Frontiers in Environmental Science and Sustainability
Frontiers in Environmental Science and Sustainability is a peer-reviewed journal advancing research on global environmental challenges. It covers climate change, resource management, renewable energy, and sustainable practices. Fostering collaboration among researchers and policymakers, it promotes innovative solutions for environmental protection, ecosystem restoration, and a sustainable future.
Traffic Flow Forecasting with Dynamic Graph Neural Networks and Incident-Aware Attention
Traffic flow forecasting constitutes a pivotal component of Intelligent Transportation Systems (ITS), enabling proactive congestion management and optimized urban planning. Traditional approaches typically model traffic networks as static graphs, relying on fixed adjacency matrices determined by Euclidean distances or physical connectivity. However, such static representations fail to capture the dynamic spatial-temporal dependencies that evolve rapidly, particularly under non-recurrent events such as traffic accidents, road closures, or adverse weather conditions. This paper introduces a novel framework, the Dynamic Graph Neural Network with Incident-Aware Attention (DGNN-IA), designed to address these limitations. The proposed model integrates a dynamic graph learning module that infers time-varying network topologies from data, coupled with a specialized attention mechanism that explicitly encodes incident information to modulate internode influence weights. By fusing traffic state tensors with incident embedding vectors, the model dynamically adjusts the information propagation path, allowing for robust prediction even in the presence of abrupt network perturbations. Extensive experiments on real-world traffic datasets augmented with incident logs demonstrate that DGNN-IA achieves state-of-the-art performance, significantly outperforming baseline models in both short-term and long-term forecasting horizons.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Vision-Based Crack Segmentation with Geometry-Constrained Transformers for Field Concrete Inspection
The structural integrity of concrete infrastructure is paramount to public safety and economic stability. Automated pavement and surface inspection via computer vision has emerged as a critical alternative to labor-intensive manual surveys. However, traditional Convolutional Neural Networks (CNNs) often struggle to preserve the high-frequency topological details of thin cracks against complex, texture-heavy heterogeneous backgrounds. While Vision Transformers (ViTs) offer superior global context modeling, they frequently lack the inductive biases required to capture the fine-grained local geometry inherent to fracture mechanics. This paper proposes a novel architecture: the Geometry-Constrained Transformer (GCT). By integrating a dedicated geometric edge-alignment module within a hierarchical Transformer encoder-decoder structure, we explicitly enforce curvilinear continuity and boundary sharpness during the segmentation process. We introduce a dual-stream attention mechanism that leverages low-level morphological cues to guide high-level semantic tokens, ensuring that the global attention map remains anchored to physical structural defects. Extensive experiments on three public benchmark datasets demonstrate that the proposed GCT outperforms state-of-the-art CNN-based and Transformer-based methods, particularly in scenarios characterized by varying illumination, shadowing, and biological staining.
Synergistic Computational and Machine Learning Models for Optimized Solar Thermal Energy Harvesting
Solar thermal energy harvesting represents a critical frontier in renewable energy technologies, offering substantial potential for sustainable power generation across both direct and indirect energy conversion pathways. This research presents a comprehensive investigation of synergistic computational and machine learning models specifically designed to optimize solar thermal energy harvesting systems through systematic parameter optimization and intelligent control strategies. The study integrates advanced computational fluid dynamics methodologies with multi-parameter optimization algorithms and thermoelectric energy harvesting circuits to create hybrid frameworks that significantly enhance energy conversion efficiency. Through systematic analysis of thermal gradient modeling, heat transfer optimization, and predictive energy forecasting using Response Surface Methodology and machine learning algorithms, this research demonstrates that combined computational and optimization approaches can achieve substantial improvements in energy harvesting efficiency compared to conventional fixed-parameter methods. The proposed hybrid framework incorporates real-time environmental parameter monitoring and multi-objective optimization to dynamically adjust system parameters including collector orientation, thermal storage management, and thermoelectric generator configurations for optimal performance. Experimental validation conducted using thermoelectric generator systems with integrated power management circuits confirms the robustness and practical applicability of the developed optimization models. The findings indicate that systematic parameter optimization using computational methods can achieve power output improvements with correlation coefficients exceeding 0.94 across diverse environmental conditions. Furthermore, the integration of advanced power management circuits within the computational framework demonstrates enhanced energy harvesting capacity and extended operational periods. This research contributes significantly to the advancement of intelligent solar thermal systems by providing a comprehensive methodology for real-time optimization and systematic parameter control, ultimately facilitating broader adoption of renewable energy technologies in industrial and residential applications.
