Atmospheric turbulence is widespread in the atmosphere, in which the slant turbulence has a significant impact on aerospace and military activities. On the one hand, the high spatial and temporal resolution wind field data obtained from the coherent Doppler lidar is used. On the other hand, according to the velocity structure function method based on Kolmogorov's local uniform homogeneous theory, i.e. the characteristics of turbulence in the inertial sub-region are only related to the turbulent eddy dissipation rate ε, the velocity structure function method is applied to the lidar glide-path scans mode for aircraft landing period. By fitting the measured data velocity structure function with the model velocity structure function least square, the atmospheric turbulence parameters (wind speed variance, turbulence integration scale and ε, etc) within the lidar swath are estimated. Furthermore, based on the spatial and temporal distribution characteristics of the data in the slant scanning area, the slant spatial distribution of the atmospheric turbulence parameters is presented and compared with the wind-shear intensity data obtained from the lidar glide-path scans mode developed by Ocean University of China. It is found that the two data are in good agreement, which validates the reliabiltiy of the proposed slant turbulence parameter retrieval method.

In order to enrich the measurment methods of aerosol optical depth of the whole atmosphere layer, a detection method integrating micro-pulse lidar and surface visibility data is proposed. In the method, the aerosol vertical extinction coefficient profile is retrieved from lidar data firstly, and the aerosol scale height is calculated accordingly. Then, the relationship between visibility and extinction coefficient is used to obtain the surface horizontal extinction coefficient. Finally, the aerosol scale height is combined with surface extinction coefficient to obtain the aerosol optical depth of the entire atmosphere. This method was applied to Hefei area, China, and the diurnal variation trend of the entire atmospheric aerosol optical depth in this area was succesfully obtained, which verifies the applicability of the method.

Based on NCEP/NCAR monthly reanalysis data from 1981 to 2020, the temporal and spatial variation characteristics, as well as the seasonal variations, of tropopause temperature over China in recent 40 years were studied. Linear regression, Mann-Kendall mutation test and empirical orthogonal decomposition (EOF) were used to study the interannual variation trend and spatio-temporal distribution characteristics of tropopause temperature. The results show that the tropopause temperature in China has an overall decreasing trend from 1981 to 2020, with a significant decreasing trend from 1995 to 2020 and a decreasing mutation occurred in 1993. Tropopause temperature in the period also has obvious seasonal characteristics. Both spring and winter climate trend rates are small and contribute little to the annual decreasing trend of tropopause temperature, while the climate trend rates in summer and autumn are larger, and contribute more to the decreasing trend of tropopause temperature in the whole year. The analysis of the spatial variation characteristics of tropopause temperature by EOF method shows that, the spatial distribution of the first mode reflects that the variation trend of tropopause in China is basically consistent in space, and the distribution presents a zonal structure of "+, -" from south to north,while the spatial distribution of the second mode presents a zonal structure of "+, -, +, -" from south to north, with an obvious north-south opposite distribution with 34°N as the boundary, and the spatial distribution of the third mode presents a zonal structure of "+, -, +" from south to north.

To quantitatively study the impact of short-duration high-strength human tourism activities on urban air quality, a novel model (EEMD-DCCA) integrating ensemble empirical mode decomposition (EEMD) and detrended cross-correlation analysis (DCCA) was proposed and applied to study the impact of the number of tourists in Wulingyuan scenic spot of Zhangjiajie on local atmospheric NO2 concentration in 2015. The high-frequency components of the number of tourists in Wulingyuan scenic spot and the concentration of NO2 in Zhangjiajie City were obtained firstly through EEMD. And then, DCCA method was used to analyze the correlation between high-frequency components of the number of tourists and NO2 concentration. The results show that there is a significant positive correlation between the two groups of the high-frequency components of the tourist number and NO2 concentration in Zhangjiajie, and the positive correlation has a long-term sustainability feature, which means that the rapid increase of the number of tourists in short-duration holidays will continue to affect the future change trend of NO2 concentration in a certain time scale. The research in Wulingyuan scenic area shows that the model proposed can provide a new research approach for quantitative assessment of the impact of tourism activities on the air quality of natural scenic spots.

