Journal of Environmental & Earth Sciences

Bridging the Gap: How Water Conservancy Projects Shape a Greener Future

Jing Zhang, Qinghe Zeng — Wed, 19 Aug 2026 00:00:00 +0800

Projects on water conservation are gaining prominence as the primary intervention of climate adaptation, low-carbon development, and ecosystem restoration. However, their sustainability performance is exceptionally situational, guided by the basin hydrology, sediment interactions, operating regulations, and governmental capacity. This review summarizes the interdisciplinary evidence of how water conservation projects can be used to achieve a cleaner future by reducing the negative externalities, such as dams and reservoirs, irrigation and conveyance systems, flood control structures, managed aquifer recharge, and hybrid gray-green strategies. We establish an integrative model between drivers, interventions, system reactions, and results along four dimensions: environmental integrity, social equity, economic performance, and resilience to climate non-stationarity. Synthesis reveals that, co-benefits of scoring well, a fully functioning project operates in an adaptable way, evaluated on a basin level, and in conjunction with demand management and land-use planning. In contrast, net harms are mainly probable in the context of the flow regulation, where biodiversity is eroded due to flow fragmentation, downstream exposure to sediment trapping, water quality reduction due to residence-time effects, and salinization induced by irrigation, and the unequal distribution of benefits because of weak procedural protection. To address these gaps, the priority should be placed on retrofit and re-operation of the old assets, environmental flows, and sediment-conscious operation, scaling nature-based and hybrid solutions, and using digital monitoring and forecast-informed operations with transparent and responsible rules of decision-making. Lastly, the review identifies gaps in research, including standardized indicators, long-term monitoring, and causal assessment, and governance changes, and financing tools that incentivize proven sustainability performance as opposed to capacity building.

From Biofilters to Biocatalysts: Advances in Biological Treatment of VOCs in Atmospheric Environments

Shujing Zhang — Mon, 03 Aug 2026 00:00:00 +0800

Volatile organic compounds (VOCs) have become a major concern that requires a viable and sustainable remedy as a substitute for conventional air pollution control technologies. This review follows the trend of biological VOC treatment, from the development of conventional biofilter systems to sophisticated biocatalytic systems. It also looks into the principles of VOC biodegradation and the importance of microbial communities, as well as the technology development of reactors such as biofilters, biotrickling filters, and hybrid biological systems. The efficiency and stability of biological treatment systems have been improved recently by material science, microbial resource engineering, and molecular biology, resolving the mass transfer limitation, microbial instability, and complexity of treating complex VOC mixtures. Nevertheless, technical bottlenecks, including fouling, clogging, and the formation of by-products, still hinder large-scale industrial use. The environmental trade-offs related to the treatment of biological VOCs mentioned in this article are the formation of toxic intermediates and the emission of greenhouse gases. However, with such challenges, process intensification, intelligent monitoring, and hybrid systems have been proposed to provide some promising directions toward limiting these constraints. The future of biological VOC treatment lies in its potential for combination with engineering innovations and biological processes, which will guarantee its scalability and long-term sustainability in atmospheric emissions management.

Coupling Transit, Land Use, and Ecology: A Critical Review of Urban Rail Transit's Environmental Co-Benefits and Trade-Offs

Cui Hu — Tue, 18 Aug 2026 00:00:00 +0800

Urban rail transit systems have now become a part of sustainable city development, bringing about environmental co-benefits such as lower greenhouse gas emissions, better air quality, and enhanced transit-oriented development. These advantages do not, however, come without trade-offs in the form of ecological costs associated with construction and habitat fragmentation, as well as the indirect effects of land-use changes. The article critically reviews the relationships among urban rail transit, land use, and ecology, with a focus on the intricate relationship between co-benefits and trade-offs. It provides a review of the main environmental impacts, including the decrease in car dependency and improvements in population health, as well as possible ecological impacts, including the carbon footprints of infrastructure and the disturbance of local ecosystems. The review highlights the significance of implementing an integrated planning approach with a focus on the ecological transit-oriented development (TOD) zoning concept, green rail design, and cross-sectoral policies that ensure the alignment of transportation, land-use, and ecological objectives. It also addresses spatial heterogeneity, determining how impacts can differ across urban densities, core-periphery processes, and regional contexts. The article ends by providing recommendations on how to advance social equity via inclusive governance and long-term environmental impact monitoring, thus indicating future research prospects in sustainable urban rail transport. Urban rails can help build stronger and more inclusive cities by balancing environmental sustainability and social inclusion.

Study on the Impact of Coal Mine Floor Grouting Renovation on the Hydrogeological and Geochemical Characteristics of the Mining Area Aquifer

Zhaopeng Yang, Like Wei — Fri, 14 Aug 2026 00:00:00 +0800

Floor grouting renovation is widely applied in coal mining to control water inrush and enhance the stability of the floor strata. However, its impacts extend beyond engineering performance, significantly influencing the hydrogeological and geochemical characteristics of aquifer systems. This review provides a comprehensive synthesis of current knowledge on the mechanisms, processes, and environmental implications of floor grouting in mining regions. It examines how grouting modifies aquifer permeability, porosity, groundwater flow patterns, and pressure regimes, as well as its effects on groundwater chemistry, including pH, major ions, and trace elements. Particular emphasis is placed on the coupled hydrogeological–geochemical processes that govern system evolution, highlighting the feedback mechanisms between physical and chemical changes. The review also addresses the long-term behavior of grouted aquifers, including the stability of sealing effects and potential delayed impacts on groundwater quality. Environmental implications, such as effects on regional water resources and ecosystems, are critically evaluated. Despite significant progress, existing studies often lack cross-disciplinary integration and long-term perspectives. This review identifies key research gaps and proposes future directions, including the development of eco-friendly materials, advanced monitoring techniques, and coupled modeling approaches. The findings contribute to a more holistic understanding of grouting-induced changes and provide guidance for sustainable and safe coal mining practices.

Water Quality Monitoring in the Era of Big Data: Sensors, AI, and Integrated Frameworks

Peng Du — Fri, 14 Aug 2026 00:00:00 +0800

Water quality monitoring is rapidly evolving from traditional periodic sampling towards continuous, data-intensive, and intelligent systems. This review examines the current trends in the field of water quality monitoring within the context of big data, and specifically focuses on the intersection of sensor technologies, artificial intelligence (AI), and integrated monitoring systems. Originally, the article examines the current sensing methods, such as electrochemical sensors, optical and spectroscopic devices, biosensors, IoT-enabled networks, remote sensing, and mobile platforms, with an emphasis on their increased monitoring performance and their persistent shortcomings in the areas of fouling, calibration, selectivity, and field performance. Second, it addresses the distinguishing features of water quality big data and highlights the issues of heterogeneity, velocity, uncertainty, preprocessing, data fusion, interoperability, and governance. Third, the paper assesses the use of AI, with special focus on machine learning, deep learning, and water quality assessment, prediction, anomaly detection, and decision support, and critically discusses concerns related to transferability, interpretability, reproducibility, and quantifying uncertainty. Lastly, the article brings together the synthesis of how the intertwining of frameworks of sensing, data infrastructure, analytics, and management platforms can help to transform fragmented observations of water quality into proactive and adaptive control. This review uniquely adopts a systems-level perspective, treating sensors, big-data management, and AI as integrated layers of next-generation environmental intelligence.

From Bench to Field: Evolving Analytical Techniques in Environmental Testing

Fri, 14 Aug 2026 00:00:00 +0800

The field of environmental testing is rapidly changing from laboratory-based measurements to field-deployable, real-time, and data-integrated monitoring systems. Conventional methods such as chromatography, mass spectrometry, atomic spectroscopy, and molecular spectroscopy remain essential due to their high sensitivity, selectivity, reproducibility, and regulatory confidence. They are, however, limited in their usefulness due to the need for lab facilities, complicated preparation, and reporting lag times under changing environmental conditions. This review’s distinctive contribution is the development of a hybrid bench-to-field monitoring framework that conceptualizes environmental testing as an integrated system rather than a collection of separate analytical techniques. In this framework, laboratory-based methods provide reference accuracy, confirmatory power, and regulatory defensibility, while field-deployable sensors, portable instruments, remote platforms, and IoT-enabled networks provide rapid screening, spatial coverage, real-time warning, and adaptive decision support. By linking analytical performance, field applicability, data analytics, validation, uncertainty assessment, and regulatory translation, the review provides a conceptual pathway for transforming emerging technologies into reliable environmental monitoring systems. There is a key lesson here: future environmental testing will not be based on the elimination of lab testing practices, but on the ability to marry lab accuracy and field responsiveness. The use of hybrid monitoring frameworks and chemometrics, artificial intelligence tools, validation protocols, and uncertainty assessment will be key to producing reliable, actionable, and scientifically defensible environmental data.