Sustainable Packaging Innovation via GAN-Enabled Inverse Design
The urgent need for sustainable packaging solutions has intensified research efforts toward computational design methodologies that can accelerate the development of environmentally responsible materials and structures. This paper presents a novel framework for sustainable packaging innovation through the integration of Generative Adversarial Networks (GANs) with inverse design principles, enabling the automated generation of packaging solutions that meet stringent environmental performance criteria while maintaining functional requirements. Our approach leverages machine learning methodologies to explore vast design spaces efficiently, moving beyond traditional structure-property optimization approaches to implement intelligent search strategies that can identify optimal material compositions and structural configurations. Through the implementation of a comprehensive machine learning-enabled inverse design system, we demonstrate the capability to generate packaging designs that achieve up to 35% material reduction compared to conventional approaches while maintaining equivalent protective performance. The methodology incorporates sustainability metrics directly into the optimization process, ensuring that environmental considerations guide the search for promising design regions rather than being applied as post-hoc constraints. Validation studies using Support Vector Regression (SVR) and Gaussian Process Regression (GPR) demonstrate that machine learning approaches exhibit superior performance consistency across different training data sizes, with GPR showing particularly robust performance for sustainable packaging design applications. The findings establish machine learning-enabled inverse design as a transformative approach for sustainable packaging innovation, offering unprecedented capabilities for systematic exploration of environmentally optimized design solutions that can significantly accelerate the transition toward circular packaging economies.
Physics–Data Synergy: A Hybrid CFD–Machine Learning Framework for Smart Cold Storage Systems
This research presents a groundbreaking physics-data synergy framework that integrates thermal stratification principles with advanced machine learning algorithms to develop intelligent cold storage systems. The study addresses fundamental challenges in thermal management by leveraging the physics of temperature stratification phenomena combined with data-driven optimization methodologies. The physics-based foundation employs validated CFD models demonstrating temperature stratification behavior with thermocline formation occurring across temperature ranges from 275.6K to 363.1K (2.5°C to 90°C). Comprehensive transient analysis reveals distinct thermal evolution patterns through five-stage temperature distribution development, showing progressive stratification establishment over operational time periods. Advanced parameter analysis demonstrates critical relationships between mixing coefficients and inlet velocities, with values ranging from 50,000 to 2,500,000 across different temperature conditions (20°C, 50°C, and 90°C). The data-driven component achieves exceptional predictive accuracy with R² values of 0.96 for temperature stratification prediction and 0.93 for thermal mixing coefficient forecasting. The synergistic framework delivers remarkable performance improvements including 29% reduction in thermal mixing, 38% enhancement in stratification efficiency, and 34% improvement in energy utilization effectiveness. Smart control algorithms developed through physics-data integration enable real-time optimization of thermal gradients, inlet velocity management, and stratification maintenance across multiple operational zones. The framework successfully demonstrates scalability from laboratory-scale thermal storage systems to industrial cold storage applications while maintaining high performance standards essential for commercial deployment.
Machine Learning Approaches to Minimize Carbon Emissions through Optimized Road Traffic Flow and Routing
Transportation systems contribute approximately 28% of global carbon emissions, with urban road traffic representing the largest single source of vehicular pollution. This research presents a comprehensive Machine Learning (ML) framework designed to minimize carbon emissions through intelligent optimization of traffic flow patterns and dynamic routing algorithms. The proposed system integrates real-time traffic monitoring, predictive modeling, and adaptive control mechanisms to reduce vehicle emissions while maintaining transportation efficiency. Our approach employs deep neural networks and reinforcement learning techniques to analyze traffic patterns, predict congestion hotspots, and optimize signal timing and route recommendations across urban transportation networks. Through extensive empirical evaluation conducted across three metropolitan areas encompassing 2,847 intersections and 156,000 vehicles over a 24-month period, our findings demonstrate substantial reductions in carbon emissions averaging 19.4% compared to conventional traffic management systems. The framework achieved remarkable improvements in traffic flow efficiency with average reductions of 26.7% in travel time and 31.2% in fuel consumption per vehicle-kilometer traveled. Additionally, the system demonstrated exceptional performance in congestion prediction with 92.8% accuracy for 30-minute forecasts and dynamic adaptation capabilities with average response times of 4.2 minutes to changing traffic conditions. These results establish ML-based traffic optimization as a highly effective strategy for sustainable urban transportation, contributing significantly to environmental protection goals while enhancing overall transportation system performance and user experience with satisfaction scores of 4.3 out of 5.0.
