Search Results (1,230)

Background: Manure-amended agricultural soil is a critical reservoir of antibiotic resistance genes (ARGs), posing escalating threats to environmental health and food safety. However, the temporal trajectories of ARG prevalence throughout the complete reproductive cycle of cash crops, and their mechanistic linkages with fertilization regimes and microbial community succession, remain inadequately understood. Methods: To bridge this knowledge gap, we conducted an in situ field experiment over the entire growth period of Chinese cabbage at a long-term manure-amended farm in Tianjin, China. Six contrasting fertilization strategies were evaluated: unfertilized control (CK1), unfertilized baseline control (CK2), traditional full-rate combined manure–chemical fertilization (TF), traditional half-rate combined manure–chemical fertilization (T1), half-dose sole manure fertilizer (T2), and half-dose sole chemical fertilizer only (T3). Results: Our results demonstrated that ARG abundance and associated mobile genetic elements (MGEs) exhibited a pronounced transient surge immediately post-fertilization, yet reverted to baseline levels by harvest, revealing a tangible resilience of the soil resistome. Notably, the optimized half-organic fertilization (T2) effectively curtailed the proliferation of manure-derived pathogenic taxa while preserving beneficial keystone phyla (e.g., Acidobacteria and Proteobacteria), indicating a trade-off between nutrient provisioning and ecological filtering. Co-occurrence network analysis further identified MB-A2-108, Saccharimonadales, and Rokubacteriales as pivotal hosts for multidrug-resistant ARGs, underscoring that microbial interspecific interactions—rather than taxonomic richness alone—are the primary drivers of resistome succession. Quantitative risk assessment confirmed that the T2 regimen reduced the composite ARG contamination index (CF_zone) by 25% relative to conventional full fertilization (TF), while maintaining comparable cabbage yields. Conclusions: Collectively, our findings advocate for precision organic fertilization as a nature-based solution that synchronizes nutrient supply with crop demand, curtails ARG propagation, and mitigates long-term agroecological risks. Full article

Natural gas production from mature fields is progressively shifting toward low-rate wells operating near their economic limit, creating challenges for long-term asset management. This study proposes an integrated stochastic decision-support framework combining Arps decline curve analysis, a calibrated Schwartz Type-I mean-reverting jump-diffusion model, Monte Carlo simulation, and Bellman dynamic programming to optimize marginal dry gas well management. The framework evaluates pipeline commercialization and a hybrid strategy integrating on-site electricity generation, while incorporating monetized environmental externalities associated with CO₂ emissions from gas combustion and potential post-abandonment CH₄ emissions. Application to the Mareógrafo 100 well in Mexico shows that the environmentally adjusted Bellman policy yields a mean NPV of USD 34.84 thousand, exceeding the comparable pipeline-only and hybrid strategies. Internalizing environmental costs reduces the mean optimal NPV by 46.6% relative to the economic-only formulation, while the mean abandonment time is approximately 200 days. Sensitivity analysis identifies electricity price, natural gas price, and pipeline distance as the dominant profitability drivers. The proposed framework provides a transferable methodology for jointly evaluating commercialization, environmental externalities, and abandonment decisions in mature dry gas fields under uncertainty. Full article

Airborne bacterial diseases driven by pathogen aerosols in enclosed greenhouses spread rapidly, challenging traditional early-warning methods. This study developed a two-step monitoring system for cucumber angular leaf spot using low-cost particulate matter (PM) sensors, qPCR, and machine learning. Evaluated across spatially independent greenhouse trials using 732 plot-days of data, PM sensors were utilized as dynamic physical proxies alongside microclimate data. These proxies continuously estimated the fluctuations of pathogen aerosols suspended in the greenhouse air. When estimated aerosol risks exceeded a pathogenic threshold, targeted air sampling and qPCR quantification were triggered. For pathogen monitoring, the Extra Trees (ET) surveillance model accurately predicted the accumulation of airborne pathogen aerosols (R² = 0.884). For disease forecasting, by integrating the quantified aerosol loads with environmental factors, the XGBoost prediction model forecasted the daily disease index change rate with high precision (R² = 0.874). SHapley Additive exPlanations (SHAP) analysis confirmed that the concentration of airborne pathogen aerosols and vapor pressure deficit were primary drivers of disease expansion. By combining continuous physical sensing of greenhouse air with risk-triggered biological quantification, this framework provides a feasible strategy to partly compensate for the lack of biological specificity of PM sensors and supports early-warning management of airborne bacterial diseases in protected agriculture. Full article

