Optimization of Mach-Zehnder interferometer of high-spectral-resolution lidar for large-scale wind measurement.

The optimization design of a quadri-channel Mach-Zehnder interferometer (QMZI) of the high-spectral-resolution lidar is presented for the large-scale wind measurement. The optimized QMZI can discriminate the Doppler frequency shift generated by atmospheric wind from aerosol Mie scattering echo signals and molecular Rayleigh scattering echo signals, and then the wind information can be retrieved. The optimal optical path differences (OPDs) of QMZI are determined by theoretical and simulation analysis. The wind measurement simulation experiments prove that the designed QMZI can measure the large-scale wind with an accuracy of meter level.

Power-modulated integrated path differential absorption lidar for probing benzene concentration.

Aimed at the regional open-path detection of benzene (C 6 H 6) in the atmosphere, a power-modulated integrated path differential absorption (PM-IPDA) lidar is introduced and demonstrated. Two tunable interband cascade lasers (ICLs) with about 3.2 µm wavelength are utilized to generate the required PM optical signal. These two operation central wavelengths (CWs) of the PM-IPDA lidar are, respectively, 3236.6 and 3187.1 nm, which can mitigate the influence of significant gases such as H 2 O, C H 4, and HCl on the detection performance. In this work, the fast Fourier transform algorithm is used to retrieve the measured values with the time resolution of 0.1 s corresponding to 104 sampling bins at the sampling rate of 100 kSps/s. The modulated frequency of the PM-IPDA lidar is selected as 10 kHz by laboratory experiments. The slow fluctuation characteristic of the benzene absorption spectrum within the vicinity region of 3.2 µm reduces the impact of small wavelength fluctuations on the performance of PM-IPDA lidar, although a scheme modulated only the driving current causes wavelength fluctuations of ∼±0.2n m. These laboratory experiments also indicate the PM-IPDA lidar can reduce the error resulting from 1/f noise. Open-path observation experiments show that the detection limit is about 0.60m g⋅m -3 and that the PM-IPDA lidar can be used for the regional open-path real-time detection of benzene.

Dietary Zinc-Loaded Montmorillonite Supplementation Improves Growth Performance, Diarrhea, Intestinal Barrier Function and Regulating Gut Microbiota in Weaned Piglets.

This experiment was conducted to investigate whether low-dose zinc-loaded montmorillonite (Zn-MMT) could be used as a potential alternative for high-dose conventional ZnO in preventing diarrhea in weaned piglets. In total, 180 piglets were randomly divided to receive either of the three treatments, with six replicates per treatment and 10 piglets per replicate. The treatments were the control group (CT), the Zn-MMT group (ZM), and the ZnO group (ZO). Compared with the CT group, the ZM and ZO groups exhibited increased ADG at 14-28 days and during the whole period (p < 0.05), and a significantly decreased diarrhea rate during the whole period (p < 0.01). The activities of T-AOC and SOD were significantly increased (p < 0.05), whereas the MDA level decreased (p < 0.05) in the serum and colonic mucosa of Zn-MMT- and ZnO-fed piglets. Dietary supplementation with Zn-MMT and ZnO decreased the contents of IFN-γ, TNF-α, IL-1ß, IL-6, DAO, and LPS in the serum and colonic mucosa (p < 0.01), and increased the IL-10 level (p < 0.01). The relative mRNA expressions of TLR-4, claudin 2, Pbd1, and MUC2 were elevated in the colonic mucosa of the Zn-MMT and ZnO groups (p < 0.05). 16S rRNA gene sequencing analysis revealed that the abundances of Proteobacteria and Actinobacteria in the ileum and the populations of Ruminnococcus and Faecalibacterium in the cecum were higher in the CT group than in the other two groups. Collectively, dietary addition of Zn from Zn-MMT was comparable to Zn from ZnO for increasing growth performance, alleviating diarrhea, as well as improving mucosal barrier integrity, and regulating the gut microbiota of weaned piglets.

Error simulation of atmospheric scattered radiance and its influence on slant visibility based on the SBDART model and Monte Carlo method.

Atmospheric scattered radiance is an important factor affecting slant visibility measurement in the daytime. This paper explores atmospheric scattered radiance errors and their influence on slant visibility measurements. Considering the difficulty in error synthesis of the radiative transfer equation, an error simulation scheme based on the Monte Carlo method is proposed. An error simulation and error analysis for atmospheric scattered radiance was carried out based on the Santa Barbara DISTORT atmospheric radiative transfer (SBDART) model and the Monte Carlo method. The error in aerosol parameters including the single-scattering albedo (SSA), the asymmetry factor, and the aerosol optical depth (AOD), was simulated by a random number and random error under different normal distributions, and the error influence of aerosol parameters on the error in the solar irradiance and 33-layer atmosphere scattered radiance is discussed in detail. The maximum relative deviations of the output scattered radiance at a certain slant direction are 5.98%, 1.47%, and 2.35%, when SSA, the asymmetry factor, and the AOD obey the normal distribution of (0, 5). The error sensitivity analysis also confirms that the SSA is the most sensitive factor affecting atmospheric scattered radiance and the total solar irradiance. Then, according to the error synthesis theory, we investigated the error transfer effect of three error sources related to the atmosphere based on the contrast ratio between the object and the background. The simulation results show that the error in the contrast ratio caused by solar irradiance and scattered radiance is lower than 6.2% and 2.84%, indicating the main role in contributing to the error transfer of slant visibility. Further, the comprehensive process of the error transfer in slant visibility measurements was demonstrated by a set of lidar experiments and the SBDART model. The results provide a reliable theoretical basis for the measurement of atmospheric scattered radiance and slant visibility, which is of great significance to improve the measurement accuracy of slant visibility.

Atmospheric visibility prediction by using the DBN deep learning model and principal component analysis.

Measuring and predicting atmospheric visibility is important scientific research that has practical significance for urban air pollution control and public transport safety. We propose a deep learning model that uses principal component analysis and a deep belief network (DBN) to effectively predict atmospheric visibility in short- and long-term sequences. First, using a visibility meter, particle spectrometer, and ground meteorological station data from 2016 to 2019, the principal component analysis method was adopted to determine the influence of atmospheric meteorological and environmental parameters on atmospheric visibility, and an input dataset applicable to atmospheric visibility prediction was constructed. On the basis of deep belief network theory, network structure parameters, including data preprocessing, the number of hidden layers, the number of nodes, and activation and weight functions, are simulated and analyzed. A deep belief network model suitable for atmospheric visibility prediction is established, where a double hidden layer is adopted with the node numbers 70 and 50, and the Z-score method is used for normalization processing with the tanh activation function and Adam optimizer. The average accuracy of atmospheric visibility prediction by the deep belief network reached 0.84, and the coefficient of determination reached 0.96; these results are significantly superior to those of the back propagation (BP) neural network and convolutional neural network (CNN), thus verifying the feasibility and effectiveness of the established deep belief network for predicting atmospheric visibility. Finally, a deep belief network model based on time series is used to predict the short- and long-term trends of atmospheric visibility. The results show that the model has good visibility prediction results within 3 days and has an accuracy rate of 0.79. Covering the visibility change evaluations of different weather conditions, the model demonstrates good practicability. The established deep learning network model provides an effective and feasible technical solution for the prediction of atmospheric meteorology and environmental parameters, which enjoys a wide range of application prospects in highway transportation, navigation, sea and air, meteorology, and environmental research.

Improved algorithm of aerosol particle size distribution based on remote sensing data.

The optical parameters (extinction or backscatter coefficients) of multi-wavelength beams can be used for the retrieval of the aerosol particle size distribution (APSD). An improved algorithm for APSD and aerosol microphysical parameters (AMPs) is studied and discussed by using only multi-wavelength extinction coefficients data. The regularized algorithm and prior value are combined for the retrieval of APSD and AMPs. The regularization algorithm, based on minimum discrepancy principle and averaging procedure, is used for the retrieval of fine-mode APSD and an averaging procedure that can achieve stable outputs is proposed. The 1% averaging result near the minimum of the discrepancy is selected and verified. Based on the inversion results of fine mode from the regularization algorithm, the lognormal distribution with a prior value (model radius) is applied to reconstruct the coarse mode of APSDs through fitting the data. The comprehensive application of the regularization algorithm and averaging process improves the stability of the inversion in the fine mode, and the use of the prior value broadens the inversion radius range of APSD. The complex refractive index need not be assumed for this method. The inversion error for different types of aerosols is analyzed and studied. The reliability of the algorithm is tested and verified by many typical APSDs and the measured APSDs by particle size spectrometer in different pollution days. The algorithm sensitivity analysis is also provided and discussed. The algorithm can obtain reliable inversion of APSD and AMPs with large radius range.

Optimized retrieval method for atmospheric temperature profiling based on rotational Raman lidar.

