Online Water Quality Monitoring Research Articles

Integrated STL-DBSCAN Algorithm for Online Hydrological and Water Quality Monitoring Data Cleaning

Integrated STL-DBSCAN Algorithm for Online Hydrological and Water Quality Monitoring Data Cleaning

Online water quality monitoring: a case study in the umbulan drinking water supply system, East Java, Indonesia

Conventional systems that monitor the supply system periodically are considered to be very inadequate. This can cause the water supply distributed to the community to be of poor quality. Therefore, this study aims to evaluate water quality periodically by measuring water parameters such as pH, chlorine and turbidity for 30 days using an online system and portable unit. Portable tools used to measure pH, turbidity and residual chlorine are as follows, namely pH meters (Hanna, HI98017), Turbidity Meters (Hanna, HI 98703), Residual Chlorine meters and measurements with an online system using "Supervisory Control and Data Acquisition”. The average parameter value of the results of online daily monitoring for a month for the pH parameter was a minimum 6.92, a maximum of 7.46, a minimum turbidity of 0.08 nephelometric turbidity unit (NTU) and a maximum of 0.94 NTU. While the residual chlorine minimum of 0.29 mg/L and a maximum of 0.58 mg/L. To determine the accuracy of the offline measurements, measurements were also performed with a portable device four times a month. The comparison of the measurement results is very small, which is the difference in pH parameters is between 0.37-0.50, turbidity is between 0.05-0.21 NTU, and residual chlorine is 0.002-0.006 mg/L. Offline measurements using the portable device are consistent with the online daily monitoring. Future research should focus on optimizing sensor technology for enhanced accuracy and reliability, and developing automated data analysis algorithms to streamline evaluation processes.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named “Prediction - Detection - Judgment” is proposed with a method framework of “Time series increment - Hierarchical clustering - Bayes' theorem model”. Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

CSD: An Online Spectral Sensing Method for Wastewater Quality Monitoring Based on Compressed Sensing and Incremental Learning

The outstanding spectral resolution of full-spectrum detection (FSD) ensures excellent performance for complex scenarios in which target signals are affected by particle interference. However, FSD is limited by the cost of its measurement devices and demanding install requirements. Therefore, it is not always practical to utilize FSD for online water quality monitoring in wastewater treatment plants (WWTPs). In this study, we developed a novel detection approach entitled “compressive spectrum detection (CSD)” to overcome the hardware restrictions posed by FSD. An intelligent source-encoding laser is first developed to perform spectrum encoding, which avoids the utilization of sophisticated equipment and reduces the detection cost. Subsequently, a dual-stage compressive detection framework was proposed based on incremental learning to accurately reconstruct spectral signals with full-spectrum resolution, which consisted of three innovative algorithms and could solve difficulties brought about by sample insufficiency and changing environments in practical situations. Based on our constructed online detection prototype, the superiority of CSD was evaluated through practical application in which the training samples were reduced to a quarter of their original size with an overall improvement in detection precision greater than 50%.

Design and Research of Multi-point Water Quality Monitoring System Based on Internet of Things

With the increasing demand for water quality monitoring and the increasing severity of water pollution, the water environmental quality in China faces a worrying situation, and water body monitoring has become an extremely important task. Now more than ever, new strategies for water quality monitoring are needed, and the rapid development of wireless monitoring technology perfectly meets people’s pursuit of low-cost, automated and real-time water quality monitoring. However, there are still problems such as few monitoring points cannot reflect the overall situation of water bodies, high artificial costs, and time-consuming analysis. This paper designs and implements a new water monitoring platform built on an Internet of Things platform. Taking water quality monitoring sensors as the core and small boats as the basic framework, it carries control, positioning, navigation equipment, uses wireless network as the data transmission means, monitors multi-point cruising sampling in different areas, realizes real-time collection, data storage and data analysis to complete related environmental monitoring. The multi-point online water quality monitoring system comprehensively applies sensor technology, Internet of Things technology and cloud computing technology to form an integrated online monitoring system. It aims to solve the problems of harsh working environment, strong repetitiveness and long sampling time of water quality monitoring tasks, and achieve fast, efficient and accurate completion of water quality monitoring tasks.

