Search Results (276)

South Dongting Lake is an essential aquatic ecosystem that receives substantial water inflows from the Xiangjiang and Zishui Rivers. However, it is significantly impacted by human activities, including mining, smelting, and farming. These activities have led to serious contamination of the lake’s sediments with heavy metals (HMs). This study investigated the distribution, mobility, and influencing factors of HMs at the sediment–water interface. To this end, sediment samples were analyzed from three key regions (Xiangjiang River estuary, Zishui River estuary, and northeastern South Dongting Lake) using traditional sampling methods and Diffusive Gradients in Thin Films (DGT) technology. Analysis of fifteen HMs (Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, V, Cr, Cu, Tl, Co, and Fe) revealed significant spatial heterogeneity. The results showed that Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibited high variability (CV > 0.20), whereas V, Tl, and Co demonstrated stable concentrations (CV < 0.20). Concentrations were found to exceed background values of the upper continental crust of eastern China (UCC), Yangtze River sediments (YZ), and Dongting Lake sediments (DT), particularly at the Xiangjiang estuary (XE) and in the northeastern regions. Speciation analysis revealed that V, Cr, Cu, Ni, and As were predominantly found in the residual fraction (F4), while Pb and Co were concentrated in the oxidizable fraction (F3), Mn and Zn appeared primarily in the exchangeable fractions (F1 and F2), and Cd was notably dominant in the exchangeable fraction (F1), suggesting a high potential for mobility. Additionally, DGT results confirmed a significant potential for the release of Pb, Zn, and Cd. Contamination assessment using the Pollution Load Index (PLI) and Geoaccumulation Index (Igeo) identified Pb, Bi, Ni, As, Se, Cd, and Sb as major pollutants. Among these, Bi and Cd were found to pose the highest risks. Furthermore, the Risk Assessment Code (RAC) and the Potential Ecological Risk Index (PERI) highlighted Cd as the primary ecological risk contributor, especially in the XE. The study identified sediment grain size, pH, electrical conductivity, and nutrient levels as the primary influencing factors. The PMF modeling revealed HM sources as mixed smelting/natural inputs, agricultural activities, natural weathering, and mining/smelting operations, suggesting that remediation should prioritize Cd control in the XE with emphasis on external inputs. Full article

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

The Yangtze River Delta urban agglomeration, characterized by high population density, an advanced transportation system, and a concentration of industrial activity, is one of the regions severely affected by O₃ pollution in central and eastern China. Using data collected from 251 monitoring stations between 2015 and 2025, this paper analyzed the spatio-temporal variation of 8 h O₃ concentrations and instances of exceedance. On the basis of exploring the influence of meteorological factors on regional 8 h O₃ concentration, the potential source contribution areas of pollutants under the exceedance condition were investigated using the HYSPLIT model. The results indicate a rapid increase in the 8 h O₃ concentration at a rate of 0.91 ± 0.98 μg·m⁻³·a⁻¹, with the average number of days exceeding concentration standards reaching 41.05 in the Yangtze River Delta urban agglomeration. Spatially, the 8 h O₃ concentrations were higher in coastal areas and lower in inland regions, as well as elevated in plains compared to hilly terrains. This distribution was significantly distinct from the concentration growth trend characterized by higher levels in the northwest and lower levels in the southeast. Furthermore, it diverged from the spatial characteristics where exceedances primarily occurred in the heavily industrialized northeastern region and the lightly industrialized central region, indicating that the growth and exceedance of 8 h O₃ concentrations were influenced by disparate factors. Local human activities have intensified the emissions of ozone precursor substances, which could be the key driving factor for the significant increase in regional 8 h O₃ concentrations. In the context of high temperatures and low humidity, this has contributed to elevated levels of 8 h O₃ concentrations. When wind speeds were below 2.5 m·s⁻¹, the proportion of 8 h O₃ concentrations exceeding the standards was nearly 0 under almost calm wind conditions, and it showed an increasing trend with rising wind speeds, indicating that the potential precursor sources that caused high O₃ concentrations originated occasionally from inland regions, with very limited presence within the study area. This observation implies that the main cause of exceedances was the transport effect of pollution from outside the region. Therefore, it is recommended that the Yangtze River Delta urban agglomeration adopt economic and technological compensation mechanisms within and between regions to reduce the emission intensity of precursor substances in potential source areas, thereby effectively controlling O₃ concentrations and improving public living conditions and quality of life. Full article

