Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Environ. Sci. Eng.    0, Vol. Issue () : 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
 Download: PDF(2426 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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     
PACS:     
Fund: 
Corresponding Author(s): 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., 0, (): 14.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-016-0866-6
https://academic.hep.com.cn/fese/EN/Y0/V/I/14
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
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
https://doi.org/10.1029/2001JD001409
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
https://doi.org/10.1023/A:1009604003981
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
https://doi.org/10.5194/gmd-3-205-2010
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
https://doi.org/10.5194/gmd-3-257-2010
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
https://doi.org/10.5194/acp-11-6639-2011
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 NOx 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+-SO42–NO3–Cl–H2O aerosols. Atmospheric Chemistry and Physics, 2007, 7(17): 4639–4659
https://doi.org/10.5194/acp-7-4639-2007
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
https://doi.org/10.1016/0960-1686(92)90358-R
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
https://doi.org/10.1016/0004-6981(83)90247-0
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
https://doi.org/10.1021/es00047a009
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
https://doi.org/10.1016/j.atmosenv.2012.07.012
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
https://doi.org/10.5194/acp-5-1053-2005
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
https://doi.org/10.1016/j.atmosenv.2004.02.034
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): 1131–1149
pmid: 22168097
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
https://doi.org/10.1016/j.atmosenv.2006.05.039
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
https://doi.org/10.1115/1.2128636
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
https://doi.org/10.1021/es048664m pmid: 16190234
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
https://doi.org/10.1175/1520-0450-34.1.16
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
https://doi.org/10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2
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
https://doi.org/10.5194/acp-14-5415-2014
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): 2153–2165
https://doi.org/10.5194/gmd-8-2153-2015
23 Geocounts Georgia Department of Transportation. Traffic Counts in Georgia.
24 US Energy Information Administration. California: State Profile and Energy Estimates.
25 US Energy Information Administration. Pennsylvania: State Profile and Energy Estimates.
26 National Emissions Inventory. 2005-Based Modeling Platform. U.S. Evironmental Protection Agency,2011
[1] FSE-16029-OF-CEI_suppl_1 Download
[1] Zizhen Ma, Jingkun Jiang, Lei Duan, Jianguo Deng, Fuyuan Xu, Zehui Li, Linhua Jiang, Ning Duan. Synergistic promotion of particulate matter reduction and production performance via adjusting electrochemical reactions in the zinc electrolysis industry[J]. Front. Environ. Sci. Eng., 2024, 18(1): 2-.
[2] Yuting Wei, Xiao Tian, Junbo Huang, Zaihua Wang, Bo Huang, Jinxing Liu, Jie Gao, Danni Liang, Haofei Yu, Yinchang Feng, Guoliang Shi. New insights into the formation of ammonium nitrate from a physical and chemical level perspective[J]. Front. Environ. Sci. Eng., 2023, 17(11): 137-.
[3] Yue Wang, Mengshuang Shi, Zhaofeng Lv, Huan Liu, Kebin He. Local and regional contributions to PM2.5 in the Beijing 2022 Winter Olympics infrastructure areas during haze episodes[J]. Front. Environ. Sci. Eng., 2021, 15(6): 140-.
[4] Noshan Bhattarai, Shuxiao Wang, Yuepeng Pan, Qingcheng Xu, Yanlin Zhang, Yunhua Chang, Yunting Fang. δ15N-stable isotope analysis of NHx: An overview on analytical measurements, source sampling and its source apportionment[J]. Front. Environ. Sci. Eng., 2021, 15(6): 126-.
[5] In-Sun Kang, Jinying Xi, Hong-Ying Hu. Photolysis and photooxidation of typical gaseous VOCs by UV Irradiation: Removal performance and mechanisms[J]. Front. Environ. Sci. Eng., 2018, 12(3): 8-.
[6] Mengqian Lu, Bin-Le Lin, Kazuya Inoue, Zhongfang Lei, Zhenya Zhang, Kiyotaka Tsunemi. PM2.5-related health impacts of utilizing ammonia-hydrogen energy in Kanto Region, Japan[J]. Front. Environ. Sci. Eng., 2018, 12(2): 13-.
[7] Yifei Song, Lei Sun, Xinfeng Wang, Yating Zhang, Hui Wang, Rui Li, Likun Xue, Jianmin Chen, Wenxing Wang. Pollution characteristics of particulate matters emitted from outdoor barbecue cooking in urban Jinan in eastern China[J]. Front. Environ. Sci. Eng., 2018, 12(2): 14-.
[8] Ningning Zhang, Mazhan Zhuang, Jie Tian, Pengshan Tian, Jieru Zhang, Qiyuan Wang, Yaqing Zhou, Rujin Huang, Chongshu Zhu, Xuemin Zhang, Junji Cao. Development of source profiles and their application in source apportionment of PM2.5 in Xiamen, China[J]. Front. Environ. Sci. Eng., 2016, 10(5): 17-.
[9] Xuemei WANG,Weihua CHEN,Duohong CHEN,Zhiyong WU,Qi Fan. Long-term trends of fine particulate matter and chemical composition in the Pearl River Delta Economic Zone (PRDEZ), China[J]. Front. Environ. Sci. Eng., 2016, 10(1): 53-62.
[10] Liu YANG,Ye WU,Jiaqi LI,Shaojie SONG,Xuan ZHENG,Jiming HAO. Mass concentrations and temporal profiles of PM10, PM2.5 and PM1 near major urban roads in Beijing[J]. Front. Environ. Sci. Eng., 2015, 9(4): 675-684.
[11] Huixia WANG, Hui SHI, Yangyang LI, Ya YU, Jun ZHANG. Seasonal variations in leaf capturing of particulate matter, surface wettability and micromorphology in urban tree species[J]. Front Envir Sci Eng, 2013, 7(4): 579-588.
[12] Xiaoyan SHI, Kebin HE, Weiwei SONG, Xingtong WANG, Jihua TAN. Effects of a diesel oxidation catalyst on gaseous pollutants and fine particles from an engine operating on diesel and biodiesel[J]. Front Envir Sci Eng, 2012, 6(4): 463-469.
[13] Gaoxiang YING, John J. SANSALONE, . Particulate matter and metals partitioning in highway rainfall-runoff[J]. Front.Environ.Sci.Eng., 2010, 4(1): 35-46.
[14] Xiaoyan SHI, Kebin HE, Jie ZHANG, Yongliang MA, Yunshan GE, Jianwei TAN, . A comparative study of particle size distribution from two oxygenated fuels and diesel fuel[J]. Front.Environ.Sci.Eng., 2010, 4(1): 30-34.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed