Environmental Monitoring Technology Research Articles

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.

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. This article is protected by copyright. All rights reserved.

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.

Rapid determination of polyhalogenated carbazoles in water chestnut based on vortex assisted solvent extraction coupled with dispersive filter extraction followed by gas chromatography-tandem mass spectrometry

Polyhalogenated carbazoles (PHCZs), originating from the production and use of halogenated indigo dyes, photoelectric materials, and p-chloraniline structure herbicides, pose a threat to water environments, aquatic products, and consequently human health. Therefore, establishing an accurate detection method for PHCZs in aquatic products is of paramount importance. In this study, trace PHCZs (36-CCZ, 36-BCZ, and 1368-BCZ) in water chestnut were detected for the first time using the prepared polyvinyl alcohol-functionalized graphene aerogel (PGA) as a novel adsorbent for dispersive filter extraction (DFE), coupled with vortex assisted solvent extraction (VASE), followed by gas chromatography-tandem mass spectrometry (GC–MS/MS). The extraction process (VASE-DFE) has the advantages of good purification effect, short extraction time (<15 min), and low adsorbent dosage (1.0 mg). This method demonstrated excellent linearity in the range of 0.10/0.50/1.00–200 ng/g for the three PHCZs (R2 ≥ 0.9995), and the recoveries at three spiked levels (2, 20, 100 ng/g) ranged from 82 % to 93 %, with relative standard deviations (RSDs) ≤ 7.4 %. Notably, the method outperforms previously reported adsorbents in terms of RSDs, adsorbent dosage, and extraction time. The VASE-DFE-GC–MS/MS method provides a new approach for the detection of trace pollutants in complex samples, introduces a novel technology for environmental and food safety monitoring, and contributes to enhancing food safety supervision.

Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications

AbstractThis paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.

Research about functions, fabrications and applications of the microstructures in sensors

Microstructures in sensors are of immense importance in technology and scientific research. Microstructures play a vital role in achieving enhanced sensitivity, accuracy, and miniaturization in sensors. Understanding their importance is crucial for advancing sensor technology in healthcare, environmental monitoring, robotics, and other fields. To conduct this research, a systematic review of academic and industry sources is performed. Existing studies, experimental techniques, and real-world applications are analyzed. The paper covers diverse topics related to microstructures in sensors. It highlights the importance of microstructures, explores types such as Micro-electromechanical Systems (MEMS), and nanomaterials, and discusses their unique characteristics. Fabrication techniques like lithography, thin-film deposition, and additive manufacturing are examined. In addition, the advantages and limitations of each method are discussed, emphasizing manufacturing challenges and opportunities. Additionally, the paper explores applications of microstructures in biomedical sensing, environmental monitoring, consumer electronics, and industrial automation. Existing research and success stories showcase their potential to revolutionize various sectors.

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

Maritime Emission Monitoring: Development and Testing of a UAV-Based Real-Time Wind Sensing Mission Planner Module

Maritime emissions contribute significantly to global pollution, necessitating accurate and efficient monitoring methods. Traditional methods for tracking ship emissions often face limitations in real-time data accuracy, with wind measurement being a critical yet challenging aspect. This paper introduces an innovative mission planner module for unmanned aerial vehicles (UAVs) that leverages onboard wind sensing capabilities to enhance maritime emission monitoring. The module’s primary objective is to assist operators in making informed decisions by providing real-time wind data overlays, thus optimizing flight paths and data collection efficiency. Our experimental setup involves the testing of the module in simulated maritime environments, demonstrating its efficacy in varying wind conditions. The real-time wind data overlays provided by the module enable UAV operators to adjust their flight paths dynamically, reducing unnecessary power expenditure and mitigating the risks associated with low-battery scenarios, especially in challenging maritime conditions. This paper presents the implementation of real-time wind data overlays on an open-source state-of-the-art mission planner as a C# plugin that is seamlessly integrated into the user interface. The factors that affect performance, in terms of communication overheads and real-time operation, are identified and discussed. The operation of the module is evaluated in terms of functional integration and real-time visual representation of wind measurements, and the enhanced situational awareness that it can offer to mission controllers is demonstrated. Beyond presenting a novel application of UAV technology in environmental monitoring, we also provide an extensive discussion of how this work will be extended in the context of complete aerial environmental inspection missions and the future directions in research within the field that can potentially lead to the modernization of maritime emission monitoring practices.

