Key Environmental Parameters Research Articles

Optimize Stormwater Management for Green Roofs Based on IoT Technology and Sensor Networks

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

Enhancing IoT Security through an Artificial Neural Network Approach

This study aims to fortify Internet of Things (IoT) security through the strategic implementation of Artificial Neural Networks (ANNs). With the rapid expansion of IoT devices, traditional security measures have struggled to cope with the dynamic and complex nature of these environments. ANNs, known for their adaptability, are explored as a promising solution to enhance security. The central objective is to significantly improve the accuracy of IoT security measures by optimizing ANN architectures. Using a curated dataset with key environmental parameters, the study evaluates three ANN models—Backpropagation Neural Network (BPNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM). The evaluation metrics include accuracy, precision, recall, and F1-score across different train-test splits. Results show that LSTM consistently outperforms BPNN and MLP, demonstrating superior accuracy and the ability to capture temporal dependencies within IoT security data. Implications stress the importance of aligning model selection with specific application goals, considering factors like computational efficiency. In conclusion, this research contributes valuable insights into the practical implementation of ANNs for IoT security, guiding future optimization efforts and addressing real-world deployment challenges to safeguard sensitive data and ensure system resilience in the evolving IoT landscape.

Key environmental parameters and numerical prediction model of jellyfish bloom in Qingchuan Bay Nuclear Power Plant, China

In recent years, there has been a notable increase in cooling water intake blockage caused by marine organism blooms at coastal nuclear power plants worldwide, resulting in shutdowns of nuclear power plants and large economic losses. A sizable portion of these incidents were caused by blooms from jellyfish, a planktonic invertebrate with a unique growth pattern. Suitable external conditions are conducive to the rapid growth of jellyfish, and blooms can occur within a few days. In order to better predict jellyfish bloom and enable nuclear power plants to prepare for it in advance, this study explores the numerical relationship between jellyfish biomass and environmental parameters. A series of time windows (evaluation intervals) were defined and constructed by a time-series recursive approach, which solved the problem of poor correlation between jellyfish biomass and environmental parameters at non-bloom points. The optimal time window length D = 10 was obtained, and the key environmental parameters affecting jellyfish biomass were screened as sea surface temperature, salinity, voltage value, dissolved oxygen, and chlorophyll. According to the ADF and KPSS tests, the key parameters have no significant time dependence, which ensures the stability and reliability of the subsequent predictions. The jellyfish bloom prediction model was derived by calculating the score F through the recursive Principal Component Analysis of the key environmental parameter in the time interval preceding the prediction point. The sudden change moments of score F correspond well to the jellyfish bloom moments, and the sudden change moments are all advanced for a period of time compared to the bloom time, which can provide valuable time for the nuclear power plant to organize manpower to deal with the blockage. Finally, a maximum score F threshold model was proposed to be coupled with the jellyfish bloom prediction model to provide a more robust basis for early warning of jellyfish at nuclear power plants.

A Modular Smart Ocean Observatory for Development of Sensors, Underwater Communication and Surveillance of Environmental Parameters

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations.

District-wise evaluation of meteorological factors and outdoor thermal comfort in India using UTCI – Insight into future climatic scenario

The study aims to comprehensively analyze outdoor meteorological conditions and thermal comfort across 592 districts in India, addressing the critical issues of thermal comfort which significantly impacts health, well-being and productivity. Given the anticipated global warming, understanding these factors becomes crucial, particularly for low-income populations who spend considerable time outdoors. While most previous studies in India have focused on indoor environments or on broad climatic regions, our research provides a granular analysis at the district level, incorporating future climate scenarios from 2050 to 2080. Using the Universal Thermal Climate Index (UTCI), our findings reveal that the northwestern part of India experiences ‘Strong’ to ‘Extreme’ heat stress, with temperatures exceeding 50 °C, while the Himalayan regions face ‘Strong’ cold stress, with temperatures dropping below -20 °C. The geographical distribution of UTCI classes shows that the western and central regions suffer from high thermal stress during summer afternoons, whereas coastal areas, benefiting from higher relative humidity and wind speeds, exhibit moderate UTCI values. The Himalayan regions consistently present lower UTCI values, indicating colder conditions. We have developed a district-wise climate atlas of India, mapping key environmental parameters and the outdoor thermal stress of UTCI values. The main objective of this study is to provide localized insights into how climate change will affect outdoor thermal comfort, facilitating informed decision-making for public health planning, energy infrastructure, and climate adaptation strategies across India.

