Assisted infant toilet training: A pathway to planetary health?

Prestige competition: Inter-city striving for excellence and the low-carbon transition in residential energy use

The increasing water pollution calls for more effective and sustainable treatment technologies. Conventional technologies face critical challenges such as fouling, struggling with emerging micropollutants, and limited mass transfer. Electrocatalytic membranes (ECMs) couple convective transport through a porous membrane-electrode with interfacial electrochemical reactions, enabling simultaneous separation and contaminant transformation in compact process trains. High removal efficiencies are governed by permeation-enhanced mass transfer and current distribution rather than intrinsic catalytic activity. The dominant removal pathway shifts among electrosorption, direct electron transfer, and indirect oxidation. ECMs also reduce fouling via electrostatic repulsion and in-situ redox reactions, suitable for various organic pollutants, including pharmaceuticals and micropollutants, while also enabling resource recovery from wastewater. However, they are limited by durability and secondary burdens, including catalyst deactivation or leaching, pore blockage, catalytic instability, sludge management, and energy-intensive fabrication routes for ceramic ECMs. This review provides a comprehensive overview of ECM fabrication strategies, system configurations, and their multifunctional roles in water treatment. Emerging frontiers include single-atom ECMs that maximize atom utilization and enable tunable reactive-species selectivity, and machine-learning-assisted design frameworks that accelerate multi-objective optimization of catalyst-architecture-operation for durability and by-product control. Standardized metrics linking energy use, current efficiency, by-products, and long-term ageing and life-cycle analyses are essential for scalable deployment of ECM technology. • Electrocatalytic membranes (ECMs) couple filtration with in-pore redox chemistry. • We classify ECM systems and link each configuration to its separation mechanisms. • Electrocatalytic membrane fabrication strategies are assessed. • Industrial-scale deployment is limited by cost, stability, and process complexity. • Challenges and future directions of electrocatalytic membranes are analyzed.

Pervasive sensing enables diverse wearable event detection (WED) applications, but deploying machine learning models on resource-constrained microcontrollers (MCUs) poses significant challenges, particularly in ensuring prediction reliability under data shifts or out-of-distribution (OOD) inputs. While Uncertainty quantification methods offer a way to assess this reliability, many are computationally prohibitive for MCUs, and detecting multiple events concurrently further exacerbates resource constraints. Addressing these combined challenges, this paper presents an uncertainty and resource-aware framework designed for reliable and efficient multi-event WED on MCUs, significantly extending our preliminary work. The proposed framework achieves this by integrating Evidential Deep Learning (EDL) for efficient, single-pass uncertainty estimation with a novel cascade learning architecture. This architecture promotes resource efficiency via: (i) intra-event sharing using uncertainty-aware early exits within a staged model (shallow, medium, deep), allowing simpler samples to terminate inference earlier; and (ii) inter-event sharing using a multi-head design where multiple event detectors share a common backbone, minimizing overhead. System efficiency is further enhanced through MCU-specific optimizations, including targeted architecture search, quantization, efficient uncertainty operator implementation using standard TensorFlow Lite Micro (TFLM) operations, and library footprint reduction. We conducted extensive experiments on four distinct wearable datasets (Oesense, KWS, ECG5000, and HHAR) and two MCU platforms (STM32F446ZE, STM32H747XI), comparing the proposed framework against strong baselines including Deep Ensembles and Vanilla EDL. Results demonstrate the proposed framework’s effectiveness, achieving competitive accuracy and uncertainty performance (e.g., up to 22% lower NLL than data augmentation) while drastically reducing resource consumption, offering up to 8.64 × faster inference, up to 8.57 × lower energy use, and 55% smaller memory footprint compared to ensemble methods. The proposed framework enables the deployment of reliable, uncertainty-aware multi-event detection on a wider range of low-power MCUs.

