Water quality degradation and eutrophication of lakes are global ecological and environmental concerns, especially shallow lakes. This study collected hydrochemical data from 2935 samples of the Chinese part of Xingkai (Khanka) Lake, based on 40 published papers spanning the period from 2001 to 2023. Using the water quality index (WQI), improved geo-accumulation index (Igeo), and redundancy analysis (RDA), we analyzed the overall contamination characteristics of the water environment in Xingkai Lake. Additionally, we explored the impact of climate change and human activities on the lake’s water quality. The results showed that the annual WQI for Xingkai Lake ranged from 47.3 to 72, with a general downward trend, indicating improving water quality. Notably, the average WQI in May and total nitrogen (TN) content decreased significantly, signaling further improvement in water quality. The average concentration of TN in sediments was 1401.3 mg/kg, reflecting mild contamination. The Igeo values for the heavy metals Hg and Cr were greater than 1, indicating moderate contamination, while the Igeo values for Cd and Pb were between 0 and 1, which is in the range of uncontaminated to moderately contaminated. Land use and climate change (average annual temperature and annual precipitation) were key factors influencing water quality, with cumulative explanatory ratios of 67.3% and 50.1%. This study utilized land-use change as a metric for human activities, highlighting the potential impacts of climate change and human activities on the water quality of Xingkai Lake. It offers vital insights for the sustainable management of Xingkai Lake and provides valuable references into the management of similar transboundary lakes.

The United Nations Sustainable Development Goal (SDG) 11.1 emphasizes improving well-being, ensuring housing security, and promoting social equity. Informal settlements, one of the most vulnerable groups, require significant attention due to their dynamic changes and habitat quality. These areas limit the ability to comprehensively capture spatial heterogeneity and dynamic shifts in regional sustainable development. This study proposes an integrated approach using multi-source remote sensing data to extract the spatial distribution of informal settlements in Mumbai and assess their habitat environment quality. Specifically, seasonal spectral indices and texture features were constructed using Sentinel and SAR data, combined with the mean decrease impurity (MDI) indicator and hierarchical clustering to optimize feature selection, ultimately using a random forest (RF) model to extract the spatial distribution of informal settlements in Mumbai. Additionally, an innovative habitat environment index was developed through a Gaussian fuzzy evaluation model based on entropy weighting, providing a more robust assessment of habitat quality for informal settlements. The study demonstrates that: (1) texture features from the gray level co-occurrence matrix (GLCM) significantly improved the classification of informal settlements, with the random forest classification model achieving a kappa coefficient above 0.77, an overall accuracy exceeding 0.89, and F1 scores above 0.90; (2) informal settlements exhibited two primary development patterns: gradual expansion near formal residential areas and dependence on natural resources such as farmland, forests, and water bodies; (3) economic vitality emerged as a critical factor in improving the living environment, while social, natural, and residential conditions remained relatively stable; (4) the proportion of highly suitable and moderately suitable areas increased from 65.62% to 65.92%, although the overall improvement in informal settlements remained slow. This study highlights the novel integration of multi-source remote sensing data with machine learning for precise spatial extraction and comprehensive habitat quality assessment, providing valuable insights into urban planning and sustainable development strategies.

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin.

The spatial law and influencing factors of microplastics in lakes are an important part of microplastic research. This study focused on exploring the influences of factors such as the underwater slope, water depth, and pollution source/shore distance of a lake on the spatial distribution of lake MPs (microplastics). The relationships between the underwater slope, other factors, and the concentration of MPs were analyzed. The results showed that the average abundance of MPs in Dianchi Lake was 129 n/m³, and the spatial distribution pattern was higher in the lake center and lower in the lake shore. The correlation analysis showed that MP abundance was not significantly associated with distance to pollution sources or water depth, but it was significantly correlated with slope (p < 0.05) and offshore distance (p < 0.01). There was a significant negative correlation between the abundance of MPs and the underwater slope. The greater the underwater slope, the lower the abundance of MPs; the smaller the underwater slope, the higher the abundance of MPs. There was a significant positive correlation between the abundance of MPs and offshore distance. The abundance of MPs increased with increasing offshore distance. These new discoveries will help us better understand the spatial patterns of MPs in lakes.

The Arctic river basins, among the most sensitive regions to climate warming, are experiencing rapid temperature rise and permafrost thawing that profoundly affect their hydrological and hydrochemical systems. However, our understanding of chemical export from Arctic basins to oceans remains limited due to scarce data, particularly in permafrost-dominated regions. This study examines the spatiotemporal variations and seasonal dynamics of major ions (Na⁺, K⁺, Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻) and dissolved organic carbon (DOC) concentrations across three river basins with varying permafrost extents: the Severnaya Dvina (2006–2008, 2012–2014), the Pechora (2016–2019) and the Taz Rivers (2016–2020). All the data were sourced from published Chemical Geological researches and were taken from Mendeley and PANGAEA datasets. Our results showed that DOC concentrations ranged from 1.75 to 26.40 mg/L, with the Severnaya Dvina River exhibiting the highest levels of DOC concentrations, alongside significantly elevated ion concentrations compared to the other two basins. A positive correlation was observed between DOC concentrations and river discharge, with peaks during the spring flood and summer baseflow due to leaching processes. The Severnaya Dvina and Pechora Rivers exhibited the highest DOC values during the spring flood, reaching 26.40 mg/L and 8.07 mg/L, respectively. In contrast, the Taz River had the highest runoff during the spring flood season, but the DOC concentration reached its highest value of 11.69 mg/L in the summer. Specifically, a 1% increase in river discharge corresponded to a 1.25% rise in DOC concentrations in the Severnaya Dvina River and a 1.04% increase in the Pechora River, while there was no significant correlation between runoff and DOC concentrations in the Taz River. Major ion concentrations demonstrated a negative correlation with river discharge, remaining relatively high during winter low-flow period. A robust power-law relationship between river discharge and concentration of DOC and major ions was observed, with distinct variations across the three river basins depending on permafrost extent. The Pechora and Taz Rivers, characterized by extensive permafrost, exhibited increasing trends in river discharge and DOC concentrations, accompanied by decreasing major ion concentrations, whereas the non-permafrost-dominated Severnaya Dvina River basin showed the opposite pattern. The Taz River, with the most extensive permafrost, also displayed a delayed DOC peak and more complex seasonal ion concentration patterns. These findings highlight the importance of varying permafrost extents and their implications for water quality and environmental protection in these vulnerable regions.