摘要
目的分析济南市大气颗粒物PM10、PM2．5日均浓度与当地居民呼吸系统疾病日就诊人次的相关性。方法收集2013—2015年济南市空气污染数据、气象数据和某综合医院每日呼吸系统就诊数，采用基于Poisson分布的广义相加模型的时间序列分析，控制长期趋势、星期几效应、气象因素等混杂因素的影响后，分析济南市大气颗粒物PM10、PM2．5；日均浓度与居民呼吸系统疾病日就诊人次问的关系，并考虑滞后效应和其他空气污染物的影响。结果大气颗粒物PM10、PM2．5，与呼吸系统就诊人次数存在关联，差异有统计学意义。当PM10、PM2．5，浓度上升10μg／m^3时，当天呼吸系统疾病就诊人次数分别增加0．36％（95％CI：0．30％～0．43％）和0．50％（95％CI：0．30％～0．70％）；滞后6d的PM10、PM2．5，浓度的健康效应最强，超额危险度为0．65％（95％CI：0．58％-0．71％）和0．54％（95％CI：0．42％-0．67％）；当纳入NO2拟合多污染物模型时，大气颗粒物PM10浓度上升10μg／m^3时，当天呼吸系统疾病就诊人次数增加0．83％（95％CI：0．76％-0．91％）。结论济南城区大气颗粒物PM10、PM2．5，污染与居民呼吸系统疾病就诊人次间存在正相关，NO2污染浓度可增加其效应。
Objective To estimate the influence of the ambient PM10 and PM2.5 pollution on the hospital outpatient department visit due to respiratory diseases in local residents in Jinan quantitatively. Methods Time serial analysis using generalized addictive model （GAM） was conducted. After controlling the confounding factors, such as long term trend, weekly pattem and meteorological factors, considering lag effect and the influence of other air pollutants, the excess relative risks of daily hospital visits associated with increased ambient PM10 and PM2.5 levels were estimated by fitting a Poisson regression model. Results A 10 μg/m3 increase of PM10 and PM2.5 levels was associated with an increase of 0.36% （95% CI： 0.30%-0.43% ） and 0.50% （95% CI： 0.30%-0.70%） respectively for hospital visits due to respiratory diseases. Lag effect of 6 days was strongest, the excess relative risks were 0.65% （95%CI： 0.58%-0.71% ） and 0.54%（95%CI： 0.42%- 0.67% ） respectively. When NO2 concentration was introduced, the daily hospital visits due to respiratory disease increased by 0.83% as a 10 μg/m^3 increase of PM,o concentration （95%CI： 0.76%- 0.91%）. Conclusion The ambient PM10 and PM2.5 pollution was positively associated with daily hospital visits due to respiratory disease in Jinan, and ambient NO2 concentration would have the synergistic effect.
出处
《中华流行病学杂志》
CAS
CSCD
北大核心
2017年第3期374-377,共4页
Chinese Journal of Epidemiology
关键词
可吸入颗粒物
呼吸系统疾病
广义相加模型
时间序列分析
日就诊人次
Inhalable particulates
Respiratory disease
Generalized addictive model
Time series analysis
Daily hospital visit
