Ammonia (NH3) is an important alkaline gas in the atmosphere, which can react with acid gas in the air leading to the formation of haze through dry and wet deposition. Therefore, the content of NH3 in the air is one of the important criteria for measuring the quality of air. The anthropogenic emission sources of NH3 are mainly divided into agricultural and non-agricultural sources, with agricultural sources accounting for 80% of the total NH3 emission, and the main agricultural sources include livestock and poultry breeding, animal husbandry excreta and nitrogen fertilizer application in cropland. Given the significant environmental effects of NH3, accurate measurement of NH3 emission flux from agriculture has become an important research field for establishing NH3 emission inventory and controlling NH3 emission. In this paper the basic principle of measuring ammonia by three infrared spectroscopy measurement techniques, namely Fourier transform spectrum, photoacoustic spectrum and tunable absorption spectrum, and the development status and applicable scenarios of the corresponding measurement techniques at home and abroad are summarized. The principle and application of eddy covariance method (EC) and backward Lagrange method (bLS), which are widely used in the estimation of ammonia emission flux from animal husbandry and cropland, and several mature NH3 flux calculation software based on EC or BLS algorithm are also introduced. Finally, the future development direction of NH3 measurement technology and ammonia flux emission estimation is prospected.

As a powerful tool for active remote sensing of the atmosphere, lidar can be used to monitor the spatial and temporal distribution of clouds automatically and continuously. An improved cloud detection algorithm based on differential enhancement is proposed in this work. By introducing integral area and peak ratio algorithm, the improved algorithm cannot only effectively reduce the number of data fitting points during differentiation, improve the authenticity of cloud data, but also avoid misjudgment caused by strong noise points after differentiation in the traditional differential enhancing cloud detection algorithm. In addition, the improved algorithm also adds the merging of characteristic clouds, which can effectively reduce the mission caused by the traditional algorithm in the case of single-layer and multi-peak signals. Finally, the three kinds of simulated lidar signals and the measured Mie scattered lidar signals are used to analyze and compare the algorithms before and after the improvement. The results show that the improved algorithm maintains the superiority of cloud detection under the conditions of low signal-to-noise ratio, while further reduces the misjudgment and mission of characteristic clouds.

The available extinction coefficient of the main land areas in China are studied from the perspective of optical engineering application. Firsty, the relevant historical data are analyzed from three aspects: overall average situation, half-year change of warm and cold weather, and diurnal change. Results show that the overall distribution of horizontal extinction coefficient is basically low in the north and high in the south. There are slight differences in the distribution of horizontal extinction coefficient between the cold and warm half year, with a larger area having a lower horizontal extinction coefficient in the warm half year than in the cold half year. As for the diurnal change, it is shown that the horizontal extinction coefficient during the daytime is lower than that at night. Furthermore, the regional classification of horizontal extinction coefficient in China is discussed on the basis of overall average, half-year change and dirunal variation, and it is found that the first level areas suitable for optical engineering application are mainly in the north and west of China. The result is helpful for understanding the regions suitable for optical engineering applications in whole country, and can provide support for further research on the quantitative impacts of horizontal extinction coefficient on optical engineering applications.

Large-aperture scintillometer can be used for regional surface heat flux observations and is also an effective tool for measuring surface heat fluxes at the satellite image scale. For the improvement of satellite product algorithms and observation methods, it is crucial to validate the accuracy of satellite products utilizing surface relative truth of scintillator. To preliminarily solve the problem of satellite product calibration, two scintillometer stations in Hebi, Henan Province, and Ulanqab, Inner Mongolia, China were selected. Based on the theory of near ground similarity and combined with the meteorological information observed, the refractive index structure constant output from the scintillometers was processed. Then the sensible and latent heat fluxes were calculated using an iterative algorithm, and the accuracy of scintillometers was verified taking the measurement results of the eddy correlation meter in Hebi area as reference. Additionally, the latent heat flux data in MOD16A2 remote sensing evapotranspiration products were ground-checked using scintillometer data from the Ulanqab region. The results demonstrate that the scintillometer inversion results have high accuracy, high correlation with the eddy data, and highly match the scale of the elements in satellite images, allowing for the use of these data as ground calibration of satellite products.

To accurately grasp the changes of the temporal and spatial pattern of carbon emissions and formulate regional differential emission reduction policies according to local conditions is a widely concerned in current society. Based on the night lighting data from 2000 to 2019 and the estimation of national energy carbon emissions, a fitting model is established to deduce the carbon emission data of prefecture-level cities, and then the spatial dependence of carbon emissions under the background of the rapid development of urbanization is studied from three aspects: carbon emission type, spatial aggregation and center of gravity transfer. The results show that the total carbon emission in China shows an upward trend from 2000 to 2019, with the growth rate and carbon emission intensity gradually slowing down as a whole. In the five inversion models for total night lighting data and provincial carbon emission data from 2000 to 2019, the quadratic model has the highest fitting degree, with R2 reaching 0.8261. Energy carbon emissions in China show an uneven distribution in space, with a basic pattern of high in the east and low in the west, and high in the north and low in the south. From 2000 to 2019, the regional differencesand spatial accumulation of carbon emissions is becoming more and more obvious, and the spatial distribution of carbon emission center has generally shifted from west to east.