Level 3 automated driving necessitates rapid driver reengagement following a takeover request, making human–machine interface design critical for safety. As a sensor-based in-vehicle interface, the augmented reality head-up display (AR-HUD) integrates real-time environmental sensing with visual information delivery, yet how different information presentation strategies influence driver visual attention during takeover remains underexplored. We examined the effects of presentation form (static/dynamic) and presentation timing (concurrent/progressive) using a 2 × 2 within-subject design. In the experiment, twenty-one licensed drivers viewed prerecorded automated driving takeover scenarios. Eye tracking measured mean fixation duration, mean saccade amplitude, time to first fixation, and fixation count, while ratings assessed usability, acceptance, and intention comprehension. Progressive presentation significantly reduced all eye-tracking measures and improved perceived usability ratings compared with concurrent presentation. This pattern indicates more concentrated and orderly gaze allocation and is consistent with lower visual-search and information-integration demands. However, the lower fixation count may partly reflect reduced information exposure. Static presentation reduced mean fixation duration but showed no consistent overall advantage on the remaining measures. Significant form-by-timing interactions showed that progressive presentation reduced saccade amplitude and time to first fixation and improved intention comprehension under static, but not dynamic, presentation. Among the four combinations, static-progressive presentation yielded the most favorable overall pattern. Overall, presentation timing affected more measured outcomes than presentation form. These findings provide preliminary evidence that presentation form and presentation timing shape gaze behavior and subjective evaluations of AR-HUD takeover displays. Full article

Multifunctional land use has gained increasing attention for reconciling societal (life function), economic (production function), and environmental (ecological function) development needs and addressing sustainable land use challenges in rapidly urbanizing regions. Consistent with mainstream international land use functions (LUFs), this study’s production-living-ecological (PLE) framework covers three key land functions, matching global research paradigms. To develop a sustainable county-level land use quantitative structure optimization model, this study innovatively integrates a mechanical equilibrium model with the multi-objective genetic algorithm (NSGA-II), overcoming the limitations of conventional qualitative production-living-ecological spaces (PLES) optimization. Furthermore, by establishing a mapping relationship between urbanization drivers and PLES functional evolution, it also enables structural optimization across urbanization subsystems. The optimization results indicate the following adjustment directions: southern coastal and southwestern counties require targeted population and socioeconomic urbanization improvements, while northern counties demand differentiated ecological urbanization regulation, and southeastern coastal areas should prioritize ecological protection. Most counties exhibit cropland and construction land expansion alongside woodland shrinkage, featuring expanded production and living spaces but contracted ecological functions. In contrast, certain counties achieve coordinated sustainability by eliminating inefficient construction land. This study operationalizes macroscopic PLE coordination into feasible quantitative strategies, enriching optimization methodologies and providing transferable insights for territorial spatial governance. Full article

Tourism is a major driver of economic growth in tropical island destinations but also places increasing pressure on fragile marine ecosystems. This study examined tourists’ environmental awareness, perceptions, and conservation-related behaviors on Cozumel Island, part of the Mesoamerican Reef System and home to multiple protected areas, including the Arrecifes de Cozumel National Park. Between June and September 2022, surveys were conducted with 144 tourists at major tourism hubs across the island. Although 86% of respondents identified themselves as environmentally conscious, awareness of local environmental impacts associated with tourism remained limited, and only 6% reported engaging in environmentally conscious actions during their visit. Awareness of the protected status and regulations of the Arrecifes de Cozumel National Park was also low (39%), and only 19% reported purchasing the required conservation bracelet or park pass. Short-stay cruise visitors represented 65% of respondents, potentially limiting opportunities for deeper environmental engagement and conservation education. These findings highlight the need for stronger pre-trip environmental communication, visible on-site interpretation, and targeted outreach strategies to support sustainable tourism and coral reef conservation in the Mexican Caribbean. Full article

