Articles | Volume 14, issue 11
https://doi.org/10.5194/acp-14-5415-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-14-5415-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jun 2014
Research article |
| 04 Jun 2014
Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach
Y. Hu
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
S. Balachandran
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
J. E. Pachon
now at: Program of Environmental Engineering, Universidad de La Salle, Bogota, Colombia
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
J. Baek
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
C. Ivey
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
H. Holmes
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
M. T. Odman
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
J. A. Mulholland
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
A. G. Russell
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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Cited
40 citations as recorded by crossref.
- An explainable integrated optimization methodology for source apportionment of ambient particulate matter components J. Shen et al. 10.1016/j.jenvman.2022.114789
- Characterization of Compound-Specific Chlorine Isotopologue Distributions of Polychlorinated Organic Compounds by GC-HRMS for Source Identification C. Tang et al. 10.1021/acs.analchem.1c00059
- Improve regional distribution and source apportionment of PM2.5 trace elements in China using inventory-observation constrained emission factors Q. Ying et al. 10.1016/j.scitotenv.2017.12.138
- Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks L. Henneman et al. 10.1080/10962247.2016.1242518
- Cross-comparison and evaluation of air pollution field estimation methods H. Yu et al. 10.1016/j.atmosenv.2018.01.045
- Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia M. Huang et al. 10.1016/j.envint.2019.105167
- An Integrated Source Apportionment Methodology and Its Application over the Yangtze River Delta Region, China L. Li et al. 10.1021/acs.est.8b01211
- Exposure to Source-Specific Particulate Matter and Health Effects: a Review of Epidemiological Studies J. Xu et al. 10.1007/s40726-022-00235-6
- Simulation of the influence of residential biomass burning on air quality in an urban area E. Siouti et al. 10.1016/j.atmosenv.2023.119897
- Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model C. Ivey et al. 10.5194/gmd-8-2153-2015
- Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities L. Henneman et al. 10.1007/s11869-017-0463-2
- Air quality modeling for accountability research: Operational, dynamic, and diagnostic evaluation L. Henneman et al. 10.1016/j.atmosenv.2017.07.049
- A hybrid method for PM2.5 source apportionment through WRF-Chem simulations and an assessment of emission-reduction measures in western China J. Yang et al. 10.1016/j.atmosres.2019.104787
- Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model K. Liao et al. 10.1038/s41612-023-00393-4
- Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires T. Sturtz et al. 10.1021/es502749r
- An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps C. Bray et al. 10.1016/j.atmosenv.2019.03.013
- Simulation of fresh and chemically-aged biomass burning organic aerosol L. Posner et al. 10.1016/j.atmosenv.2018.09.055
- Public health costs accounting of inorganic PM2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model J. Heo et al. 10.1016/j.envint.2017.06.006
- Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA R. Huang et al. 10.1007/s11869-017-0511-y
- Composition and oxidation state of sulfur in atmospheric particulate matter A. Longo et al. 10.5194/acp-16-13389-2016
- Source impact modeling of spatiotemporal trends in PM2.5 oxidative potential across the eastern United States J. Bates et al. 10.1016/j.atmosenv.2018.08.055
- Source apportionment of fine secondary inorganic aerosol over the Pearl River Delta region using a hybrid method W. Chen et al. 10.1016/j.apr.2021.101061
- Integrating Chemical Mass Balance and the Community Multiscale Air Quality models for source identification and apportionment of PM2.5 C. Zhang et al. 10.1016/j.psep.2021.03.033
- Insight into characteristics and sources of PM2.5 in the Beijing–Tianjin–Hebei region, China M. Hu et al. 10.1093/nsr/nwv003
- A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter C. Ivey et al. 10.1007/s11783-016-0866-6
- Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago C. Milando et al. 10.1016/j.atmosenv.2016.01.012
- Review on formation mechanism analysis method and control strategy of urban haze in China W. Huang et al. 10.1016/j.cjche.2018.08.016
- A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data K. Liao et al. 10.1016/j.scitotenv.2020.144282
- Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure J. Servadio et al. 10.1007/s11524-018-0318-7
- Identification of biased sectors in emission data using a combination of chemical transport model and receptor model K. Uranishi et al. 10.1016/j.atmosenv.2017.06.039
- Evaluation of PM2.5 air pollution sources and cardiovascular health E. Slawsky et al. 10.1097/EE9.0000000000000157
- Spatial PM2.5mobile source impacts using a calibrated indicator method X. Zhai et al. 10.1080/10962247.2018.1532468
- Development of PM2.5 Source Profiles Using a Hybrid Chemical Transport-Receptor Modeling Approach C. Ivey et al. 10.1021/acs.est.7b03781
- Investigating fine particulate matter sources in Salt Lake City during persistent cold air pool events C. Ivey et al. 10.1016/j.atmosenv.2019.06.042
- A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing Y. Ma et al. 10.1080/15567036.2022.2075991
- Estimating source-attributable health impacts of ambient fine particulate matter exposure: global premature mortality from surface transportation emissions in 2005 S. Chambliss et al. 10.1088/1748-9326/9/10/104009
- Local and distant source contributions to secondary organic aerosol in the Beijing urban area in summer J. Lin et al. 10.1016/j.atmosenv.2015.08.098
- Source apportionment of settleable particles in an impacted urban and industrialized region in Brazil J. Santos et al. 10.1007/s11356-017-9677-y
- Review of receptor-based source apportionment research of fine particulate matter and its challenges in China Y. Zhang et al. 10.1016/j.scitotenv.2017.02.071
- Dynamic harmonization of source-oriented and receptor models for source apportionment X. Zhang et al. 10.1016/j.scitotenv.2022.160312
40 citations as recorded by crossref.
