Quality Monitoring Research Articles

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions The model was trained on the dataset over the period 2022-2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

Surface quality prediction in-situ monitoring system: A deep transfer learning-based regression approach with audible signal

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

Chinese ink-coated elastic PU/PET fibers as multifunctional flexible wearable sensors

Textiles as conductive substrates are widely used in sensing applications due to their good elasticity, high stretchability and recoverable deformation. Here, the mask rope (polyurethane/polyester blended fibers) was selected as flexible substrate to load of conductive Chinese ink using surface swelling technique in N, N-dimethylformamide. The presence of carbon nanoparticles in the ink imparts electrical conductivity to the elastic fiber while maintaining the mechanical properties and extensibility of the fiber. This simple and low-cost method allows excellent interfacial adhesion between the conductivity ink layer and the elastic fibers, and the carbon particles do not fall off even when the modified fiber was treated by sonication in water. The ink coated elastic fiber as strain sensor shows excellent stability and sensitive responsiveness after 500 stretch-release cycles. The response and recovery times during the sensitivity experiment of the flexible fiber sensor are 133.79 and 134.76 ms. The sensor exhibits physiological motion detection and quality monitoring capabilities, which can accurately monitor human movements such as walking, wrist movement, throat swallowing and articulation. The sensor also has a good Joule heating effect, and the temperature of the conductive fiber can increase 88.9°C in the 60s under 15V voltage. This work develops a simple and efficient, low-cost, mass-producible flexible sensor with potential for physiological activity, temperature, and humidity detection.

Impact of Landscape Patterns on Water Quality in Urbanized Rivers at Characteristic Scale: A Case of Pearl River Delta, China.

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos

Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new approach combining zebrafish embryo behavioral phenotyping with deep learning was used to identify and classify water pollutants. Zebrafish embryos were exposed to 17 psychoactive compounds at environmentally relevant concentrations (1 and 10 μg/L), and their locomotor behavior was recorded at 5 and 6 days post-fertilization (dpf). We constructed six distinct zebrafish locomotor track datasets encompassing various observation times and developmental stages and evaluated multiple deep learning models on these datasets. The results demonstrated that the ResNet101 model performed optimally on the 1-min track dataset at 6 dpf, achieving an accuracy of 65.35 %. Interpretability analyses revealed that the model effectively focused on the relevant locomotor track features for classification. These findings suggest that the integration of zebrafish embryo behavioral analysis with deep learning can serve as an environmentally friendly and economical method for detecting water pollutants. This approach offers a new perspective for water quality monitoring and has the potential to assist existing chemical analytical techniques in detection, thereby advancing environmental toxicology research and water pollution control efforts.

Characterisation and manufacturing methods of material extrusion 3D printing composite filaments based on polylactide and nanohydroxyapatite

As the pace of scientific research and technological advancement accelerates, there is an increasing demand for rapid on-site prototyping with specific materials. Additive manufacturing through material extrusion 3D printing has emerged as an economically viable solution to this need. The production of custom filaments for this process frequently depends on low-volume filament manufacturing lines. A significant challenge in such production processes is maintaining the quality of the filament throughout its entire length, as this has a considerable impact on the quality of the final printed object. To ensure consistent quality, it is essential to monitor and manage a range of essential properties, including diameter consistency, material homogeneity and compound ratio. This study presents the implementation of a real-time filament quality monitor (R-FQM), which provides detailed insight into the longitudinal characteristics of the filament during the manufacturing process and its crystallinity analysis. In conjunction with a new proof-of-concept drawing process for reducing filament diameter by multi stage drawing die solid-state extrusion, this approach addresses the challenge of maintaining filaments quality. Several PLA-based filaments were produced for research purposes: pure PLA and composites containing 5 and 10wt% nanohydroxyapatite. Produced composite filaments had significantly greater diameter variation compared to pure PLA (largest for 5% filler ± 0.29mm). The diameter of the resulting filaments was reduced in the range of 2.5-1.4mm using the SSE approach. In addition to the R-FQM method, the filaments were characterised by structural, thermal and mechanical property analysis. The analyses showed that the 10% hydroxyapatite content in the filament subjected to the SSE process can lead to significant weakening of the material. However, all the filaments were suitable for 3D printing for biomedical applications. The research carried out demonstrated the complementarity of the R-FQM method with classical methods, confirming the effectiveness of the approach described.

