Combining the travel modes of human activities, fully mining multi-source data, and analyzing the relationship between the urban ecological environment and human activities are important topics in urban ecological environment planning. Human activity indicators were constructed based on the data of POI points, OSM road network, and residential areas. Machine learning models such as support vector regression machine, extreme gradient boosting regression, polynomial regression, and random forest regression were combined with remote sensing images to construct an urban ecological environment indicator system. These models were used to conduct regression analysis of urban ecological environment indicators and human activity indicators in Chengdu, China. The research shows that the three indicators of human activities all show a trend of increasing in the center and gradually decreasing in the surrounding areas, while the sustainable urban ecological environment indicators show the opposite trend. On the relationship between urban ecological environment and human activities, XGB has the best effect; the correlation between the street vitality index and the urban function mixing index and the sustainable urban ecological environment is stronger, and the correlation between the walkability measure index of the residential area and the sustainable urban ecological environment is even worse.

The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management.

The anomalous enrichment of arsenic (As) and fluoride (F) in groundwater in the oasis area at the southern margin of the Tarim Basin has become a critical environmental and sustainability challenge. It poses not only potential health risks but also profound socio-economic impacts on local communities, threatening the long-term security of water resources in arid regions. Therefore, an in-depth investigation of the hydrochemical characteristics of groundwater and the co-enrichment mechanism of As and F is essential for advancing sustainable groundwater management. In this study, 110 phreatic water samples and 50 confined water samples were collected, and mathematical and statistical methods were applied to analyze the hydrochemical characteristics, sources, and co-enrichment mechanisms of As and F. The results show that (1) the groundwater chemistry types are mainly Cl·SO₄-Na, SO₄·Cl-Na·Mg, Cl·SO₄-Na·Mg, and Cl-Na, and the chemistry is primarily controlled by evaporation and concentration processes, with additional influence from human activities and cation exchange; (2) As and F mainly originate from soils and minerals, and are released through dissolution; (3) As and F enrichment is positively correlated with pH, Na⁺, and HCO₃⁻, but negatively correlated with Ca²⁺, Mg²⁺, and SO₄²⁻, indicating that a weakly alkaline hydrochemical environment with high HCO₃⁻ and Na⁺, and low Ca²⁺ promotes their enrichment; (4) strong evaporative concentration in retention zones, combined with artificial groundwater extraction, further intensifies As and F accumulation. This study not only provides an innovative theoretical and methodological framework for exploring trace element enrichment mechanisms in groundwater under arid conditions but also delivers critical scientific evidence for developing sustainable water resource management strategies, mitigating water-related health risks, and supporting regional socio-economic resilience under global climate change.

Addressing the mismatch between agricultural development and water resource conditions, this study selected 22 provinces and 4 municipalities in China from 2014 to 2023 as the research area and incorporated terrain factors into the analytical framework. An evaluation system for the Agricultural Emphasis Index and Water Resource Condition Index was established. The entropy weight method, coupling coordination degree model, standard deviation ellipse model, spatial difference coefficient, and two-way fixed effects model were employed to investigate the spatiotemporal variations and coordination relationships between agricultural development and water resource conditions. The results showed that the Agricultural Emphasis Index continuously increased during the study period, whereas water resource conditions remained relatively stable, indicating different evolutionary patterns between the two systems. The coordination level of the Agriculture–Water Resources System generally improved, but significant regional disparities remained. Plain regions exhibited stronger coordination foundations, while mountainous and plateau regions showed relatively lower coordination levels due to terrain constraints and resource limitations. Spatial displacement existed between agricultural advantage regions and water resource advantage regions, although the matching degree between the two systems gradually improved. The evaluation framework established in this study provides a scientific basis for optimizing regional agricultural layouts, improving water resource allocation, and promoting coordinated agricultural and water resource development.

To address the mismatch between agricultural development and water resource conditions, this study examined 22 provinces and four municipalities in China over the period 2014–2023 and incorporated terrain factors into the analytical framework. An integrated evaluation system was developed for the Agricultural Development Index (ADI) and Water Resource Condition Index (WCI). The entropy weight method, coupling coordination degree model, standard deviation ellipse model, spatial difference coefficient, and two-way fixed-effects model were employed to characterize the spatiotemporal dynamics and coordination between agricultural development and water resource conditions. The results indicate that the ADI increased steadily throughout the study period, whereas the WCI remained relatively stable, reflecting distinct evolutionary trajectories of the two systems. Although the coordination level of the Agriculture–Water Resources System improved overall, pronounced regional disparities persisted. Plain regions exhibited stronger coordination, whereas mountainous and plateau regions showed lower coordination levels because of terrain constraints and limited resource endowments. Spatial mismatches between agricultural and water resource advantage regions were evident, although the overall matching relationship between the two systems gradually strengthened. The proposed evaluation framework provides a scientific basis for optimizing regional agricultural layouts, improving water resource allocation, and promoting coordinated agricultural and water resource development.