Quality Monitoring System Research Articles

Remote Water Quality Monitoring System for Use in Fairway Applications

In the context of climate change, there is a growing need for accurate, real-time data on water quality in river waterways. This results in the development of advanced monitoring systems. This article presents a remote water quality monitoring system designed specifically for use in inland waterways, the basic elements of which are placed in a buoy with an IoT unit. The proposed system uses a network of sensors strategically placed along the waterway to continuously measure critical parameters: temperature, pH, dissolved oxygen, and conductivity. Various compatibility, efficiency, and ease-of-use tests have been conducted to verify each aspect of the monitoring system. It has been shown that the sensors operate within the intended accuracy ranges. The central unit equipped with a GSM (Global System for Mobile Communications) module can wirelessly transmit data to a main server, enabling remote access and analysis via a user-friendly interface of the developed application. The paper details the technical architecture of the system, the integration of GSM technology to ensure reliable data transmission, and the results of the monitoring studies of the proposed parameters. The remote monitoring system offers significant benefits in terms of early detection of pollution events, ensuring the safety of aquatic life, and supporting sustainable navigation practices. The research results highlight the potential of GSM-based remote monitoring systems to revolutionize water quality management in waterways in various regions.

Development of a Long-Range IoT Air Quality Monitoring System Using LoRa Mesh Repeaters for Real-Time Pollution Tracking

Numerous researchers are currently presenting innovative methods to measure air quality in a cost-effective and real-time manner. The primary significance of the air quality monitoring system lies in its ability to facilitate communication via the Internet. This capability enables monitoring stations to measure air quality over extensive regions, spanning several kilometers. Presently, existing technology has constraints on transmitting data beyond distances of a few kilometers. This study introduces an air quality monitoring station capable of measuring NO2, SO2, CO2, CO, PM2.5, and PM10. To extend the transmission range, a LoRa mesh repeater was designed, utilizing point-to-point LoRa technology for extended data transmission. The signal strength between the air quality monitoring station and the repeater varies from -84 dBm to -92 dBm. The observed RSSI signal between the LoRa repeater and the LoRa gateway in the experimental results varies from -69 dBm to -112 dBm. The practical, effective operating distance is 1.7 km, making it suitable for potential utilization in long-distance IoT applications.

Comparing surgical readmission, in-hospital complications, and charges between total hip arthroplasty and hemiarthroplasty for geriatric femoral neck fractures.

For displaced femoral neck fractures (FNFs) in geriatric patients, there remains uncertainty regarding the effect of total hip arthroplasty (THA) compared with hemiarthroplasty (HA) in the guidelines. We aimed to compare 90-day surgical readmission, in-hospital complications, and charges between THA and HA in these patients. The Hospital Quality Monitoring System was queried from 1 January 2013 to 31 December 2019 for displaced FNFs in geriatric patients treated with THA or HA. After propensity score matching, which identified 33,849 paired patients, outcomes were compared between THA and HA using logistic and linear regression models. The HA group had a lower incidence of 90-day surgical readmission than the THA group (odds ratio (OR) 0.75 (95% CI 0.68 to 0.83)). Meanwhile, the HA group had lower incidence of dislocation (OR 0.42 (95% CI 0.33 to 0.52)), aseptic loosening (OR 0.50 (95% CI 0.38 to 0.66)), and joint pain (OR 0.63 (95% CI 0.40 to 0.98)), but a higher incidence of periprosthetic fracture (OR 1.41 (95% CI 1.07 to 1.87)) for readmission, compared to the THA group. The incidence of in-hospital complications did not differ significantly between the two groups. Moreover, the HA group had lower mean charges than the THA group (47,578.29 Chinese Yuan (CNY) (SD 20,069.71) vs 57,641.00 CNY (SD 21,524.07)). When considering 90-day surgical readmission rate, in-hospital complications, and mortality, HA resulted in a significantly lower surgical readmission rate within 90 days compared to THA, despite the patients being older and having a higher Charlson Comorbidity Index.

