In order to study the impact of land use change on the surface albedo accurately and quantitatively, a long-term global spectral surface albedo dataset (near-infrared, visible, shortwave) is obtained by using land use types dataset from 850 A.D. to 2100 A.D. provided by land-use harmonization (LUH2) project, and then detailed analysis and comparative verification are carried out. The results show that, the spatial distribution characteristics of spectral surface albedo obtained in this work are consistent with those of MODIS. The visible albedo in non-snow and non-ice covered area varies from-0.0081 to 0.0029 with high precision, while the largest difference is found in snow and ice covered area inthe middle and high latitudes of the southern and northern hemisphere. It is also shown that, since 1860, the urban built-up area has increased year by year in all typical regions (including Eastern China, Southeastern Europe, Mid-east Unite States and Southern Brazil), while the natural vegetations have decreased in Southern Brazil. From 1860 to 1980, natural vegetations were mainly transformed into urban and croplands in each region. In 1980～2015, croplands continued to increase in Southern Brazil, while the other regions showed a trend of gradual restoration of natural vegetations. In addition, the spectral albedo of different land cover types varies greatly. For example, the visible albedo in spring is 0.5069 on the surface of ice/snow, while itis 0.0444 in mixed forest and 0.0870 in urban. Under the influence of different latitudes and underlying surface properties, even the spectral albedo of the same land use type also has obvious spatial differences. For example, the maximum visible albedo of grassland in spring is 0.4915, and the minimum is 1.127×10-4. It is believed that the long-term global spectral albedo dataset can lay a data foundation for the quantitative research on the climate change driven by land use change.

Based on the meteorological data of Global Data Assimilation System (GDAS) and PM2.5 concentration data from the China National Environmental Monitoring Station of the Ministry of Ecology and Environment, using MeteoInfo and TrajStat plug-in, a cluster analysis on the airflow trajectory reaching the main urban area of Yining City, China, from December 2018 to January 2019 was carried out, and then the weighted potential source contribution probability (WPSCF) and the weighted concentration weight trajectory (WCWT) of PM2.5 in the main urban area of Yining City was analyzed by using potential source contribution function (PSCF) analysis and concentration weighted trajectory model (CWT) analysis. The results show that: a) Trajectory clustering shows that the region is most affected by the southwest air mass, mainly from Kazakhstan and Kyrgyzstan. b) The WPSCF and WCWT distribution feature are similar, and the highest WPSCF and WCWT in the heavily polluted source area are above 0.7～0.8 and 80～110 μg·m-3 respectively, which are mainly concentrated in Zhaosu-Tekes Basin.

To analyze the multi-scale characteristics of PM2.5 in Changsha-Zhuzhou-Xiangtan region, China, and explain the main dynamic mechanism of PM2.5 evolution, a novel ensemble empirical mode decomposition and multifractal detrended fluctuation analysis (EEMD-MFDFA) model is proposed, and then the dynamic evolution of PM2.5 hourly average concentrations in Xiangtan, Changsha and Zhuzhou from January 1, 2015 to December 31, 2019 is studied. Through EEMD, the high-frequency modes and trend terms of PM2.5 are obtained for the three cities. The results of the trend term show a decreasing trend of PM2.5 concentrations, and the high-frequency mode of PM2.5 reflects the nonlinear characteristics of PM2.5 concentration fluctuation. Furthermore, MFDFA method is used to analyze the high-frequency cumulative mode of PM2.5. The results indicate that the high-frequency mode of PM2.5 has strong multifractal characteristics. In addition, the main sources of multifractal characteristics are studied by using shuffling procedure and phase randomization. The results indicate that the long-term persistence of PM2.5 concentration fluctuation in different time scales is the maindynamic factor for the emergence of high concentration PM2.5. Finally, the influence of meteorological conditions on the multifractal strength of high-frequency modes of PM2.5 is discussed. The results show that the multifractal strength of PM2.5 in winter was stronger than that in other seasons. The analysis shows that although the air pollution in Changsha-Zhuzhou-Xiangtan Urban Agglomeration has been effectively controlled through the air pollution action plan, in winter, the long-term persistence mechanism of air pollution plays a more dominant role in controlling the PM2.5 evolutions, and the non-linear long-term persistent mechanism of PM2.5 at different time scales can lead to the risk of emergence of high concentration PM2.5 in winter, and even more serious air pollution events. The results have great significance for studying the dynamic characteristics of regional PM2.5 multi-scale evolution and forecasting haze.

With the acceleration of urbanization, urban diseases caused by the rising land surface temperature (LST) are becoming more and more serious. In order to deeply understand the influencing factors of LST in coastal cities and provide scientific data support for improving human health and ecological environment, the single window algorithm is used to inverse the LST of Ganjingzi District, Xigang District, Shahekou District and Zhongshan District in Dalian, China, and multiscale geographical weighted regression (Multiscale-GWR) model combined with normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and normalized difference bareness index (NDBAI) is used to explore the spatial heterogeneity relationship between LST and the underlying surface index. The results show that: (1) the surface temperature of the four districts in Dalian presents a decreasing distribution trend from east to west, the surface temperature in the north of Zhongshan District, Shahekou District and Xigang District is higher than that in the south, and the surface temperature in the southwest of Ganjingzi District is lower than that in other areas. (2) The relationship between surface temperature and underlying surface index in the four districts of Dalian has no global effect, and the spatial heterogeneity is very strong. The Multiscale-GWR model can better fit the surface temperature correlation in the selected underlying surface exponential domain. (3) In terms of correlation coefficient, the impact of underlying surface index on surface temperature is as follows: NDBAI > NDVI > MNDWI > NDBI, and NDBAI, NDVI and MNDWI indexes show negative correlation effect on the whole, while NDBI shows positive correlation effect on the whole.

