Journal of Environmental & Earth Sciences

Next-Generation 3D Mapping Techniques for Geological Hazard Monitoring

Shuai Fu — 2026-06-01

The increasing frequency and intensity of geological hazards such as earthquakes, landslides, volcanic eruptions, and floods underscore the need for advanced monitoring techniques. Light Detection and Ranging (LiDAR), satellite-based technologies, and Unmanned Aerial Vehicles (UAVs) are next-generation 3D mapping technologies that have transformed geological hazards monitoring due to their high-resolution, real-time data that could improve hazard detection, risk evaluation, and disaster management. Through these technologies, detailed and three-dimensional models of geological features can be produced, and this helps in the detection of hazards like fault lines, unstable slopes, and volcanic activities with more accuracy than before. The combination of several sources of data and the development of machine learning and predictive modeling has further increased the abilities of 3D mapping systems, which have allowed them to monitor hazards in real-time and provide early warning systems. The challenges associated with data quality, computational requirements, environmental issues, and data integration still persist despite the great advancement. The future development of sensor technology, autonomous systems, and predictive modeling has the potential to enhance hazard prediction and early warning and risk mitigation approaches. Due to the use of 3D mapping technologies, disaster preparedness can be enhanced, negative consequences of natural calamities can be decreased, and the overall resilience to geological threats can be improved. In this review, the development, present status, use, challenges, and future trends of 3D mapping in monitoring geological hazards have been discussed.

Smart Environmental Technologies for Safeguarding Tangible Heritage: From Microclimate Control to Predictive Deterioration Modelling

Shuang Li — 2026-06-04

The environmental conditions determine the long-term conservation of tangible cultural heritage, affecting the processes of physical, chemical, and biological degradation of materials and situations over an extensive spectrum. The last few years have seen expansive gains in sensing technology, data acquisition, and analysis procedures, which have facilitated the creation of intelligent environmental practices that surpass the conventional and unchanging conservation policies. This review presents a recap on existing studies in smart environmental technologies to preserve tangible heritage with a focus on the spectrum between environmental monitoring and intelligent microclimate control, predictive deterioration modelling, and decision support. The paper reviews the main environmental hazards to build, movable, and outdoor heritage sites and outlines how high-resolution surveillance systems, sensor networks, and non-invasive methods deliver the data base of adaptive conservation management. Intelligent microclimate control strategies are studied in the context of the ability to achieve conservation performance, energy efficiency, and sustainability. Special focus is made on predictive deterioration modelling, which can include physics-based, empirical, and data-driven models, and the issues of validation, uncertainty, and interpretability in heritage. The combination of these elements as part of decision support structures is noted as a critical move to preventive and risk-based conservation. Through a critical analysis of the existing capacities and capacities, the review outlines the main gaps in the current research and the way forward in the future of designing resilient, data-infused heritage conservation systems that can address the strategic shifts in the environmental and climatic forces.

Environmental IoT under Attack: A Review of Adversarial Machine Learning in Monitoring Networks

Hui Zeng — 2026-06-12

Environmental Internet of Things (E-IoT) networks are also used in real-time air, water, soil, and ecological systems monitoring, which provides high-resolution data that is essential in environmental management and policy decision-making. When used in these networks, the implementation of machine learning (ML) will improve the predictive potential, anomaly detection, and automated decision-making. Nevertheless, the use of ML models brings with it additional security threats in the guise of adversarial machine learning (AML) attacks, which can alter the input to models, their training data, or parameters in order to trigger inaccurate predictions or to prevent the detection of events of interest. This review essentially offers a thorough analysis of E-IoT network AML threats, the distinct characteristics of resource-constrained, distributed, and dynamic environmental sensing environments. Along with the mechanisms, the attack surface, and the impacts that such attacks may have on monitoring reliability and decision-making, we classify AML attacks into evasion, poisoning, model extraction, and backdoor attacks. The review also provides a survey of defense techniques at data, model, and system levels, such as preprocessing, robust modeling, adversarial training, secure aggregation, and self-healing networks, and the advantages, weaknesses, and trade-offs of these techniques. Lastly, the challenges that are recognized as open include a lack of realistic datasets, coping with concept drift, resource limitations, and interdisciplinary research. Through the synthesis of existing information, this review will inform the development of resilient, safe, and reliable E-IoT networks that will be able to support reliable environmental monitoring when subjected to adversarial attacks.

