Sensors For Environmental Monitoring Research Articles

Automated evaluation of environmental coupling for Advanced LIGO gravitational wave detections

The extreme sensitivity required for direct observation of gravitational waves by the Advanced LIGO detectors means that environmental noise is increasingly likely to contaminate Advanced LIGO gravitational wave signals if left unaddressed. Consequently, environmental monitoring efforts have been undertaken and novel noise mitigation techniques have been developed which have reduced environmental coupling and made it possible to analyze environmental artifacts with potential to affect the 90 gravitational wave events detected from 2015–2020 by the Advanced LIGO detectors. So far, there is no evidence for environmental contamination in gravitational wave detections. However, automated, rapid ways to monitor and assess the degree of environmental coupling between gravitational wave detectors and their surroundings are needed as the rate of detections continues to increase. We introduce a computational tool, PEMcheck, for quantifying the degree of environmental coupling present in gravitational wave signals using data from the extant collection of environmental monitoring sensors at each detector. We study its performance when applied to 79 gravitational waves detected in LIGO’s third observing run and test its performance in the case of extreme environmental contamination of gravitational wave data. We find that PEMcheck’s automated analysis identifies only a small number of gravitational waves that merit further study by environmental noise experts due to possible contamination, a substantial improvement over the manual vetting that occurred for every gravitational wave candidate in the first two observing runs. Building on a first attempt at automating environmental coupling assessments used in the third observing run, this tool represents an improvement in accuracy and interpretability of coupling assessments, reducing the time needed to validate gravitational wave candidates. With the validation provided herein; PEMcheck will play a critical role in event validation during LIGO’s fourth observing run as an integral part of the data quality report produced for each gravitational wave candidate.

Assessing the informativeness of a coupled surface–subsurface watershed model for understanding debris flow: a hydrological perspective

ABSTRACT Characterization of debris flow is critical to both risk assessment and hazard mitigation. Recent technologies enable onsite environmental monitoring sensors for geological disaster monitoring. However, the spatiotemporal understanding of debris flow in remote mountainous areas is still limited due to difficulties in observation networks and its complex driving conditions. Here we apply a coupled surface–subsurface hydrological model to examine the characteristics of the water movements near debris flow sites in southwest China. Our approach captured the temporal dynamics of infiltration, redistribution of soil moisture, groundwater storage, and lateral groundwater fluxes. The lateral groundwater flux and groundwater storage were informative indicators in identifying debris flow location. Such informativeness was only effective when hourly dynamics were analysed. Our findings provide new insight into quantifying debris flow susceptibility. This study suggests that the coupled surface–subsurface watershed modelling approach can be informative for preliminary monitoring, planning and management of debris flow.

2D Hybrid Perovskite Sensors for Environmental and Healthcare Monitoring.

Layered perovskites, a novel class of two-dimensional (2D) layered materials, exhibit versatile photophysical properties of great interest in photovoltaics and optoelectronics. However, their instability to environmental factors, particularly water, has limited their utility. In this study, we introduce an innovative solution to the problem by leveraging the unique properties of natural beeswax as a protective coating of 2D-fluorinated phenylethylammonium lead iodide perovskite. These photodetectors show outstanding figures of merit, such as a responsivity of >2200 A/W and a detectivity of 2.4 × 1018 Jones. The hydrophobic nature of beeswax endows the 2D perovskite sensors with an unprecedented resilience to prolonged immersion in contaminated water, and it increases the lifespan of devices to a period longer than one year. At the same time, the biocompatibility of the beeswax and its self-cleaning properties make it possible to use the very same turbidity sensors for healthcare in photoplethysmography and monitor the human heartbeat with clear systolic and diastolic signatures. Beeswax-enabled multipurpose optoelectronics paves the way to sustainable electronics by ultimately reducing the need for multiple components.

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

Development of Environmental Monitoring Sensor for Steel Corrosion in Concrete based on Micro-Electro-Mechanical Systems (MEMS)

