China’s largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing’an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China.

Impact-Echo (IE) testing is a promising nondestructive evaluation technique for concrete structures, but interpreting its frequency response in cylindrical bridge piers remains challenging because structural geometry, propagation paths, and internal discontinuities can produce overlapping spectral characteristics. This study investigates IE responses of cylindrical concrete bridge piers through field measurements and three-dimensional finite-element simulations. Field tests were conducted on two in-service bridges to characterize dominant-frequency responses and measurement repeatability under practical conditions. The numerical investigation considered hollow voids and segregation-like low-stiffness regions with different sizes and spatial configurations. The effects of defect type, size, and location on dominant frequency, waveform timing, and transmission characteristics were examined to interpret the underlying response mechanisms. The simulations showed that hollow voids generally produced stronger transmission blockage and more pronounced temporal shifts in the backside response than segregation-like regions in the representative cases, although their frequency responses were not uniquely distinguishable. A normalized dominant-frequency-shift index was subsequently evaluated as a candidate measure of relative spectral deviation. Within the deduplicated numerical parameter set, hollow-void cases generally exhibited larger dominant-frequency shifts than segregation cases. However, the distributions overlapped, and one segregation case was highly sensitive to dominant-peak selection because of two nearly equal spectral peaks. The field measurements demonstrated repeatable dominant-frequency responses under the investigated conditions, while spatial variations and measurement geometry highlighted the limitations of single-frequency interpretation. The findings support comparative interpretation of IE responses in cylindrical members but also highlight the need for further validation using independently confirmed defect conditions.

Subgrades are vulnerable to hydrological effects, repeated loading, and material degradation, which may induce settlement, voids, loosening, and differential deformation and ultimately threaten the safety and serviceability of transportation infrastructure. With the growing demand for continuous and intelligent condition assessment, conventional single modality inspection methods are increasingly insufficient for complex field environments. This paper reviews recent advances in intelligent subgrade inspection and distress identification following a “sensing–identification–application” framework. Multi-physics sensing technologies, including ground-penetrating radar, elastic-wave testing, intelligent compaction, and embedded sensing, are summarized with emphasis on their applicable distress types, sensing characteristics, and life-cycle stages. AI-based methods for distress identification and condition prediction are then discussed, covering image recognition, object detection, semantic segmentation, time-series forecasting, and physics-informed data fusion. Engineering applications involving vehicle, rail, UAV, and robot-based inspection platforms are also examined. Future research should focus on multisource sensing fusion, stronger integration of data-driven models with engineering mechanics, improved cross-scenario adaptability, and closer links between inspection results and maintenance decisions. These advances are expected to support a transition from passive distress detection toward proactive prediction and intelligent infrastructure management.