中国的空气污染是近年来公众最关注的问题之一，因此，地面污染物时空分布的监测、预测和反演是当今的研究热点，而卫星遥感技术具有覆盖面积大、成本低、空间连续性高等多项优势，逐渐成为空气质量的有效监测手段。然而，由卫星观测参数向地面污染物的反演过程中还存在许多问题，因此，本文旨在利用卫星遥感来研究城市内部的大气污染。由于京津冀地区受雾霾影响严重，而内蒙古城市群受传统产业结构的影响，NO2和SO2污染显著，故本研究分别选用了京津冀地区和呼包鄂城市群两个区域来进行气溶胶细颗粒物（PM2.5）和污染气体（NO2和SO2）的研究。 以PM2.5与AOD的比值η及相关系数R2为评价指标，本文首先利用2011至2015年北京地面站点的AERONET AOD产品、PM2.5数据、气象数据及卫星遥感产品（MODIS及CALIPSO AOD数据），定性及定量分析了各个因子对AOD-PM2.5关系的影响，其中包括相对湿度（RH）、大气边界层高度（PBLH）、气溶胶类型、风速、风向及气溶胶的垂直分布结构。研究发现：PBLH与RH显著影响η值。RH越大，η越大，PBLH越大，η越小。经RH校正的AOD月均值与经PBLH校正的PM2.5月均值的相关性显著增加，R2由0.632变为0.756，趋势也更加吻合；散射型气溶胶的η低于吸收型气溶胶，而粗模态粒子的η低于细粒子，同时，将气溶胶粒子分为不同类型之后，PM2.5与AOD的相关性提高（R2介于0.5与0.7之间）；气溶胶的垂直分布结构的季节差异显著，夏季气溶胶粒子主要集中在500m以下，而在冬季集中于150m以下。500m以下的AOD与PM2.5的相关性更高，R2为0.769；与PM2.5与AOD的变化趋势相似，η随风速的增加而减小，表明地面污染物对AOD的贡献随风速的增加而减少。其次，本文通过MODIS卫星的二级AOD数据与地面79个站点的PM2.5观测数据，利用地理加权回归模型，得到了京津冀地区PM2.5的空间分布，并分析了气溶胶污染物的季节变化与跨区域传输。 此外，结合OMI卫星的NO2对流层柱浓度产品与SO2边界层柱浓度产品，以及呼包鄂城市群31个监测站点NO2与SO2的近地面浓度数据，本文在第三章中分析了内蒙古NO2与SO2的时空分布特征。研究发现：从长时间的变化趋势而言，NO2柱浓度由2005至2011年间的年均值增长率为14.3%，而从2011年至2016年的年均值减少率为8.1%；SO2柱浓度在2005至2007年的年均值增长率为9.7%，而在2007年至2016年期间，除2011年出现峰值，整体显示降低的趋势，其年均值减少率为1.6%，该趋势与NO2与SO2排放量的时间变化表现一致；从空间格局上而言，呼和浩特与包头的NO2污染最严重，其次是乌海与鄂尔多斯，巴彦淖尔最轻；相比于NO2，SO2的空间分布稍有不同，乌海的污染最严重，其次是呼和浩特与包头，鄂尔多斯与巴彦淖尔最轻。NO2与SO2的日变化规律基本一致，由0:00至6:00呈现降低的趋势，随后上升，8:00达至峰值，8:00至15:00下降，随后上升。无论是NO2与SO2的时间变化，还是空间格局，都与人类活动密切相关。 本文在第四章中对整项研究进行了工作总结，并对未来的研究给予了一定的展望。

Air pollution in China has been one of the most public concerns in recent years. Therefore, the monitoring, prediction, and inversion of the near-surface pollutants’ spatial-temporal distribution is an important research topic. Compared with the traditional ground-station observation method, satellite remote sensing technology has many advantages, such as large area of coverage, low cost, and high space continuity. Thus, it has gradually become an effective monitoring tool for air quality. However, there are still many problems in the process of inversion of ground contaminants from satellite observation parameters. Against this backdrop, this paper aimed to study the urban pollution based on satellite remote sensing technology. Because of the industrialization and urbanization, the haze caused by high PM2.5 concentration is serious in Beijing-Tianjin-Hebei region while in the Inner Mongolia urban agglomerations, the pollution of NO2 and SO2 is significant due to the impact of traditional industrial structure. Therefore, this study selected this two regions (Beijing-Tianjin-Hebei and Hu-Bao-E agglomerations) to conduct research on fine particles (PM2.5) and pollutant gases (NO2 and SO2) respectively. We first collected the AERONET AOD product, PM2.5 data, meteorological data of Beijing, and the satellite remote sensing products (MODIS and CALIPSO), qualitatively and quantitatively analyzed the influence of various factors on the AOD-PM2.5 relationship. These factors include relative humidity (RH), atmosphere boundary layer height (PBLH), the aerosol type, wind speed and direction, and the aerosol vertical distribution. The ratio of PM2.5 to AOD, which is defined as η, and the square of their correlation coefficient (R2) are used as evaluation indicators. It shows that η is smaller for scattering-dominant aerosols than for absorbing-dominant aerosols, and smaller for coarse mode aerosols than for fine mode aerosols. Both RH and PBLH affect the η value significantly. The higher the RH, the larger the η, and the higher the PBLH, the smaller the η. For AOD and PM2.5 data with the correction of RH and PBLH compared to those without, R2 of monthly averaged PM2.5 and AOD at 14:00 LT increases from 0.63 to 0.76. Similar to the variation of AOD and PM2.5, η also decreases with the increasing surface wind speed, indicating that the contribution of surface PM2.5 concentrations to AOD decreases with surface wind speed. The vertical structure of aerosol exhibits a remarkable change with seasons, with most particles concentrated within about 500 m in summer and within 150 m in winter. Compared to the AOD of the whole atmosphere, AOD below 500 m has a better correlation with PM2.5, for which R2 is 0.77. Then we analyzed the level 2 AOD data of MODIS product and PM2.5 data from 79 stations on the ground, and then obtained the spatial distribution of PM2.5 in the Jing-Jin-Ji region using the geographically weighted regression model (GWR). Meanwhile, this study discussed the seasonal variation and transregional transmission of aerosol pollutants. Based on the combination of tropospheric NO2 and SO2 column density products derived from OMI satellite with the ground station observations, this study analyzed the spatial-temporal distribution characteristic of NO2 and SO2 in Inner Mongolia urban agglomerations. In terms of long-term changing trend, NO2 increased continually from 2005 to 2011 at 14.3% per year and then decreased from 2011 to 2016 at -8.1% per year. As for SO2, there is a consistent increase from 2005 to 2007 at 9.7% per year. During the period of 2007 to 2016, despite of a peak value in 2011, it showed a decreasing trend of -1.6% per year as a whole. With regard to the spatial pattern of NO2, the highest levels of pollution occur in Hohhot and Baotou, followed by Wuhai and Ordos, the least polluted area is in Bayannur. Compared with NO2, the SO2 spatial distribution is slightly different. The pollution of SO2 is the most serious in Wuhai, followed by Hohhot and Baotou, and the lightest in Ordos and Bayannur. The diurnal variation of NO2 and SO2 is basically the same, which decrease from 0:00 to 6:00, then rise with a peak at 8:00, and decrease from 8:00 to 15:00. The diurnal variation of NO2 and SO2 is highly related to the diurnal variation of both anthropogenic emission and boundary layer height. Differently, the long-term spatial-temporal distribution of NO2 and SO2 are more closely related to human activities. In the fourth chapter, this article summarizes the work of the entire study and gives some prospects for future research.