This study introduces a novel scaling analysis to investigate the transient solid-phase gasification of horizontal clear poly(methyl methacrylate) (PMMA) slabs under both ambient and oxygen-depleted conditions. Small samples were exposed to controlled external radiant heating in normal and nitrogen-diluted atmospheres to simulate low-oxygen fire environments. The energy-based scaling framework removes variability in characteristic timescales and response amplitudes, standardising the results and enabling consistent comparisons across oxygen levels and heat fluxes. It reveals new relationships between governing parameters, clarifies the role of competing mechanisms, and facilitates identification of transitions between heat-transfer–limited and kinetically controlled regimes. Scaling metrics provide predictive tools to discriminate between regimes, quantify energy use, and assess the role of boundary layer dynamics in mass transfer efficiency. Results highlight critical mechanisms: oxygen acts as both a reactant and a catalyst at the onset of gasification, reducing the energy required for decomposition, while external heat flux dominates regime control, with a threshold near 20 kW·m -2 separating flux-dependent from flux-independent behaviour. This framework simplifies modelling and improves CFD predictive capability by describing gasification through energy thresholds rather than time scales. • Energy-based scaling enables predictive PMMA gasification across conditions. • Oxygen acts as catalyst lowering gasification energy barriers in PMMA combustion • Scaling metrics identifies diffusion-to-kinetic regime transitions • Heat flux controls regimes; ∼20 kW·m -2 separates flux-dependent and independent.

Environmental sustainability represents an emerging priority for cardiology, owing to the close interconnection between planetary health and human health, with cardiovascular diseases constituting the main clinical outcome. The global healthcare sector accounts for approximately 4-5% of total greenhouse gas emissions, and cardiology contributes substantially to this burden because of its high resource intensity in diagnostic testing, interventional procedures, and energy consumption. At the same time, environmental factors such as air pollution, extreme temperatures, defined as values significantly above or below the regional average caused by climate change, and exposure to emerging contaminants, including heavy metals (lead, cadmium, arsenic) and micro- and nanoplastics are increasingly recognized as major determinants of cardiovascular risk. Chronic exposure to these pollutants is associated with oxidative stress, systemic inflammation, and accelerated progression of atherosclerosis. Strategies for sustainable cardiology primarily aim to reduce emissions related to energy use and supply chains. Priority actions include adopting circular economy principles (reduce, reuse, recycle), improving the appropriateness and optimization of diagnostic testing favoring lower environmental impact modalities, such as echocardiography, over carbon-intensive techniques and implementing telemedicine to reduce patient and provider travel. Furthermore, primary cardiovascular prevention can be considered an effective "double-benefit strategy", capable of simultaneously reducing disease burden and the demand for emission-intensive healthcare. In this context, healthcare professionals and scientific societies, including ANMCO, are called upon to lead a cultural shift by integrating environmental sustainability as a core ethical principle of contemporary cardiology practice.

The traditional notion of productivity in ergonomics -primarily focused on efficiency and performance- is insufficient to address environmental responsibility in the sustainable management of resources. Eco-productivity has emerged as a concept that can transform ergonomic design and product development through regenerative models that optimize the use of materials, energy, and other resources. This study aims to examine how eco-productivity is defined and discussed in the literature and to explore its application in ergonomic-sustainable design from an ergoecological perspective. Eco-productivity incorporates systemic and regenerative principles aimed at reducing environmental degradation while enhancing human and ecological well-being -which are consistent with the contemporary scope of ergonomics. Despite its conceptual relevance, eco-productivity remains less developed than related approaches such as eco-efficiency and eco-effectiveness. The findings highlight the need for clear metrics and interdisciplinary methodologies to effectively operationalize eco-productivity. By linking energy, material, and information flows with ergonomic practices, this concept offers an integrative framework for advancing ergonomic-sustainable design and promoting more balanced interactions among humans, technology, and the environment. Using the Roses standard, this study conducts a systematic review of the eco-productivity concept. This analysis deepens understanding of how eco-productivity can guide future ergonomic research and practice within sustainability transitions.

• A Python model for semi-islanded green ammonia production was developed. • The LCOA ranges from 669.30 to 867.94 €/tNH 3 . • Power generation represents the largest share of total system costs. • The LCOA decreases by up to 15.15 % over the next two decades. • Dynamic operation achieves up to a 6 % reduction compared with continuous operation. The study presents a technical and economic assessment of green ammonia production in several counties in Ireland. The system is based on renewable energy sources, namely photovoltaic and offshore wind. Three locations were chosen based on their renewable potential and the availability of export ports to EU (European Union) markets. A high-temporal-resolution model for green ammonia production has been developed for the first time in Ireland. The WSA (Wind Solar Ammonia) model was developed specifically for this research. It uses MILP (Mixed Integer Linear Programming) and optimisation techniques to simulate scenarios at the lowest possible cost. The Python-based model incorporates all relevant energy subsystems and use functions from specialised libraries. The WSA model includes large-scale hydrogen production with proton exchange membrane electrolysers, air separation to produce nitrogen, Haber-Bosch ammonia synthesis, desalination unit. Storages buffers were implemented for green hydrogen, green ammonia, purified sea water, and nitrogen. Both continuous and dynamic operation were simulated, continuous operation reflects industrial reliability, stable equipment performance, and maximised lifetime, while dynamic operation captures renewable intermittency, curtailment reduction, and system flexibility. Cork is identified as the least-cost location, with the dynamic operation system achieving the lowest LCOA (Levelised Cost Of Ammonia) at 791.07 €/t in 2030 and 731.45 €/t in 2040, outperforming the continuous operation system, which records 834.27 €/t in 2030 and 741.62 €/t in 2040. The system achieve a carbon saving up to 94.63 % compared to the ammonia fossil fuel-based comparator.

