Water Quality Detection Research Articles

An Innovative Approach to Delineate and Differentiate Clear and Turbid Water Ponds in Indian Sundarban Area Using Sentinel-2 MSI Data

Pond water is the main source of irrigation in the Sundarbans area of West Bengal. The serious issues faced in agricultural lands and ponds are due to salinity. As salinity cannot be detected on high scales through satellite images, in this study the remotely sensed turbidity has been compared and studied with laboratory-measured salinity. The high-resolution Sentinel-2 MSI data with optimum bands for water quality detection have been used in this study. The small agricultural ponds in the study area were not detected by the conventionally used water indices, as the ponds were surrounded by homestead vegetation. Hence, a new index called Pond Probability Spectral Index (PPSI) was formulated and used in the decision tree algorithm to delineate all the ponds. Turbidity indices were used over the extracted ponds to classify them into three classes, clear, turbid and highly turbid. From the field accuracy assessment, one of the turbidity indices outperformed in segregating the ponds with an overall accuracy of 83.3%. It was also found that salinity was inversely proportional to turbidity only when the turbidity value was greater than 100 NTU. The total estimated pond area (average of 3 seasons) in the Sundarban region was 471.66 km2.

Achieving a Large Driving Force on Triboelectric Nanogenerator by Wave‐Driven Linkage Mechanism for Harvesting Blue Energy toward Marine Environment Monitoring

AbstractWater wave energy is regarded to be a promising renewable energy source to relieve the pressure of fossil energy shortage and achieve a sustainable future. A cylindrical wave‐driven linkage mechanism triboelectric nanogenerator (WLM‐TENG) with unidirectional rotation is designed and fabricated to effectively harvest water wave energy. The enormous force generated by water waves, which is up to 424.3 N m−2, is used to drive the TENG through its simple and smart mechanical structure. This force allows the rotor to overcome the large resistance caused by the full contact between rabbit furs and electrodes. The output current can reach about 30 µA and the output power arrives at 50 mW. Moreover, self‐powered applications powered by the WLM‐TENG are successfully demonstrated, including indicating light‐emitting diodes (LEDs), multifunctional barometer, portable anemometer, and multifunctional water quality detection pen. This work renders an effective approach to harness the enormous force generated by the water waves, enhancing the water wave energy harvesting ability of the TENG, toward practical applications of marine environment monitoring.

Design of Online Detection System of Boiler Water Quality Based on STM32

Industrial boilers have strict requirements for water quality. It is necessary to adjust water treatment measures and regular blowdown according to the monitoring of phosphate and soluble silica content in boiler water to avoid boiler scaling, corrosion, and deterioration of steam quality, and prevent major accidents. The traditional boiler water quality detection technology has a strong lag and long detection time, which cannot meet real-time online detection. Aiming at the requirement of detection of phosphate radical and soluble silica content in boiler water quality, a portable and low-cost online detection system of boiler water quality based on STM32 was studied. The pump valve flow path is constructed with a syringe pump equipped with a syringe and a distribution valve, LED and silicon photocell are used for the light source and detector of the system respectively, and the photoelectric detection part is composed of a designed Z-shaped flow cell, which reduces the experimental cost; Design a data acquisition circuit based on STM32 microcontroller and a 16-bit high-precision analogy-to-digital converter, as well as a constant-current drive circuit for driving LED light sources, to improve the stability and high precision of the automatic measuring instrument. The system test results show that the boiler water quality online detection system based on STM32 can accurately detect the phosphate and soluble silica concentrations in boiler water quality.

Simultaneous Cross-type Detection of Water Quality Indexes via a Smartphone-App Integrated Microfluidic Paper-Based Platform.

Water quality guarantee in remote areas necessitates the development of portable, sensitive, fast, cost-effective, and easy-to-use water quality detection methods. The current work reports on a microfluidic paper-based analytical device (μPAD) integrated with a smartphone app for the simultaneous detection of cross-type water quality parameters including pH, Cu(II), Ni(II), Fe(III), and nitrite. The shapes, baking time, amount, and ratios of reaction reagent mixtures of wax μPAD were optimized to improve the color uniformity and intensity effectively. An easy-to-use smartphone app was established for recording, analyzing, and directly reading the colorimetric signals and target concentrations on μPAD. The results showed that under the optimum conditions, the current analytical platform has reached the detection limits of 0.4, 1.9, 2.9, and 1.1 ppm for nitrite, Cu(II), Ni(II), and Fe(III), respectively, and the liner ranges are 2.3-90 ppm (nitrite), 3.8-400 ppm (Cu(II)), 2.9-1000 ppm (Ni(II)), 2.8-500 ppm (Fe(III)), and 5-9 (pH). The proposed portable smartphone-app integrated μPAD detection system was successfully applied to real industrial wastewater and river water quality monitoring. The proposed method has great potential for field water quality detection.