While significant progress has been made in controlling point source pollution, agricultural non-point source pollution (AGNPSP) has emerged as a major contributor to global water pollution, posing a severe threat to ecological quality. According to China’s Second National Pollution Source Census, AGNPSP constitutes a substantial proportion of water pollution, making its mitigation a critical challenge. Identifying AGNPSP risk zones is essential for targeted management and effective intervention. This study focuses on Yongchuan District, a representative hilly–mountainous area in the Yangtze River Basin. Applying the landscape ecology “source–sink” theory, we selected seven natural factors influencing AGNPSP and constructed a minimum cumulative resistance model using remote sensing post-processing data. An attempt was made to classify the “source” and “sink” landscapes, and ultimately conduct a risk assessment of AGNPSP in Yongchuan District, identifying the key areas for AGNPSP control. Key findings include: 1. Vegetation coverage is the most significant natural factor affecting AGNPSP. 2. Extremely high- and high-risk zones cover 90% of Yongchuan, primarily concentrated in the central and southern regions, indicating severe AGNPSP pressure that demands urgent management. 3. The levels of ammonia nitrogen and total phosphorus in the typical sections are related to the risk levels of the corresponding sections. Consequently, the risk level of AGNPSP directly correlates with the pollutant concentrations measured in the sections. This study provides a robust scientific basis for AGNPSP risk assessment and targeted control strategies, offering valuable insights for pollution management in Yongchuan and similar regions. Full article

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM_2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

Rapid urbanization and climate extremes expose cities to multi-dimensional risks, necessitating the coordinated development of new urbanization and urban resilience for achieving urban sustainability. While existing studies focus on core economic zones like the Yangtze River Delta, secondary economic cooperation regions remain understudied. This study examined the Huaihai Economic Zone (HEZ)—a quadri-provincial border area—by constructing the evaluation systems for new urbanization and urban resilience. The development indices of the two systems were measured using the entropy weight-CRITIC method. The spatiotemporal evolution characteristics of their coupling coordination degree (CCD) were analyzed through a CCD model, while key driving factors influencing the CCD were investigated using a grey relational analysis model. The results indicated that both the new urbanization construction and urban resilience development indices in the HEZ exhibited a steady upward trend during the study period, with the urban resilience development index surpassing the new urbanization construction index. The new urbanization index increased from 0.3026 (2013) to 0.4702 (2023), and the urban resilience index increased from 0.3520 (2013) to 0.6366 (2023). The CCD between new urbanization and urban resilience reached 0.7368 by 2023, with 80% of cities in the HEZ achieving good coordination types. The variation of the CCD among cities was minimal, revealing a spatially clustered coordinated development pattern. In terms of driving factors, economic development level, public service capacity, and municipal resilience level were identified as core drivers for enhancing coupling coordination. Infrastructure construction, digital capabilities, and spatial intensification served as important supports, while ecological governance capacity remained a weakness. This study establishes a transferable framework for the coordinated development of secondary economic cooperation region, though future research should integrate diverse data sources and expand indicator coverage for higher precision. Moreover, the use of linear models to analyze the key driving factors of the CCD has limitations. The incorporation of non-linear techniques can better elucidate the complex interactions among factors. Full article

Against the backdrop of global climate change and rapid urbanization, understanding the spatiotemporal dynamics and driving mechanisms of vegetation net primary productivity (NPP) is critical for ensuring regional ecological security and achieving carbon neutrality goals. This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA) and integrates multi-source remote sensing data with socioeconomic statistics. By combining interpretable machine learning (XGBoost-SHAP) with multiscale geographically weighted regression (MGWR), and incorporating Theil–Sen trend analysis and Mann–Kendall significance testing, we systematically analyze the spatiotemporal variations in NPP and its multiscale driving mechanisms from 2001 to 2020. The results reveal the following: (1) Total NPP in the YRDUA shows an increasing trend, with approximately 24.83% of the region experiencing a significant rise and only 2.75% showing a significant decline, indicating continuous improvement in regional ecological conditions. (2) Land use change resulted in a net NPP loss of 2.67 TgC, yet ecological restoration and advances in agricultural technology effectively mitigated negative impacts and became the main contributors to NPP growth. (3) The results from XGBoost and MGWR are complementary, highlighting the scale-dependent effects of driving factors—at the regional scale, natural factors such as elevation (DEM), precipitation (PRE), and vegetation cover (VFC) have positive impacts on NPP, while the human footprint (HF) generally exerts a negative effect. However, in certain areas, a dose–response effect is observed, in which moderate human intervention can enhance ecological functions. (4) The spatial heterogeneity of NPP is mainly driven by nonlinear interactions between natural and anthropogenic factors. Notably, the interaction between DEM and climatic variables exhibits threshold responses and a “spatial gradient–factor interaction” mechanism, where the same driver may have opposite effects under different geomorphic conditions. Therefore, a well-balanced combination of land use transformation and ecological conservation policies is crucial for enhancing regional ecological functions and NPP. These findings provide scientific support for ecological management and the formulation of sustainable development strategies in urban agglomerations. Full article