Angus-based heifers (F0; n = 72; 14 to 15 mo; initial body weight [BW] = 380.4 ± 50.56 kg) were ranked by BW, bred via artificial insemination (AI) with female-sexed semen, and assigned to receive a basal diet (CON; n = 36) or the basal diet plus a vitamin and mineral supplement (VTM; n = 36) with the total mixed ration. Treatments were applied from breeding through calving, after which cow-calf pairs (n = 14 CON; n = 17 VTM) received a common diet. A subset of F1 heifers (n = 7 CON; n = 9 VTM) were bred via AI with female-sexed semen and evaluated from breeding through d 250 of gestation when pregnant heifers were slaughtered. Nutrient balance and energy metabolism were measured each trimester via apparent total tract digestibility and indirect calorimetry. Blood samples were collected each trimester and at harvest for hormone/metabolite analysis. Data were analyzed using the MIXED procedure of SAS with repeated measures where appropriate, with treatment, time, and interaction included as fixed effects and animal as the experimental unit. In F1 heifers, digestibility of dry matter, organic matter, neutral detergent fiber, acid detergent fiber, and N was not influenced by treatment (P ≥ 0.21) but decreased (P ≤ 0.03) as gestation advanced. Fecal energy (FE) losses, heat production (HP) as a percentage of gross energy intake, and retained energy (RE) were not affected by treatment (P ≥ 0.52); however, FE and HP increased (P ≤ 0.03) while RE decreased (P = 0.01) with advancing gestation. Circulating insulin concentration was greater (P < 0.01) in VTM heifers, whereas glucose decreased (P < 0.01) and non-esterified fatty acids and blood urea nitrogen increased (P ≤ 0.01) as gestation progressed. Body weight was greater (P < 0.01) in VTM heifers and at slaughter, VTM heifers tended (P ≤ 0.10) to have heavier carcasses and greater ribeye area than CON. The F1 CON heifers had heavier spleens relative to BW (P = 0.04) whereas other organs did not differ (P ≥ 0.17). In F2 fetuses, CON tended to have heavier reproductive tracts and spleens (P ≤ 0.10) with no other differences in organ weights (P ≥ 0.13), whereas VTM fetuses had greater (P = 0.01) blood glucose concentration. These results indicate that prenatal micronutrient supplementation programs growth and metabolic function across generations, with minimal effects on organ mass. Advancing gestation reduced efficiency of energy and nitrogen use, reflecting nutrient partitioning shifts supporting fetal growth.

Model-based analysis of fuel pathways is essential for informing energy and environmental policies. Two major model types are typically used: multisector dynamics models, which capture the broader energy economy, such as GCAM (Global Change Analysis Model), and life-cycle assessment models, such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation). Each has distinct strengths and limitations, and recent studies have increasingly adopted hybrid approaches to harness the advantages of both. However, such integration is often time-consuming and complicated by inconsistencies in the system boundaries and technology definitions. We present LC-GCAM, a new tool that enables estimation of life-cycle greenhouse gas emissions and primary energy use for any fuel pathway represented in GCAM. We apply LC-GCAM to 300 scenarios designed to explore key uncertainties affecting the life-cycle performance of future fuel options in the U.S. freight sector. To evaluate LC-GCAM, we compared its results with those from GREET for nine fuel types in a 2030 reference scenario. When input assumptions are modestly aligned, LC-GCAM and GREET estimates typically agree within 13% (aggregate absolute-sum error), although discrepancies can be larger for pathways involving large amounts of land use change emissions. LC-GCAM offers a flexible and efficient approach to generating life-cycle metrics within an integrated modeling framework, supporting robust policy analysis across a wide range of interacting energy system uncertainties.