To address the problem of the detection performance calibration of differential absorption lidar in mid-infrared band, a straight-through gas cell for differential absorption detection in mid-infrared band was designed and constructed to carry out experiments on differential absorption spectroscopy detection of atmospheric benzene. The structural and optical parameters of the gas cell were designed by using a mid-infrared interband cascade laser as the light source and combining the emission beam parameters of the interband cascade laser. Then the transmittance characteristics and sealing performance of the gas cell were tested at 3187.0 nm and 3235.3 nm wavelengths. Finally, differential absorption lidar detection calibration experiments were carried out using benzene as an example. The results showed that the light transmission rate of the gas cell in the test band was greater than 85%, the negative air pressure was reduced by about 13 kPa after 100 h, and the correlation coefficient of the differential absorption spectroscopy detection calibration experiment was 0.995, indicating that the designed gas cell fully meets the calibration requirements of the mid infrared differential absorption lidar.

The concentration of chlorophyll a in inland water determines the plankton content, which is a main index to measure the eutrophication degree of water, and is also one of the important factors affecting the apparent reflectance spectrum of water. The three-band bio-optical model can effectively reduce the influence of suspended matter and chemical substances in Class II water during the inversion process and accurately estimate the concentration of chlorophyll a. Taking Baiyangdian Lake in Xiongan New Area, China, as the research area, the spectral characteristics of water were analyzed by using the measured water spectrum and chlorophyll a concentration, the position of three-band factors was determined by using the exhaustive method, the three-band inversion model was established and the accuracy was verified, and finally the results wreanalyzed and evaluated. The results show that at λ1=673 nm, λ2=729 nm, λ3=782 nm, the maximum correlation coefficient between the inversion factors of the three-band model [Rrs-1673-Rrs-1729]Rrs782 and chlorophyll aconcentration is 0.82. The determination coefficient, root mean square error, mean absolute error and mean relative error of the model are 0.67, 3.61 μg/L, 2.86 μg/L and 26.06%, respectively, indicating that for the water with high suspended matter and chemical substances, the three-band model has a good inversion accuracy compared with the wave segment ratio and single-band model. This study provides a certain theoretical basis and technical support for the inversion of chlorophyll a concentration in Baiyangdian Lake, which is helpful for the continuous monitoring of water quality in Baiyangdian Lake, and also provides an algorithmic support for the inversion of chlorophyll a concentration in Class II water using three-band model in the future.

Based on the daily average concentration monitoring data of important pollutants and the meteorological data at ground national control stations from January 1, 2019 to December 31, 2020, a comprehensive assessment of urban air quality in Hefei City, China, was conducted using the principal component analysis method. The pollutant data includes daily values of important atmospheric pollutants such as fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), and the corresponding daily mean meteorological parameters include temperature, wind speed, sunshine duration, precipitation, atmospheric pressure, and relative humidity. Firstly, the indicators that have a significant impact onthe air quality were selected through stepwise regression, and then these significant indicators were reduced to a lower dimension through the principal component analysis. According to the theory of principal component analysis, two principal components were extracted from five significant impact indicators, and the cumulative variance contribution rate of the two extracted principal components reached 82.9%. The first principal component was characterized by high loadings of PM2.5, CO, and PM10 concentrations, implying the significant impact of these parameters on the air quality of Hefei. The second principal component was characterized by high loadings of O3 concentration and sunshine duration, demonstrating their important role on the air quality of Hefei. Overall, good correlation was observed between the results from principal component analysis and the air quality index with a correlation coefficient of 0.78. In addition, it is found that the proposed method is more suitable to assess the air quality in winter and under good air quality conditions. These findings highlight the good performance of principal component analysis method on the assessment of urban air quality.

Based on ground-based Fourier transform infrared remote sensing and in-situ surface measurement, the variation of atmospheric CH4 and CO2 concentrations in Hefei City, China, from January 2016 to December 2017 were investigated. The observation results show that there were obvious seasonal variations in CO2 and CH4 over Hefei. CO2 showed a minimum value in August 2016, and then gradually increased over time, reaching the maximum in April 2017. After that, it declined gradually, reaching another minimum value in early September 2017. In contrast, CH4 showed high level in summer and early autumn, and low concentrations in winter and early spring. In 2016, the seasonal amplitudes of the monthly mean concentrations of XCO2 and XCH4 measured by ground-based remote sensing were 5.96 × 10-6 and 0.047 × 10-6 respectively, while those of CO2 and CH4 measured in-situ were 31.46 × 10-6 and 0.26 × 10-6 respectively, about 5 times of the corresponding values measured by ground-based remote sensing. The correlation between atmospheric CO2 and CO, as well as CH4 and CO2, at Hefei Station was analyzed. It is shown that the correlation coefficient R between XCO2 and XCO measured by ground-based remote sensing in autumn is 0.75, and the ratio of XCO2 and XCO is 79.5. The strong correlation between XCO2 and XCO indicates that most of CO2 and CO in Hefei area in autumn originate from the same sources. On the other hand, the correlation coefficient R of CO2 and CO, CH4 and CO2 measured by in-situ surface measurement in winter is 0.80, the ratio of CO2 and CO is 52.2, and the ratio of CH4 and CO2 is 0.0036. The strong correlations between gases indicate that anthropogenic sources are main sources of CO2 at Hefei Station in winter, and CH4 and CO2 partially originate from biomass burning.