Environmental Monitoring System Research Articles

Subwavelength Grating Cascaded Microring Resonator Biochemical Sensors with Record-High Sensitivity.

Photonic integrated circuit biochemical and biomedical sensors show promising applications in medical diagnosis, food security, healthcare, and environmental monitoring. Silicon-on-insulator subwavelength grating waveguides and cascaded microring resonator structures enhance photon-analyte interaction, offering superior sensing performance (higher sensitivity with lower limit of detection and larger free spectral range) compared to traditional strip and slot waveguide microring resonator structures. In this study, we design, simulate, and experimentally demonstrate a novel and compact biochemical sensor integrating subwavelength grating cascaded microring resonators and multibox subwavelength grating straight waveguides on a silicon-on-insulator platform. We achieve a record-high refractive index sensitivity of 810 nm/RIU with a limit of detection value of 2.04 × 10-5 RIU. The measured concentration sensitivity for sodium chloride solutions is 1430 pm/% with a limit of detection of 0.04%. The free spectral range is 35.8 nm, and the measured Q factor is 1.9 × 103. By combining the advantages of cascaded microring resonators with those subwavelength gratings, our sensor offers unprecedented sensitivity for biochemical sensing applications, promising significant enhancements in healthcare diagnostics and environmental monitoring systems.

The technological assessment of green buildings using artificial neural networks

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented to collect real-time environmental parameters both inside and outside the building. To evaluate the technical suitability of green buildings, this study employs a multifaceted approach encompassing various criteria, including energy efficiency, environmental impact, economic benefits, user comfort, and sustainability. Specifically, it involves real-time monitoring of environmental parameters, analysis of energy consumption data, and indoor environmental quality indicators derived from user satisfaction surveys. Subsequently, a Multi-Layer Perceptron (MLP) is selected as a conventional artificial neural network (ANN) model, while a Long Short-Term Memory (LSTM) model is chosen as an advanced recurrent neural network model in the realm of deep learning. These models are utilized to process and explore the collected data and assess the technical suitability of green buildings. The dataset comprises physical quantities such as temperature, humidity, and light intensity, as well as economic indicators including energy efficiency and building operating costs. Furthermore, the assessment process considers the building's life cycle assessment and indoor environmental quality factors such as health, comfort, and safety. By incorporating these comprehensive criteria, a holistic evaluation of green building technologies is achieved, ensuring the selected technologies' suitability and effectiveness. The model prediction results demonstrate that the proposed hybrid evaluation model exhibits high accuracy and robust stability in predicting building environmental parameters. For instance, the Root Mean Square Error (RMSE) for temperature prediction is 1.2 °C, the Mean Absolute Error (MAE) is 0.9 °C, and the determination coefficient (R2) reaches 0.95. Similarly, for humidity prediction, the RMSE, MAE, and R2 are 3.5 %, 2.8 %, and 0.88. Compared to the traditional MLP and LSTM models alone, the proposed hybrid model shows significant improvements in predicting building energy consumption, with approximately 15 % and 12 % reductions in RMSE and MAE, respectively, and an increase in R2 values of approximately 7 percentage points. These findings indicate that by amalgamation of the IoT and ANNs, this study successfully establishes a comprehensive model for accurately assessing technologies suitable for green buildings. This approach offers a novel perspective and methodology for the design and evaluation of green buildings.

Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization.

The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guidelines, World Health Organization (WHO) standards, and Sustainable Development Goals (SDGs) related to clean water and life on land. This study developed the predictive-based monitoring of HM concentrations, including cadmium (Cd), chromium (Cr), and mercury (Hg) in the coastal aquifers of Dammam, influenced by industrial, agricultural, and urban activities. For this purpose, dynamic system identification and machine learning (ML) models integrated with three ensemble techniques, namely, simple averaging (SAE), weighted averaging (WAE), and neuro-ensemble (N-ESB), were employed to enhance the accuracy, reliability, and efficiency of environmental monitoring systems. The experimental data were calibrated and validated in addition to k-fold cross-validation to ensure the predictive skills of the models. The methodology integrates extensive data collection across varied land uses in Dammam and accurate model calibration and validation phases to develop highly accurate predictive models. The findings proved that the N-ESB and Hammerstein-Wiener (HW) models surpassed other models in predicting the concentrations of all HM. For Cd, the N-ESB model achieved a root mean square error (RMSE = 0.0010mg/kg). Similarly, Cr demonstrated superior performance (RMSE = 0.0179mg/kg). Further numerical results indicated that the HW algorithm proved the most effective for Hg, with RMSE = 0.0000mg/kg. The quantitative comparison suggested that the N-ESB model's consistently high performance and low error rates make it an optimal choice for real-time, precise monitoring and management of HM pollution in coastal aquifers. The outcomes of this research highlighted the importance of integrating advanced predictive modeling techniques in environmental science, providing significant and practical implications for policymaking and ecological management.