Low-cost and Efficient Quadrotor for Medicine Delivery and IoT based Air Quality Monitoring

Low-cost and Efficient Quadrotor for Medicine Delivery and IoT based Air Quality Monitoring

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Evidence-driven indoor air quality improvement: An innovative and interdisciplinary approach to improving indoor air quality.

Indoor air pollution is a recognized emerging threat, claiming millions of lives annually. People are constantly exposed to ambient and indoor air pollution. The latest research shows that people in developed countries spend up to 90% of their time indoors and almost 70% at home. Although impaired IAQ represents a significant health risk, it affects people differently, and specific populations are more vulnerable: children, the elderly, and people with respiratory illnesses are more sensitive to these environmental risks. Despite rather extensive research on IAQ, most of the current understanding about the subject, which includes pollution sources, indoor-outdoor relationships, and ventilation/filtration, is still quite limited, mainly because air quality monitoring in the EU is primarily focused on ambient air quality and regulatory requirements are lacking for indoor environments. Therefore, the EDIAQI project aims to improve guidelines and awareness for advancing the IAQ in Europe and beyond by allowing user-friendly access to information about indoor air pollution exposures, sources, and related risk factors. The solution proposed with EDIAQI consists of conducting a characterization of sources and routes of exposure and dispersion of chemical, biological, and emerging indoor air pollution in multiple cities in the EU. The project will deploy cost-effective/user-friendly monitoring solutions to create new knowledge on sources, exposure routes, and indoor multipollutant body burdens. The EDIAQI project brings together 18 organizations from 11 different European countries that provide interdisciplinary skills and expertise in various fields, including environmental science and technology, medicine, and toxicology, as well as policy design and public engagement.

Label-free detection of formaldehyde in water by titanium doped MoS2 gold nanoislands LSPR sensor

Formaldehyde is a known human carcinogen widely present in various industrial processes. Dissolved formaldehyde in water poses a significant threat to human health, not solely gases. Here, a novel label-free detection scheme of formaldehyde in aqueous environment via Ti-doped MoS2 as the functionalized layer on a localized surface plasmon resonance (LSPR) sensor is reported. It has been found that the strong adsorption of formaldehyde on Ti-doped MoS2 attached on gold nanoislands induces a strong local refractive index change and enables sensitive detection. Besides, the characterization and LSPR experimental results also highlight the contribution of the nanoislands morphology to facilitate improved surface attachment of Ti-doped MoS2, and in turn improves the sensing performance. Along with experimental work, density functional theory (DFT) calculation has shown the strong formaldehyde adsorption on Ti-doped MoS2 with water molecules included, therefore confirming its feasibility and effectiveness for sensing. The sensor has successfully achieved an excellent detection limit of 0.104 ppb with its selectivity studied and confirmed using DFT calculations and experiment. The practical value of this research lies in its potential development of this light-based sensor for water quality monitoring with the major advantages of facile synthesis, fast detection, and label-free sensing offered by LSPR.

Construction and Practice of Education and Teaching Quality Assurance Systems in Applied Colleges and Universities under the Background of Digital Intelligence

This paper discusses the optimization strategy of education and teaching quality assurance systems in applied colleges and universities under the background of digital intelligence. It first summarizes the relevant theories of digital intelligence transformation and analyzes the impact of digital intelligence transformation on higher education. Secondly, this paper puts forward the principles of constructing the quality assurance system of applied colleges, including strengthening the quality assurance consciousness, improving teachers’ digital literacy, and implementing digital intelligence governance. From the practical perspective, this paper expounds on strategies such as optimizing educational teaching resource allocation, constructing a diversified evaluation system of teaching quality, strengthening the construction and training of teaching staff, and innovating teaching management methods. Specific optimization measures are put forward, such as improving policies, regulations, and system guarantees, strengthening cooperation between schools and enterprises, integrating industry, school, and research, building an educational information platform, and improving the monitoring and feedback mechanism of educational quality.