Cancer is increasingly recognized as an emerging concern in wildlife health and biodiversity conservation in the Anthropocene. Although traditionally viewed as an individual disease associated primarily with aging, wildlife cancer is shaped by complex interactions among environmental changes, species-specific evolutionary adaptations, and ecological processes. This review synthesizes current knowledge on the ecological and evolutionary drivers of wildlife cancer by integrating evidence from comparative oncology, environmental toxicology, wildlife pathology, and conservation biology. We examine how anthropogenic stressors, including pollution, habitat degradation, climate change, and infectious agents, influence cancer susceptibility in wild populations, and summarize intrinsic mechanisms underlying interspecific variation in cancer vulnerability, including Peto’s paradox, enhanced tumor suppression, and adaptive immune surveillance. We further highlight major methodological challenges, including limited surveillance capacity, fragmented datasets, taxonomic biases, and insufficient integration between cancer biology and conservation science. Finally, we discuss emerging interdisciplinary approaches, such as standardized monitoring frameworks, multi-omics technologies, artificial intelligence-assisted diagnosis, and One Health-based strategies, to advance wildlife cancer research and management. Collectively, this review positions wildlife cancer as an important ecological and evolutionary phenomenon and provides perspectives for incorporating cancer surveillance into biodiversity conservation and ecosystem health assessment. Full article

Consumer food waste is a major challenge for food system sustainability and a governance problem that cannot be addressed by individual behavioral change alone. Previous reviews have examined the causes of food waste or evaluated specific intervention strategies, but less attention has been paid to how behavioral drivers, intervention strategies, and the roles of governance actors can be examined within an integrated analytical framework. Against this background, this review aims to synthesize the behavioral drivers of consumer food waste, classify the main types of behavioral interventions, and develop a multi-level governance perspective that connects these elements. Using a narrative review approach supported by a structured literature search, the review identifies three broad categories of behavioral drivers, namely cognitive biases, motivational drivers, and environmental and situational influences. It then classifies behavioral interventions into five types, namely information-based interventions, social norm interventions, choice architecture and environmental design interventions, incentive-based interventions, and education and capacity-building interventions. The review indicates that no single intervention type is likely to be effective across all settings. Instead, intervention outcomes are closely linked to the alignment among targeted behavioral drivers, intervention strategies, consumption contexts, and governance support. From a multi-level governance perspective, this review views consumers as the actors engaged in everyday food-related practices, businesses as the designers of the consumption environments and operational routines, and policymakers as the providers of institutional, informational, and economic support. By bringing behavioral drivers, intervention strategies, and governance roles into a shared analytical framework, this review moves beyond fragmented, individual-centered approaches to reducing consumer food waste. The framework suggests that food waste reduction efforts may be more effectively implemented and maintained when consumer practices, business environments, and policy conditions are mutually supportive. Full article

As the global agri-food system faces escalating climate-related challenges, developing localized low-carbon short food supply chains (SFSCs) has become a vital strategy for sustainable agriculture. To elucidate the psychological drivers of green consumer behavior within this context, this study integrates the Stimulus–Organism–Behavior–Consequence (SOBC) framework with the Theory of Planned Behavior (TPB). The aim is to investigate the continuous purchase intentions of Taiwanese consumers for organic cocoa. Data collected via an online survey from 377 consumers with prior cocoa consumption experience were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that personal attitudes and perceived behavioral control positively drive consumption intentions, whereas subjective norms do not. This suggests that consumption behaviors rely on substantive product evaluation rather than social pressures. Additionally, green product attributes and environmental values significantly enhance TPB constructs. Importantly, consumption intentions translate positively into perceived consumer effectiveness and continuous purchase intentions. By elucidating the psychological mechanisms underlying sustainable food choices, this study highlights the value of Taiwan’s “Tree-to-Bar” model. Practically, the findings highlight potential implications for policymakers and agribusinesses regarding how environmental attributes are communicated through front-of-pack (FOP) labels that emphasize local production and low-carbon mileage. Such approaches may help make environmental information more accessible to consumers, although their effectiveness should be evaluated in future research. Full article

