Search Results (360)

Enhanced Rock Weathering (ERW) is a promising carbon dioxide removal (CDR) strategy that accelerates mineral dissolution, sequestering atmospheric CO₂ while improving soil health. This study builds on prior applications of soil calcimetry by investigating its ability to resolve short-term carbonate fluxes and rainfall-modulated weathering dynamics in wollastonite-amended croplands. Conducted over a single growing season (May–October 2024) in temperate row-crop fields near Port Colborne, Ontario—characterized by fibric mesisol soils (Histosols, FAO-WRB)—this study tests whether calcimetry can distinguish between dissolution and precipitation phases and serve as a proxy for weathering flux within the upper soil horizon, under the assumption that rapid pedogenic carbonate cycling dominates alkalinity retention in this soil–mineral system. Monthly measurements of soil pH (Milli-Q and CaCl₂) and calcium carbonate equivalent (CCE) were conducted across 10 plots, totaling 180 composite samples. Results show significant alkalinization (p < 0.001), with average pH increases of ~+1.0 unit in both Milli-Q and CaCl₂ extracts over the timeline. In contrast, CCE values showed high spatiotemporal variability (−2.5 to +6.4%) without consistent seasonal trends. The calcimetry-derived weathering proxy, log (Σ ΔCCE/Δt), correlated positively with pH (r = 0.652), capturing net carbonate accumulation, while the kinetic dissolution rate model correlated strongly and negatively with pH (r ≈ −1), reflecting acid-promoted dissolution. This divergence confirms that the two metrics capture complementary stages of the weathering–precipitation continuum. Rainfall strongly modulated short-term carbonate formation, with cumulative precipitation over the previous 7–10 days enhancing formation rates up to a saturation point (~30 mm), beyond which additional rainfall yielded diminishing returns. In contrast, dissolution fluxes remained largely independent of rainfall. These results highlight calcimetry as a direct, scalable, and dynamic tool not only for monitoring solid-phase carbonate formation, but also for inferring carbonate migration and dissolution dynamics. In systems dominated by rapid pedogenic carbonate cycling, this approach captures the majority of alkalinity fluxes, offering a conservative yet comprehensive proxy for CO₂ sequestration. Full article

This study investigated the mechanisms of soil water–salt and nitrogen transport and optimal strategies under freeze–thaw (F-T) cycles in the salinized farmlands of the Hetao Irrigation District. A combined approach of field monitoring and laboratory simulation, utilizing both undisturbed and repacked soil columns subjected to 0–15 F-T cycles and five irrigation treatments, was employed to analyze the spatiotemporal dynamics in Gleyic Solonchaks. The results demonstrated that freeze–thaw processes play an important role in salt migration in surface soil layers, driving salt redistribution through phase changes of soil moisture. Increased freeze–thaw cycles reduced surface soil moisture content while promoting upward salt accumulation, salt dynamics exhibited pronounced spatial heterogeneity and irrigation source dependency, and the surface layer exhibited lower salinity levels after irrigation compared to pre-irrigation levels. These cycles also enhanced short-term soil nitrogen transformation and facilitated inorganic nitrogen accumulation. Different irrigation regimes exhibited a significant impact on the dynamics of water–salt and nitrogen in soil, with low-salinity treatment (S2) and moderate-nitrogen irrigation (N2) effectively reducing surface salt accumulation while improving nitrogen utilization efficiency (moderate-nitrogen irrigation exhibited higher mineralization rates, which facilitated the release of inorganic nitrogen from soil). This study reveals the synergistic transport mechanisms of water–salt and nitrogen under freeze–thaw driving forces and provides a scientific basis and practical pathway for sustainable agricultural management in cold arid irrigation districts. Full article

This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas seepage processes under such conditions. The simulations systematically reveal the spatiotemporal evolutionary patterns of the velocity and direction of the gas seepage and elucidate the migration mechanism driven by excavation-induced pressure gradients. The model specifically analyzes how geological structures, such as rock joints and fractures, control the seepage pathways. The model also demonstrates the dynamic variations in and enrichment behavior of the gas escape velocities near these discontinuities. Field measurements obtained from the Hongdoushan Tunnel validated the simulated emission patterns along jointed fissures. The findings clarify the intrinsic relationships between the outburst dynamics and key factors that include pressure differentials, geological structures, and temporal effects. This work provides a crucial theoretical foundation and practical strategy for the prediction and prevention of hazardous gas disasters in analogous tunnel engineering projects, thereby enhancing overall construction safety. Full article