Multi-Sensor Platform in Precision Livestock Farming for Air Quality Measurement Based on Open-Source Tools

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.

Fish diversity, assemblage pattern along with the environmental variables in tidal fresh water stretch in the Hooghly estuary of Gangetic delta, West Bengal, India

The greatest estuary created by the Gangetic delta in India is the Hooghly estuary. Despite their importance to the estuarine biodiversity, the tidal freshwater areas of this estuary have gained little interest from ecologists. Present study aims to explore the fish diversity with its assemblage pattern and their relationship with physico-chemical parameters from June 2020 to May 2021. There have been identified 118 fish species in total, from 14 orders and 47 families, with seven (7) being near threatened, fifteen (15) vulnerable, four (4) endangered, and eight (8) not evaluated. Perciformes (29.66 %), Cypriniformes (22.88 %), and Siluriformes (21.18 %) accounted for 73.72 % of the overall species count. Station-1 has significantly higher species richness, rarefied richness, and abundance. Station-3 and station-2 followed station-1 respectively. The composition of fish species changed significantly among the stations, as shown by cluster analysis, nMDS (nonmetric multidimensional scaling), ANOSIM (analysis of similarities), and SIMPER (similarity percentage analysis) (p <0.05). The canonical correspondence analysis revealed that alkalinity, conductivity, phosphate, and total carbon dioxide of the water were the key environmental parameters. In addition to defining a sustainable management method for the fish resources in the Hooghly estuary, this effort will act as a baseline study for further investigation.

Aromatic Polymethacrylates from Lignin-Based Feedstock: Synthesis, Thermal Properties, Life-Cycle Assessment and Toxicity.

There is currently a great need for rigid, high-performance and processable bio-based polymers and plastics as alternatives to the fossil-based materials used today. Here, we report on the straightforward synthesis and polymerization of lignin-derived methacrylate monomers based on the methyl esters of syringic, vanillic, and 4-hydroxybenzoic acid, respectively. The corresponding homopolymethacrylates exhibit high glass transition temperatures (Tgs) at 106, 128, and 197°C, respectively. Rheological properties and thermal stability up to at least 277 °C indicate that these polymers are melt-processable. In addition, copolymers with methyl methacrylate are prepared to further vary and tune the polymer properties. An integrated ex-ante and prospective life-cycle assessment of key environmental impact parameters indicates similar or only slightly higher values compared to well-established fossil-based methyl methacrylate. Moreover, the toxicity towards human HeLa cell lines compares well with that of poly(methyl methacrylate). Hence, the potential availability of lignin-derived acids, combined with the straightforward and potentially upscalable monomer synthesis, make these rigid polymers appealing alternatives towards bio-based high-Tgthermoplastic materials with low toxicity.

Plant antagonistic facilitation across environmental gradients: a soil-resource ecosystem engineering model.

Theory questions the persistence of nonreciprocal interactions in which one plant has a positive net effect on a neighbor that, in return, has a negative net impact on its benefactor - a phenomenon known as antagonistic facilitation. We develop a spatially explicit consumer-resource model for belowground plant competition between ecosystem engineers, plants able to mine resources and make them available for any other plant in the community, and exploiters. We use the model to determine in what environmental conditions antagonistic facilitation via soil-resource engineering emerges as an optimal strategy. Antagonistic facilitation emerges in stressful environments where ecosystem engineers' self-benefits from mining resources outweigh the competition with opportunistic neighbors. Among all potential causes of stress considered in the model, the key environmental parameter driving changes in the interaction between plants is the proportion of the resource that becomes readily available for plant consumption in the absence of any mining activity. Our results align with theories of primary succession and the stress gradient hypothesis. However, we find that the total root biomass and its spatial allocation through the root system, often used to measure the sign of the interaction between plants, do not predict facilitation reliably.