To explore the characteristics of PM2.5 and PM10 in Chengdu, China, and their relationship with meteorological factors, monthly average concentrations of PM2.5 and PM10 in Chengdu from 2015 to 2018 were collected, and their relationships with meteorological factors were analyzed by using Pearson correlation analysis method. The results show that: (1) Pue to the implementation of a series of air pollution control measures since 2015, the average annual concentration of PM2.5, PM10 in Chengdu shows a slowly decreasing trend year by year. And the seasonal PM2.5, PM10 concentration in Chengdu from 2015 to 2018 decreased in the order of winter, spring, autumn and summer. (2) Different meteorological factors have different effects on the monthly average concentration of PM2.5 and PM10 in Chengdu. The concentrations of PM2.5 and PM10 show a high negative linear correlation with temperature and precipitation, which indicates that temperature and precipitation are two key factors affecting the monthly average concentrations of PM2.5 and PM10 in Chengdu. Sunshine duration, monthly average wind speed and relative humidity are also negatively correlated with PM2.5 and PM10 monthly average concentrations. However, compared with precipitation and temperature, the linear correlation between sunshine duration, monthly average wind speed and PM2.5, PM10 is lower, while the linear correlation between relative humidity and PM2.5, PM10 is even weaker, and less significant, indicating that the change of relative humidity has little effect on the accumulation and diffusion of PM2.5 and PM10 in Chengdu.

The aerosol optical depth (AOD) product of Himawari-8 satellite can be used to estimate PM2.5 concentration near the earth surface with wide spatial coverage and high temporal resolution. Based on the three-dimensional variational assimilation system, the PM2.5 data estimated by AOD are assimilated into the WRF-Chem atmospheric chemistry model. Then through the comparative analysis of control experiment and assimilation experiment, the improvement of PM2.5 pollution simulation by assimilation of PM2.5 data estimated by AOD is discussed. The results show that the assimilation of PM2.5 data estimated by AOD has a significant effect on the improvement of the background field, resulting in the significant improvement of PM2.5 pollution prediction. And in other hand, the improvement of PM2.5 pollution from AOD variational assimilation is different in time and space. In addition, compared with the other methods using AOD observation operators directly to assimilate AOD, this method has the advantage of easy operation.

According to the problems that the change of temperature can affect the observation results of sun photometer and the temperature correction coefficient is inconvenient to obtain, a temperature control system of automatic sun photometer based on thermo-electric cooler (TEC) was designed. The whole design, especially the temperature control system design, of automatic sun photometer was introduced, and the effect of temperature on detector response was analyzed. The performance of the automatic sun photometer was tested in the field. In Hefei area, China, the automatic sun photometer was compared with the commercial instrument CE318, and the results show that the aerosol optical depth retrieved by the automatic sun photometer is consistent with the corrected result of CE318, with the difference less than 0.01. The long-term test results in Dunhuang area show that the temperature control system of the automatic sun photometer can be kept within (25 ± 0.2) ℃ for long-term field observation with large temperature changes, which verifies the effectiveness and reliability of the system design.

An organic matter detection system based on hydrogen flame ionization detection method is designed. The working principle of the system is introduced firstly, and then the structural design of the system is optimized based on the principle. According to the reaction conditions of the system, the control circuit of the system is designed. In view of the weak detection signal and wide dynamic range of the system, a current detection circuit with switchable range is designed by using the method of transimpedance amplification, which realizes a wide range of micro-current detection. The performance of the system is quantified by methane standard gas, and it is shown that the relative standard deviation of standard sample repeatability measurement is less than 2%, and the linear correlation coefficient is greater than 0.99.