Design and Implementation of a Lightweight AI-Driven Real-Time Preprocessing System for Remote Sensing Imagery

Hongyu Yu, Fangyuan Chen, Lianxiu Hao — Tue, 11 Aug 2026 00:00:00 +0800

Thanks to the intensive development of remote sensing systems, Earth observation has never been accessed at such a scale, and thus, the urgency has been revealed to develop an effective and timely preprocessing response. Conventional preprocessing techniques, mostly operating on physical models, are frequently computationally expensive, fixed, and cannot be implemented in resource-constrained systems, such as satellites, unmanned aerial vehicles and edge devices. Recent technological progress in artificial intelligence, and especially lightweight deep learning models, presents a potential alternative since it provides adaptive, data-driven preprocessing at the cost of less computation. This review provides an in-depth discussion of the design and implementation of lightweight AI-based real-time preprocessing platforms for remote sensing imagery. It analyzes the basic properties of remote sensing data, important preprocessing activities, and how model-driven and data-driven approaches can be changed to each other. The paper goes on to discuss lightweight AI methods, such as model compression and efficient architecture, and system-level design considerations, such as hardware platforms, edge and cloud integration, and pipeline optimization. Practical challenges and opportunities are presented with references to the real-life situations in disaster monitoring, precision agriculture, and urban analytics. This work is a novel one due to its holistic approach, which focuses on the introduction of the innovation of algorithms and the deployment of systems. Lastly, future directions are mentioned to help formulate scalable, efficient, and robust preprocessing structures.

Chinese Anthropogenic Emissions and Air Quality Control: From the Perspective of Environment and Earth Sciences

Chunxiao Li, Jihong Xiao, Xiaojie Mo — Wed, 05 Aug 2026 00:00:00 +0800

The fast industrialization and urbanization in China have created intricate man-made emissions that have led to gross air degradation with immense environmental, climatic, and health consequences. This is a synthesis of the existing knowledge on Chinese emissions, considering the lenses of environmental and Earth science and incorporating the knowledge on the origins of emissions, atmospheric change, transportation of the pollutants, and environmental effects. It also takes a critical look at ground and satellite-based monitoring, emission inventories, and chemical transport modeling and their importance in assessing the dynamics of pollutants and the effectiveness of control measures. The air quality management policies of China are also examined in the article, such as policy frameworks, technological interventions, and socio-economic measures, and their effectiveness in reducing PM_2.5 and SO_2, as well as the current challenges of NO_x, ozone, and secondary aerosols. The main new challenges, including aerosol-climate interactions, formation of secondary pollutants, and transboundary transport, are outlined, with the importance of integrated, adaptive, multi-sectoral responses. The review determines knowledge gaps in quantification of emissions, chemical transformation, and policy review, providing guidelines on future research and management policies that can maximize environmental, climatic, and public health co- benefits. This work offers an integrated approach to the science of emission, atmospheric processes, and environmental management that can be used to help us understand and control air pollution in China.

Green Development Empowers Rural Revitalization: Research on the Path and Mechanism of Achieving High-Quality Rural Development in the Process of Chinese Modernization

Rong Wang, Xia Rong — Wed, 12 Aug 2026 00:00:00 +0800

The process of Chinese modernization has seen the introduction of green development as a strategic force of rural transformation. The paper is a systematic review of the role of green development in empowering rural revitalization and high-quality rural development using multidimensional processes and synergies. Drawing on sustainable development theory, ecological modernization theory, endogenous development theory, resilience perspectives, and multi-level governance frameworks, the article develops a unified analytical model that links green development inputs, transmission mechanisms, and rural development outcomes. The analysis results in the recognition of ecological value realization, industrial upgrading, technological innovation, institutional reform, financial support, and social capital accumulation as fundamental mechanisms that together reform rural ecological, economic, social, and governance subsystems. The results of the empirical evidence, consisting of the regional case synthesis and quantitative studies, show that green transformation increases green total factor productivity, industrial diversification, income stability, and governance capacity, and it also improves environmental quality and resilience. The paper also postulates a multidimensional rating scale to determine high-quality rural development and talks about challenges in structure, policy implications and further research. This study enhances the theoretical concept of sustainable rural transformation by defining how the systemic logic exists in regard to the green-based rural revitalization, and provides policy implications on how a multi-level, inclusive and environmentally sustainable modernization could be achieved.

Community-Based Adaptation to Climate Change Management: Global Evidence, Effectiveness, and Policy Directions

Daoling Zhang, Qi Liu — Fri, 21 Aug 2026 00:00:00 +0800

Community-Based Adaptation has emerged as a vital approach to addressing the local impacts of climate change Management , particularly in vulnerable communities. This review examines the development, efficacy, and policy implications of Community-Based Adaptation, with a focus on its contribution to resilience and transformative adaptation. Community-Based Adaptation works with communities to design and implement climate adaptation strategies tailored to local contexts through the integration of local knowledge, community empowerment, and participatory decision-making. Although Community-Based Adaptation has proved successful in areas such as agriculture, water management, and disaster risk reduction, issues of scalability, financial sustainability, and social equity have been problematic. The review addresses how Community-Based Adaptation can be involved in transformative adaptation by addressing the cause of the vulnerability, such as social and economic inequalities. It further emphasizes the need to incorporate Community-Based Adaptation in the national and global strategies of climate change, long-term support, and inclusive governance. This review, in the end, offers a broad framework of the future development of Community-Based Adaptation as a fundamental aspect of climate resilience, providing policy ideas to maximize and maintain local adaptation activities.

Nature-Based Solutions and Circular Approaches in Professional Water Pollution Prevention and Remediation

Yufei Deng — Wed, 12 Aug 2026 00:00:00 +0800

Water pollution is one of the most pressing issues in the world that requires new approaches to guarantee the sustainable use of water. This paper discusses the combination of Nature-Based Solutions (NbS) and Circular Economy (CE) concepts as viable solutions to water pollution prevention and treatment. NbS, including wetlands, riparian buffers, and green infrastructure, utilize natural processes to filter and treat pollutants, whereas CE emphasizes resource recovery, water reuse, and waste reduction. These methods have complementary benefits, improved water quality, resource efficiency, and ecosystem resilience. Nevertheless, there are difficulties in scaling such solutions, including technical design, economic investment, regulatory frameworks, and social acceptance. In spite of these challenges, technology, policy, and funding are making inroads into wider adoption. The paper has pointed out the overlaps between NbS and CE, which have created a model for a more sustainable and resilient water management system worldwide. Through these combined strategies, communities will be able to not only solve water pollution but also support environmental sustainability and loop-based resource utilization in the long term.

Low-Carbon Building Codes and Standards: Comparative Evaluation of Global Regulatory Frameworks and Their Environmental Impacts

Haibo Lu, Zhanpo Yuan, Xiaoming Zhang — Wed, 19 Aug 2026 00:00:00 +0800

The construction industry contributes a considerable portion of the world's energy demand and emissions of greenhouse gases; therefore, it is an important area where efforts should be directed in reducing the climate change. Building codes and standards. Low-carbon building codes and standards have become major controlling tools with the aim of enhancing building performance and minimizing environmental impacts. This review offers a comparative analysis of key low-carbon building codes and standards across the world, how they are designed, how they are implemented, and what environmental impacts they report. Both the mandatory building codes and voluntary standards are taken into consideration in both mature and developing areas with varied climates, economic, and institutional backgrounds. The analysis draws out the significant difference in regulatory strategies, such as prescriptive and performance-based models, disparities in implementation capacity, and uneven adoption of carbon-based and life-cycle performance indicators. Literature evidence suggests that reinforced building codes have provided substantial operational energy consumption and emissions containment, especially where a robust compliance system is in place. Voluntary standards tend to show a better level of performance, but are restricted by the market coverage. With the decarbonization of electricity systems, the proportion of embodied carbon and life-cycle emissions becomes significant, and the drawbacks of the existing regulatory frameworks, which continue to emphasize mainly operational energy efficiency, can be seen. The review provides such methodological issues in the evaluation of environmental effectiveness as the use of ex-ante modeling and a lack of post-occupancy data. It concludes with policy implications and further suggestions for improving the low-carbon building regulation through life-cycle-oriented metrics, enhanced enforcement, and the exchange of international knowledge.

Reimagining Oil Spill Response: Innovations in Detection, Risk Assessment, and Sustainable Remediation Pathways

Wed, 12 Aug 2026 00:00:00 +0800

A leaking refined-oil pipeline is a significant, but frequently overlooked, source of soil and groundwater contamination. The leakage from underground pipelines can be undetected for extended periods and can travel vertically through the vadose zone and horizontally in an aquifer, where gasoline, diesel, and associated petroleum hydrocarbons may accumulate. This review aims to compile existing understanding of detecting, migrating, assessing risk, and remediating a refined-oil pipeline contamination in a coupled soil groundwater system. Special attention is devoted to some of the most important pollutants, such as benzene, toluene, ethylbenzene, and xylenes (BTEX), polycyclic aromatic hydrocarbons, methyl tert-butyl ether, volatile petroleum hydrocarbons, and extractable petroleum hydrocarbons. The discussion highlights the fact that contamination in the upper layers of soil may not provide a reliable assessment of subsurface contamination, since unweathered residual petroleum can cause a prolonged secondary source of contamination to groundwater plumes. Contaminant transport and remediation feasibility are highly influenced by hydrogeological conditions, such as soil texture, fractures, loess structure, aquifer properties, and groundwater flow direction. While conventional remediation technologies can deliver quick risk reduction, integrated approaches that combine bioremediation, monitored natural attenuation, nature-based solutions, emerging treatment technologies, and long-term monitoring are increasingly required to ensure sustainable management. A representative pipeline leakage case (Luoyang–Zhumadian) is used throughout to illustrate key concepts, but the review synthesizes findings from multiple studies. The article proposes a proactive approach, one that moves beyond a reactive cleanup model to adaptive and predictive management, using intelligent monitoring, contaminant transport modelling, source-pathway-receptor analysis, and risk-based decision-making. The integrated approach can enhance protection of the environment, the effectiveness of remediation, and the security of groundwater resources in areas impacted by refined-oil pipeline spills.