Design of Intelligent Security System for Higher Vocational Colleges Based on Internet of Things

Abstract The increase in Internet of Things (IoT) technologies for security in higher vocational institutions provides new solutions to enhance campus security. The challenges faced by campus security management include break-ins, emergency response, and environmental monitoring. Effective security system design can improve the ability to deal with these challenges and ensure the safety and comfort of students and faculty. In this study, an intelligent security system based on IoT technology is designed to realize dynamic management and real-time response to campus security by integrating advanced face recognition, environmental monitoring and data analysis technologies, thus improving campus security and management efficiency. Haar feature recognition, face detection optimized by Adaboost algorithm, and ecological monitoring technologies are some of the technological approaches introduced in the study. The system design integrates multiple aspects such as video surveillance, environmental parameter monitoring, and data analysis to build an efficient and reliable security monitoring system. In this paper, the system can monitor the campus security situation in real time, the face recognition accuracy rate reaches 95%, and the illegal intrusion detection time is reduced to within 2 seconds, effectively improving the response speed and accuracy of campus security management. This study proves the effectiveness and feasibility of IoT technology in designing campus intelligent security systems, which is of great significance to improve campus security management.

Spatial and temporal urban air pollution patterns based on limited data of monitoring stations

Urban air pollution is one of the serious environmental problems in the development of high-density cities, especially for traffic-related pollution. Effective urban environmental monitoring technologies are helpful to predict and control the air pollution emitted by traffic sectors. However, the complex temporal and spatial characteristics of air pollution in urban areas pose a great challenge for its monitoring, prediction, and control. This work investigated on the spatiotemporal air pollution patterns and its correlation mechanism with urban elements (such as traffic) based on limited data of monitoring stations, which is significant for the accurate prediction and regulation of urban environment. Regional Cluster Monitoring (RCM) methods were proposed through integrating multi-source data, including data from U.S. Geological Survey (USGS) Landsat, OpenStreetMap (OSM) road network, and environmental monitoring stations to categorize different monitoring stations. According to different urban areas (defined as U: ‘urban’ or S: ‘suburban’) and traffic densities (T: ‘traffic’ or B: ‘background’), monitoring stations areas were categorized into U-B, U-T, S-B, and S-T. Then the spatiotemporal characteristics of traffic-related pollutants in different region categories were analyzed. The rapid prediction of air pollution distribution in different categories of urban regions was realized by RCM method-based Long Short-Term Memory (LSTM) neural network. Results showed that the proposed methods could accurately represent the spatiotemporal distribution characteristics of urban air pollution. The mean absolute percentage errors (MAPE) between the predicted results and actual observed values were 20.31% to 28.05% (absolute errors were 3.9-14.3 μg/m³). This work can significantly impact on low-cost & effective monitoring and control strategies for urban area, and further contribute to comprehend the spatial-temporal distribution patterns of urban traffic-related air pollution from limited data of monitoring stations.

Smart sensors and Internet of Things (IoT) for sustainable environmental and agricultural management

With the advancement of smart sensors and the Internet of Things, innovative technologies for environmental monitoring and rural installations have become solutions to increase production sustainably. Thus, this work analyzes the application of smart sensors and the Internet of Things (IoT) in the environmental monitoring of rural facilities, aiming to promote sustainability and efficiency in agricultural production. Smart sensors can collect environmental data in real-time, which is essential in rural environments. IoT enables communication and integration of this data. These technologies bring several benefits, such as improvements in efficiency, productivity, and sustainability. The study addresses types of sensors used in monitoring, such as optical, acoustic, chemical, and physical, in addition to monitored parameters, such as air and soil quality. Emerging technologies are also explored, including wireless network sensors, multispectral monitoring, microsatellites, blockchain, and virtual reality. The review includes case studies on successful applications of IoT and sensors in contexts such as smart irrigation, precision agriculture, and animal facility monitoring. It is concluded that these technologies have a high potential to contribute to more sustainable agricultural systems and animal production. Challenges such as privacy, security, and ethical use of data are highlighted.

Green Supply Chain Management Research Trends and Linkages to UN Sustainable Development Goals

Growing awareness about environmental responsibility among businesses and policymakers has increased interest in Green Supply Chain Management (GSCM). For the first time, using the SDG mapping algorithms, this article focused on 7009 publications from 2013 to 2022 mapped explicitly to 17 SDGs. This study uses bibliometric and science mapping techniques to create a detailed mapping between GSCM research and SDGs. This study created an SDG citation network to show how specific SDGs are related to GSCM practices. The most mapped SDGs in this study are SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 17 (Partnership for the Goals). Our results showed that GSCM research mainly focuses on three thematic areas: industry 4.0 and sustainable supply chains, GSCM practice coordination, and sustainable environmental policy management. This also includes integrating the circular economy into GSCM, sustainable sourcing, supply chain resilience, digital transformation in GSCM for better environmental performance, and the social impact of GSCM. A shift towards integrating sustainability into supply chain processes has been found. Environmental management, performance monitoring, and new technologies like Industry 4.0 and AI have been used to support the SDGs. International interest and collaboration in GSCM research can be seen from the bibliographic coupling study covering countries like Europe, Western Countries, the Middle East, Southeast Asia, and Eastern Europe. This study shows how GSCM can directly address global challenges identified by the United Nations by mapping GSCM research to specific SDGs.

