中文摘要：

目的比较和分析不同大气温度指标在定量评价气温与人群呼吸系统疾病死亡相关性方面的不同特点及优劣。方法收集北京市H区2004--2008年气象数据、呼吸系统疾病每日死亡人数及同期大气污染物数据，采用时间序列分析中广义相加模型（GAM），通过平滑样条函数控制长期趋势、短期波动、其他气象因素及大气污染物带来的混杂效应，利用GCV值的大小来判断模型拟合程度的优劣，分析比较不同温度指标（日均温，13最高温，日最低温，当日温差，隔日温差）与全人群呼吸系统死亡的相关性。结果半参数模型拟合效果最优指标为日最高温（GCV=1．290）；日最高温每升高1℃，发生呼吸系统疾病死亡的RR值为1．021（P〈0．05）。季节分层结果显示，夏季日最高温、日均温每升高1℃，发生呼吸系统疾病死亡的RR值分别为1．040和1．053；冬季日最高温、日均温、当日温差每升高1℃，发生呼吸系统疾病死亡的RR值分别为1．042、1．033和1．026，均有统计学意义（P〈0．05或P〈0．01）；春、秋季尚未发现有统计学意义的温度指标。春、夏季最优温度指标分别为日最低温（GCV=1．374）、日均温（GCV=1．516），秋、冬季最优温度指标均为日最高温（GCV分别为1．439和1．500）。结论日最高温与呼吸系统疾病死亡率的相关性最强，是该类研究的优选指标。不同季节各温度指标的拟合优度不同，就北京市的气候特点而言，春、夏季分别优选日最低温、日均温，秋、冬季均优选日最高温。

英文摘要：

Objective To compare and analyze the effects of different temperature indicators on daily mortality due to respiratory diseases, with adjustment of the weather factors and air pollutants. Methods The data of daily mortality due to respiratory diseases, air temperature and the other meteorological factors, as well as the air pollutants were collected in H district of Beijing from 2004 to 2008 and time series analysis was used. Generalized additive model （GAM） with smoothing spline function was used to adjust the long term trend, short term volatility and mixed effect caused by weather changes. Different temperature indicators were put into the model, which were daily average temperature, daily maximum temperature, daily minimum temperature, daily temperature difference and temperature difference in two days, and judgments of the quality of fitting degrees were performed by comparing general cross validation （GCV） values of them. Results The best indicator for air temperature was daily maximum temperature （GCV=1.290）. The higher for every 1℃ in the highest daily temperature, the 2.1% of increase for the risk .of excess death due to respiratory diseases was （P〈0.05）. The result of stratified analysis on seasons showed that 1℃ increase of daily maximum temperature and daily average temperature, the excess risks of mortality due to respiratory system diseases were 0.040 and 0.053 respectively in summer （P〈0.05）. In winter, 1℃ increase of daily maximum temperature, daily average temperature and daily temperature difference, the excess risks of mortality were 0.042 （P〈0.01）, 0.033 （P〈0.05）, 0.026 （P〈0.05） respectively. No statistically significant difference was found for other seasons. Conclusion The daily maximum temperature is closely associated with the risk of respiratory mortality, and is the most appropriate temperature indicators around Beijing. The appropriate indicators are daily minimum temperature in spring, daily average temperature in summer, and daily maximu