ALBERT

Description: To obtain a better understanding of carbon cycle and accurate climate prediction models, highly accurate and temporal resolution observation of atmospheric CO2 is necessary. Differential absorption LIDAR (DIAL) remote sensing is a promising technology to detect atmospheric CO2. However, the traditional DIAL system is the dual-wavelength DIAL (DW-DIAL), which has strict requirements for wavelength accuracy and stability. Moreover, for on-line and off-line wavelengths, the system’s optical efficiency and the change of atmospheric parameters are assumed to be the same in the DW-DIAL system. This assumption inevitably produces measurement errors, especially under rapid aerosol changes. In this study, a multi-wavelength DIAL (MW-DIAL) is proposed to map atmospheric CO2 concentration. The MW-DIAL conducts inversion with one on-line and multiple off-line wavelengths. Multiple concentrations of CO2 are then obtained through difference processing between the single on-line and each of the off-line wavelengths. In addition, the least square method is adopted to optimize inversion results. Consequently, the inversion concentration of CO2 in the MW-DIAL system is found to be the weighted average of the multiple concentrations. Simulation analysis and laboratory experiments were conducted to evaluate the inversion precision of MW-DIAL. For comparison, traditional DW-DIAL simulations were also conducted. Simulation analysis demonstrated that, given the drifting wavelengths of the laser, the detection accuracy of CO2 when using MW-DIAL is higher than that when using DW-DIAL, especially when the drift is large. A laboratory experiment was also performed to verify the simulation analysis.

Description: Journal of the American Chemical Society DOI: 10.1021/jacs.5b03042

Description: The accurate estimation of deposits adhering on insulators is of great significance to prevent pollution flashovers which cause huge costs worldwide. Researchers have developed sensors using different technologies to monitor insulator contamination on a fine time scale. However, there is lack of analysis of these data to reveal spatial and temporal characteristics of insulator contamination, and as a result the scheduling of periodical maintenance of power facilities is highly dependent on personal experience. Owing to the deployment of novel sensors, daily Equivalent Salt Deposit Density (ESDD) observations of over two years were collected and analyzed for the first time. Results from 16 sites distributed in four regions of Hubei demonstrated that spatial heterogeneity can be seen at both the fine and coarse geographical scales, suggesting that current polluted area maps are necessary but are not sufficient conditions to guide the maintenance of power facilities. Both the local emission and the regional air pollution condition exert evident influences on deposit accumulation. A relationship between ESDD and PM10 was revealed by using regression analysis, proving that air pollution exerts influence on pollution accumulations on insulators. Moreover, the seasonality of ESDD was discovered for the first time by means of time series analysis, which could help engineers select appropriate times to clean the contamination. Besides, the trend component shows that the ESDD increases in a negative exponential fashion with the accumulation date (ESDD = a − b × exp(−time)) at a long time scale in real environments.

Description: Aerosol scattering and absorption properties were continuously measured and analyzed at the urban Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) site in Wuhan, central China, from 1 December 2009 to 31 March 2014. The mean aerosol scattering coefficient , absorption coefficient , and single scattering albedo (SSA) were 377.54 Mm−1, 119.06 Mm−1, and 0.73, respectively. Both  and  showed obvious annual variability with large values in winter and small values in summer, principally caused by the annual characteristics of meteorological conditions, especially planetary boundary layer height (PBLH) and local emissions. The SSA showed a slight annual variation. High values of SSA were related to formation of secondary aerosols in winter hazes and aerosol hygroscopic growth in humid summer. The large SSA in June can be attributed to the biomass combustion in Hubei and surrounding provinces. Both  and  showed double peak phenomena in diurnal variation resulting from the shallow stable PBLH at night and automobile exhaust emission during morning rush hours. The SSA also exhibited a double peak phenomenon related to the proportional variation of black carbon (BC) and light scattering particulates in the day and night. The long-term exploration on quantified aerosol optical properties can help offer scientific basis of introducing timely environmental policies for local government.

Description: Sensors, Vol. 18, Pages 2362: Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform Sensors doi: 10.3390/s18072362 Authors: Chengzhi Xiang Ge Han Yuxin Zheng Xin Ma Wei Gong Atmospheric CO2 plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO2 vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO2 detection due to its characteristics of high precision, high time resolution, and high spatial resolution. Ground-based CO2-DIAL can provide the continuous observations of the vertical profile of CO2 concentration, which can be highly significant to gaining deeper insights into the rectification effect of CO2, the ratio of respiration photosynthesis, and the CO2 dome in urban areas. A set of ground-based CO2-DIAL systems were developed by our team and highly accurate long-term laboratory experiments were conducted. Nonetheless, the performance suffered from low signal-to-noise ratio (SNR) in field explorations because of decreasing aerosol concentrations with increasing altitude and surrounding interference according to the results of our experiments in Wuhan and Huainan. The concentration of atmospheric CO2 is derived from the difference of signals between on-line and off-line wavelengths; thus, low SNR will cause the superimposition of the final inversion error. In such a situation, an efficient and accurate denoising algorithm is critical for a ground-based CO2-DIAL system, particularly in field experiments. In this study, a method based on lifting wavelet transform (LWT) for CO2-DIAL signal denoising was proposed. This method, which is an improvement of the traditional wavelet transform, can select different predictive and update functions according to the characteristics of lidar signals, thereby making it suitable for the signal denoising of CO2-DIAL. Experiment analyses were conducted to evaluate the denoising effect of LWT. For comparison, ensemble empirical mode decomposition denoising was also performed on the same lidar signal. In addition, this study calculated the coefficient of variation (CV) at the same altitude among multiple original signals within 10 min and then performed the same calculation on the denoised signal. Finally, high-quality signal of ground-based CO2-DIAL was obtained using the LWT denoising method. The differential absorption optical depths of the denoised signals obtained via LWT were calculated, and the profile distribution information of CO2 concentration was acquired during field detection by using our developed CO2-DIAL systems.

Description: Remote Sensing, Vol. 10, Pages 985: Feasibility Study on Measuring Atmospheric CO2 in Urban Areas Using Spaceborne CO2-IPDA LIDAR Remote Sensing doi: 10.3390/rs10070985 Authors: Ge Han Hao Xu Wei Gong Jiqiao Liu Juan Du Xin Ma Ailin Liang Since over 70% of carbon emissions are from urban areas, it is of great importance to develop an effective measurement technique that can accurately monitor atmospheric CO2 in global urban areas. Remote sensing could be an effective way to achieve this goal. However, due to high aerosol loading in urban areas, there are large, inadequately resolved areas in the CO2 products acquired by passive remote sensing. China is planning to launch the Atmospheric Environment Monitoring Satellite (AEMS) equipped with a CO2-light detecting and ranging (LIDAR) system. This work conducted a feasibility study on obtaining city-scale column CO2 volume mixing ratios (XCO2) using the LIDAR measurements. A performance framework consisting of a sensor model, sampling model, and environmental model was proposed to fulfill our demand. We found that both the coverage and the accuracy of the LIDAR-derived city-scale XCO2 values were highly dependent on the orbit height. With an orbit height of 450 km, random errors of less than 0.3% are expected for all four metropolitan areas tested in this work. However, random errors of less than 0.3% were obtained in only two metropolitan areas with an orbit height of 705 km. Our simulations also showed that off-nadir sampling would improve the performance of a CO2-Integrated Path Differential Absorption (IPDA) LIDAR system operating in a 705 km orbit. These results indicate that an active remote sensing mission could help to effectively measure XCO2 values in urban areas. More detailed studies are needed to reveal the potential of such equipment for improving the verification of carbon emissions and the estimation of urban carbon fluxes.

Description: Remote Sensing, Vol. 9, Pages 768: Performance Evaluation for China’s Planned CO2-IPDA Remote Sensing doi: 10.3390/rs9080768 Authors: Ge Han Xin Ma Ailin Liang Tianhao Zhang Yannan Zhao Miao Zhang Wei Gong Active remote sensing of atmospheric XCO2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO2-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO2 globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO2-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading.

Description: Remote Sensing, Vol. 9, Pages 1033: Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON Remote Sensing doi: 10.3390/rs9101033 Authors: Ailin Liang Wei Gong Ge Han Chengzhi Xiang CO2 is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO2 of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO2 distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014–2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO2. Furthermore, the XCO2 values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO2 distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites’ measurements reflect the growth trend of the global XCO2 at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape.

Description: Mechanical vibration signal mapped into a high-dimensional space tends to exhibit a special distribution and movement characteristics, which can further reveal the dynamic behavior of the original time series. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, the tensor decomposition algorithm has broad application prospects in signal processing. High-dimensional tensor can be obtained from a one-dimensional vibration signal by using phase space reconstruction, which is called the tensorization of data. As a new signal decomposition method, tensor-based singular spectrum algorithm (TSSA) fully combines the advantages of phase space reconstruction and tensor decomposition. However, TSSA has some problems, mainly in estimating the rank of tensor and selecting the optimal reconstruction tensor. In this paper, the improved TSSA algorithm based on convex-optimization and permutation entropy (PE) is proposed. Firstly, aiming to accurately estimate the rank of tensor decomposition, this paper presents a convex optimization algorithm using non-convex penalty functions based on singular value decomposition (SVD). Then, PE is employed to evaluate the desired tensor and improve the denoising performance. In order to verify the effectiveness of proposed algorithm, both numerical simulation and experimental bearing failure data are analyzed.