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1、北京市热环境变化与空气质量分析研究 北京市热环境变化与空气质量分析研究 Analysis on Urban Heat Island and Air Quality【中文摘要】 采用传统的气象资料不能实时、动态、连续的反映地表温度空间分布的差异。卫星遥感为地表温度的求取提供了新思路,利用MODIS ( Moderate Resolution Imaging Spectroradiometer,中分辨率成像光谱仪)数据宏观、实时、周期性的特点,可以很好地对地表温度进行分析。随着遥感应用的深入,现有的热红外遥感数据地表温度反演算法分为三大类:单通道算法、分裂窗算法和多波段算法。本文比较了这三种算法,
2、并建立了基于MODIS和分裂窗算法的北京市地表温度反演模型。其中,用来演算地表温度的分裂窗算法是以NOAA-AVHRR所观测到的热辐射数据为基础。根据Planck热辐射函数,将AVHRR的两个热通道(即通道4和5)数据转化为相应的亮度温度。选择与AVHRR的两个热通道(4,10.5-11.3m和5,11.5-12.5m)波段范围接近的MODIS 31(10. 780- 11. 280m),32(11. 770- 12. 270m)两波段来计算。通过对Planck方程的辐射强度和温度计算模拟,发现温度和辐射强度的近似关系,由此建立线性方程,以简化计算。然后通过模拟计算得到大气透过率与大气水汽含量
3、的关系,从相同MODIS影像数据中计算得到MODIS 31/32波段透过率。最后运用分裂窗算法和MODIS的31和32通道反演地表温度。研究表明:利用MODIS数据和分裂窗算法反演北京市地表温度是可行的。结果发现:随着北京城市建设和城市化速度的加快,城市地表热量分布有着明显的季节变化分布特征。具体如下:春季,由于地表植被覆盖低,城市热岛强度很低、近似为0;夏季热岛强度和热岛面积都增至最大,热岛面积甚至超过五环,到达通州;秋季热岛强度和面积减小;冬季有冷岛现象存在,即城市地表温度低于乡村。揭示出植被覆盖对城市地表温度的影响显著。城市植被的分布及季节变化影响着城市热岛的强度与时空分布,对于降低热岛
4、效应有显著效果。大气气溶胶是指由自然过程和人类活动造成,悬浮在大气中沉降速度小、尺度范围为0.001-20m大小的液态或固态粒子。气溶胶对天气和气候起着重要作用,同时对人类的居住环境产生巨大的影响。遥感具有面状观测、准实时获取、更新周期短、成本低的特点,对城市地区大气污染的重要组成部分气溶胶,可以进行快速的监测。本文利用MODIS 1B数据为数据源,使用6S进行辐射传输计算构建查找表,进行气溶胶光学厚度的反演,并对其结果进行了比较。结果表明,该算法在假定气溶胶与大气分子的散射相函数相同的情况下,能较好得监测气溶胶,反映城市气溶胶的区域变化,结合北京市及周边地区的地理状况进行分析,具体如下:气溶
5、胶光学厚度的高值区基本分布在人口密集、交通和工商业活动频繁的地区,高分辨率卫星遥感结果对污染物的排放源分布监测具有潜在的应用价值,能够在一定程度上表征北京市空气质量的状况。通过对北京市地表温度和气溶胶光学厚度在空间水平尺度上的对比,以夏季为例,可以发现北京市地表温度和气溶胶光学厚度高值区虽然大致都是北京城区即人口密集、交通和商业活动频繁的地方,但北京城市热岛效应和空气污染主要来源于城市本身,温度对气溶胶浓度变化的影响并不大。由于温度在一定程度上可以表征城市热环境,气溶胶光学厚度在经过垂直和湿度影响因子订正后,可以作为监测颗粒物污染物地面分布的一个有效手段,用以表征城市污染,而可吸入颗粒物是目前
6、北京首要空气污染物,在此基础上,本文结合北京市环保局发布的空气质量状况和地面实测数据,从微观上对温度和可吸入颗粒物进行分析,发现城市环境中温度与可吸入颗粒物个数关联影响不大。从温度的变化来看,可吸入颗粒物的个数随着温度的升高反而减少,也就是说,温度高,可吸入颗粒物个数反而少,这可能是高温时地面湍流强烈,大气垂直方向输送较大,垂直方向有利于可吸入颗粒物的扩散,所以空气中的可吸入颗粒物个数偏少。【英文摘要】 With the acceleration of urbanization, the phenomenon of temperature in urban cities which is hi
7、gher than that in suburbs is called Urban Heat Island (UHI), which is put forward by Lake Howard in 1833. This paper chose split window algorithm to retrieve land surface temperature after comparing with single window algorithm, split window algorithm and multi-band algorithm. And this paper improve
8、s the split window algorithm of AVHRR, so as to be suitable for MODIS image data. In this paper, land surface temperature is retrieved by split window algorithm based on MODIS images, and the temporal and spatial characteristics of Beijing urban heat island as well as its influencing factors are als
9、o analyzed. The result indicates that there is a clear urban heat island in summer, and it is quite close with vegetation index. Besides, the negative correlation relationship between land surface temperature and PM10 is also emphasizely analyzed.Land surface temperature (LST) plays an important rol
10、e in energy balance of land surface and widely used in quite many fields. For example, soil moisture survey, forest fire monitoring, discrimination of geothermal locations, military camouflage applications and even deposit research, all these can not be separated from the land surface temperature. W