Copyright (c) 2025 Lei Qiu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Reinforcement Learning for Real-Time Energy Management in Electric Vehicles
The increasing penetration of electric vehicles (EVs) in modern transportation requires advanced energy management strategies to optimize power distribution, enhance battery longevity, and improve driving range. Traditional rule-based or model predictive control systems often struggle with dynamic and uncertain driving conditions, limiting their adaptability. This paper explores the application of reinforcement learning (RL), particularly deep reinforcement learning (DRL), as a data-driven and adaptive solution for real-time energy management in EVs. By formulating energy control as a sequential decision-making problem, RL agents learn optimal policies through interaction with the EV environment, adjusting strategies based on speed, terrain, state-of-charge (SOC), and driver behavior. We present a hybrid RL framework that integrates battery aging models, regenerative braking, and thermal constraints. Simulation results show that our approach significantly outperforms traditional baselines in terms of energy efficiency, charge preservation, and system responsiveness. The paper also discusses challenges in real-world deployment, including safety, explainability, and transferability of learned policies.
Nano-Enhanced Biodegradable Packaging Materials for Extended Food Shelf-Life
Conventional plastic packaging, while effective in food preservation, poses significant environmental threats due to its non-biodegradable nature. In response, biodegradable packaging materials enhanced with nanotechnology have emerged as promising alternatives, offering sustainable and functional solutions to extend food shelf-life. This paper reviews recent advancements in nano-enhanced biodegradable packaging, focusing on materials such as starch, cellulose, chitosan, and polylactic acid (PLA) integrated with nanoparticles like nanoclay, silver, zinc oxide, and titanium dioxide. The inclusion of these nanoparticles significantly improves mechanical strength, barrier properties, and antimicrobial activity, thus effectively delaying spoilage and enhancing food safety. Experimental results from recent studies demonstrate improved shelf-life and reduced microbial contamination compared to conventional biodegradable and non-biodegradable packaging. Despite their potential, concerns regarding nanoparticle migration, consumer acceptance, regulatory compliance, and cost-effectiveness must be addressed to facilitate widespread adoption. Overall, nano-enhanced biodegradable packaging represents a critical advancement toward sustainable food preservation technologies.
Role and Application Strategies of Biocontrol Bacteria in Sustainable Agricultural Development
In the contemporary context of global agriculture, confronted with numerous challenges including ecological degradation, chemical pesticide misuse and food safety concerns, biocontrol bacteria are garnering increased attention as a green and sustainable approach to disease prevention and control. This paper systematically explores the mechanisms by which biocontrol bacteria promote healthy crop growth, inhibit pathogenic microorganisms, improve soil ecosystems and reduce chemical inputs, from the perspective of sustainable agricultural development. The paper synthesises current research and industrial development at home and abroad, highlighting the application of biocontrol bacteria, including Bacillus spp., Pseudomonas spp. and Streptomyces spp., in disease control of rice, wheat, tomato and other crops. The paper demonstrates the potential of biocontrol bacteria in driving the green transformation of agriculture. A thorough analysis was conducted on the key issues, including the unstable field application effect, the weak adaptability of strains, the outdated formulation process, and the inadequate market and policy support. Based on these findings, targeted application strategies were proposed from the dimensions of strain selection and breeding, formulation optimization, technology integration, and policy guidance. Finally, the trend of the deep integration of biocontrol bacteria with the frontiers of histotechnology, smart agriculture, and agricultural carbon neutrality is anticipated. The paper's objective is twofold: firstly, to provide a theoretical foundation and technical reference framework for in-depth research and practical application of biocontrol bacteria in the sustainable development of agriculture; and secondly, to offer a comprehensive review of the current state of research and practical application of biocontrol bacteria in the sustainable development of agriculture.
Copyright (c) 2025 Yi Hu, Ye Chen, Liuyao Wang, Zhenxing Xie, Qiwen Tao, Kaijun Song (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Ecological Restoration: Revitalizing Degraded Landscapes for Long-Term Sustainability
Ecological restoration plays a critical role in revitalizing degraded landscapes and promoting long-term sustainability. This paper examines the principles, practices, and outcomes of ecological restoration projects aimed at rehabilitating ecosystems that have been degraded by human activity. By analyzing case studies from various geographical contexts, the paper highlights the successes, challenges, and methodologies involved in restoring ecological balance. The study also explores the role of community involvement, policy frameworks, and innovative techniques in enhancing restoration efforts. Ultimately, it emphasizes the importance of integrating ecological restoration into broader environmental management strategies to achieve sustainable and resilient landscapes.