Forest fire regimes are undergoing systematic reorganization under climate change, particularly in monsoon–human coupled ecosystems such as Southeastern China, where risk dynamics remain poorly quantified. This study proposes a meteorology-driven machine learning model designed to assess long-term forest fire risk. Using kernel density estimation and standard deviational ellipse analysis, we assessed the spatiotemporal patterns of fire risk during the observational period and their future shifts across the SSP1-2.6 and SSP5-8.5 scenarios. The results indicate a significant overall decline in fire frequency from 2008 to 2024 (−467.3 fires/year, representing an annual average reduction of 10.8%, p < 0.001), which is attributed primarily to enhanced regional fire prevention and control measures, yet with a notable reversal after 2016 in Guangdong and Fujian. Fires are highly seasonal, with 74% occurring in the dry season (December–March). The meteorologically driven random forest model exhibited excellent performance (R² = 0.889), validating meteorological conditions as key drivers of regional fire dynamics. It is projected that intensified warming (+5.5 °C under SSP5-8.5) and increased precipitation variability (+23%) are likely to drive pronounced northward and inland migration in high-risk zones. Our projections indicate that by the end of the century, high-risk area coverage could expand to 19.2%, with a shift from diffuse to clustered patterns, particularly in Jiangsu and Zhejiang. These findings underscore the critical role of hydrothermal reconfiguration in reshaping fire risk geography and highlight the need for dynamic, region-specific fire management strategies in response to compound climate risks. Full article

The coastal waters of the northern Shandong Peninsula have abundant fishery resources, which serve as a critical transitional fishing ground for economic fish migrating into the Bohai Sea for spawning and departing for overwintering habitats. However, anthropogenic pressures such as garbage dumping have led to severe degradation of local fishery resources and concomitant adverse effects on zooplankton communities. To assess these impacts, we analyzed the spatiotemporal distribution, community structure, dominant species, and diversity indices of zooplankton based on sampling data collected in spring from 2015 to 2018 in this region. A total of 24 zooplankton species and 11 larval classes were identified, with the highest species richness observed in 2016. Calanus sinicus and Centropages abdominalis were the primary dominant species, with C. sinicus consistently predominant across all four years. Notably, the dominant species exhibited marked annual variability. The abundance and biomass of zooplankton in the surveyed area exhibited significant annual variations, both showing a trend of first decreasing and then increasing. Peak abundance occurred in 2015 (594.36 ind/m³), while the lowest was recorded in 2017 (118.73 ind/m³). Spatially, abundance and biomass were heterogeneous, with coastal waters exhibiting higher concentrations than offshore areas. The overall low level of community diversity and its significant annual variations indicated that the zooplankton community structure in the surveyed sea area was unstable and showed a trend of degenerative succession. The community structure of zooplankton and larger-bodied dominant species showed stronger correlations with phytoplankton dynamics, whereas smaller-bodied species were more influenced by water temperature. Full article

Beaches are important geomorphic units shaped by land–sea interactions. Changes in their profiles and surface sediments are directly influenced by both natural processes and human activities. This study is based on continuous topographic and sediment monitoring from 2021 to 2023 on the open and sheltered beaches of Puqian Bay, Hainan Island. It investigates the interannual profile evolution and the spatiotemporal response of sediment grain size under the influence of an artificial island. The results show that the Guilinyang Beach profile is mainly characterized by seasonal erosion–accretion cycles and the seaward migration of sandbars, while the Hilton Beach profile has undergone long-term erosion. At Hilton, sediment grain size changes are strongly coupled with profile erosion and accretion. Seasonal waves drive spatial differences in both profile and grain-size variation across Puqian Bay. The artificial island has reshaped local alongshore sediment transport and wave energy distribution. This has led to continuous erosion and coarsening in the open sector, while the sheltered sector remains morphologically stable. These findings reveal the spatiotemporal response patterns of headland-bay beaches under both natural and anthropogenic forcing, and provide scientific evidence for understanding coastal sediment dynamics and the impacts of artificial structures. Full article

