北京市大气PM10浓度与居民呼吸系统疾病死亡的时空分析研究  

Spatial-temporal analysis for correlation between ambient inhalable particles and population respiratory disease mortality in Beijing, China

作　　者：张亚娟[1,2] 周健[2] 尹洪磊[3] 胥美美[1] 王五一[4] 潘小川[1] 

机构地区：[1]北京大学公共卫生学院,北京100191 [2]宁夏医科大学公共卫生学院 [3]赤峰学院第三附属医院 [4]中国科学院地理科学与资源研究所

出　　处：《环境与健康杂志》2014年第9期753-756,F0003,共5页Journal of Environment and Health

基　　金：国家自然科学基金(81273033)

摘　　要：目的 探讨北京市2008—2009年大气PM10浓度的时空分布特征和人群暴露水平,分析北京市大气PM10浓度对居民呼吸系统疾病死亡的暴露-反应关系。方法 采用克里格插值模型对研究期间北京市大气PM10的日均浓度进行估计,采用时间序列的广义相加混合效应模型分析大气PM10浓度对居民呼吸系统疾病死亡的暴露-反应关系。结果 北京市2008—2009年大气PM10的日均浓度为118.6μg/m3,高于GB 3095—1996《环境空气质量标准》二级标准。研究期间大气PM10浓度呈现自北向南逐渐升高的空间分布规律。北京市大气PM10浓度每升高10μg/m3对呼吸系统疾病死亡的超额危险度为0.56%（95%CI：0.28%~0.83%）。引入多污染物模型后大气PM10浓度对呼吸系统疾病死亡的超额危险度略有减小,但仍有统计学意义（P〈0.05）。结论 采用克里格插值模型能够较为精确地估计北京市大气PM10浓度的空间分布状况;大气PM10浓度对北京市居民呼吸系统死亡存在一定的暴露-反应关系。Objective To explore the spatial-temporal characteristics of ambient particulate matter （PM10） mass concentration and formulate the exposure-response relationship between the ambient PM10 level and respiratory disease mortality of the exposed population in Beijing. Methods Kriging interpolation model was used to estimate the spatial-temporal characteristics and concentration of ambient PM10 geographically around Beijing and optimized based on the results of cross-validation. Generalized additive mixed model （GAMM） was used to examine the city-wide association between PM10 and respiratory disease mortality adjusting for the random effects of districts. Results The daily average concentration of ambient PM10 of Beijing was 118.6 μg/m3 during 2008 to 2009, higher than secondary standard of Ambient Air Quality Standard （AAQS, GB 3095-1996）. The spatial distribution of ambient PM10 concentration presented to increase gradually from the northern to the southern in Beijing. A 10 μg/m3 increase in the PM10 was associated with an increase of 0.56% （95%CI： 0.28%-0.83%） in respiratory mortality of the population in Beijing. Although, the odds ratio decreased after introducing the multiple pollutants models, it was also statistically significant （P〈0.05）. Conclusion Kriging interpolation models performs a good estimation of spatial distribution of the PM10 concentration across Beijing. The methods used in this study which combined the Kriging interpolation and GAMM may assess the exposure-response relationship between PM10 level and respiratory mortality in Beijing.

关 键 词：大气污染 呼吸系统疾病死亡 克里格插值 时空分析 

分 类 号：R181.3[医药卫生—流行病学]