Environmental Monitoring Technology Research Articles

Advancements in Binary Solvent-Assisted Hydrogel Composites for Wearable Sensing Applications

The advancement of wearable sensing technologies has been pivotal in revolutionizing healthcare, environmental monitoring, and personal fitness. Among the diverse materials employed in these technologies, multifunctional hydrogel composites have emerged as critical components due to their unique properties, including high water content, flexibility, and biocompatibility. This review provides a comprehensive overview of the state-of-the-art in binary solvent-assisted hydrogel composites for wearable sensing applications. It begins by defining hydrogel composites and their essential attributes for wearable sensors, specifically focusing on binary solvent-assisted methods that enhance their performance and functionality. The review then delves into the applications of these composites in health monitoring, environmental detection, and sports and fitness, highlighting their role in advancing wearable technologies. Despite their promising features, there are significant challenges related to durability, sensitivity, and integration that need to be addressed to fully exploit these materials in wearable devices. This review discusses these challenges and presents potential solutions, including the development of new materials, improvement in fabrication processes, and strategies for achieving multifunctionality and sustainable design. Looking forward, the paper outlines future directions for research in this field, emphasizing the need for innovative materials and technologies that can lead to more effective, reliable, and eco-friendly wearable sensors. This review aims to inspire further research and development in the field of wearable sensing, paving the way for new applications and advancements in healthcare, environmental monitoring, and personal fitness technologies.

A Portable Electronic Nose Coupled with Deep Learning for Enhanced Detection and Differentiation of Local Thai Craft Spirits

In this study, our primary focus is the biomimetic design and rigorous evaluation of an economically viable and portable ‘e-nose’ system, tailored for the precise detection of a broad range of volatile organic compounds (VOCs) in local Thai craft spirits. This e-nose system is innovatively equipped with cost-efficient metal oxide gas sensors and a temperature/humidity sensor, ensuring comprehensive and accurate sensing. A custom-designed real-time data acquisition system is integrated, featuring gas flow control, humidity filters, dual sensing/reference chambers, an analog-to-digital converter, and seamless data integration with a laptop. Deep learning, utilizing a multilayer perceptron (MLP), is employed to achieve highly effective classification of local Thai craft spirits, demonstrated by a perfect classification accuracy of 100% in experimental studies. This work underscores the significant potential of biomimetic principles in advancing cost-effective, portable, and analytically precise e-nose systems, offering valuable insights into future applications of advanced gas sensor technology in food, biomedical, and environmental monitoring and safety.

Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring

During the past decade, substantial efforts have been dedicated to advancing cost-effective sensor platforms for air quality monitoring. Calibration is a crucial step in ensuring that the data quality of low-cost air monitoring systems meets established standards. Recent research has extensively evaluated low-cost air monitoring platforms against the data quality objectives set by the European Directive. This paper introduces a novel calibration model for low-cost air quality sensors, significantly improving the accuracy of the measurement of nitric oxide (NO), sulfur dioxide (SO2), and particulate matter (PM1, PM2.5, and PM10), while promoting accessibility and adaptability in environmental monitoring technologies. This study extends the evaluation of a developed platform capable of integrating a diverse array of sensors to measure up to 12 parameters. Our proposed models demonstrate a significant improvement, achieving a 60% better accuracy for SO2. Additionally, these models deliver similar results for PMx and NO or exceed those of state of the art research. The calibration methodology meets the requirements of the Data Quality Objectives (DQO) for all monitored parameters and also achieves indicative levels for PM parameters.