Smart Industrial Water Treatment: Integrating AI and IoT for Real-Time Monitoring and Optimization

Junying Wang, Weining Feng, Minsong Jia — Fri, 07 Aug 2026 00:00:00 +0800

Smart industrial water treatment is progressively sought after to achieve stricter discharge and product-water requirements, decreased energy and chemical use, and enhanced stability at fluctuating influent and working regimes. This article provides a review of how the Internet of Things (IoT-based) sensing and artificial intelligence analytics may be combined in order to facilitate real-time monitoring and optimization in industrial treatment trains, including pretreatment and biological treatment systems, membranes, and zero-liquid-discharge (ZLD) systems. The initial contextual framework (smart treatment) is placed on industrial performance goals and limitations, focusing on partial observability, sensor pollutions/fouling, measurement delay, and multi-objective optimization amongst compliance, cost, particle recovery objectives, and asset health. We next consider IoT and architectures of sensing used to ensure trustworthy monitoring, such as time-based pipelines of data, edge cloud pattern of installation and engineering of data quality, which is used to validate, redundancy, and fault detection. It is based on these backgrounds that we consider artificial intelligence (AI) techniques in anomaly detection, fault diagnosis, soft sensing, and probabilistic forecasting, and point out how regime awareness, explainability, and uncertainty quantification must be applied to risk-sensitive operations. Prescriptive capabilities are considered in a control maturity perspective, between decision support and constrained supervisory and closed-loop control. We contrast classical methods, e.g., model predictive control, with data-based optimal control, e.g., Bayesian optimization, safe/offline reinforcement learning, and explain why digital twins can be used to enable validation and operator training. Last but not least, we discuss deployment facts-OT/IT (operational technology/information technology) integration, cybersecurity, lifecycle management, and human factors, and offer a vision of the future based on interoperable data models, strong cross-site transfer, and optimization that is proven to be safe.

Optimization and Resource Utilization of Urban Solid Waste Classification and Recycling System under the Goal of Low Carbon Environmental Governance

Chenchen Zhang, Yizhen Du — Wed, 12 Aug 2026 00:00:00 +0800

The classification and recycling of urban solid waste have become key channels to realizing a low-carbon environmental governance, enhance resource efficiency, and encourage a circular urban development. This review critically analyzes the existing status, optimization, governance and resource utilization strategies of urban solid waste classification and recycling systems. It emphasizes that the high performance of waste classification is based not just on the participation of residents, but also on coordination of source separation, classified collection, transportation, intelligent sorting, recycling technologies, market mechanisms and carbon accounting. Unstable classification behavior, fragmented management responsibilities, insufficient infrastructure, low-value recycling, weak life-cycle carbon assessment, and low level of demand of secondary resources are still challenges to the current systems. In order to overcome these barriers, this article offers an integrated optimization model comprising of digital monitoring, route optimization, sorting using artificial intelligence, life-cycle assessment, extended producer responsibility, economic incentives, participation of the populace, and green procurement. The review also highlights the fact that the utilization of the resources should focus on high-quality material recycling, organic waste recovery, industrial symbiosis, and verifiable reduction of carbon. In contrast to the traditional approaches to waste management, according to which the primary focus is on the disposal reduction or recycling rate, this article places the classification and recycling on the broader context of the low-carbon governance and urban material metabolism. The results offer both theoretical explanations and practical suggestions to implement effective, inclusive, and climate-oriented urban waste management systems.

Cross-Cultural Media Representations of Climate Change: Implications for Global Environmental Cooperation and Earth System Protection

Ruihe Sun, Chaofeng Sun, Xindi Liu — Wed, 12 Aug 2026 00:00:00 +0800

This article discusses how climate change is cross-culturally mediated in the media and what this entails in terms of maintaining environmental collaboration and guardianship of the Earth system globally. Although the science of climate change is not disputed, the media representations of the problem diffuse widely in various cultural settings depending on the historical, social, and political conditions. These variations may provide opportunities as well as challenges towards the creation of a coherent global response to the climate crisis. The article discusses how the media in North America, Europe, the Global South, and Indigenous communities are framing climate change, and it focuses on the aspects of justice, technological solutions, and collective responsibility. It emphasizes the possibility of the media to create cross-cultural communication, prompt social action, and impact policy that will create sustainability in the environment. Also, the article addresses the significance of media approaches with a cultural understanding within the development of behavior change and the promotion of international collaboration on climate change action. The article concludes that the media, when well utilized, can become a potent instrument in the protection of the Earth's systems and the climate crisis experienced by the global world, despite the difficulties of misinformation, political and unequal access to the media.

Toward High-Quality Development: Reviewing Eco-Regulation Mechanisms and Industrial Integration in the Yellow River Basin

Jihong Xiao, Xiaojie Mo, Chunxiao Li — Wed, 12 Aug 2026 00:00:00 +0800

The Yellow River Basin (YRB) has turned into an important place to study the ways of how ecological protection and economic transformation can be synchronized in the conditions of resource deficit, ecological vulnerability, and significant regional heterogeneity. The present review discusses the correlation between eco-regulation processes, industrial integration, and the high-quality development of the YRB in terms of the coupled analytical perspective. It suggests that eco-regulation should not be seen as resulting in environmental constraint, but as an institutional force that can transform the industrial structure, incentives to innovation, and developmental trajectories in regions. Similarly, the concept of industrial integration is not seen as a mere economic connection, but it is a process of structural and spatial reorganization of sectors, regions, and value chains within ecological constraints. The article summarizes the development of the eco-regulation instruments, the primary ways of industrial integration, and the mechanisms of the interaction of the two in the basin. It demonstrates that eco-regulation can facilitate cleaner production, green innovation, and industrial upgrading and integration can be effective in regulation by increasing efficiency, spillover in technology and coordinated development. Such synergies are, however, conditional and can be counterbalanced by compliance costs, pollution transfer, uneven regional capabilities, and fragmentation in governance. The review also gives an assessment of empirical literature, major gaps in the methodology and conceptualization, and presents research directions for the future. It finds that the quality development of high-quality in the YRB requires basin-wide coordination, regional differentiation of strategies, and institutional linkage between ecological governance and industrial transformation.

Reimagining Agricultural Cultivation: An Integrative Review of Pathways to Sustainable Crop Production and Environmental Resilience

Jia-Liu Zhu, Qing Feng, Qi-Xiao Zhang, Jia-Wen Guo — Mon, 10 Aug 2026 00:00:00 +0800

Agricultural production is facing increased pressure to increase yields and address soil degradation, water scarcity, biodiversity loss, climate change, and the environmental impacts of input-intensive production. This review critically examines how sustainable crop production can be promoted through integrative pathways that enhance environmental resilience while improving productivity. It initially examines the fundamental structural issues in modern agriculture, with the underlying idea that deteriorating soil health, hydrological stress, chemical addiction, and ecosystem simplification are intertwined rather than distinct limitations. The review subsequently summarises five key avenues for transformation: agroecological diversification, soil-health-centered management, precision and digital agriculture, climate-smart crop improvement, and circular, resource-efficient systems. Instead of treating these approaches as rival models, the article argues that they are most valuable when combined strategically and integrated into context. Their collective impacts include increased yield stability, greater efficiency in input use, improved soil and water management, reduced pollution, and enhanced biological management of pests and diseases. The review also notes that environmental resilience must be a key measure of agricultural development, rather than an incidental co-benefit. It further highlights the significance of good governance, policy provision, and socioeconomic viability in scaling sustainable cultivation practices. On the whole, the article suggests a systems-based approach to reconsidering agriculture as an adaptive, regenerative, and resilient venture that could ensure long-term food production amid accelerating environmental change.

