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

Abstract

A method for quantifying source impacts for secondary PM_2.5 species is derived.

The method provides estimates of bias in modeled concentrations.

Adjusted concentrations match corresponding observations at monitored locations.

Sources impacts on secondary species are estimated over the US for 20 sources.

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 PM_2.5 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. 10-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.

Graphical abstract

Keywords

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

References

[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): 4183

[2] Nenes A, Pandis S N, Pilinis C. ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquatic Geochemistry, 1998, 4(1): 123–152

[3] Foley K M, Roselle S J, Appel K W, 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): 205–226

[4] Kelly J T, Bhave P V, Nolte C G, Shankar U, Foley K M. Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model. Geoscientific Model Development, 2010, 3(1): 257–273

[5] Shrivastava M, Fast J, Easter R, Gustafson W IJr, 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): 6639–6662

[6] Kim S W, Heckel A, Frost G J, Richter A, Gleason J, Burrows J P, McKeen 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 NO_x emissions. Journal of Geophysical Research, D, Atmospheres, 2009, 114 (D11): 1

[7] Fountoukis C, Nenes A. ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K⁺-Ca²⁺-Mg²⁺-NH₄⁺-Na⁺-SO₄^2–NO₃^–Cl–H₂O aerosols. Atmospheric Chemistry and Physics, 2007, 7(17): 4639–4659

[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): 2269–2282

[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): 949–964

[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): 2045–2055

[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: 124–139

[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): 1053–1123

[13] Bessagnet B, Hodzic A, Vautard R, Beekmann M, Cheinet S, Honore C, Liousse C, Rouil L. Aerosol modeling with CHIMERE- preliminary evaluation at the continental scale. Atmospheric Environment, 2004, 38(18): 2803–2817

[14] Malm W C, Schichtel B A, Pitchford M L. Uncertainties in PM_2.5 gravimetric and speciation measurements and what we can learn from them. Journal of the Air & Waste Management Association, 2011, 61(11): 1131–1149

[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): 6112–6121

[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): 51–77

[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): 6739–6748

[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): 16–32

[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): 192–209

[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.

[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): 5415–5431

[22] Ivey C E, Holmes H A, Hu Y T, Mulholland J A, Russell A G. Development of PM_2.5 source impact spatial fields using a hybrid source apportionment air quality model. Geoscientific Model Development, 2015, 8(7): 2153–2165

[23] Geocounts Georgia Department of Transportation. Traffic Counts in Georgia. http://geocounts.com/gdot/

[24] US Energy Information Administration. California: State Profile and Energy Estimates. http://www.eia.gov/state/?sid=CA

[25] US Energy Information Administration. Pennsylvania: State Profile and Energy Estimates. http://www.eia.gov/state/?sid=PA

[26] National Emissions Inventory. 2005-Based Modeling Platform. U.S. Evironmental Protection Agency,2011

Acknowledgements

This publication was made possible in part by USEPA STAR grants R833626, R833866, R834799 and RD83479901, STAR Fellowship FP-91761401-0, and by NASA under grant NNX11AI55G. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the US government. Further, US government does not endorse the purchase of any commercial products or services mentioned in the publication. We also acknowledge the Southern Company and the Alfred P. Sloan Foundation for their support.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s11783-016-0866-6 and is accessible for authorized users.

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg