When the dynamic characteristics of a bridge structure are analyzed though Hilbert–Huang transform (HHT), the noise contained in the bridge dynamic monitoring data may seriously affect the performance of the first natural frequency identification. A time-frequency analysis method that integrates wavelet threshold denoising and HHT is proposed to overcome this deficiency. The denoising effect of the experimental analysis on the simulated noisy signals proves the effectiveness of the proposed method. This method is used to perform denoising pre-processing on the dynamic monitoring data of Sutong Bridge, and the denoised results of different methods are compared and analyzed. Then, the best denoising data are selected as the input data of Hilbert spectrum analysis to identify the structural first natural frequency of the bridge. The results indicate that the wavelet-empirical mode decomposition (EMD) method effectively reduces the interference of random noise and eliminates useless intrinsic modal function (IMF) components, and the excellent properties of the signal evaluation index after denoising make the method suitable for processing non-stationary signals with noise. When Hilbert spectrum analysis is applied to the denoised data, the first natural frequency of the bridge structure can be identified clearly and is consistent with the theoretical calculation. The proposed method can effectively determine the natural vibration characteristics of the bridge structure.

The rising sea level caused by global climate change might impact the human living environment. Global navigation satellite systems (GNSS)-multipath reflection (MR) technology holds significant potential for monitoring tide level changes. GNSS-MR technology typically employs low-order polynomials to extract the signal-to-noise ratio (SNR) residuals containing GNSS interference signals. It utilizes Lomb-Scargle (LSP) spectral analysis or empirical mode decomposition (EMD) to obtain the dominant frequency of the SNR residuals, which is then converted into tidal heights. However, as the satellite elevation angle increases, the GNSS interference signals decrease and the traditional method does not adapt well to the extraction of SNR residuals under such conditions. A series of improved EMD-kind algorithms, namely ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEDMAN), have been proposed to address the shortcomings of EMD algorithms such as end effect and mode aliasing. However, these improved EMD-kind algorithms have yet to be reported in sea level inversion. This study investigates the mitigation effects of EMD-kind algorithms on GNSS-MR direct signal and noise to improve the stability and accuracy of an SNR residual sequence with high satellite elevation angles. Experimental data from the HKQT station for one week and the SC02 station for one year are utilized to validate the effectiveness and accuracy of these algorithms in extracting SNR residuals. Compared to the traditional polynomial method, the experimental results demonstrate that all EMD-kind algorithms effectively address the distortion issue in traditional inversion methods under long periods, higher satellite elevation angles, and low GNSS receiver sampling rates. Among these algorithms, the results from the experiments show that ICEEMDAN consistently provides the best inversion accuracy. The results of the comparative analysis show that ICEEMDAN effectively reduces non-interference signals in SNR residuals at higher satellite elevation angles, expanding the useable range of satellite elevation angles and improving the utilization and temporal resolution of GNSS data inversion. Hence, it is an effective and appropriate approach to improving the accuracy of GNSS-MR tide level monitoring.

There is extremely strict precision requirement for the lateral breakthrough error of long immersed tunnel in the offshore island and tunnel project, but the point location layout range of the outer plane control network is limited in the artificial island, and the space inside the tunnel is long and narrow, which is not conducive to high-precision through-measurement control. In order to further reduce the influence of the error of the plane control network on the lateral breakthrough error of the immersed tunnel, a mathematical model of the influence of the lateral breakthrough error caused by the orientation direction of the outer control point and the position of the breakthrough point has been established through the theoretical analysis in this study, and the favorable orientation directions and the favorable penetration point are analyzed. It is suggested through simulation analysis that the direction that is as consistent with that from the entrance (outlet) point to the penetration point as possible is as the advantageous orientation, which could minimize the lateral penetration error and ensure high-precision rendezvous and docking of the immersed tunnel. This study is instructive for similar engineering practices.

Purpose: To explore the impact of anthropogenic emissions reductions on air quality in China during the COVID-19 pandemic. The COVID-19 outbreak was accompanied by various global lockdown measures. These measures led to a long - term decline in industrial activities and air pollution emissions, offering a valuable chance to study the influence of anthropogenic emissions cuts on air quality. Methods: Based on the anomaly percentage model, we analyzed the time-varying trends of fine particulate matter (PM_2.5), precipitable water vapor (PWV), and temperature (T) in 33 provincial-level administrative regions in China from 2010–2020, explored the spatial distribution of their anomalies from 2019–2020, and selected 170 cities with different population density classes to analyze the changes of related indicators during the epidemic. Results: During the COVID-19 period, the anomaly values of PM_2.5 fluctuated negatively in all provinces, and 69.7% and 78.8% of the anomaly values of PWV and T fluctuated positively; during the non-COVID-19 period, the anomaly values of PM_2.5 fluctuated positively, and the fluctuations of PWV and T were not obvious; PM_2.5 decreased significantly in southeastern China, and PWV and T increased significantly. As the urban population density increases, the average anomaly percentages and rates of change of PM_2.5, precipitable water vapor (PWV), and T increase. Conclusion: The human activity restrictions implemented during the COVID-19 period greatly changed the pollutant emission situation, resulting in an overall decrease in PM_2.5 and an overall increase in PWV and T. This fully confirms that there is a strong correlation between the intensity of human activities and the atmospheric air quality, and that the reduction of the intensity of human activities has a positive effect on the improvement of air quality. Graphical