International and National Mechanisms Combating Ecosystems’ Damage and Environmental Crimes to Foster Sustainable Development

This article examines international and national mechanisms for combating crime against the environment. Arguments are presented to add the crime of ecocide into the Rome Statute of the International Criminal Court, as well as to create a special international human rights tribunal that can potentially hear cases related to ecocide. A number of bodies, programs, international networks, and transnational non-governmental non-profit organizations should be promoted to directly or indirectly aim at combating crime against the environment. At the same time, the necessity of adopting a comprehensive approach to the international cooperation among these institutions and law enforcement agencies is warranted, given contemporary challenges and threats, in particular with Interpol, a robust institutional framework to combating environmental crime. The importance of establishing universal national and interstate mechanisms for the restoring and preserving ecosystems to combat environmental crimes effectively is underscored. These international regulations should be recognized through relevant resolutions of the UN General Assembly and other international documents. A conceptual inference drawn is the imperative for comprehensive national measures to combat ecocide, including the development of cohesive state policies, the establishment of an effective environmental monitoring system, and the documentation of incurred damages.

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

Environmental monitoring of the agrosphere: theory, methodology, practice

The article deals with the theory, methodology and practice of conducting comprehensive ecological monitoring of the agrosphere of Ukraine, which is caused by ecologically unreasonable land use, insufficient technical and technological support, implementation of ineffective investment and innovative economic and technological solutions, and violation of balance of agrolandscapes due to their significant plowing, soil compaction, deterioration of the ratio of arable land to ecological stabilization lands and natural and nature reserve fund, ineffective implementation of ecological and emerald networks, destruction of soil cover (reduction of soil cover (reduction of soil buffering), and a decrease in the level of agrobiodiversity, an increase in the area of degraded land. A sciencebased methodology is proposed for improving environmental monitoring of the Ukrainian agrosphere for different types of agrolandscapes, their optimization, efficient use and conservation of biodiversity. The stages and specifics of conducting comprehensive environmental monitoring of the agrosphere are presented for different types of agrolandscapes, their functional and structural elements, the main directions and practical approaches are proposed. The necessity of conducting the proposed integrated system of environmental monitoring of the agrosphere for different types of agrolandscapes in the territory of the state and the creation of regional information and advisory centers on agro-environmental issues are proved. For the effective implementation of comprehensive environmental monitoring of Ukrainian agro-sphere for different types of agro-landscapes, it is recommended to create a bank of reference soils (for the purpose of comparative analysis). It is proposed to create appropriate environmental information databases for advisory and implementation centers on agro-environmental issues, which will make it possible to effectively implement programs for sustainable development of the agricultural sector of Ukraine.

Information and Logic Model Development Natural Engineering System Environmental Monitoring

Environmental monitoring is the main source of information on the current environment state and its changes associated with man-made and anthropogenic impact. The system of environmental monitoring of increased danger technogenic objects includes state observations of air and water environments, soil snow cover, as well as monitoring of flora and fauna, including biotesting and bioindication. An example of an integrated approach to organizing the environment conditionobserving system is environmental monitoring in the zone of chemical weapons destruction facilities. The results of monitoring activities are numerical arrays of quantitative indicators of the environment state related to specific monitoring objects: concentrations of pollutants in natural environments (air, water, and soil); indicators of the state of flora and fauna; bioindication results. The complexity of the monitoring result integrated analysis is primarily associated with the diversity of the structure and number of indicators for individual natural environments, different dimensions of indicators, as well as the sparseness of the matrices of data arrays. Taking into account the above-mentioned information, it is relevant to develop methods for storing and updating environmental monitoring data arrays, as well as algorithms for operational analysis of the results of monitoring studies. The paper presents an information and logical model that allows to form databases for storing and replenishing data on the state of the natural engineering system. The developed information and logic model is focused on building OLAP cubes for subsequent analysis of environmental monitoring data. The presented information and logical model is the main stage of developing a system for storing, updating and analyzing the results of environmental monitoring of the TCP. Systematization and structuring of indicators of natural environments and ecosystems makes it possible to formulate a number of practical tasks for assessing and predicting the state of the environment in the presence of technogenic impact.

