The following International Journal of Oil, Gas and Coal Technology article, "Influence of process parameters on conversion efficiency in plasma cracking of heavy oil residues", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Critical Infrastructures article, "Knowledge graph and reinforcement learning-based automatic decision-making for distribution network fault handling", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A technique for detecting potentially harmful metals in regenerated cellulose food packaging is discussed in the International Journal of Environment and Pollution. The new tool can be used for safety assessments of materials increasingly used as alternatives to conventional plastics.
The method measures cadmium, chromium, lead, and titanium in one pass. These metallic elements might be present in processed packaging after manufacture or remanufacture and, under certain conditions, could leach into food contained in the packaging. Tests on five commercially available packaging samples showed that none of these elements were above safety thresholds, but the researchers point out that this might not always be the case and that improved detection methods are needed to strengthen routine monitoring and allow regulatory standards to be applied across the industry.
The new technique uses microwave digestion with heated acidic reagents to break down a sample and release any metallic elements present. The resulting sample is then analysed with inductively coupled plasma atomic emission spectrometry (ICP-AES). This type of analysis heats the sample to form a high-temperature plasma and identifies any metallic elements present based on the characteristic light they emit. It is essentially a high-tech version of the school-lab flame test for metals, where the colour of the flame reveals the presence of a given element.
While regenerated cellulose is at the forefront of environmentally friendly packaging, its manufacture can involve chemical additives and other substances requiring safety assessment. The new method could help manufacturers and regulators identify potential contamination more reliably, improving quality control and so the safety of this kind of packaging.
Wang, X., Chen, X., Liu, D. and Geng, H. (2026) ‘Study on detection method for harmful chemical residues in regenerated cellulose food packaging materials’, Int. J. Environment and Pollution, Vol. 76, No. 9, pp.1–19.
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The following International Journal of Environment and Pollution article, "Study on detection method for harmful chemical residues in regenerated cellulose food packaging materials", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Artificial intelligence (AI) combined with virtual reality (VR) could guide us through tomorrow's cities before they are even built, according to research in the International Journal of Environment and Pollution. Such models could be useful for architects, planners, and policymakers.
The team describes a modelling method that uses computer vision, technology that enables computers to interpret images, to scan architectural drawings and buildings. Machine learning (ML), a form of AI that identifies patterns in data, then processes these scans to allow VR technology to construct realistic virtual architectural scenes.
The researchers suggest that greater automation of this process could reduce the labour and resources required by conventional 3D modelling, in which scanned material is often assembled and processed manually. This will be increasingly important as expanding cities place greater demands on infrastructure management and public services. Aside from applications in architecture and construction, the virtual city environments could be used to simulate transport conditions, investigate how tourism might be supported, and also allow responses to emergencies to be rehearsed. Such simulations would allow officials and other professionals to test scenarios in a controlled digital environment before acting in the physical city.
The work points towards digital representations of urban areas becoming increasingly useful in the management of infrastructure and the coordination of complex projects.
Dai, D., Tu, B. and Liu, J. (2026) 'Generation of virtual reality landscape scenes for digital urban architecture based on AI', Int. J. Environment and Pollution, Vol. 76, No. 8, pp.109-122.
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A digital scheduling framework combining machine learning, optimisation and blockchain technology could allow carbon emissions to be reduced at lower cost in regional energy systems, according to research in the International Journal of Environment and Pollution. The system cut the marginal emission-reduction cost, the additional expense of removing one more tonne of carbon, by about 12 per cent when the carbon quota was restricted to half the system’s baseline emissions.
The new framework addresses a problem facing those managing increasingly complex regional energy networks, where electricity, heat and other sources must be coordinated while emissions are controlled. The problem being that the differences in data quality can make it difficult to assign emissions to specific sources and to then respond to rapidly changing demand.