IOT Based Waterproof System for Water Quality and Level Monitoring

Abstract: The Internet of Things (IoT) represents a significant advancement in the monitoring and management of environmental resources. This review explores the design and implementation of a waterproof IoT-based system for the remote monitoring of water quality and levels [2]. Water pollution, particularly in developing regions such as India, poses a substantial environmental threat, primarily driven by factors like untreated sewage, agricultural runoff, and industrial waste. Conventional approaches to monitoring water quality, which rely on physical sampling and laboratory testing, tend to be less efficient and often fail to deliver the prompt information required for effective resource management. To overcome these limitations, the proposed system utilizes waterproof IoT devices equipped with various sensors to continuously assess critical water quality parameters, including pH, turbidity [3], and pollutant levels. The collected data is transmitted in real time to a central monitoring unit, allowing for immediate assessment and timely interventions when quality thresholds are exceeded. This review emphasizes the innovations in sensor technology and communication protocols that enhance the efficiency and reliability of the monitoring system. Additionally, the paper addresses challenges such as sensor calibration, durability, and data transmission reliability in different environmental conditions. Solutions to these challenges are discussed to provide a comprehensive understanding of how to develop and maintain effective water quality monitoring systems. The review concludes by outlining future research directions aimed at improving system robustness and expanding its application across various water bodies, thereby contributing to enhanced water resource management and environmental sustainability [1].

The Construction of Practical Teaching Quality Assurance System of Hotel Management Major under the Perspective of Collaborative Innovation of Government-Industry-University-Research

This paper discusses the construction of quality assurance system for practical teaching of hotel management major from the perspective of collaborative innovation of government-industry-university-research. Firstly, this paper analyzes the problems in the practical teaching of hotel management, such as lack of resources and traditional teaching methods, and emphasizes the importance of constructing the quality assurance system of practical teaching. Then, combined with the theoretical framework of collaborative innovation of government-industry-university-research, the strategies for constructing the quality assurance system of practical teaching are put forward, including the longitudinal curriculum system of “four continuous and three in-depth”, the horizontal operation system of "three classroom interactions" and the overall management system of "double cooperation and mutual supervision". It is found that the collaborative innovation of educational mechanism between government-industry-university-research can significantly improve the quality of personnel training, but the implementation process needs to overcome the challenges such as imperfect collaborative mechanism. Finally, the article puts forward some concrete measures, such as strengthening the cooperation between government and industry-university-research, optimizing the curriculum system, strengthening the construction of teachers, improving the quality monitoring and evaluation system and promoting students' professional development. Through the implementation of these measures, the quality assurance system of practical teaching of hotel management specialty can be effectively constructed, which provides strong talent support for the sustainable development of hotel industry.

Correlation of Vehicle Traffic to Air Quality, Temperature, and Noise in Malang City Through an Internet of Things (IoT) Approach

In response to the issues of air pollution, temperature, and noise, this project attempts to create an air quality, temperature, and noise monitoring system using Internet of Things (IoT) technology. This system will comprise physical components and a web platform that delivers real-time environmental reports. Users can readily obtain information regarding air quality, temperature, and noise levels via this platform, which takes advantage of the internet's accessibility. This Internet of Things-based device monitors environmental quality in six high-traffic areas in Malang, Indonesia. The system uses various sensors to monitor air quality, temperature, humidity, dust levels, carbon monoxide (CO), carbon dioxide (CO2), and noise pollution in real-time. Data was collected during peak traffic hours, demonstrating the direct influence of car emissions on air quality. The findings show that some regions' CO and particulate matter levels surpass safe criteria, notably during peak traffic periods, but CO2, humidity, and noise levels are below acceptable norms. These findings highlight the necessity for urban air pollution reduction initiatives. Additional sensor calibration and communication modifications are recommended to increase system accuracy and dependability. This study gives significant insights for local authorities to manage urban environmental quality and safeguard human health.

