Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing

In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was...

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Published inEnvironmental pollution (1987) Vol. 246; pp. 225 - 236
Main Authors Shang, Jing, Khuzestani, Reza Bashiri, Tian, Jingyu, Schauer, James J., Hua, Jinxi, Zhang, Yang, Cai, Tianqi, Fang, Dongqing, An, Jianxiong, Zhang, Yuanxun
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2019
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Abstract In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time. [Display omitted] •PMF was used to identify the main sources of personal PM2.5 exposure.•Secondary source varied between subjects with different personal behavior.•Cadmium source was found to be a factor related to smoking exposure. Primary ambient sources presented significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behaviors.
AbstractList In the study, personal PM₂.₅ exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM₂.₅ monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM₂.₅ data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM₂.₅ exposures and produced poor correlations between personal and ambient PM₂.₅. These results demonstrate that individual behavior strongly affects personal PM₂.₅ exposure. Six primary sources of personal PM₂.₅ exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM₂.₅ exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.
In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.
In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time. [Display omitted] •PMF was used to identify the main sources of personal PM2.5 exposure.•Secondary source varied between subjects with different personal behavior.•Cadmium source was found to be a factor related to smoking exposure. Primary ambient sources presented significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behaviors.
Author Schauer, James J.
Zhang, Yang
Zhang, Yuanxun
Khuzestani, Reza Bashiri
An, Jianxiong
Fang, Dongqing
Shang, Jing
Tian, Jingyu
Hua, Jinxi
Cai, Tianqi
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  fullname: Khuzestani, Reza Bashiri
  organization: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
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  givenname: Jingyu
  surname: Tian
  fullname: Tian, Jingyu
  organization: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
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  givenname: James J.
  surname: Schauer
  fullname: Schauer, James J.
  organization: Environmental Chemistry and Technology Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
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  givenname: Jinxi
  surname: Hua
  fullname: Hua, Jinxi
  organization: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
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  organization: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
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  givenname: Jianxiong
  surname: An
  fullname: An, Jianxiong
  organization: Department of Anesthesiology, Pain Medicine and Critical Care Medicine, Aviation General Hospital of China Medical University, Beijing, 100012, China
– sequence: 10
  givenname: Yuanxun
  surname: Zhang
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Snippet In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The...
In the study, personal PM₂.₅ exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The...
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SubjectTerms cadmium
China
combustion
dust
metropolitan areas
particulates
Personal behavior
Personal exposure
questionnaires
roads
seasonal variation
Smoking
smoking (habit)
soil
Source apportionment
students
suburban areas
summer
volunteers
winter
Title Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing
URI https://dx.doi.org/10.1016/j.envpol.2018.11.076
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