To address the escalating urban heat stress driven by global warming and rapid urbanization, this study integrates multi-source remote sensing data to assess the spatiotemporal dynamics of summer thermal comfort across the Yangtze River Delta Urban Agglomeration (YRDUA) from 2000 to 2020. By combining 2D landscape pattern metrics with 3D building morphological features, this study employs an XGBoost model enhanced with SHAP and PDP techniques to reveal the nonlinear and threshold effects of landscape configurations on the Universal Thermal Climate Index (UTCI). The results show the following: (1) during the study period, over 90% of the region experienced strong or extreme heat stress, and 76.8% of the area exhibited a rising UTCI trend, with an average increase of 0.09 °C per year; (2) forest coverage exceeding 50% reduced the UTCI by approximately 2.5 °C, and an increased water area lowered the UTCI by around 1.5 °C, while highly clustered cropland intensified the UTCI by about 1.5 °C; and (3) a moderate increase in building height and shape complexity improved ventilation and shading, reducing the UTCI by roughly 0.5 °C. These findings highlight that optimizing the blue–green infrastructure and 3D urban form are effective strategies to mitigate urban heat stress, offering scientific guidance for sustainable urban planning. Full article

The Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR), serving as a pivotal hub for coordinated economic and ecological development in central China, is characterized by marked ecological fragility and climate sensitivity. Investigating the land use dynamics and ecological benefit changes within this region holds critical strategic significance for balancing regional development with the construction of ecological security barriers. This study systematically analyzed the spatiotemporal variations in land use/land cover (LULC) across the UAMRYR, using multi-source remote sensing data, climatic factors, land conditions, and anthropogenic influences. By integrating the four-quadrant model and the coupling degree model, we developed a remote sensing ecological index (RSEI)–ecological service index (ESI) coupling evaluation framework to assess the spatiotemporal evolution patterns of changes in ecological benefits in the region. Furthermore, we employed Geodetector analysis to identify the key influencing factors driving the RSEI–ESI coupling relationship and their interactive mechanisms. The research findings are as follows: (1) The ecological regional pattern has changed. The area of Quadrant I (RSEI > 0.5 and ESI > 0.5) decreased by 13,800 km², whereas Quadrants II (RSEI < 0.5 and ESI > 0.5) and IV (RSEI > 0.5 and ESI < 0.5) increased by 14,900 km² and 3500 km², respectively. Quadrant III (RSEI < 0.5 and ESI < 0.5) remained relatively stable. This indicates that the imbalance in ecological functional spaces has intensified, affecting key ecological processes. (2) The quantitative analysis of the spatiotemporal evolution characteristics of the RSEI and ESI revealed contrasting trends: the RSEI decreased by 0.006, whereas the ESI showed a slight increase of 0.001. (3) The ranking of the driving factors indicated that the Normalized Difference Vegetation Index (NDVI) and the mean annual rainfall (MAP) were the primary factors driving ecological evolution, while the influence of economic driving factors was relatively weak. This study establishes a three-pillar framework (quadrant-based diagnosis, Geodetector-driven analysis, and RSEI–ESI coupled interventions) to guide precision-based ecological restoration and spatial governance. Full article

Vegetation carbon sequestration (CS) is critical for mitigating climate change in urban agglomerations, yet its driving mechanisms remain poorly understood in rapidly urbanizing regions. This study introduces an integrated attribution and influence analysis framework, GWR-RF-SEM, to quantitatively assess the driving forces, mechanisms, and pathways of CS using multi-source remote sensing data at the county scale within the Yangtze River Delta Urban Agglomeration (YRDUA), China, from 2001 to 2020. Our results reveal an overall increase in CS across 70.14% districts in the YRDUA, with municipal districts exhibiting significantly lower CS compared to the outside districts. Photosynthesis and human activities emerged as the dominant drivers, collectively accounting for 73.1% of CS variation, significantly surpassing the influence of climate factors. Although most factors influenced urban vegetation CS either directly or indirectly, photosynthesis, afforestation, and urban green space structure were identified as the primary direct drivers of CS enhancement in both districts. Notably, we found significant spatial heterogeneity in CS drivers between municipal districts and the outside districts, highlighting the need for targeted strategies to enhance CS efficiency. These findings advance our understanding of urban vegetation CS mechanisms, providing essential support for the enhancement of nature-based solutions depending on ecosystem services under urbanization and climate change. Full article