The Effect of Pollutantmigration on the Environmental Situation in the Zone of Chemical Weaponinfluence

The article is devoted to the issues of monitoring the environment state in the influence zone of chemically hazardous objects on the example of the environmental monitoring system of a chemical weapons storage and destruction facility (CWDF) in the city of Kambarka, Udmurt Republic. Among the components there are: production control systems and site monitoring, environmental monitoring and health monitoring, however, it is environment in the zone of the chemical waste disposal facility influence which hasthe largest area and is the most difficult to control. Here, due to large areas, the process of migration of pollutants over considerable distances is observedclearly. The importance of the system for arranging sampling points on the topographic map of the waste disposal facility is shown in order to take into account the process of pollutantmigration. Vertical and horizontal migration of pollutants can lead to the emergence of unobvious local areas of pollutantaccumulation both within and outside the influence zone of the chemical waste disposal facility. A solution to the problem of migration process modeling on a conditionally selected area of the territory within the facility influence zone is presented to show the effect pollutantmigration on the dynamics of the environmental situation within the chemical waste disposal area. It has been proven that within the area under consideration there will be a significant amount of pollutant, which, subject to further evaporation of rainwater runoff, will remain in this area. This indicates the inevitability of the appearance of local areas of pollutantaccumulation in the influence zone of chemically-disposable substances in excess of permissible standards for their concentration, which requires a revision of the arranging sampling pointsystem, taking into account the application of migration processmodeling methods. The need for dynamic observations in individual sites of the area, selected with respect to the analysis results of the pollutantmigration routes within the entire influence zone of the chemically-disposable waste disposal facility, in order to assess the degree of impact of the non-toxic waste disposal facility on the environment, is substantiated.

An improved cohesive hierarchical clustering for indoor air quality monitoring in smart gymnasium with healthy sport areas

With the rise of healthy lifestyles, sports arenas have become important exercise venues for people. However, indoor air quality issues are becoming increasingly prominent, posing a potential threat to the health of athletes. Traditional indoor air quality monitoring systems often suffer from problems such as unreasonable layout of monitoring points and inaccurate data processing, making it difficult to effectively cope with complex indoor environments. This article proposes an intelligent monitoring system for indoor air quality in sports venues based on an improved cohesive hierarchical clustering algorithm to address these issues. The system optimizes the layout of monitoring points, combines real-time data collection on the Android platform, and accurately processes non smooth data using an improved cohesive hierarchical clustering algorithm based on BiLSTM. The experimental results show that the algorithm not only improves the accuracy of monitoring point selection, but also optimizes the number of monitoring points and effectively supplements missing data. In addition, the algorithm significantly reduces computational complexity and improves computational efficiency while ensuring clustering quality. Application testing shows that the indoor air environment monitoring system constructed in this article can obtain and analyze data in real time, effectively control the concentration of key pollutants such as PM2.5 and carbon dioxide, and create a healthier sports environment for sports venues. This study provides new ideas and methods for indoor air quality monitoring, which has important practical application value.

Analysis of passenger transportation and the public transportation impact on the reduction in a smart city

The state of atmospheric pollution is determined by the growth of the population, the amount of transport and the generated volumes of emissions. The object is the process of analyzing passenger transportation in the city. The subject is passenger transport analysis methods. Purpose: analysis of passenger transportation and approaches to optimization of public transport based on the concept of a smart city. Tasks: analysis of passenger transportation, classification of existing conceptual approaches to optimization of public transport with low carbon emissions, systematization of existing methods, means and types of neural networks in smart cities, analysis of successful implementation projects. Methods of statistical analysis, linear and non-linear interpolation, logical generalization, comparison, grouping, analysis and synthesis. Results: the analysis of passenger transportation in the city revealed that statistical data sets indicate a decrease in the main indicators of passenger traffic and an increase in the volume of emissions of carbon-containing compounds. The classification of existing approaches to the optimization of public transport is carried out according to the priority of public transport, hybridization and electrification of vehicles and the implementation of IT monitoring. During the systematization of methods and means in smart cities, the following are highlighted: smart transport systems; electric vehicles; transport sharing networks; smart applications and information systems; innovative payment systems; unmanned vehicles; information boards and announcement systems; networks of bicycle paths and equipped sidewalks; environmental monitoring systems. Among neural networks, recurrent, convolutional, and deep neural networks have been proposed as those that contribute to route optimization and traffic prediction. Conclusions: the statistical analysis of passenger transportation established that reducing carbon dioxide emissions is an unresolved task for both public transport and the transportation system. It is proposed to include methods and means that optimize public transport, reducing the carbon footprint of the initiatives of implementing the concept of a smart city, which are successful all over the world. It is proposed to use recurrent, convolutional and deep neural networks to optimize passenger transportation in smart cities.