Water Quality Prediction Based on Hybrid Deep Learning Algorithm

Pollution from many different sources severely affects the quality of our water supply. Over the past few years, a large number of online water quality monitoring stations have been used to gather time series data on water quality monitoring. These numbers are the foundation for deep learning techniques for forecasting water quality. In particular, typical deep learning approaches struggle to accurately estimate water quality in the presence of net promoter system (NPS) contamination. To overcome this shortcoming, a new deep learning model called long short-term memory (LSTM)–gray wolf optimization (GWO)–fish swarm optimization (FSO) was developed to enhance the precision of water quality prediction with NPS pollution. The well-established model may remedy the mechanism models’ inability to foretell changes in water quality on a minute-by-minute basis. Thamirabarani river watershed was used for the model’s application. Based on experimental data, the suggested model outperformed the mechanism model and the LSTM model in predicting extreme values. Maximum relative errors in anticipated against observed dissolved oxygen, chemical oxygen demand, and NH3─N values were 7.58%, 18.45%, and 22.25%, respectively. In comparison to the artificial neural network (ANN), back propagation neural network (BPNN), and recurrent neural network (RNN) models, the created LSTM–GWO–FSO model was shown to have greater computational performance (RNN). LSTM–GWO–FSO outperformed ANN, BPNN, and RNN regarding R2 of 3.1%–38.4% improvements. The suggested approach may provide a fresh perspective when predicting water quality in the presence of NPS contamination.

Methodological Approach for an Online Water Quality Monitoring System in an Iron Ore Tailing Dam

Monitoring the concentration of potentially toxic elements (PTEs) in the aquatic ecosystems of the Amazon is critical to guarantee the maintenance of the ecological balance and the life quality of human populations that reside in or use these environments for survival. In this sense, many rivers in the region are dammed to form lakes for depositing mining tailings. Among these, the Gelado Project has the largest iron ore dam in the Amazon that occupies about 13.5 km2 of surface area with 142 million m3 of water and tailings volume, which are currently being mined for exploration, and its upstream waters and downstream are historically used by traditional populations. Based on this, to monitor the impacts of this activity, an online system for sampling and automatic analysis of water quality, composed of three process analyzers monitoring more than 20 parameters, including the PTEs (Cd, Pb, Fe, Mn, Ni, and Cu), was installed downstream of this dam. Therefore, this short communication describes this system’s development, installation, operation, and main advantage over conventional methods.

Water Quality Monitoring Data Transmission Network Design

With the continuous advancement of electronic technology and the introduction of new concepts, people's demands on life are increasing and the concept of intelligent automation is gaining popularity. With the continuous consumption of the source of life, people's concern about fresh water resources is also increasing. With the theme of intelligent detection, a water quality online monitoring system that integrates detection and monitoring is designed. The system can monitor temperature, humidity, water level and turbidity in real time, and can transmit monitoring values through wireless module. Users can use mobile applications to view the data detected at this time, which is very convenient. The system hardware design includes STM32 system processor circuit, temperature and humidity sensor module, water level monitoring module, WIFI module, and turbidity monitoring module design.

Water Quality Data Analysis from the Onlimo Monitoring System at 16 Stations in Priority Watersheds and Lakes

