Power Monitoring Research Articles

Design of Power Outage Monitoring and Warning System Based on Internet of Things

With the rapid development of distribution network and the high popularity of distributed generation, the research on risk assessment of distribution network is accelerated. When quantifying the degree of harm caused by abnormal situations, it is necessary to consider the severity of the harm and the impact on society to measure the severity of power failure and reduce the loss caused by power risk. To provide data support for the fine perception of the situation and its development trend, the monitoring system constructed by the power microservice further improves the ability and accuracy of sudden fault prediction. In this paper, through the monitoring system information to explore the transmission line fault early warning, the data analysis model is established. The particle swarm optimization algorithm and BP neural network are used to build the early warning model to predict the potential fault situation and cause analysis of the power grid under various indicators in a certain period in the future, which is to provide more scientific and reasonable risk management and control for the power grid. Through the statistical analysis of all kinds of information data collected by the power microservice, a power monitoring system based on early warning of power outage risk is established to enhance the operation adaptability of the power grid system under uncertain conditions and prevent the occurrence of tripping blackouts so that this study has an important function of intelligent decision-making. The experimental results show the feasibility and high efficiency of this study. The comparison of algorithm results shows that the performance of this method is improved by more than 10%. The stability is improved by 17%. The response time to various situations is shortened by 32% to judge the abnormal operation state of the power grid more efficiently. It can make emergency preparations in time, reduce the damage caused by accidents to the power grid and provide practical tools for the power grid to further enhance the disaster prevention ability of transmission equipment and effectively resist risk events.

Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations

A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations.

A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.

Two datasheet-based approaches for double-diode PV modeling with an efficient trade-off between accuracy and simplicity

A reliable photovoltaic (PV) model with logical trade-off between accuracy and simplicity can be utilized for optimal design, monitoring, fault detection and prediction of operational power. In this research work, two novel datasheet-based approaches are developed for PV module identification through double-diode model (DDM). In both methods the four unknown parameters are derived as functions of series resistance (Rs). It is shown that each parameter estimation algorithm has its own unique solution. Effective numerical methods are applied for fast convergence and with absolute certainty to the corresponding unique solution. The established methods are both reasonably simple and accurate while one is simpler (faster) and the other is more accurate. The performances of the proposed algorithms are evaluated by real experimental data and through a comparison with other similar datasheet-based algorithms. The results reveal that the developed methods have superb performance in comparison with other techniques.

Advanced remote sensing technologies for tracking landscape changes and environmental conditions

The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.

Prompt-critical power excursion measurements with optical fibers in the CABRI experimental reactor

This paper presents our experimental work to assess the capability of estimating the transient-induced power distribution evolution in the CABRI experimental reactor, using Cherenkov radiation in optical fibers. The CABRI reactor is designed to produce power transients from 100 kW up to 21 GW in less than 100 ms, in order to irradiate a test fuel pin under conditions representative of a Reactivity Insertion Accident in pressurized water reactors. Because of the wide response range and short response time required to follow the reactor power evolution during a complete transient, the usual means of detection, such as ionization or fission chamber, are rendered inoperable, especially for in-core or core vicinity measurements. For this reason, we suggest measuring the Cherenkov light produced within the optical fibers. The Cherenkov light emission is linked to local electron production, which is proportional to local gamma flux through the Compton or pair production cross-section. In other words, the intensity of Cherenkov radiation is related to the photon flux intensity. Knowledge of the fission photons emitted by the reactor gives direct insight into the fission rate, hence a spatial power density distribution could be reconstructed by using the measurement of Cherenkov light at different points in the reactor. The radio-luminescence measurements taken using photodiodes show very good linearity with the reactor power monitoring system despite the transient radiation induced attenuation observed in the optical fibers. The radio-luminescent light shows steady values with around 5% of variations over the 15 transients for low OH fibers and high OH fibers. Low OH tends to overestimate the FWHM of the power peak by 1–4%.