Pulmonary fibrosis (PF) is a progressive and fatal lung disease characterized by irreversible alveolar destruction and pathological extracellular matrix (ECM) deposition. Currently approved agents (pirfenidone and nintedanib) slow functional decline but do not reverse established fibrosis or restore functional alveoli. Multifunctional bioscaffolds present a promising therapeutic strategy through targeted modulation of critical cellular processes, including proliferation, migration, and differentiation. This review synthesizes recent advances in scaffold-based interventions for PF, with a focus on their dual mechano-epigenetic regulatory functions. We delineate how scaffold properties (elastic modulus, stiffness gradients, dynamic mechanical cues) direct cell fate decisions via mechanotransduction pathways, exemplified by focal adhesion–cytoskeleton coupling. Critically, we highlight how pathological mechanical inputs establish and perpetuate self-reinforcing epigenetic barriers to regeneration through aberrant chromatin states. Furthermore, we examine scaffolds as platforms for precision epigenetic drug delivery, particularly controlled release of inhibitors targeting DNA methyltransferases (DNMTi) and histone deacetylases (HDACi) to disrupt this mechano-reinforced barrier. Evidence from PF murine models and ex vivo lung slice cultures demonstrate scaffold-mediated remodeling of the fibrotic niche, with key studies reporting substantial reductions in collagen deposition and significant increases in alveolar epithelial cell markers following intervention. These quantitative outcomes highlight enhanced alveolar epithelial plasticity and upregulating antifibrotic gene networks. Emerging integration of stimuli-responsive biomaterials, CRISPR/dCas9-based epigenetic editors, and AI-driven design to enhance scaffold functionality is discussed. Collectively, multifunctional bioscaffolds hold significant potential for clinical translation by uniquely co-targeting mechanotransduction and epigenetic reprogramming. Future work will need to resolve persistent challenges, including the erasure of pathological mechanical memory and precise spatiotemporal control of epigenetic modifiers in vivo, to unlock their full therapeutic potential. Full article

Difference in Differences (DiD) is a useful statistical technique employed by researchers to estimate the effects of exogenous events on the outcome of some response variables in random samples of treated units (i.e., units exposed to the event) ideally drawn from an infinite population. The term “effect” should be understood as the discrepancy between the post-event realisation of the response and the hypothetical realisation of that same outcome for the same treated units in the absence of the event. This theoretical discrepancy is clearly unobservable. To circumvent the implicit missing variable problem, DiD methods utilise the realisations of the response variable observed in comparable random samples of untreated units. The latter are samples of units drawn from the same population, but they are not exposed to the event under investigation. They function as the control or comparison group and serve as proxies for the non-existent untreated realisations of the responses in treated units during post-treatment periods. In summary, the DiD model posits that, in the absence of intervention and under specific conditions, treated units would exhibit behaviours that are indistinguishable from those of control or untreated units during the post-treatment periods. For the purpose of estimation, the method employs a combination of before–after and treatment–control group comparisons. The event that affects the response variables is referred to as “treatment.” However, it could also be referred to as “causal factor” to emphasise that, in the DiD approach, the objective is not to estimate a mere statistical association among variables. This review introduces the DiD techniques for researchers in economics, public policy, health research, management, environmental analysis, and other fields. It commences with the rudimentary methods employed to estimate the so-called Average Treatment Effect upon Treated (ATET) in a two-period and two-group case and subsequently addresses numerous issues that arise in a multi-unit and multi-period context. A particular focus is placed on the statistical assumptions necessary for a precise delineation of the identification process of the cause–effect relationship in the multi-period case. These assumptions include the parallel trend hypothesis, the no-anticipation assumption, and the SUTVA assumption. In the multi-period case, both the homogeneous and heterogeneous scenarios are taken into consideration. The homogeneous scenario refers to the situation in which the treated units are initially treated in the same periods. In contrast, the heterogeneous scenario involves the treatment of treated units in different periods. A portion of the presentation will be allocated to the developments associated with the DiD techniques that can be employed in the context of data clustering or spatio-temporal dependence. The present review includes a concise exposition of some policy-oriented papers that incorporate applications of DiD. The areas of focus encompass income taxation, migration, regulation, and environmental management. Full article