Research on Monitoring Technology and Analysis of Urban Green Space Ecology and Environment

Urban green land plays an active role in the process of improving urban ecological environment and the harmony between human and nature. With the economic development, industrial progress and the improvement of people's living standard, urban greenness as the regulator of urban environment has been generally concerned. The dynamic monitoring of urban green land is conducive to scientific and effective urban management, and provides scientific basis and evaluation criteria for urban green land system planning. In this paper, we use remote sensing as the observation means to systematically carry out the research of urban green land ecological environment monitoring technology and analysis. Firstly, it introduces the pre-processing techniques of remote sensing data for urban monitoring, and selects the area method, grid cell method, buffer zone method and moving window method as the key techniques for spatial measurement of urban greenness spaces, and finally evaluates the ecological effects of urban green land with the greenness environment index model of urban buildings. This paper provides the key techniques and evaluation process of urban green space remote sensing in a more systematic way, which will play an important role in garden city evaluation, ecological city evaluation, garden planning, smart city and ecological city support, urban building planning, and sponge map city construction.

Reparative futures of smart urban governance: A speculative design approach for multispecies justice

In recent years, urban governance within smart city contexts has increasingly turned towards processes and practices of “futuring” to guide city visions and planning. Particularly with the increased focus on sustainability within smart urban governance, these futuring practices often rely on environmental monitoring technologies to guide reparative actions for environmental harms and promote multispecies justice. Yet, these futuring practices often overlook or are disconnected from the past where these environmental harms and injustices were sustained. Little attention has been paid to how smart urban governance might consider both past and future to better account for multispecies justice. This paper introduces a speculative design methodology within a reparative futures framework as it explores multispecies justice in smart urban governance. It reports on a methodological study conducted in Brisbane, Australia, in which participants—including policymakers, environmental lawyers, scientists, urban planners, and climate activists—undertook a speculative futuring design process intending to address past harms faced by multiple species and to repair relations within a more-than-human world. Direct engagement with the past in futuring practices helps us reflect on how smart urban governance might take accountability and institute better reparative actions to materialise multispecies justice.

Present situation of satellite remote sensing technology in China

Satellite remote sensing technology has become widely used in all sectors of life as satellite equipment has improved and remote sensing technology has evolved. Satellite remote sensing technology has the advantages of objective acquisition mode, accurate picture information, rapid data acquisition, and a large number of applications, and is primarily utilized in a wide variety of dynamic monitoring and analytical work in diverse fields. China has achieved fruitful results in the development and application of remote sensing technology in recent years, launching a variety of satellites including land, meteorology, oceans, cities, water bodies, environment, ecology, disasters, mapping, and other series, and applying the information obtained in many fields, including land, agriculture, forests, grassland, meteorology, oceans, cities, water bodies, environment, ecology, disasters, mapping, and other fields. This study examines and summarizes the present application results of satellite remote sensing technology in environmental monitoring, urban planning, and finance, and serves as a reference for people interested in the field of satellite remote sensing technology. This technology has been widely applied in the field of environmental testing in water bodies, meteorology, land, and disaster; in urban planning, it is primarily used for building location selection and engineering supervision; and in finance, it is used for economic risk assessment.

Under the Background of Industry-Education Integration--Real-Time Environmental Inspection--Exploration and Practice of Teaching Mode

This article mainly discusses the application of real-time environmental monitoring technology in educational practices. It begins by introducing the basic principles of this technology, which involves real-time monitoring of environmental parameters through sensors and the use of data analysis and processing for real-time evaluation and feedback. It then delves into the specific applications of real-time environmental monitoring technology in teaching, including classroom instruction and laboratory practices. The article analyzes the advantages of this technology in teaching, such as real-time monitoring, objectivity, and personalization, while also addressing challenges such as sensor reliability and accuracy, teacher training, and strategy improvement. Finally, it provides methods and strategies to address the challenges in the application of real-time environmental monitoring technology, including improving sensor technology, enhancing teacher training, and refining teaching strategies. Overall, real-time environmental monitoring technology has significant potential for application in education, but it also requires addressing various technological and educational challenges.

Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management.

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device's performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management.

Graphene Hybrid Metasurfaces for Mid-Infrared Molecular Sensors.

We integrated graphene with asymmetric metal metasurfaces and optimised the geometry dependent photoresponse towards optoelectronic molecular sensor devices. Through careful tuning and characterisation, combining finite-difference time-domain simulations, electron-beam lithography-based nanofabrication, and micro-Fourier transform infrared spectroscopy, we achieved precise control over the mid-infrared peak response wavelengths, transmittance, and reflectance. Our methods enabled simple, reproducible and targeted mid-infrared molecular sensing over a wide range of geometrical parameters. With ultimate minimization potential down to atomic thicknesses and a diverse range of complimentary nanomaterial combinations, we anticipate a high impact potential of these technologies for environmental monitoring, threat detection, and point of care diagnostics.