ABSTRACT Balancing thermal comfort, indoor air quality (IAQ), and energy use in office buildings located in cold semi-arid regions remains a major challenge, as most previous studies have examined these factors independently. This paper presents a replicable framework for the design and evaluation of a centralized HVAC system intended to replace conventional split-unit systems in a typical office building in Tehran. Dynamic energy simulation, computational fluid dynamics, and global sensitivity analysis are combined to assess thermal comfort, ventilation performance, and annual heating and cooling energy consumption. To provide a unified and holistic evaluation, this study introduces the Central HVAC Tri-Performance Index (CH-TPI), a composite decision-support indicator that integrates comfort (PMV, ULH), IAQ (LMA, ACE), and total HVAC energy use into a single normalized score. Results for Tehran show that the proposed HVAC system improves ventilation effectiveness and thermal comfort while lowering total HVAC energy consumption compared to the baseline case. Across the studied climates, Kerman and Isfahan achieve the highest CH-TPI values (0.803 and 0.794), matched by Shiraz (0.794) and followed by Tehran (PM) (0.756), while the baseline Tehran case shows the lowest performance (0.303). These findings highlight the effectiveness of the proposed framework and the importance of climate-responsive HVAC design.

Lithium demand is surging to support the global energy transition, raising concerns over the environmental impacts of its production. However, the quantitative contribution of advanced extraction technologies in reducing the environmental impacts of lithium is still unclear. To fill this gap, this study first conducts a comprehensive life cycle assessment (LCA) of 11 environmental impact categories from lithium extraction worldwide, covering diverse sources (brines, spodumene, clays, and geothermal brines) and extraction technologies. Results reveal that producing 1 kg of Li2CO3 generates 2.14-19.11 kg CO2-eq, where the Mg2+/Li+ ratio in brines is a key driver of environmental outcomes, influencing extraction efficiency. We further evaluate emerging Direct Lithium Extraction (DLE) technologies for low-concentration, high Mg2+/Li+ brines, finding that most DLE methods yield ∼4-fold higher impacts than traditional methods due to intensive chemical and energy use. In contrast, four advanced DLE technologies, including adsorption-coupled membrane, solvent extraction, and electrochemical deintercalation/intercalation with LFP/FP or LiMn2O4/λ-MnO2 electrodes, reduce emissions by up to 60% relative to other DLE options. Transitioning to renewable energy enhances DLE viability, potentially lowering impacts below traditional levels. Our findings highlight pathways for sustainable lithium supply through technological advancement and energy decarbonization.

Life cycle assessment of different pre-treatment methods for silicon solar panel recycling.

As the world's second-largest milk producer, the U.S. dairy industry aims to achieve net GHG neutrality for all dairy products by 2050. Meeting this target requires understanding emissions across the dairy supply chain, extending beyond farms to include full processing activities. While previous Life Cycle Assessments (LCAs) evaluated individual products like fluid milk and cheese, no comprehensive cradle-to-processing gate LCA exists for the industry. Moreover, prior LCAs often allocated facility-level emissions across multiple products, overlooking variations in energy and materials among coproducts. This study addresses these gaps, quantifying total cradle-to-processing gate emissions, emissions per kilogram of product, and product emissions per kilogram of fat and protein-corrected milk (FPCM) across 12 regions covering 99.96% of U.S. dairy production. In 2020, total emissions were 163.3 million tonnes CO2e, with raw milk production accounting for an average of 81%. Postfarm gate emissions varied regionally due to differences in product mix, energy use, electricity grid composition, and transportation distances. Results identify raw milk production as the largest emissions source across the evaluated uncertainty range, indicating that mitigation will require coordinated action across both farm and processing stages. Findings provide critical insights for policymakers and stakeholders to effectively prioritize strategies such as energy efficiency improvements and renewable energy adoption in future sustainability initiatives.

