Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong
https://orcid.org/0000-0001-6749-8358
https://orcid.org/0000-0003-1000-7241
https://orcid.org/0000-0002-2826-0950
Abstract Epidemiological studies provide evidence of the harmful effects of source-specific fine particulate matter (PM2.5) on human health. Studies regarding relative contributions of multiple sources to personal exposure are limited and inconsistent. Personal exposure monitoring for PM2.5 was conducted in 48 adult subjects (ages 18‒63 years) in Hong Kong between June 2014 and March 2015. We identified seven sources of personal PM2.5 exposure using Positive Matrix Factorization (PMF). These sources included regional pollution (associated with coal combustion and biomass burning), secondary sulfate, tailpipe exhaust, secondary nitrate, crustal/road dust, and shipping emission sources. For personal PM2.5 exposure, one additional source related to individuals' activities was found: non-tailpipe pollution (characterized by Fe, Mn, Cr, Cu, Sr). We also applied principal component analysis (PCA) for PM2.5 source identification. The results revealed similar factor/component profiles using PMF and PCA, with some discrepancies in the number of factors. PCA/absolute principal component scores (PCA/APCs) coupled with a linear mixed-effects model (LMM) was applied to the same dataset for source apportionment, adjusting for temperature and relative humidity. Furthermore, stratified PCA/APCs-LMM models were applied to estimate season- and group-specific source contributions of personal PM2.5 exposure. A mixed source contributions of secondary sulfate, secondary nitrate, and regional pollution were shown (35.1–43.6%), with no seasonal or subject group differences (p > 0.05). Shipping emissions were ubiquitous, contributing 6.3–8.8% of personal PM2.5 exposure for all subjects. Tailpipe exhaust and traffic-related particles varied by season (p < 0.01) and subject group (p < 0.05). Caution should be taken when using source-specific PM2.5 as proxies for the corresponding personal exposures in epidemiological studies.
Graphical abstract Display Omitted
Highlights Personal exposure to PM2.5 mass and chemical components often exceeds the corresponding ambient measurements. PMF analysis identified seven PM2.5 source factors from samples collected during personal monitoring of adults in Hong Kong. PCA/APCs combined with linear mixed-effects models were applied to account for discrepancies in source contributions. Daily individual activities influenced PM2.5 exposures from traffic-related sources.
Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong
https://orcid.org/0000-0001-6749-8358
https://orcid.org/0000-0003-1000-7241
https://orcid.org/0000-0002-2826-0950
Abstract Epidemiological studies provide evidence of the harmful effects of source-specific fine particulate matter (PM2.5) on human health. Studies regarding relative contributions of multiple sources to personal exposure are limited and inconsistent. Personal exposure monitoring for PM2.5 was conducted in 48 adult subjects (ages 18‒63 years) in Hong Kong between June 2014 and March 2015. We identified seven sources of personal PM2.5 exposure using Positive Matrix Factorization (PMF). These sources included regional pollution (associated with coal combustion and biomass burning), secondary sulfate, tailpipe exhaust, secondary nitrate, crustal/road dust, and shipping emission sources. For personal PM2.5 exposure, one additional source related to individuals' activities was found: non-tailpipe pollution (characterized by Fe, Mn, Cr, Cu, Sr). We also applied principal component analysis (PCA) for PM2.5 source identification. The results revealed similar factor/component profiles using PMF and PCA, with some discrepancies in the number of factors. PCA/absolute principal component scores (PCA/APCs) coupled with a linear mixed-effects model (LMM) was applied to the same dataset for source apportionment, adjusting for temperature and relative humidity. Furthermore, stratified PCA/APCs-LMM models were applied to estimate season- and group-specific source contributions of personal PM2.5 exposure. A mixed source contributions of secondary sulfate, secondary nitrate, and regional pollution were shown (35.1–43.6%), with no seasonal or subject group differences (p > 0.05). Shipping emissions were ubiquitous, contributing 6.3–8.8% of personal PM2.5 exposure for all subjects. Tailpipe exhaust and traffic-related particles varied by season (p < 0.01) and subject group (p < 0.05). Caution should be taken when using source-specific PM2.5 as proxies for the corresponding personal exposures in epidemiological studies.
Graphical abstract Display Omitted
Highlights Personal exposure to PM2.5 mass and chemical components often exceeds the corresponding ambient measurements. PMF analysis identified seven PM2.5 source factors from samples collected during personal monitoring of adults in Hong Kong. PCA/APCs combined with linear mixed-effects models were applied to account for discrepancies in source contributions. Daily individual activities influenced PM2.5 exposures from traffic-related sources.
Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong
https://orcid.org/0000-0001-6749-8358
https://orcid.org/0000-0003-1000-7241
https://orcid.org/0000-0002-2826-0950
Abstract Epidemiological studies provide evidence of the harmful effects of source-specific fine particulate matter (PM2.5) on human health. Studies regarding relative contributions of multiple sources to personal exposure are limited and inconsistent. Personal exposure monitoring for PM2.5 was conducted in 48 adult subjects (ages 18‒63 years) in Hong Kong between June 2014 and March 2015. We identified seven sources of personal PM2.5 exposure using Positive Matrix Factorization (PMF). These sources included regional pollution (associated with coal combustion and biomass burning), secondary sulfate, tailpipe exhaust, secondary nitrate, crustal/road dust, and shipping emission sources. For personal PM2.5 exposure, one additional source related to individuals' activities was found: non-tailpipe pollution (characterized by Fe, Mn, Cr, Cu, Sr). We also applied principal component analysis (PCA) for PM2.5 source identification. The results revealed similar factor/component profiles using PMF and PCA, with some discrepancies in the number of factors. PCA/absolute principal component scores (PCA/APCs) coupled with a linear mixed-effects model (LMM) was applied to the same dataset for source apportionment, adjusting for temperature and relative humidity. Furthermore, stratified PCA/APCs-LMM models were applied to estimate season- and group-specific source contributions of personal PM2.5 exposure. A mixed source contributions of secondary sulfate, secondary nitrate, and regional pollution were shown (35.1–43.6%), with no seasonal or subject group differences (p > 0.05). Shipping emissions were ubiquitous, contributing 6.3–8.8% of personal PM2.5 exposure for all subjects. Tailpipe exhaust and traffic-related particles varied by season (p < 0.01) and subject group (p < 0.05). Caution should be taken when using source-specific PM2.5 as proxies for the corresponding personal exposures in epidemiological studies.
Graphical abstract Display Omitted
Highlights Personal exposure to PM2.5 mass and chemical components often exceeds the corresponding ambient measurements. PMF analysis identified seven PM2.5 source factors from samples collected during personal monitoring of adults in Hong Kong. PCA/APCs combined with linear mixed-effects models were applied to account for discrepancies in source contributions. Daily individual activities influenced PM2.5 exposures from traffic-related sources.
Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong
Chen, Xiao-Cui (Autor:in) / Ward, Tony J. (Autor:in) / Cao, Jun-Ji (Autor:in) / Lee, Shun-Cheng (Autor:in) / Lau, Ngar-Cheung (Autor:in) / Yim, Steve HL. (Autor:in) / Ho, Kin-Fai (Autor:in)
Atmospheric Environment ; 218
20.09.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| Taylor & Francis Verlag | 2019
Source analysis of high particulate matter days in Hong Kong
| Elsevier | 2008