北京市空气质量指数与气象条件的关系及其对呼吸系统疾病的滞后影响

发布时间：2018-12-19 08:59

【摘要】：依据北京市2009年1月1日-2011年12月31日的逐日空气质量指数(AQI)与同时段常规地面气象观测数据,区分污染日与非污染日(以轻微污染下界为分界)、不同季节研究了AQI与气象要素的关系;应用分布滞后非线性模型与广义相加模型,在调整气温、相对湿度、风速等混杂效应的基础上,定量计算了AQI对呼吸系统急诊人数的影响及滞后效应.结果表明,研究时段北京AQI的年际波动并不明显,冬季中、重度污染易发;秋季空气优良率最低;春季易发生轻微污染.污染日AQI与平均气压、最高气压显著正相关,与气温、相对湿度负相关;非污染日,AQI与气压、平均/最大风速和日照时数负相关,而与气温、相对湿度、降水量正相关.就诊当天与滞后1 d的AQI对呼吸系统疾病的影响最显著,且危险度(RR)随污染物质量浓度的增大以接近线性形态增大.AQI指数每增加10,北京市呼吸系统急诊就诊人数的RR为0.22%,其95%置信区间为0.104%~0.342%.
[Abstract]:According to the daily air quality index (AQI) of Beijing from January 1, 2009 to December 31, 2011, and the conventional surface meteorological observation data of the same period, the pollution day and the non-pollution day (with the lower boundary of slight pollution as the boundary) are distinguished. The relationship between AQI and meteorological elements was studied in different seasons. On the basis of adjusting the mixed effects of air temperature, relative humidity and wind speed, the influence of AQI on the emergency population of respiratory system and the lag effect were calculated quantitatively by using the distributed lag nonlinear model and the generalized additive model. The results showed that the interannual fluctuation of AQI in Beijing was not obvious in the period of study, the severe pollution was easy to occur in winter, the excellent and good rate of air in autumn was the lowest, and the slight pollution occurred in spring. AQI was positively correlated with mean pressure, maximum air pressure, air temperature and relative humidity, while AQI was negatively correlated with air pressure, mean / maximum wind speed and sunshine hours, but positively correlated with air temperature, relative humidity and precipitation. The effects of AQI on respiratory diseases were most significant on the same day and one day later, and the risk (RR) increased with the increase of pollutant mass concentration in a linear form. The AQI index increased by 10. The RR of respiratory emergency patients in Beijing was 0.22 and its 95% confidence interval was 0.104 and 0.342.
【作者单位】：兰州大学大气科学学院甘肃省干旱气候变化与减灾重点实验室气象环境与人体健康研究中心;成都信息工程大学大气科学学院;
【基金】：公益性行业(气象)科研专项项目(GYHY201106034) 国家人口与健康科学数据共享平台建设项目
【分类号】：R12;X51

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