济南市典型空气污染物与慢性阻塞性肺炎的相关性研究

发布时间：2018-05-06 03:27

本文选题：典型空气污染物 + 慢性阻塞性肺炎　；参考：《现代预防医学》2015年18期

【摘要】：目的研究典型空气污染物(SO2、NO2、CO、PM10、O3)与慢性阻塞性肺炎的相关性,为疾病预防和污染治理提供依据。方法依托于地理信息系统操作平台,通过遥感影像反演结果与地面实测浓度的插值,得到研究区内5种污染物浓度的分布趋势;利用统计学原理,求平均、数理统计、建立回归分析等,得到最适宜于研究两者之间关系的模型;利用SPSS,分别以5种污染物月平均浓度为自变量,以每月的慢性阻塞性肺炎患者数量为因变量,进行相关性分析、建立散点图和函数模型,并比较各自参数。结果各污染物浓度分布与患者分布情况趋于一致:浓度高的区域,患者分布密集;SO2、NO2、PM10、CO、O3浓度与患者数量的相关性因子分别为0.681、0.576、0.755、0.611、0.519。结论空气污染越严重,即污染物浓度越高,病患分布越为密集;PM10浓度与病患数量之间函数模型的相关性因子最大,两者相关性最强。
[Abstract]:Objective to study the relationship between the typical air pollutant (so _ 2 / no _ 2) and chronic obstructive pneumonia (COPD) and to provide evidence for disease prevention and pollution control. Methods based on the operating platform of GIS, the distribution trend of five kinds of pollutants in the study area was obtained by interpolation of the inversion result of remote sensing image and the measured concentration on the ground, and the average and mathematical statistics were obtained by using the principle of statistics. Regression analysis was established to obtain the most suitable model for the study of the relationship between the two. Using SPSS, the monthly average concentrations of five pollutants were taken as independent variables and the monthly number of patients with chronic obstructive pneumonia as dependent variables. The scattered plot and function model are established and their parameters are compared. Results the concentration distribution of each pollutant tended to be consistent with that of the patients: in the areas with high concentration, the correlation factors between the concentration of COO _ 3 and the number of patients were 0.681n 0.576n0.7550.7550.110.6110.1919, respectively. Conclusion the more serious air pollution, that is, the higher the concentration of pollutants, the more the distribution of patients is the largest correlation factor between the concentration of PM10 and the number of patients, and the strongest correlation between the two.
【作者单位】：山东师范大学地理与环境学院;山东大学齐鲁医院呼吸内科;
【基金】：国家自然科学基金资助项目(41371395) 国家科技支撑计划项目(2012BAB11B01)
【分类号】：R563.1

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