Environmental Monitoring System Research Articles

Ventilation optimization through digital transformation

Underground mines require electricity to operate both the main surface fans and auxiliary systems, with primary ventilation systems typically accounting for approximately 40-60% of the mine's overall electricity consumption. The primary reason for this considerable expense is that most underground ventilation systems are designed for peak demand, regardless of the actual demand, which is commonly dictated by diesel equipment usage in trackless mining operations. Most platinum mines on the Great Dyke in Zimbabwe tend to operate their ventilation systems at this peak level, despite the mine's air quantity being well in excess of the 'true' ventilation needs. This is due to a lack of appropriate ventilation controls. The authors designed and installed a ventilation-on-demand system at one of the bord and pillar platinum mines to minimize the use of redundant air in underground operations. Fourth industrial revolution techniques were applied to the environmental monitoring and tracking systems thereby optimizing air quantity demands. The mine managed to reduce its annual power consumption by 23% through the implementation of manual control and time-of-day scheduling levels of the ventilation-on-demand concept. There was also a 6% productivity improvement, mainly attributable to an increased face time as a result of the significant reduction in the re-entry period following a blast. This paper introduces a novel concept of ventilation optimization through digital transformation targeting mainly room and pillar platinum mines. A system was designed, installed, and commissioned, though there is currently an ongoing optimization process to harness the full benefits such as productivity enhancement, reduced power costs, and improved worker health.

Prediction and monitoring model for farmland environmental system using soil sensor and neural network algorithm

Abstract In this study, data fusion algorithm is used to classify the soil species and calibrate the soil humidity sensor, and by using edge computing and a wireless sensor network, farmland environment monitoring system with a two-stage calibration function of frequency domain reflectometer (FDR) is established. Edge computing is used in system nodes, including the saturation value of the soil humidity sensor, the calculated soil hardness, the calculation process of the neural network, and the model of soil classification. A bagged tree is adopted to avoid over-fitting to reduce the prediction variance of the decision tree. A decision tree model is established on each training set, and the C4.5 algorithm is adopted to construct each decision tree. After primary calibration, the root mean squared error (RMSE) between the measured and standard values is reduced to less than 0.0849%. The mean squared error (MSE) and mean absolute error (MAE) are reduced to less than 0.7208 and 0.6929%. The bagged tree model and backpropagation neural network are used to classify the soil and train the dynamic soil dataset. The output of the trained neural network is closer to the actual soil humidity than that of the FDR soil humidity sensor. The MAE, the MSE, and the RMSE decrease by 1.37%, 3.79, and 1.86%. With accurate measurements of soil humidity, this research shows an important guiding significance for improving the utilization efficiency of agricultural water, saving agricultural water, and formulating the crop irrigation process.

A Step-by-Step Guide to smart environment monitoring system

A Step-by-Step Guide to smart environment monitoring system

In Vivo Plant Bio-Electrochemical Sensor Using Redox Cycling.

This work presents an in vivo stem-mounted sensor for Nicotiana tabacum plants and an in situ cell suspension sensor for Solanum lycopersicum cells. Stem-mounted sensors are mechanically stable and less sensitive to plant and air movements than the previously demonstrated leaf-mounted sensors. Interdigitated-electrode-arrays with a dual working electrode configuration were used with an auxiliary electrode and an Ag/AgCl quasi-reference electrode. Signal amplification by redox cycling is demonstrated for a plant-based sensor responding to enzyme expression induced by different cues in the plants. Functional biosensing is demonstrated, first for constitutive enzyme expression and later, for heat-shock-induced enzyme expression in plants. In the cell suspension with redox cycling, positive detection of the enzyme β-glucuronidase (GUS) was observed within a few minutes after applying the substrate (pNPG, 4-Nitrophenyl β-D-glucopyranoside), following redox reactions of the product (p-nitrophenol (pNP)). It is assumed that the initial reaction is the irreversible reduction of pNP to p-hydroxylaminophenol. Next, it can be either oxidized to p-nitrosophenol or dehydrated and oxidized to aminophenol. Both last reactions are reversible and can be used for redox cycling. The dual-electrode redox-cycling electrochemical signal was an order of magnitude larger than that of conventional single-working electrode transducers. A simple model for the gain is presented, predicting that an even larger gain is possible for sub-micron electrodes. In summary, this work demonstrates, for the first time, a redox cycling-based in vivo plant sensor, where diffusion-based amplification occurs inside a tobacco plant's tissue. The technique can be applied to other plants as well as to medical and environmental monitoring systems.