Many bridges located near marine environments usually have high risk to lose their structural performance due to corrosion of reinforcing bars with chloride attack. While a huge amount of budget to maintain may be consumed for the structural maintenance methodologies, an efficient monitoring system for Reinforced Concrete (RC) structures in the corrosive environment is strongly demanded. Thus, structural health monitoring in real situations is significant for bridge asset management. The alternative way for preventing severe damage propagation is proactively monitoring the environmental condition containing chloride ion and its effect on the durability of concrete structures, for which numerous methods have been conventionally suggested such as dry gauze method to capture chlorides in air. In this study, Micro Energy Harvester (MEH) based on Micro-Electro-Mechanical Systems (MEMS) is technically applied to develop a sensor to identify the corrosion environment with chlorides in the exposure condition. MEMS is a rapidly evolving device, which enables to monitor the changes in natural frequency and amplitude at nodal locations of the targeting structure due to vibration-based electrostatic power generation induced by the corrosive environment. The purpose of this research is to develop a system for detecting the high corrosion-prone areas and finding the suitable metal sheet material for various chloride ion conditions, using the metal sheet as a cantilever structure for the MEMS device. 4 types of metal, aluminium, copper, iron and zinc are considered for the parametric experiments. Several scenarios are considered including chloride ion concentration, temperatures, and wet and dry conditions to compare the dominant frequency changes of each metal sheet and determine their sensitivity to corrosion environment. From the preliminary results, iron and zinc have a significant drop in frequency with the elapsed exposure period, which indicates that they are highly sensitive to the external chloride ion environment. These results offer an innovative and proactive approach to structural health monitoring for further developed applications. It enables prompt maintenance and early detection of potential issues for corrosion of steel bars in concrete, ensuring the longevity and safety of RC bridge structures.

Enhanced Surface Plasmon Resonance (SPR) Fiber Optic Sensor for Environmental Monitoring: A Coreless Fiber–Based Design

Enhanced Surface Plasmon Resonance (SPR) Fiber Optic Sensor for Environmental Monitoring: A Coreless Fiber–Based Design

Sign Recognition and Voice Conversion Device for Dumb

This project proposes a comprehensive hardware setup for home automation and environmental monitoring. The system includes components such as a Bluetooth module for remote control, a voice module (APR9600) for audio feedback, an LCD for display purposes, relay modules for controlling electrical devices, bulbs with holders for lighting, a CPU fan for temperature regulation, speakers for audio output, and a 5V power supply for operation. The core functionality of the system revolves around the integration of flux sensors for environmental monitoring. When flux sensors detect specific conditions, predefined audio signals are played through the voice module. These conditions correspond to six distinct signs, facilitating real-time alerts and notifications. Moreover, the system offers remote control capabilities through Bluetooth connectivity, enabling users to manage lighting and fan operations conveniently. Additionally, temperature monitoring is implemented using a DHT11 sensor, triggering the activation of the CPU fan when temperature thresholds are exceeded. Furthermore, an LCD display enhances user interaction by providing visual feedback and status updates. The project utilizes Arduino UNO as the central processing unit and incorporates 12V 1A adapters for powering specific components. Connectors and power cables are utilized for seamless integration and electrical connectivity. Overall, this project presents an integrated solution for home automation and environmental monitoring, offering flexibility, convenience, and enhanced user experience.

AI-Powered Disease Detection and Growth Monitoring System Using IoT for Improved Tomato Yield

Abstract: This project introduces an advanced AI- powered disease detection and growth monitoring system designed specifically for enhancing tomato yield. With Internet of Things (IoT) technology, the system integrates Raspberry Pi as the central processing unit with temperature and humidity sensors for environmental monitoring. AI algorithms are used to predict tomato ripening stages and detect diseases, to ensure proactive management of plant health. If a disease is identified, the system provides the solutions to control its impact, thereby enhancing yield and quality. The captured data is analysed and results are displayed on a user-friendly webpage accessible to farmers, enabling informed decision-making and improved agricultural practices. This innovative approach combines technology with agricultural expertise, offering a solution to the challenges faced by tomato farmers and contributing to sustainable tomato production. Keywords: Image Recognition, Disease Detection

Highly sensitive and selective laser-based BTEX sensor for occupational and environmental monitoring

A mid-infrared laser-based sensor is designed and demonstrated for trace detection of benzene, toluene, ethylbenzene, and xylene isomers at ambient conditions. The sensor is based on a distributed feedback inter-band cascade laser emitting near 3.29 µm and an off-axis cavity-enhanced absorption spectroscopy configuration with an optical gain of 2800. Wavelength tuning and a deep neural network (DNN) model were employed to enable simultaneous and selective BTEX measurements. The sensor performance was demonstrated by measuring BTEX mole fractions in various mixtures. At an integration time of 10 s, minimum detection limits of 11.4, 9.7, 9.1, 10, 15.6, and 12.9 ppb were achieved for benzene, toluene, ethylbenzene, m-xylene, o-xylene, and p-xylene, respectively. The sensor can be used to detect tiny BTEX leaks in petrochemical facilities and to monitor air quality in residential and industrial areas for workplace pollution.