11、ith the development of remote sensing applications, Single channel algorithm, split window algorithm and multi-band algorithm are the three algorithms of land surface temperature retrieval of existing thermal infrared remote sensed data. At first, this paper chooses split window algorithm after comp
12、aring the three algorithms and improves the split window algorithm of AVHRR, so as to be suitable for MODIS image data. This split window algorithm is based on the thermal radiation data which is observed by NOAAAVHRR. With the basis of thermal radiation function, the brightness temperature can be t
13、ranslated from the two corresponding thermal channels (channel 4 and 5) of AVHRR, and that is to say: Choose MODIS 31(10. 780- 11. 280m)and 32(11. 770- 12. 270m)to take the place of the thermal channels (4,10.5-11.3m and 5,11.5-12.5m)of AVHRR. Secondly, this thesis discusses how to determine atmosph
14、eric transmittance and land surface emissivity, which are the two basic parameter of split window algorithm. Atmospheric transmittance can be retrieved by MODIS 2 and 19 with the relationship of the moisture of atmosphere and atmospheric transmittance. Thirdly, Land surface emissivity mainly depends
15、 on the structure of land surface substance, especially the vegetation index, and so land surface emissivity can be obtained by the land surface classification and vegetation index. Finally, land surface temperature can be retrieved through the land surface temperature model, and then the thesis ana
16、lyses the retrieval precision and error causes.The result indicates that there is a clear urban heat island in summer and autumn, especially summer, with its maximum of not only the intensity but also the area. UHI seems does not exist in spring. In winter, urban cold island effect appears and takes
17、 the place of urban heat island. Another conclusion: the land cover types are sensitive to urban heat island effect, and there is a significant negative correlation between vegetation index and distribution of the urban heat island. That is to say: the higher land surface vegetation cover, the less
18、urban heat island is. Therefore, land surface vegetation cover plays an important role on reducing urban heat island.Atmospheric aerosol refers to particles of small-scale range of 0.001-20m size of liquid or solid caused by natural processes and human activities, which are suspended in the atmosphe
19、re. Aerosol plays an important role on weather and climate, while impacts the living environment of mankind. This paper takes Beijing area as an example ,the aerosol optic depth of Beijing has been tried to retrieve using DDV(Dark Dense Vegetation) method from MODIS 1B data. Firstly , the Look Up Ta
20、ble is built by 6S , the dark dense pixel is checked based on NDVI and the retrieved AOD(Aerosol Optical Depth) is validated by the comparation. The results show the DDV method can be used to monitor the urban aerosol very well during the summer in Beijing. analysis of geographic situation combined