African swine fever (ASF) is a viral disease of domestic and wild pigs that has re-emerged as a major transboundary disease. Modelling using mechanistic, statistical, and machine learning (ML) approaches plays a key role in understanding ASF transmission and informing disease control, but the literature remains fragmented. To synthesise global ASF modelling efforts, we systematically reviewed studies applying these three approaches. We examined temporal and geographic trends, modelling objectives, explanatory variables, and model evaluation practices. A total of 151 papers published through 2024 met the inclusion criteria. Mechanistic (54.3%) and statistical (40.4%) approaches predominated, whereas ML (9.3%) was increasingly applied in recent years. Mechanistic models were primarily used to assess control strategies (48.8%) and transmission drivers (41.5%), statistical models to identify risk factors (63.9%) and spatiotemporal spread (32.8%), and ML for environmental suitability modelling (64.3%). Most were published from 2011 (99.3%) and focused on Europe (43.0%) and Asia (26.5%). Model evaluation remained inconsistent, with mechanistic papers frequently lacking model output uncertainty quantification (47.0%) and statistical papers often omitting model adequacy assessment (49.2%) and assumption checking (50.8%). Overall, ASF modelling approaches have developed complementary methodological roles, while geographic underrepresentation, limited representation of some transmission pathways, and inconsistent model evaluation remain important gaps. Full article

Understanding the mechanisms of vegetation recovery following severe ecological disturbances is essential for effective forest management. The relative importance of environmental predictors across different disturbance types and temporal phases remains poorly quantified. We utilized a comprehensive nationwide dataset of 194 restoration plots in South Korea to evaluate the factors influencing total vegetation cover. We employed random forest regression models to analyze topographic, soil, and stand structural variables across four disturbance types and two recovery periods. Model performance revealed that short-term recovery and mechanically unstable roadside slopes are highly stochastic and strictly limited by physical terrain features, including elevation and slope. Conversely, the long-term recovery phase and severely degraded environments, such as mined sites, are strongly associated with biological structural traits and soil nutrient dynamics. Partial dependence plots demonstrated severe non-linear threshold mechanisms where total cover exhibited a sharp positive response to canopy height in quarries and an abrupt negative response to high soil carbon-to-nitrogen ratios during long-term recovery. These results indicate that structural canopy facilitation and microbial nutrient immobilization co-develop with advanced community expansion rather than acting as independent drivers. Ecological restoration strategies must therefore transition from immediate physical site stabilization to proactive soil nutrient management and canopy facilitation as succession advances. Full article

Forage conservation is a strategic component of Mediterranean livestock systems, where seasonal drought, irregular rainfall, and high summer temperatures limit the availability of fresh forage. In these environments, hay and haylage are widely used as preservation strategies, yet their environmental performance remains insufficiently quantified under semi-arid conditions. It is hypothesized that the higher material and energy inputs required for haylage could be compensated for by the combined effects of biomass preservation efficiency and forage productivity, resulting in comparable or lower impacts per unit of conserved forage under Mediterranean farm conditions. To test this hypothesis, a life cycle assessment was conducted to compare hay and haylage production in a forage farm located in Sicily, Italy, considering the 2024–2025 season. The analysis adopted a cradle-to-farm-gate system boundary, and two complementary impact assessment methods, namely the CML-IA baseline method and the ReCiPe 2016 method, were applied. Environmental burdens were calculated per hectare of cultivated land and per ton of dry matter produced to capture both land-based and product-based performance. The choice of functional unit strongly influenced the interpretation of the environmental impact results. Per hectare, hay and haylage displayed comparable overall burdens, with haylage showing higher impacts in impact categories linked to plastic use and wrapping operations. Per ton of dry matter, however, haylage showed consistently lower impacts, attributed to greater biomass recovery and higher dry matter output, reflecting the combined effect of forage composition, crop productivity, and conservation efficiency. Hay systems showed greater burdens per unit of product under the evaluated farm conditions due to prolonged drying periods, repeated field operations, and higher biomass losses during harvesting and storage. The results suggest that, within the specific Mediterranean farm context analyzed, the combined effects of biomass preservation efficiency and crop productivity were important factors influencing environmental performance when impacts were expressed on an output basis. This case study contributes to current knowledge by identifying biomass recovery, rather than input minimization alone, as a key driver of sustainable forage conservation. Full article