Enhancing the energy efficiency of acetaldehyde production is essential for advancing operational performance and supporting sustainable chemical manufacturing. This study investigates the effect of integrating an additional heat exchanger into the ethanol dehydrogenation flowsheet, positioned before the feed stream enters the process unit, on overall thermal efficiency. Thermodynamic simulations were conducted to compare the baseline configuration with the modified design. The added heat exchanger recovers energy from existing process streams, thereby reducing dependence on external heating utilities and minimizing unnecessary heat losses. Simulation results reveal a significant reduction in total energy demand, with overall heat consumption lowered by 507,602.4 kJ/h relative to the original system. These findings highlight the potential of strategic heat integration to markedly enhance the energy performance of acetaldehyde production. In conclusion, incorporating a heat exchanger prior to downstream heating units offers a practical and effective means of optimizing energy use while promoting more efficient and environmentally responsible process designs. Copyright © 2026 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Background: Air pollution poses major health and environmental risks globally, with disproportionate impacts in low- and middle-income countries lacking robust monitoring systems. In Nigeria, rapid urbanization, fossil fuel dependence, and informal industrial activities have intensified urban air quality challenges. In Aba, limited continuous, pollutant-specific monitoring has constrained comprehensive understanding of long-term temporal and spatial pollution dynamics. Objectives: This study quantified tropospheric carbon monoxide (CO) and aerosol concentrations in Aba (2019–2024), identified seasonal trends and spatial hotspots, and assessed inter-annual variability using Sentinel-5 Precursor (Sentinel-5P) satellite data integrated with GIS-based spatial analysis. Methods: Satellite-derived CO and aerosol data for Aba were obtained from Sentinel-5P (2019–2024). Datasets were accessed and processed within Google Earth Engine (GEE), where CO and aerosol bands were filtered by date and spatially constrained to the metropolitan boundary. Monthly and annual means were computed using custom JavaScript. Processed rasters were exported as GeoTIFF files and analysed in ArcGIS 10.7.1 to generate spatial distribution maps and classify concentration levels. Descriptive statistics and paired-sample t-tests were performed to evaluate inter-annual variability. Time-series analyses were used to assess seasonal trends and temporal fluctuations across the study period. Results: CO and aerosol levels exhibited pronounced seasonal peaks, consistently highest in February, and inter-annual fluctuations linked to post-pandemic recovery and energy use patterns. Central commercial and industrial areas consistently emerged as pollution hotspots, while peripheral zones recorded lower concentrations. Both pollutants responded to anthropogenic activity and policy changes, such as fuel subsidy removal, and their accumulation was modulated by seasonal climatic factors. Aerosols showed more persistent atmospheric presence than CO. This multi-year, high-resolution assessment provides a pollutant-specific baseline, filling gaps left by short-term ground-based studies and enabling evidence-based urban air quality management in mid-sized cities. Conclusion: This study quantified temporal and spatial dynamics of CO and aerosol concentrations in Aba (2019–2024) using Sentinel-5P and GIS analysis, revealing seasonal peaks, urban pollution hotspots, policy-sensitive variations, and providing a high-resolution baseline addressing gaps in continuous, pollutant-specific monitoring.

Health systems are expected to reduce environmental impacts while preserving safe, continuous and equitable care. Artificial intelligence (AI) may help optimize energy, water, waste and circular-economy processes, but the evidence is dispersed across sectors and its relevance to nursing and health policy is uncertain. We conducted a cross-sector scoping review and evidence map of studies published from January 2020 to April 2025. Searches were conducted in Scopus and Web of Science, with backward reference-list checking. Eligible studies evaluated an AI-enabled intervention, decision-support pathway or optimization process and reported a quantified environmental sustainability endpoint, sustainability-relevant operational proxy or clearly linked circular-economy process outcome. Outcomes were mapped as real-world direct environmental endpoints, simulated or modeled direct endpoints, indirect operational proxies or technical enabling metrics. Full platform-specific search strategies are provided in Supplementary file 1 (Supplementary Table S1). After a tightened eligibility audit, 11 studies were included. No study evaluated AI-enabled sustainability interventions in routine healthcare delivery or nursing practice. Energy-management studies were concentrated in building heating, ventilation and air-conditioning control or microgrid scheduling. Direct environmental evidence was sparse and was either non-healthcare real-building evidence or simulated/modeled estimates, including building energy use, irrigation water use and route-derived emissions or carbon-cost indicators. Indirect evidence included demand-response flexibility, operating-cost reductions, productivity or profit improvements, operational-efficiency gains, battery reuse-pack performance and bioleaching process optimization. Technical metrics such as forecasting or classification accuracy were treated as implementation-enabling evidence and were not interpreted as environmental effects. The current evidence base is better characterized as a cross-sector evidence map than as a focused healthcare effectiveness review. AI-enabled sustainability applications may offer candidate functions for health-system infrastructure and operations; however, nursing implications are interpretive, hypothesis-generating and not directly evidenced. Future healthcare studies should evaluate environmental endpoints alongside patient safety, infection prevention, nursing workload, equity and the lifecycle footprint of AI systems.

Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysomnography are costly and impractical for long-term, home-based monitoring. This study presents an energy-efficient approach for detecting four sleep stages (wake, rapid eye movement (REM), light sleep, deep sleep) using a single-lead electrocardiogram (ECG) signal. We evaluate various machine learning and deep learning models, introducing two windowing strategies: (1) a 5-minute window with 30-second steps for machine learning and (2) a 30-second window with 10-second steps for deep learning, enabling 10-second temporal resolution for real-time predictions. While deep learning models like MobileNet-v1 achieve high accuracy (92%) and F1-score (91%), their energy demands make them unsuitable for wearables. To address this, we design SleepLiteCNN, optimized for ECG-based sleep staging, achieving 89% accuracy and 89% F1-score while minimizing energy use. Applying 8-bit quantization further reduces energy consumption to 5.48 μJ per inference, with 90% accuracy and F1-score. Additionally, field-programmable gate array (FPGA) deployment shows significant reductions in resource usage. This approach provides a practical, energy-efficient solution for continuous ECG-based sleep monitoring in resource-constrained wearable devices.

The ongoing global energy transition has intensified the need for precise modeling of renewable energy systems, especially photovoltaic–thermal (PV/T) systems that have the ability to produce both electrical and thermal energy. Improving the efficiency and reliability of PV/T systems is a key enabler of the transition toward sustainable energy. Accurate forecasting of their thermal performance is therefore essential to maximize renewable energy use and reduce energy losses. A deep learning-based method is proposed in this study for the prediction of the thermal efficiency of an air-based PV/T system. More specifically, temporal deep learning architectures are investigated to exploit the complex nonlinear relationships and temporal dependencies governing the thermal behavior of the PV/T collector. A comprehensive comparative analysis is conducted using four state-of-the-art architectures, namely Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Furthermore, the influence of sequence length is examined through a sensitivity analysis considering forecasting horizons of 1 h, 6 h, 12 h, and 24 h. The models are evaluated using the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results demonstrate that forecasting performance is strongly influenced by the selected temporal horizon. Among the investigated configurations, the 24-h horizon provided the most informative temporal context for thermal efficiency prediction. Under this common forecasting horizon, the LSTM model achieved the highest predictive accuracy, reaching an R2 of 0.9952, an RMSE of 0.5975, and an MAE of 0.2364, outperforming the TCN, GRU, and Transformer architectures. The residual error and convergence analyses further highlighted the effectiveness of recurrent neural networks in capturing the thermal dynamics of the investigated PV/T system. By enabling accurate and reliable thermal efficiency forecasting, the proposed framework supports improved energy management, higher energy efficiency, and a stronger integration of renewable energy systems, thus contributing to more sustainable operation of hybrid solar energy technologies.

Data routing protocols play a vital role in Wireless Sensor Networks (WSNs). However, large network sizes and constrained resources demand more energy-efficient routing strategies. In this context, conventional routing protocols often show weak load balancing and inefficient energy use. Low-Energy Adaptive Clustering Hierarchy (LEACH) and Low-Energy Adaptive Clustering Hierarchy Centralized (LEACH-C) remain the two most widely adopted hierarchical routing protocols in WSNs. LEACH operates as a non-geographic distributed routing protocol, whereas LEACH-C is a geographic-based centralized routing protocol. Compared with flat routing protocols, both can prolong network lifetime, but they still suffer from limited energy efficiency. To address this limitation, we in this research proposed an enhanced LEACH protocol based on cluster configuration and Quantum Beluga Whale Optimization (QBWO-LEACH). During the setup phase, the central base station (BS) employs the proposed QBWO approach, which integrates Beluga Whale Optimization (BWO) with the strengths of quantum computing, to centrally organize the clusters. This process includes determining the cluster centroids, assigning cluster members, and evaluating cluster energy, cluster priority, and cluster lifetime. In the cluster heads (CHs) rotation phase, local clusters use the position and energy information of all cluster members to perform distributed CHs switching, distributing cluster energy approximately evenly among all members. In the steady-state phase, the relay forwarding of monitored data flows is implemented. Compared with traditional LEACH and other improved variants of the LEACH protocols, the comprehensive performance of the protocol proposed in the present research is found to be superior. We compare our proposed QBWO-LEACH with the existing LEACH protocols in terms of node survival, network residual energy, half node dies (HND), last node dies (LND), and first node dies (FND), in all four cases using both simulation and statistical analysis. QBWO-LEACH demonstrates an average improvement of 51.87% over LEACH, 17.69% over Particle Filter LEACH (PF-LEACH) and 4.31% over a 2-stage Genetic Algorithm-based LEACH (GA2-LEACH) in node survival and network residual energy in all four cases.