A Design and implementation of a sustainable microcontroller based solar power automatic water irrigation control and monitoring system

The major cause of the shortage of land reserve water owing to the lack of rainfall makes the proper operation of irrigation systems extremely important. A major concern for farmers these days is water scarcity. Pakistan is an important agricultural nation and major part of economy depend on agriculture. This study analyses the soil moisture monitoring and automatic plant watering system since it is really helpful in all kinds of weather. In this study a prototype is design and implement using Arduino-based automation sensors are employed for the control and observation of a solar powered smart irrigation system. An Arduino based remote irrigation system developed for the agricultural plantation, which is placed at a remote location and requires water, provides for plantation when the moisture of the earth drives under the set-point rate which turns on the pump on autopilot. An estimation was carried for solar power energy potential agriculture system condition using soil and humidity sensors. The measurements and mathematical formulation were observed to monitor effectivity of solar based power generation. This analysis is conducted for 20 Watt power panel integrated with 5-watt electrical drive with battery capacity 1200 m-Ah. A modified energy model was used to determine the agricultural load for a 5-watt dc motor. The results showed a cumulative analysis to implement this system inspired by smart sustainable clean renewable energy technologies which meet the farmer’s demands significantly increase the productivity of farming and reduce wastage of water and energy.

CFO co-option and dividend payments: the moderating role of CFO talent

Purpose This study aims to investigate the association between the co-option of the chief financial officer (CFO) and dividend payments, assessing whether the talent of the CFO affects this association. Design/methodology/approach The empirical analyses were based on hand-collected data for 922 firm-year observations from 157 European listed firms, during the period 2013–2019. Empirical models, based on a two-step estimation procedure, involved the use of instrumental variables and the generalised moment method. Findings The results show that CFO co-option is negatively associated with the level of dividend payments. It was also found that the degree of CFO talent moderates the negative association between CFO co-option and dividend payments. Research limitations/implications This investigation responds to the call for literature which examines how chief executive officer (CEO) – CFO relationships influence firms’ policies and outcomes. The study offers novel evidence for the individual-level characteristics of CFOs which are likely to reduce the effectiveness of CEO power and increase monitoring on corporate decisions on dividends. Practical implications The study sheds light on the effect of the interactions between CEOs and CFOs, which are important for investors’ expectations. In this regard, investors may be interested in the CFO profiles which may reduce CEO power over dividend policies. Originality/value Unlike previous research, which focused on CEOs, the authors are the first to shed light on the role of CFOs as key decision makers in influencing the dividend policies in modern corporations.

An automatic robot for ultrasonic partial discharge detection of gas-insulated switchgear

PurposeGas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks.Design/methodology/approachGBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device.FindingsThe robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate.Practical implicationsThe robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields.Originality/valueThe robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

A Portable Agriculture Environmental Sensor with a Photovoltaic Power Supply and Dynamic Active Sleep Scheme

A portable environmental sensor for agricultural applications is proposed that addresses key challenges in power supply, data transmission, and monitoring efficiency. The sensor features a photovoltaic power supply and a PID-based dynamic active–sleep scheme for sustainable energy management, maintaining optimal battery levels under varying solar conditions. Its compact, waterproof, and dustproof design (90 mm × 90 mm × 150 mm, 844 g) ensures robust and reliable operation in diverse agricultural environments. High-precision digital sensors monitor temperature, humidity, light intensity, and CO2 concentration. Equipped with low-power NB-IoT technology, the sensor supports real-time remote environmental monitoring. Our experimental results show effective continuous operation, accurate environmental measurements, and performance comparable to established data loggers. The advanced power management and precise sensing capabilities make this sensor a competitive solution for improving smart agriculture practices, particularly in resource-limited or off-grid settings.