Application and recent progress of inland water monitoring using remote sensing techniques.

Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research.

A spectral learning path for simultaneous multi-parameter detection of water quality

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection. A novel spectroscopic instrument was used to obtain SPIs, which were converted into feature images of water chemistry and then combined with deep convolutional neural networks (CNNs) to train models and predict WQP. The results showed that the method of combining SPIs and DL has high accuracy and stability, and good prediction results with average relative error of each parameter (anions and cations, TOC, TP, TN, NO3−-N, NH3–N) at 1.3%, coefficient of determination (R2) of 0.996, root mean square error (RMSE) of 0.1, residual prediction deviation (RPD) of 16.2, and mean absolute error (MAE) of 0.067. The method can achieve rapid and accurate detection of high-dimensional water quality multi-parameters, and has the advantages of simple pre-processing and low cost. It can be applied not only to the intelligent detection of environmental waters, but also has the potential to be applied in chemical, biological and medical fields.

Portable Water Quality Measurenment System for Sanitation

ABSTRACTWater is one of the essential needs that play an essential role in supporting life on earth. Water for sanitary hygiene purposes is used for several things to maintain individual hygiene, such as bathing and toothbrushes. Water is also for washing foodstuffs, tableware, and washing clothes. In addition, water for hygiene sanitation can be used as raw water for drinking water. This research designs a water quality measurement tool using parameters following water quality for sanitary hygiene purposes issued by the Ministry of Health. These parameters include turbidity, acidity (pH), temperature, and solids in water. The tool is designed to use Arduino Uno as a controller, sensors according to the parameters of photodiode sensors, pH, temperature, TDS (Total Dissolved Solids), and output systems using an LCD 20 x 4. This study concluded that the water quality detection system is running well, using photodiode sensors that can read each level of turbidity based on the value of ADC (Analog Digital Converter). The TDS sensor has an accuracy rate of 93.2% with a 6.8% error percentage. The pH sensor has an occurrence rate of 97.5% with a 2.5% error percentage, and a temperature sensor has an accuracy of 97.84% with a 2.16% error percentage.

Optical Fiber Multiparameter Detection and Numerical Simulation and Characteristics of Water Quality Particulate Matter Based on Single-Photon Detection Technology

Water quality testing is of great significance for daily water use or water use in high precision manufacturing. The use of single-photon detection technology to detect water particles is one of the important methods of physical water quality detection. However, the current water quality particle detection technology needs to be improved in the range and accuracy of turbidity detection. The paper aims to use single-photon detection technology to design a fiber optic turbidity detection system to solve the current problems of small turbidity detection range and low accuracy in a large range. In response to this, this paper has made an in-depth understanding of single-photon detection technology and designed two sets of detection schemes using the performance of photons. The first set is the transmission optical fiber turbidity detection, which can effectively detect the ultralow turbidity range and a wide range of water quality particles. The second set is a scattering optical fiber turbidity detection system, which can effectively detect water particles in the low and medium turbidity range. In this paper, the two systems were integrated into a whole system, so the results of the two detection methods can also be compared, and the detected turbidity can be obtained more accurately. The experimental results showed that for the transmission optical fiber turbidity detection system, the detection effect of the system was the best under the incident light intensity of 11 wm, and the fitting value was 0.99; for the scattering optical fiber turbidity detection system, the detection effect of the system was the best under the incident light intensity of 4 wm, and the fitting value was 0.99.

Water Quality Index Classification Based on Machine Learning: A Case from the Langat River Basin Model

Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula.

