Search Results (2,819)

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as task completion time, success rate, or error count, which may not fully capture how a task is executed. This exploratory study investigated whether wearable IMU signals collected during an immersive VR sushi-making task could support binary detection of a core upper-limb manipulation phase and provide additional information about task execution beyond global performance outcomes. A total of 45 participants contributed usable motion recordings for this study, with five Xsens DOT sensors placed on the hands, forearms, and waist. Three signal modalities were analysed, including acceleration (ACC), gyroscope angular velocity (GYR), and Euler angles. The downstream recognition problem was formulated as a binary classification task (Placing vs. Non-Placing), and a self-supervised learning (SSL) pretrain–fine-tune strategy was evaluated against conventional machine learning and from-scratch deep learning baselines using five subject-wise validation splits. The strongest overall performance was achieved with hand-mounted accelerometer signals, with LeftHand–ACC achieving a Macro-F1 of $0.712\pm 0.128$ and RightHand–ACC achieving $0.679\pm 0.118$. Under both hand-ACC settings, SSL fine-tuning showed higher mean Macro-F1 than the Balanced Random Forest baseline and the same deep architecture trained from scratch. Recognition performance varied substantially across sensor locations, signal modalities, and task segments, with distal upper-limb sensors generally outperforming waist-based configurations. Cross-age analyses further showed that within-cohort and cross-cohort performance did not fully align, indicating sensitivity to age-related distribution shift. Beyond classification, Log Dimensionless Jerk (LDLJ) derived from the Placing action showed a significant positive association with Cognitron motor control time cost ($r=0.636$, $p<0.001$). These findings suggest that wearable IMU sensing can provide preliminary process-level information during immersive VR functional tasks, including task-phase detection, sensing-configuration comparison, cross-cohort generalisation assessment, and exploratory motion-quality analysis. The results should be interpreted as evidence of feasibility rather than as a mature biomechanical or clinical assessment model. Full article

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

Environmental change, accelerated by increasing global temperatures, has become a defining economic, ecological, and public health issue of the twenty-first century. This study presents an economic evaluation of early mortality associated with air pollution, a major factor underlying worldwide patterns of illness and premature mortality, through which climate change affects global population health. Using secondary global mortality estimates and an economic valuation framework, both tangible costs (e.g., economic output and income losses) and quality-of-life loss (e.g., welfare loss associated with premature mortality) are estimated. The paper contributes to planetary health scholarship by integrating established economic valuation approaches with harm-reduction and systems-based perspectives to reinterpret air pollution as an interconnected environmental, economic, and societal challenge rather than solely a public health issue. Air pollution is associated with reduced life expectancy and approximately 3 million premature deaths annually worldwide. The estimated annual tangible economic burden is approximately US$940.9 billion (range: US$550.5 billion–US$1.65 trillion), while intangible costs are estimated at US$37.8 trillion annually (range: US$13.3 trillion–US$48.8 trillion). The findings suggest that air pollution should be understood not merely as a health-related challenge but also as a broader planetary health challenge with implications for environmental sustainability, economic resilience, and long-term societal well-being. Targeted air quality interventions and pollution reduction strategies may therefore generate substantial public health and societal co-benefits worldwide. Full article

Global climate governance is undergoing a rapid transformation, and energy systems are increasingly shifting toward low-carbon development. Against this background, improving manufacturing firms’ green total factor productivity (MFGTFP) is essential for achieving sustainable industrial development. China has introduced two major policy instruments: new energy demonstration cities (NEDCs) and low-carbon city pilots (LCCPs). NEDCs focus on optimizing the energy supply structure, whereas LCCPs seek to reduce carbon emissions through demand-side regulatory constraints. This study treated the joint implementation of NEDCs and LCCPs as a quasi-natural experiment and employed panel data from Chinese A-share listed manufacturing firms from 2007 to 2024. Using a multi-period difference-in-differences model and mechanism tests, we examined the effect of the joint implementation of these policies on MFGTFP. The empirical results show that the joint implementation of NEDCs and LCCPs significantly improves MFGTFP. This effect is more pronounced when NEDCs are introduced prior to LCCPs, particularly in cities with a higher government ecological governance capacity (GEGC) and in regions characterized by a lower carbon emission intensity (CEI). Mechanism analysis revealed that the joint effects of NEDCs and LCCPs operate through supply-side innovation and partially through demand-side cost-pressure channels. On the supply side, NEDCs promote green innovation (GI), thereby enhancing firms’ supply innovation. On the demand side, the evidence mainly reflects financing constraint (FC) alleviation rather than a positive capacity utilization (CU) channel. Together, these findings suggest that improvements in MFGTFP are driven by supply-side innovation incentives and partially by demand-side cost-pressure effects through FC alleviation. These findings provide firm-level evidence on how the joint implementation of energy and carbon policies promotes green productivity improvement. Full article