China's Ground‐Based Space Environment Monitoring Network—Chinese Meridian Project (CMP)

AbstractMonitoring and investigation of the solar‐terrestrial space environment is a huge challenge for humans in space age. To this end, China has established the Ground‐based Space Environment Monitoring Network, namely Chinese Meridian Project (CMP). The project comprises three major systems: the Space Environment Monitoring System, Data and Communication System, and Scientific Application System. The Space Environment Monitoring System adopts a well‐designed monitoring architecture, known as “One Chain, Three Networks, and Four Focuses,” to achieve stereoscopic and comprehensive monitoring of the entire solar‐terrestrial space. The “One‐Chain” component utilizes optical, radio, interplanetary scintillation, cosmic ray instruments to cover the causal chain of space weather disturbances from the solar surface to near‐Earth space. For the ionosphere, middle and upper atmosphere, and magnetic field, instruments are deployed along longitudes of 120° and 100°E, and latitudes of 30° and 40°N, forming the “Three Networks.” Furthermore, more powerful monitoring facilities or large‐scale instruments have been deployed in four key regions: the high‐latitude polar region, mid‐latitude region in northern China, low‐latitude region at Hainan Island, and the Tibet region. These four regions are crucial for disturbances propagation and evolution, or possess unique geographical and topographical characteristics. The Data and Communication System and Scientific Application System are designed for data collecting, processing, storage, mining, and providing user service based on data acquired by the Space Environment Monitoring System. The data obtained by CMP will be shared with the global scientific community, facilitating enhanced collaboration on space weather and space physics research.

Design of A Real-Time Solar Power Monitoring and Controlling System Using Internet of Things

Design of A Real-Time Solar Power Monitoring and Controlling System Using Internet of Things

Detector-integrated vertical-cavity surface-emitting laser with a movable high-contrast grating mirror

In this paper, we present a detector-integrated vertical-cavity surface-emitting laser (VCSEL) with a movable high-contrast grating (HCG) mirror in an n-i-p-i-n manner. The detector-integrated VCSEL with a movable HCG can achieve three functions, including wavelength tuning, power monitoring, and resonant-cavity-enhanced (RCE) photon detection. Currently, the device can achieve a wavelength tuning range of 27 nm at room temperature when the suspended HCG is driven by the reverse-bias voltage. The n-i-p structure located at the upper part of the device can serve as an intra-cavity photodiode to monitor the output power due to the defect absorption. The RCE photon detection function of the detector-integrated VCSEL with a movable HCG is measured, and it has a peak responsivity at about 926 nm. This detector-integrated VCSEL with a movable HCG will be useful for sensing and imaging.

Revisiting the strategy for marine litter monitoring within the european marine strategy framework directive (MSFD)

Marine litter and non-degradable plastic pollution is of global concern. Regular monitoring programs are being established to assess and understand the scale of this pollution. In Europe, the goal of the European Marine Strategy Framework Directive (MSFD) is to assess trends in Good Environmental Status and support large-scale actions at the regional level. Marine litter monitoring requires tailored sampling strategies, protocols and indicators, that align with specific objectives and are tailored for local or regional needs. In addition, the uneven spatial and temporal distributions of marine litter present a challenge when designing a statistically powerful monitoring program. In this paper, we critically review the existing marine litter monitoring programs in Europe. We discuss the main constraints, including environmental, logistical, scientific, and ethical factors. Additionally, we outline the critical gaps and shortcomings in monitoring MSFD beaches/shorelines, floating litter, seafloor litter, microplastics, and harm. Several priorities must be established to shape the future of monitoring within the MSFD. Recent developments in analytical approaches, including optimizing protocols and sampling strategies, gaining a better understanding of the spatiotemporal heterogeneity of litter and its implications for survey design and replication, and the inclusion of newly validated methodologies that have achieved sufficient technical readiness, must be considered. Although there are well-established methods for assessing beaches, floating and seafloor litter, it will be necessary to implement monitoring schemes for microplastics in sediments and invertebrates as robust analytical methods become available for targeting smaller particle size classes. Furthermore, the inclusion of indicators for entanglement and injury to marine organisms will have to be considered in the near future. Moreover, the following actions will enhance the effectiveness of monitoring efforts: (1) creating an inventory of accumulation areas and sources of specific types of litter (e.g., fishing gear), (2) monitoring riverine inputs of litter, (3) monitoring atmospheric inputs including microplastics, (4) accidental inputs during extreme weather events, and (5) studying how species at risk may be transported by litter. We provide recommendations to support long-term, effective, and well-coordinated marine litter monitoring within the MSFD to achieve a comprehensive and accurate understanding of marine litter in EU waters. This will allow the development of measures to mitigate the impacts of marine pollution and eventually to evaluate the success of the respective measures.