Simulation of low energy building thermal energy cycle in IoT smart city planning based on environmental sensors and deep learning

With the intensification of global climate change, the reduction of building energy consumption has become an important goal of smart city development. By optimizing the thermal energy circulation system, low energy buildings can not only reduce energy consumption, but also improve living comfort. In recent years, with the help of environmental sensors and deep learning technology, the intelligent management of building heat energy cycle has become a research hotspot. The research constructs an intelligent thermal energy circulation system that integrates multiple environmental sensors for real-time monitoring of indoor and outdoor temperature, humidity and other key environmental parameters. A deep learning algorithm is used to analyze the collected data to optimize the control strategy of the thermal energy cycle. Through simulation, the energy efficiency performance of the scheme under different climatic conditions and building types was evaluated. The experimental results show that the control strategy based on environmental sensors and deep learning can significantly improve the thermal energy utilization efficiency of low-energy buildings, and the average energy consumption is greatly reduced compared with the traditional management mode. The system shows good adaptability and stability under different climate conditions. Therefore, the application of environmental sensors and deep learning technology in the thermal energy cycle of low-energy buildings can effectively promote the energy efficiency management of smart cities.

Meridional shift of climatological tropical cyclone genesis location in the western North Pacific

AbstractClimatological monthly mean tropical cyclone (TC) genesis location in the western North Pacific exhibits a marked meridional shift from June to November and the physical cause of such a shift was investigated through the diagnosis of observational and reanalysis datasets. Two genesis potential indices were used to examine key environmental parameters affecting the meridional shift. The diagnosis results indicate that dynamic parameters such as vertical and meridional wind shears play a dominant role in promoting both the northward advance and southward retreat of the mean genesis latitude, while the effect of vertical velocity is negligible. The thermodynamic parameters such as relative humidity and potential intensity in general play a minor role, except that the latter helps promote northward advance during the early TC season. The change of the environmental parameters is closely linked to the sub‐seasonal evolution of the monsoon trough and subtropical high in the western North Pacific. Maximum synoptic‐scale variability also experiences a similar meridional shift, contributing to the TC genesis location shift.

Design and construction of Arduino based greenhouse monitoring system using IoT

The rapid advancement of electronic device technologies has led to the creation of intelligent systems aimed at enhancing various aspects of human life. One of the most significant of these advancements is the Internet of Things (IoT), which has revolutionized monitoring, controlling, and security features across numerous applications. In agriculture, IoT-based systems are increasingly crucial for optimizing greenhouse conditions, essential for efficient crop cultivation. This research focuses on the design and construction of an Arduino-based greenhouse monitoring system utilizing IoT technology. The system automates the monitoring and regulation of key environmental parameters such as temperature, humidity, light, sodium, potassium, phosphorus and soil moisture, using sensors and actuators managed by the microcontroller. Prototyping methods was adopted. The integration of IoT enables real-time data collection and remote control, significantly reducing manual labor and enhancing crop yield. Additionally, the system incorporates dual power sources, utilizing both grid and solar energy to ensure uninterrupted operation. The lettuce crop yield increase by 20% which makes the system a better alternative to other. The implementation of this automated system showcases the potential of IoT in creating smarter, more sustainable agricultural practices.

