Rongguang Shi

Beautiful City construction is a core initiative to realize the Beautiful China vision and advance China’s progress toward the Sustainable Development Goals (SDGs). This study develops a unified evaluation framework aligned with official Chinese assessment guidelines and global sustainability objectives. Based on a 2015–2022 dataset of 126 prefecture-level cities in the Yangtze River Economic Belt (YREB), we adopt Dagum’s Gini coefficient decomposition, kernel density estimation (KDE), and Moran’s I to investigate the spatiotemporal characteristics of beautiful city construction. The results indicate that the regional Beautiful City Construction Index (BI) increased steadily at both the regional and subregional levels, with all sample cities achieving continuous improvement. A consistent development gradient of lower reach > middle reach > upper reach was identified, with gradually narrowing inter-regional gaps. Importantly, the primary source of regional disparity shifted from inter-subregional differences to intra-subregional differences around 2021. Statistically significant positive spatial clustering was observed, with high-value clusters concentrated in downstream areas and low-value clusters in upstream areas, while the overall clustering effect gradually weakened. This study provides empirical evidence for targeted urban governance and regional coordinated development, offering a typical Chinese practice for global SDG research.

Accurate source identification is essential for the prevention and control of heavy metal(loid)s (HMs) contamination in farmland soils. Conventional source-apportionment approaches often rely on limited indicators and expert judgment, which can increase uncertainty in source interpretation. In this study, 800 topsoil samples collected from farmland in Ningxia, together with 24 environmental and anthropogenic variables, were used to develop element-specific machine learning models for Cd, Cr, Hg, Pb, and As. Five algorithms, including random forest (RF), Extra Trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and least absolute shrinkage and selection operator (LASSO-stacking), were compared, and Shapley additive explanations (SHAP) were used to interpret the variables statistically associated with the spatial variability of each metal. Positive matrix factorization (PMF) was further applied to identify potential source categories. Model performance varied among metals, indicating element-specific differences in predictability and controlling factors. SHAP analysis showed that precipitation, temperature, spatial coordinates (longitude/latitude), cropping intensity, and industrial-source fine particulate matter emissions were among the most important associated factors, although their relative importance differed across elements. PMF results suggested that 73.8% of Cd was associated with agricultural inputs, 87.6% of Hg with industrial atmospheric deposition, and 68.4% of Cr and 46.7% of As with natural sources, Pb showed relatively weak predictability, suggesting that its spatial variability may be influenced by unmeasured local or legacy inputs, while model-derived associations indicated possible links with spatial gradients, terrain-hydrological conditions, wind-related variables, and soil carrier properties. Overall, this study presents an integrated framework that combines machine learning-based associated-factor analysis with receptor-model source apportionment, providing a more nuanced understanding of HMs contamination in farmland soils and supporting targeted soil pollution prevention and control.

Unsustainable land-use transitions in peri-urban watersheds threaten both food security and ecological integrity. While Patch-generating Land Use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models for ecosystem service (ES) assessment are commonly integrated, limited studies have simultaneously (i) accounted for multiple real-world spatial policies (e.g., ecological redlines) as hard constraints, (ii) targeted a comprehensive suite of ESs, and (iii) explicitly pursued synergies without relying on large-scale land conversion. To address these gaps, we developed a spatially explicit framework that integrates the PLUS and InVEST models to simulate four land-use scenarios and assess six ESs—grain yield, water yield, nitrogen export, phosphorus export, soil conservation, and carbon sequestration—in the Yuqiao Reservoir watershed, China, during 1990–2030. Against a backdrop of historical declines in cropland/grassland and key ESs due to construction expansion (1990–2020), the novel Comprehensive Development scenario—implementing slope-adaptive management and riparian buffers—synergistically increases grain yield (+0.55%) and carbon sequestration (+1.10%) while drastically reducing phosphorus export (−10.86%). It demonstrates that synergistic gains can arise from strategic spatial reconfiguration within a stable land-use area, advancing a paradigm from area-centric to configuration-centric optimization. This provides a quantifiable methodological basis and actionable policy reference for land spatial optimization in similar water-source watersheds.