Classification of protected grassland habitats using deep learning architectures on Sentinel-2 satellite imagery data

This study examines the effectiveness of five deep learning models—ViTb-19, SwinV2-t, VGG-16, ResNet-50, and DenseNet-121—in distinguishing different vegetation types in the protected grasslands of Castilla y León region, Spain, following the guidelines of the Natura 92/43/CEE directive. Among the models, ResNet-50 achieved the highest weighted overall accuracy (OA) of 0.95, closely followed by SwinV2-t with an OA of 0.94, demonstrating their strong ability to detect complex patterns in satellite imagery. DenseNet-121 also performed competitively with a weighted OA of 0.93, while ViTb-19 and VGG-16 showed slightly lower performance. SwinV2-t, a transformer-based model, outperformed traditional CNN architectures in data-rich classes but faced challenges in classifying habitats with limited representation. Consequently, this study identifies these challenges that conventional transformer architectures pose in classifying certain habitats with limited representation and intricate features. Highlighting the advantages of deep learning technologies for environmental monitoring and conservation, the study provides important insights for adjusting neural network architectures for effective habitat classification. This suggests the necessity of selecting appropriate architectures such as SwinV2-t and ResNet50 to to effectively address the intricate requirements of satellite imagery analysis.

Impact of SiGe pocket on different shape TFET structures for gas sensing application

Gas sensing requires highly sensitive and selective sensor technologies for environmental monitoring, industrial safety, and public health objectives. Although conventional MOSFET-based gas sensors are widely utilized, their detection of low quantities of gases, such as ammonia, is hindered by a number of severe constraints. TFET is emerged as the better device than MOSFET, particularly for the sensing applications. In this work we discussed the source engineered and shape engineered techniques to improve the performance of TFETs, specifically for ammonia gas detection. Comprehensive simulations on SILVACO TCAD and compared the four TFET structures SiGe-pocket DGTFET, SiGe-pocket vertical TFET SiGe-pocket Z-shape TFET, and SiGe-pocket U-shape TFET structure on the basis of various electrical characterization parameters. Significant improvements in efficiency and sensitivity are obtained by adjusting the work function of the molybdenum (4.40–4.60 eV) catalytic gate metal to find the optimal values. The findings reveal that the SiGe-pocket U-shape TFET structure exhibits superior performance, demonstrating an ION of 8.01 × 10−4 A/μm, an ION/IOFF ratio of 1.25×1013, and an OFF current sensitivity (SOFF) of 1.262. These results highlight the enhanced sensitivity and efficacy of the proposed SiGe-pocket U-shape tunnel FET in ammonia gas sensing applications, making it a promising candidate for practical uses in environmental monitoring and industrial safety.

Exploling the Scientific Model for Constructing a Platform for the International Communication of PingJiang Opera

Purpose: Considerable changes have occurred in many areas of science and technology as a result of the current information technologies' quick development and use. Specifically, new theories in mathematics have surfaced; they may be used to construct and refine current mathematical models of different processes and objects. Method: Regulatory and permitting decision-makers are worried about the ambiguity surrounding the possible environmental consequences arising from the introduction of these innovative devices in coastal and riverine ecosystems as research and testing of maritime energy devices progress. The Triton Initiative conducts studies and makes suggestions for environmental monitoring techniques and technology to give industry stakeholders with the information required to approve maritime energy system testing. Results: The efficient distribution of research findings is crucial in enhancing data accessibility for stakeholders who might use the information to make informed policy choices. Nevertheless, there are few frameworks available for carrying out scientific communications related to marine energy projects. Using the Triton Initiative's pilot scientific communication program as a case study, we discuss methods, resources, and strategies for creating a science communication framework for marine energy projects or other related fields of research in this article. Conclusion: This article uses the historic Pingjiang Road in Suzhou, China, as a case study to investigate how, in the era of globalization and digital media, urban spaces that have been revitalized for historic and cultural consumption may demonstrate the publicness or potential capacity for publicity.