Double-Swing Spring Origami Triboelectric Nanogenerators for Self-Powered Ocean Monitoring

Coastal areas often experience high population density and intense human activity owing to the considerable value of the ocean. Therefore, devices for monitoring marine disasters are crucial for ensuring the safety of human life. Herein, we develop hemispherical spring origami (SO) triboelectric nanogenerators (TENGs) (HSO-TENGs) for self-powered ocean wave monitoring. Optimization is performed using two approaches. First, swing machine experiments are conducted to investigate the monitoring performance of the HSO-TENGs regarding wave height and period with satisfactory accuracy. To increase power generation and monitoring accuracy, the internal inertia and centroid of gravity of the HSO-TENGs are optimized with respect to the structural parameters (i.e., magnet weight, hammer height, and external swing arm length). Second, numerical simulations are performed using the smoothed-particle hydrodynamics (SPH) method to determine the most suitable fixed condition for the HSO-TENGs for sensing wave changes. Subsequently, wave tank experiments are conducted on the HSO-TENGs to determine their ability to sense wave height, period, frequency, and direction. Tests related to supplying other sensors are also conducted. Eventually, the ability of the HSO-TENGs to monitor wave direction and spreading parameters is investigated in a numerical SPH circular wave tank. The results prove that the optimized HSO-TENGs can achieve powering and sensing through the same device.

Dive descent rate as a buoyancy indicator to infer body condition of Weddell seals in the Antarctic

AbstractChanges in buoyancy of marine mammals can be used to infer environmental changes. In multiple seal species, how “fast” an animal sinks reveals body condition changes through shifts in buoyancy as the ratio between lean and lipid tissue changes. However, quantifying similar at‐sea changes in Weddell seals (Leptonychotes weddellii) has remained unexplored. Here, we present a method of inferring buoyancy of Weddell seals by monitoring descent rates from 4‐s time‐depth data, to reveal in situ insight of their life cycle. We defined a Buoyancy Indicator Segment (BIS) as the descent rate of a dive segment created with the broken‐stick method that was systematically filtered to only include characteristic nonstroking and directed travel segments while excluding lung buoyancy biases. We found that BISs predicted body condition changes in Weddell seals, being a function of dive duration, mean depth, and time‐of‐year. Descent rates quickened with troughs in early April due to postmolt muscle recovery, early July due to winter conditions, and early September possibly due to pregnancy. Each trough was followed by weight gain, with slowing descent rates reaching peaks in late May, early August, and late October. This new approach showed that determining at‐sea condition is possible for Weddell seals, deriving a powerful species and environmental monitoring tool.

Integrated photodetectors embedded within fiber laser based on hybrid rGO:ZnO nanostructures

Online power monitoring is helpful in fiber-optic communication applications. This report introduced an innovative all-fiber photodetector based on the polished side of an arc-shaped fiber for the optical signals’ in situ measurements. For this purpose, a metal-semiconductor–metal photodetector was designed with multiple interdigitated metal fingers of gold electrodes. A waveguide structure containing polymethyl methacrylate (PMMA), graphene layers, and zinc oxide nanostructures was developed and covered over the arc-shaped fiber. Due to the PMMA’s refractive index (n = 1.4905), the evanescent field of propagating mode was drawn out of the core, thus increasing the light interaction with semiconductor nanostructures. To study the influence of graphene flakes on the optoelectrical behavior of zinc oxide nanostructures, a suspension of graphene oxide (GO) and ZnO nanorods was synthesized and transferred onto the PMMA thin film, which was deposited on the polished side of the arc-shaped fiber. The morphological analysis indicates the formation of ZnO nanorods with different orientations, and these nanorods have the potential to enhance incident photon trapping greatly. Upon photon exposure, the guiding photons in the fiber evanescently coupled with the deposited nanostructures, and the photoexcited electron–hole pairs were generated in the semiconductor material. Using applied voltage across the interdigitated electrodes, the generated excitons were separated and increased the photocurrent, which was monitored precisely by source-measure equipment. The optoelectrical properties of fabricated devices showed that incorporating graphene layers can improve the photon detection performance of the fabricated all-fiber photodetectors based on ZnO nanostructures.

