Yongtao Hu 1 , Sergey L. Napelenok 2 , M. Talat Odman 1 and Armistead G. Russell 1

1. 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

2. 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?

3. Approach • 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.

4. Canada Midwest RPO WRAP MANE-VU United States CENRAP VISTAS Mexico Scale BC emissions by five source categories at five RPO regions as well as Canada and Mexico totals RPO regions Source Categories On-road Non-road Fire Wood-fuel “Others”

5. Rural Sites (green dots) vs. Urban Sites (red and pink dots) BC monitoring networks: IMPROVE,STN,SEARCH and ASACA. The 36-km Modeling Domain

6. 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)

7. Annual totals: the a priori vs. the a posteriori obtained using different obs. networks • By Region • By Category

8. Seasonal Variation (1) • Total • Fire

9. Seasonal Variation (2) • on-road • off-road • wood fuel • “others”

10. Robustness of the inverse estimates: Model Performance comparison • 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 .

11. Monthly Model Performance Comparison • FB • FE

12. Model Performance for RPO Regions • FB, May • FE, May

13. 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

14. Spatial Pattern: |FBa posteriori| - |FBa priori| • using R+U sites • using Rural sites • using Urban sites

15. 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 …