Date Leaves-Derived Submicron/Nano Carbon-Modified Glassy Carbon Electrode for Highly Sensitive and Simultaneous Detection of 1-Naphthol and 2-Naphthol

This study introduces a novel approach for synthesizing biomass derived carbon from date leaves waste, employing a straightforward combination of pyrolysis and ball milling processes. This process yields active carbon in submicron and nanometer sizes, characterized by detailed surface, structural, and compositional analyses, confirming suitability of the materials for electrochemical applications. Utilizing this synthesized carbon, we have developed a modified glassy carbon electrode (DLSNC/GCE) for the highly-sensitive and simultaneous detection of phenolic contaminants, specifically 1-naphthol (1-NP) and 2-naphthol (2-NP), which are crucial for environmental monitoring. The study describes two innovative fabrication methods for electrochemical sensors. The first method controls the direct oxidation of 1-NP and 2-NP, while the second method exploits redox peaks associated with quinone formation from dihydroxy naphthalene, revealing superior analytical performance for the simultaneous detection of the analytes. The electrochemical sensor demonstrated exceptional sensitivity and selectivity towards 1-NP and 2-NP. This revolution emphasizes the potential of using date leaves-derived carbon materials in constructing low-cost, efficient electrochemical sensors for environmental monitoring and electrochemical applications.

Artificial Intelligence based Rover with Robotic Arm using Raspberry Pi

This paper presents the design, implementation, and evaluation of an Artificial Intelligence (AI)-Based Rover with a Robotic Arm, powered by Raspberry Pi technology. The integration of AI algorithms with Raspberry Pi provides a versatile platform for developing intelligent robotic systems capable of autonomous navigation and manipulation tasks. The rover's autonomous navigation system utilizes computer vision and machine learning algorithms to perceive its environment, plan optimal paths, and navigate autonomously while avoiding obstacles. Additionally, the inclusion of a robotic arm extends the rover's capabilities, enabling it to perform manipulation tasks with precision and versatility. Key components of the system include Raspberry Pi as the central processing unit, cameras for environmental perception, motors for locomotion and manipulation, and sensors for feedback and environmental monitoring. The integration of these components facilitates seamless communication and coordination, enabling the rover to execute complex tasks autonomously. Experimental results demonstrate the effectiveness of the proposed system in various scenarios, showcasing its ability to navigate challenging environments, avoid obstacles, and manipulate objects with precision. The versatility and scalability of the platform make it suitable for a wide range of applications, including search and rescue missions, environmental monitoring, and industrial automation. Keywords- Robotics, Artificial Intelligence, Raspberry pi, Autonomous Navigation, Machine Learning

Assessment of the microbial electrochemical sensor (SENTRY™) as a potential wastewater quality monitoring tool for common pollutants found in Malaysia.

Bioelectrochemical sensors for environment monitoring have the potential to provide facility operators with real-time data, allowing for better and more timely decision-making regarding water and wastewater treatment. To assess the robustness and sensitivity of the Sentry™ biosensor in local conditions, it was tested in Malaysia using domestically available wastewater. The study objectives included (1) enrich the biosensor locally, (2) operate and test the biosensor with local domestic wastewater, and (3) determine the biosensor's responsiveness to model pollutants through pollutant spike and immersion test as well as response to absence of wastewater. Lab-scale operation shows the biosensor was successfully enriched with (1) local University Kebangsaan Malaysia's, microbial community strain collection and (2) local municipal wastewater microflora, operated for more than 50days with a stable yet responsive carbon consumption rate (CCR) signal. Meanwhile, two independent biosensors were also enriched and operated in Indah Water Research Centre's crude sewage holding tank, showing a stable response to the wastewater. Next, a pilot scale setup was constructed to test the enriched biosensors for the spiked-pollutant test. The biosensors showed a proportional CCR response (pollutant presence detected) towards several organic compounds in the sewage, including ethanol, chicken blood, and dilution of tested sewage but less to curry powder, methanol, and isopropanol. Conversely, there was no significant response (pollutant presence not detected) towards hexane, Congo red, engine oil, and paint, which may be due to their non-biodegradability and/or insoluble nature. Additionally, the biosensors were exposed to air for 6h to assess their robustness towards aerobic shock with a positive result. Overall, the study suggested that the biosensor could be a powerful monitoring tool, given its responsiveness towards organic compounds in sewage under normal conditions.

Wireless flow-powered miniature robot capable of traversing tubular structures.