The research on cadmium (Cd) pollution remediation technologies in farmland is of great significance for ensuring food security. However, there is currently a lack of empirical research on the passivation effects of the related repair materials on alkaline farmland in arid regions. This study selected a typical experimental area in a dryland corn farmland in Ningxia, Northwest China. Field experiments were conducted on four typical remediation materials: mercapto clay minerals, sepiolite remediation materials, microbial inoculants, and bio-organic fertilizers. The effects of these four materials on the available cadmium in the soil, cadmium content in corn stems and leaves, and enrichment coefficients were analyzed. The results show that the four types of remediation fertilizers have significant differences in their effects on the available Cd content in the soil, with a reduction range of 3.33–60.94%. The order of the inhibitory effect from strong to weak is as follows: mercapto clay mineral passivation material, bio-organic fertilizer, sepiolite, and microbial inoculant. The cumulative distribution pattern of Cd in the organs of corn plants is leaf > stem > grain. It reduces the cadmium content in corn stems by 7.01–37.16% and reduces the cadmium content in corn leaves by 1.45–26.56%. Under the four types of remediation fertilizer treatments, the enrichment coefficients of corn stems and leaves all decreased. The enrichment coefficient of stems decreased by 3.78% to 29.42%, and the enrichment coefficient of leaves decreased by 3.41% to 31.92%. The mercapto clay minerals passivation material has the best effect on reducing the available cadmium in the soil of dryland corn in the arid areas of Northwest China and also has the best effect on inhibiting the absorption of cadmium by various organs of corn. It can be further verified in the field and promoted for application, providing support for the restoration of heavy metal pollution in farmland based on local conditions and differentiated measures.

Monitoring and assessing the spatial heterogeneity of soil elements within cultivated land presents core challenges in precision agriculture. Uncertainty analysis methods and optimal sampling densities for arsenic (As) pollution risk assessments in typical high-altitude regions of the southeastern Qinghai–Tibet Plateau remain insufficiently studied. This study investigates arsenic contamination in cultivated soils of a representative high-altitude region. By combining multi-level grid deployment with random sampling, seven datasets with varying sampling densities (62, 98, 139, 221, 289, 394, and 570 samples) were collected from 612 monitoring sites. The results indicate significant arsenic enrichment in the study area, with concentrations reaching 3.6 times the national soil background value and 2.4 times the plateau soil background value. Compared to arithmetic mean and median analyses, the geometric mean evaluation demonstrates lower uncertainty across different sampling densities in ecological environment assessments, averaging 4.3%, thereby providing more accurate results. Significant directional anisotropy exhibits a pronounced quadratic trend. The strongest spatial correlation occurs along the northwest–southeast direction, with the spatial autocorrelation distance in the vertical direction being 2.39 times greater than in other directions. Increasing sampling density is a macro-level requirement for accurately assessing the environmental risk characteristics of arsenic in plateau ecosystems; however, it is not the only factor influencing the spatial variability of arsenic concentrations. By comprehensively considering model fitting parameters, spatial distribution patterns, and cost–benefit analysis, a moderate sampling density of 20 points per square kilometer was determined to be optimal. This density provides a basis for exploring the distribution patterns and dynamic monitoring of arsenic as a risk element in plateau environments, while also provide monitoring guidance for developing countries with limited and fragmented agricultural land areas.

Approximately 1.1% of global soils exceed the safety thresholds. Yinchuan is one of the key grain production bases, and the quality of its agricultural soil directly impacts the quality of agricultural products. To investigate the heavy metal contamination status of surface agricultural soil in Yinchuan, this study collected 325 agricultural soil samples from the city to analyze the concentrations of five heavy metal elements—As, Hg, Pb, Cd, and Cr—and conducted a risk assessment and quantitative source apportionment of soil heavy metal contamination. The results indicate that the majority of the study area is classified as having a lightly polluted level with moderate ecological risks. The order of the over-standard rates is Hg > Cd > Pb > Cr > As. The soil in the study area is generally weakly alkaline, which has a relatively low impact on the migration and transformation of heavy metal elements. High-value areas of heavy metals are all located near the Yellow River floodplain. They are significantly affected by the agricultural and industrial wastewater discharge from the upper reaches of the Yellow River. The Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model analysis identified the sources of soil heavy metal pollution as natural sources (37.29%), agricultural sources (25.50%), coal combustion sources (20.18%), and industrial-transportation sources (17.04%). The positive matrix factorization (PMF) model explained that the sources of heavy metals in the soil were natural sources (22.42%), agricultural activities (24.46%), coal combustion sources (26.70%), and traffic sources (26.42%). Overall, the results indicate that there is a certain degree of metal pollution in the agricultural soil of Yinchuan, which is significantly influenced by human activities.

