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 Hu 1, Sergey L. Napelenok 2, M. Talat Odman 1 and Armistead G. Russell 1 1 School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA 2 Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC Presented at the 6th Annual CMAS Conference, October 2 nd, 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.  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

4. 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-VU Midwest RPO VISTAS CENRAP WRAP Mexico United States

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 Category  By Region

8. Seasonal Variation (1)  Total  Fire

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

10.  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

11.  FB  FE Monthly Model Performance Comparison

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

13. Improvement at sites: |FB a posteriori | - |FB a 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: |FB a posteriori | - |FB a priori |  using Rural sites  using R+U 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 …