Nomograph derivation for underwater sour gas releases in shallow waters based on computational fluid dynamics simulations

Ruptured underwater pipelines releasing sour gas (i.e., natural gas with high concentrations of H2S) pose significant threats to environmental safety due to their toxic and contaminative properties. This study suggests a transient Eulerian-Eulerian multiphase computational fluid dynamics (CFD) model for assessing the environmental impact of sour gas releases in shallow, ecologically sensitive waters. Employing the Realizable k-ε turbulence model and additional bubble plume mechanisms, the model was rigorously validated against well-documented experimental data, showing high levels of agreement across multiple metrics. Key environmental safety parameters—such as rise time and H2S surface gas concentration, indicative of toxic exposure risk to aquatic life and human health—were extracted for multiple release scenarios. To present generalized and easily accessible data, these parameters were nondimensionalized and incorporated into a nomograph, enabling environmental risk assessments to be conducted four orders of magnitude faster than with high-fidelity simulations. The study emphasizes the nomograph's utility in rapid, environmentally relevant risk assessment and emergency planning for mitigating the impact of sour gas spills. An error and sensitivity analysis revealed uncertainties of ±7 % for rise time and ±58 % for surface gas concentration, pinpointing avenues for future research.

Packaging performance evaluation and freshness intelligent prediction modeling in grape transportation

Vibrations during transportation inevitably lead to mechanical damage, endangering grape freshness and directly impacting their economic worth. While adequate packaging serves as a viable solution, current studies on packaging efficacy lack depth. Moreover, conventional methods for forecasting fruit freshness fail to accommodate the varying freshness levels of grapes across different packaging techniques. Consequently, a novel approach for predicting fruit freshness leveraging multi-sensing technology and machine learning algorithms is introduced. By reasonably evaluating packaging performance, the automation, intelligence, and accuracy of fruit freshness prediction are enhanced. Initially, critical control points in grape supply chain logistics were scrutinized using the HACCP method to identify key environmental parameters (vibration, temperature, and humidity) and their interaction with grape freshness. Subsequently, an environmental monitoring platform was devised for the grape supply chain, facilitating environmental surveillance under distinct packaging types (corrugated carton, foam box, plastic box, and inflatable package). Through a blend of environmental monitoring outcomes and physical-chemical indicators, the protective efficacy of diverse transport packaging was meticulously analyzed and appraised alongside finite element analysis. Notably, environmental data proved capable of characterizing grape freshness in lieu of quality data, with vibration metrics exhibiting strong correlations with quality metrics. Machine learning models were developed to predict grape freshness based on environmental cues, yielding prediction accuracies of 92.512% (SVM) and 94.334% (GA-ANN). The automated, non-destructive data acquisition and novel machine learning approaches offer a fresh avenue for evaluating packaging, predicting freshness, and managing food quality within grape logistics operations.

Design and modeling of a co-flow reactor for turquoise hydrogen production

This work focuses on the design of a reactor for producing clean hydrogen from methane pyrolysis in the form of the so-called “turquoise hydrogen”. In addition to its simple geometry, the fundamental concept and the main novelty of the proposed method rely on using part of the methane to produce the required heat needed for the thermal decomposition of methane (TDM). The reactor configuration for hydrogen production is shown to produce significant advantages in terms of greenhouse gas (GHG) emissions. A reactive flow CFD model incorporating also soot formation mechanism has been first developed and validated with experimental results available in the literature and then used to design and characterize the performances of proposed reactor configuration. 3D CFD simulations have been carried out to predict the behavior of the reactor configuration; a sensitivity analysis is used for clearing the aspect related to key environmental parameters, e.g., the global warming impact (GWI). The real potential of the proposed design resides in the low emissions and high efficiency with which hydrogen is produced at the various operating conditions (very flexible reactor), albeit subject to the presence of carbon by-product. This suggests that this type of methane conversion system could be a good substitute for the most common hydrogen production technologies.

Remotely controlled smart monitoring system of hermetic paddy storage to reduce postharvest losses in Bangladesh