Sensor Partitioning Placements via Random Walk and Water Quality and Leakage Detection Models within Water Distribution Systems

Sensor Partitioning Placements via Random Walk and Water Quality and Leakage Detection Models within Water Distribution Systems

An Optimized Approach for Predicting Water Quality Features Based on Machine Learning

Traditionally, water quality is assessed using costly laboratory and statistical methods, rendering real-time monitoring useless. Poor water quality requires a more practical and cost-effective solution. The machine learning classification approach appears promising for rapid detection and prediction of water quality. Machine learning has been used successfully to predict water quality. However, research on machine learning for water quality index (WQI) prediction is generally lacking. Therefore, this research aims to identify the important features for the WQI, which necessitated the classification of numerous indicators. This study develops four machine learning models (Artificial Neural Network, Support Vector Machine, Random Forest, and Naïve Bayes) based on the WQI and chemical parameters. The Langat Basin in Selangor dataset from the Department of Environment of Malaysia trains and validates each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by several feature selection strategies such as information gain, correlation, and symmetrical uncertainty. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized Random Forest classifier with the WQI parameter selected by the information gain feature selection method achieved the highest performance. The experimental results show that the WQI parameters are more relevant in predicting the WQI than the other variables. Consequently, this result shows that parameter oxygen (DO) and biochemical oxygen demand (BOD) are important features for predicting WQI. The proposed model achieved reasonable accuracy with minimal parameters, indicating that it could be used in real-time water quality detection systems.

Prevalence Analysis of Hypodermal Infectious and Haematopoetic Necrosis Virus (IHHNV) in Vaname Shrimp (Litopenaeus vannamei) in Bengkalis District

The disease is a limiting factor and causes economic losses in the cultivation of white shrimp (Litopenaeus vannamei). Diseases in vaname shrimp are caused by viruses, microbes, parasites, organisms, and natural conditions. One of these diseases is infectious hypodermal and hematopoietic necrosis (IHHNV). This study was conducted from February to April 2022. This study aimed to analyze the prevalence of IHHNV that attacks white shrimp in the Bengkalis Regency and to observe and observe clinical symptoms in whitewashed shrimp infected with IHHNV in the pond area of the Bengkalis Regency. The research method used in this study is a survey method. The primary data taken included water quality observations, morphological observations, sampling, and detection of IHHNV using the polymerase chain reaction (PCR) method. The results showed that the prevalence of IHHNV in white shrimp was 0%. PCR testing using a specific primer measuring 392bp showed that white shrimp samples from three locations in Bengkalis District, namely Bengkalis District, Bukit Batu District, and Rupat District, were negative for IHHNV and did not show any clinical symptoms of IHHNV on morphological examination

A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment

Great changes have been brought about by the coastal environment when the economy develops rapidly. Coastal environmental monitoring is the basis and technical guarantee for coastal environmental protection supervision and management. It is one of the important tasks to detect and timely discover coastal seawater anomalies. Usually, a single sensor cannot determine whether the coastal environment or ship operation is an anomaly. Recently, an unmanned surface vehicle for coastal environment monitoring was developed, and stacked autoencoders are used for seawater anomaly detection using multisensor data fusion methods. The multisensor data of pH, conductivity, and ammonia nitrogen are employed to judge the anomaly of seawater. The mean, standard deviation, mean square root, and normalized power spectrum features of multisensor data are extracted, and a stacked autoencoder is employed to fuse these features for anomaly detection. The proposed method is feasible and effective for anomaly detection of coastal water quality and ship operation. Compared with other commonly used methods, the proposed method has a higher recall, precision, and F1 score performance.

Water Quality Substance Detection System Based on Internet of Things

According to the sources of pollution in different river regions, the characteristics of rivers and lakes, and on the basis of investigation and analysis of the actual situation of water environment monitoring in different river regions, this paper carries out a research on the optimization of water quality detection and gives the definition of a super network as a super network based on a hypergraph structure. Moreover, according to the simulation results of topological parameters, this paper analyzes the structural characteristics of the super network and combines the Internet of Things technology to construct a water quality material detection system. In addition, this paper evaluates the water quality detection effect of the system proposed in this paper through a control experiment. According to the quality inspection results, it can be seen that the accuracy rate of the water quality material detection system based on the Internet of Things proposed in this paper is more than 92%, which verifies that the system model proposed in this paper has a good effect.

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network

pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction () was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.

Design and Implementation of Online Water Quality Monitoring System

Water quality detection plays an important role in water pollution alarm, water source pollution detection and water source diagnosis and treatment. The online water quality monitoring system based on the Internet of Things technology can monitor the water quality in real time and dynamically, and can serve the strategic requirements of the country for water resources. Currently, the water quality on-line monitoring system is mainly composed of upper computer system and lower base station in domestic researches. This mode of construction has deficiencies such as lack of channel diversity, large investment in communication network, and insufficient capacity of data. Aiming at these problems, this study attempts to use LoRa network technology, cloud architecture combined with water quality monitoring sensors to build a low cost and high reliable water quality monitoring system. Indexes of water quality are collected by intelligent sensors, and are transmitted to cloud server through LoRa network. They can be intelligently analysed by the technology of big data. The system includes both mobile and computer terminals. Indexes of water quality can be monitored timely and accurately by the system, which can further enhance the omnidirectional intelligent management of rivers. By case analysis to verify that the system has certain value of promotion and application.