This study collected 820 topsoil samples from cultivated lands across Ningxia, covering the Yellow River irrigation area, the central arid zone, and the southern mountainous region. The ordinary kriging were spatially interpolated to analyze As, Hg, Cd, Cr, and Pb heavy-metal pollution spatial patterns. Pollution was evaluated using the Nemerow and geoaccumulation (I_(geo)) indices, and sources quantified via Pearson correlations, PCA (Principal Component Analysis), and PMF (Positive Matrix Factorization). The results indicated that Hg and Cd posed the highest ecological risks. The overall mean concentrations (mg.kg⁻¹) of Hg, Cd, As, Pb, and Cr were 0.04, 0.27, 9.91,23.81, and 57.34, respectively. Compared with the background values, they were 1.90, 2.41, 0.83, 1.14, 2.74 times higher, respectively. Geospatially, regions with higher pollution probabilities for Cd, Cr, Pb, Hg, and As were concentrated in the northern and central parts of Ningxia, whereas the southern region exhibited lower pollution probabilities. pH significantly influenced the accumulation and spatial distribution of heavy metals in soil. Source apportionment identified three primary contributors: transportation and natural parent materials (As, Pb, Cr), industrial activities (Hg), and agricultural practices (Cd). Hg and Cd were identified as the key risk elements requiring prioritized management. These results enhance understanding of the pollution levers of heavy metals in Ningxia cultivated soils, and also provide foundation for developing more scientific and precise soil risk control policies, offering significant practical value for environmental risk management.

This study collected 820 topsoil samples from cultivated lands across Ningxia, covering the Yellow River irrigation area, the central arid zone, and the southern mountainous region. The concentrations of Arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), and lead (Pb) were analyzed to evaluate the pollution levels and spatial distribution patterns of heavy metals, which were interpolated using Kriging. Pollution status was assessed via the Nemerow composite index and the geoaccumulation index (Igeo). Source identification and contribution quantification were performed using Pearson correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF). Results indicated that the cultivated soils in Ningxia are generally clean, with isolated instances of warning and light pollution. Ecological risk assessment revealed differential risk levels among elements, with Hg and Cd presenting the highest ecological risks. Geospatially, regions with higher pollution probabilities for Cd, Cr, Pb, Hg, and As were concentrated in the northern and central parts of Ningxia, whereas the southern region exhibited lower pollution probabilities.pH significantly influences the accumulation and spatial distribution of heavy metals in soil. Source apportionment identified three primary contributors: transportation and natural parent materials (As, Pb, Cr), industrial activities (Hg), and agricultural practices (Cd). Hg and Cd were identified as the key risk elements requiring prioritized management.

Urban green spaces (UGSs), including parks, forests, and community gardens, play a critical role in enhancing public health and well-being by providing essential ecosystem services such as improving air quality, reducing surface temperatures, and mitigating harmful substances. As urbanization accelerates, especially in rapidly growing cities like Jinan, China, the demand for UGSs is intensifying, necessitating careful urban planning to balance development and environmental protection. While previous studies have often focused on city-level green coverage, this study shifts the analytical focus from UGS as a whole to urban functional units (UFUs), allowing for a more detailed examination of how green space is distributed across different land use types. We investigate UGS changes in Jinan over the past two decades and assess the influence of socio-economic factors—such as housing prices, land use types, and building age—on UGS distribution within UFUs. Remote sensing technology was employed to analyze the spatiotemporal dynamics of UGS and its correlation with these variables. Our findings reveal a significant shift in UGS distribution, with parks and leisure areas becoming primary drivers of UGS expansion. This study also highlights the growing influence of economic factors, particularly housing prices, on UGS distribution in more affluent UFUs. Additionally, while UGS in Jinan has generally expanded, challenges remain in balancing green space with urban expansion, especially in commercial and residential UFUs. This paper contributes to a more nuanced understanding of UGS distribution by integrating the UFU framework and identifying socio-economic drivers—including housing prices, construction age, and land use type—that shape green space patterns in Jinan. Our findings demonstrate that the spatial pattern of UGS in Jinan mirrors socio-economic and land use disparities observed in other global cities, highlighting both the universality of these patterns and the need for targeted planning in rapidly urbanizing contexts.

The Tibetan Plateau, a globally significant ecological region, is experiencing escalating pollution from heavy metals (HMs). This study applies a machine learning approach based on the self-organizing map hyper-clustering, alongside advanced methodologies such as Positive Matrix Factorization (PMF), Incremental Spatial Autocorrelation, and Bivariate Local Indicators of Spatial Association (BiLISA), to analyze the ecological risk of soil HMs in representative watersheds of the southeastern Tibetan Plateau, focusing on spatial pattern clustering, pollutant source identification, and interaction risk assessment. The results indicated higher HMs concentrations in the middle and downstream areas. A comprehensive ecological risk assessment integrating the Improved Potential Ecological Risk Index, Enrichment Factor, Contamination Factor, and Geo-accumulation Index identified Cd, Pb, and As as the primary pollutants of concern. By combining PMF with Mantel analysis, pollution was attributed to geological background, agricultural activities, traffic emissions, and atmospheric deposition. The BiLISA method revealed significant spatial interactions among HMs, with the composite pollution of As and Cd occupying the largest proportion in High (As)-High (Cd) aggregation zones, underscoring the need for integrated management strategies. This study offers novel insights into the spatial pollution patterns and source apportionment of soil HMs, providing an advanced analytical framework for their precise control and ecological restoration.