Russian-Made Wireless Lighting and Microclimate Monitoring and Control Systems Accounting for Features of Museum Premises

Microclimate and light environment monitoring systems form a relatively new (emerging) hightech market segment in Russia. Projects being implemented in Russia include microclimate control based on imported equipment, while intelligent lighting systems are not produced at all. The barriers preventing the segment from growing also include lack of standards (each company develops its own technology) and inability of the Russian-produced measuring instruments registered in the State Register to work as part of such systems. These all are the key aspects are taken into account when making decisions regarding automation. Based on its proprietary developments, TKA has manufactured microclimate monitoring systems and introduced the same in museum and library premises (with wall thickness of up to 1 m, high remoteness of the premises (buildings) and other specific conditions). In particular, the specific features of buildings and some premises of the Library of the Academy of Sciences required pre-testing in order to determine permeability of walls and doors in terms of wireless communication. As a result, in the course of project development, it was found that it was necessary to install 8 repeaters to enable reliable radio signal passage. The goal of the ongoing study is to create a native system with adjustable operation conditions, number of sensors, selection of monitored parameters and their metrological characteristics, which would increase the accuracy of metrological monitoring in museum, library and archive premises as well as in greenhouse facilities and in urban and industrial infrastructural facilities by means of the lighting environment spectrometric and radiometric analysis and intelligent microclimate control.

Toxic impacts of the war on Ukraine

ABSTRACT Set against the human suffering and daily destruction that war brings, its impacts on the environment may seem secondary. But they are long-lasting; they threaten both the stability of natural ecosystems and human health; and overcoming their consequences can take many years. An action plan is needed to combat the environmental crimes against the territory of Ukraine but we can only estimate their impacts by mathematical simulation because the system of instrumental environmental monitoring has been destroyed. Here, we assess the most significant environmental impacts of this assault, and draw interim conclusions based on open-source data.

Multilayered Helical Spherical Triboelectric Nanogenerator with Charge Shuttling for Water Wave Energy Harvesting.

As an important part of natural resources, islands support the marine economy and build a blue barrier for marine ecological civilization. However, the power supply on these islands is difficult, limiting the development of marine internet of things (IoTs). In order to break the status quo, this work applies triboelectric nanogenerators (TENGs) to island power supply and ecological monitoring. A spherical TENG with two multilayered helical units is designed to harvest water wave energy, in which the space utilization rate reaches 92.5%. Then a charge shuttling mechanism is developed to improve the electrical output. The output current and power of a single TENG without power management reach 200.3µA and 16.2mW respectively, corresponding to a peak power density of 23.2W m-3 . Moreover, a scheme of the power managed TENG is proposed for realizing large-scale wave energy harvesting. The TENG is demonstrated to successfully power a water quality detector, a Bluetooth thermo-hygrometer, and an intelligent wireless alarm system for remote environmental monitoring. This work not only proposes a new type of TENG for water wave energy harvesting with improved performance, but also provides a new strategy for intelligent ocean IoTs, which even contributes to the carbon neutralization.

Microservice architecture for a remote management platform for pastured poultry farming using Amazon Web Services and wireless mesh sensor networks

Introduction: A variety of innovative solutions known as Precision Livestock Farming (PLF) technologies have been developed for the management of animal production industries, including Wireless Sensor Networks (WSN) for poultry farming.
 Problem: Current WSN-based systems for poultry farming lack the design of robust but flexible software architectures that ensure the integrity and proper delivery of data.
 Objective: Designing a microservice-based software architecture (MSA) for a multiplatform remote environmental management system based on Wireless Mesh Sensor Networks (WMSN) to be deployed in pastured poultry farming spaces.
 Methodology: A review about MSAs designed for animal farming was conducted, to synthesize key factors considered for the design process of the system data flow, microservice definition and the environmental monitoring system technology selection.
 Results: A cloud MSA with a multi layered scheme using the Amazon Web Services (AWS) platform was developed, validating the persistence of environmental data transmitted from WMSN prototype nodes to be deployed in mobile chicken coops.
 Conclusion: Defining an End-to-End data flow facilitates the organization of tasks by domains, allowing efficient event communication between components and network reliability both at the hardware and software levels.
 Originality: This study presents a novel design for a remote environmental monitoring system based on WMSN for mobile coops used in pastured poultry and a multi layered MSA cloud management platform for this specific type of food production industry.
 Limitations: Software architecture technology selection was based only on services offered, to the date of the study, in the free tier of the Amazon Web Service platform.

Development of boron doped diamond electrodes material for heavy metal ion sensor with high sensitivity and durability

We report on the optimized substrate pretreatment and deposition process conditions for boron-doped diamond (BDD) electrodes fabricated by hot-filament chemical vapor deposition (HFCVD). The optimized BDD electrode with a doping concentration of 8000 ppm showed high accuracy and precision in detecting Cd(II), Pb(II), and Cu(II) ions. In addition, this demonstrates excellent selectivity against external metal ions under the optimized stripping voltammetry measurement conditions. The detection limits of the target ions of Cd(II), Pb(II), and Cu(II) were 0.55 (±0.05), 0.43 (±0.04), and 0.74 (±0.06) μg/L (S/N = 3), respectively. In real samples spiked with 100 μg/L Cd(II), Pb(II), and Cu(II), both the accuracy and precision of the BDD electrode were within 5%; the interference with organic matter was also negligible. The excellent selectivity and long-term stability indicate that the BDD electrode developed in this study are potentially useful for online water environment monitoring systems.