Water Quality Monitoring System Based on IoT

Water quality is a crucial factor in public health, agriculture, and industry, and it has become increasingly important to monitor and maintain it effectively. Traditional water quality testing methods are often time-consuming, require manual sampling, and can involve complex equipment and processes, which may not be suitable for continuous monitoring. This project, titled "IoT- Based Water Quality Monitoring System," aims to create a real-time, automated solution that continuously monitors essential water quality parameters, namely pH, Total Dissolved Solids (TDS), and turbidity, using IoT technology. The system utilizes a suite of sensors connected to an ESP32 microcontroller to capture data for each of these parameters. The pH sensor monitors acidity or alkalinity, the TDS sensor measures the total amount of dissolved solids to determine purity, and the turbidity sensor assesses the clarity of water, detecting particulate matter that may indicate contamination.The gathered data is displayed locally on a 16x2 LCD screen, providing instant access to real-time readings. Additionally, the ESP32 microcontroller is connected to the Blynk cloud platform, where data is transmitted and stored, enabling remote access and monitoring of water quality. Users can access the data from any location via the Blynk mobile application, making it highly versatile and accessible. By using the Blynk cloud, the system provides valuable flexibility for applications in remote or unattended environments, where it may not be practical to monitor water quality manually.The IoT-based water quality monitoring system holds significant potential for application across a range of sectors. In drinking water supply systems, it allows for continuous monitoring, which helps maintain safety standards by identifying variations in water quality immediately. For agricultural irrigation, the system ensures that crops receive water that meets quality standards, contributing to better crop health and yield. Industrial applications, where specific water standards are often required for processes, can also benefit from the system's ability to detect deviations in water quality.In conclusion, this IoT-enabled water quality monitoring system provides an efficient, real-time, and remotely accessible solution for monitoring water parameters. With the integration of IoT, it presents a cost-effective approach to water management, enhancing responsiveness to quality changes and contributing to health and environmental sustainability. This system not only reduces the reliance on periodic manual testing but also enables proactive water quality management, thus supporting the sustainability of natural resources in a variety of applications.

The Use off Fuzzy Mamdani to Predict Tilapia Production Based on Freshwater Quality

Fish cultivation is a form of animal husbandry that is widely practiced by Indonesian people, whether cultivation is in ponds, rivers, or the sea, fish cultivation in ponds requires regular maintenance and feeding of fish. Excessive feeding also affects the water quality of the cultivation media which also affects the survival of the fish. One solution is to build a water quality monitoring system. A monitoring system can be created based on Fuzzy logic which makes it easier to control water quality. In this research, the sensors used were temperature, pH, DO, and ammonia. The data will then be processed by Matlab and will display the results on the screen.

IoT based real-time water quality monitoring system in water treatment plants (WTPs)

This study presents the development and implementation of an Internet of Things (IoT)-based real-time water quality monitoring system tailored for water treatment plants (WTPs). The system integrates advanced sensor technologies to continuously monitor key water quality parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected by these sensors is transmitted through a robust communication network to a centralized monitoring platform that utilizes cloud-based storage and analytics. The system's design includes a PLC-based control mechanism, allowing for flexible setup modifications and the easy addition of new monitoring parameters. The IoT-based system, powered by a low-energy 29-W configuration, offers accurate and reliable data with a minimal error margin of 0.1–0.2 across various parameters. The research highlights the system's ability to provide real-time alerts, historical data logging, and remote monitoring, all of which contribute to enhanced operational efficiency, proactive maintenance, and informed decision-making. This innovative approach to water quality management not only improves the effectiveness of WTP operations but also ensures environmental sustainability and public health safety. The study underscores the significant potential of IoT technologies in revolutionizing water quality monitoring practices.