In order to solve the problems of mass transfer polarization spatiotemporal distribution variations, uncontrollable regulation error, and accelerated capacity decay caused by ion crossover in high-power vanadium liquid flow batteries (VFBs), a three-dimensional battery model with a flow-type flow field based on the three-dimensional transient COMSOL Multiphysics^® 6.1 numerical modeling method was developed in this study. The model combines the ion transmembrane migration equation with the mass transfer polarization theory, constructs an objective function to quantify the regulation error, and is validated by multifluid-field structural simulations. The results indicate the following: (1) Ion crossover induces a 3–5% electrolyte concentration deviation and a current density distribution bias reaching 11%; (2) The intensity of mass transfer polarization exhibits a linear increase with the flow rate difference between the positive and negative electrodes; (3) Ion crossover significantly degrades system performance, causing Coulombic efficiency (CE) and Energy efficiency (EE) to decrease by 1.1% and 1.5%, respectively. This research demonstrates that unlike conventional flow field optimization, our strategy quantifies the regulation error by directly compensating for the ΔQ caused by ion crossing, and further regulation minimizes the effect, providing a theoretical basis for mass transfer intensification and capacity recovery in flow batteries. Full article

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. However, the spatial and temporal dynamics underlying its development remain poorly characterized. This study employs spatial transcriptomics (ST) to investigate the progression of intestinal tumors in APC ^Min/+ mice across multiple time points. We identified distinct transcriptional profiles between tumor and normal tissues, resolving six major cell types through integrated dimensionality reduction and pathological annotation. Pseudo-time trajectory analysis revealed increased expression of MMP11 and MYL9 in later stages of tumor progression. Analysis of human CRC cohorts from the TCGA database further confirmed that high expression of these genes is associated with advanced clinical stages and promotes tumor proliferation and invasion. Temporal gene expression dynamics indicated enrichment of cancer-related pathways concurrent with suppression of lipid and amino acid metabolism. Notably, genes in the DEFA family were significantly upregulated in normal tissues compared to tumor tissues. Functional validation showed that DEFA3 inhibits colon cancer cell migration and proliferation in vitro. These demonstrate the value of ST in resolving spatiotemporal heterogeneity in CRC and identify both MMP11/MYL9 and DEFA3 as potential biomarkers and therapeutic targets. Full article

A spatiotemporal transmission epidemic model is proposed based on human mobility, spatial factors of population migration across multiple regions, individual protection, and government quarantine measures. First, the model’s basic reproduction number and disease-free equilibrium are derived, and the relationship between the basic reproduction number in a single region and that across multiple regions is explored. Second, the global asymptotic stability of both the disease-free equilibrium and the endemic equilibrium is proved by constructing a Lyapunov function. The impact of population migration on the spread of the virus is revealed by numerical simulations, and the global sensitivity of the model parameters is analyzed for a single region. Finally, a protection isolation strategy based on the optimal path is proposed. The experimental results indicate that increasing the isolation rate, improving the treatment rate, enhancing personal protection, and reducing the infection rate can effectively prevent and control the spread of the epidemic. Population migration accelerates the spread of the virus from high-infected areas to low-infected areas, aggravating the epidemic situation. However, effective public health measures in low-infected areas can prevent transmission and reduce the basic reproduction number. Furthermore, if the inflow migration rate exceeds the outflow rate, the number of infected individuals in the region increases. Full article

