Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-2153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
20 Jul 2015
Methods for assessment of models |
| 20 Jul 2015
Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model
C. E. Ivey
CORRESPONDING AUTHOR
Georgia Institute of Technology, Atlanta, Georgia, USA
H. A. Holmes
University of Nevada Reno, Reno, Nevada, USA
Y. T. Hu
Georgia Institute of Technology, Atlanta, Georgia, USA
J. A. Mulholland
Georgia Institute of Technology, Atlanta, Georgia, USA
A. G. Russell
Georgia Institute of Technology, Atlanta, Georgia, USA
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Cited
34 citations as recorded by crossref.
- Cross-comparison and evaluation of air pollution field estimation methods H. Yu et al. 10.1016/j.atmosenv.2018.01.045
- An Integrated Source Apportionment Methodology and Its Application over the Yangtze River Delta Region, China L. Li et al. 10.1021/acs.est.8b01211
- 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
- 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
- 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
- Exposure to Source-Specific Particulate Matter and Health Effects: a Review of Epidemiological Studies J. Xu et al. 10.1007/s40726-022-00235-6
- Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data X. Yu et al. 10.1016/j.envint.2020.105772
- The impact of fire-specific PM2.5 calibration on health effect analyses X. Jiang et al. 10.1016/j.scitotenv.2022.159548
- Development of PM2.5 Source Profiles Using a Hybrid Chemical Transport-Receptor Modeling Approach C. Ivey et al. 10.1021/acs.est.7b03781
- Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China Z. Li et al. 10.1016/j.scitotenv.2021.151757
- Spatial PM2.5mobile source impacts using a calibrated indicator method X. Zhai et al. 10.1080/10962247.2018.1532468
- Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments X. Jiang & E. Enki Yoo 10.1021/acs.est.9b02660
- Calibrating R-LINE model results with observational data to develop annual mobile source air pollutant fields at fine spatial resolution: Application in Atlanta X. Zhai et al. 10.1016/j.atmosenv.2016.10.015
- Characterizing population exposure to coal emissions sources in the United States using the HyADS model L. Henneman et al. 10.1016/j.atmosenv.2019.01.043
- 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
- 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
- Relations between indoor and outdoor PM2.5 and constituent concentrations C. Liu & Y. Zhang 10.1007/s11783-019-1089-4
- Spatial and temporal source apportionment of PM 2.5 in Georgia, 2002 to 2013 X. Zhai et al. 10.1016/j.atmosenv.2017.04.039
- 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
- Linked Response of Aerosol Acidity and Ammonia to SO2 and NOx Emissions Reductions in the United States A. Lawal et al. 10.1021/acs.est.8b00711
- 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
- Application of an improved gas-constrained source apportionment method using data fused fields: A case study in North Carolina, USA R. Huang et al. 10.1016/j.atmosenv.2022.119031
- Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data G. Shi et al. 10.1016/j.atmosenv.2017.04.021
- Inequitable Exposures to U.S. Coal Power Plant–Related PM2.5: 22 Years and Counting L. Henneman et al. 10.1289/EHP11605
- Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models M. Diao et al. 10.1080/10962247.2019.1668498
- Sensitivities of Simulated Source Contributions and Health Impacts of PM2.5 to Aerosol Models Y. Morino et al. 10.1021/acs.est.7b04000
- Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions L. Henneman et al. 10.1038/s41370-020-0219-1
- A review of the technology and applications of methods for evaluating the transport of air pollutants X. Wang et al. 10.1016/j.jes.2022.06.022
- 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
- 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
- Accountability Assessment of Health Improvements in the United States Associated with Reduced Coal Emissions Between 2005 and 2012 L. Henneman et al. 10.1097/EDE.0000000000001024
- Source and sectoral contribution analysis of PM2.5 based on efficient response surface modeling technique over Pearl River Delta Region of China Y. Pan et al. 10.1016/j.scitotenv.2020.139655
- High-Resolution Modeling and Apportionment of Diesel-Related Contributions to Black Carbon Concentrations S. Hamilton & R. Harley 10.1021/acs.est.1c03913
34 citations as recorded by crossref.