Influence of Particulate Matter and Carbon Dioxide on Students’ Emotions in a Smart Classroom

The effects of air quality on health and cognition are well documented, but few studies have focused on its impact on emotions, leaving this area underexplored. This study investigates the influence of environmental factors—specifically particulate matter (PM1, PM2.5, and PM10) and carbon dioxide (CO2)—on students’ basic emotions in secondary school classrooms. For the collection of environmental data, we used low-cost sensors, which were carefully calibrated to ensure acceptable accuracy for monitoring air quality variables, despite inherent precision limitations compared to traditional sensors. Emotions were recorded via camera and analyzed using a custom-developed code. Based on these data, we found significant but modest correlations, such as the negative correlation between PM levels and happiness, and positive correlations of CO2 concentrations with fear and disgust. The regression models explained between 36% and 62% of the variance in emotions like neutrality, sadness, fear, and happiness, highlighting nonlinear relationships in some cases. These findings underscore the need for improved classroom environmental management, including the implementation of real-time air quality monitoring systems. Such systems would enable schools to mitigate the negative emotional effects of poor air quality, contributing to healthier and more conducive learning environments. Future research should explore the combined effects of multiple environmental factors to further understand their impact on student well-being.

Two stage iterative approach for addressing missing values in small-scale water quality data

Handling missing values in real water quality monitoring systems is essential for environmental analysis, particularly in some small-scale datasets. In the case of insufficient data size, the observed data cannot provide adequate information, inhibiting some imputing methods from working well. This study proposes a two-stage approach for addressing missing water quality data of small size on the basis of accuracy assessment. Missingness is formulated as the coexistence of ‘random missing over short periods’ and ‘long-term continuous missing’. In the first stage, the traditional mean imputation, median imputation, linear interpolation, k-nearest neighbor imputation, random forest imputation, and multiple imputation by chained equations are compared to select the optimal method. As the most suitable method across all variables, linear interpolation is used to fill in small random missing portions of the original data, providing an opportunity to expand the dataset to perform subsequent imputation. In the second stage, together with the autoregressive integrated moving average, the filling methods are similarly evaluated on the basis of data already filled in the first step. The most suitable method obtained from the comparison is used to populate the remaining long-term continuous missing data. The efficacy of the proposed approach is validated on a real water quality dataset. The results demonstrate that the two-stage iterative approach offers a feasible roadmap to impute missing values on the small-scale water quality dataset.

Research on machine vision online monitoring system for egg production and quality in cage environment

In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. The project involved upgrading traditional henhouses with enhanced wireless connectivity and developing data transmission techniques for video streams and image data. The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. Experimental results revealed that the henhouse average network latencies of 35 ms, with signal strengths between -30 and -71 dBm, ensuring data transmission to the poultry management system. The enhanced YOLOv8s model, deployed on the Jetson AGX Orin, demonstrated well improvements: a Precision of 94.0 % (+2.4 %), Recall rate of 92.8 % (+4.6 %), Average Precision50:95 of 91.5 % (+3 %) and F1 score of 93.4 % (+3.9 %), with a minor decrease in detection speed to 91.7 Frame Per Second (-18.2). Field experiment in 60 chicken cages during robotic patrols achieved an egg recognition rate of 98.9 %, validating the system's effectiveness. In fixed settings, an 83-minute experiment managed to analyze egg numbers and abnormalities, attaining a 100 % recognition rate with all scoring data promptly relayed back to the management system. Overall, this research introduces a comprehensive system for monitoring egg production and quality in cage environments, addressing manual recording and quality assessment challenges in caged poultry farming. This study is crucial for optimizing modern livestock management, enhancing production efficiency, and ensuring animal welfare.

Kalman Filter-Based Data Stabilization for an Automated Water Quality Monitoring System in Macrobrachium Rosenbergii Larvae Culture

Manual monitoring and management of water quality parameters in Macrobrachium Rosenbergii (freshwater prawn) larval culture are labourintensive, time-consuming, and susceptible to human error. This research aims to develop and evaluate an automated water quality monitoring system for freshwater prawn larval culture, with a specific emphasis on data stabilization. An ESP32 microcontroller is implemented to allow remote communication using an internet connection. The system continuously monitors critical parameters using sensors such as temperature, pH, turbidity, and Total Dissolved Solids (TDS), facilitating remote real-time data acquisition. The Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol is employed for seamless communication between the microcontroller and the monitoring dashboard. A Kalman filter is integrated into the system, enabling real-time sensor noise reduction and dynamic adaptation to changes. The filtered data are displayed remotely using a Node-RED dashboard with graphical representations. The implementation of the ESP32 microcontroller with the MQTT protocol and Node-RED has proven to be a robust platform for seamless data communication and presentation. Integration of the Kalman Filter significantly mitigates fluctuations in sensor readings. This is further proven by comparisons of the filtered data with known-good sensors, which have shown minimal to acceptable error percentages between 1% - 8.5%, where the error can mainly be attributed to environmental factors. Overall, this study has established a framework that can contribute to improving aquaculture practices and promoting environmental sustainability infreshwater prawn culture operations. The validated system provides valuable insights that assist farmers in facilitating a more efficient prawn culture operation.

Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset

Air pollution is an urgent global environmental problem, with significant impacts on public health and ecosystem stability. This research aims to develop an air quality classification model using the Global Air Pollution dataset from Kaggle, which consists of 23,463 rows of data and 12 features, including important variables such as Air Quality Index (AQI), PM2.5, NO2, and O3. Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. The research results show that the Decision Tree provides the best results with an accuracy of 99.89% after tuning hyperparameters using the Grid Search method. The SVM model showed an improvement of 94.89% to 99.32%, while Random Forest recorded an accuracy of 96.87% with no significant improvement after tuning. Importance feature analysis identified PM2.5 and AQI as the dominant factors in influencing air quality, with PM2.5 having the highest importance value of 0.93. This research confirms that machine learning can be an effective tool for integrating and classifying air pollution. It is hoped that the integration of this model into a real-time air quality monitoring system can help make more responsive and precise decisions in dealing with air pollution problems.

Predictive Urban Air Quality Monitoring for Healthier Cities

The research work proposal outlines an innovative and crucial project for addressing urban air quality issues using advanced technological solutions. Establishing a real-time air quality monitoring system with integrated big data and machine learning will enable precise tracking and analysis of pollution levels. The research paper begins with the key aspects of air quality. It further focuses upon the need for utilizing advanced techniques to curb the issues due to poor air quality. A system is proposed to determine the air quality utilizing machine learning techniques, predicting the quality of air index. Evaluation measures are then undertaken for predicting the same using Random forest, Support vector and Catboost regression techniques.

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

Aquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue.

Indoor Air Quality Monitoring System with High Accuracy of Gas Classification and Concentration Prediction via Selective Mechanism Research.

The efficacy of sensors, particularly sensor arrays, lies in their selectivity. However, research on selectivity remains notably obscure and scarce. In this work, indoor pollutants (C7H8, HCHO, CH4, and NO2) were chosen as the target gas. Following the screening of six oxides from previous work, temperature-programmed desorption/reduction experiments were conducted to delve into the origins of selectivity. The results explicate the superiority of NiO in detecting toluene and unveil the distinctive NO2 sensing mechanism of WO3 sensors. Based on the sensor array comprising these oxides, it can clearly detect low concentrations of C7H8 (S = 1.6 to 50 ppb), HCHO (S = 1.4 to 50 ppb), and NO2 (S = 3.3 to 50 ppb), which satisfies the requisites of indoor air monitoring. Meanwhile, three machine learning models (Extreme Gradient Boosting, Support Vector Machine, and Back Propagation Neural Network) are employed for gas classification. The classification accuracies of these models are 95.45%, 100%, and 100%, while the R2 values of the concentration prediction are 99.65%, 94.9%, and 98.04%, respectively, indicating the rationality of material selection. Furthermore, it can still achieve relatively high accuracy in gas classification (94.12%) and concentration prediction (89.36%), even for gas mixtures of four gases. Finally, an indoor air quality monitoring system is developed, which enables real-time monitoring of indoor gas quality through the Internet of Things.

Upgrade of the PandaX-4T online data quality monitoring system and perspectives for future multi-tons PandaX upgrades

Upgrade of the PandaX-4T online data quality monitoring system and perspectives for future multi-tons PandaX upgrades

Air quality monitoring system based on low power wide area network technology at public transport stops

<span>Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.</span>