Data-Driven Water Monitoring System with Descriptive Analytics for Aquaculture Prawn Farms

This study aimed to develop a data-driven water monitoring system with descriptive analytics for aquaculture prawn farms. This system was designed to automate the monitoring of the water quality of the prawn farm so as to minimize labor and time spent for manually testing the water. Utilizing a rapid prototyping methodology, which is a rapid or quick creation of a prototype or a physical model of a device used in the development of a project, the said monitoring system was evaluated through a comparative analysis between the data gathered manually and the data collected by the monitoring system. To ensure reliability and validity of this monitoring system, it was used various times over several consecutive days. Also, a t-test was used to determine the significant differences between manual and system readings. Findings of the study showed that the designed water monitoring system provided accurate results, and the objectives of the study were met, particularly in terms of integrating a Raspberry Pi 3 microprocessor to automate the system in monitoring the water quality. It was also found out that the use of different sensors to test the water condition in terms of its temperature, pH level, salinity, dissolved oxygen, and turbidity was accurate. Furthermore, the system also met its objective of developing an Android mobile application to monitor the status remotely. With this, it is recommended for prawn farmers to utilize the system (with internet connectivity) for their efficient water quality management and to improve their remote monitoring capabilities.

Exploring the Potential of Pectin as a Source of Biopolymers for Active and Intelligent Packaging: A Review

The use of fossil-based plastics in food packaging poses a serious environmental concern. Pectin, a natural biodegradable polymer, offers a potential solution for environmentally friendly and sustainable food packaging to replace fossil-based plastics. This article reviews the applications of pectin in active and intelligent packaging and analyzes the latest research trends. Bibliometric analysis was used to review the existing literature on pectin in food packaging. Data were collected from the Scopus database, which covers research on film manufacturing and pectin-based coating. Pectin-based active packaging contains antimicrobial and antioxidant compounds such as ascorbic acid and essential oils, which effectively prevent bacterial growth while absorbing oxygen and water vapor. In contrast, pectin-based intelligent packaging allows real-time monitoring of food quality through integrated color-changing indicators, eliminating the need for open packaging. Research trends have shown a significant increase in publications on pectin-based packaging, reflecting the growing interest in sustainable packaging solutions. With a focus on innovation and sustainability, pectin can replace conventional plastics and provide safer and more durable packaging solutions, thereby supporting global efforts to reduce the environmental impact of plastic waste.

Sustainable agriculture through qanat systems in Karabakh: Water and soil characteristics in the context of climate change

This study investigates the water quality and soil characteristics associated with qanat systems in the Cebrail district of the Karabakh region, Azerbaijan. Qanat systems, traditional underground channels designed for water transport, play a crucial role in providing reliable water sources for drinking and irrigation. Water and soil samples were collected from seven qanat systems and analyzed for various physicochemical properties. Water quality parameters included pH, electrical conductivity, hardness, mineralization, and concentrations of calcium, magnesium, sodium, and other ions. Soil analyses focused on pH, electrical conductivity, organic matter content, salinization degree, and the presence of key ions like sulfate and nitrate. The results indicated that qanat water is generally of high quality, with pH levels suitable for both drinking and irrigation. However, some qanat systems exhibited high electrical conductivity and mineralization levels, suggesting potential salinity issues for sensitive crops. Soil samples showed favorable conditions for agriculture, with good pH levels, low salinity, and high organic matter content. The analysis revealed a significant interaction between water quality and soil characteristics, emphasizing the importance of integrated management practices. In the context of climate change, the sustainability of qanat systems is critical. Recommendations include regular monitoring of water and soil quality, soil amendments to mitigate salinity, efficient irrigation techniques, and the use of climate-resilient infrastructure. This study underscores the importance of qanat systems in arid and semi-arid regions and provides practical recommendations for sustainable land and water resource management, enhancing the socio-economic well-being of local communities.

