The organic matter of lake sediment plays an important role in paleolimnological reconstruction. Here, we report a detailed study of organic matter components (C_org%, N%, δ¹³C, δ¹⁵N) in a dated sediment core of Hulun Lake in northern China. Multiple mixing models based on the stoichiometric ratios and stable isotopic compositions were applied to quantify the contributions of organic matter sources in lake sediment. The results show that the organic matter in the sediments from Hulun Lake mainly comes from terrestrial organic matter: the proportion of terrestrial organic matter is more than 80%. The results of the SIAR mixing model further reveal that the proportions of terrestrial C₃ plants-derived organic matter, soil organic matter, and lake plankton-derived organic matter were 76.0%, 13.9%, and 10.1%, respectively. The organic matter content of lake sediment from terrestrial sources began to increase significantly from 1980 onward, which is consistent with the growth in overgrazing in the Hulun Lake basin. The content of organic matter from endogenous lake-derived sources began to increase significantly after 2000 due to the nutrients gradually becoming concentrated in lake water, indicating that the reduction in rivers’ discharge and the downgrade of the lake water level were the immediate causes of the lake’s environmental deterioration during this period.

Frequent wildfires associated with emplacement of the Central Atlantic Magmatic Province (CAMP) are thought to have been important drivers of two significant changes in terrestrial plant communities and diversity during the Triassic-Jurassic Mass Extinction (TJME, ca. 201.51 Ma). ​However, it remains to be investigated whether these two changes are potentially related to different wildfire types. To better understand this relationship, we used a new method to reanalyze fossil pollen and spores across the Triassic-Jurassic transition in the Jiyuan Basin from the North China Plate. Results show that two peaks in wildfire frequency experienced different types of wildfires, with each linked to significant changes in plant communities and diversity losses. In the first wildfire peak, canopy fires dominated and are accompanied by significant losses of canopy forming plants, while in the second wildfire peak, ground cover fires dominated accompanied by significant losses of ground cover plants. ​Changes in atmospheric humidity conditions were an important control on the two different wildfire peaks. Relatively humid climatic conditions corresponded to the prevalence of canopy fires and hindered the spread and development of ground cover fires in wet surface conditions. Conversely, relatively arid climatic conditions corresponded with the prevalence of ground cover fires in dry surface environments. Our results provide a potential relationship between terrestrial plant communities and wildfire types, which is important to further understanding of terrestrial environmental and floral changes driven by Large Igneous Provinces.

Microbial communities play a pivotal role in material cycling, energy flow, and pollutant degradation within river ecosystems. Thus, gaining a clear understanding of how wastewater discharge affects microbial community structure and function is essential for the protection and management of the surface water environment. In this study, a total of 9 samples were collected from the Sha River in March 2024. Subsequently, 16S rRNA sequencing technology combined with investigation of physicochemical properties of water was used to investigate the compositional diversity, spatial distribution, and explore the environmental effects of wastewater discharged on microorganisms. The sequencing results of species at the phylum level revealed that the dominant microbial phyla in the Sha River were primarily Proteobacteria (55.4%), Actinobacteriota (24.0%), Bacteroidota (14.3%), and Verrucomicrobiota (2.6%). The most dominant phylum, Proteobacteria, exhibited varying abundances across different sampling sites in the Sha River basin, with the highest abundances observed at Sites S2, S4, S5, and S6. This is mainly due to the fact that the upstream areas of Sites S2, S4, S5, and S6 are characterized by high concentrations of COD and NH₃-N, which are caused by wastewater discharge. Quantitative analysis was also conducted using the Source Tracker model; the results showed that S2 (36.7%) and S4 (31.3%) in the upper reaches of the Sha River are the primary contributors to the microbial community in the downstream catchment area (S6). The study found that the impact of wastewater discharge on the microbial community in the downstream water body exhibits a “longitudinal persistence of microbial signatures” even though the physicochemical pollution indicators of the water body have decreased. These findings of this study represent the application in microbial source tracking in the upstream and downstream sections of rivers, providing strong support for formulating more effective environmental protection strategies in the Sha River basin.

