The International Agency for Research on Cancer and the World Health Organization have designated airborne particulates, including particulates of median aerodynamic diameter 〈 2.5 gm （PM2.5）, as Group 1 carcinogen...The International Agency for Research on Cancer and the World Health Organization have designated airborne particulates, including particulates of median aerodynamic diameter 〈 2.5 gm （PM2.5）, as Group 1 carcinogens. It has not been determined, however, whether exposure to ambient PM2.5 is associated with an increase in respiratory related diseases. This meta-analysis assessed the association between exposure to ambient fine particulate matter （PM2.5） and the risk of respiratory tract diseases, using relevant articles extracted from PubMed, Web of Science, and Embase. In results, of the 1,126 articles originally identified, 35 （3.1%） were included in this meta-analysis. PM2.5 was found to be associated with respiratory tract diseases. After subdivision by age group, respiratory tract disease, and continent, PM2.5 was strongly associated with respiratory tract diseases in children, in persons with cough, lower respiratory illness, and wheezing, and in individuals from North America, Europe, and Asia. The risk of respiratory tract diseases was greater for exposure to traffic-related than non-traffic-related air pollution. In children, the pooled relative risk （RR） represented significant increases in wheezing （8.2%）, cough （7.5%）, and lower respiratory illness （15.3%）. The pooled RRs in children were 1.091 （95%CI： 1.049, 1.135） for exposure to 〈 25 gg/m3 PM2.5, and 1.126 （95%CI： 1.067, l. 190） for exposure to 〉 25 gg/m3 PM2.5. In conclusion, exposure to ambient PM2.5 was significantly associated with the development of respiratory tract diseases, especially in children exposed to high concentrations of PM2.5.展开更多
考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统...考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.81473013 and No.81673210)Jiangsu Province Blue Project of UniversityInnovation of Graduate Student Training Project in Jiangsu Province(KYLX15_0976)
文摘The International Agency for Research on Cancer and the World Health Organization have designated airborne particulates, including particulates of median aerodynamic diameter 〈 2.5 gm （PM2.5）, as Group 1 carcinogens. It has not been determined, however, whether exposure to ambient PM2.5 is associated with an increase in respiratory related diseases. This meta-analysis assessed the association between exposure to ambient fine particulate matter （PM2.5） and the risk of respiratory tract diseases, using relevant articles extracted from PubMed, Web of Science, and Embase. In results, of the 1,126 articles originally identified, 35 （3.1%） were included in this meta-analysis. PM2.5 was found to be associated with respiratory tract diseases. After subdivision by age group, respiratory tract disease, and continent, PM2.5 was strongly associated with respiratory tract diseases in children, in persons with cough, lower respiratory illness, and wheezing, and in individuals from North America, Europe, and Asia. The risk of respiratory tract diseases was greater for exposure to traffic-related than non-traffic-related air pollution. In children, the pooled relative risk （RR） represented significant increases in wheezing （8.2%）, cough （7.5%）, and lower respiratory illness （15.3%）. The pooled RRs in children were 1.091 （95%CI： 1.049, 1.135） for exposure to 〈 25 gg/m3 PM2.5, and 1.126 （95%CI： 1.067, l. 190） for exposure to 〉 25 gg/m3 PM2.5. In conclusion, exposure to ambient PM2.5 was significantly associated with the development of respiratory tract diseases, especially in children exposed to high concentrations of PM2.5.
文摘考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.
