Atmospheric downward radiation is a key parameter in surface radiation and energy balance. It plays a key role in retrieving surface temperature and emissivity and in the study of climate change. Based on the analysis and comparison of the existing atmospheric downward radiation measurement methods, the research of atmospheric downward longwave radiation of Hefei, China, is carried out. Hefei downward atmospheric radiant flux is simulated and calculated by using MODTRAN radiation transmission model, and then taking the atmospheric downward radiant flux obtained by this method as benchmark, the performance of the widely used sunny day empirical model is evaluated, and the applicability of Idso model and ngstrm model to Hefei atmospheric conditions is verified. In addition, the parameters in the Idso model are modified by using MODTRAN radiation transmission model simulation data to improve the simulation accuracy of the model. This study provides a reliable method for conveniently, quickly and accurately obtaining Hefei atmospheric downward radiant flux.

In order to realize non-contact high-resolution and high-precision measurement of small-scale wind field, and make up for the measurement blind area of pulse coherent wind lidar in the close wind field, a high power CW fiber laser with working wavelength of 1550 nm, which is safe for human eyes, was selected as the light source of lidar, and then based on the principle of laser coherent wind measurement, the wind speed detection at different positionsin the wind field was realized by changing the focusing position of continuous wave laser. The local oscillator power of the current system was optimized to improve the signal-to-noise ratio of the system, and by optimizing the effective spectral centroid algorithm, the data of multiple velocity components in the measured interval of continuous wave were processed so as to improve the accuracy of data inversion. The comparative experiment between the lidar system and the ultrasonic anemometer was carriedout, the correlation of wind speed data of the two systems is 0.98 and the standard deviation is 0.16 m·s-1. Then the long-term wind speed observation experiment was carried out by using the lidar system, and several groups of data with different aerosol content under different weather were selected for analysis. Meanwhile, the change of wind speed in the wind field near the ground and near buildings was explored. The experimental results of local wind speed observation show that the wind speed measurement range of the system is 0.3～18 m·s-1, and the wind field measurement position is 2～30 m, which can realize the continuous measurement of wind speed gradient.

Dust aerosols have a significant impact on the local atmospheric environment. Based on the satellite/ground-based remote sensing observation data of eastern China in March 2021, the periodic characteristics and driving factors of aerosol environment in Beijing and Xuzhou, China, the upstream and downstream cities along the transport pathway of dust, are studied in this work. The results show that: (1) The two consecutive large-scale dust storms were driven by cold air from Mongolia to eastern China. The first dust entered the Yellow Sea in the middle part of Jiangsu province, and the second dust recirculation occurred near Jiangsu Province under the influence of southwest warm wind, resulting in continuous pollution. (2) The sand dust was mainly coarse particles, which made the PM10 concentration in the near surface layer rise sharply to 20 times of the background value and PM2.5 to 3 times. Xuzhou in the downstream was 500 μg·m-3 lower than Beijing in the upstream, and the time was delayed by 12 hours. (3) The background aerosol in Xuzhou was mainly scattered fine particles, the aerosol optical thickness (AOD ) was less than 0.5, and the single scattering albedo (SSA) was about 0.99. When the sand storm arrived in Xuzhou, it floated in the upper layer of 2～4 km and mixed with the aerosol underlying layer with the action of gravity, so that AOD instantly increased to more than 1.5. After 24 hours, it began to gradually transition from sand dust dominated to local pollutant particles dominated (the aerosol depolarization ratio decreased from more than 0.25 to less than 0.1). It is also found that some particles in the dust had strong absorption to 440 nm spectrum, which was related to the source of dust.

Urban green plants provide purification functions for the urban ecosystem, which play a variety of environmental protection roles such as air purification, dust retention and dust fall. On the other hand, dust retention can also affect green plants in turn. To study how dust retention affects the spectral characteristics of urban plant leaves, the point cloud data of four kinds of evergreen leaf samples (Fatsia japonica, Photinia stenophylla, Deyeuxia langsdorffii and Magnolia grandiflora) are collected with hyperspectral lidar, and then the effect of dust retention on leaf spectral characteristics is analyzed. The results show that dust retention has an influence on the reflectance in visible light band despite of plant species, and has a great impact on the reflectance difference in the near-infrared band for the same kind of leaves. The reflectance difference in visible light band is 1.21%～3.41%, and that in near infrared is 1.76%～8.49s of leaves to dust retention is the smallest (less than 3.7%), that of the ratio vegetation index (RVI) is the largest (more than 20.0%, except Deyeuxia langsdorffii), and that of the red edge index (SDr), simple ratio index (SR) and leaf chlorophyll index (LCI) is low and unstable for all leaves. Furthermore, the fitting models based on the correlation between the dust retention vegetation index and responsiveness are established and tested, it is shown that the best one is based on LCI, which can be expressed as b = -1.527a + 0.659, and the determination coefficient R2 is about 0.88.