Copyright (c) 2024 Frontiers in Environmental Science and Sustainability
The Role of Education in Promoting Environmental Awareness and Sustainable Practices
Education plays a critical role in shaping individuals’ understanding of environmental issues and fostering sustainable practices. This article explores how educational institutions, curricula, and pedagogical approaches contribute to raising environmental awareness and promoting sustainability. Through a review of current educational strategies and their effectiveness, the study highlights the importance of integrating environmental education across all levels of schooling. It also examines case studies and best practices from various educational settings worldwide. The findings underscore the need for continued investment in environmental education to address global challenges such as climate change, resource depletion, and biodiversity loss.
Sustainable Fisheries: Balancing Marine Biodiversity and Economic Growth
The sustainable management of fisheries is crucial for maintaining marine biodiversity while supporting economic growth. This article explores the intricate balance between conserving marine ecosystems and fostering economic development through sustainable fisheries practices. We examine key challenges and strategies in implementing sustainable fishing policies, assess the impact of overfishing and habitat degradation on marine biodiversity, and evaluate the economic implications for fishing communities and global markets. Case studies from various regions highlight successful models of sustainability. The article concludes with recommendations for policy-makers, industry stakeholders, and conservationists to ensure a harmonious balance between environmental preservation and economic benefits.
Carbon Sequestration: Natural and Technological Approaches to Combat Climate Change
Carbon sequestration is a critical strategy for mitigating climate change by capturing and storing atmospheric carbon dioxide (CO₂). This paper explores both natural and technological approaches to carbon sequestration, evaluating their effectiveness, challenges, and future prospects. Natural methods include afforestation, reforestation, and soil carbon storage, which leverage ecosystems' inherent capabilities. Technological approaches, such as carbon capture and storage (CCS) and direct air capture (DAC), offer more controlled means of reducing atmospheric CO₂. This study provides a comparative analysis of these methods, highlighting their potential roles in achieving global climate targets and the integration of strategies for optimal impact.
Waste to Energy: Exploring Technologies for Sustainable Waste Management
The rapid urbanization and population growth have led to an exponential increase in waste generation, posing significant environmental and health challenges. This paper explores the potential of waste-to-energy (WTE) technologies as a sustainable solution for waste management. By examining various WTE technologies, including incineration, anaerobic digestion, gasification, and pyrolysis, this study highlights their effectiveness in reducing waste volumes and generating energy. The paper also discusses the environmental impacts, economic viability, and technological advancements associated with these methods. The findings suggest that while WTE technologies offer promising solutions for sustainable waste management, careful consideration of their implementation and regulation is crucial for maximizing benefits and minimizing negative impacts.
The Role of Ecosystem Services in Sustainable Development
Ecosystem services are vital components in the pursuit of sustainable development, providing essential benefits that support human well-being and environmental health. This paper explores the role of ecosystem services in sustainable development, focusing on their contributions to economic stability, social equity, and environmental conservation. By examining various types of ecosystem services—including provisioning, regulating, supporting, and cultural services—the paper highlights their importance in addressing global challenges such as climate change, biodiversity loss, and resource depletion. Through a review of case studies and current research, this paper demonstrates how integrating ecosystem services into policy and practice can enhance sustainability outcomes and promote resilient communities.
Green Infrastructure: Designing Cities for Sustainability and Resilience
Green infrastructure integrates natural systems into urban planning to enhance sustainability and resilience in cities. This approach leverages natural processes to manage environmental challenges such as stormwater, air quality, and heat islands while promoting biodiversity and human well-being. This paper explores the principles and practices of green infrastructure, highlighting successful case studies and emerging trends. By examining various design strategies and their impacts on urban environments, this study underscores the role of green infrastructure in achieving sustainable urban development and mitigating the effects of climate change.
Environmental Policies and Their Impact on Global Sustainability
Environmental policies are critical for shaping sustainable development and addressing global environmental challenges. This paper examines various environmental policies implemented worldwide and assesses their impact on global sustainability initiatives. By analyzing case studies from different countries and international agreements, the paper highlights how effective policies contribute to environmental protection, resource management, and climate change mitigation. Additionally, it explores the role of policy integration and collaboration among nations in achieving sustainable development goals. The findings emphasize the need for robust, adaptive, and collaborative policy frameworks to foster global sustainability.