Enhanced Visibility for Real-time Monitoring and Alerting in Kubernetes by Integrating Prometheus,Prometheus, Grafana, Loki, and Alerta

With the increasing popularity of Kubernetes as the go-to platform for containerized applications, numerous companies are adopting Kubernetes as their preferred platform for containerized applications. As organizations embrace this container orchestration technology for its scalability, flexibility, and portability benefits, the need for robust monitoring solutions becomes paramount. Monitoring Kubernetes environments is essential to ensure the health, performance, and availability of applications running within the cluster. This paper aims to provide a comprehensive approach for monitoring Kubernetes via Prometheus, Grafana, and Alerta Prometheus, a powerful open-source monitoring system, col- lects metrics from Kubernetes pods and nodes, enabling real- time monitoring of resource utilization, performance metrics, and application health. Grafana complements Prometheus by providing intuitive visualization of collected metrics through customizable dashboards, facilitating comprehensive insights into cluster performance and trends. Loki and Promtail by Grafana are used to collect and aggregate the logs associated with the cluster. Alerta enhances the monitoring setup by enabling alerting based on predefined thresholds and conditions, ensuring prompt notification of potential issues or anomalies. Together, this stack empowers administrators to gain deep visibility into their Kubernetes infrastructure, proactively identify and mitigate issues, and maintain the high availability and reliability of their applications and services. Index Terms—Kubernetes, Prometheus, Grafana, Alerta, Loki, Promtail, Monitoring, Alerting, Logging.

EXPERIMENTAL STUDY: IMPLEMENTING IOT TECHNOLOGY FOR MONITORING ENTERPRISE POWER SUPPLY SYSTEMS

This study has demonstrated the necessity and effectiveness of a well-designed power monitoring system in the current year. The integration of IoT devices with this system has enhanced its effectiveness and applicability across various disciplines. Moreover, using this monitoring system facilitates easy and efficient connections with GSM and embedded controllers. The incorporation of multiple sensors in smart design monitoring systems proves beneficial in this context. Additionally, the study's critical discussion highlights that smart power monitoring systems positively impact motion direction, home security, augmented reality, and technological disruption. Data was collected from 100 respondents out of a total population of 500, selected using a simple random sampling method. This method was preferred due to its simplicity in selecting samples for the study. The researcher distributed questionnaires to the respondents and collected their responses to conduct the research.

Green radiography: Exploring perceptions, practices, and barriers to sustainability

IntroductionPrevious research has delved into the attitudes and behaviors of diverse professions regarding environmental sustainability. However, there needs to be more research specifically targeting radiographers. This study aims to survey radiographers' perceptions, practices, and barriers to change concerning environmental sustainability in radiology. MethodsInstitutional ethical approval was obtained (IRB–COHS–FAC-110-2024) and data collection was conducted using Google Forms (Google Inc., Mountain View, CA). The survey targeted 104 practicing radiographers across several countries. Questions were structured around five domains to gather insights into demographics, training in global warming and climate change, perceptions of sustainability and climate change, sustainability barriers, and current radiology practices on sustainability. Data analysis utilized descriptive and d inferential statistics. ResultsOne hundred and four radiographers completed the study. Females had a significantly higher attendance rate in environmental protection campaigns (P = 0.01). The majority of respondents (68%) believe in climate change's knowledge and impact on the natural world. Our survey findings demonstrate that 74% of respondents believe there's a need to improve sustainability practices. The most commonly used strategies to decrease energy consumption and emissions were low-energy lighting (60%), real-time power monitoring tools (41%), and energy-efficient heating systems (32%). A significant concern regarding sustainability emerges among respondents: time (50%) and lack of leadership (48%) are prevalent concerns among the identified barriers. ConclusionParticipants are recognising the importance of environmental sustainability in radiology, but lack of leadership, support, authority, and facility limitations hinder their adoption. Impact on practiceRadiology must prioritize environmental sustainability by providing resources and training for radiographers and collaborating with healthcare professionals, policymakers, and environmental experts to develop comprehensive strategies for a sustainable healthcare system.