Yihang Gao

Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, between 2019 and 2024. Using InSAR-derived datasets for training and validation, three machine learning architectures, namely two-dimensional convolutional long short-term memory (ConvLSTM2D), hybrid convolutional neural network–long short-term memory (hybrid CNN-LSTM), and hybrid convolutional neural network–bidirectional long short-term memory (hybrid CNN-BiLSTM), were developed to further analyze ground deformation and make future predictions. It was found that from SBAS-InSAR, the deformation rates for the whole Dongli District, Tianjin, ranged from −40.98 to 27.18 mm/year, with a mean of −2.41 mm/year from 2019 to 2024. Model performance was evaluated using held-out validation samples derived from the InSAR deformation dataset. The ConvLSTM2D model achieved the best performance, with an R² value of 0.99 and root mean squared error (RMSE) of 1.37 mm, compared with the hybrid CNN-LSTM (R² = 0.99, RMSE = 2.16 mm) and hybrid CNN-BiLSTM (R² = 0.99, RMSE = 2.19 mm). This optimized ConvLSTM2D model was applied to estimate the predictions of the ground deformation rate with −43.71 mm/year in the high-deformation zone between 2025 and 2028. These findings predict a continuing trend of land instability, highlighting the necessity for urgent geohazard mitigation and urban planning strategies in the affected regions.

China’s accelerated urbanization has instigated construction land expansion and ecological land attrition, aggravating the carbon emission disequilibrium. Notably, the “land carbon emission elasticity coefficient” in urban agglomerations far exceeds international benchmarks, underscoring the contradiction between spatial expansion and low-carbon goals. Existing research predominantly centers on single-spatial-type or static-model analyses, lacking cross-scale mechanism exploration, policy heterogeneity consideration, and differentiated carbon metabolism assessment across functional spaces. This study takes Xiong’an New Area as a case, delineating the spatiotemporal evolution of land use and carbon emissions during 2017–2023. Construction land expanded by 26.8%, propelling an 11-fold escalation in carbon emissions, while emission intensity decreased by 11.4% due to energy efficiency improvements and renewable energy adoption. Cultivated land reduction (31.8%) caused a 73.4% decline in agricultural emissions, and ecological land network restructuring (65.3% forest expansion and wetland restoration) significantly enhanced carbon sequestration. This research validates a governance paradigm prioritizing “structural optimization” over “scale expansion”—synergizing construction land intensification with ecological restoration to decelerate emission growth and strengthen carbon sink systems.

(1) Background: Ecological restoration is crucial to improve ecological functions and optimize its security patterns. The Zhangbei of Zhangjiakou, a typical agro-pastoral transition zone, was studied as an example to conduct ecological restoration suitability evaluation in northern China. (2) Methods: suitability of ecological restoration in Zhangbei was assessed by both single factor analysis and comprehensive factor analysis, which were based on the data of regional water resources, ecosystem service function, and ecosystem sensitivity obtained from a high-precision environmental survey. (3) Results and conclusions: The results show that in Zhangbei County, areas classified as important and extremely important for ecosystem service functions account for 50.32%, ecologically sensitive and highly sensitive areas represent 5.95%, and regions designated as important and extremely important for ecological protection cover 52.70%. Furthermore, ecological restoration of Zhangbei was divided into four ecological restoration zones: agro-forest–wetland ecological restoration and soil erosion control zone, agro-forest–wetland ecological restoration and water conservation zone, forest–grassland soil erosion and soil–water conservation zone, and mountain forest conservation and biodiversity maintenance zone. The study can be a scientific case study for local ecosystem restoration and conservation. In the future, this study will further explore multi-source data fusion, the establishment of a multi-scale evaluation system, and the trade-off analysis between conservation and development.

With rapid urbanization, urban underground space (UUS) development has become crucial for sustainable urban growth. This paper systematically reviews geological suitability evaluation (GSE) methods for UUS, integrating theoretical frameworks, indicator systems, and assessment techniques. We establish a comprehensive evaluation framework based on environmental strategic assessment (ESA) principles, analyzing key geological factors, including rock/soil properties, hydrogeological conditions, geological hazards, and existing underground structures. The study compares weighting methods (AHP, EWM, CRITIC) and comprehensive evaluation models (FCE, TOPSIS, BNM), highlighting their advantages and application scenarios. A case study of Xiong’an New Area demonstrates how multi-layer UUS planning integrates geological constraints with sustainable development goals. The results show that combining 3D geological modeling with hybrid evaluation methods significantly improves decision-making accuracy. The review provides practical guidance for optimizing UUS utilization while addressing current challenges in indicator selection, weight rationalization, and heterogeneity management.

Baiyangdian Lake, recognized as the largest freshwater body in northern China, plays a vital role in maintaining the regional eco-environment. Prior studies have pointed out the contamination of sediments with heavy metals, raising concerns about eco-environmental challenges. Therefore, it is imperative to evaluate the current pollution levels and ecological threats related to heavy metals found in the sediments of Baiyangdian Lake as well as in its inflow rivers. In May 2022, surface sediments with a depth of less than 20 cm were analyzed for Cu, Zn, Pb, Cr, Ni, As, Cd, and Hg to determine the pollution status, identify sources of pollution, and evaluate potential ecological risks. A range of evaluation methods used by predecessors such as geo-accumulation index (I_geo), enrichment factor (EF), ecological risk index (RI), sediment quality guidelines (SQGs), positive matrix factorization (PMF), absolute principal component score-multiple linear regression model (APCS-MLR), chemical mass balance (CMB), and UNMIX model were analyzed. After comparison, multi-methods including the geo-accumulation index (Igeo), absolute principal component score-multiple linear regression model (APCS-MLR), ecological risk index (RI), and sediment quality guidelines (SQGs) were utilized this time, leading to a better result. Findings reveal that pollution levels are generally low or non-existent, with only 1.64% of sampling sites showing close to moderate pollution levels for Cu, Pb, and Zn, and 4.92% and 1.64% of sites exhibiting close to moderate and moderate pollution levels for Cd, respectively. The main contributors to heavy metal presence are pinpointed as industrial wastewater discharge, particularly Cu, Zn, Pb, Cd, and Hg. The ecological risks are also relatively low, with 4.92%, 1.64%, and 1.64% of sampling sites demonstrating close to moderate, moderate, and strong risks in the inflow rivers, respectively. Additionally, only one site shows moderate potential biological toxicity, while the rest display non-toxicity. These findings will update our cognition and offer a scientific basis for pollution treatment and ecosystem enhancement for government management.

The development of Xiongan New Area in Hebei Province, China, as a significant national choice, has considerable strategic significance for the integrated growth of Beijing, Tianjin, and Hebei. This paper proposes a cloud model for the suitability evaluation of the construction of Xiongan New Area based on entropy weight analysis, taking into account the geological conditions, groundwater environment, environmental geological problems, and other factors of the suitability of image city development. According to the research, the suitability evaluation findings for the project building employing the cloud model are in strong accord with those of the traditional model and have some application potential. The evaluation’s findings indicate that the project construction in Xiongan New Area is acceptable, with suitable and relatively suitable sites making up 81.4% of the total area and excellent circumstances for project development, construction, and usage. This study offers helpful direction for Xiongan New Area’s urban land-space design and serves as a useful point of comparison for studies looking at the viability of other deep Quaternary Plain region engineering buildings.