Mechanical intelligent wave energy harvesting and self-powered marine environment monitoring

Harvesting wave energy to power wireless sensors can realize marine environment monitoring. However, the low frequency and irregularity of waves, as well as the harsh marine environment, have become bottlenecks limiting the development of wave energy harvesting. Herein, we propose a novel concept of mechanical intelligent energy harvesting, i.e., adaptive external excitation and regulation of energy harvesting system by mechanical structure or mechanism rather than electrical components, and illustrate this concept by designing an irregular wave energy harvesting system. The proposed gravity-driven roller and seesaw-inspired structure are sensitive to low-frequency and irregular excitation, thereby wave energy can be more easily harvested into the system. Further, the bidirectional swinging of the seesaw is converted into a high-speed unidirectional rotation of the permanent magnet disc in one direction, resulting in a significant increase in the efficiency of electromechanical conversion. Moreover, symmetrical discs on both sides can rotate synchronously via magnetic coupling mechanism, which increases the magnetic flux in the coils for higher output power and the consistent phase of the voltage is conducive to the use of electricity. Besides, the rolling behavior is used by the triboelectric nanogenerator (TENG) to convert more mechanical energy into electricity without affecting the electromagnetic energy harvester. The experimental results show that the harvester can work effectively at the ultra-low frequency (0.1 Hz) and it can charge a 0.47 F capacitor to 5 V within 6 min to achieve a self-powered wireless marine environment monitoring system under simulated wave excitation. This work demonstrates that mechanical intelligent energy harvesting is a potential solution for irregular energy harvesting and self-powered Internet of Things (IoT).

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Machine learning has been used to mine the massive data collected by automatic environmental monitoring systems and predict the changes in the environmental factors in lakes. However, further study is needed to assess the feasibility of the development of a universal machine-learning-based turbidity model for a large shallow lake with considerable spatial heterogeneity in environmental factors. In this study, we collected and examined sediment and water quality data from Lake Taihu, China. Three monitoring stations were established in three lake zones to obtain continuous time series data of the water quality and meteorological variables. We used these data to develop three turbidity models based on long short-term memory (LSTM). The three zones differed in terms of environmental factors related to turbidity: in West Taihu, the Lake Center, and the mouth of Gonghu Bay, the critical shear stress of bed sediments was 0.029, 0.055, and 0.032 N m−2, and the chlorophyll-a concentration was 23.27, 14.62, 30.80 μg L−1, respectively. The LSTM-based turbidity model developed for any zone could predict the turbidity in the other two zones. For the model developed for West Taihu, its performance to predict the turbidity in the local zone (i.e., West Taihu) was inferior to that for the other zones; the reverse applied to the models developed for the Lake Center and Gonghu Bay. This can be attributed to the complex hydrodynamics in West Taihu, which weakens the learning of LSTM from the time series data. This study explores the feasibility of the development of a universal LSTM-based turbidity model for Lake Taihu and promotes the application of machine learning algorithms to large shallow lakes.

Construction of electrochemiluminescence aptasensor for acetamiprid detection using flower-liked SnO2 nanocrystals encapsulated Ag3PO4 composite as luminophore

Acetamiprid is a kind of broad-spectrum pesticides that has been largely applied in agriculture and household pest control. In this paper, a simple, novel and highly sensitive electrochemiluminescence aptamer sensor (ECL aptasensor) based on Ag3PO4-NH2-SnO2 was proposed. The ECL signal was greatly amplified with the synergistic effect of Ag3PO4 and NH2-SnO2 (Ag3PO4-NH2-SnO2) which was dropped on a glass carbon electrode (GCE) and used as working electrode. We designed an “on–off” ECL sensing strategy for the sensitive and specific detection of acetamiprid. Under the optimal conditions, the linear range of the sensor for acetamiprid detection was 1.0 × 10−11 ∼ 1.0 × 10−5 M, with a detection limit of 2.5 × 10−12 M (S/N = 3). The method showed simple and fast operation, high sensitivity and selectivity, strong anti-interference ability and good stability. More importantly, the developed aptasensor exhibited excellent recognition ability towards residual acetamiprid in actual water samples, which might open promising avenues to develop new ECL systems for biological analysis and environmental water monitoring.