Rapid and accurate diagnosis of infectious diseases in aquaculture is essential for preventing major economic and ecological losses. Traditional culture-based methods focus on isolation of individual pathogens, and often are burdened with extended processing times, particularly during investigations of polymicrobial infections. Application of Oxford Nanopore Technologies (ONT) sequencing offers a rapid, culture-independent workflow for the identification of bacterial and fungal pathogens directly from fish tissues. Swab and organ samples from four cases (1: Salmo spp.; 2: Cyprinus carpio; 3: Salvelinus fontinalis; 4: Heniochus acuminatus) were analyzed using ONT long-read sequencing for metagenomic screening and bioinformatic classification. The results revealed case-, species-, and tissue-specific microbial profiles, with external tissues showing higher microbial diversity and internal organs enriched in pathogenic taxa. Dominant pathogens included Streptococcus iniae, Aeromonas hydrophila, Pseudomonas spp., and Saprolegnia parasitica, alongside opportunistic zoonotic bacteria such as Escherichia coli and Acinetobacter baumannii. We demonstrate the potential for diagnostic application of ONT sequencing in investigations and detection of multi-pathogen infections, including assessments of microbial community structure changes during disease outbreaks in aquatic species. The presented workflow enables rapid, cost-effective, and comprehensive pathogen profiling, supporting early disease surveillance and improved management in aquatic veterinary practice. Full article

Facing global problems such as the energy crisis and climate change, in recent years, the bioelectrochemical system represented by plant microbial fuel cell (PMFC) has been widely studied. It is a frontier bioelectrochemical technology that combines plant photosynthesis, rhizosphere microbial metabolism, and electrochemical energy conversion. This paper focuses on the linkage application of PMFC and intelligent sensing technology in the paddy-field environment, systematically expounds the basic composition, working principle, and integration mode of this technology with paddy field ecology, and emphatically analyzes its realization path and application potential in self-powered external sensor deployment, rhizosphere biosensor, and multi-node sensor network integration. The analysis shows that PMFC has the unique advantage of in situ and continuous micro-power generation in flooded rice fields. Its output not only supports the intermittent operation of low-power sensors, but the output electrical signals can also reflect plant stress and environmental conditions, thereby possessing biosensing potential. However, the current system still faces key bottlenecks, such as low power density, easily disturbed electrical signals, and high cost of high-performance electrode materials, which restrict the actual deployment of rice fields. Through the collaborative optimization of electrode interface engineering, microbial community directional control, and low-power sensing fusion strategy, it is expected to promote the transformation of PMFC from principle verification to field intelligent monitoring application. Full article

The Biological Monitoring Working Party (BMWP) and Average Score Per Taxon (ASPT) indices, which rely on family-level environmental sensitivity values (FESVs), are widely used in freshwater bioassessment. However, regional differences in taxonomic composition often render existing FESV systems incomplete or incompatible, and the influence of rare families remains poorly understood. Using a historical dataset from the temperate Taizi River basin in Northeast China, we developed a regional FESV system for benthic macroinvertebrates. A total of 67 FESVs were established, including 10 families not previously scored in the UK system. These values followed a normal distribution and were ecologically validated using canonical correspondence analysis (CCA). Both BMWP and ASPT indices showed significant correlations with water quality parameters, the Water Quality Index (WQI), and the Habitat Quality Index (HQI). Notably, excluding rare families (occurrence frequency < 1%) did not reduce but slightly enhanced the responsiveness of both indices. CCA identified HQI, conductivity, and ammonia nitrogen as the primary drivers of community composition, and the inferred ecological preferences aligned well with the assigned FESVs. This study provides a robust, regionally adapted framework for family-level bioassessment in temperate East Asian rivers and supports the exclusion of rare taxa to improve cost-effectiveness and index sensitivity. Full article

A new method has been established for determining trace amounts of platinum in water using ion liquid (IL)-dispersive liquid–liquid microextraction (DLLME) combined with graphite furnace atomic absorption spectroscopy (GFAAS). The method is based on the use of a self-prepared reagent, 5-(5-cyano-2-pyridineazo)-2,4-diaminotoluene (5-CN-PADAT), as a chelating agent, which reacts with Pt(IV) to form a hydrophobic chelate. The extraction solvent is 1-octyl-3-methylimidazolium hexafluorophosphate ([C₈mim][PF₆]), and ethyl acetate is used as the dispersive solvent. After the extraction is completed, the extraction phase formed by [C₈mim][PF₆] and ethyl acetate has a relatively low viscosity and can be directly used for the determination of GFAAS. A single-factor rotational method was employed to optimize conditions affecting DLLME extraction efficiency. The interactions among the factors affecting DLLME were analyzed using response surface optimization (RSM). Under optimal conditions, platinum concentrations exhibited good linearity within the range of 40–280 ng/mL, with a detection limit of 0.3 ng/mL. AGREEprep was used to discuss the ecological friendliness of the method, demonstrating its low cost, ease of operation, simple equipment requirements, and environmental friendliness. When applied to determining trace amounts of platinum in water samples, the results were satisfactory. Full article