25 soil dust samples collected outside Nanjing Iron and Steel Group, China, from September to October 2020 are analyzed by a handheld X-ray fluorescence spectrometer. Ten heavy metal elements, including chromium (Cr), magnesium (Mn), iron (Fe), copper (Cu), zinc (Zn), mercury (Hg), lead (Pb), strontium (Sr), zirconium (Zr) and molybdenum (Mo), are quantified, and their impacts on local soil pollution are assessed by various statistical models. In addition, a receptor model, positive matrix factorization (PMF), is used for source apportionment of these heavy metals. It is shown that the concentrations of these heavy metals in the ground surface soils are Cr (260 ± 14 mg·kg-1), Mn (1550 ± 22 mg·kg-1), Fe (1.65 ± 0.01 mg·kg-1), Cu (67 ± 9 mg·kg-1), Zn (600 ± 13 mg·kg-1), Hg (16 ± 6 mg·kg-1), Pb (102 ± 7 mg·kg-1), Sr (275 ± 10 mg·kg-1), Zr (302 ± 5 mg·kg-1) and Mo (13 ± 3 mg·kg-1), respectively. Their averaged surface accumulation index is 2.13, indicating a moderate level of pollution. Individually, Hg is severely polluted, Mo is heavily polluted, Zn is moderately polluted, Cr, Fe and Pb are slightly-to-moderately polluted, Mn, Cu and Sr are slightly polluted, Zr is below the pollution level. It is found that most of the high concentration samples are taken from the north and northwest parts of the study area, where heavy metals are prone to accumulation. Three major sources of heavey metals in the study area are identified by the PMF analysis: industrial emissions (49.3%), domestic activities (30.7%) and automobile emissions (20.0%).

Chlorobenzene compounds and their high-temperature pyrolysis products are one kind of the important pollutants in the atmosphere. The research on the pyrolysis path of chlorobenzene can provide basic theoretical guidance for preventing or reducing the emission of related pollutants. The pyrolysis of chlorobenzene in a flow tube reactor was studied at 4 kPa and 873～1373 K by in-situ synchrotron radiation photoionization mass spectrometry (SR-PIMS). 16 products including olefin, aromatic hydrocarbons, and chlorinated aromatic hydrocarbons were detected in real time. Mole fractions of these products were obtained as function of pyrolysis temperature. The results show that o-benzyne is a key intermediate for the formation of small molecules in the pyrolysis of chlorobenzene. Besides, compared with the pyrolysis results of chlorobenzene at atmospheric-pressure observed before in this group, it was found that low pressure can reduce the formationpathways of chlorine-containing organics such as chlorinated aromatic hydrocarbons, an important secondary pyrolysis product of chlorobenzene, which indicates that pressure conditions can affect the formation and the following atmospheric emission of chlorinated aromatic hydrocarbons.

Effective cloud detection and cloud phase identification are of great significance to agriculture, climate and human life, and the acquisition of these data is mainly from satellite remote sensing. Satellite remote sensing data plays a vital role in the production and life of current society, and the development of many fields is inseparable from the support of satellite remote sensing data. With the development of high-precision sensors, traditional research methods cannot meet the requirements of large-scale and high-dimensional data mining and processing, so deep learning technology has been rapidly developed in the field of remote sensing. Based on deep learning technology, a method of cloud detection and cloud phase recognition combined with multi-band remote sensing images is proposed in this work. MODIS cloud product images are used as samples, the different waveband information is used as eigenvalue to establish multiple databases for cloud detection and cloud phase state recognition research tasks, and then Deeplab V3+ model is used for training and prediction, so as to complete the high-precision cloud detection and cloud phase state recognition. Compared with the traditional methods, the proposed method is more efficient and convenient, and has stronger feature extraction ability. When multi-band is used as the eigenvalue input model for prediction, the method shows good performance.

The traditional method of adjusting the gain of photomultiplier tube (PMT) with mechanical knobs not only has disadvantages of manual operation based on experience and poor accuracy, but also has a disadvantage that the gain of PMT is susceptible to temperature and the high voltage of PMT needs to be dynamically adjusted according to the ambient temperature. These limitations are not conducive to its application in lidar system. In order to adjust thegain of PMT easily and keep the stability of PMT gain, the control circuit board of PMT applicable to lidar system is designed to realize gain regulation and high-voltage temperature self-adaptive adjustment by computer, thus simplifying the structure and volume of the signal detection system and improving the stability of the signal. In this work, H10721-20 type PMT with built-in high voltage power supply is used, and the corresponding control circuit board based on STM32 single chip microcomputer, digital-to-analogue converter and peripheral circuit is designed and manufactured. Further, the high voltage stability of PMT controlled by potentiometer is compared with that of PMT adjusted by the control method proposed in this work, and then the performance of Mie scattering lidar using the designed PMT control circuit board is tested. The experimental results show that the control method proposed in this work can achieve more stable PMT gain control, which indicates that the designed PMT control circuitboard has good reliability.

Water mist environment, which is very common in industrial production, has a strong attenuation effect on infrared radiation. The artificial water mist system is often used in the cooling device of continuous casting slab in secondary cooling zone, and it is of great significance to accurately measure the slab surface temperature in the secondary cooling zone of continuous casting to improve the stability of product quality. In view of the interferenceof a large amount of water mist in the secondary cooling zone on the non-contact temperature measurement of continuous casting slab surface, a correction method based on Mie scattering theory is proposed. Firstly, according to the air-water nozzle used in the field, the simulation experiment is carried out to analyze the flow distribution of the water mist, and the water mist state model is established. Then the attenuation characteristics of the infrared radiation under the water mist state model is analyzed, the corresponding extinction coefficient is calculated through the simulation experiment, and the correction factor of the infrared radiation attenuation is obtained by using Lambert Beer law. Finally, the correction factor is introduced into the non-contact infrared temperature measurement system to correct the real-time temperature value. The research shows that the accuracy of the non-contact infrared temperature measurement system can be effectively improved by introducing the correction factor.