Journal of Shandong University (Health Sciences) ›› 2018, Vol. 56 ›› Issue (11): 68-75.doi: 10.6040/j.issn.1671-7554.0.2018.538

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Effect of airborne particulates concentration on outpatient visits of respiratory diseases among children in Shijiazhuang City

CHEN Lang, ZHAO Chuan, CHEN Fengge, BAI Ping   

  1. 1. Department of Preventive Medicine, North China University of Science and Technology, Tangshan 063210, Hebei, China;
    2. Institute of Environmental Health, Shijiazhuang Center for Disease Control and Prevention, Shijiazhuang 050011, Hebei, China
  • Published:2022-09-27

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Abstract: Objective To investigate the effect of the concentrations of airborne particulates PM10 and PM2.5 on daily outpatient visits of respiratory diseases among children in Shijiazhuang City. Methods The data of PM10 and PM2.5 concentrations, meteorological factors(daily average temperature and relative humidity), and daily outpatient visits of respiratory diseases among children during 2013 and 2017 were collected and described in Shijiazhuang City. A time series analysis using a generalized addictive model(GAM)was applied to evaluate the association between pediatric outpatient visits for respiratory diseases and PM10 and PM2.5 exposure. Results The average concentrations of PM10 and PM2.5 in Shijiazhuang City were respective 195.03 and 107.13 μg/m3. Time series analysis showed that an increase of 10 μg/m3 of PM10 and PM2.5 could increase the risk of outpatient visits of respiratory diseases by 0.11%(95%CI: 0.07%-0.16%)and 0.22%(95%CI: 0.15%-0.28%), respectively. After adjusting the gaseous pollutant O3/SO2/NO2, the acute health effects of PM10 disappeared and the health effects of PM2.5 declined slightly. Conclusion The increase of PM10 and PM2.5 concentrations may increase outpatient visits of respiratory diseases in children, recommending the relevant departments should strengthen control of air pollution and protect childrens health.

Key words: Air pollution, Respiratory disease, Daily outpatient visits, Children, Time series analysis

CLC Number: 

  • R122.7
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