Yangcheng Lake, the third largest freshwater lake in the Taihu Plain (118.68 km²), serves critical functions in drinking water supply, aquaculture, and ecological regulation. This study aims to address the challenge of optimizing total phosphorus load allocation and engineering project configuration in the Yangcheng Lake basin by developing a multi-objective optimization model that integrates environmental, social, and economic dimensions with the goal of achieving three specific objectives: (1) maximizing ecological benefits, (2) minimizing life-cycle costs, and (3) minimizing the environmental Gini coefficient. The NSGA-II algorithm was used, with hyperparameters calibrated via orthogonal experiments and HV-GD evaluation. Under a normal flow year scenario, total phosphorus (TP) load allocation was optimized for an agricultural watershed where livestock manure contributes 86.5% of TP pollution. Five selection strategies (Economic Priority, Ecological Priority, Equity Priority, Ideal Point Method, Game Theory) were applied to the Pareto front. Results show synergy between ecological and equity objectives, both competing with economic cost. Optimal hyperparameters were a population size of 1000 and 1000 iterations. Among strategies, the Ideal Point Method achieved the best compromise (economic cost: 5772.7; Gini coefficient < 0.30). The proposed framework provides scientific support for pollution load allocation in plain river network regions, helping decision-makers balance economic development, ecological protection, and social equity. Full article

Fire is an essential component of species, ecosystems, and atmospheric dynamics. However, human activity has caused changes in fire regimes over the past two decades. In many cases, the spatial patterns of vegetation change after fire at the landscape scale remain unknown. The aim of this study was to evaluate spatial vegetation trends in burned areas across the Americas (2001–2024), using non-parametric tests and analyzing Normalized Difference Vegetation Index (NDVI) remote sensing products. Over a period of 24 years, fire activity burned a total area of 429.7 million hectares in 44 countries or territories and 269 ecoregions in the Americas. Regarding fire recurrence, the data indicates that 244.7 Mha (56.9%) burned only once (≤1), while 185.0 Mha (43.1%) burned multiple times (≥2), with certain regions experiencing up to 39 fires. The NDVI trend analysis showed that burned areas with increasing trends (p < 0.05) represented a total of 149.6 Mha (34.8%), primarily in Brazil (54.6 Mha, 12.7%), Argentina (17.8 Mha, 4.2%), the United States (14.4 Mha, 3.4%). In terms of decreasing NDVI trends (p < 0.05), these represented a total of 91.8 Mha (21.37%), primarily in Brazil (29.1 Mha, 6.8%), Canada (23.4 Mha, 5.4%), and the United States (14.2 Mha, 3.3%). The ecoregions with the largest areas showing increasing NDVI trends (p < 0.05) were the Cerrado (33.8 Mha, 7.8%), the Llanos (13.3 Mha, 3.1%) and the Humid Chaco (7 Mha, 1.6%). In contrast, the ecoregions with the largest areas showing decreasing NDVI trends (p < 0.05) were the Dry Chaco (9.2 Mha, 2.1%), the Cerrado (8.6 Mha, 2.0%), and the Boreal Shield (8.3 Mha, 1.9%). In terms of land cover types, savannas (37.2%) exhibited the highest proportions of increasing NDVI trends (p < 0.05), while decreasing trends were also present in savannas (28.0%) and grasslands (22.1%). Identifying spatiotemporal trends in vegetation change after fires is a fundamental step in implementing strategies and public policies to ensure ecological restoration. Moreover, given the high costs of restoration efforts, governments must work together to prevent these ecosystems from burning repeatedly. Full article

This paper highlights the eco-efficiency of a sustainable digital solution to support decision-making in resolving the problem of sudden production drops and associated energy waste in industrial power plants, especially those operating with a steam turbomachine. The solution involves a multi-interface digital dashboard that generates insightful visual reports and gives proactive alerting to the decision-makers about potential underperformances to ensure resource optimization. For the studied use case, it involves the development of three interfaces of the dashboard, so as to perform the sustainable monitoring of a thermoelectric power plant based on the Rankine–Hirn cycle as follows: the first interface is about real-time monitoring of thirty-two key physical parameters equipped with a notification system. The second interface displays the historical trends of all the plant variables, in order to help in detecting incipient abnormal deviations before they impact environmental efficiency. Lastly, the third platform covers a predictive model using the XGBoost algorithmic method to forecast the future behavior of the electrical power as the target variable of the power plant. The XGBoost method was selected after a comparative assessment which also included the algorithms of Random Forest Regressor (RFR) and Gated Recurrent Unit (GRU). As a final step, this solution was later tested in a simulation environment built under the “Node-Red” platform, through an industrial decision scenario. The concrete findings validate the framework’s sustainability metrics, demonstrating the ability of the solution to help in preserving, for each production cycle of two years, up to 7.6 GWh of electrical energy that would otherwise be wasted, which translates into a potential cost-saving exceeding 633,247.9 USD, as well as an ecological impact by preventing the emission of 4628 tons of CO₂. Full article

