| 摘要(英) | Abstract
Objective: Many panel studies have indicated the relationship between the concentrations of particulate matter (PM) and the health status of children with asthma. Most of the studies used PM (or other air pollutants) data obtained from the ambient air monitoring sites to access the health effects for susceptible groups, while the data gathered from the ambient sites might introduce certain statistical errors or biases. As a result, it is difficult to estimate the relation between PM exposures and health effect. Currently, results of previous studies have been inconsistent with the effects of fine particle (PM2.5) and coarse particle (PM2.5-10) on the peak expiratory flow rate (PEFR) for asthmatic children. Therefore, we conducted the research to achieve further understanding of the effects of different PM diameters and time frames on the peak expiratory flow rate for asthmatic children.
Method: In this study, we recruited 30 asthmatic children form SinTai Elementary School, SinChuang City, and used a portable particle monitor (GRIMM 1.108, Germany) to measure their microenvironmental particle exposures (PM10, PM2.5, and PM1). Subjects’ PEFR data were obtained using PF-100 (Microlife, Taiwan) twice a day in both of the morning and the evening. In order to estimate the actual PM exposures in different microenvironments, activity data were also recorded via time-activity diary.
Linear mixed-effect model was used to analyze the association between PM exposures and peak expiratory flow rate in different time frames and different diameters. In the study, lag effects of PM concentrations on PEFR were observed by different time frames, including 24-hour PM concentrations before the PEFR tests (Lag 0), 24-hour to 48-hour PM concentrations before the PEFR tests (Lag 1), 48-hour to 72-hour PM concentrations before the PEFR tests (Lag 2), 2-day cumulative PM concentrations before the PEFR tests (2-day mean), and 3-day cumulative PM concentrations before the PEFR tests (3-day mean). In addition to microenvironmental PM exposures, PM concentrations measured at the particle Supersite were also applied in the analysis to evaluate the PM effects on the PEFR changes.
Results: The mean microenvironmental concentrations of each PM size for asthmatic children (PM10 = 51.9 ?g/m3, PM2.5-10 = 17.9 ?g/m3, and PM2.5 = 34.0 ?g/m3) were significantly higher than the corresponding concentrations gathered from the Supersite (PM10 = 48.4 ?g/m3, PM2.5-10 = 17.0 ?g/m3, PM2.5 = 31.4 ?g/m3). Although results obtained from the linear mixed-effect model analysis were not statistically significant, several relationship trends were observed. For example, PM2.5-10 levels showed greater lag effects (Lag 0, Lag 1, and Lag 2) on the morning PEFR, while no apparent trends were found for the cumulative effects of coarse particle and fine particle. Furthermore, PM1 and PM2.5 had stronger lag effect of Lag 2 on the morning PEFR. PM2.5-10 was the only size of particles shown effects (lag and cumulative effects) on evening PEFR. Lastly, stronger Pearson’s coefficients were found for the association between microenvironmental exposures and Supersite ambient levels for fine particles, as compared to those for coarse particles. Results of the linear mixed-effect model also suggested microenvironmental PM data were better fit for children’s PEFR change assessment.
Conclusion: In this study, asthmatic children’s averaged microenvironmental PM exposures were significantly higher than the corresponding ambient PM concentrations measured at the Supersite. Stronger variability was found for microenvironmental PM exposures as compared to the corresponding ambient levels. Although no statistically significant trends were shown between PM exposures and asthmatic children’s PEFR decreases, some relationship trends were observed. Finally, compared to the Supersite, the PM data monitored in different microenvironments were better fit for children’s PEFR change assessment.
Key words: particulate matter, asthmatic children, peak expiratory flow rate, microenvironmental exposures, lag effect, cumulative effect
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