Aerosol Nanoparticle Growth Dynamics and Urban Air Quality in Megacities of China

Ling Huang, Hong-Tao Li, Hai-Bo Zhang, Bin Yan, Hui-Jun Wan — Fri, 14 Aug 2026 00:00:00 +0800

The role of aerosol nanoparticles in the urban atmosphere is significant but underappreciated, owing to the rapid formation, growth, and aging, which link gaseous precursors to particulate pollution, haze formation, and human exposure. The review summarizes existing knowledge on the dynamics of aerosol nanoparticle growth and its implications for air quality in China’s megacities. It studies the basic mechanisms of nanoparticle formation, condensational growth, coagulation, and chemical aging, and how the high-emission, high-humidity, and meteorologically complex conditions in large Chinese urban clusters alter these processes. The review also elucidates the contributions of traffic, industry, regional transport, atmospheric oxidation, and boundary-layer dynamics in governing nanoparticle behavior across various cities and seasons. New developments in field observations, chemical characterization, and process modeling are considered, along with the still-remaining uncertainties in determining early growth phases, precursor inputs, and urban-regional interactions. The environmental importance of nanoparticle growth as an intermediate between ultrafine particle pollution and PM2.5 formation, and its effects on haze, visibility, atmospheric chemistry, and public health and air quality management are given special attention. The review concludes that further pollution control in Chinese megacities should cease to rely solely on mass-based measurements and should be organized within a more comprehensive framework that accounts for particle counts, precursor chemistry, and growth-driven atmospheric changes. This kind of approach is critical for explaining current shifts in urban aerosol regimes and for developing more effective approaches to multipollutant control.

Connected Environments: Advancing Monitoring and Decision Support through the Fusion of IoT Sensors and Intelligent Systems

Jun Zhan, Wei Yu — Wed, 12 Aug 2026 00:00:00 +0800

Interrelated environments are also becoming the driving force behind how intricate physical, social, and cyber-physical systems are observed and handled. The key in this development is the incorporation of Internet of Things (IoT) sensing infrastructures and smart systems that have data analytics, learning, and decision support capabilities. The review is a well-organized account of the development of recent connected environments with a specific emphasis on the interactions of IoT sensors and intelligent systems to improve monitoring and decision-making. First, the article discusses IoT sensing infrastructures, which comprise architectural foundations, sensor technologies, communication mechanisms, and deployment considerations that facilitate the acquisition of large volumes of data. It then looks at intelligent systems data processing and analytics, with the contribution of machine learning, artificial intelligence, and edge-cloud design to the conversion of raw sensor information into insights. Based on these premises, the review examines frameworks and strategies of fusion that combine heterogeneous sensor data and intelligent analytics across multiple layers of systems. The application domains, such as smart cities, healthcare, industrial automation, and environmental monitoring, are mentioned as examples of the representative areas where the concept of intelligent connected environments has practical implications and issues. The Major technical, organizational, and societal issues—scalability, data heterogeneity, security, privacy, and trust—are discussed critically. Lastly, the article also describes the emerging trends and future research directions, such as distributed intelligence, privacy-preserving learning, and autonomous decision systems. With its holistic view of connected environments, this review is expected to help researchers and practitioners in developing robust, scalable, and trustworthy next-generation monitoring and decision support in connected environments.

Circular Logistics Engineering: Minimizing Waste and Carbon Footprint

Qing Wang — Fri, 14 Aug 2026 00:00:00 +0800

The shift from linear to circular economic systems has created new needs for the design of logistics systems, especially for minimising waste and reducing the carbon footprint. This review discusses how logistics engineering can incorporate the principles of a circular economy to support the realisation of more resource-efficient, low-carbon, and recovery-oriented supply systems. The article begins by elucidating the theoretical principles of circular logistics by contrasting it with traditional, green, reverse, and closed-loop logistics, and by emphasising the necessity of considering logistics as a multi-directional system to retain value rather than distributing products in a one-way manner. It subsequently develops key engineering advances, including sustainable transport, smart warehousing, reverse logistics infrastructure, reusable packaging, and digital technologies such as IoT, AI, and digital twins. The review also examines the optimisation models and decision-support strategies applied to balance cost, service, and waste reduction and carbon mitigation under uncertainty. Moreover, it assesses the barriers to implementation in terms of infrastructure, economics, organisational capacity, regulation and sector-specific conditions of operation. The article posits that the originality of logistics driven by the circular economy lies in its comprehensive approach to waste and carbon goals through engineering design, rather than distinct green interventions. The review concludes that the idea of circular logistics can become a vital facilitator of sustainable industrial change, but it will require the assistance of lifecycle-based assessment, integrated infrastructure, and context-specific implementation strategies.

ESG as a Strategic Asset in Emerging Markets: A Case Study of Renewable Energy Technology Transfer and Marketing

Yue Ma — Tue, 11 Aug 2026 00:00:00 +0800

Environmental, social, and governance (ESG) considerations are increasingly framed as sources of competitive advantage. Yet evidence remains fragmented on how ESG creates strategic value in emerging markets where institutional uncertainty, stakeholder contestation, and financing constraints shape renewable energy deployment. This review conceptualizes ESG as a strategic resource, an embedded competency that improves project administration, validity, and learning practices, and reviews the role of ESG in renewable energy technology transfer and downstream marketing/commercialization results. By combining findings on resource-based view, institutional theory, stakeholder theory, signaling, and technology transfer, we come up with an explicatory pathway between ESG maturity and partner selection, choice of transfer mode, bankability, absorptive capacity, and lifecycle performance. We also relate the outcome of operations to market-facing processes, as the argument is that ESG influences tender eligibility, cost of capital, ecosystem coordination, and adoption through lower perceived risk and an increase in credibility among regulators, financiers, communities, and buyers. The comparison of a case of China and Germany demonstrates two prevailing ways: an execution-centered way in which ESG builds value by reliability and reduction of disruption, and a verification-centered way in which ESG builds value by safeguards, standards, and credible signaling. At cross-pathways, the strategic usefulness of ESG relies on local fit as well as on how the commitments can be converted into quantifiable results like transfer of skills, development of suppliers, affinity of communities, and reliable energy supply. Managerial and policy implications and a research agenda are concluded at the end of the review in the form of causal identification, longitudinal design, and distributional just transition outcomes in contexts of emerging markets.

Enhancing Circular Bioeconomy through Aerobic Granular Sludge: Experimental Insights into Nutrient Recovery from Kitchen Waste Leachate

Xiaosu Wu — Wed, 12 Aug 2026 00:00:00 +0800

Kitchen waste leachate is a variable, nutrient-rich liquid stream generated during the collection, storage, and processing of food waste. While it significantly increases the organic load, ammonium, phosphate, suspended solids, oils, and fermentation products are very troublesome in the treatment process; it also provides for the recovery of carbon, nitrogen, and phosphorus. This review critically analyzes the use of aerobic granular sludge (AGS) as a biological platform to improve circular bioeconomy strategies for recovering nutrients from kitchen waste leachate. The review combines leachate characteristics, granulation mechanisms in AGS, microbial nutrient pathways, experimental evidence, and integration pathways with downstream technologies for leachate recovery. The difference between pollutant removal and resource recovery is discussed, as is the fact that conventional nitrogen and phosphorus removal may not yield reusable products. The advantages of AGS treatment include compact dimensions, superior settleability, high biomass retention, and the simultaneous occurrence of carbon degradation and nitrification, anoxic and anaerobic microenvironments, and phosphorus accumulation. But high organic loading, ammonia inhibition, fats, salinity, influent inconsistency, and granule instability remain significant challenges. For recovery-oriented AGS systems, it is therefore beneficial to combine pretreatment, fermentation, struvite precipitation, ammonium recovery, membrane separation, or biomass valorization. Nutrient mass balances, product safety, pilot-scale validation, assessment of greenhouse gas(es) (GHG), and techno-economic analysis should be given greater importance in future research. In general, AGS is a potential bridge between biological treatment and nutrient circularity in sustainable food waste management systems, and between decentralized urban resource recovery and future cities.

The Digital Frontier: AI Applications in Environmental Monitoring and Earth Science Research

Xuebin Wang, Huanle Zhang — Wed, 12 Aug 2026 00:00:00 +0800

Emerging as the new frontier of environmental monitoring and environmental science studies, artificial intelligence (AI) promises to allow the process of scalable inferences when new volumes of satellite, airborne, in situ, and model-generated data are taken into account. This review brings together the data ecosystems, methodological principles and application evidence that shape the new frontier of Earth observation, namely the digital frontier. Measurement physics: We explain the effects of measurement physics on problem formulation, label uncertainty, and missingness, and how current machine-learning practices are naively transferred to other domains, despite these domains exhibiting different possibilities that could affect model performance. After this, we discuss principal AI strategies focusing on representation learning and self-supervised pretraining, spatio-temporal deep learning in map and prediction, multi-modal fusion, and generative learning in gap filling, downsizing, and reconstruction. Specific focus is made on physics-guided and hybrid modeling approaches that jointly integrate learned components with mechanistic models to enhance plausibility, extrapolation and uncertainty quantification, calibration, and interpretability needed to gain scientific credibility and operational decision support. In the fields of application that we have considered, land systems, atmosphere and air quality, hydrology and water resources, cryosphere, ocean and coasts, natural hazards and urban environments, we discuss common patterns of success and failure, with operational readiness spanning almost equally evaluation design, data governance, lifecycle maintenance, and architecture choice. Our final contribution is research and community priorities such as Earth system foundation models, resilient extremes and out of distribution beneficial products, decision facing probabilistic products and responsible governance that deal with bias, privacy and dual-use risks. This combination of directions defines a roadmap on the way to credible prototypes to reliable and reproducible and beneficial Earth AI systems.