- An explainable integrated optimization methodology for source apportionment of ambient particulate matter components J. Shen et al. 10.1016/j.jenvman.2022.114789
- Characterization of Compound-Specific Chlorine Isotopologue Distributions of Polychlorinated Organic Compounds by GC-HRMS for Source Identification C. Tang et al. 10.1021/acs.analchem.1c00059
- Improve regional distribution and source apportionment of PM2.5 trace elements in China using inventory-observation constrained emission factors Q. Ying et al. 10.1016/j.scitotenv.2017.12.138
- Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks L. Henneman et al. 10.1080/10962247.2016.1242518
- Cross-comparison and evaluation of air pollution field estimation methods H. Yu et al. 10.1016/j.atmosenv.2018.01.045
- Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia M. Huang et al. 10.1016/j.envint.2019.105167
- An Integrated Source Apportionment Methodology and Its Application over the Yangtze River Delta Region, China L. Li et al. 10.1021/acs.est.8b01211
- Exposure to Source-Specific Particulate Matter and Health Effects: a Review of Epidemiological Studies J. Xu et al. 10.1007/s40726-022-00235-6
- Simulation of the influence of residential biomass burning on air quality in an urban area E. Siouti et al. 10.1016/j.atmosenv.2023.119897
- Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model C. Ivey et al. 10.5194/gmd-8-2153-2015
- Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities L. Henneman et al. 10.1007/s11869-017-0463-2
- Air quality modeling for accountability research: Operational, dynamic, and diagnostic evaluation L. Henneman et al. 10.1016/j.atmosenv.2017.07.049
- A hybrid method for PM2.5 source apportionment through WRF-Chem simulations and an assessment of emission-reduction measures in western China J. Yang et al. 10.1016/j.atmosres.2019.104787
- Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model K. Liao et al. 10.1038/s41612-023-00393-4
- Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires T. Sturtz et al. 10.1021/es502749r
- An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps C. Bray et al. 10.1016/j.atmosenv.2019.03.013
- Simulation of fresh and chemically-aged biomass burning organic aerosol L. Posner et al. 10.1016/j.atmosenv.2018.09.055
- Public health costs accounting of inorganic PM2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model J. Heo et al. 10.1016/j.envint.2017.06.006
- Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA R. Huang et al. 10.1007/s11869-017-0511-y
- Composition and oxidation state of sulfur in atmospheric particulate matter A. Longo et al. 10.5194/acp-16-13389-2016
- Source impact modeling of spatiotemporal trends in PM2.5 oxidative potential across the eastern United States J. Bates et al. 10.1016/j.atmosenv.2018.08.055
- Source apportionment of fine secondary inorganic aerosol over the Pearl River Delta region using a hybrid method W. Chen et al. 10.1016/j.apr.2021.101061
- Integrating Chemical Mass Balance and the Community Multiscale Air Quality models for source identification and apportionment of PM2.5 C. Zhang et al. 10.1016/j.psep.2021.03.033
- Insight into characteristics and sources of PM2.5 in the Beijing–Tianjin–Hebei region, China M. Hu et al. 10.1093/nsr/nwv003
- A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter C. Ivey et al. 10.1007/s11783-016-0866-6
- Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago C. Milando et al. 10.1016/j.atmosenv.2016.01.012
- Review on formation mechanism analysis method and control strategy of urban haze in China W. Huang et al. 10.1016/j.cjche.2018.08.016
- A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data K. Liao et al. 10.1016/j.scitotenv.2020.144282
- Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure J. Servadio et al. 10.1007/s11524-018-0318-7
- Identification of biased sectors in emission data using a combination of chemical transport model and receptor model K. Uranishi et al. 10.1016/j.atmosenv.2017.06.039
- Evaluation of PM2.5 air pollution sources and cardiovascular health E. Slawsky et al. 10.1097/EE9.0000000000000157
- Spatial PM2.5mobile source impacts using a calibrated indicator method X. Zhai et al. 10.1080/10962247.2018.1532468
- Development of PM2.5 Source Profiles Using a Hybrid Chemical Transport-Receptor Modeling Approach C. Ivey et al. 10.1021/acs.est.7b03781
- Investigating fine particulate matter sources in Salt Lake City during persistent cold air pool events C. Ivey et al. 10.1016/j.atmosenv.2019.06.042
- A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing Y. Ma et al. 10.1080/15567036.2022.2075991
- Estimating source-attributable health impacts of ambient fine particulate matter exposure: global premature mortality from surface transportation emissions in 2005 S. Chambliss et al. 10.1088/1748-9326/9/10/104009
- Local and distant source contributions to secondary organic aerosol in the Beijing urban area in summer J. Lin et al. 10.1016/j.atmosenv.2015.08.098
- Source apportionment of settleable particles in an impacted urban and industrialized region in Brazil J. Santos et al. 10.1007/s11356-017-9677-y
- Review of receptor-based source apportionment research of fine particulate matter and its challenges in China Y. Zhang et al. 10.1016/j.scitotenv.2017.02.071
- Dynamic harmonization of source-oriented and receptor models for source apportionment X. Zhang et al. 10.1016/j.scitotenv.2022.160312
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