The low-carbon economy is becoming a critical global development paradigm. As the world’s largest carbon emitter, China’s transition toward low-carbon practices in its construction sector is pivotal for achieving its carbon peaking and carbon neutrality goals. Research into the decarbonization pathways and driving factors of this energy- and emission-intensive industry is essential. It not only reduces the sector’s dependence on traditional energy sources but also provides vital support for China’s national energy conservation and emissions reduction strategy. As the construction industry transitions toward low-carbon sustainability, traditional unidimensional assessments based solely on socio-economic and ecological factors are inadequate. This study proposed an integrated evaluation framework using the CRITIC–TOPSIS model, incorporating technological, social, economic, industrial, and energy dimensions. Panel data on energy consumption in the Yangtze River Delta (YRD) region were employed to assess the construction sector’s low-carbon development level and an ARIMA model was utilized to forecast its low-carbon potential. The results indicate that from 2011 to 2022, the sector’s total carbon emissions followed a unimodal trajectory (initial increase followed by decline), with indirect emissions exceeding 90%, primarily from cement, steel, and other building materials. The regional construction industry exhibited a unimodal trajectory in low-carbon development, characterized by an initial increase followed by a decline. Average construction carbon emissions reached 41,637.5877 million tons, with a transient surge (69.67% increase) occurring between 2011 and 2014. This was followed by a 41.83% reduction from 2014 to 2022, with emissions projected to stabilize and gradually increase through 2030. Technological and industrial factors constitute the primary drivers of sectoral low carbon. Quantitative analysis identified the capital utilization rate, industrial structure, and construction industry gross domestic product (GDP) as key impediments to low-carbon transition, with average impedance degrees of 8.713%, 12.280%, and 12.697%, respectively. This study has revealed the key driving factors for the low-carbon development of the construction industry, extending theoretical frameworks for construction industry sustainability. These findings offer empirical support for formulating regionally differentiated carbon mitigation policies. Full article

Ozone (O₃) and fine particulate matter (PM_2.5) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O₃ and PM_2.5 in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O₃ and PM_2.5 concentrations. The results revealed a distinct inverse relationship between O₃ and PM_2.5, with meteorologically normalized PM_2.5 decreasing significantly (−5.0 μg/m³ compared to the pre-lockdown baseline of 0.6 μg/m³), while O₃ increased substantially (15.2 μg/m³ compared to the baseline of 5.3 μg/m³). Partial dependency analysis revealed that PM_2.5-O₃ relationships evolved from linear to non-linear patterns across lockdown periods, while NO₂-O₃ interactions indicated shifts from VOC-limited to NO_x-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM_2.5 levels while avoiding O₃ increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments. Full article

To characterize the temporal variation in and source contribution of volatile organic compounds (VOCs) in a polymer industrial park, a two-year offline monitoring campaign (2018–2019) at Shangyu Industrial Park in the Yangtze River Delta was conducted. The study quantified the VOCs composition, seasonal variation, and ozone formation potential (OFP), with source apportionment performed using the Positive Matrix Factorization (PMF) model. During the observation period, the average concentration of total VOCs in 2019 was 286.1 ppb, showing a 22.6% reduction compared to that in 2018. Seasonal analysis revealed decreases in the total VOCs concentration by 41.8%, 38.4%, and 6.1% during spring, summer and winter, respectively, while an increase of 13.8% was observed in autumn, primarily attributed to industrial restructuring in the second half of 2019. Notable reductions were observed in specific VOCs components: oxygen-containing volatile organic compounds (OVOCs), alkane, halogenated hydrocarbon, alkene, and alkyne decreased by 34.5%, 27.9%, 26.3%, 24.6%, and 20.4%, respectively. The average OFP in 2019 was 2402.0 μg/m³, representing a 1.8% reduction from 2018. Contributions to total OFP from alkane, OVOCs, alkyne, and alkene decreased by 32.9%, 26.0%, 20.7%, and 15.0%, respectively, while halogenated hydrocarbons and aromatic hydrocarbons increased by 50.1% and 7.0%. PMF analysis identified four major VOCs sources: industrial production (44.9%), biomass combustion (17.8%), vehicle exhaust (11.0%), and solvent usage (26.3%). From 2018 to 2019, contributions from vehicle exhaust and solvent usage increased by 4.8% and 5.9%, respectively, while industrial production and biomass combustion decreased by 10.5% and 0.3%. Full article