The TDLAS system combined with multi-path absorption cell is prone to interference noise in practical application, which has a very adverse impact on its detection performance. In this work, the causes of this interference are analyzed, and a wavelet denoising method is proposed to improve the detection performance of the system. Firstly, the appropriate wavelet function and decomposition layers are selected according to the theoretical research results, and then the simulation signal with superimposed interference is filtered through this wavelet. The results show that this noise reduction technology has good application performance. Finally, the direct absorption spectrum (DAS) and second harmonic signal of each carbon dioxide concentration collected in the experiment are processed by wavelet denoising technology. Compared with the original signal, the signal-to-noise ratio of the denoised signal is increased from 0.4 to 259, and the detection limitof the system reaches 7×10-6, which indicates that the proposed small wave noise reduction method has high application value in the detection of ambient gases.

In order to understand the distribution characteristics and influencing factors of road dust in Weinan City, China, and obtain the localization parameter of road dust and the basis for road dust control, the vehicle-mounted rapid monitoring system was used to measure the road dust load in Weinan based on light scattering method. The characteristics of dust load on different types of roads were analyzed, and the factors affecting the road dust load were explored based on the factors such as traffic flow, vehicle weight and road dust suppression methods. The results show that the average road dust load in Weinan is 1.13 g·m-2, and the dust load of different types of roads is characterized by branch road (1.79 g·m-2) > secondary trunk road (1.22 g·m-2) > provincial road (1.08 g·m-2) > main trunk road (0.94 g·m-2) > national road (0.71 g·m-2). It is also shown that traffic flow and vehicle speed are negatively correlated with the dust load value, that is, the greater the vehicle flow and speed, the smaller the dust load value. In addition, sprinkling water can effectively reduce the value of road dust load, but the retention time isshort. Therefore, it is suggested that the operation mode of heavy watering and weak cleaning should be changed, with the focus on dust cleaning and removal.

PM2.5 is one of the important pollutants in the atmosphere, so simulating the spatial distribution of PM2.5 concentration is of great significance to the prevention and control of air pollution. The Land Use Regression (LUR) model was applied to the heavily polluted Northern Anhui region in Anhui Province, China. Taking the monitoring points as the center, the buffer zones with radius of 0.5, 1, 1.5, 2, 3, 4 and 5 km were established respectively. Combined with 105 variables including land use factor, road factor, pollution source factor, meteorological factor, elevation factor and population factor, a four-season and annual average LUR model for this district was established, and the accuracy of the model was verified by leave-one-out cross validation. The results show that the PM2.5 concentration in the study area is greatly affected by grassland, wetland, rainfall, relative humidity, atmospheric pressure, wind speed, secondary roads,tertiary roads, air-polluting enterprise, and population. The adjusted R2 is 0.828 (spring), 0.731 (summer), 0.831 (autumn), 0.775 (winter) and 0.892 (annual average) respectively. The root mean square error (RMSE) is 6.34 μg·m-3 (spring), 7.01 μg·m-3 (summer), 6.28 μg·m-3 (autumn), 6.71 μg·m-3 (winter) and 5.33 μg·m-3 (annual average). The simulation accuracy R2 is 0.825 (spring), 0.730 (summer), 0.834 (autumn), 0.772 (winter) and 0.897 (annual average). The model shows good performance and strong explanatory power. As can be seen from the simulated spatial distribution of PM2.5 concentration, the spatial distribution characteristics in the area are obviously different in different seasons, which is related to a large number of pollution particles from the north, local coal mining, straw burning during autumn tillage and other potential pollution sources.

In order to evaluate the amount of vertical information on aerosol extinction profiles extracted from the O2 A-band polarization spectrum and the influence of improving spectral resolution on aerosol vertical profile information measured by polarization, the Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used to analyze the information from satellite measurements of O2 A-band degree of linear polarization (DoLP) and the influencing factors of aerosol peak height information of DoLP under different observation conditions and spectral resolution. Simulation results show that DoLP contains more information content for aerosol peak height than radiation intensity, and the maximum degree of freedom of signal (DFS) of radiation intensity is 0.49, while the DFS of DoLP is 0.76. In addition, with the improvement of spectral resolution, the information content of aerosol peak height increases, and the dependence on surface albedo, observation geometry and prior error is greatly reduced.

In order to study the vibration response characteristics of space-borne equipment in complex mechanical environment, simulation analysis of the optical path switching assembly is carried out by using finite element analysis software. Firstly, according to the mechanical environment of onboard load, the excitation curve of the input spectral density of onboard load is established through the calculation method of spectral shape parameters. Then, the modal analysis and random vibration analysis of the optical path switching assembly are carried out, and the vibration response results and the maximum equivalent stress of the assembly are obtained under the excitation spectrum condition. The calculation results of strength check show that the safety margin is 1.57, which meets the requirements of stiffness and strength. Finally, the vibration of the whole machine is tested and verified. The verification results show that the structure of the module is in good condition, and the optical performance comparison test results show that the imaging range and spectral intensity characteristics of the three channels of the halogen lamp are basically the same before and after the vibration, and the optical path switching components have no abnormal changes, which verifies the reliability of the simulation analysis.