Spatial water quality analysis of the watercourses in Northwestern New Territories (Hong Kong)

Abstract River water is an important source for drinking water supply in Northwestern New Territories of Hong Kong. Thus, there is no denying the fact that monitor the quality of river water is a must for the locals. In this study, a mixed multivariate analysis method was used to lower monitoring costs by optimizing the layout of water quality monitoring stations. To this purpose, the data from a period of five years and over 36,000 observations was evaluated in this article. The cluster analysis approach was also used to categorize monitoring stations into three groups. What's more, three latent factors that predominantly influence the river water quality were assessed using factor analysis: anthropogenic pollution, seawater intrusion and geological processes, and the nitrification process. A spatial pattern using the three latent factor scores was plotted and six redundant monitoring stations were identified by this pattern. Finally, discriminant analysis was used to extract seven significant parameters. The results showed that the surface water-monitoring program of the watercourses in the Northwestern New Territories (Hong Kong) could be adjusted by reducing the monitoring stations to 18 and the measured chemical parameters to seven to ensure the detection of water quality and reduce the cost.

Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification

With the development of society and the accelerated industrialization, the problem of water pollution has become increasingly prominent. In order to stop the gathering and diffusion of harmful substances in water bodies, leading to further deterioration of water quality and more serious environmental problems, environmental management departments have developed a series of pollutant discharge standards to prevent water pollution in real time. Common testing methods are the colorimetric method and TDS (total dissolved solids) value testing method, which are mostly through water bodies that contain acid, alkali, salt, and other indicators of the concentration test, to produce an assessment of water quality. However, the traditional methods of water quality testing, whether in the measurement time or in the accuracy of the test, are certain defects. In order to be able to quickly detect the concentration of water quality indicators in water bodies, timely response and treatment of highly polluted water bodies are urgently needed. In this paper, we propose a water quality detection classification model based on multimodal machine learning algorithm. Firstly, we preprocessed and analyzed the collected water quality dataset and determined the reasonable and perfect water quality classification influencing factors. Then, we successively built 15 kinds of classification models based on machine learning algorithms for water quality detection. At the same time, we evaluated the performance of each model. From the four evaluation indexes of precision, recall rate, F1 value, and accuracy, respectively, the real value is compared with the predicted value of each model. The experimental results show that sulfate, pH, solids, and hardness are the important influencing factors to perform water quality testing. And the three models XGBoost (Extreme Gradient Boosting), CatBoost (Categorical Boosting), and LGBM (Light Gradient Boosting Machine) have better performances in conducting water quality testing. Finally, we further optimized the classification models based on XGBoost, CatBoost, and LGBM by using two major tools: cross-validation and hyperparameter tuning.

A comprehensive review on nanotechnology based sensors for monitoring quality and shelf life of food products

Nanosensors are chemical or mechanical sensors used in food and water quality detection to detect the presence of chemical species. Biosensor enabled packaging systems can detect food quality and extend shelf life. Nanosensors provide faster and more accurate detection of microbes, toxins, and adulterants. Nanoparticles may also be used to detect biodegradable food components such as vitamins and antioxidants. The review focuses on the emerging application of nanotechnology based sensors for shelf life analysis of food products, including freshness monitoring, food safety measurement, and detection of spoiled food components. The focused categories of nanomaterials for developing sensors in food applications are inorganic, organic, and carbon allotropes. In this context, the characteristics features of inorganic nanomaterials, organic nanomaterials, and carbon allotropes for developing nanotechnology-based sensors have been discussed. Further, a detailed discussion on the use of inorganic nanomaterials such as gold, silver, titanium dioxide, and zinc oxide nanoparticles for developing sensors in determining food shelf life (freshness, spoilage, and food safety) has been made. Due to available significant properties, including biocompatibility, nontoxicity, photochemical activity, large surface area, and electronic properties, the inorganic nanomaterials are a promising candidate for developing sensors to be used in food sectors. Besides, the inclusion of organic or biopolymeric nanomaterials and carbon allotropes for developing sensors for food shelf life has also been discussed. This study revealed that nanosensors have a substantial role in the food industry as identification and detection tool.

Task Offloading Strategy Based on Mobile Edge Computing in UAV Network.

When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs.