly on fractional bias but only slightly on fractional error. Other errors remain, e.g. cell-point comparison, spatial inhomogeneity, temporal variation of current emissions inventory …