Sensitivity of inverse estimation of 2004 elemental carbon emissions inventory in the United States to the choice of observational networks

[1] Choice of observational networks for inverse re-estimation of elemental carbon (EC) emissions in the United States impacts results. We convert the Thermal Optical Transmittance (TOT) EC measurements to the Thermal Optical Reflectance (TOR) equivalents to make full utilization of available networks in inverse modeling of EC using regional air quality model. Results show that using the Interagency Monitoring of Protected Visual Environments (IMPROVE) network gives significantly lower emissions estimate compared to using the Speciation Trends Network (STN) and other networks or using all available networks together. The re-estimate obtained by using IMPROVE sites alone made overall model performance worse compared to the bottom-up estimate of EC emissions, while both re-estimates, using STN (and others) sites and using all sites together, significantly improved the performance, showing higher robustness. Further analysis suggests that site density with respect to geographical location (downwind of source) impacts the robustness of a network's inverse re-estimate.

[1]  Armistead G Russell,et al.  Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis. , 2005, Environmental science & technology.

[2]  D. Streets,et al.  A technology‐based global inventory of black and organic carbon emissions from combustion , 2004 .

[3]  Robin L. Dennis,et al.  Seasonal NH3 emission estimates for the eastern United States , 2003 .

[4]  Norman R. Draper,et al.  Ridge Regression and James-Stein Estimation: Review and Comments , 1979 .

[5]  Peter Bergamaschi,et al.  Inverse modeling of the global CO cycle: 1 , 2000 .

[6]  A. Russell,et al.  Mass conservation in the Community Multiscale Air Quality model , 2006 .

[7]  Hans Moosmüller,et al.  Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. , 2004, Environmental science & technology.

[8]  Christine A. O'Neill,et al.  Effects of Aerosol from Biomass Burning on the Global Radiation Budget , 1992, Science.

[9]  Jonathan E. Pleim,et al.  Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model , 2001 .

[10]  J. Coakley,et al.  Climate Forcing by Anthropogenic Aerosols , 1992, Science.

[11]  Ruixin Yang,et al.  Evaluations of Mesoscale Models' Simulations of Near-Surface Winds, Temperature Gradients, and Mixing Depths , 2001 .

[12]  A. Russell,et al.  Daily sampling of PM2.5 in Atlanta: Results of the first year of the Assessment of Spatial Aerosol Composition in Atlanta study , 2003 .

[13]  John C. Gille,et al.  Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P) observations to estimate Asian sources of carbon monoxide , 2004 .

[14]  Shaocai Yu,et al.  A performance evaluation of the 2004 release of Models-3 CMAQ , 2006 .

[15]  Mian Chin,et al.  Sources of carbonaceous aerosols over the United States and implications for natural visibility , 2003 .

[16]  Gail S. Tonnesen,et al.  CMAQ/CAMx annual 2002 performance evaluation over the eastern US , 2006 .

[17]  O. Talagrand,et al.  4D-variational data assimilation with an adjoint air quality model for emission analysis , 2000, Environ. Model. Softw..

[18]  Kevin R. Gurney,et al.  TransCom 3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices , 2003 .

[19]  Yongtao Hu,et al.  Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM) , 2006 .

[20]  James G. Wilkinson,et al.  Fast, Direct Sensitivity Analysis of Multidimensional Photochemical Models , 1997 .

[21]  C. Liousse,et al.  Construction of a 1° × 1° fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model , 1999 .

[22]  A. Petzold,et al.  Intercomparison of thermal and optical measurement methods for elemental carbon and black carbon at an urban location. , 2006, Environmental science & technology.

[23]  Iterative Inverse Modeling and Direct Sensitivity Analysis of a Photochemical Air Quality Model , 2000 .

[24]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[25]  Greg Yarwood,et al.  Comparison of source apportionment and source sensitivity of ozone in a three-dimensional air quality model. , 2002, Environmental science & technology.

[26]  A. Russell,et al.  Estimation of emission adjustments from the application of four-dimensional data assimilation to photochemical air quality modeling , 2001 .

[27]  Tami C. Bond,et al.  Export efficiency of black carbon aerosol in continental outflow: Global implications , 2005 .

[28]  D. Byun,et al.  Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , 2006 .

[29]  J. Chow,et al.  Results of the "Carbon Conference" International Aerosol Carbon Round Robin Test Stage I , 2001 .

[30]  Shamil Maksyutov,et al.  Sensitivity of inverse estimation of annual mean CO2 sources and sinks to ocean‐only sites versus all‐sites observational networks , 2006, Geophysical Research Letters.

[31]  Adrian Sandu,et al.  Adjoint inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment , 2005 .

[32]  J. Brook,et al.  Evaluation of Elemental and Black Carbon Measurements from the GAViM and IMPROVE Networks , 2003 .

[33]  George M Hidy,et al.  The Southeastern Aerosol Research and Characterization Study, Part 3: Continuous Measurements of Fine Particulate Matter Mass and Composition , 2006, Journal of the Air & Waste Management Association.