Geological Engineering at the Climate Nexus: Innovations in Hazard Assessment, Resource Stability, and Environmental Protection

Maokang Pang, Shaoyong Li, Dongqin Li — Fri, 14 Aug 2026 00:00:00 +0800

Geological engineering is changing because of climate change, as it changes the environmental boundary conditions that affect the stability of the ground, its security as a resource and the behavior of contaminants. Geological hazards are becoming more complex due to intensifying rainfall, sea-level rise, drought, permafrost degradation, coastal erosion, groundwater fluctuations, and extreme events, which are affecting the basic design assumptions of historical stationarity. This review explores the field of geological engineering in the context of climate change, with three interrelated themes: hazard identification, resource stability, and environmental protection. It brings together developments in slope-risk assessment, geohazard assessment in the coastal and riverine environment, cold-region instability, compound hazards, remote sensing, field monitoring, predictive modeling, and digital decision-support systems. Additionally, the review assesses the importance of sustainable subsurface engineering for the security of groundwater, critical mineral development, geothermal energy, carbon storage, and containment of my waste. It also covers an analysis of the geoenvironmental risks associated with climate change, adaptive remediation, nature-based and hybrid solutions, resilient infrastructure, land-use planning, and new policy needs. Analysis indicates that, going forward, geological engineering should shift from retrospective, single-hazard models to dynamic, multi-hazard, performance-based models. They need to be technologically innovative, but integration with the understanding of geological processes, uncertainty quantification, long-term monitoring, and adaptive governance are essential. Geological engineering can play a vital role in supporting resilient infrastructure, responsible resource development, and sustainable climate adaptation by connecting climate-related hazards, resources, and environmental protection.

Mechanically Intelligent Infrastructure: Integrating Autonomous Design with Real-Time Environmental Process Control

Shilei Zhang — Wed, 12 Aug 2026 00:00:00 +0800

Mechanical intelligence infrastructure (MII) is a paradigm shift in the design and management of the built environment through a combination of autonomous architecture and real-time control over environmental processes. This paper discusses the intersection of these two technologies with reference to their ability to develop adaptive, long-term, and efficient infrastructure systems. Autonomous design uses AI-based and generator-based design algorithms, which allow the design of highly optimized forms capable of evolving to external influences. Controlled by IoT sensors and AI-based processes, real-time environmental process control constantly monitors and adjusts the performance of the infrastructure to maximize the use of energy, increase comfort, and resilience. The combination of these systems allows the infrastructure to conform to the evolving conditions in a proactive manner with long-term sustainability and effectiveness. MII has been applied in smart buildings, smart cities, industrial systems, and critical infrastructure, where it has significant value in terms of energy efficiency, resource optimization, and disaster resilience. Nevertheless, there are issues of data interoperability, high cost, and regulatory requirements. With the further advancement of technology, the opportunity to change the city environment, the workplace, and the infrastructure is enormous. This paper gives a detailed analysis of the present, the new trend, and the future of mechanically intelligent infrastructure.

From Raw Materials to Recycling: Environmental Challenges and Opportunities of Solid-State Battery Technologies

Dejun Yang — Wed, 12 Aug 2026 00:00:00 +0800

Solid-state batteries (SSBs) are gaining prominence as a dependable next-generation energy storage solution, offering higher energy density, improved safety, and extended cycle life compared with conventional lithium-ion batteries. There has been a lot of research concentrated on evaluating the electrochemical performance of SSBs. Still, the environmental implications across the entire life cycle, from raw material extraction and manufacturing to operational use and end-of-life management, remain underexplored. This paper provides a comprehensive assessment of the environmental opportunities and challenges associated with SSBs. Critical raw materials, including nickel, lithium, cobalt, and solid electrolytes, are assessed for availability, extraction impacts, and toxicity. Industrial processes, particularly solvent-based processing, high-temperature sintering, and lithium-metal handling, are evaluated for energy consumption, chemical usage, and potential environmental hazards. Operational benefits, such as abridged risk of thermal runaway, extended lifetime, and higher energy efficiency, are analyzed alongside challenges, including interface degradation, dendrite formation, and thermal management. End-of-life considerations, including recycling, circular economy strategies, and material recovery, are discussed with attention to sulfide, oxide, and polymer electrolytes. By integrating life-cycle perspectives and sustainability considerations with technical performance, this paper highlights pathways to environmentally responsible SSB deployment. The conclusions emphasize that future research must balance material innovation, manufacturing efficiency, operational stability, and end-of-life recyclability to realize the full environmental potential of SSB skills.

Integration of Big Data, Electronic Information, and GIS in Atmospheric Monitoring

Zhi-Chen Sun, Tong-Tong Jiang, Xiang-Liang Yan — Wed, 19 Aug 2026 00:00:00 +0800

Monitoring of the atmosphere is experiencing a paradigm shift due to a combination of big data, electronic information technologies, and geographic information systems (GIS). Conventional methods of monitoring, which are largely based on sparse fixed-site stations, usually experience spatial constraints, limited time responsiveness, and challenges in integrating multiple sources. This paper provides a systematic review of how the joint evolution of dense sensing networks, real-time communication systems, big-data analytics, and geospatial platforms is transforming existing atmospheric observation systems into smarter and decision-oriented structures. The paper first presents the technological background of integrated atmospheric monitoring, such as heterogeneous data sources, sensor and communication infrastructure, and GIS-based spatial intelligence. It then examines key processes of data preprocessing and quality control, multi-source fusion, spatiotemporal modeling, visualization, and uncertainty-conscious interpretation. Significant application areas are also examined, among which are urban air quality control, regional pollution monitoring, emergency response, and public health assessment. The current limitations are also critically assessed in the review (including data heterogeneity, unstable calibration, scale and location issues, interoperability, propagation of uncertainties, and poor model interpretability). Based on this assessment, future trends are determined to include explainable artificial intelligence, edge intelligence, digital twin platforms, and more standardized and resilient monitoring architectures. The originality of this review is its integrated approach, which does not consider big data, electronic information, and GIS as independent technical solutions, but rather views them as complementary components and pillars of next-generation atmospheric monitoring. The review serves as a systematic source for the development of more accurate, adaptive, and governance-oriented atmospheric monitoring systems.

Research on State Evaluation Method of Energy Storage Lithium-Ion Batteries Based on Multi-Source Non-Invasive Big Data

Junqi Zhang, Ruisheng Diao — Wed, 12 Aug 2026 00:00:00 +0800

Lithium-ion batteries are the backbone of electric vehicles, renewable energy storage, and new emerging smart grid applications. However, the safety and the economic value of such batteries depend heavily on the proper assessment of State of Health (SOH). Conventional invasive measurements provide detailed information; however, they are difficult to apply to sealed or in-service battery packs. This makes non-invasive techniques such as voltage, current, temperature, impedance, and data-driven modeling better suited for battery management at scale. In this paper, three representative research directions, including comprehensive SOH characterization, fast impedance-based SOH estimation, and machine-learning diagnosis of degradation patterns from electrochemical impedance spectroscopy, are reviewed and synthesized. Rather than analyzing each study in isolation, the paper proposes a comparative framework to identify methodological strengths, data requirements, limitations, and potential for practical deployment. A unified SOH evaluation framework is proposed, which combines Gaussian Process Regression-Automatic Relevance Determination (GPR-ARD)-based feature selection, fast impedance calculation, lightweight Extreme Learning Machine (ELM)-based online estimation, and cloud–edge model updating. The practical feasibility is discussed in terms of computational cost, latency, bandwidth, temperature variation, data quality, cybersecurity, and real-time battery management constraints. The study concludes that, to support safer electric vehicles, second-life battery use and sustainable energy storage systems, future non-invasive battery diagnostics should combine physical interpretability, multi-source data fusion, and deployment-oriented model design.

Eco-Functional Polymers at the Nexus of Material Science and Environmental Resource Management

Lanbo Luo — Wed, 12 Aug 2026 00:00:00 +0800

Eco-functional polymers have become one of the potential high-performance materials that combine functionality and environmental concerns with sustainable design. These polymers lie at the interface of polymer science, materials engineering, and environmental resource management, and are increasingly being explored to find uses in the field of water purification, soil remediation, air pollution, carbon management, nutrient recovery, and waste valorization. The present review discusses the basic principles and molecular design approaches of eco-functional polymers and their environmental applications, where the structure of the material, functional groups, morphology, and hybridization largely determine the performance. There is a specific focus on intelligent and multifunctional polymer systems that integrate adsorption, separation, catalysis, sensing, responsiveness, and regenerative ability. The review also addresses performance evaluation and mechanistic understanding, which are crucial in the conditions of a complex environment, such as selectivity, kinetics, stability, regeneration, and realistic benchmarking. In addition to the technical performance, the article is critical of sustainability, safety, and translation to real-world deployment, such as feedstock choice, green synthesis, lifecycle issues, degradation behavior, toxicity, and scale-up issues. Current research gaps exist in understanding the structure–function–sustainability relationship, establishing standardized testing protocols, elucidating environmental fate, and demonstrating practical deployment. This review argues that the future of eco-functional polymers lies in moving beyond linear, single-use remediation materials toward systems-conscious, lifecycle-responsible platforms that support circular environmental management.