Aimed at addressing the disadvantages of restricted retrieval height caused by signal-to-noise ratio (SNR) differences between high-quantum-number and low-quantum-number pure rotational Raman scattering signals (PRRSs) obtained with the traditional retrieval method, an optimized retrieval method is proposed for atmospheric temperature profiling based on rotational Raman lidar. This method allows independent alternating solutions to high- and low-quantum-number PRRSs, where high-quantum-number PRRS lidar returns are used to solve the channel constant, and low-quantum-number PRRS returns with a high SNR are used for retrieving temperature profiles. The system sensitivity, SNR, and statistical error in temperature measurements by the two methods are first simulated and discussed, and the results are then compared to show that a higher SNR and stable sensitivity can be attributed to stable statistical errors with the optimized method. A further assessment is demonstrated by three sets of lidar data from a multifunctional Raman-Mie lidar system at the Xi'an University of Technology (34.233°N, 108.911°E). The retrieved atmospheric temperature profiles under different weather conditions are compared with radiosonde data; then, the temperature deviations are further evaluated, and a correlation analysis is performed to evaluate the reliability and correctness of the temperature data obtained by the optimized retrieval method. The results show that the effective temperature retrieval height can be greatly improved from 17 to 25 km under clear weather conditions, and a high correlation >0.99 and stable relative deviations of less than 5 K can be obtained up to 25 km. Additionally, the retrieval height can be extended from 8 to 16 km in cloudy weather, and the existence of an inversion layer can be successfully captured as well. It is evident that the proposed optimized method will provide a new and reliable retrieval theorem for atmospheric temperature profiles, and the proposed method is propitious for retrieving temperature profiles over a larger height range, even up to the lower stratosphere. It is also deduced that the proposed algorithm can favorably simplify the spectroscopic system for temperature detection in the future when the channel constant is determined in advance.

Research on an adaptive filter for the Mie lidar signal.

The statistical properties of the noise in the Mie lidar signal are analyzed by the statistical hypotheses testing method. Based on this, an adaptive filter is proposed to eliminate the noise. The least mean square error algorithm is used to achieve optimal filtering, in which the mean square error is minimized by adjusting the filter's weight matrix. The validity of the adaptive filter is verified by numerical simulation and experimental data retrieving. In the numerical simulation, the signal-to-noise ratio of the adaptive filter is larger than that of the wavelet transform filter, and the mean square error of the output of the adaptive filter is less than the wavelet transform filter. In experimental data retrieving, the filtered lidar signals of the adaptive filter and wavelet transform filter are used to retrieve the extinction coefficient respectively in different weather conditions. The amplitude of the ripples in the extinction coefficient profile of the adaptive filter is less than that of the wavelet transform filter. Additionally, the adaptive filter's extinction coefficient profile is smoother than that of the wavelet transform filter. The detail of the extinction coefficient is displayed more clearly in the profile of the adaptive filter. The research result is of great importance for improving the accuracy of lidar data retrieving.

Variations in the water vapor distribution and the associated effects on fog and haze events over Xi'an based on Raman lidar data and back trajectories.

A combination of more than two years of water vapor lidar data with back trajectory analysis using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model was used to study the long-range transport of air masses and the water vapor distribution characteristics and variations over Xi'an, China (34.233° N, 108.911° E), which is a typical city in Northwest China. High-quality profiles of the water vapor density were derived from a multifunction Raman lidar system built in Xi'an, and more than 2000 sets of profiles with >400 nighttime observations from October 2013 to July 2016 were collected and used for statistical and quantitative analyses. The vertical variations in the water vapor content were discussed. A mutation height of the water vapor exists at 2-4 km with a high occurrence rate of ∼60% during the autumn and winter seasons. This height reflects a distinct stratification in the water vapor content. Additionally, the atmospheric water vapor content was mainly concentrated in the lower troposphere, and the proportion of the water vapor content at 0.5-5 km accounted for 80%-90% of the total water vapor below 10 km. Obvious seasonal variations were observed, including large water vapor content during the spring and summer and small content during the autumn and winter. Combined with back trajectory analysis, the results showed that markedly different water vapor transport pathways contribute to seasonal variations in the water vapor content. South and southeast airflows dominated during the summer, with 30% of the 84 trajectories originating from these areas; however, the air masses during the winter originated from the north and local regions (64.3%) and from the northwest (27%). In addition, we discussed variations in the water vapor during fog and haze weather conditions during the winter. A considerable enhancement in the mean water vapor density at 0.5-3 km exhibited a clear positive correlation (correlation coefficient >0.8) with the PM2.5 and PM10 concentrations. The results indicate that local airflow trajectories mainly affect water vapor transport below the boundary layer, and that these flows are closely related to the formation of fog and haze events in the Xi'an area.

Correction technology of a polarization lidar with a complex optical system.

A complex optical system used in polarization lidars often modifies the input polarization of the return signal so that it may significantly impact depolarization estimates and introduce errors to polarization lidar measurements. In most cases, retardation, depolarization, and misalignment of the system exist at the same time and interact with each other. Polarization effects of the system cannot be represented by a simple correction coefficient, so they cannot be removed using a traditional calibration method. Detailed analysis and correction technologies were provided to remove systematic biases in estimating depolarization values from a polarization lidar owing to multiple optical components. The Mueller matrices from an emitter to a receiver were calculated, and the expression for an aerosol depolarization parameter including system polarization effects was derived and obtained. In addition, the correction algorithm based on the Mueller matrix was introduced and provided. A polarization lidar was established, and the polarization characteristics of its optical components were measured with a laboratory ellipsometer; then, the Mueller matrix of the receiver was calculated and obtained. Lidar observations were performed, and our correction algorithm was applied to lidar field data. The results show that the correction method can significantly remove systematic polarization effects.