Dynamics of Physicochemical Parameters as Indicator of Water Quality: A Study of Ogun River, Nigeria

Environmental pollution is increasingly becoming a major global problem. Surface water pollution impacts human life and the ecosystem, emphasizing the need for water quality assessment and monitoring. In this study, water Samples were collected in Ogun River for two years (twice a year) and analyzed for the physicochemical parameters using standard methods. The results show that there was spatial and seasonal variation in the water quality of the river. The physical parameters Turbidity, EC, and TDS had significant positive correlations with each other but correlated negatively with DO and pH. A significant positive correlation also existed between pH and DO (0.918). Cluster analysis grouped the parameters into three clusters, while the PCA yielded 2-components, which explained 85.61 % of the total variance in the data set. Component 1 accounts for 69.26 % of the total variance and has strong positive loadings for Turbidity, EC, and TDS and strong negative loadings for pH and DO. Component 2 accounts for 16.35 % of the total variance, with strong positive loading for BOD. The results indicate that factors that increase the Turbidity, EC, and TDS levels will decrease the levels of pH and DO while exerting a less significant effect on BOD. This study provides valuable insight into the changes and interactions of parameters that affect the water quality, which is crucial for the sustainability and quality management of the water system.

AUTOMATED LEAK AND WATER QUALITY DETECTION SYSTEM FOR PIPED WATER SUPPLY

The volume of water loss because of leakage in the conveyance pipe has been alarming. Old and poorly constructed pipelines, inadequate corrosion protection, poorly maintained valves, and mechanical damage contribute to leakage. Water-carrying pipes were buried underground, so tracing leak points manually could be tasking, if not impossible. This work was to report on the effectiveness of a developed Automated Leak and Water Quality Detection (ALWQD) system. This device can detect leaks in the piped water system automatically and can also report any deterioration in the quality of water that flows through affected pipes. The ALWQD consisted of several drainpipe connections, pipe accessories, electronic components, and sensors to monitor water quality impairment. The control signal was the solenoid valves that interfaced with the ESP-32 microcontroller boards placed on the pipe manifold at intervals, along with water quality monitoring sensors of turbidity, Total Dissolved Solids (TDS), and pH. The fabrication and testing of the device followed standard procedures. Testing of ALWQD was done at 0, 5, and 10 minutes under load and no-load conditions, with average variation in reading recorded after three trials. The findings indicated that the efficiency of ALWQD was between 70% and 80%, which could be improved upon. The trend in the results of the monitored parameters was not different from that of similar previous work. Leaks caused pressure drops and disallowed the full flow of water found at pipe joints, which could be a pathway for the intrusion of contaminants into the water conveyance system.

Microscale Flow Control and Droplet Generation Using Arduino-Based Pneumatically-Controlled Microfluidic Device.

Microfluidics are crucial for managing small-volume analytical solutions for various applications, such as disease diagnostics, drug efficacy testing, chemical analysis, and water quality monitoring. The precise control of flow control devices can generate diverse flow patterns using pneumatic control with solenoid valves and a microcontroller. This system enables the active modulation of the pneumatic pressure through Arduino programming of the solenoid valves connected to the pressure source. Additionally, the incorporation of solenoid valve sets allows for multichannel control, enabling simultaneous creation and manipulation of various microflows at a low cost. The proposed microfluidic flow controller facilitates accurate flow regulation, especially through periodic flow modulation beneficial for droplet generation and continuous production of microdroplets of different sizes. Overall, we expect the proposed microfluidic flow controller to drive innovative advancements in technology and medicine owing to its engineering precision and versatility.

Recent Trends in Air Pollution in the Most Important City of The Romanian Black Sea Coast

Air pollution is one of the greatest environmental issues of contemporary society, affecting virtually all major cities worldwide, including those in Romania. Consequently, evaluating the dynamics of pollution conditions in urban environments is essential for designing control measures and adaptation strategies to this environmental disruption. This paper aims to analyze the recent dynamics of pollution and air quality in Constanta, the largest city on the Romanian coastline and one of the most important cities along the entire Black Sea coast. The investigations were based on the official concentrations of major atmospheric pollutants recorded at air quality monitoring stations located within or near Constanta. The dynamics of pollutant concentrations were explored at annual and seasonal levels, and the results showed a mixed picture of changes in the atmospheric pollutant concentrations, in recent years. Essentially, the findings highlighted both increases and decreases in air pollution conditions, emphasizing the need for constant air quality monitoring and controlling throughout Constanta city.

Spatial Localization of Air Pollutants in Lima: Air Quality Monitoring in the Troposphere

Spatial Localization of Air Pollutants in Lima: Air Quality Monitoring in the Troposphere