Search Results (25,024)

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article

This study offers a detailed environmental, energy, and economic evaluation of thermal modernisation options for an existing single-family home in southern Poland. A total of 24 variants, combining different heat sources (solid fuel, biomass, natural gas, and heat pumps) with various levels of building insulation, were analysed using energy performance certification methods. Results show that, from an energy perspective, the most advantageous scenarios are those utilising brine-to-water or air-to-water heat pumps supported by photovoltaic systems, reaching final energy demands as low as 43.5 kWh/m²year and primary energy demands of 41.1 kWh/m²year. Biomass boilers coupled with solar collectors delivered the highest renewable energy share (up to 99.2%); however, they resulted in less notable reductions in primary energy. Environmentally, all heat pump options removed local particulate emissions, with CO₂ reductions of up to 87.5% compared to the baseline; biomass systems attained 100% CO₂ reduction owing to renewable fuels. Economically, biomass boilers had the lowest unit energy production costs, while PV-assisted heat pumps faced the highest overall costs despite their superior environmental benefits. The findings highlight the trade-offs between ecological advantages, energy efficiency, and investment costs, offering a decision-making framework for the modernisation of sustainable residential heating systems. Full article

The structural reliability of agricultural machinery is critically dependent on bolted joints, with loosening being a significant and prevalent failure mode. Harsh operational environments (intense vibration, impact, corrosion) severely exacerbate loosening risks, compromising machinery performance and safety. Traditional periodic inspections are inadequate for preventing sudden failures induced by loosening, leading to impaired efficiency and safety hazards. This review comprehensively analyzes the unique challenges and opportunities in monitoring bolted joint reliability within agricultural machinery. It covers the following: (1) the status of bolted joint reliability issues (failure modes, impacts, maintenance inadequacies); (2) environmental challenges to joint integrity; (3) evaluation of conventional detection methods; (4) principles and classifications of modern detection technologies (e.g., vibration-based, acoustic, direct measurement, vision-based); and (5) their application status, limitations, and techno-economic hurdles in agriculture. This review identifies significant deficiencies in current technologies for agricultural machinery bolt loosening surveillance, underscoring the pressing need for specialized, dependable, and cost-effective online monitoring systems tailored for agriculture’s demanding conditions. Finally, forward-looking research directions are outlined to enhance the reliability and intelligence of structural monitoring for agricultural machinery. Full article

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

Clarifying the spatiotemporal relationship between urban ecosystem services and changes in landscape patterns is essential, as it has significant implications for balancing ecological protection with socio-economic development. However, existing studies have largely focused on the one-sided impact of landscape patterns on either the supply or demand of ESs, with limited investigation into how changes in these patterns affect the growth rates of both supply and demand. The central urban area, characterized by complex urban functions, intricate land use structures, and diverse environmental challenges, further complicates this relationship; yet, the spatiotemporal differentiation patterns of ecosystem services’ supply–demand dynamics in such regions, along with the underlying influencing mechanisms, remain insufficiently explored. To address this gap, the present study uses Shenyang’s central urban area, China as a case study, integrating multiple data sources to quantify the spatiotemporal variations in landscape pattern indices and five ecosystem services: water retention, flood regulation, air purification, carbon sequestration, and habitat quality. The XGBoost model is employed to construct non-linear relationships between landscape pattern indices and the supply–demand ratios of these services. Using SHAP values and LOWESS analysis, this study evaluates both the magnitude and direction of each landscape pattern index’s influence on the ecological supply–demand ratio. The findings outlined above indicate that: there are distinct disparities in the spatiotemporal distribution of landscape pattern indices at the patch type level. Additionally, the changing trends in the supply, demand, and supply–demand ratios of ecosystem services show spatiotemporal differentiation. Overall, the ecosystem services in the study area are developing negatively. Further, the impact of landscape pattern characteristics on ecosystem services is non-linear. Each index has a unique effect, and there are notable threshold intervals. This study provides a novel analytical approach for understanding the intricate relationship between landscape patterns and ESs, offering a scientific foundation and practical guidance for urban ecological protection, restoration initiatives, and territorial spatial planning. Full article