The researchers used a machine-learning algorithm known as extreme gradient boosting, XGBoost, to identify complex relationships in the data and so forecast daily energy loads using historical consumption, electricity prices, weather, and industrial activity. The forecasts continuously update a mixed-integer linear programming (MILP) model, an optimisation method that selects decisions subject to constraints such as energy balances and carbon limits.
In addition, the framework records encrypted emissions data using a blockchain platform. Blockchain technology is perhaps more familiar as a cryptocurrency system, but it is essentially an immutable digital ledger that can be used to record data and information exchanges of all kinds. The blockchain thus provides a traceable record of emissions and assigns responsibility across the energy network.
The researchers suggest that integrating forecasting, scheduling, and verification into a single system could provide regional energy systems with a more responsive mechanism for managing emissions as carbon constraints tighten.
Liu, W. and Ma, W. (2026) ‘Marginal emission reduction costs of regional energy system transformation enabled by digital economy from the perspective of supply chain’, Int. J. Environment and Pollution, Vol. 76, No. 8, pp.1–26.
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A new AI model for predicting energy consumption could help managers plan ahead as well as open up opportunities for cutting carbon emissions, according to research in the International Journal of Information and Communication Technology.
The system uses a transformer-based generative adversarial network (GAN) to learn complex patterns in energy use. GANs are machine-learning systems in which two neural networks work against each other to produce realistic data. By adding Bayesian statistical optimisation, the team could set the system for best performance.
They tested the model on 2000 hourly energy and emissions records drawn from smart meters, building management systems, and industrial grids. They removed references to missing data and deleted outliers to normalise the input. The model made predictions that closely matched the observed energy data while taking only a few seconds to train and run.
The researchers explain that their computer-generated scenarios will allow operators to test demand-shifting measures, integrate renewable generation, and identify inefficiencies ahead of actual power generation and emission formation.
The approach addresses several weaknesses in conventional predictive models, which are known to struggle with non-linear consumption patterns, sudden spikes and troughs, and multiple external factors. This carbon-aware energy optimisation could be used across sites with internal power generation, such as industrial sites and in wider energy systems.
Liu, Y. and Li, B. (2026) ‘Modelling and predicting energy consumption patterns using generative adversarial networks for effective carbon management’, Int. J. Information and Communication Technology, Vol. 27, No. 92, pp.79–110.
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The following International Journal of Tourism Anthropology article, "Developing community-based waste management for public spaces in coastal tourism villages: establishing institutions with local champions", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Local action may offer a model for waste management in rural tourism, according to research in the International Journal of Tourism Anthropology.
A study of waste problems in Kampung Nipah, a coastal tourism hamlet in North Sumatra, Indonesia, argues that rural communities with limited public services may need locally governed waste systems rather than relying solely on municipal provision. The researchers found that fly-tipping and littering in Kampung Nipah is linked to inadequate infrastructure. However, it also occurs because of established cultural practices, weak collective responsibility, and an absence of an institution responsible for managing waste across the wider village. The team explains that government waste-collection services do not reach the area, while rubbish from residents, tourists and rivers can accumulate along the coast.
The study involved ethnographic methods, a close, but extended, study of people and their everyday practices. It was able to show how social behaviour and institutional arrangements contribute to the problem of local waste in such a place. To overcome this problem, the team proposes a community-based model centred on local champions, residents who take an active leadership role, who work alongside tourism managers and other stakeholders to change the waste culture.
While the work focuses on only one location, it may well prove to have wider implications for rural coastal tourism elsewhere.
Zuska, F. and Zulkifli, Z. (2026) ‘Developing community-based waste management for public spaces in coastal tourism villages: establishing institutions with local champions’, Int. J. Tourism Anthropology, Vol. 10, No. 5, pp.1–22.
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On hearing the first cuckoo of spring, the poet’s heart might sing, and while one swallow does not a summer make, the annual migrations of bird species across continents have fascinated us for centuries. Today, however, technology is giving ornithologists the means to follow these incredible journeys in ways that would have been unimaginable to earlier generations of naturalists.