Internet of Things (IoT)-Based Environmental Monitoring and Control System for Home-Based Mushroom Cultivation

The control and monitoring of the environmental conditions in mushroom cultivation has been a challenge in the mushroom industry. Currently, research has been conducted to implement successful remote environmental monitoring, or, in some cases, remote environmental control, yet there is not yet a combination of both these systems providing live stream images or video. As a result, this research aimed to design and develop an Internet of things (IoT)-based environmental control and monitoring system for mushroom cultivation, whereby the growth conditions of the mushrooms, such as temperature, humidity, light intensity, and soil moisture level, are remotely monitored and controlled through a mobile and web application. Users would be able to visualize the growth of the mushroom remotely by video and images through the Internet. The respective sensors are implemented into the mushroom cultivation process and connected to the NodeMCU microcontroller, which collects and transfers the data to the cloud server, enabling remote access at any time through the end device with internet connection. The control algorithm regulates the equipment within the cultivational chamber autonomously, based on feedback from the sensors, in order to retain the optimum environment for the cultivation of mushrooms. The sensors were tested and compared with manual readings to ensure their accuracy. The implementation of IoT toward mushroom cultivation would greatly contribute to the advancement of the current mushroom industry which still applies the traditional cultivation approach.

NO2 Sensor Based on Faraday Rotation Spectroscopy Using Ring Array Permanent Magnets.

Faraday rotation spectroscopy (FRS) exploits the magneto-optical effect to achieve highly selective and sensitive detection of paramagnetic molecules. Usually, a solenoid coil is used to provide a longitudinal magnetic field to produce the magneto-optical effect. However, such a method has the disadvantages of excessive power consumption and susceptibility to electromagnetic interference. In the present work, a novel FRS approach based on a combination of a neodymium iron boron permanent magnet ring array and a Herriott multipass absorption cell is proposed. A longitudinal magnetic field was generated by using 14 identical neodymium iron boron permanent magnet rings combined in a non-equidistant form according to their magnetic field's spatial distribution characteristics. The average magnetic field strength within a length of 380 mm was 346 gauss. A quantum cascade laser was used to target the optimum 441 ← 440 Q-branch nitrogen dioxide transition at 1613.25 cm-1 (6.2 μm) with an optical power of 40 mW. Coupling to a Herriott multipass absorption cell, a minimum detection limit of 0.4 ppb was achieved with an integration time of 70 s. The low-power FRS nitrogen dioxide sensor proposed in this work is expected to be developed into a robust field-deployable environment monitoring system.

Research and Development of Cellar Pool Environmental Monitoring System Based on NB-IOT and Cloud Platform

NB-IOT and cloud platform technology are used to monitor the cellar pool environment, which is difficult to collect by hand. Under the zigbee self-organizing network, the system collects the parameters of the terminal nodes distributed in each cellar pool through a coordinator, and then sends them to the ali cloud internet of things platform for display and control through the nb-IOT communication module. Experiments show that the system can display the environment data of the cellar pool in real time and open the execution device outside the threshold range, which proves the practicability and convenience of the system.

Designing an Evaluation Framework for IoT Environmental Monitoring Systems

Environmental monitoring systems have been evolving dynamically to embrace modern Internet of Things (IoT) technology in the last decade. Despite this progress there are, however, continuing limitations and issues with some IoT designs. Thus, past research has identified areas of concern in areas such as communications, interoperability, reliability, and scalability. As enabling technologies evolve in an accelerated manner, there will no doubt be a plethora of environmental monitoring solutions under development. Such solutions need to be well evaluated so that potential users are knowledgeable in relation to the best solution for their specific applications. Along these lines, this paper puts forward a framework to evaluate proposed IT designs relevant to smart environmental monitoring systems. This framework is based on model standardized software engineering requirements found in ISO 25010, which it uses as an aid to develop business-driven ‘smart’ environmental monitoring systems.

Re-emergence of Rift Valley fever: an update and preventive measures.

Re-emergence of Rift Valley fever: an update and preventive measures.

Methodology for forecasting and modeling the migration of pollutants in the system of integrated environmental monitoring of chemically hazardous objects

Methodology for forecasting and modeling the migration of pollutants in the system of integrated environmental monitoring of chemically hazardous objects

Effective resource monitoring and management system for cloud environment

Effective resource monitoring and management system for cloud environment

Design of smart learning environment monitoring system based on multi-mode

The traditional smart campus pays attention to improving the management efficiency and neglects to improve the learning effect. This paper studies that the physical factors of smart learning environment affect students' learning effect, and designs a monitoring system of smart learning environment. The sensor is used to sense the temperature, humidity and air quality of the learning environment. The microcontroller system processes the sensing information of the sensor, and the server analyzes the data and generates the physical environment report and equipment control information to build a multi-mode multi-user smart learning environment monitoring system.