All-printed MXene/WS2-based flexible humidity sensor for multi-scenario applications

As non-contact sensing, non-invasive medical diagnostics and environmental humidity monitoring technologies advance, the demand for high-performance humidity sensors is becoming increasingly apparent. Ti3C2Tx MXene is an attractive humidity-sensitive candidate due to its excellent chemical activity and hydrophilic surface. However, achieving fast responsiveness and high sensitivity of dynamic changes in humidity is still challenging. Herein, this work introduced a WS2-modified Ti3C2Tx MXene composite material as humidity sensing material with superior sensing performance. Then sodium carboxymethyl cellulose solution was added to the Ti3C2Tx MXene/WS2 composite material to adjust viscosity of the composite material, enabling the formulation of printable humidity-sensitive ink. Utilizing screen-printing technology, a mass-producible humidity sensor (CMXW2) was successfully fabricated. Experimental results show that the sensor has short response and recovery times of 4.65 s/1.33 s in the relative humidity of 11–97 % with a high humidity response of 1707 % and excellent mechanical properties. The efficacy of the humidity sensor for skin moisture monitoring, real-time respiration monitoring, and non-contact switch was also evaluated. The successful demonstration of multi-scenario applications using the CMXW2 humidity sensor opens up new possibilities and applications in the fields of smart healthcare and non-contact sensing.

Internet of Things (IoT) for Environmental Monitoring

Purpose: The general objective of this study was to explore the Internet of Things for environmental monitoring. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the Internet of Things for environmental monitoring. Preliminary empirical review revealed that IoT technologies have significantly enhanced environmental management practices by revolutionizing data collection and analysis across various ecosystems. By integrating IoT sensors with existing monitoring frameworks, real-time data on air and water quality, agriculture, wildlife habitats, and urban green spaces was efficiently gathered. This data facilitated proactive decision-making, early detection of environmental risks, and evidence-based policy formulation to address climate change, biodiversity conservation, and sustainable resource management challenges. Despite challenges like data security and interoperability, collaborative efforts among stakeholders paved the way for more effective environmental monitoring and sustainable development initiatives globally. Unique Contribution to Theory, Practice and Policy: The Complex Adaptive Systems Theory, Diffusion of Innovations Theory and Resource Dependence Theory may be used to anchor future studies on the Internet of Things technology. The study provided several recommendations that contributed significantly to theory, practice, and policy in environmental management. The study emphasized interdisciplinary approaches to enhance theoretical frameworks, advocating for advanced models and algorithms integrating IoT with environmental science and data analytics. In practice, it recommended widespread adoption of IoT-enabled sensor networks with enhanced capabilities for precise and reliable data collection. Policy-wise, the study called for regulatory frameworks supporting IoT integration, data standards, and international cooperation to address global environmental challenges collaboratively. Capacity building and continuous research and development were also highlighted to optimize IoT technologies for sustainable environmental monitoring and management globally.

Towards chemical source tracking and characterization using physics-informed neural networks

This paper presents a new algorithm using physics-informed neural networks (PINNs) to track and characterize spatio-temporally varying chemical sources based on time-resolved measurements of chemical concentration, fluid velocity, and pressure. Chemical source emission functions, chemical concentration, fluid velocity and fluid pressure fields are modeled as multi-layer perceptrons (MLPs). During the training process, sensors readings of chemical concentration, fluid velocity, and fluid pressure are matched to the output of the respective MLPs at the given spatio-temporal locations. Simultaneously, the physics of fluid flow and chemical dispersion at an arbitrary number of spatio-temporal locations in the domain of interest are enforced through regularization. Once trained, the constituent MLPs can be evaluated to generate the time resolved maps of source emissions, chemical concentration, fluid velocity and pressure. Extensive numerical simulation validate the method, including scenarios with both static and dynamic sources in complex flow fields. This innovative approach marks a significant advancement in environmental monitoring technologies.

Enhancing Identification of Meteorological and Biological Targets Using the Depolarization Ratio for Weather Radar: A Case Study of FAW Outbreak in Rwanda

Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda’s C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR’s performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats.

Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning

Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests that plants have the potential to be used as biosensors for monitoring environmental information. However, there are current challenges in linking environmental information with plant electrical signals, especially in collecting and classifying the corresponding electrical signals under soil moisture gradients. This study documented the electrical signals of clivia under different soil moisture gradients and created a dataset for classifying electrical signals. Subsequently, we proposed a lightweight convolutional neural network (CNN) model (PlantNet) for classifying the electrical signal dataset. Compared to traditional CNN models, our model achieved optimal classification performance with the lowest computational resource consumption. The model achieved an accuracy of 99.26%, precision of 99.31%, recall of 92.26%, F1-score of 99.21%, with 0.17M parameters, a size of 7.17MB, and 14.66M FLOPs. Therefore, this research provides scientific evidence for the future development of plants as biosensors for detecting soil moisture, and offers insight into developing plants as biosensors for detecting signals such as ozone, PM2.5, Volatile Organic Compounds(VOCs), and more. These studies are expected to drive the development of environmental monitoring technology and provide new pathways for better understanding the interaction between plants and the environment.

Application of Electrochemical Sensing Technology in Environmental Monitoring and Its Development Prospects

Application of Electrochemical Sensing Technology in Environmental Monitoring and Its Development Prospects

Recent Progress in Solar-Blind Photodetectors Based on Ultrawide Bandgap Semiconductors.

Ultrawide bandgap (UWBG) semiconductors, including Ga2O3, diamond, Al x Ga1-x N/AlN, featuring bandgaps greater than 4.4 eV, hold significant promise for solar-blind ultraviolet photodetection, with applications spanning in environmental monitoring, chemical/biological analysis, industrial processes, and military technologies. Over recent decades, substantial strides in synthesizing high-quality UWBG semiconductors have facilitated the development of diverse high-performance solar-blind photodetectors (SBPDs). This review comprehensively examines recent advancements in UWBG semiconductor-based SBPDs across various device architectures, encompassing photoconductors, metal-semiconductor-metal photodetectors, Schottky photodiodes, p-n (p-i-n) photodiodes, phototransistors, etc., with a systematic introduction and discussion of their operational principles. The current state of device performance for SBPDs employing these UWBG semiconductors is evaluated across different device configurations. Finally, this review outlines key challenges to be addressed, aiming to steer future research endeavors in this critical domain.

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study1

Abstract The intersection between artificial intelligence (AI) and climate change mitigation has received increased attention as a result of pressing global environmental concerns and technological advancements. This paper will cover key scientific literature about the development and implementation of AI in tackling climate change challenge. Methodologically, bibliometric and scientometric analyses, including cooccurrence and co-citation analysis, were employed to systematically examine research trends and patterns. The major research question focuses on tracing the evolution of AI research in relation to climate change, investigating important drivers of interest and identifying influential works and research groups. The data find a considerable increase in AI and climate change papers after 2015, demonstrating a growing appreciation for AI’s potential to provide novel solutions. Implications of this trend include the use of AI technologies for environmental monitoring, prediction, and policy makers. Moreover, interdisciplinary collaborations on AI and climate change between experts from various fields shown the importance of global climate issues. This paper provides a new idea for the study on AI in climate change, highlighting opportunities for further exploration and collaboration.

The model of choosing a microcontroller for the environmental monitoring system module

Problem. The development and application of modern digital technologies for environmental monitoring is an extremely relevant issue. Such environmental monitoring systems allow for dynamic monitoring of the current state of the studied territories in order to further analyze changes and make decisions on their conservation and development. To improve the environmental situation, it is necessary to constantly improve environmental monitoring systems by creating local environmental monitoring systems operating in real time. Goal. The aim of the study is to develop a model for selecting a microcontroller for an environmental monitoring module based on many functional and cost criteria, which will reduce the time of information collection for a regional environmental monitoring system. Methodology. To achieve this goal, we analyzed the characteristics of microcontrollers. A model for selecting a microcontroller for an environmental monitoring module has been developed based on many functional and cost criteria. An example of using the microcontroller selection model for the environmental monitoring module is given. The method of multicriteria optimization allows the selection of a microcontroller for the module of the environmental monitoring system by calculating the utility function. Results. The paper considers the main tasks of the environmental monitoring system. A mathematical model for selecting a microcontroller for the module of an environmental monitoring system has been developed. It belongs to the class of multicriteria discrete programming models and, unlike the known ones, takes into account the system requirements for the module of the environmental monitoring system as a whole. Originality. The developed model allows us to make a microcontroller according to many functional and cost criteria under conditions of fuzzy input information. Practical value. The use of the proposed model for selecting a microcontroller for the module of the environmental monitoring system will reduce the time of collecting information for the regional environmental monitoring system.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception.