21、with Beijing and its surrounding area, the specific is as follows: AOD has higher value in densely populated areas, transportation, and areas of industrial and frequent commercial activities. High-resolution satellite remote sensing results has potential value in monitoring the distribution of emiss
22、ion sources,and can be characterized to the status of air quality in Beijing on a certain extent.Compared the land surface temperature to aerosol optical depth of Beijing on the horizontal scale in the space, this paper taking summer as an example, and find both land surface temperature and AOD have
23、 higher value in densely populated areas, transportation, and areas of industrial and frequent commercial activities, but urban heat island and air pollution of Beijing come mainly from the city itself, and temperature effects AOD is not great. the temperature can be characterized urban heat environ
24、ment on a cetain extent. AOD after revising the vertical and humidity impact can be used to monitor the distribution of particulate matter pollutants in the ground as an effective means,and AOD can be the characterization of urban pollution. Respirable particulate matter is the most important air po
25、llutant of Beijing at present. This paper combines the Municipal Environmental Protection Agency of Beijing air quality conditions and ground measurements, and analyses the relationship between temperature and respirable particulate matter from the microscopic, and finds that the temperature affecte
26、s the number of respirable particulate matter little. The number of particles can be decreased as the temperature increases,that is to say: The higher the temperature is, the lower the number of respirable particulate matter is.【中文关键词】 地表温度; 分裂窗算法; 大气透过率; 地表比辐射率; 热岛效应; 气溶胶; 可吸入颗粒物 【英文关键词】 land surfa
27、ce temperature; split window algorithm; land surface emissivity; atmospheric transmittance; urban heat island; AOD; PM10 【毕业论文目录】摘要 4-6 ABSTRACT 6-7 第一章 绪论 12-18 1.1 研究背景及意义 12-13 1.2 国内外研究现状 13-16 1.2.1 热环境研究相关进展 13-16 1.2.2 空气质量相关研究进展 16 1.3 论文思路与技术路线 16-18 第二章 研究目标和研究内容 18-21 2.1 研究区概况 18-20 2.1.
28、1 城市热环境概况 18-19 2.1.2 空气质量概况 19-20 2.2 研究目标和内容 20-21 第三章 基于RS的北京市热环境分析 21-34 3.1 数据源 21-22 3.1.1 数据来源 21-22 3.1.2 数据预处理 22 3.2 分裂窗算法及其推导 22-26 3.2.1 地表温度反演算法的比较 22-23 3.2.2 分裂窗算法的推导和流程 23-26 3.3 星上亮温及地表比辐射率和大气透过率两参数的确定 26-30 3.3.1 星上亮温的确定 26 3.3.2 地表比辐射率的计算 26-27 3.3.3 大气透过率的计算 27-28 3.3.4 遥感器视角校正和温
29、度校正 28-30 3.4 反演结果与分析 30-32 3.5 反演结果精度及误差分析 32-33 3.6 热岛效应形成机制分析 33-34 第四章 北京市空气质量分析研究 34-43 4.1 基于MODIS的北京市气溶胶光学厚度反演 34-37 4.1.1 气溶胶光学厚度反演过程 34-36 4.1.2 气溶胶反演结果分析及验证 36-37 4.2 北京市环境保护局空气质量监测 37-40 4.2.1 数据来源 37 4.2.2 空气质量等级 37-40 4.3 地面实测数据 40-43 4.3.1 采样点的位置 40-41 4.3.2 采样仪器及方法 41-43 第五章 城市热环境和空气质量关系分析研究 43-47 5.1 北京城市热岛效应与空气污染关系分析 43-44 5.2 温度与可吸入颗粒物关系分析 44-47 第六章 总结与展望 47-50 6.1 总结 47-49 6.1.1 结论 47-48 6.1.2 不足 48 6.1.3 特色及创新点 48-49 6.2 展望 49-50 参考文献 50-56 致谢 56-57
