Estimation of PM2.5 infiltration factors and personal exposure factors in two megacities, China. (February 2019)

• Record Type: Journal Article
• Authors: Li, Na; Liu, Zhe; Li, Yunpu; Li, Ning; Chartier, Ryan; McWilliams, Andrea; Chang, Junrui; Wang, Qin; Wu, Yaxi; Xu, Chunyu; Xu, Dongqun
• Is Part Of: Building and environment. Volume 149(2019)
• Page Start: 297
• Page End: 304
• Publication Date: 2019-02
• Subjects: PM2.5 -- Infiltration -- Personal exposure -- Personal-indoor-outdoor relationship -- Seasonal variability -- Predictive model; PM2.5 fine particulate matter -- Finf infiltration factor -- Fpex ambient personal exposure factor -- HVAC heating, ventilation, and air-conditioning systems -- NHS non-heating season -- HS heating season -- AC air conditioning -- GPS global position system -- AQMS air quality monitoring sites -- TAD time-activity diary; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2018.12.033
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: This study estimates infiltration factors ( F inf ) and ambient personal exposure factors ( F pex ) for fine particulate matter (PM2.5 ) in two Chinese megacities, and constructs predictive models to explore their determinants. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days from 33 residences (of retired adults) in Nanjing (NJ) and Beijing (BJ), China, in both the non-heating season (NHS) and the heating season (HS). Elemental sulfur in filter deposits was determined by energy-dispersive X-ray fluorescence for PM2.5 F inf and F pex estimations. Season-specific models developed by stepwise multiple linear regression were evaluated using R 2 and root mean square error (RMSE). The median [interquartile range (IQR)] of F inf varied from 0.76 (0.15) in the HS to 0.93 (0.11) in the NHS in NJ; and from 0.67 (0.16) to 0.86 (0.12) in BJ. Similarly, F pex was significantly higher during the NHS [NJ: 0.95 (0.07); BJ: 0.89 (0.14)] than during the HS [NJ: 0.76 (0.17); BJ: 0.67 (0.11); p < 0.0001]. Common predictors of F inf and F pex included window opening behaviors, meteorological variables, and building age. Moreover, air conditioning and distance to the nearest major road had an influence on F inf, while predictors of F pex were more related to human behavior and activity (e.g., time spent outdoors and transportation). The models accounted for 35.4%–68.1% (RMSE: 0.065–0.101) and 41.6%–77.0% (RMSE: 0.033–0.103) of the variance in F inf and F pex, respectively. By indicating the determinants of F inf and F pex, these models can improve ambient PM2.5 exposure assessment and reduce exposure misclassification. Highlights: Both F inf and F pex were significantly higher during non-heating season than heating season. Considerable variations in F inf and F pex between cities were found. F pex were highly correlated with F inf except in non-heating season in Nanjing. Key factors influencing F inf and F pex were identified by predictive model.