RFID-Based Multisensory System for Environmental Monitoring and Consumable Management of Intelligent Tracking

RFID-Based Multisensory System for Environmental Monitoring and Consumable Management of Intelligent Tracking

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

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

30 years of the environmental health monitoring system in the Czech Republic

30 years of the environmental health monitoring system in the Czech Republic

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT

This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies.

Optimising the network of air quality monitoring stations

This study explores the application of genetic algorithms (GAs) for optimizing the network of air quality monitoring stations. Recognizing the complexity of accurately assessing air pollution levels across diverse urban and rural landscapes, the research focuses on finding the most effective station placements to maximize coverage and data fidelity while minimizing costs. The methodology entails simulating natural selection processes, including selection, crossover, and mutation, to evolve a population of potential solutions. Each candidate solution in the population represents a unique configuration of monitoring stations. A fitness function evaluates the efficiency of each configuration based on criteria such as population coverage, proximity to pollution sources, and installation and operational expenses. The research employs a genetic algorithm developed in Python, which iteratively refines the population of solutions over thousands of generations. The algorithm's performance is assessed through experimental validation, with an emphasis on the adaptability of the approach to accommodate various environmental, economic, and regulatory constraints. Results indicate that GAs can effectively balance multiple optimization objectives, leading to a network design that is both cost-efficient and comprehensive in its monitoring capabilities. The outcome of the study is an optimized network that significantly improves upon the initial state in terms of coverage and cost-effectiveness. The study concludes that genetic algorithms offer a promising avenue for addressing the challenges of air quality monitoring network design. The flexibility and global search capabilities of GAs make them suitable for the complex, multi-objective nature of the task. Moreover, the findings suggest potential for further improvements and applications of GAs in environmental monitoring and other complex systems optimization scenarios.

Taking Shenzhen and Shanghai as Examples to Analyze the Construction and Development of Smart Port Cities

In recent years, the enthusiasm for discussing building smart cities has been boosted greatly worldwide, and the residents of major super cities seem to be eager to understand what a smart city is. Unfortunately, due to the International Business Machines Corporations (IBM) recent introduction of the concept of smart cities, many people have misunderstood the concept of smart cities, and even mistakenly classify urban digitization as the scope of smart cities. The core element of a smart city is to use various Smart City Facilities (geographic information systems, urban security systems, intelligent traffic control systems, environmental monitoring systems, residents feedback systems) to bring the city onto an intelligent, automated, efficient, and livable road. This article aims to compare two significant Chinese port cities, Shenzhen and Shanghai, in their efforts to establish smart cities. It delves into the challenges they encountered during this process, analyzes the benefits accrued, and offers valuable reminding for cities aspiring to embark on their own smart city journey in the future.

Illuminating tetracycline: The role of metal coordination in fluorescence emission and its optical detection potential

Rapid on-site detection of antibiotics such as tetracycline (TC) is crucial for understanding their elusive environmental fate. Fluorescence-based strategies are promising owing to their sensitivity and ease of use. This study demonstrates that Al3+–TC coordination significantly enhances the intrinsic fluorescence of TC at excitation/emission wavelengths of 395/490 and 270/490 nm. The fluorescence emission intensity is proportional to the TC concentration between 0.005 and 7.5 μmol/L TC, which increases the detection sensitivity by 40 times compared with the commonly used absorption signals. Density functional theory calculations, when combined with the independent gradient model, indicate that the six-membered chelate ring in the octahedral Al3+–TC complex restricts molecular motion, along with two intramolecular hydrogen bonds formed between TC and water ligands. These factors lead to a narrowing of the energy gap between the highest occupied and lowest unoccupied molecular orbitals upon complexation with Al3+. Consequently, the enhanced molecular rigidity and facilitated electron transfer result in a significant increase in the fluorescence quantum yield. This captivating fluorescence response significantly enhances the proficiency of optical signals in detecting TC, thereby enriching future environmental monitoring systems with greater diversity and adaptability.