Wireless millimeter-scale robots capable of navigating through fluid-flowing tubular structures hold substantial potential for inspection, maintenance, or repair use in nuclear, industrial, and medical applications. However, prevalent reliance on external powering constrains these robots' operational range and applicable environments. Alternatives with onboard powering must trade off size, functionality, and operation duration. Here, we propose a wireless millimeter-scale wheeled robot capable of using environmental flows to power and actuate its long-distance locomotion through complex pipelines. The flow-powering module can convert flow energy into mechanical energy, achieving an impeller speed of up to 9595 revolutions per minute, accompanied by an output power density of 11.7 watts per cubic meter and an efficiency of 33.7%. A miniature gearbox module can further transmit the converted mechanical energy into the robot's locomotion system, allowing the robot to move against water flow at an average rate of up to 1.05 meters per second. The robot's motion status (moving against/with flow or pausing) can be switched using an external magnetic field or an onboard mechanical regulator, contingent on different proposed control designs. In addition, we designed kirigami-based soft wheels for adaptive locomotion. The robot can move against flows of various substances within pipes featuring complex geometries and diverse materials. Solely powered by flow, the robot can transport cylindrical payloads with a diameter of up to 55% of the pipe's diameter and carry devices such as an endoscopic camera for pipeline inspection, a wireless temperature sensor for environmental temperature monitoring, and a leak-stopper shell for infrastructure maintenance.

Delafossite CuGaO2-Based Chemiresistive Sensor for Sensitive and Selective Detection of Dimethyl Disulfide.

Dimethyl disulfide (DMDS) is a common odor pollutant with an extremely low olfactory threshold. Highly sensitive and selective detection of DMDS in ambient humid air background, by metal oxide semiconductor (MOS) sensors, is highly desirable to address the increased public concern for health risk. However, it has still been a critical challenge up to now. Herein, p-type delafossite CuGaO2 has been proposed as a promising DMDS sensing material owing to its striking hydrophobicity (revealed by water contact angle measurement) and excellent partial catalytic oxidation properties (indicated by mass spectroscopy). The present CuGaO2 sensor shows a selective DMDS response, with satisfied humidity resistance performance and long-term stability at a relatively low operation temperature of 140 °C. An ultrahigh response of 100 to 10 ppm DMDS and a low limit of detection of 3.3 ppb could be achieved via a pulsed temperature modulation strategy. A smart sensing system based on a CuGaO2 sensor has been developed, which could precisely monitor DMDS vapor in ambient humid air, even with the presence of multiple interfering gases, demonstrating the practical application capability of MOS sensors for environmental odor monitoring.

Growth Processing and Strategies: A Way to Improve the Gas Sensing Performance of Nickel Oxide-Based Devices

The review paper provides a comprehensive analysis of nickel oxide (NiO) as an emerging material in environmental monitoring by surveying recent developments primarily within the last three years and reports the growth processing and strategies employed to enhance NiO sensing performance. It covers synthesis methods for pristine NiO, including vapor-phase, liquid-phase, and solution-processing techniques, highlighting advantages and limitations. The growth mechanisms of NiO nanostructures are explored, with a focus on the most recent research studies. Additionally, different strategies to improve the gas sensing performance of NiO are discussed (i.e., surface functionalization by metallic nanoparticles, heterostructure formation, carbon-based nanomaterials, and conducting polymers). The influence of these strategies on selectivity, sensitivity, response time, and stability of NiO-based sensors is thoroughly examined. Finally, the challenges and future directions that may lead to the successful development of highly efficient NiO-based gas sensors for environmental monitoring are introduced in this review.

Synergistic Integration of Nanogenerators and Solar Cells: Advanced Hybrid Structures and Applications

AbstractThe rapid growth of global energy consumption and the increasing demand for sustainable and renewable energy sources have urged vast research into harnessing energy from various sources. Among them, the most promising approaches are nanogenerators (NGs) and solar cells (SCs), which independently offer innovative solutions for energy harvesting. This review paper presents a comprehensive analysis of the integration of NGs and SCs, exploring advanced hybrid structures and their diverse applications. First, an overview of the principles and working mechanisms of NGs and SCs is provided for seamless hybrid integrations. Then, various design strategies are discussed, such as piezoelectric and triboelectric NGs with different types of SCs. Finally, a wide range of applications are explored that benefit from the synergistic integration of NGs and SCs, including self‐powered electronics, wearable devices, environmental monitoring, and wireless sensor networks. The potential for these hybrid systems is highlighted to address real‐world energy needs and contribute to developing sustainable and self‐sufficient technologies. In conclusion, this review provides valuable insights into the state‐of‐the‐art developments in NGs and SCs integration, shedding light on advanced hybrid structures and their diverse applications.