Multi-Sensor Fusion Techniques for Monitoring Emissions and Health of New Energy Vehicle Powertrains

Tianyin Cao, Wei Hu — Wed, 12 Aug 2026 00:00:00 +0800

The rapid development of new energy vehicles (NEVs) has intensified the demand for accurate and reliable monitoring of both emission-related performance and powertrain health. In contrast to traditional vehicles, NEV powertrains are characterized by a high degree of interconnection between electrical, thermal, mechanical, and chemical subsystems that results in complicated dynamic behavior and degradation behavior. The conventional single-sensor or isolated-model monitoring systems are becoming less effective in dealing with the challenges during real-world driving conditions. Multi-sensor fusion in this regard has become one of the key enabling technologies to achieve higher levels of observability, robustness, and diagnostic capability in NEV powertrain monitoring. The paper gives an in-depth review of multi-sensor fusion that is being used to monitor emissions and powertrain conditions in NEVs. The review initially examines the monitoring requirements that are linked to various NEV architectures and sensing technologies, and the heterogeneous and uncertain character of multi-sensor data. Significant sensor fusion strategies, such as model, data, and hybrid strategies, are also presented systematically about their theoretical basis, implementation architecture, and trade-offs. The applications in emission monitoring and health management in powertrains are then considered, including real-time estimation of emission-related factors, fault detection, state estimation, and prognostics. Other critical issues in terms of data quality, computational requirements, strength, and explicability are also underscored. The synthesis of existing research advances and the uncovering of gaps are what make this review give a single view on the importance of multi-sensor fusion to develop intelligent, reliable, and sustainable NEV powertrain monitoring systems.

Materials for a Regenerative Built Environment: A Review of Novel Green Construction Technologies

Yayuqiong Zeng, Qi Huang, Liuchun Zhang, Zhiwei Lin — Wed, 19 Aug 2026 00:00:00 +0800

The built environment is a significant contributor to global resource use, Greenhouse Gases (GHGs) and construction and demolition waste, and the current approach of sustainable construction needs to evolve from a linear supply and use model, towards a more regenerative material system. The review article discusses new green construction technologies that can contribute to a regenerative built environment, which minimizes embodied carbon, carbon sequestration, circularity, and supports ecological functions and the health of the occupants. The article critically reviews the progress of the materials that are bio-derived and carbon-storing, such as engineered timber, bamboo, hempcrete, straw, cork, mycelium composites, algae-derived materials, cellulose and lignin-based materials. It also examines low-carbon or circular mineral-based alternatives like low-clinker cement, limestone calcined clay cement, geopolymers, alkali-activated binders, recycled aggregate concrete, carbon-cured concrete, re-used metals, re-used glass and earth-based materials. Furthermore, new technologies such as additive manufacturing, prefabrication and modular construction, smart materials, living systems, digital twin and material passports are assessed as means of system-level regeneration. The review emphasizes that regeneration can only be measured through whole life assessment and is not just a part of the material origin or embodied carbon but also through durability, circularity, toxicity, land use, scalability, ecological value and social acceptance. The results indicate that future buildings that are regens will rely on integrated material systems, not just material substitutions. The research priorities include standardized assessment frameworks, long-term field validation, circular design protocols, digital traceability and policy support for low-carbon and reusable construction materials.

Satellite-Based Change Detection Techniques for Land Use and Land Cover Dynamics

Weilin Ou, Jun Qiu, Yuanzhi Li, Xin Dai, Qingchun Wang — Tue, 11 Aug 2026 00:00:00 +0800

The detection of change using satellites has become a fundamental feature to track the land use and land cover (LULC) dynamics at local to global levels. Through this review, the synthesis of progress in the Earth observation data sources, preprocessing, methodological frameworks, and validation practices has been taken into consideration, upon which credible mapping of LULC changes relies. We then give a brief overview of applications of optical, synthetic aperture radar (SAR), thermal, and emerging hyperspectral/structural products, with the outlook of how resolution-revisit trade-offs, co-registration, atmosphere/terrain normalization, vapor and speckle suppressing capabilities, and time-series separation of performance. We then classify change detection methodologies as formulations of the problem, including binary no change, multi-class transition mapping, and the estimation of change timing. Classical differencing and change vector analysis will be useful in interpretable settings with low data, whereas statistical breakpoint and trajectory models allow time and persistence reasoning that are sensitive to seasonality. Deep learning designs have been used to learn spatial-temporal representations using engineered multi-temporal features, and machine learning algorithms, such as Siamese and encoder-decoder networks and temporal convolutional and transformer models, can learn directly on imagery. New self-supervised and foundation model paradigms seek to enhance transferability in domain shift as well as label scarcity. We also check multi-sensor fusion and object-based approaches to the enhancement of semantic coherence at high resolution. Lastly, we speak about benchmark datasets, evaluation measures, and validation procedures, pointing out pitfalls due to spatial leakage and class imbalance, and emphasizing uncertainty-aware statistically defendable area estimation. We finish with operational monitoring that is driven by application and is open to challenges towards scalable, interpretable, and trustworthy operational monitoring.

Efficiency Evaluation and Path Optimization of Regional Atmospheric Environment Governance from a Synergistic Emission Reduction Perspective Synergistic Control of PM2.5 and O3

Yizhen Du, Chenchen Zhang — Wed, 12 Aug 2026 00:00:00 +0800

There is a growing challenge posed by the coexistence of fine particulate matter (PM_2.5) and ground-level ozone (O₃). Despite the significant reductions in primary particles and a variety of gaseous pollutants achieved by conventional air pollution control, O₃ pollution has remained or even increased in many areas, demonstrating the shortcomings of single-pollutant regulation. This review examines how the efficiency evaluation and path optimization of regional atmospheric governance can be viewed through the prism of synergistic emission reduction to coordinate control of PM_2.5 and O₃. It initially explains the chemical linkage between PM_2.5and O₃ through common precursors, the atmosphere’s capacity for oxidation, photolysis, heterogeneous reactions, and regional transport. It subsequently discusses efficiency evaluation methods, such as indicator systems, data envelopment analysis, slack-based measure models, Malmquist indices, spatial econometric methods, and integrated benefit assessment. The review also discusses pathways for emission reduction across the industrial, power, transport, residential, and agricultural sectors, with a focus on differentiated VOCs-NO_x sensitivity, NH₃ regulation, structural adjustment, clean-energy transition, process optimization, and scenario-based cost-benefit analysis. Lastly, it suggests a regional path-optimization framework that combines air-quality modeling, economic analysis, cross-regional responsibility sharing, policy synergy, and adaptive, data-driven governance. The review claims that successful atmospheric governance needs to shift toward mechanism-based, regionally coordinated, and welfare-oriented decision-making to achieve sustainable air-quality improvements, health protection, carbon co-benefits, and regional equity.

Geophysical Controls on River Turbulence: Implications for Bank Erosion and Pollutant Dispersion

Yifan Zhang — Wed, 12 Aug 2026 00:00:00 +0800

Turbulence plays a critical role in the transfer of momentum, sediment, and pollutants in rivers. We review the state of knowledge on geophysical controls of river turbulence and their role in bank erosion and contaminant transport. Controlling factors, such as channel geometry, boundary roughness, flow conditions, and forcing, have been shown to affect not only the magnitude of turbulence but also its spatial structure and temporal dynamics. The review underscores the role of organized structures, intermittent bursts, and scale interactions in driving near-boundary mechanisms and mixing processes. For bank erosion, turbulence produces fluctuating stresses and pressures that interact with spatially varying bank materials, resulting in complex erosion processes that go beyond threshold-based models. Intermittent high-energy events, such as floods, are shown to play a significant role in erosion. In terms of pollutant dispersion, turbulence dictates mixing and transport processes and can lead to non-Fickian transport behavior in complex channels with temporary storage zones. The review highlights the link between erosion and dispersion, both of which are controlled by turbulence and partially by similar geophysical factors. It also discusses current shortcomings of the models and suggests the need for integrated, multi-scale models considering unsteady turbulence. The development of such approaches is critical to enhance predictions and inform river protection and management.