Urban sustainability poses a deeply interdisciplinary challenge, spanning technical fields like data science and environmental science, design-oriented disciplines like architecture and spatial planning, and domains such as economics, policy, and social studies. While numerous advanced tools are used in these domains, ranging from geospatial systems to AI and network analysis-, they often remain fragmented, domain-specific, and difficult to integrate. This paper introduces a semantic framework that aims not to replace existing analytical methods, but to interlink their outputs and datasets within a unified, queryable knowledge graph. Leveraging semantic web technologies, the framework enables the integration of heterogeneous urban data, including spatial, network, and regulatory information, permitting advanced querying and pattern discovery across formats. Applying the methodology to two urban contexts—Thessaloniki (Greece) as a full implementation and Marine Parade GRC (Singapore) as a secondary test—we demonstrate its flexibility and potential to support more informed decision-making in diverse planning environments. The methodology reveals both opportunities and constraints shaped by accessibility, connectivity, and legal zoning, offering a reusable approach for urban interventions in other contexts. More broadly, the work illustrates how semantic technologies can foster interoperability among tools and disciplines, creating the conditions for truly data-driven, collaborative urban planning. Full article

This study presents a mathematical model to describe the transmission dynamics of asthma, explicitly accounting for the impact of dust waves and airborne particulate matter in the environment, recognized as key triggers of asthma exacerbations. The model incorporates a single endemic equilibrium point, which is shown to be locally asymptotically stable. To mitigate the burden of asthma, we employed the Pontryagin Maximum Principle within an optimal control framework, incorporating three time-dependent intervention strategies: vaccination, treatment, and avoidance of environmental triggers such as dust exposure. The model was numerically solved using the fourth-order Runge–Kutta method in conjunction with a forward–backward sweep algorithm to investigate the effects of various control combinations on the prevalence of asthma. Additionally, a comprehensive cost-effectiveness analysis was conducted to evaluate the economic viability of each strategy. The results indicate that the combined application of vaccination and treatment is the most cost-effective approach among the strategies analyzed, significantly reducing the number of asthma cases at minimal cost. All simulations and numerical experiments were performed to validate the theoretical findings and quantify the effectiveness of the proposed interventions under realistic environmental conditions driven by dust activity. The model highlights the importance of integrated medical and environmental control policies in mitigating asthma outbreaks, particularly in regions frequently exposed to dust storms. Full article

Cryogenic upgrading represents a promising route for the production of high-purity biomethane, aligning with current decarbonization goals and the increasing demand for renewable gases. This review provides a critical assessment of cryogenic technologies applied to biogas purification, focusing on process fundamentals, technological configurations, energy and separation performance, and their industrial integration potential. The analysis covers standalone cryogenic systems as well as hybrid configurations combining cryogenic separation with membrane or chemical pretreatment to enhance efficiency and reduce operating costs. A comparative evaluation of key performance indicators—including methane recovery, specific energy demand, product purity, and technology readiness level—is presented, along with a discussion of representative industrial applications. In addition, recent techno-economic studies are examined to contextualize cryogenic upgrading within the broader landscape of CO₂ separation technologies. Environmental trade-offs, investment thresholds, and sensitivity to gas prices and CO₂ taxation are also discussed. The review identifies existing technical and economic barriers, outlines research and innovation priorities, and highlights the relevance of process integration with natural gas networks. Overall, cryogenic upgrading is confirmed as a technically viable and environmentally competitive solution for biomethane production, particularly in contexts requiring liquefied biomethane or CO₂ recovery. Strategic deployment and regulatory support will be key to accelerating its industrial adoption. The objectives of this review have been met by consolidating the current state of knowledge and identifying specific gaps that warrant further investigation. Future work is expected to address these gaps through targeted experimental studies and technology demonstrations. Full article