Behavioral interventions are widely used to promote sustainable consumption, but their effectiveness under high-friction real-world conditions remains uncertain, especially when multiple tools are combined. We report a quasi-experimental natural field study conducted in a busy urban café in Tel Aviv, Israel, examining the isolated and combined effects of a localized identity-based social-norm cue and a small financial incentive on reusable cup adoption. Across four consecutive weeks and 9414 hot-beverage transactions, a baseline week was followed by a norm condition, a 1 NIS discount condition, and a combined condition. Reusable cup use increased from 3.33% at baseline to 3.59% in the norm week, 4.19% in the incentive week, and 3.72% in the combined week, but none of these changes reached statistical significance. The financial incentive produced the largest descriptive increase, whereas the combined intervention did not outperform the incentive alone. Across the intervention period, reusable cup use exceeded the number expected under the baseline rate by approximately 35 purchases. These bounded null findings suggest that low-cost behavioral tools may yield only modest gains in convenience-driven consumption settings and that combining policy tools does not necessarily generate additive effects. The study contributes ecologically grounded evidence on the boundary conditions of sustainable behavior change and highlights the importance of testing behavioral policies under realistic implementation constraints. Full article

Headwater stream dynamics are vital for understanding hydrological and ecological processes in watersheds; however, traditional monitoring methods can be costly and time-consuming. This technical note documents the limitations and challenges encountered when deploying a low-cost camera system for continuous streamflow monitoring in a forested headwater stream in Ohio, USA. The study stream, with a channel width of less than 1 m and watershed of 0.4 km², is much smaller than previously studied streams. The camera system was constructed using inexpensive and easily accessible electronics, and it enabled application of large-scale particle image velocimetry (LSPIV) to videos collected at a frequency of 15 min. The application of LSPIV to much larger streams is well-established in previous studies; however, its application to extremely small headwater streams is understudied. Preliminary testing in a flume showed that this system was capable of providing accurate discharge measurements. In the field, however, a rating curve calibrated based on the LSPIV-derived flow estimates had an R² value of 0.70, which was weaker than relationships previously reported in the literature. The rating curve overestimated flows at lower channel stages and underestimated them at higher stages when compared to physical discharge measurements. Examination of the videos collected during field deployment revealed that unsteady flow conditions introduced significant variability in the rating curve analysis. Environmental noise from raindrops, illumination conditions, and leaf litter also caused erroneous flow measurements in the LSPIV results. This technical note presents a critical evaluation of the performance of LSPIV-based camera system in extremely small streams, and practitioners and researchers are advised to follow several best practices, offered as lessons learned from our study, to minimize specific sources of error during implementation. Full article

Sorghum halepense is widely recognised as one of the most aggressive invasive perennial grasses affecting agricultural ecosystems worldwide. This systematic review synthesises existing scientific evidence on the ecological invasion dynamics, origin, distribution patterns, impacts on both biodiversity and livestock, and management strategies. A systematic literature review approach was employed to identify and evaluate peer-reviewed and grey literature. Relevant studies were retrieved from major scientific databases, including Google Scholar, PubMed, and ResearchGate, using predefined search terms related to S. halepense, invasion, impact on native plants and livestock, and possible control measures. Articles were screened based on relevance, methodological quality, and thematic alignment with the objectives of the review. The results showed that Johnsongrass is making a gradual invasion in South Africa through seed production and rhizome systems. Sorghum halepense alters native species composition, subsequently reduces biodiversity, and outcompetes native species. Although it may provide forage under certain conditions, its accumulation of cyanogenic compounds and nitrates poses serious poisoning risks to herbivores. Management strategies such as mechanical, burning, and chemical methods vary in terms of effectiveness. Some of these measures are influenced by the genetic make-up of the plant, costs associated with each control measure and other environmental factors. This review highlights the need for integrated management approaches that balance invasive weed control with sustainable forage production. This review emphasises the importance of adopting integrated management strategies that effectively control both seed production and underground stems. Future research should prioritise climate-responsive management approaches, improved understanding of invasion ecology, and the development of cost-effective control measures. Bringing together policy makers and specialists in weed science, natural conservation science, and animal health will be essential for reaching consensus on the actions required to curb the expansion and reduce the economic losses associated with the abundance of Sorghum halepense in our ecosystems. Full article