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Evaluating the Performance and Practicality of a Multi-Parameter Assessment System with Design, Comparative Analysis, and Future Directions

This study introduces a developed environmental quality assessment system, detailing its hardware, software, and comparative analysis against a publicly available system. While showing larger deviations in particulate matter and air humidity parameters, the proposed system demonstrates sufficient accuracy in other environmental characteristics. It establishes a standardized operating procedure and evaluates uncertainty and quality assurance measures, ensuring reliability in environmental measurements. The system offers comprehensive capabilities, measuring parameters like total volatile organic compounds, carbon dioxide, temperature, humidity, particulate matter, noise, nitrogen oxides, sulfur oxides, ozone, and carbon monoxide, with real-time monitoring functions for detecting environmental changes. Its user-friendly interfaces, scalability, and potential integration with existing systems enhance its versatility and cost-effectiveness across diverse settings. This study underscores the need for future research to enhance accuracy, reliability, and real-time operability and explore integration with smart city initiatives and environmental management systems. Overall, the proposed system represents a promising advancement in environmental monitoring technology, facilitating comprehensive environmental quality assessment and management.

Real-time Fishpond Water Temperature Monitoring with Internet of Things (IoT) Technology

The quality of pond water is an important factor in ensuring the health and sustainability of the organisms within it. Internet of Things (IoT) technology has enabled the development of sophisticated and effective monitoring systems to monitor water quality in real-time. One important parameter that needs to be monitored is the temperature of the pond water, as unstable temperatures can harm aquatic organisms. To obtain good quality and disease-free fish at normal times, the optimal temperature for catfish fry is around 28˚C - 30˚C. Manual monitoring or using thermometer measuring instruments is still very traditional and limited to the ability of supervisors to carry out continuous monitoring and may cause delays in detecting significant temperature changes. This research aims to analyze the monitoring system of pool water quality based on temperature by utilizing IoT technology. The proposed system uses a DS18B20 temperature sensor that is wirelessly connected to the IoT network. The temperature data collected in real time will be uploaded to Firebase for storage and further analysis. Through the development of this IoT-based pond water quality monitoring system, it is expected to increase efficiency and reliability in monitoring pond water quality. From the monitoring conducted for approximately 24 hours, the temperature in the fishpond is 28°-30°. After analyzing with direct monitoring, the good temperature is at night at 18.00-00.00 WIB. In conjunction with a standard thermometer, resulted in an average temperature sensor error rate of less than 5%, highlighting the reliability of the IoT technology in real-time environmental monitoring.

Porphyrin based-optical fiber sensor for pattern recognition of chlorides and nitrates

The paper introduces a novel approach to develop a photonic device for chemical applications using an evanescent wave absorption technique-based optical fiber probe layered with a Tetraamine porphyrin (TAPP) monolayer exploiting the optical properties of TAPP. The interactions between the TAPP and nitrate and chloride ions in the solution phase were monitored and the changes in the absorption spectra of TAPP molecules were recorded. Distinct optical patterns emerge, and ion-dependent shifts in the Soret and Q-bands are analyzed for eight samples of nitrates and chlorides. Principal Component Analysis (PCA) is employed to discern ion groups based on the spectral pattern recognition. These fiber probes exhibit sensitivity to nitrate and chloride ions, with enhanced selectivity, robustness, and a rapid response time of 2–3 seconds. The study demonstrates the emergence of porphyrin-based optical fiber sensors as a useful sensor technology in environmental monitoring.

Synergy in Action: Integrating Environmental Monitoring, Energy Efficiency, and IoT for Safer Shared Buildings

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, the integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces is relevant. The outcomes of the study reveal the delicate nature of all the involved components, which need to be carefully developed in an integrated manner.