北京市MODIS气溶胶光学厚度与PM2.5浓度关系研究

发布时间：2018-01-01 09:28

本文关键词：北京市MODIS气溶胶光学厚度与PM2.5浓度关系研究　出处：《成都理工大学》2015年硕士论文　论文类型：学位论文

【摘要】：近年来全国各地频现的雾霾天气,不但制约和影响着我们国民经济的发展,更威胁着我们每个人的生命健康。气溶胶是雾霾形成的基础和前提,人类活动排放的污染物中包括直接排放的气溶胶和各种气态污染物,通过光化学转化,这些物质可形成二次气溶胶,进而演变成灰霾,这就使得危害人体健康的细颗粒物PM2.5的浓度进一步升高。目前我国大气环境常规监测手段仍是通过建立地面监测站的方式,我国幅员辽阔,想要监测大区域尺度的空气质量状况,实现区域、全球的大气环境质量监测,显然现有的地面监测站的数量远不能满足需求。卫星遥感监测手段为我们提供了天地一体化的监测体系,卫星遥感在大气环境监测方面具有广覆盖、连续性、空间性和预测性的独特优势,能够在更大尺度的空间范围内快速、实时、准确地获取大气环境状况。气溶胶光学厚度是气溶胶胶体的重要光学特征,通过对气溶胶光学厚度的反演,建立其和地基空气质量监测的PM2.5浓度的关系模型,可以获得大尺度区域的近地表PM2.5的浓度,弥补了地基空气质量监测中由于地面监测站的数量有限而无法监测大范围区域空气质量指标的不足。本文以近年来空气污染较为严重的北京市为研究区域,选取2013年到2014年晴朗天气拍摄的Terra-MODIS卫星遥感影像数据作为研究区的样本数据,利用当前发展较成熟的暗像元方法,基于ENVI5.0平台反演出北京各天的气溶胶光学厚度。与此同时收集与遥感影像获取相同时间段内监测得到的北京市12个监测站点PM2.5的瞬时浓度值,并利用空间地统计分析原理对北京市PM2.5浓度的整体特征分别在空间和时间维度进行分析。由于利用暗像元算法反演气溶胶光学厚度有其特定的适用条件,比如在植被密度较高的地方反演效果较低植被覆盖度的区域好,在春夏季节由于植被较秋冬季节密度高,所以春夏季节的反演效果好,秋冬季节存在反演结果误差大的情况,所以要选取气溶胶光学厚度反演结果中质量好的部分,对应各个监测站点的位置提取出瞬时气溶胶光学厚度值和监测站得到的PM2.5的浓度值,然后对这两个值做相关分析和回归分析,分别建立以线性函数、二次函数、三次函数、指数函数、对数函数和乘幂函数为基础的回归模型,通过对回归模型拟合优度R2及模型检验精度的对比选择出最优拟合模型。论文基于上述研究方法,取得的成果如下:(1)利用暗像元算法对北京地区的气溶胶光学厚度值进行了反演,并筛选出满足要求的反演结果,得到6幅夏季的反演结果图像,3幅春季的反演结果图像,1幅秋季的反演结果图像。(2)对北京市PM2.5值的变化趋势分别在时间维度和空间维度上进行了分析,冬季PM2.5值普遍较高,春季次之;夏、秋季节PM2.5值较低。从空间变化趋势上看PM2.5值呈现由北向南逐渐递增的趋势。(3)证明了气溶胶光学厚度与PM2.5相关性的存在。并且分季节进行相关性分析后,气溶胶光学厚度与PM2.5的pearson相关系数与双侧显著性都有了明显的提高,具有统计学意义,可以进行回归分析并建立回归模型。(4)通过回归分析,针对夏季和春季建立了气溶胶光学厚度与PM2.5值的6种回归模型,并筛选出拟合优度R2较高的三次模型、指数模型、乘幂模型做模型验证,最终确定乘幂模型为夏季和春季的最优拟合模型。
[Abstract]:The haze weather around the country are frequent in recent years, not only affects the development of our national economy, more threatening our lives. Aerosol health is the basis and prerequisite for the formation of haze, including direct emissions of aerosols and various gaseous pollutants emissions of human activities, through photochemical transformation, these substances can form two secondary aerosol, and then evolved into the haze, which makes the concentration of fine particles PM2.5 harmful to human health is still further increased. The conventional means of monitoring the atmospheric environment in China through the establishment of ground monitoring station, China's vast territory, the status of air quality monitoring, to achieve large scale regional atmospheric environmental quality monitoring the world, obviously the existing ground monitoring stations can not meet the demand. The number of satellite remote sensing monitoring provides world integrated monitoring for us Measuring system of satellite remote sensing in atmospheric environmental monitoring has wide coverage, continuity, the unique advantages of space and predictive, can quickly, in the space scope of the larger scale in real-time, accurately obtain atmospheric environmental conditions. The aerosol optical thickness is an important optical characteristics of aerosol colloid, through the inversion of aerosol optical thickness the relationship between the concentration of PM2.5, the model and establish the foundation for air quality monitoring, can obtain the PM2.5 concentration near the surface of large scale area, make up the foundation of air quality monitoring due to the limited number of ground monitoring stations to monitoring large area air quality index in this paper. In recent years, air pollution is more serious in Beijing city as the study area, from 2013 to the Terra-MODIS satellite remote sensing data in 2014 sunny weather shooting as the sample data of the study area, using the current development Dark pixel method is more mature, the anti ENVI5.0 platform performance in Beijing every day based on the instantaneous concentration of aerosol optical thickness. At the same time collecting and remote sensing images obtained in the same time monitoring of the 12 monitoring stations in Beijing city PM2.5, and using the spatial statistical analysis of overall characteristics of the concentration of PM2.5 in Beijing city in space and principle. The time dimension is analyzed. Due to the use of dark pixel algorithm to retrieve aerosol optical thickness has its special application condition, such as in the local areas of high vegetation density inversion effect of low vegetation coverage, in spring and summer than in autumn and winter festival because the vegetation density is high, so the inversion effect of spring and summer, autumn and winter are the inversion results the error is large, so we should choose the aerosol optical thickness inversion results in good quality parts, each monitoring site location to extract transient The concentration of aerosol optical thickness and the value of air monitoring station get the value of PM2.5, then do the correlation analysis and regression analysis of these two values were established with linear function, quadratic function, cubic function, exponential function, logarithmic function and power function regression model as the foundation, through the comparison of the goodness of fit of R2 and the model testing precision of regression model to select the best fitting model. The method is based on the above research, the results are as follows: (1) the dark pixel algorithm of aerosol optical depth in Beijing area were selected and inversion, the inversion results meet the requirements, the inversion results obtained 6 summer images, 3 pieces of spring the inversion results of images, 1 images of the image retrieval results fall. (2) the change trend of Beijing city PM2.5 values in the dimension of time and space to carry on the analysis, the PM2.5 value is generally higher in winter, summer, autumn and spring; Seasonal low PM2.5 value. The PM2.5 value showed gradually increasing trend from the south to the north from the spatial change trend. (3) proved that the aerosol optical thickness and PM2.5 correlation. And seasonal correlation analysis, Pearson correlation coefficient and significant bilateral aerosol optical thickness and PM2.5 have been significantly improved, have statistical significance, can carry out regression analysis and the regression model (4). Through regression analysis, 6 Regression Model of aerosol optical thickness and PM2.5 value have been set up for the summer and spring, and selected the three model, the goodness of fit R2 high index model, model verification power model, and ultimately determine the power the model was the best fitting model for the spring and summer months.

【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：X513

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