- Cross-comparison and evaluation of air pollution field estimation methods H. Yu et al. 10.1016/j.atmosenv.2018.01.045
- An Integrated Source Apportionment Methodology and Its Application over the Yangtze River Delta Region, China L. Li et al. 10.1021/acs.est.8b01211
- 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
- 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
- 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
- Exposure to Source-Specific Particulate Matter and Health Effects: a Review of Epidemiological Studies J. Xu et al. 10.1007/s40726-022-00235-6
- Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data X. Yu et al. 10.1016/j.envint.2020.105772
- The impact of fire-specific PM2.5 calibration on health effect analyses X. Jiang et al. 10.1016/j.scitotenv.2022.159548
- Development of PM2.5 Source Profiles Using a Hybrid Chemical Transport-Receptor Modeling Approach C. Ivey et al. 10.1021/acs.est.7b03781
- Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China Z. Li et al. 10.1016/j.scitotenv.2021.151757
- Spatial PM2.5mobile source impacts using a calibrated indicator method X. Zhai et al. 10.1080/10962247.2018.1532468
- Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments X. Jiang & E. Enki Yoo 10.1021/acs.est.9b02660
- Calibrating R-LINE model results with observational data to develop annual mobile source air pollutant fields at fine spatial resolution: Application in Atlanta X. Zhai et al. 10.1016/j.atmosenv.2016.10.015
- Characterizing population exposure to coal emissions sources in the United States using the HyADS model L. Henneman et al. 10.1016/j.atmosenv.2019.01.043
- 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
- 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
- Relations between indoor and outdoor PM2.5 and constituent concentrations C. Liu & Y. Zhang 10.1007/s11783-019-1089-4
- Spatial and temporal source apportionment of PM 2.5 in Georgia, 2002 to 2013 X. Zhai et al. 10.1016/j.atmosenv.2017.04.039
- 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
- Linked Response of Aerosol Acidity and Ammonia to SO2 and NOx Emissions Reductions in the United States A. Lawal et al. 10.1021/acs.est.8b00711
- 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
- Application of an improved gas-constrained source apportionment method using data fused fields: A case study in North Carolina, USA R. Huang et al. 10.1016/j.atmosenv.2022.119031
- Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data G. Shi et al. 10.1016/j.atmosenv.2017.04.021
- Inequitable Exposures to U.S. Coal Power Plant–Related PM2.5: 22 Years and Counting L. Henneman et al. 10.1289/EHP11605
- Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models M. Diao et al. 10.1080/10962247.2019.1668498
- Sensitivities of Simulated Source Contributions and Health Impacts of PM2.5 to Aerosol Models Y. Morino et al. 10.1021/acs.est.7b04000
- Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions L. Henneman et al. 10.1038/s41370-020-0219-1
- A review of the technology and applications of methods for evaluating the transport of air pollutants X. Wang et al. 10.1016/j.jes.2022.06.022
- 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
- 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
- Accountability Assessment of Health Improvements in the United States Associated with Reduced Coal Emissions Between 2005 and 2012 L. Henneman et al. 10.1097/EDE.0000000000001024
- Source and sectoral contribution analysis of PM2.5 based on efficient response surface modeling technique over Pearl River Delta Region of China Y. Pan et al. 10.1016/j.scitotenv.2020.139655
- High-Resolution Modeling and Apportionment of Diesel-Related Contributions to Black Carbon Concentrations S. Hamilton & R. Harley 10.1021/acs.est.1c03913
Saved (final revised paper)
Latest update: 24 Apr 2025
Short summary
An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). This work presents a novel spatiotemporal source apportionment method that generates source impacts for the continental USA. Key sources presented include fossil fuel combustion, biomass burning, dust, sea salt, as well as agricultural activities, biogenics, and aircraft.
An integral part of air quality management is knowledge of the impact of pollutant sources on...
