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目的采用灰色关联分析探索北京市PM_(10)、SO_2和NO_2长期暴露与居民肺癌发病相关性及其在健康结局研究方面的应用。方法收集2004年1月—2010年12月北京市大气PM_(10)、SO_2和NO_2日平均浓度和气象因素日平均值以及北京市肿瘤医院2008—2010年的肺癌患者就诊资料,以北京市肺癌入院人数作为参考数列,以大气污染指标和气象指标作为比较数列,进行大气污染与居民肺癌入院数的灰色关联分析。结果不同污染物与北京市肺癌月均入院数的灰色关联度排序为PM_(10)>NO_2>SO_2;与累积1~3年污染物滑动平均浓度数列相比,累积4年的污染物滑动平均浓度数列与肺癌入院的关联度最大。结论本研究涉及的三种污染物中,对肺癌入院影响最大的是PM_(10)。灰色关联方法在空气污染与肺癌的相关性方面的应用有待进一步探索,需在进一步研究中补充更为详实和长期的数据。
Objective To explore the relationship between the long-term exposure of PM_ (10), SO_2 and NO_2 in Beijing and the incidence of lung cancer in residents in Beijing by gray relational analysis and its application in health outcomes study. Methods The data of daily average concentrations of PM 10, SO 2 and NO 2 and average daily values of air pollutants in Beijing from January 2004 to December 2010 were collected. The data of lung cancer patients from 2003 to 2010 in Beijing were analyzed retrospectively. Admission number as a reference series to the air pollution indicators and meteorological indicators as a comparative series of air pollution and residents of lung cancer hospital admission number of gray correlation analysis. Results The gray correlation degree of different pollutants and monthly average number of hospitalized lung cancer in Beijing was PM_ (10)> NO_2> SO_2. Compared with the series of average pollutant concentration in 1 ~ 3 years, the average of four years of pollutant moving average The correlation between the concentration series and lung cancer admission is the largest. Conclusion Among the three pollutants involved in this study, the most significant contributor to lung cancer was PM_ (10). The application of gray correlation method in the correlation between air pollution and lung cancer needs to be further explored, and more detailed and long-term data need to be supplemented in further research.