Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Source apportionment of stack emissions from research and development facilities using positive matrix factorization
https://orcid.org/0000-0003-1153-7201
Abstract Research and development (R&D) facility emissions are difficult to characterize due to their variable processes, changing nature of research, and large number of chemicals. Positive matrix factorization (PMF) was applied to volatile organic compound (VOC) concentrations measured in the main exhaust stacks of four different R&D buildings to identify the number and composition of major contributing sources. PMF identified between 9 and 11 source-related factors contributing to stack emissions, depending on the building. Similar factors between buildings were major contributors to trichloroethylene (TCE), acetone, and ethanol emissions; other factors had similar profiles for two or more buildings but not all four. At least one factor for each building was identified that contained a broad mix of many species and constraints were used in PMF to modify the factors to resemble more closely the off-shift concentration profiles. PMF accepted the constraints with little decrease in model fit.
Graphical abstract Display Omitted
Highlights Positive Matrix Factorization (PMF) was used for the first time on measured stack data. PMF was applied to air chemical emission sample data from research and development facilities. PMF identified between 9 and 11 sources contributing to the measured emissions. Some source profiles from the PMF application were common to all facilities, but others were unique. At least one source from each facility resembled the source profile of off-shift samples.
Positive Matrix Factorization applied to measured air emissions from research facilities can provide useful insight into sources contributing to the emissions.
Source apportionment of stack emissions from research and development facilities using positive matrix factorization
https://orcid.org/0000-0003-1153-7201
Abstract Research and development (R&D) facility emissions are difficult to characterize due to their variable processes, changing nature of research, and large number of chemicals. Positive matrix factorization (PMF) was applied to volatile organic compound (VOC) concentrations measured in the main exhaust stacks of four different R&D buildings to identify the number and composition of major contributing sources. PMF identified between 9 and 11 source-related factors contributing to stack emissions, depending on the building. Similar factors between buildings were major contributors to trichloroethylene (TCE), acetone, and ethanol emissions; other factors had similar profiles for two or more buildings but not all four. At least one factor for each building was identified that contained a broad mix of many species and constraints were used in PMF to modify the factors to resemble more closely the off-shift concentration profiles. PMF accepted the constraints with little decrease in model fit.
Graphical abstract Display Omitted
Highlights Positive Matrix Factorization (PMF) was used for the first time on measured stack data. PMF was applied to air chemical emission sample data from research and development facilities. PMF identified between 9 and 11 sources contributing to the measured emissions. Some source profiles from the PMF application were common to all facilities, but others were unique. At least one source from each facility resembled the source profile of off-shift samples.
Positive Matrix Factorization applied to measured air emissions from research facilities can provide useful insight into sources contributing to the emissions.
Source apportionment of stack emissions from research and development facilities using positive matrix factorization
https://orcid.org/0000-0003-1153-7201
Abstract Research and development (R&D) facility emissions are difficult to characterize due to their variable processes, changing nature of research, and large number of chemicals. Positive matrix factorization (PMF) was applied to volatile organic compound (VOC) concentrations measured in the main exhaust stacks of four different R&D buildings to identify the number and composition of major contributing sources. PMF identified between 9 and 11 source-related factors contributing to stack emissions, depending on the building. Similar factors between buildings were major contributors to trichloroethylene (TCE), acetone, and ethanol emissions; other factors had similar profiles for two or more buildings but not all four. At least one factor for each building was identified that contained a broad mix of many species and constraints were used in PMF to modify the factors to resemble more closely the off-shift concentration profiles. PMF accepted the constraints with little decrease in model fit.
Graphical abstract Display Omitted
Highlights Positive Matrix Factorization (PMF) was used for the first time on measured stack data. PMF was applied to air chemical emission sample data from research and development facilities. PMF identified between 9 and 11 sources contributing to the measured emissions. Some source profiles from the PMF application were common to all facilities, but others were unique. At least one source from each facility resembled the source profile of off-shift samples.
Positive Matrix Factorization applied to measured air emissions from research facilities can provide useful insight into sources contributing to the emissions.
Source apportionment of stack emissions from research and development facilities using positive matrix factorization
Ballinger, Marcel Y. (Autor:in) / Larson, Timothy V. (Autor:in)
Atmospheric Environment ; 98 ; 59-65
19.08.2014
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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