Articles | Volume 15, issue 2
https://doi.org/10.5194/acp-15-815-2015
© 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/acp-15-815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Jan 2015
Research article |
| 23 Jan 2015
A comparison of four receptor models used to quantify the boreal wildfire smoke contribution to surface PM2.5 in Halifax, Nova Scotia during the BORTAS-B experiment
M. D. Gibson
CORRESPONDING AUTHOR
Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada
J. Haelssig
Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada
J. R. Pierce
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA
M. Parrington
School of GeoSciences, The University of Edinburgh, Edinburgh, Scotland, UK
European Centre For Medium Range Weather Forecasts, Reading, UK
J. E. Franklin
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
J. T. Hopper
Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
Z. Li
Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada
College of Environmental Science and Engineering, Ocean University of China, Qingdao, China
T. J. Ward
Centre for Environmental Health Sciences, University of Montana, MT, USA
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Cited
17 citations as recorded by crossref.
- Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method Y. Tian et al. 10.1016/j.chemosphere.2015.12.132
- Characteristics and source apportionment of PM2.5 in Jiaxing, China Z. Zhao et al. 10.1007/s11356-019-04205-2
- Large global variations in measured airborne metal concentrations driven by anthropogenic sources J. McNeill et al. 10.1038/s41598-020-78789-y
- SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications G. Snider et al. 10.5194/amt-8-505-2015
- Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment M. Khan et al. 10.5194/acp-16-597-2016
- Potential sources and processes affecting speciated atmospheric mercury at Kejimkujik National Park, Canada: comparison of receptor models and data treatment methods X. Xu et al. 10.5194/acp-17-1381-2017
- Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics A. Targino et al. 10.1016/j.envpol.2016.07.027
- Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018 X. Qin et al. 10.5194/acp-20-10985-2020
- An iterative method for evaluating the inter-comparability between chemical mass balance and multivariate receptor models G. Argyropoulos et al. 10.1016/j.chemolab.2016.03.032
- Fire Influences on Atmospheric Composition, Air Quality and Climate A. Voulgarakis & R. Field 10.1007/s40726-015-0007-z
- Characteristics of atmospheric mercury in a suburban area of east China: sources, formation mechanisms, and regional transport X. Qin et al. 10.5194/acp-19-5923-2019
- Approaches for identifying PM2.5 source types and source areas at a remote background site of South China in spring K. Zhang et al. 10.1016/j.scitotenv.2019.07.178
- Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning X. Qin et al. 10.5194/acp-22-15851-2022
- Evaluating the potential of waste plastics as fuel in cement kilns using bench-scale emissions analysis E. Asamany et al. 10.1016/j.fuel.2016.12.054
- Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm J. Zhang et al. 10.1016/j.scitotenv.2021.150149
- Quantifying variation in occupational air pollution exposure within a small metropolitan region of Brazil W. Pattinson et al. 10.1016/j.atmosenv.2018.03.011
- Simulation and Forecasting Study on the Influential Factors of PM2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region D. Li et al. 10.3390/su16083152
17 citations as recorded by crossref.
- Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method Y. Tian et al. 10.1016/j.chemosphere.2015.12.132
- Characteristics and source apportionment of PM2.5 in Jiaxing, China Z. Zhao et al. 10.1007/s11356-019-04205-2
- Large global variations in measured airborne metal concentrations driven by anthropogenic sources J. McNeill et al. 10.1038/s41598-020-78789-y
- SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications G. Snider et al. 10.5194/amt-8-505-2015
- Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment M. Khan et al. 10.5194/acp-16-597-2016
- Potential sources and processes affecting speciated atmospheric mercury at Kejimkujik National Park, Canada: comparison of receptor models and data treatment methods X. Xu et al. 10.5194/acp-17-1381-2017
- Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics A. Targino et al. 10.1016/j.envpol.2016.07.027
- Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018 X. Qin et al. 10.5194/acp-20-10985-2020
- An iterative method for evaluating the inter-comparability between chemical mass balance and multivariate receptor models G. Argyropoulos et al. 10.1016/j.chemolab.2016.03.032
- Fire Influences on Atmospheric Composition, Air Quality and Climate A. Voulgarakis & R. Field 10.1007/s40726-015-0007-z
- Characteristics of atmospheric mercury in a suburban area of east China: sources, formation mechanisms, and regional transport X. Qin et al. 10.5194/acp-19-5923-2019
- Approaches for identifying PM2.5 source types and source areas at a remote background site of South China in spring K. Zhang et al. 10.1016/j.scitotenv.2019.07.178
- Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning X. Qin et al. 10.5194/acp-22-15851-2022
- Evaluating the potential of waste plastics as fuel in cement kilns using bench-scale emissions analysis E. Asamany et al. 10.1016/j.fuel.2016.12.054
- Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm J. Zhang et al. 10.1016/j.scitotenv.2021.150149
- Quantifying variation in occupational air pollution exposure within a small metropolitan region of Brazil W. Pattinson et al. 10.1016/j.atmosenv.2018.03.011
- Simulation and Forecasting Study on the Influential Factors of PM2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region D. Li et al. 10.3390/su16083152
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Latest update: 23 Nov 2024
Short summary
This paper presents a quantitative comparison of the four most commonly used receptor models, namely absolute principal component scores, pragmatic mass closure, chemical mass balance, and positive matrix factorization. The receptor models were used to predict the contributions of boreal wild-fire smoke and other sources to PM2.5 mass in Halifax, Nova Scotia, Canada during the BORTAS-B experiment. This paper also presents a new woodsmoke PM2.5 enrichment factor (levoglucosan x 52).
This paper presents a quantitative comparison of the four most commonly used receptor models,...
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