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目的定量研究广州市大气细颗粒污染物(PM_(2.5))对呼吸系统疾病门诊量的影响。方法采用环境流行病学方法,收集2015年1月1日—12月31日大气PM_(2.5)等污染物资料、气象资料以及广州市1家三级甲等综合性医院呼吸系统疾病日门诊量资料,采用广义相加模型研究广州市大气PM_(2.5)与呼吸系统疾病日门诊量的关系(同时控制长期趋势、星期几效应和气象因素等混杂因素的影响)。结果 2015年广州市大气PM_(2.5)平均浓度为38.6μg/m~3,Spearman相关性分析表明,呼吸系统疾病日门诊量与SO_2、NO_2、CO、气压均成正相关,均有统计学意义(P<0.05)。GAM模型分析结果显示,PM_(2.5)对呼吸系统疾病日门诊量呈现出0~3 d的明显的滞后效应,均有统计学意义。PM_(2.5)滞后1 d效应最大,滞后1 d时,模型结果预测ER(%)为2.847(1.310,4.408),PM_(2.5)每增加10μg/m3,呼吸系统疾病门诊量增加2.847%。结论广州市大气PM_(2.5)与呼吸系统疾病日门诊量成正相关关系,且存在滞后效应。
Objective To quantitatively study the effects of fine particulate pollutants (PM_ (2.5)) on the outpatient respiratory diseases in Guangzhou. Methods Environmental epidemiological methods were used to collect the data of atmospheric PM2.5 and other meteorological data from January 1 to December 31, 2015 and the daily outpatient visits of respiratory diseases in one Grade III A general hospital in Guangzhou Data, the generalized additive model was used to study the relationship between the PM_ (2.5) and the daily outpatient respiratory disease in Guangzhou (while controlling the long-term trend, the day of the week and the meteorological factors). Results The average PM 2.5 concentration in Guangzhou was 38.6 μg / m 3 in 2015, and Spearman correlation analysis showed that the outpatient volume of respiratory diseases was positively correlated with SO 2, NO 2, CO and air pressure (P < P <0.05). The results of GAM model analysis showed that the PM_ (2.5) showed a significant lag effect on day outpatients with respiratory diseases for 0 ~ 3 days, both of which were statistically significant. PM1.5 lagged 1 d, and the lagged 1 d, model predictions ER (%) was 2.847 (1.310, 4.408) and PM2.5 increased by 10%. Conclusion The PM_ (2.5) in Guangzhou has a positive correlation with the outpatient respiratory disease, and there is a lagging effect.