Currently, the decline in the quality of river and lake water continues, for this reason, activities to control pollution and improve river and lake water quality are required. One effort to prevent this problem is to implement online water quality monitoring technology continuously and in real time. The information generated from the water quality monitoring system can be used as a basis for planning watershed management and determining river and lake water management policies at the national and regional levels. The purpose of this activity is to analyze river and lake water quality data and evaluate the performance of the monitoring systems in 16 water quality monitoring stations that have been built. The data being analyzed is from 2016 to 2020 and includes the parameters of temperature, conductivity, TDS, DO, pH, and turbidity. The 16 station locations are spread over the Ciliwung River, Cisadane River, Citarum River, Serayu River, Bengawan Solo River, and Lake Toba. This research method uses descriptive statistical analysis and the STORET method, which refers to the Decree of the Minister of Environment and Forestry Number 115 of 2003 and Government Regulation of the Republic of Indonesia Number 82 of 2001. The results of this study indicate that most of the rivers monitored fall into the moderate to heavily polluted category, especially the rivers downstream of the Ciliwung, Cisadane, and Bengawan Solo rivers. The results of the analysis of DO, TDS, conductivity, and turbidity parameters from 16 monitoring stations, mostly met the Class II quality standards.

Application of the concept of smart city and smart water management for the new capital city

The Indonesian government has decided to move the State Capital (IKN) from DKI Jakarta to East Kalimantan Province. One of IKN’s visions is to build a smart, modern city that meets international standards. This paper examines the concept of smart city and smart water management (SWM) that will be applied to the new IKNs to build an integrated, sophisticated and sustainable clean water supply system. The method used was analyzing concepts related to smart cities and SWM applications in the world and Indonesia. Based on the results of the analysis of the abovementioned concepts, several conclusions were obtained, namely that an SWM support system is indispensable in water system management, leak detection, and water quality monitoring. Then, a smart water grid is used to develop control over water resources, infrastructure, and water discharge with high efficiency and sustainability. The flood early warning system must be utilized to minimize losses or victims caused by flood disasters. In addition, an online water quality monitoring system is needed to monitor water quality online and in real time. The integrated implementation of the smart city and SWM concepts described in the paper is expected to produce safe, healthy, and sustainable drinking water services.

Research on The Impact of Ciliwung River Water on The Surrounding Environment in The DKI Jakarta Area

Ciliwung River flows through central Jakarta, passing through many residential and densely populated areas that cause heavy pollution in the Ciliwung River resulting from wastewater and solid waste. This research aims to know the water quality and pollution level in the Ciliwung river and determine the locations to install water quality online monitoring stations. The research method used in this study is to analyze the quality of water samples taken from several points of the Ciliwung River. The analysis results show that the Ciliwung river is highly polluted, especially in the areas going downstream of the river. In addition, the analysis results have determined 8 (eight) points/locations where the online water quality monitoring devices will be installed. Due to limited funds, however, only 3 (three) online monitoring devices are currently installed on site, representing upstream, middle, and downstream areas of the Ciliwung river.

High-throughput determination of lipopolysaccharide by flow injection analysis using dipicolylamine-type fluorescent probe

Endotoxin (lipopolysaccharide: LPS), a bacterial toxin, is currently quantified by batch analysis using a reagent made from Limulus amebocyte lysate (LAL). Although the LAL method is sensitive, a long measurement time and gelation have hindered its application to online water quality monitoring. In this paper, we report a novel high-throughput analytical technique that combines flow injection analysis (FIA) and a dipicolylamine-type fluorescent probe, which was previously reported to exhibit strong fluorescence emission in response to LPS. A comparison of normal and reverse FIA systems revealed that the normal FIA offers more rapid detection with throughput of 30 samples per hour, which is the highest among known LAL methods and alternative LPS detection methods that have been recently reported. On the other hand, the reverse FIA requires much less reagent (480 μL per sample). Continuous online monitoring of water quality and analysis of real samples spiked with LPS were realized by this technique.

A high-performance pH-sensitive electrode integrated with a multi-sensing probe for online water quality monitoring

Water quality monitoring is an essential tool in effective environmental and public health protection.