Environmental Stewardship in Civil Infrastructure: A Comprehensive Review of AI Applications in Road and Bridge Engineering

Yi Su — 2026-06-11

The road and bridge infrastructure systems are also among the most resource-intensive systems in the built environment, which produce massive environmental impacts on their life cycles. Civil infrastructure environmental stewardship must therefore demand decision-making methods that are no longer limited to the static analysis of sustainability, but rather to the adaptive and data-driven management of long-lived resources. Recent innovations in artificial intelligence (AI) can provide transformative potential to facilitate this transition by making predictions, optimization, and continuous monitoring in the process of planning, design, construction, operation, maintenance, and end-of-life stages. The article is a comprehensive review of AI applications that promote environmental stewardship in road and bridge engineering. The review combines the state-of-the-art approaches, which involve AI in combination with the life-cycle assessment, digital twins, sensing systems, and asset management systems to minimize greenhouse gas emissions, energy consumption, material use, waste, and ecosystem disruption. The focus is specifically on the way AI advances environmentally responsible planning and design, low-impact construction delivery, predictive and network-level maintenance approaches, and circular end-of-life approaches. The review also explores cross-cutting issues to do with the quality of data, model transferability, interpretability, governance, and equity that impact the practical efficacy of AI-based stewardship. This article forms a systematic basis for future research and implementation by categorizing the current research on environmental impact domains, life-cycle stages, and AI methods. The results show how AI can transform the infrastructure management system into a proactive, measurable, and robust environmental custodianship, as well as the requirement of interconnected systems, reliable models, and institutional preparedness.

Research on the Resilience Evaluation and Enhancement Mechanism of Sustainable Development in Cultural Tourism under Environmental Regulatory Pressure

Qiming Wang — 2026-06-09

The pressure from environmental regulations is emerging as an effective and constant factor that is developing the sustainable development of cultural tourism destinations. The review is a synthesis of studies on the process of resilience evaluation and improvement of cultural tourism systems that are increasingly subject to governance by the environment of very high standards. The article combines separate literatures on tourism sustainability, environmental regulation, and adaptive governance based on the resilience theory and coupled socio-ecological-economic systems perspective to explain how regulatory pressure influences resilience processes. The review indicates that regulation has dual and context-specific effects, including compliance costs, capacity constraints, and uncertainty in operations, which can undermine short-term absorptive capacity. Credible and well-coordinated regulatory regimes can boost green innovation, resource-use efficiency, product upgrading, and institutional learning, and enhance adaptive and transformative resilience. Methodologically, the current resilience measurements are dependent on composite indicator systems in terms of the environmental, economic, social, cultural, and institutional facets, but they are constrained by the lack of dynamism in measurements, the lack of uniformity in the selection of indicators, and a deficit of multi-scale interactions. The mechanism-oriented evidence focuses on four interacting channels: constraint, incentive, structural adjustment, and governance mediation channels, in which the regulation determines destination pathways. The review also reveals intervention strategies for resilience enhancement that focus on policy alignment, technology-driven visitor and environmental management, low-impact cultural experiences, industrial diversification, inclusive community involvement, and adaptive and data-driven governance. Dynamic assessment schemes, causal findings, and distribution-sensitive studies should be developed in the future to enable sustainable transformation under the influence of a regulation.

The Impact of Bio Compost Use on the Agricultural Soil Microbiome: Evidence from a Laboratory-Scale Study

Angelantonio Calabrese — 2026-06-05

Composted organic amendments are increasingly recognized as viable alternatives to synthetic fertilizers, driven by the rising interest in organic waste recycling and sustainable soil management. This study aims to evaluate the long-term effects of bio compost, derived from organic waste, on the chemical and microbiological properties of agricultural soils. A one-year laboratory-scale experiment was conducted using nine microcosm replicates with increasing doses of compost (ratios 1:1, 1:2, 1:4), applied in both powder and granular forms, with and without the addition of water. Microbiological analysis based on 16S rRNA gene sequencing revealed significant shifts in microbial composition. The results demonstrated a consistent increase in microbiological DNA concentration in samples amended with bio-compost, showing a 50% increase from the initial concentration. Furthermore, regarding species composition, bio-compost altered the bacterial population in favor of the predominant species introduced into the soil, indicating a selective enhancement of the bacterial community. Additionally, the addition of water did not affect either the quantity or quality of the bacterial composition. Microbial biomass significantly improved following compost application, with powdered formulations proving more effective than granular ones, and drought conditions often eliciting more pronounced responses. These findings demonstrate that the amendment can improve soil quality by minimizing disruption to microbial communities and promoting long-term soil fertility. Adopting this approach appears beneficial for circular, bio-based agricultural systems.