Based on the improved three-dimensional variational assimilation system, the assimilation of ground fine particulate matter (PM2.5) and satellite aerosol optical depth (AOD) is conducted and the effectof analysis field assimilation on the PM2.5 forecast improvement is evaluated. A continuous pollution process is selected in this study, and the ground PM2.5 and AOD observation data are assimilated individually and simultaneously. The results show that compared with PM2.5 individual assimilation, AOD assimilation alone is more effective in improving the accuracy of the AOD analysis field, but the accuracy of the PM2.5 analysis field is reduced obviously. While simultaneous assimilation of PM2.5 and AOD makes the simulation of aerosol optical-physical properties achieve the best overall effect. Moreover, assimilation tests can effectively reduce the missing report rate. For the case of mild and moderate pollution, the choice of assimilation PM2.5 or AOD does not have a significant impact on the forecast. However, in the case of severe pollution, the comprehensive forecast of PM2.5 is the best when the ground PM2.5 and the whole layer AOD are assimilated simultaneously.

Based on moderate resolution imaging spectroradiometer (MODIS) 3 km aerosol optical depth (AOD) products, PM2.5 concentration and meteorological data measured near the ground, the spatio-temporal characteristics of AOD and PM2.5 over Xiamen, China, as well as their relationship were studied through comparison of the total and local, unitary and multivariate by using the methods of spatial analysis and statistical regression . The results show there were obvious changes in space-time characteristics of AOD, with highest monthly mean value of 1.133 occurred in April and lowest of 0.635 in January, and seasonal mean value shows a descending trend in spring, summer, autumn and winter, while the annual average value shows a trend of slowly rising, stabilizing and then slightly declining. The higher values of seasonal average AOD were mainly distributed in coastal regions and Xiamen Island, and the lower were mostly located in northwest, north and northeast regions. PM2.5 presented similar spatio-temporal characteristics to AOD, for example, it had obvious seasonal characteristics in time sequence, and in spatial distribution, its higher seasonal mean values were main in central coastal regions, while the lower values appeared mostly in northwest and east regions. There was a moderate positive overall correlation between AOD and PM2.5, with the overall correlation coefficient of unitary linear regression of 0.575. There were obvious differences in the partial coefficients of unitary linear regression between AOD and PM2.5, and the average partial correlation coefficient was 0.432, which was slightly lower than the overall correlation coefficient. The average correlation coefficient of multiplelinear regression of AOD, PM2.5 and meteorological factors was 0.625, and it is shown that the multiple linear regression model of three independent variables has the best fitting effect, but the effect does not tend to be better with the increase of the number of the meteorological factors.

To explore the correlation between the concentrations of PM2.5, NO2 in atmosphere and tourism activities as well as weather factors in Zhangjiajie, China, an ecotourism city, the method of multifractal detrended cross-correlation analysis (MFDCCA) was used. Firstly, the cross-correlation test was carried out and the results indicate that cross-correlation behaviors exist statistically between them.Then the multifractal characteristics between PM2.5, NO2 and number of tourists, average temperature and relative humidity in both low and high tourism seasons were compared. It is found that in the off-season of tourism, PM2.5 and NO2 concentration of Zhangjiajie is affected by relative humidity in a wide range of fluctuations, and is more likely to have lower concentrations. While in the peak tourism season, PM2.5 and NO2 concentration of Zhangjiajie fluctuates widely under the influence of tourism activities, and tends to appearhigh concentration values.

Fourier transform infrared spectroscopy (FTIR) has been developed rapidly in recent years as a comprehensive detection technology, which has a wide potential application in monitoring of volatile organic compounds (VOCs). The system structure and the quantitative analysis process of VOCs of underway observation based on portable FTIR are introduced. Based on the combination of FTIR and vehicle mobile platform, the VOCs concentration information in the catering gathering area of Taixing City, China, was obtained through the navigation observation, and the spatial and temporal characteristics of VOCs in the catering gathering area were determined. The results show that the combination of portable FTIR technology and vehicle-mounted mobile platform has certain advantages in the navigation observation of urban VOCs emission, and the temporal and spatial emission characteristics of VOCs in urban catering can be obtained.

In order to achieve the comprehensive monitoring of on-road vehicle emissions in urban areas, the road network vehicle emission remote sensing system is constructed. In the system, the location of remote sensing detectors has a great effect on the effectiveness of monitoring on-road vehicles. Thus the location of detectors is a key factor in the construction of road network vehicle emission remote sensing system. In this work, a location strategy based on the topology structure of traffic network is proposed. First, a mathematical model for the problem of interest is established, where the connection degree between road sections is described with the aid of the reachability matrix of the line graph of the traffic network graph. Then, a dual structure code based genetic algorithm is employed to obtain the solution. The experimental results show that the proposed location strategy in this work can capture more individual vehicles in the road networkthan the traditional schemes.

Atmospheric aerosols have an important impact on the global environment and human health. Secondary aerosols generated in the troposphere are one of the main sources of atmospheric aerosols. Its formation is generally divided into two stages, the formation of critical nuclear and the following rapid growth process. Phthalic acid and sulfuric acid are common nucleation precursor in the atmosphere, however, their nucleation mechanism is still seldom studied. The structure and thermodynamic parameters of the cluster are obtained by high-precision quantitative calculation. The evaporation rate analysis shows that compared to sulfate clusters, the heterodimer formed by phthalic acid and sulfuric acid is more stable, that is, the addition of phthalic acid greatly reduces the evaporation rate of the sulfuric acid cluster, which indicates that phthalic acid has the effect of promoting nucleation of sulfuric acid. Optical property analysis shows that the cluster size is positively correlated with Rayleigh scattering and isotropic average polarizability. Moreover, infrared spectrum analysis also confirms the presence of hydrogen bonds in the heterodimer, which is in favor of the formation of stable nucleation clusters.

Cloud detection is one of the important tasks for remote sensing image processing. At present, the multi-spectral and multi-channel information is often used in cloud detection of remote sensing image, but the research on the influence of multi-angle information on cloud detection is still insufficient. To explore the influence of multi-angle information as cloud feature on the accuracy of cloud classification, a cloud detection method with multi-angles remote sensing based on deep learning is proposed. The proposed method takes SegNet as backbone network, and trains a multi-angle information based cloud detection model by extracting the remote sensing image feature with multi-angle information. Extensive experimental results demonstrate that the Global Accuracy and the mean intersection over union (MeanIoU) of the proposed method are 91.39% and 83.99% respectively. And the method proves the limitations of single angle cloud detection and the effectiveness of multi-angle information on the improvement of the cloud detection accuracy. In addtion, the influence of different angles on the cloud detection in POLDER is also explored.

Aiming at the problem of anti bait interference in infrared precise guidance, the simulation and experimental research of target spectrum recognition technology based on spectral angle and spectral distance were carried out. On the premise that target and bait were both gray body radiation, the infrared radiation distribution of target and bait under different radiation temperature and different spectral emissivity models was modeled. The relationship between temperature, spectral emissivity and spectral angle, spectral distance of target and bait was quantitatively analyzed, and the spectral difference between target and bait was numerically characterized. In order to verify the effectiveness of the spectral discrimination method, the target and bait spectral data were collected and analyzed. The results show that the spectral angle and spectral distance can be used to distinguish target from bait, which can provides theoretical and experimental basis for target classification in complex background in the future.