Smart Water Systems: Advances in Monitoring, Modeling, and Management of Water Resources in China

Fei Lin, Qing Gao, Lixin Wang — Fri, 14 Aug 2026 00:00:00 +0800

Smart water systems are a revolutionary way of utilizing water resources, based on the use of modern technologies (sensors, data analysis, machine learning, and real-time monitoring). Such systems are becoming part and parcel of water management practices in China in order to deal with the fact that the country is facing water scarcity, problems of pollution, and even an unpredictable climate. This article examines the design and deployment of smart waters in China and how monitoring, modeling, and decision-making technologies have been integrated into water management systems in different sectors of water management, such as river basins, urban utilities, agriculture, and groundwater management. We consider how decision support systems (DSS) can be used to improve water allocation, infrastructure management processes, and resilience to floods and droughts. The article, however, also notes that there are various challenges to scaling these systems, such as the lack of technological interoperability, data management concerns, cybersecurity risks, and financial limitations. Moreover, institutional fragmentation and social refusal to accept also complicate the popularisation of smart water technologies. The article wraps up by underscoring the fact that more investment needs to be made in data standards, cross-agency coordination, and capacity building to eliminate these obstacles and to make smart water systems a success in the long term. The activities of China in this respect are a worthy example to other countries that are trying to deal with the challenge of water management, like China.

Next-Generation Environmental Radiation Monitoring: Advances in Sensors, Data Analytics, and Earth System Integration

Wenbing Wang — Tue, 11 Aug 2026 00:00:00 +0800

The convergence of next-generation sensors, sophisticated data analytics, and Earth system models has fundamentally transformed the landscape of environmental radiation monitoring. These technological synergies have collectively enhanced the accuracy, spatial coverage, and real-time responsiveness of detection systems, enabling more precise identification and assessment of radiation exposure risks. This article systematically reviews recent progress in sensor technologies, including improved sensitivity and durability, alongside the growing role of machine learning algorithms in processing complex monitoring data and distinguishing anomalous radiation patterns from background noise. A central focus is the integration of radiation data into Earth system models, which facilitates the simulation of radionuclide dispersion and environmental fate, thereby supporting predictive capabilities and informed decision-making during radiological emergencies. Furthermore, the article highlights the practical benefits of real-time monitoring networks and early warning systems in strengthening disaster preparedness and safeguarding public health. Despite these notable advances, significant challenges remain, including issues related to sensor calibration, cross-platform data integration, infrastructure deployment, and cybersecurity. Looking ahead, continued research, technological innovation, and international collaboration are essential to enhance the reliability, scalability, and global interoperability of these systems. Ultimately, these developments not only contribute to reducing radiation-related risks but also align with broader sustainability objectives, particularly in supporting environmental health, climate resilience, and evidence-based policy formulation under the United Nations Sustainable Development Goals.

Cost-Optimized Pathways for Carbon-Neutral Wastewater Treatment in China by 2035

Hong Tao Li, Ling Huang, Hai Bo Zhang, Hui Jun Wan, Bin Yan — Fri, 14 Aug 2026 00:00:00 +0800

The wastewater treatment industry in China is under pressure to balance tighter pollution control with the national carbon-neutrality objectives. This review evaluates cost-effective solutions to achieve carbon-neutral wastewater treatment in China by 2035, focusing on the integration of carbon reduction, resource reuse, and regional infrastructure planning. Direct emissions of methane and nitrous oxide in the carbon profile of wastewater treatment plants result directly from biological and sludge processes and indirectly from electricity, chemicals, construction, and sludge disposal. Due to the large-scale variation in plants in China, the quality of influent, climate, grid carbon intensity, water shortage, and financial capability, it is unlikely that the uniform application of technology will be cost-effective. The review has found the following near-term priorities to be highly economically viable: energy-efficient aeration, intelligent process control, chemical optimization, and improved sludge management. More extreme decarbonization involves anaerobic digestion, co-digestion, biogas, heat recovery, nutrient recovery, reuse of reclaimed water, and integration of renewable electricity. The proposed alternative is a differentiated approach: large urban plants are to be transformed into resource-recovery centres; small and medium cities are to implement gradual retrofits and regional sludge management; the rural focus is to be on strong, low-energy decentralized treatment; and industrial parks are to be encouraged to establish source control and water-energy recycling. The life-cycle cost assessment, marginal abatement analysis, plausible monitoring, tariff reform, green finance, and integrated water-energy-waste governance will be essential to achieving carbon neutrality across sectors by 2035.

Circular Retrofit Approaches: Linking Building Renovation, Resource Efficiency, and Environmental Sustainability

Ao Li, Pingkun Deng, Xiao Wei — Wed, 19 Aug 2026 00:00:00 +0800

Circular retrofit practices are now being more firmly placed as a tool to reshape building renovation to be resource efficient and environmentally sustainable by adding life to assets, minimizing material throughput, and minimizing waste without compromising or impairing building performance. This report summarizes theoretical underpinnings, lifecycle plans, evaluation procedures, and implementation terms to describe the circular retrofits, as opposed to traditional energy-focused renovation. We explain the workings of circular retrofit thinking, with the focus on adaptability and reversibility, as well as value retention as internal design logic by the material, component, building, and district loop levels. Our next approach involves strategies in the pre-retrofit diagnostics, circular design and digital documentation, procurement and contracting, selective deconstruction, and operational practices that support the sustainability of performance and extend the service life of the components. The review assesses impact assessment methods, such as whole-life life cycle assessment, dynamic carbon accounting, material flow analysis, circularity indicators, and life cycle costing, which are vulnerable to system boundaries, baselines, service-life assumptions, and end-of-life allocation. Lastly, we examine barriers and enablers by policy, markets, business models, digital governance, and skills, and determine conditions for pathways that can be taken during the transition of pilot projects to mainstream practice. Future studies ought to focus on common typologies and accountability, longitudinal execution indicators, and composite evaluation systems that are unified in terms of carbon, resources, expense, and resilience.

Economic Cost-Benefit Analysis and Environmental Resource Management Performance Evaluation under the Carbon Trading Market

Tianzhi Ouyang, Qi Liu — Mon, 17 Aug 2026 00:00:00 +0800

The carbon trading markets have emerged as a key policy tool to achieve climate mitigation with economic flexibility. Current research, however, tends to examine their impacts from either an economic or an environmental perspective, yielding mixed results regarding market performance. This review is developing an integrated framework for the analysis of economic cost-benefit effects and environmental resource management performance under carbon trading markets. It first summarizes the theoretical basis of carbon trading, including internalizing externalities, property rights allocation, the cap-and-trade system, and carbon price formation. It then reviews key economic costs, such as compliance costs, transaction costs, administrative costs, and costs of low-carbon investment, and key economic benefits, which include abatement-cost savings, increasing energy efficiency, green innovation, and auction revenue. The review also examines aspects of environmental performance, including emission reduction, carbon productivity, resource-use efficiency, pollution-control co-benefits, green innovation, and governance capacity. The article proposes to consider carbon trading as a dynamic governance system, rather than a single trading system, by connecting these facets. The results indicate that well-designed carbon markets require credible price signals, adequate caps, robust monitoring, sufficient liquidity, and policy support. Future research should focus on dynamic assessment, causal analysis, sectoral and regional differences, firm-level dynamics, digital governance, and distributional justice. This holistic view offers insights into theory and practice for the design of carbon markets and the sustainable management of resources.

From Smart Data to Sustainable Decisions: Intelligent Technologies in Environmental Monitoring and Resource Management

Xu Han — Tue, 11 Aug 2026 00:00:00 +0800

The rapid advancement of intelligent technologies, such as machine learning, optimization algorithms, and decision support systems (DSS), presents significant opportunities for improving environmental monitoring and resource management. Such technologies are able to handle large, complicated datasets to deliver actionable insights that are able to support sustainable decision-making. Nonetheless, data quality, model reliability, model uncertainty, and stakeholder participation are important issues that should be considered carefully during the transformation of raw environmental data into informed decisions. The review describes the application of intelligent systems to different contexts of environmental decisions, such as compliance and enforcement, early warning systems, resource allocation, and conservation planning. We argue that these systems must be reliable and fair, highlighting the importance of data governance, transparency, and ethical considerations. We also discuss the practical demands of being deployed in the real world, including the interoperability of the data, adapting the model to new circumstances, and the financial and technical constraints of deploying the more sophisticated technologies. Based on case studies, we determine not only the achievements but also the drawbacks of the existing applications, providing information on how intelligent technologies could be better combined into the decision-making process. In conclusion, we present a research and policy agenda wherein we would focus on integrating the system, assessing the model continuously, and involving a wide range of stakeholders to make sure that these technologies are used in a way that enhances environmental sustainability and social equity.