In Bangladesh, where agriculture forms the backbone of the economy, efficient grain storage is crucial, particularly for paddy storage. Traditional methods like gunny bags and plastic containers are common, but the recent introduction of hermetic storage technologies, such as GrainPro bags and hermetic cocoons, have significantly reduced postharvest losses paddy. However, most research focuses on oxygen (O2) and carbon dioxide (CO2) levels in those open storage systems. This study introduces an innovative, cost-effective remote monitoring system for hermetically sealed grain storage, designed using locally sourced components, including a Digital Hygrometer, WiFi Mini IP Camera, and a Remote Controller unit. Tested over a 125-day period at Advanced Storage Lab, Bangladesh Agricultural University, the system effectively monitored the internal environment of the storage units. Key findings show that the internal temperature of the storage bags varied between 16.7°C and 29.2°C, demonstrating lesser fluctuation compared to the ambient temperature range of 16.1°C to 33.5°C. The relative humidity inside the storage units remained stable between 45 % and 54 %, in contrast to the external humidity range of 45 % to 81.2 %. Additionally, using the Steffe and Singh equation, an increase of 0.512 % in Equilibrium Moisture Content (EMC) of paddy was recorded. Notably, there was no evidence of insect or mold growth after 125 days of storage. This study's remote monitoring system not only marks a significant advancement in hermetic grain storage technology but also contributes to sustainable agricultural practices. It provides a practical, real-time solution to monitor and manage key environmental parameters, ensuring the preservation of grain quality over time.

GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

Energy Consumption Behavior Analysis in the UAE Educational Buildings for Sustainable Economy: A Case Study of Ras Al Khaimah Schools

Energy-saving in educational building has a significant contribution and a vital role in the economic and social development of nations. In this paper, the energy consumption of some educational buildings in the United Arab Emirates (UAE) are collected and analyzed. Four schools within the region of Ras Al Khaimah were selected to study their buildings’ energy consumption behavior. Key energy, economic and environmental parameters were calculated and investigated such as the total annual energy consumption, energy consumption per person, energy consumption per area, energy consumption by category (AC, lighting, other devices, etc.), energy cost, and the amount of greenhouse gas (GHG) emissions. A detailed energy audit was applied to one of the selected schools in order to identify the major areas of energy usage within the school, and to suggest the appropriate measures for potential energy and cost savings. Results revealed that total annual energy consumption of the investigated schools is very high with a range of 3700 - 3900 MWh. Significantly, in these hot climatic regions, AC systems alone accounts for over 80% of the overall annual energy consumption, standing out as the predominant energy consumers particularly during the summer months. The annual energy consumption per unit area was also excessive with values ranging from 200 to 500 kWh per m2. Total annual cost of energy was relatively substantial for all schools with values in the order of 1.2 - 1.5 million AED ($330,000 - $400,000). The negative effects of the CO2 emissions were estimated to be around 500 - 600 ton of CO2/year per a single school.

Toward a common methodological framework for the sampling, extraction, and isotopic analysis of water in the Critical Zone to study vegetation water use

AbstractThe analysis of the stable isotopic composition of hydrogen and oxygen in water samples from soils and plants can help to identify sources of vegetation water uptake. This approach requires that the heterogeneous nature of plant and soil matrices is carefully accounted for during experimental design, sample collection, water extraction and analyses. The comparability and shortcomings of the different methods for extracting water and analyzing isotopic composition have been discussed in specialized literature. Yet, despite insightful comparisons of extraction methods and benchmarking methodologies of laboratories worldwide, the community still lacks a roadmap to guide sample collection, extraction, and isotopic analyses, and many practical issues for potential users remain unresolved: for example, which (soil or plant) water pool(s) does the extracted water represent? These constitute a hurdle for the implementation of the approach by newcomers. Here, we summarize discussions led in the framework of the COST Action WATSON (“WATer isotopeS in the critical zONe: from groundwater recharge to plant transpiration”—CA19120). We provide guidelines for (1) sampling soil and plant material for isotopic analysis, (2) methods for laboratory or in situ water extraction, and (3) measurements of isotopic composition. We highlight the importance of considering the process chain as a whole, from experimental design to isotopic analysis to minimize biased estimates of the relative contribution of different water sources to plant water uptake. We conclude by acknowledging some of the limitations of this methodology and advice on the collection of key environmental parameters prior to sample collection for isotopic analyses.This article is categorized under: Science of Water &gt; Hydrological Processes Science of Water &gt; Water and Environmental Change Science of Water &gt; Water Extremes