Quality Monitoring System Research Articles

Design and Implementation of Multi-Node CO Air Quality Monitoring System Based on Wireless Sensor Network and Internet of Things Integrated with Solar Panel

Design and Implementation of Multi-Node CO Air Quality Monitoring System Based on Wireless Sensor Network and Internet of Things Integrated with Solar Panel

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Water Quality Monitoring System Based on the Internet of Things (IoT) for Vannamei Shrimp Farming

As Internet of Things (IoT) technology develops, water quality monitoring systems for Vannamei shrimp farms have become more inventive and straightforward. The prototype IoT system monitors and controls the pool using sensors that can measure water quality parameters, such as temperature, pH, and salinity. The research aimed to design an automated water quality monitoring system for Vannamei shrimp aquaculture. The research used the E-4052C sensor, DS18B20 sensor, and DFRobot V1.0 sensor as data transmitting hardware (transmitter) and the receiving hardware microcontroller NodeMCU ESP32 as data processing, management, and control system tools. Then, the system used a Wi-Fi network to transfer data from the microcontroller to the Message Queue Telemetry Transport (MQTT) server as a data cloud. Several software programs, including Telegram, Node-Red, and ThingSpeak, help Android devices display real-time data. Test results for the accuracy of the sensor’s reading on water pH are 99.71, with an error rate of 0.29%. Meanwhile, the accuracy of the temperature sensor is 98.03 with an error rate of 1.7%. On the other hand, the accuracy of the salinity sensor is 99.49, with an error rate of 0.41%. The results indicate that all sensors have excellent performance. The real-time monitoring display and Android Telegram notification functions are good, and the automatic water quality monitoring tool is successfully operating in the Vannamei shrimp pool in Pamandati, South Konawe District, Southeast Sulawesi ssProvince, Indonesia.

The perils of building big: Desalination sustainability and brine regulation in the Arab Gulf countries

Seawater desalination has become an accessible option for augmenting freshwater supplies worldwide. In the countries of the Gulf Cooperation Council (GCC), it has been practiced for decades as the main source for domestic water use. Sustainable desalination requires addressing environmental impacts including damage to ecosystems from the high volumes of brine in the Gulf. This paper examines challenges related to environmental regulation of brine management in the Arab Gulf countries using the example of Qatar. It analyzes the brine challenge through infrastructure planning policies and stakeholders’ perceptions. The brine issue has been identified as a major environmental concern that requires action through discharge infrastructure, brine management technologies, and regulatory approaches based on quality thresholds and monitoring systems. Although there is a high level of agreement on the solvability of the brine issue, there are limitations with regard to the high reliance on desalination rendered through large-scale infrastructure. These limitations necessitate complementary water supply infrastructure for storage or the development of other sources through water reuse and storage. While water security considerations require prioritization of protection and supply continuity through desalination, incremental change through a stepwise dual approach of brine management and regulation is still possible.

Design and Development of a Quality Evaluation and Monitoring System for the Short-term Weather Forecast in Gansu Province

In order to further improve the development of the meteorological observation quality management system in Gansu Province, this article takes the organic integration of quality management and meteorological observation as the concept, and combines practical business applications to develop a scoring model to quantitatively evaluate the quality of short-term warning signals and observation data transmission in Gansu Province. At the same time, a visual model is developed to achieve a personalized monitoring and analysis system for the entire process of “monitoring warning service”. System users can monitor the actual situation of various meteorological disasters, analyze disasters, and assess risks in the system, and display and apply decision information, thereby improving the scientific and standardized level of meteorological observation business management.

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

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.

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass production. The monitoring of parts quality directly during manufacturing promises to solve this problem, while machine learning showed efficient performance correlating versatile manufacturing measurements with different quality grades. Today, most monitoring algorithms are based on semi- or supervised learning, thus, requiring a collection and ground-truth validation of training sets. This is costly and time consuming in real-life conditions. Our work is a feasibility study of the application of unsupervised machine learning to monitor different manufacturing regimes and quality in metal additive manufacturing. The algorithm combines the kernel Bayes rule for inference and Bayesian adaptive resonance for structuring the incoming data. Airborne acoustic emission from laser powder bed fusion is used as an algorithm input. The recognition of the main manufacturing regimes (conduction mode, stable, and unstable keyholes) are shown on real-life data, while the self-learning accuracy of developed algorithm exceeds 88 %. Our approach promises future development of plug-and-play quality monitoring systems for laser processing technology, requiring minimum modifications of the existing machines, reducing time/cost for algorithm preparation and providing continuous data driven adaptation of the algorithm to changes in manufacturing conditions.

