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Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis.

Li-Sha LuoYunquan ZhangJunfeng JiangHanghang LuanChuanhua YuPeihong NanBin LuoMao You
Published in: International journal of environmental research and public health (2018)
In this study, we estimated the short-term effects of ambient air pollution on respiratory disease hospitalization in Taiyuan, China. Daily data of respiratory disease hospitalization, daily concentration of ambient air pollutants and meteorological factors from 1 October 2014 to 30 September 2017 in Taiyuan were included in our study. We conducted a time-series study design and applied a generalized additive model to evaluate the association between every 10-μg/m³ increment of air pollutants and percent increase of respiratory disease hospitalization. A total of 127,565 respiratory disease hospitalization cases were included in this study during the present period. In single-pollutant models, the effect values in multi-day lags were greater than those in single-day lags. PM2.5 at lag02 days, SO₂ at lag03 days, PM10 and NO₂ at lag05 days were observed to be strongly and significantly associated with respiratory disease hospitalization. No significant association was found between O₃ and respiratory disease hospitalization. SO₂ and NO₂ were still significantly associated with hospitalization after adjusting for PM2.5 or PM10 into two-pollutant models. Females and younger population for respiratory disease were more vulnerable to air pollution than males and older groups. Therefore, some effective measures should be taken to strengthen the management of the ambient air pollutants, especially SO₂ and NO₂, and to enhance the protection of the high-risk population from air pollutants, thereby reducing the burden of respiratory disease caused by ambient air pollution.
Keyphrases
  • air pollution
  • particulate matter
  • lung function
  • heavy metals
  • physical activity
  • chronic obstructive pulmonary disease
  • risk assessment
  • deep learning
  • electronic health record
  • artificial intelligence