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Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing

OBJECTIVE: Ambient fine particulate matter (PM(2.5)) pollution is currently a major public health concern in Chinese urban areas. However, PM(2.5) exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM(2.5) mass concentrations for...

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Detalles Bibliográficos
Autores principales: Huang, Lihui, Pu, Zhongnan, Li, Mu, Sundell, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580321/
https://www.ncbi.nlm.nih.gov/pubmed/26397734
http://dx.doi.org/10.1371/journal.pone.0138559
Descripción
Sumario:OBJECTIVE: Ambient fine particulate matter (PM(2.5)) pollution is currently a major public health concern in Chinese urban areas. However, PM(2.5) exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM(2.5) mass concentrations for urban residences in Beijing. METHODS: In this study, 24-h real-time indoor and ambient PM(2.5) mass concentrations were concurrently collected for 41 urban residences in the non-heating season. The diurnal variation of pollutant concentrations was characterized. Pearson correlation analysis was used to examine the correlation between indoor and ambient PM(2.5) mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM(2.5) mass concentration. RESULTS: Hourly ambient PM(2.5) mass concentrations were 3–280 μg/m(3) with a median of 58 μg/m(3), and hourly indoor counterpart were 4–193 μg/m(3) with a median of 34 μg/m(3). The median indoor/ambient ratio of PM(2.5) mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM(2.5) mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM(2.5) mass concentrations on the community basis (coefficients: r≥0.90, p<0.0001), and the ambient data explained ≥84% variance of the indoor data. Regression analysis suggested that the variables, such as traffic conditions, indoor smoking activities, indoor cleaning activities, indoor plants and number of occupants, had significant influences on the indoor PM(2.5) mass concentrations. CONCLUSIONS: PM(2.5) of ambient origin made dominant contribution to residential indoor PM(2.5) exposure in the non-heating season under the high ambient fine particle pollution condition. Nonetheless, the large inter-residence variability of infiltration factor of ambient PM(2.5) raised the concern of exposure misclassification when using ambient PM(2.5) mass concentrations as exposure surrogates. PM(2.5) of indoor origin still had minor influence on indoor PM(2.5) mass concentrations, particularly at 11:00–13:00 and 22:00–0:00. The predictive models suggested that particles from traffic emission, secondary aerosols, particles from indoor smoking, resuspended particles due to indoor cleaning and particles related to indoor plants contributed to indoor PM(2.5) mass concentrations in this study. Real-time ventilation measurements and improvement of questionnaire design to involve more variables subject to built environment were recommended to enhance the performance of the predictive models.