Urban tree planting on single-family-home lots represents a critical yet underexplored component of municipal greening strategies. This study examines residents’ perceptions of tree planting in Westpointe, a diverse neighborhood in Salt Lake City, Utah, as part of the city’s Reimagine Nature Public Lands Master Plan development effort. Through a mixed-methods approach combining qualitative interviews (n = 24) and a tree signup initiative extended to 86 residents, with 51 participating, this research explores the complex interplay of demographic, economic, social, and infrastructure factors influencing residents’ willingness to plant trees on single-family-home lots. The findings reveal significant variations based on gender, with women expressing more positive environmental and aesthetic motivations, while men focused on practical concerns including maintenance and property damage. Age emerged as another critical factor, with older adults (65+) expressing concerns about long-term maintenance capabilities, while younger families (25–44) demonstrated future-oriented thinking about shade and property values. Property characteristics, particularly yard size, significantly influenced receptiveness, with owners of larger yards (>5000 sq ft) showing greater willingness compared to those with smaller properties, who cited space constraints. Additional barriers, i.e., maintenance, financial, and knowledge barriers, included irrigation costs, lack of horticultural knowledge, pest concerns, and proximity to underground utilities. Geographic analysis revealed that Spanish-speaking social networks were particularly effective in promoting tree planting. The study contributes to urban forestry literature by providing nuanced insights into single-family homeowners’ tree-planting decisions and offers targeted recommendations for municipal programs. These include gender-specific outreach strategies, age-appropriate support services, sliding-scale subsidy programs based on property size, and comprehensive education initiatives. The findings inform evidence-based approaches to increase urban canopy coverage through private property plantings, ultimately supporting climate resilience and environmental justice goals in diverse urban neighborhoods. Full article

This study examines the impact of renewable energy, economic growth, and trade openness on CO₂ emissions in the EU-15 countries over the period 1980–2022, employing the ARDL modeling framework. In addition, a panel PMG-ARDL model is employed as a robustness check. The analysis identifies cointegration among the variables in 11 out of the 15 countries studied. Economic growth is found to increase CO₂ emissions, highlighting the ongoing challenge of aligning economic expansion with environmental objectives. The estimated coefficients for economic growth range from 0.43 to 5.70, depending on the country. Renewable energy significantly reduces emissions, highlighting its critical role in achieving sustainability (the corresponding coefficient moves in the range −0.13 to −0.96). Trade openness generally shows a neutral impact on emissions across most cases. Overall, renewable energy contributes to reducing CO₂ emissions, whereas the effects of economic growth and trade openness remain mixed and country-specific. These findings highlight the need to promote cleaner technologies, enhance energy efficiency, and ensure broader access to environmentally friendly energy sources. Full article

Hippophae rhamnoides ssp. sinensis Rousi a species of significant ecological and economic value that is native to the Qinghai–Tibet Plateau and arid/semi-arid regions. Investigating the mitochondrial genome can elucidate stress adaptation mechanisms, population genetic structure, and hybrid evolutionary history, offering molecular insights for ecological restoration and species conservation. However, the genetic information and evolutionary mechanisms of its mitochondrial genome remain poorly understood. This study aimed to assemble the complete mitochondrial genome of H. rhamnoides L. ssp. sinensis using Illumina sequencing, uncovering its structural features, evolutionary pressures, and environmental adaptability and addressing the research gap regarding mitochondrial genomes within the Hippophae genus. The study assembled a 454,444 bp circular mitochondrial genome of H. rhamnoides ssp. sinensis, with a GC content of 44.86%. A total of 73 genes and 3 pseudogenes were annotated, with the notable absence of the rps2 gene, which is present in related species. The genome exhibits significant codon usage bias, particularly with high-frequency use of the alanine codon GCU and the isoleucine codon AUU. Additionally, 449 repetitive sequences, potentially driving genome recombination, were identified. Our evolutionary pressure analysis revealed that most genes are under purifying selection, while genes such as atp4 and nad4 exhibit positive selection. A nucleotide diversity analysis revealed that the sdh4 gene exhibits the highest variation, whereas rrn5 is the most conserved. Meanwhile, phylogenetic analysis showed that H. rhamnoides ssp. sinensis from China is most closely related to Hippophae tibetana, with extensive homologous sequences (49.72% of the chloroplast genome) being identified between the chloroplast and mitochondrial genomes, indicating active inter-organellar gene transfer. Furthermore, 539 RNA editing sites, primarily involving hydrophilic-to-hydrophobic amino acid conversions, were predicted, potentially regulating mitochondrial protein function. Our findings establish a foundation for genetic improvement and research on adaptive evolutionary mechanisms in the Hippophae genus, offering a novel case study for plant mitochondrial genome evolution theory. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