Research on inspection route of hanging environmental robot based on computational fluid dynamics

The environment of a closed piggery is commonly characterized by spatial unevenness, and there are currently no specific standards for installation points of various environmental monitoring sensors. Therefore, the project team used the hanging track inspection robot (HTIR) as an environmental mon-itoring platform to seek the environmental monitoring points and ensure the scientific layout of moni-toring points. Ansys-CFD software was used to study the change rules of environmental parameters at 1.6 m (α plane), 0.7 m (β plane), and 0.4 m (γ plane) above the ground. The 300 monitoring points ((x1~x30) ×(y1~y10)) in each plane were analyzed to determine the most suitable monitoring points and inspection routes for HTIR. The results showed that: (1) All monitoring points could be arranged directly below the y3 track. (2) Monitoring points (x1, y3), (x10, y3) and (x30, y3) were environmental feature points. At (x1, y3), the maximum relative humidity and NH3 concentration on the α plane could be detected, and the maximum wind speed, maximum temperature, and maximum NH3 concentration on other planes could also be detected; At (x10, y3), the minimum temperature and maximum relative humidity of the β and γ planes could be detected; At (x30, y3), the maximum NH3 concentration in the α plane and the minimum relative humidity in all planes could be detected. This study scientifically arranged the inspection track and monitoring points for HTIR, improved the accuracy of environmental monitoring, and put forward suggestions for reducing NH3 concentration in closed piggeries, laying the foundation for the next step.

Long-term field studies of a distributed network of sensors for environmental radiological monitoring

The W-MON project goal is to establish an automatic control mechanism of the presence of radioactive material in conventional waste containers at CERN using a distributed network of interconnected low-power radiation sensors. This network facilitates continuous data recording, transfer and storage in a database while allowing online and offline data analysis, in addition to alarm triggering. Data transmission, processing and evaluation is achieved by a centralized IoT end-to-end data architecture that has been developed for real-time monitoring and visualization of the radiation levels in waste containers. In this paper the results of field tests of the W-MON system described in two previous papers are presented for three different types of sensors. Estimation of failure detection probability, long-term stability tests and sensitivity studies carried out using radioactive samples of various activities placed in standard waste containers are described. A comparison between the manual monitoring procedure currently used at CERN and the W-MON system is discussed in detail.

Application research of intelligent system based on BIM and sensors monitoring technology in construction management

Architectural engineering is a dynamic process, and it is difficult to achieve ideal results with fixed standard management. This article proposes research on intelligent building construction management methods based on BIM and information soft sensor monitoring technology, attempting to construct a construction building model and an information database that includes basic data and expanded data. Establish a BIM model, split it into various modules, and graft the real-time dynamic database detected by environmental monitoring sensors with the BIM model to simulate the entire construction process, conduct dynamic monitoring, and adjust emergency plans in real-time through intelligent internet to ensure the rationality of the entire construction stage of multiple blocks. Based on the expected construction process requirements, resources are scheduled and managed. Compared with traditional management models, information sensors and monitoring intelligent management systems are more efficient, effectively improving resource utilization, achieving low-carbon construction, and significantly accelerating project progress.

Oxidized carboxymethyl cellulose/polyaniline-based hybrid nanocomposite for sensitive detection of environmentally hazardous nitrophenol in real samples

Present work aims at fabricating chemically active, reproducible, and reliable electrode material by Ag nanoparticles anchored oxidized-carboxymethyl cellulose (O-CMC) embedded Pani (Ag@Pani/O-CMC) based nanocomposite for the trace detection of 4-nitrophenol. The Ag@Pani/O-CMC was physiochemically characterized in detail using FTIR, SEM, TEM, UV–vis, XPS, and XRD to analyze functional groups, structural morphology, optical properties, and chemical compositions. Differential pulse voltammetry and cyclic voltammetry were adopted to explore the electrochemical performance of Ag@Pani/O-CMC modified glassy carbon electrode (Ag@Pani/O-CMC/GCE). The observed reduction peak current was proportional to the 4-NP concentrations with a high sensitivity (2.48 µA nM−1 cm−2) and displayed a detection limit of 0.58 nM and a good linear range of 10–100 nM. In addition, the sensor exhibited comparable sensing capability in real biological samples, including milk, mixed fruit juice, and tap water, with excellent recovery from 97.20 % to 101 %. It also depicted high repeatability as the current response remained at 84.52 % after 28 days of operation for 4-NP detection. This approach can be projected to result in the electrochemical sensor for safe industrial processing and environmental monitoring, which is expected to have good stability, excellent selectivity, and sensitivity for detecting traces of 4-NP in actual samples.