From Data to Decisions: Integrative Frameworks for Environmental Monitoring in the Anthropocene

Fei Zhao — Wed, 19 Aug 2026 00:00:00 +0800

Environmental monitoring in the Anthropocene is at a pivotal moment from data accumulation towards decision-oriented environmental intelligence. The monitoring systems must be able to integrate multiple data streams and transform these data into actionable, credible and timely knowledge to address the critical issues of rapid climate change, biodiversity loss, pollution, land-use transformation and increasing disaster risks. This review discusses integrative frameworks bringing together the generation of environmental data, analytical synthesis, decision support systems and policy translation. It emphasizes the synergistic use of in situ observations, remote sensing, citizen science, Internet of Things sensors, environmental DNA, bioacoustics and emerging digital platforms. The article examines the role of data fusion, geospatial analysis, statistical modelling, AI, socio-ecological modelling, indicator frameworks, and uncertainty assessment as means to convert observations of diverse nature to usable evidence. It also maintains that interoperability, transparency, institutional capacity, ethical data governance, stakeholder involvement, and environmental justice are equally critical to successful monitoring. The relevance of connecting the outputs of climate monitoring to unambiguous decision pathways is illustrated in applications to climate adaptation, the conservation of biodiversity, pollution control, urban sustainability, water–land systems and disaster risk reduction. The review finds that next-generation environmental monitoring needs to be an adaptive system of knowledge-action that can learn from results, be transparent about uncertainty, and enable accountable governance in a world of dynamic environmental conditions.

A Simplified Analytical Method for Assessing Fluid Mixing Homogeneity in Flow Battery Storage Tanks

Dabin Mi, Qiang Guo, Jiang Wei, Lihui Sui, Yaze Wang, Tianhao Wang — Wed, 12 Aug 2026 00:00:00 +0800

The electrolyte storage tank, as a key component in the vanadium redox flow battery (VRFB) system, directly influences the battery’s operational performance and the accuracy of state-of-charge (SOC) estimation through the mixing homogeneity of the internal electrolyte. To address the challenge in engineering applications of rapidly evaluating mixing processes in storage tanks, this paper proposes a simplified analytical method for assessing fluid mixing homogeneity in VRFB tanks. The proposed method is a computational fluid dynamics (CFD)-based methodology that combines an internal flow-field simulation with monitoring of outlet parameters. It provides a qualitative and semi-quantitative analysis of the tank’s overall mixing performance by comparing the simulated inlet electrolyte scalar concentration curve with the theoretical response curve under ideal, complete-mixing conditions. The research focuses on a multi-inlet conical-bottom self-mixing tank, and a traditional cylindrical flat-bottom tank was used for comparison, with their internal flow fields and mixing mechanisms analyzed using simulation software. The simulation results suggest that the flow streamlines within the conical-bottom self-mixing tank are more uniformly distributed, and its outlet response curve more closely matches the theoretical complete-mixing curve, indicating superior mixing homogeneity compared with the conventional structure. The simulation results were further confirmed by experiments using scaled-down physical models, and the experimentally measured characteristics of the conductivity distributions were in good agreement with the numerical inferences. The paper demonstrates that the suggested approach has the following strengths: simplified modelling, low computational cost, intuitive criteria, and utility as a guide for structural design optimisation of VRFB tanks and for system-level modelling.

Migration and Transformation Mechanism and Source Analysis of "Three Nitrogen" Pollutants in Groundwater

Ji Long Shang, Tao Liu, Lin Guo, Xu Wang, Qi Fa Sun — Fri, 14 Aug 2026 00:00:00 +0800

Contamination of groundwater with the three forms of nitrogen (nitrate N, nitrite N, ammonium N) is a common environmental issue in agricultural, urban, industrial and animal production areas. The nitrogen species are highly mobile, stable, toxic, and redox-sensitive, but are also strongly coupled by biogeochemical reactions. This review systematically summarises the occurrence characteristics, migration behaviour, transformation mechanisms, and source analysis methods for three nitrogen pollutants in groundwater. Nitrate is highly soluble, poorly adsorbed, and the most mobile of the different forms in toxic aquifers. Although nitrite is normally transient, its presence may indicate incomplete nitrification, incomplete denitrification, or a lack of redox stability. Across the levels of ammonium, adsorption, ion exchange, organic nitrogen mineralization, dissimilatory nitrate reduction to ammonium (DNRA), and reducing environments are more strongly affected. The largest nitrogen sources are fertilizer application, manure, sewage leakage, septic systems, livestock wastewater, landfill leachate, industrial discharges, atmospheric deposition, soil organic nitrogen, and geological release of N. But the identification of sources is complicated by the transformation processes as groundwater moves. Thus, a combination of hydrochemical, stable isotopes, microbial functional genes, groundwater flow analysis, laboratory experiments and reactive transport modeling is crucial for the use of integrated approaches. This review emphasizes the importance of moving away from the traditional single-species approach towards a coupled source–migration–transformation approach and offers scientific guidance to support groundwater nitrogen pollution tracing, risk assessment, and remediation.

Unsupervised Intelligent Framework for Earth Science Remote Sensing Applications Based on Clustering Algorithms

Hongyan Zhang, Li Zhao — Wed, 12 Aug 2026 00:00:00 +0800

Archives of Earth observations have grown and continue to grow in volume, modality, and temporal density, providing unprecedented access for monitoring the Earth system and exacerbating the paucity and high cost of quality labels. This is a review of unsupervised intelligent methods for Earth science remote sensing based on clustering algorithms and capable of analyzing data in a scalable, interpretable manner under domain shift. We begin by summarizing the remote sensing data properties that make it difficult to learn unsupervised, and they are: high-dimensionality, mixed-pixels, sensor-specific noise, spatial autocorrelation, non-uniform time series, heterogeneous across sensors. We next list key clustering families that are applied in remote sensing, including centroid-based and medoid-based, probabilistic mixtures, density-based, hierarchical, graph clustering, spectral clustering, subspace/low-rank models of hyperspectral data, fuzzy clustering to ambiguity, and deep clustering using self-supervised representation learning. On these algorithmic mammoths, we introduce end-to-end patterns of framework design, i.e., preprocessing, harmonization, sampling, and tiling on a scale of archive computations, spatial-context synthesis with super pixels and regularization, multi-modal fusion with shared latent spaces and spatiotemporal clustering with consistency and drift management. Lastly, we also talk about application areas like stratifying land-covers, grouping of hyperspectral materials, discovering SAR regimes, hazard surveillance, and analyzing climate-hydrology regimes with the focus on not analyzing by labels but by stability tests, uncertainty reporting, and process-based validation. The review summarizes practical advice and presents open dilemmas to effective, operational unsupervised intelligence for Earth observation.

Complex-Strata Horizontal Borehole Drilling and Multi-Stage Reaming Technology for Water Conservancy Investigation

Changyou Dai, Zijia Ge, Shulei Zhang — Wed, 12 Aug 2026 00:00:00 +0800

Horizontal drilling of boreholes using complex-strata drilling and multi-stage reaming technologies has gained greater significance in water investigations, as vertical drilling often fails to fully characterise laterally distributed seepage pathways, weak interlayers, fractures, karst formations, and structurally controlled defects. The review explores the geology, drilling principles, major equipment systems, fluid and cuttings transport systems, borehole stability problems, and multi-stage reaming techniques for constructing horizontal boreholes in complex strata. Close consideration is given to the interplay among lithological heterogeneity, structural discontinuities, groundwater conditions, and operational parameters on drilling performance and borehole quality. The discussion demonstrates that multi-stage reaming is not merely a borehole enlargement process but a formation-adaptive design process to enhance the ultimate usability of the borehole and minimise the risk of instability throughout construction. Common engineering uses in the foundations of dams, reservoir banks, underground hydraulic structures, and post-treatment checking are also covered, along with evaluation criteria that include trajectory control, wall integrity, process stability, and reliability of the investigation. The review also lists existing constraints, such as a lack of standardisation, a lack of integration between geology and drilling control, and excessive reliance on experience-based design in heterogeneous formations. The further development should focus on intelligent drilling, real-time interpretation of responses, process optimisation using digital twins, and integrated investigation systems. The article is systematically presented as a source for the development of horizontal-borehole construction and staged-enlargement technologies in more challenging landscapes in water-conservancy engineering.

Contribution of Landsat 9 OLI-2/TIRS-2 Imagery to the Mapping of Dolerites in Aboutelfan (Central Chad Massif): Structural Implications

Wed, 12 Aug 2026 00:00:00 +0800

This study focuses on the structural and lithological characterization of dolerite dykes in the Aboutelfan Massif, central Chad, using an integrated remote sensing and field-based approach. Landsat 9 OLI-2/TIRS-2 multispectral imagery was processed through radiometric calibration, spectral band combinations, and principal component analysis to discriminate doleritic bodies from surrounding lithologies. Field observations and petrographic analyses provided ground verification and detailed mineralogical descriptions. The dolerite dykes, generally post-tectonic, intrude the granitic basement with sharp contacts and exhibit variable thicknesses ranging from 3 to 25 m. Rose diagrams reveal dominant structural orientations along N 30° E, N 65° E, N 135°E, and N 153° E, reflecting the regional Pan-African tectonic control on dyke emplacement. The integration of satellite imagery and structural data led to the production of a high-resolution lithological and structural map of the Aboutelfan region. The results highlight the efficiency of Landsat 9 OLI-2/TIRS-2 imagery for identifying mafic intrusions and deciphering tectonic patterns in arid environments. This work provides new insights into the structural evolution of central Chad and establishes a methodological framework for future studies combining hyperspectral imagery and geochemical analyses to better constrain the petrogenesis and geochronology of doleritic intrusions.