Research in the International Journal of Global Environmental Issues shows that artificial intelligence (AI) is being used by conservationists to help predict bird migration timings and patterns. The technology might allow them to identify how climate change and habitat loss are affecting the journeys of many migratory bird species.
The team looked at research into machine learning and deep learning models used to analyse migration, habitat preferences, and bird populations. They explain that machine learning refers to computer systems that identify patterns in data, while deep learning is a form of machine learning that uses layered neural networks to detect more complex patterns. Increasingly, automated sound recording devices that pick up bird calls and songs as they migrate overhead are providing useful data where visual monitoring is simply not possible.
These remote monitoring systems can operate for long periods with relatively little human intervention, building up a picture of migration activity over many nights, seasons, and even years. They offer researchers a way to monitor birds at a scale that would be extremely difficult to achieve with conventional field observations. This technology could thus strengthen monitoring, as habitats and migration routes respond to rising temperatures, changing rainfall, and land development. More accurate forecasts will help determine which habitats require greater protection and inform policy to ensure migratory bird conservation remains central to climate adaptation strategies.
Musale, P.P. and Sonawani, S.S. (2026) ‘Advancements and challenges in bird migration models: a comprehensive survey’, Int. J. Global Environmental Issues, Vol. 25, No. 1, pp.1–26.
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Research in the International Journal of Sustainable Development has looked at eight of the nations involved in the Second World War, both Allied and Axis countries, and shows how that period of history helped set societies on the path to the resource-intensive modern economies we have today. The research links wartime mobilisation to the period known as the Great Acceleration in energy use, material consumption, and ultimately detrimental environmental impact.
The study looks at societal change from before the war, 1935, to the post-war recovery period and the boomer years up to 1960. Demographics, economic activity, power supply, material and resource flow, and environmental impact are all examined. Three major consequences of WWII are seen. First, acceleration, in which existing trends become even more intense. Secondly, redirection where development shifts towards new technologies and the opening up of novel resources. Thirdly, reset, in which destruction or political upheaval changed the direction of nations from the paths there were on before the war.
The researchers use the term “socio-metabolic transition” to describe the various changes in power consumption and physical resources in society. By adopting this almost biological model, they were able to connect wartime production and resource mobilisation with institutional and technological changes that persisted long after 1945.
Abrari, L., Rezaei, N. and Linnanen, L. (2026) ‘World War II and its lasting legacy: an overview of socio-metabolic transition, environmental impacts and resource flows’, Int. J. Sustainable Development, Vol. 29, No. 5, pp.1–62.
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A new diagnostic method could allow electricity substations to detect hidden insulation faults in relay-protection circuits earlier than conventional testing, according to research in the International Journal of Energy Technology and Policy.
The researchers have combined three measurements into a diagnostic model: zero-sequence current, an electrical signal indicating unintended current paths, waveform similarity, which compares measured current patterns with those expected under normal conditions, and third-harmonic content, a component of the electrical waveform that can reveal non-linear grounding faults.
In tests, they saw a 98.6 per cent detection rate for insulation faults and 94.3 per cent accuracy in locating multiple grounding points. Average diagnostic latency was less than 45 milliseconds, with a 1.5 per cent false-positive rate during normal operation. The team adds that the system can also detect insulation deterioration when resistance falls to between 50 and 100 kilohms. This offers an early warning sooner than conventional, offline methods, potentially allowing maintenance to be carried out before faults become critical.
A six-month pilot test at a 500-kilovolt substation in Nanjing with linked inspection robots demonstrated rapid communication between substation devices. Overall, the average fault-resolution time was reduced from more than four hours to just over one hour, and manual inspections were cut by 76 per cent.
Shi, H., You, H., Chen, X., Xu, S. and Chen, J. (2026) ‘Enabling holistic insulation monitoring in secondary AC protection circuits: a diagnostic algorithm’, Int. J. Energy Technology and Policy, Vol. 21, No. 5, pp.20–49.
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