To address the issues of false detections and missed detections in object detection for intelligent driving scenarios, this study focuses on optimizing the YOLOv10 algorithm to reduce model complexity while enhancing detection accuracy. The method involves three key improvements. First, it involves the design of multi-scale flexible convolution (MSFC), which can capture multi-scale information simultaneously, thereby reducing network stacking and computational load. Second, it reconstructs the neck network structure by incorporating Shallow Auxiliary Fusion (SAF) and Advanced Auxiliary Fusion (AAF), enabling better capture of multi-scale features of objects. Third, it improves the detection head through the combination of multi-scale convolution and channel adaptive attention mechanism, enhancing the diversity and accuracy of feature extraction. Results show that the improved YOLOv10 model has a size of 13.4 MB, meaning a reduction of 11.8%, and that the detection accuracy mAP@0.5 reaches 93.0%, outperforming mainstream models in comprehensive performance. This work provides a detection framework for intelligent driving scenarios, balancing accuracy and model size. Full article

Climate change is a global crisis that disproportionately affects vulnerable agrarian communities, exacerbating food insecurity and health risks. This qualitative study explored the relationship between climate change and health in the following two rural sub-counties of Kilifi County, Kenya: Ganze and Magarini. In fall 2023, we conducted 16 focus group discussions with adolescent girls (14–17), young adults (18–30), and older adults (31+). Thematic analysis revealed that climate change adversely affects health through key social determinants, including economic instability, environmental degradation, limited healthcare access, food insecurity, and disrupted education. Participants reported increased food scarcity, disease outbreaks, and reduced access to medical care due to droughts and floods. Economic hardship contributed to harmful survival strategies, including transactional sex and school dropout among adolescent girls. Mental health concerns, such as stress, substance use, and suicidal ideation, were prevalent. These findings highlight the wide-ranging health impacts of climate change in agrarian settings and the urgent need for comprehensive, community-informed interventions. Priorities should include improving nutrition, reproductive and mental health services, infectious disease prevention, and healthcare access. Full article

Green space (GS) equity is a crucial component of environmental justice. From the perspective of environmental justice, this study focuses on the equity of GS across sub-districts with varying GDP levels in Guangzhou, quantitatively assessing and comparing GS equity in areas with different development statuses. However, existing research still lacks sufficient exploration of the relationship between micro-scale socioeconomic indicators and GS equity. To address this gap, this study investigates the inequality of GS availability across neighborhoods during the rapid urbanization process in Guangzhou’s central urban area from 2000 to 2020. Key indicators for measuring GS availability—including GS area, per capita GS area, and NDVI—were selected and calculated for each sub-district in 2000 and 2020. This approach reveals spatial disparities in GS distribution between the two years. Subsequently, the Theil index and Gini index were employed to assess the degree of inequality in GS. Using GS area data and NDVI data, this study analyzes per capita GS area and NDVI values across sub-districts with different development levels in Guangzhou’s central urban area. Statistical methods such as the Theil index were then applied to evaluate the equity of these indicators. The findings indicate that between 2000 and 2020, Guangzhou experienced significant urbanization, a notable decline in total GS area, a marked improvement in NDVI values, and a substantial improvement in GS equity. There is a conflict between the supply of green resources and the demand for high-density economic/population centers. This research provides scientific evidence for urban planners and policymakers to promote the equitable distribution and sustainable development of GS. Full article