Desertification represents a critical environmental challenge in arid and semi-arid regions, driven by the combined impacts of climate change and unsustainable land-use practices. This review provides a comparative assessment of desertification dynamics, mitigation strategies, and ecological restoration approaches in the Ningxia Hui Autonomous Region (China) and Egypt. Both regions are characterized by severe water scarcity, increasing climatic variability, and fragile ecosystems; however, they differ in ecological conditions, institutional frameworks, and dominant land degradation processes. The study synthesizes major drivers of desertification, including rising temperatures, precipitation variability, recurrent droughts, soil salinization, overgrazing, wind erosion, and unsustainable agricultural expansion. It further evaluates key control measures implemented in both regions, such as afforestation and ecological engineering, sand dune stabilization, water-efficient irrigation systems, soil rehabilitation practices, and the integration of remote sensing and GIS-based monitoring technologies. The analysis highlights China’s large-scale, long-term ecological restoration programs, which have significantly improved vegetation cover and reduced land degradation, compared to Egypt’s emphasis on irrigation efficiency, land reclamation, and salinity management under extreme aridity constraints. Importantly, the comparison underscores institutional differences, with China’s state-led ecological engineering contrasting against Egypt’s multi-actor reclamation initiatives, offering novel insights into governance pathways for combating desertification. The comparative synthesis demonstrates that effective desertification control requires integrated strategies combining ecological restoration, sustainable water resource management, technological innovation, and strong policy support. Despite contextual differences, both regions offer complementary lessons for dryland management. The study emphasizes the potential for enhanced China–Egypt cooperation in climate-smart agriculture, digital environmental monitoring, and nature-based solutions, thereby advancing sustainable land restoration and contributing to global efforts toward land degradation neutrality under future climate change scenarios. Full article

Accurately predicting fine particulate matter (${\mathrm{PM}}_{2.5}$) concentrations in regions with sparse monitoring networks remains a critical challenge for air quality management and public health. This study evaluates a machine learning (ML) data fusion approach that integrates daily federal regulatory observations, daily low-cost community sensor measurements, and monthly satellite-derived aerosol products (functioning as a regional background field) to improve ${\mathrm{PM}}_{2.5}$ prediction across under-monitored environments. Using a Long Short-Term Memory (LSTM) neural network architecture, the analysis examines how combining heterogeneous data sources influences predictions. Results show that pooled multi-source training was associated with higher holdout skill relative to some single-source configurations under this parsimonious baseline, though associations are city- and configuration-dependent and cannot be attributed solely to fusion because evaluation populations are not common. Comparisons against tree-based baselines (Random Forest, Gradient Boosting, XGBoost) indicate that overall predictive skill, not just the LSTM’s, is constrained by data availability, suggesting that data composition, rather than model choice, is the primary driver of the observed performance patterns. These findings highlight both the potential and the practical constraints of multi-source ML approaches for air quality prediction and exposure assessment, with implications for model design, monitoring strategy, and environmental equity. This study is intentionally scoped as an applied evaluation of data fusion performance rather than a comprehensive assessment of algorithmic optimality or operational forecasting readiness. The analysis focuses on daily ${\mathrm{PM}}_{2.5}$ prediction across a selected set of U.S. cities and does not address sub-daily variability, real-time deployment constraints, or event-specific model optimization. Model performance is therefore interpreted in the context of data availability, consistency, and representativeness, rather than as an upper bound on achievable predictive skill. Full article

The Inner Mongolia Autonomous Region, located in northern China, spans a vast territory. It serves as a crucial transitional zone connecting three major ecosystems—forest, grassland, and desert—and constitutes an essential component of the nation’s ecological security barrier. Over the past three decades, significant changes have occurred in the region’s land use structure and landscape patterns. Based on long-term time-series land use data, this study systematically analyzed land cover changes and landscape diversity characteristics by dividing the region into climatic zones, ecological zones, and administrative areas. Additionally, a driver analysis framework based on the eXtreme Gradient Boosting (XGBoost) model was constructed, and the SHapley Additive exPlanations (SHAP) method was employed to quantitatively identify the contribution rates of driving factors influencing the Shannon Diversity Index (SHDI). Results indicate pronounced spatial heterogeneity in land-use structure dynamics across regions. Among all land use types, grasslands, croplands, and bare land exhibited the most pronounced changes under the combined influence of natural environmental variations and human disturbances. Against this backdrop of landscape pattern evolution, the regional Shannon Diversity Index (SHDI) also showed differentiated variation trends. Based on SHAP contribution analysis, population density (POP) was identified as the primary driver affecting SHDI changes, with a contribution rate of 29.9%, followed by precipitation (PRE, 19.5%), elevation (DEM, 17.2%), and GDP (11.9%). Further SHAP response analysis revealed the nonlinear effects and characteristics of different driving factors on SHDI, indicating that the proposed framework can effectively elucidate the driving mechanisms of landscape changes. The findings provide scientific support for developing sustainable and differentiated land-use management strategies and optimizing ecological conservation and restoration measures across regions with diverse ecological backgrounds. Full article