Sustainable Agriculture: Enhancing Productivity While Preserving Ecosystems
Sustainable agriculture represents a critical approach to enhancing agricultural productivity while preserving ecosystems. This paper explores various methods and practices in sustainable agriculture that aim to balance the need for increased food production with environmental conservation. It discusses innovative techniques such as organic farming, agroecology, and precision agriculture, highlighting their impact on soil health, water conservation, and biodiversity. The study also examines policy frameworks and community engagement strategies that support sustainable agricultural practices. The findings suggest that integrating these approaches can lead to more resilient agricultural systems and contribute to long-term environmental sustainability.
Water Resource Management: Strategies for Sustainable Use and Conservation
Effective water resource management is crucial for ensuring sustainable development and environmental conservation. This article explores various strategies for sustainable water use and conservation, emphasizing integrated water resource management (IWRM) approaches, policy frameworks, technological innovations, and community engagement. It highlights best practices from around the world, discusses the challenges faced in different regions, and offers recommendations for improving water resource management. By examining case studies and recent advancements, the article aims to provide actionable insights for policymakers, researchers, and practitioners to enhance water sustainability.
Integrated Water Resource Management (IWRM)
Renewable Energy Technologies: Transitioning to a Low-Carbon Future
The transition to a low-carbon future is crucial for mitigating climate change and ensuring sustainable development. This paper explores various renewable energy technologies and their role in reducing greenhouse gas emissions. We examine the technological advancements, economic implications, and policy frameworks that support the adoption of renewable energy sources. By analyzing case studies and current trends, we identify the challenges and opportunities associated with renewable energy technologies. The study aims to provide a comprehensive overview of how these technologies can facilitate the shift towards a low-carbon economy.
Biodiversity Conservation in the Face of Urbanization: Challenges and Solutions
Urbanization presents significant challenges to biodiversity conservation, as expanding cities encroach upon natural habitats and ecosystems. This paper explores the impact of urbanization on biodiversity and offers insights into strategies for mitigating its adverse effects. Through a review of case studies and literature, we examine how urban sprawl affects species distribution, habitat fragmentation, and ecosystem services. We also discuss innovative solutions, including green infrastructure, urban planning policies, and community engagement, to enhance biodiversity conservation in urban settings. The paper highlights successful examples from around the world and provides recommendations for policymakers, urban planners, and conservationists to address the growing conflict between urban development and biodiversity preservation.
Circular Economy Models: Redefining Waste in Sustainable Development
The circular economy (CE) represents a transformative shift in waste management and sustainable development by emphasizing the continuous use of resources and minimizing waste. This paper explores various circular economy models and their effectiveness in redefining waste management practices within the context of sustainable development. We examine case studies from different industries to illustrate how CE principles—such as resource efficiency, closed-loop systems, and waste minimization—are being implemented globally. The analysis highlights the benefits of CE models, including reduced environmental impact, enhanced resource efficiency, and economic growth. Challenges and future directions for integrating circular economy practices into mainstream policy and business strategies are also discussed.
Climate Change Mitigation: Innovative Strategies for Reducing Global Carbon Footprints
Climate change is one of the most pressing challenges facing humanity, driven primarily by increased greenhouse gas emissions. Effective mitigation strategies are crucial for reducing global carbon footprints and averting catastrophic impacts on the environment and human societies. This paper explores innovative approaches to climate change mitigation, focusing on emerging technologies, policy measures, and community-based initiatives. We examine advancements in renewable energy, carbon capture and storage, and sustainable agricultural practices. Additionally, the role of international cooperation and economic incentives in driving climate action is analyzed. By highlighting successful case studies and proposing new strategies, this study aims to contribute to a more sustainable future.
Sustainable Resource Management: Balancing Human Needs and Environmental Protection
Sustainable Resource Management is a critical field that seeks to balance human needs with environmental protection. This paper explores the principles, strategies, and challenges associated with managing natural resources sustainably. It examines the importance of integrating ecological, economic, and social considerations to achieve long-term sustainability. By reviewing current practices and case studies, the paper highlights successful models and identifies gaps in current approaches. The discussion extends to policy recommendations and future directions for enhancing resource management strategies to better align human activities with environmental conservation.