A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

  • Cesunica E. IveyEmail author
  • Heather A. Holmes
  • Yongtao Hu
  • James A. Mulholland
  • Armistead G. Russell
Research Article
Part of the following topical collections:
  1. Understanding the processes of air pollution formation (Responsible Editors: Min SHAO, Shuxiao WANG & Armistead G. RUSSELL)


Community Multi-Scale Air Quality (CMAQ) estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority of PM2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is −0.28 (OC), 0.11 (NO3), 0.05 (NH4), and −0.08 (SO4). The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO3, NH4, and SO4 after applying the secondary bias correction. Ten-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.


Particulate matter Source apportionment Secondary particulate matter Chemical transport modeling Receptor modeling 

Supplementary material

11783_2016_866_MOESM1_ESM.pdf (1 mb)
Supplementary material, approximately 1.00 MB.


  1. 1.
    Binkowski F S, Roselle S J. Models-3 community multiscale air quality (CMAQ) model aerosol component-1. Model description. Journal of Geophysical Research, D, Atmospheres, 2003, 108(D6): 4183CrossRefGoogle Scholar
  2. 2.
    Nenes A, Pandis S N, Pilinis C. ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquatic Geochemistry, 1998, 4(1): 123–152CrossRefGoogle Scholar
  3. 3.
    Foley KM, Roselle S J, Appel KW, Bhave P V, Pleim J E, Otte T L, Mathur R, Sarwar G, Young J O, Gilliam R C, Nolte C G, Kelly J T, Gilliland A B, Bash J O. Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 2010, 3(1): 205–226CrossRefGoogle Scholar
  4. 4.
    Kelly J T, Bhave P V, Nolte C G, Shankar U, Foley KM. Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model. Geoscientific Model Development, 2010, 3(1): 257–273CrossRefGoogle Scholar
  5. 5.
    Shrivastava M, Fast J, Easter R, Gustafson W I, Zaveri R A, Jimenez J L, Saide P, Hodzic A. Modeling organic aerosols in a megacity: comparison of simple and complex representations of the volatility basis set approach. Atmospheric Chemistry and Physics, 2011, 11(13): 6639–6662CrossRefGoogle Scholar
  6. 6.
    Kim S W, Heckel A, Frost G J, Richter A, Gleason J, Burrows J P, Mc Keen S, Hsie E Y, Granier C, Trainer M. NO2 columns in the western United States observed from space and simulated by a regional chemistry model and their implications for NOx emissions. Journal of Geophysical Research, D, Atmospheres, 2009, 114 (D11): 1CrossRefGoogle Scholar
  7. 7.
    Fountoukis C, Nenes A. ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+-Ca2+-Mg2+-NH4+ -Na+-SO42– NO3–Cl–H2O aerosols. Atmospheric Chemistry and Physics, 2007, 7(17): 4639–4659CrossRefGoogle Scholar
  8. 8.
    Pandis S N, Harley R A, Cass G R, Seinfeld J H. Secondary Organic Aerosol Formation and Transport. Atmospheric Environment. Part A, General Topics, 1992, 26(13): 2269–2282CrossRefGoogle Scholar
  9. 9.
    Russell A G, Mcrae G J, Cass G R. Mathematical-Modeling of the Formation and Transport of Ammonium-Nitrate Aerosol. Atmospheric Environment, 1983, 17(5): 949–964CrossRefGoogle Scholar
  10. 10.
    Hildemann L M, Cass G R, Mazurek M A, Simonelt B R T. Mathematical-Modeling of Urban Organic Aerosol-Properties Measured by High-Resolution Gas-Chromatography. Environmental Science & Technology, 1993, 27(10): 2045–2055CrossRefGoogle Scholar
  11. 11.
    Simon H, Baker K R, Phillips S. Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012. Atmospheric Environment, 2012, 61: 124–139CrossRefGoogle Scholar
  12. 12.
    Kanakidou M, Seinfeld J H, Pandis S N, Barnes I, Dentener F J, Facchini M C, Van Dingenen R, Ervens B, Nenes A, Nielsen C J, Swietlicki E, Putaud J P, Balkanski Y, Fuzzi S, Horth J, Moortgat G K, Winterhalter R, Myhre C E L, Tsigaridis K, Vignati E, Stephanou E G, Wilson J. Organic aerosol and global climate modelling: a review. Atmospheric Chemistry and Physics, 2005, 5(4): 1053–1123CrossRefGoogle Scholar
  13. 13.
    Bessagnet B, Hodzic A, Vautard R, Beekmann M, Cheinet S, Honore C, Liousse C, Rouil L. Aerosol modeling with CHIMEREpreliminary evaluation at the continental scale. Atmospheric Environment, 2004, 38(18): 2803–2817CrossRefGoogle Scholar
  14. 14.
    Malm W C, Schichtel B A, Pitchford M L. Uncertainties in PM2.5 gravimetric and speciation measurements and what we can learn from them. Journal of the Air & Waste Management Association, 2011, 61(11): 1131–1149Google Scholar
  15. 15.
    Napelenok S L, Cohan D S, Hu Y T, Russell A G. Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmospheric Environment, 2006, 40(32): 6112–6121CrossRefGoogle Scholar
  16. 16.
    Byun D, Schere K L. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews, 2006, 59(1–6): 51–77CrossRefGoogle Scholar
  17. 17.
    Cohan D S, Hakami A, Hu Y, Russell A G. Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis. Environmental Science & Technology, 2005, 39(17): 6739–6748CrossRefGoogle Scholar
  18. 18.
    Pleim J E, Xiu A. Development and testing of a surface flux and planetary boundary-layer model for application in mesoscale models. Journal of Applied Meteorology, 1995, 34(1): 16–32CrossRefGoogle Scholar
  19. 19.
    Xiu A J, Pleim J E. Development of a land surface model. Part I: Application in a mesoscale meteorological model. Journal of Applied Meteorology, 2001, 40(2): 192–209CrossRefGoogle Scholar
  20. 20.
    CEP. Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE) User Manual, edited. In The University of North Carolina at Chapel Hill: Chapel Hill, NC, 2003.Google Scholar
  21. 21.
    Hu Y, Balachandran S, Pachon J E, Baek J, Ivey C, Holmes H, Odman M T, Mulholland J A, Russell A G. Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach. Atmospheric Chemistry and Physics, 2014, 14(11): 5415–5431CrossRefGoogle Scholar
  22. 22.
    Ivey C E, Holmes H A, Hu Y T, Mulholland J A, Russell A G. Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model. Geoscientific Model Development, 2015, 8(7): 2153–2165CrossRefGoogle Scholar
  23. 23.
    Geocounts Georgia Department of Transportation. Traffic Counts in Georgia. Scholar
  24. 24.
    US Energy Information Administration. California: State Profile and Energy Estimates. = CAGoogle Scholar
  25. 25.
    US Energy Information Administration. Pennsylvania: State Profile and Energy Estimates. = PAGoogle Scholar
  26. 26.
    National Emissions Inventory. 2005-Based Modeling Platform. U.S. Evironmental Protection Agency,2011Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Cesunica E. Ivey
    • 1
    Email author
  • Heather A. Holmes
    • 2
  • Yongtao Hu
    • 1
  • James A. Mulholland
    • 1
  • Armistead G. Russell
    • 1
  1. 1.School of Civil and Environmental EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of PhysicsUniversity of Nevada RenoRenoUSA

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