Objective: To estimate the influence of the ambient PM(l0) and PM(2.5) 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 pattern 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 PM(10) and PM(2.5) levels were estimated by fitting a Poisson regression model. Results: A 10 μg/m(3) increase of PM(10) and PM(2.5) 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 NO(2) concentration was introduced, the daily hospital visits due to respiratory disease increased by 0.83% as a 10 μg/m(3) increase of PM(10) concentration (95% CI: 0.76%-0.91%). Conclusion: The ambient PM(l0) and PM(2.5) pollution was positively associated with daily hospital visits due to respiratory disease in Jinan, and ambient NO(2) concentration would have the synergistic effect.
目的： 分析济南市大气颗粒物PM(10)、PM(2.5)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PM(10)、PM(2.5)日均浓度与居民呼吸系统疾病日就诊人次间的关系，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PM(10)、PM(2.5)与呼吸系统就诊人次数存在关联，差异有统计学意义。当PM(10)、PM(2.5)浓度上升10 μg/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6 d的PM(10)、PM(2.5)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NO(2)拟合多污染物模型时，大气颗粒物PM(10)浓度上升10 μg/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PM(10)、PM(2.5)污染与居民呼吸系统疾病就诊人次间存在正相关，NO(2)污染浓度可增加其效应。.
Keywords: Daily hospital visit; Generalized addictive model; Inhalable particulates; Respiratory disease; Time series analysis.