Monthly runoff prediction plays a crucial role in water resource management, flood prevention, and disaster reduction. This study proposed a novel hybrid model for predicting monthly runoff by combining variational modal decomposition (VMD) with an extreme learning machine (ELM) and adaptive boosting (AdaBoost) algorithm. First, VMD is used to decompose the monthly runoff data, simplifying it and addressing its non-stationarity by extracting subsequences at different frequency scales. Next, the ELM model is applied to each subsequence within the AdaBoost algorithm to enhance prediction accuracy and stability. To contextualise its performance, the proposed model was systematically compared with four representative comparable models (VMD-ELM, ELM-AdaBoost, LSTM, and VMD-TPE-LSTM) using the same training/validation datasets (80% for training and 20% for validation) and evaluation metrics (root mean square error, RMSE; mean absolute percentage error, MAPE). The results indicate that the VMD-ELM-AdaBoost model outperforms all comparative models: at Yanshan Station, it achieves an RMSE of 2.521 mm and MAPE of 8.56% (34.8–45.1% lower RMSE than VMD-ELM, ELM-AdaBoost, and LSTM); at Baiguishan Station, it yields an RMSE of 2.906 mm and MAPE of 9.02% (22.3–42.6% lower RMSE than VMD-TPE-LSTM and other alternatives). This study demonstrates that the VMD-ELM-AdaBoost model balances accuracy, efficiency, and data adaptability, providing a practical tool for monthly runoff prediction in data-limited basins.

Tectonic movement and magmatic activity are complex geological processes resulting from the interaction of multiple factors. These processes are often characterized by local singularities with nonlinear characteristics. This study drew on the Multifractal Local Singularity theory and took the Heyu granite magmatism and the Mesozoic tectonic evolution of the Xiong’er Mountain as examples to explore singularity characteristics in the process of tectonic evolution and magmatism. The aim was to enhance the understanding of geological evolution and offer new perspectives for studying geological evolution processes. The research showed that during the four stages of the Heyu granite magmatism, the early phase exhibited a low singularity index, weak magmatic activity, and relatively rapid crystallization. In the middle phase, the singularity index and eruption intensity gradually increased, whereas the crystallization rate decreased. The third stage had the highest singularity index and peak eruption intensity, with the lowest crystallization rate. In the final stage, the singularity index decreased, eruption intensity weakened, and crystallization rate increased. In the case of the Mesozoic tectonic evolution of the Xiong’er Mountain, three distinct stages were identified: early-stage compression and warming, mid-stage structural transformation, and late-stage decompression and extension. The singularity characteristics show a progressive increase from the early to the late stages, indicating a gradual intensification of tectonic activity.

This work focused on the Xinghua River, a typical urbanizing river, to investigate how different anthropogenic activities affect the composition, sources, and environmental impact of dissolved organic matter (DOM) during urbanization. Using fluorescence spectroscopy combined with multivariate statistics, we systematically explored DOM characteristics and their response to urbanization. A total of four fluorescent components were identified, including protein-like components C1 and C3, and humic-like components C2 and C4, with protein-like substances constituting the major fraction of DOM. Fluorescence indices indicated that DOM in the Xinghua River was primarily derived from autochthonous sources (FI > 1.9), with a low degree of humification reflecting the dominance of fresh organic matter input during urbanization. Spatial analysis revealed that from upstream to downstream, the source of DOM gradually shifted from autochthonous dominance to increased allochthonous input, accompanied by increasing trends in both protein-like and humic-like components, indicating an accumulative effect of anthropogenic activities along the river. 2D-COS further revealed that the transformation sequence of DOM components along the flow direction was C3 → C1 → C4 → C2, suggesting that tyrosine/tryptophan-like substances were the most sensitive to anthropogenic disturbance. Redundancy analysis identified total phosphorus (TP), total dissolved solids (TDS), and permanganate index (COD_Mn) as the key environmental factors influencing DOM distribution, highlighting the synergistic regulatory roles of nitrogen and phosphorus nutrients and organic pollution loads on DOM composition. This study not only elucidates the gradient effects of human activities on DOM in the Xinghua River but also provides a scientific basis for water management in urban rivers worldwide, particularly for zone-based control and source-oriented management.