Monitoring and management of green information in water ecological environment based on sensors and big data

With the continuous development of industrial production, the pollution of water resources is becoming more and more serious, and the damage to aquatic organisms is also increasing. Therefore, strengthening water environment monitoring has become a key measure to prevent and solve this problem. Aiming at the limitation of traditional water environment monitoring methods, a water ecological environment monitoring scheme is proposed. This study uses advanced sensor technology, including water quality sensor, water level sensor, weather sensor, etc., to monitor the indicators of water in real time. Through data acquisition and storage technology, the data obtained by the sensor is integrated and analyzed. At the same time, big data analysis method is used to predict and simulate the change trend of water ecological environment. This scheme designs and develops a sensor network monitoring system, which can collect water temperature data in real time at the monitoring point, transmit the sampled information to the aggregation node through the sensor network, and finally transmit to the information intelligent monitoring equipment through GPRS, so as to realize timely display and early warning. At the same time, combined with the water quality monitoring instrument, it can realize the remote query and processing of monitoring data. The experimental results show that the water ecological environment monitoring and management system based on sensor and big data technology can efficiently and accurately monitor the indicators of water. Through the comprehensive monitoring of the water ecological environment, abnormal situations can be found in time, and corresponding measures can be taken to protect and repair. The results of big data analysis provided by the system can provide scientific basis and guidance for decision makers.

Wind-driven suspended triboelectric-electromagnetic hybrid generator with vibration elimination for environmental monitoring in the high-voltage power transmission line

The sustainable development of the high-voltage power transmission system puts forward more significant requirements IoT sensor network. Currently, a large number of the IoT sensors are installed on the high-voltage transmission lines, and environmental wind energy harvesting around the outdoor power transmission lines enables a promising and feasible solution to supply distributed sensors. However, the traditional floor-mounted wind energy nanogenerator with high requirements for placement and fixation is no longer applicable to the suspension scenario of the high-voltage power transmission lines. In this work, a wind-driven suspended triboelectric-electromagnetic hybrid generator (WS-TEHG) is designed as a suspended wind cup with a three-layered integrated structure, which can be integrated with the damping structure that hanged and installed on the power transmission line, demonstrating the simultaneously achieving high efficiency wind energy harvesting and the operation stability of the device. After the structural and material optimization, WS-TEHG can achieve efficient energy collection under wide-range of the wind speed from 2.6 to 17.3 m s−1, whose TENG and EMG produced instantaneous peak power from 0.2 to 7.9 mW and 0.6–85.1 mW, respectively. Moreover, a standardized power management based on LTC 3588-based double-channel circuit was established to co-manage the outputs of the two generating modules in the hybrid generator and enhance the continuous stability of the standardized voltage output, demonstrating a shorter undervoltage charge accumulation, a faster start-up process, a lower power consumption compared with the traditional power management. Finally, a self-powered environmental monitoring system by a WS-TEHG equipped with a standardized power management circuit was developed to provide the weather and climate information around the power transmission lines, thereby demonstrating a potential and feasible for large-scale self-powered application in the field of the high-voltage power transmission lines.

Innovations of water pollution traceability technology with artificial intelligence

Water pollution traceability is a critical component of environmental management, essential for ensuring the safety of water supplies and maintaining ecological balance. Although many countries have established relatively mature water environment quality monitoring (WEQM) systems, these systems often face limitations such as inadequate professional sensing equipment, insufficient model stability, and high technical expertise requirements. Monitoring, early warning, and traceability of water pollution in complex watersheds remain challenging tasks. The advent of artificial intelligence (AI), the Internet of Things (IoT), and big data is driving the digital transformation of traditional WEQM systems, significantly enhancing their capabilities in digitalization, intelligence, and networking. This perspective paper examines the potential impacts of emerging technologies such as AI and big data on water environment monitoring and pollution traceability in watersheds. It identifies innovation barriers and the transformation potential of WEQM systems from various perspectives, including monitoring and early warning systems, pollution location and tracking, fingerprint spectroscopy technology, and the detection of new pollutants. This exploration aims to provide insights and inspiration for the accurate diagnosis of water pollution and the enhancement of global environmental monitoring systems.