On 2 December 2020, an earthquake with a magnitude of M_w 4.5 occurred near the city of Thiva (Greece). The aftershock sequence, triggered by ruptures on or near the Kallithea fault, continued until January 2021. Seven months later, new seismic activity began a few kilometers west of the initial events, with the swarm displaying a general trend of spatiotemporal migration toward the east–southeast until the middle of 2022. In order to understand the physical and statistical pattern of the swarm, the seismicity was relocated using HypoDD, and the magnitude of completeness was determined using the frequency–magnitude distribution. In order to define the existence of spatiotemporal seismicity clusters in an objective way, the DBSCAN clustering algorithm was applied to the 2020–2022 Thiva earthquake sequence. The extracted clusters permit the analysis of the spatiotemporal scaling properties of the main clusters using the Non-Extensive Statistical Physics (NESP) approach, providing detailed insights into the nature of the long-term correlation of the seismic swarm. The statistical pattern observed aligns with a Q-exponential distribution, with q_D values ranging from 0.7 to 0.8 and q_T values from 1.44 to 1.50. Furthermore, the frequency–magnitude distributions were analyzed using the fragment–asperity model proposed within the NESP framework, providing the non-additive entropic parameter (q_M). The results suggest that the statistical characteristics of earthquake clusters can be effectively interpreted using NESP, highlighting the complexity and non-additive nature of the spatiotemporal evolution of seismicity. In addition, the analysis of the properties of the seismicity clusters extracted using the DBSCAN algorithm permits the suggestion of possible physical mechanisms that drive the evolution of the two main and larger clusters. For the cluster that activated first and is located in the west–northwest part, an afterslip mechanism activated after the 2 September 2021, M 4.0 events seems to predominately control its evolution, while for the second activated cluster located in the east–southeast part, a normal diffusion mechanism is proposed to describe its migration pattern. Concluding, we can state that in the present work the application of the DBSCAN algorithm to recognize the existence of any possible spatiotemporal clustering of seismicity could be helping to provide detailed insight into the statistical and physical patterns in earthquake swarms. Full article

Mining-induced seismicity poses significant safety risks in deep coal mining operations, necessitating advanced monitoring and accurate hazard assessment. Based on 15,584 microseismic events from a coal mine in Gansu, China, in 2024, this study investigates the spatiotemporal characteristics of mining-induced seismicity and its quantitative relationship with excavation disturbances. The methodology integrates Gaussian Mixture Model (GMM) clustering analysis with maximum likelihood estimation of b-value. Key findings include: (1) GMM clustering effectively identifies distinct seismic zones under different stress states, with significant variations in b-values (0.64–0.70). Low b-value zones correspond to high stress concentration and potential for strong events, enabling refined hazard assessment; (2) The time-sliding window analysis reveals the dynamic evolution of the b-value, which exhibits a clear negative correlation with high-energy seismic activity. When the b-value drops sharply to 0.6 or below, the likelihood of high-energy events increases markedly. Notably, 7 out of 8 high-energy seismic events occurred below this threshold. (3) Seismicity migrates with working face advancement, with monthly excavation length positively correlating with seismic energy release, confirming excavation as the primary trigger. This b-value spatiotemporal analysis framework provides scientific basis for early warning and mining optimization in deep coal mines. Full article

This study focuses on Hohhot (the capital city of Inner Mongolia Autonomous Region, northern China), a representative arid-semi-arid town in northern China. Against the backdrop of concurrent rapid urbanization and ecological constraints, it undertakes a systematic investigation into the spatiotemporal evolution and driving mechanisms of ecological asset utilization efficiency, aiming to furnish scientific evidence for sustainable development in ecologically fragile urban areas. Employing a “technology-scale-structure” analytical framework and constructing an “input-output-benefit” evaluation system, this research integrates the super-efficiency slack-based measure (SBM) model with spatial analysis methodologies to conduct multidimensional assessments of ecological asset utilization efficiency across all administrative districts and counties from 2000 to 2020. Empirical results demonstrate an overall upward trajectory in Hohhot’s ecological asset utilization efficiency, with comprehensive efficiency increasing from 1.132 in 2000 to 1.397 in 2020. However, pure technical efficiency and scale efficiency exhibit significant asynchrony, reflecting inherent tensions between technological advancement and scale expansion. Spatially, efficiency distribution manifests substantial spatial clustering and heterogeneity, with identified hotspots demonstrating temporal migration patterns. Peripheral counties exhibit distinct “technological isolation” phenomena and diseconomies of scale. Mechanism analysis reveals that industrial structure optimization constitutes the primary driver of efficiency enhancement, while the catalytic effects of economic development and governmental investment exhibit diminishing marginal returns. Urbanization maintains a moderate influence, transitioning from extensive spatial expansion toward intensive functional upgrading. This study recommends a synergistic enhancement of ecological asset utilization efficiency through strategic pathways, including the following: First, advancing green industrial transformation. Second, establishing regional technology-sharing platforms. Third, implementing systematic ecological compensation mechanisms. Fourth, adopting spatially differentiated governance approaches. These measures are projected to foster coordinated environmental and economic development. This research provides theoretical underpinnings and policy implications for urban ecological asset management in arid and semi-arid regions globally. Full article

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience. Full article