Analysis and design of smart food packaging monitoring model based on chipless RFID sensor

Abstract Logistics plays a crucial role in shaping the efficiency and effectiveness of business operations. The manual monitoring approach often entails labor-intensive procedures, leading to inefficiencies and ineffectiveness within the logistics sector. The incorporation of sensors into product packaging holds the potential to benefit consumers by ensuring freshness and quality. Simultaneously, it enables retail industries to manage their inventory and verify product authenticity more effectively. Chipless RFID sensors, a wireless technology, offer advantages as a data input medium in the series of activities involved in product distribution systems. The amalgamation of sensor technology and RFID introduces novel prospects for overseeing and controlling perishable products. This study introduces an intelligent radio frequency label equipped with a sensor capable of measuring temperature and humidity. The purpose of this research is to analyze and design a chipless RFID-based product quality monitoring system. The research methodology begins with an identification system, an analysis of the interaction between stakeholders, and analysis and design using SAP Power Design. The implementation of chipless RFID involves industry, chipless RFID architecture, and buyers, as well as the primary environment of the system. Business Process Modeling and Notation Diagrams describe the flow of material and data between actors and describe their processing to produce quality data and information.

Halochromic Polyaniline as smart detector packaging for fresh food: a review

Abstract Early spoilage is a challenge that is often encountered in fresh food. One prevention that can be done is by using packaging technology. There has been an increasing interest in using smart detector packaging for fresh products. Smart detector has high sensitivity and is responsive to visual changes when exposed to pH, temperature, water activity, or composition. The advantage of using a smart detector is that it can detect and give information on which products are safe to consume and which are not. Overall, the concept of a smart detector is not much different from the function of smart packaging. Smart packaging that is sensitive to pH is called halochromic properties. The mechanism of halochromic depends on the compound or the indicator used. Polyaniline (PANI) is one of the candidates for halochromic materials, which have high stability and sensitivity to pH changes. PANI also has a high sensitivity to volatile compounds such as ammonia, indicated by a change in colour from green to blue. This review will overview the use of PANI as a smart detector halochromic packaging and its potential application in the food industry. It has been found that the application of PANI has opportunities to advance the development of modern smart packaging and better food quality monitoring systems.

Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network.

Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model's air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques.

Insights from the development of an innovative air quality monitoring system

Insights from the development of an innovative air quality monitoring system

IOT based noise pollution and water quality monitoring system

Presently, noise pollution has become a very big issue around the world. The adverse effects of this pollution include hearing impairment, negative social behaviour, annoyance, sleep disturbance and intelligibility to understand people’s speech. In learning context, noise can affect understanding and behaviour of people and places with high noise level are not suitable for learning and teaching process. Internet of Things (IoT) technology is one of the best choices to monitor the noise or sound intensity in the environment for the safety of human being. The aim of this paper is to deliver a development of an IoT based noise monitoring system comprises of a sound sensor, an IoT platform called Node MCU, LCD and LEDs. The system will provide a real-time alert if the noise exceeds the threshold noise limit set by Environmental Department of Health standard. Equipped with an Android application, the data from the sound sensor will be transferred into the cloud server and subsequently transferred into the app for display and to enable remote monitoring. A case study is carried out using the developed prototype to ascertain the noise comfort for University Technology Malaysia (UTM) students for studying or learning purpose. The sound level is measured for two different days during weekend and weekday. Based on Charted Institution of Building Service Engineers (CIBSE), 60dBA is the permissible ambient level and any readings that above 60dBA can interrupt speech intelligibility.

Development and Validation of Low-Cost Indoor Air Quality Monitoring System for Swine Buildings.

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Effect of agriculture on surface water quantity and quality in Gilgel Gibe watershed, southwestern Ethiopia.

Monitoring water quality and quantity is crucial to be sure that water resources are sustainably used. However, there is no monitoring system of water quantity and quality in southwestern Ethiopia, despite expansion of agricultural activities demanding water resources. The objective of this study was to investigate the effect of agriculture on water quantity and quality with special emphasis on irrigation in southwestern Ethiopia. Data of water quantity was collected from four rivers and four irrigation canals during dry season of 2023. Physico-chemical water quality data was collected from 35 sites. Water quantity was calculated by estimating the water discharge of the rivers and irrigation canals. Weighted arithmetic water quality index was calculated to assess the status of the studied rivers. Principal component analysis was used to identify the relation of the sites with water quality parameters. This study revealed that the average amount of abstracted water for irrigation from the four studied rivers was 22,399 m3/day during the studied period, and the average percentage of abstracted water was 17%. Sites downstream of the irrigation site were characterized by poor water quality compared with the upstream sites. Sites surrounded by agricultural land use were correlated with chemical oxygen demand, electric conductivity, nitrate, orthophosphate, water temperature, and pH, whereas all sites surrounded by forest were positively correlated with dissolved oxygen. This study indicates that agricultural activities have a negative impact on surface water quality and quantity if not managed properly. Hence, we recommend sustainable use of water resources for the planned irrigation expansion.

