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Frontiers of Environmental Science & Engineering

Front. Environ. Sci. Eng.    2016, Vol. 10 Issue (5) : 14     https://doi.org/10.1007/s11783-016-0866-6
RESEARCH ARTICLE |
A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter
Cesunica E. Ivey1,*(),Heather A. Holmes2,Yongtao Hu1,James A. Mulholland1,Armistead G. Russell1
1. School of Civil and Environmental Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA 30332, USA
2. Department of Physics, University of Nevada Reno, 1664 N Virginia St, Reno, NV 89557, USA
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Abstract

A method for quantifying source impacts for secondary PM2.5 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 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. 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.

Keywords Particulate matter      Source apportionment      Secondary particulate matter      Chemical transport modeling      Receptor modeling     
This article is part of themed collection: Understanding the processes of air pollution formation (Responsible Editors: Min SHAO, Shuxiao WANG & Armistead G. RUSSELL)
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Corresponding Authors: Cesunica E. Ivey   
Issue Date: 23 August 2016
 Cite this article:   
Cesunica E. Ivey,Heather A. Holmes,Yongtao Hu, et al. A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter[J]. Front. Environ. Sci. Eng., 2016, 10(5): 14.
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http://journal.hep.com.cn/fese/EN/10.1007/s11783-016-0866-6
http://journal.hep.com.cn/fese/EN/Y2016/V10/I5/14
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Cesunica E. Ivey
Heather A. Holmes
Yongtao Hu
James A. Mulholland
Armistead G. Russell
Fig.1  CSN monitors (blue circles) used for model development, application, and evaluation
species rank winter (µg·m-3) spring (µg·m-3) summer (µg·m-3) fall (µg·m-3)
PM2.5 11.8 15.0 22.3 15.3
OC 1 1.17 biog 1.54 biog 2.51 biog 1.83 biog
2 0.30 slv 0.22 ot 0.24 ord 0.25 slv
3 0.29 org 0.19 org 0.24 ot 0.22 org
4 0.22 ord 0.18 ord 0.20 org 0.20 ord
5 0.19 ot 0.15 slv 0.15 nrg 0.17 ot
NO3 1 1.09 ag 0.78 ag 0.38 org 0.94 ag
2 0.96 org 0.41 org 0.34 ag 0.63 org
3 0.16 ord 0.15 ord 0.14 ord 0.17 ord
4 0.10 ot 0.09 nrd 0.07 nrd 0.10 nrd
5 0.08 nrd 0.05 ot 0.06 coal 0.06 ot
NH4 1 0.37 ag 0.52 coal 0.98 coal 0.57 coal
2 0.28 org 0.47 ag 0.44 ag 0.44 ag
3 0.19 coal 0.23 org 0.40 org 0.30 org
4 0.04 ot 0.09 ord 0.15 ord 0.09 ord
5 0.04 ord 0.07 foil 0.11 foil 0.07 foil
SO4 1 0.83 coal 2.44 coal 4.61 coal 2.30 coal
2 0.13 foil 0.28 foil 0.44 foil 0.24 foil
3 0.04 ot 0.13 ord 0.42 ord 0.13 ord
4 0.01 nro 0.12 ot 0.25 org 0.07 ot
5 0.01 metal 0.08 nrd 0.21 nrd 0.06 nrd
Tab.1  Source impacts on secondary PM2.5 for Atlanta, GA
species rank winter (µg·m-3) spring (µg·m-3) summer (µg·m-3) fall (µg·m-3)
PM2.5 17.2 12.8 16.8 15.2
OC 1 2.41 biog 0.81 biog 0.85 biog 1.68 biog
2 0.61 slv 0.33 meat 0.29 meat 0.29 meat
3 0.26 meat 0.31 slv 0.27 org 0.28 org
4 0.22 org 0.21 org 0.19 nrd 0.27 slv
5 0.14 wood 0.15 nrd 0.12 slv 0.19 nrd
NO3 1 2.36 org 2.22 org 1.78 org 1.77 org
2 1.09 ag 1.38 ag 0.96 ng 0.91 ng
3 0.97 ng 1.29 ng 0.66 ag 0.91 ag
4 0.74 ord 0.58 nrd 0.35 nrd 0.47 nrd
5 0.72 foil 0.49 ord 0.32 nro 0.45 ord
NH4 1 0.64 foil 0.67 org 0.46 org 0.44 org
2 0.59 org 0.42 ng 0.36 nro 0.35 foil
3 0.28 ng 0.40 ag 0.26 ng 0.25 ng
4 0.22 ag 0.29 foil 0.24 foil 0.23 ag
5 0.18 ord 0.22 nrd 0.20 nrd 0.15 nrd
SO4 1 0.51 foil 0.56 foil 0.70 nro 0.46 foil
2 0.10 ot 0.31 nro 0.51 foil 0.14 nro
3 0.07 coal 0.20 ot 0.39 nrd 0.12 ot
4 0.04 nro 0.17 nrd 0.31 ot 0.07 nrd
5 0.02 ng 0.09 coal 0.17 coal 0.07 coal
Tab.2  Source impacts on secondary PM2.5 for Los Angeles, CA
species rank winter (µg·m-3) spring (µg·m-3) summer (µg·m-3) fall (µg·m-3)
PM2.5 11.0 12.0 12.0 10.3
OC 1 0.37 biog 0.59 biog 1.55 biog 0.77 biog
2 0.24 slv 0.18 slv 0.19 slv 0.30 slv
3 0.22 org 0.18 org 0.18 meat 0.18 org
4 0.12 meat 0.17 meat 0.17 nrd 0.16 meat
5 0.10 wood 0.14 wood 0.17 nrg 0.12 nrd
NO3 1 1.62 ag 1.20 ag 0.65 ag 1.21 ag
2 0.67 org 0.47 org 0.35 org 0.51 org
3 0.18 ot 0.15 ot 0.10 ord 0.14 ot
4 0.12 ord 0.13 ord 0.08 ot 0.09 ord
5 0.11 mg 0.07 nrd 0.08 nrd 0.07 nrd
NH4 1 0.51 ag 0.57 ag 0.83 ag 0.57 ag
2 0.23 org 0.32 coal 0.76 coal 0.30 coal
3 0.21 coal 0.22 org 0.35 org 0.27 org
4 0.17 ot 0.19 ot 0.26 ot 0.18 ot
5 0.03 foil 0.04 ord 0.10 ord 0.03 foil
SO4 1 1.29 coal 2.26 coal 6.00 coal 1.98 coal
2 0.13 foil 0.13 ot 0.31 ord 0.11 foil
3 0.06 ot 0.13 foil 0.28 ot 0.07 ot
4 0.05 metal 0.06 metal 0.28 nrd 0.06 metal
5 0.02 nrg 0.02 otc 0.19 foil 0.01 ord
Tab.3  Source impacts on secondary PM2.5 for Pittsburgh, PA
Fig.2  Seasonally-averaged spatial fields of SCij values in µg·m-3 for top secondary organic carbon sources
Fig.3  Seasonally-averaged spatial fields of SCij values in µg·m-3 for top nitrate sources
Fig.4  Seasonally-averaged spatial fields of SCij valuesin µg·m-3 for top ammonium sources
Fig.5  Seasonally-averaged spatial fields of SCij values in µg·m-3 for top sulfate sources
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