TitleExposure and health impact evaluation based on simultaneous measurement of indoor and ambient PM2.5 in Haidian, Beijing
AuthorsQi, Meng
Zhu, Xi
Du, Wei
Chen, Yilin
Chen, Yuanchen
Huang, Tianbo
Pan, Xuelian
Zhong, Qirui
Sun, Xu
Zeng, Eddy Y.
Xing, Baoshan
Tao, Shu
AffiliationPeking Univ, Coll Urban & Environm Sci, Lab Earth Surface Proc, Beijing 100871, Peoples R China.
Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China.
Jinan Univ, Sch Environm, Guangzhou Key Lab Environm Exposure & Hlth, Guangzhou 510632, Guangdong, Peoples R China.
Jinan Univ, Guangdong Key Lab Environm Pollut & Hlth, Guangzhou 510632, Guangdong, Peoples R China.
Univ Massachusetts, Coll Nat Sci, Stockbridge Sch Agr, Amherst, MA 01003 USA.
KeywordsPM2.5
Indoor air
I/O
Modeling
Population exposure
PARTICULATE MATTER
AIR-POLLUTION
FINE PARTICLES
OUTDOOR
CHINA
ENVIRONMENTS
AEROSOLS
REGION
WINTER
Issue Date2017
PublisherENVIRONMENTAL POLLUTION
CitationENVIRONMENTAL POLLUTION.2017,220,704-712.
AbstractBecause people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM2.5 concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM2.5 concentrations suggests rapid transitions between polluted and non-polluted situations. The PM2.5 concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM2.5 concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM2.5 concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM2.5 concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM2.5 and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future. (C) 2016 Elsevier Ltd. All rights reserved.
URIhttp://hdl.handle.net/20.500.11897/457319
ISSN0269-7491
DOI10.1016/j.envpol.2016.10.035
IndexedSCI(E)
PubMed
Appears in Collections:城市与环境学院
地表过程分析与模拟教育部重点实验室

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