ANALYSIS OF THE PROSPECTS OF APPLICATION OF NATURAL GAS LEAK DETECTION METHODS TO INCREASE THE EFFICIENCY OF ATMOSPHERIC AIR MONITORING AT HTS FACILITIES

The activities of the oil and gas industry enterprises, in particular the objects of gas transportation systems (GTS), are accompanied by a significant anthropogenic impact on the environment. The atmospheric air itself is significantly affected, since many technological processes are accompanied by emissions of natural gas (organized, unorganized and emergency) and, as a result, a significant amount of volatile substances, many of which are dangerous and harmful, enter the atmosphere. There is a wide range of methods for detecting gas leaks from elements of gas transportation systems. Ways of classifying methods directly depend on the features that form the basis of their distribution. However, the insufficient definition of the classification features, their "one-sidedness" do not allow a full assessment of the shortcomings and advantages of certain methods and means, and, therefore, the possibility of their application to increase the effectiveness of atmospheric air monitoring at HTS facilities in order to ensure environmental safety. The main tasks solved in this article are the formulation of the optimal classification of existing methods of controlling the presence of gas leaks in terms of applicability in environmental monitoring systems of atmospheric air; analysis of their advantages and disadvantages; substantiation of the expediency and perspective of using these means and methods to ensure constant monitoring of the state of atmospheric air at GTS facilities. A classification of methods and means of control is proposed based on the level of application of technical means, according to which all methods are divided into organoleptic, hardware and computational (software-mathematical). According to the results of the analysis of the data of the groups, it was determined that, from the point of view of the prospects of application in the atmospheric air quality monitoring systems at the GTS facilities, hardware means, namely gas analyzers, have the most advantages.

Participatory Water Quality Monitoring System for the Gajahwong River, Yogyakarta City, Indonesia

This paper presents the development of Suka Peta (an integrated web-based system of participatory water quality monitoring), which has been developed by the researcher team of the State Islamic University Sunan Kalijaga, Yogyakarta, Indonesia. The system is designed to collect various types of data to generate a report on the water quality status of a river. The study followed a method for research and development that includes the design, development, implementation, and evaluation stages. The final product of this research is a web-based application that could be accessed publicly through www.status-sungai.com. During the evaluation stage, we conducted an FGD with stakeholders in the Gajahwong River community. The result confirmed that the application has a high potential for engaging the community in water quality monitoring. In addition, the participants have been able to use the application and participate in water quality monitoring efforts.

Design of Water Quality Monitoring System Based on Internet of Things Technology

Water quality is an assessment of how appropriate water is for a certain use or purpose, taking into consideration various physical, chemical, and biological factors that can affect its suitability. These factors can include pH, turbidity, dissolved oxygen, temperature, and the presence of pollutants or pathogens. The outdated method has been used by scientists and researchers to monitor the quality of water from the sources. The objective of this project is to create an efficient Internet of Things (IoT) system that can various sensors to continuously monitor water quality. The system is implemented using Arduino as the microcontroller, and sensors. A real-time monitoring system that is IoT-based was done to improve the examination process of the water sample. The system device is containing a NodeMCU ESP8266 microcontroller, pH, temperature, and turbidity sensors and uses the Blynk application. The system experiment results show that the device can show different readings based on the variety of water samples from different water bodies.

Index Air Quality Monitoring for Light and Active Mobility.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

Advancement of sustainable trypan blue degradation through rice husk ash based-heterogenous Fenton catalyst and study with gaussian process regression

Dyes and their derivatives have been found to be harmful to aquatic organisms, and discharging dye effluent without proper treatment into the environment can lead to affect the usability of water for distinct purposes, including drinking water, agriculture, and commercial processes. To overcome this environmental crisis a cost-effective rice husk ash (RHA) derived catalyst was synthesized as Fenton catalyst and a new, environmentally, and friendly treatment was used to treat trypan blue (TB) from synthetic wastewater. SEM, EDX, XRD, and FTIR were used to describe the prepared catalyst's comprehensive characterization. The optimization of degradation was observed with the batch study, while the reaction time was 1 h at 313 K with 180 ppm catalyst and 200 ppm of H2O2 under pH of 3. DNA study of treated and untreated wastewater demonstrated the non-toxic nature of treated trypan blue containing wastewater. For the betterment of water quality monitoring system Gaussian process regression (GPR) model was introduced for dye degradation determination and prediction in aqueous medium. By managing and controlling dye levels, treatment plants can mitigate the negative effects on receiving water bodies and ecosystems. The production cost of catalyst per kg of rice husk ash was estimated to $ 15.4803 which can be reused up to four cycles with minimum efficiency of 87 %. The outcomes unequivocally demonstrated that the suggested approach offered a completely sustainable resolution for reducing various forms of waste.