Imprinting of different types of graphene oxide with metal cations

It is known that graphene oxide (GO) particles bear negatively charged oxygen moieties which can interact with cations, what has been several times employed in fabrication of electrochemical sensors for metals. In this work, for the first time GO was treated with Cu2+, Cd2+ or Pb2+ cations in a way similar to the ion imprinting by simple incubation of the two components and the synthesized nanomaterials were then deposited on carbon electrode surface forming electrochemical sensors towards lead cations. It was found that simple filtration of GO is very efficient way for collection of GO particles that could be imprinted most efficiently in terms of high electrochemical response towards lead cations. Part of the success also lies in high redox potential of Cu2+ that turned to be the best choice for the GO imprinting. It was also proven that the electrodes modified with ion-imprinted GO could work as the sensors of Pb2+ in drop amount of solution as well as they provide stable response under flow-through cell conditions suitable, for example, for online monitoring of water quality.

A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods

• The established model could reflect the impact of non-point source pollution on water quality. • The hybrid model performed very well in the water quality prediction. • The hybrid model improved the accuracy of extreme water quality prediction. Non-point source (NPS) pollution is an important factor affecting the quality of water environment. In recent years, a large number of online water quality monitoring stations have been used to obtain continuous time series water quality monitoring data. These data provide the necessary basis for the application of deep learning methods in water quality prediction. However, the prediction accuracy of traditional deep learning methods is low, especially in predicting the water quality with NPS pollution. Aiming to address this limitation, a novel deep learning model named SOD-VGG-LSTM with the simulation-observation difference (SOD) modular based on physical process, the visual geometry (VGG) modular reflecting spatial characteristics, and the long short-term memory (LSTM) modular based on deep learning method was developed to improve the accuracy of the water quality prediction with NPS pollution. The established model can overcome the problem that mechanism models can not predict the changes of water quality on the hourly or minute time scale. The model was applied in Lijiang River watershed. Experimental results indicated that the proposed model had the highest accuracy in the extreme value prediction compared with the mechanism model and LSTM model. The maximum relative errors between the predicted and observed results for DO, COD Mn , NH 3 -N, and TP were 8.47%, 19.76%, 24.1%, and 35.4%, respectively. The model evaluation demonstrated that the established SOD-VGG-LSTM model achieved superior computational performance compared to Auto Regression Integreate Moving Average model (ARIMA), Support Vector Regression model (SVR), and Recurrent Neural Network model (RNN). The evaluation results showed that SOD-VGG-LSTM achieved 3.2–39.3% higher R 2 than ARIMA, SVR and RNN. The proposed model can provide a new method for water quality prediction with NPS pollution.

Online water quality monitoring based on UV–Vis spectrometry and artificial neural networks in a river confluence near Sherfield-on-Loddon

Water quality monitoring is very important in agricultural catchments. UV–Vis spectrometry is widely used in place of traditional analytical methods because it is cost effective and fast and there is no chemical waste. In recent years, artificial neural networks have been extensively studied and used in various areas. In this study, we plan to simplify water quality monitoring with UV–Vis spectrometry and artificial neural networks. Samples were collected and immediately taken back to a laboratory for analysis. The absorption spectra of the water sample were acquired within a wavelength range from 200 to 800 nm. Convolutional neural network (CNN) and partial least squares (PLS) methods are used to calculate water parameters and obtain accurate results. The experimental results of this study show that both PLS and CNN methods may obtain an accurate result: linear correlation coefficient (R2) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R2 between predicted value and true values of TSS concentrations is 0.827 with PLS model and 0.915 with CNN model. CNN method may obtain a better linear correlation coefficient (R2) even with small number of samples and can be used for online water quality monitoring combined with UV–Vis spectrometry in agricultural catchment.

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.

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things.

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country's strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.

Associated Sensors, Innovative Sensor Deployment furthermore, Intelligent Data Analysis for Online Water Quality Monitoring

Abstract: The sensor innovation for water quality checking (WQM) has improved during late years. The financially savvy sensorised instruments that can independently gauge the fundamental physical - substance and organic (PCB) factors are presently promptly accessible and are being sent on floats, boats and ships. However, there is a distinction between the information quality, information gathering and information examination because of the absence of normalized approaches for information assortment and handling, spatio-fleeting variety of key boundaries in water bodies and new pollutants. Such holes can be spanned with an organization of multiparametric sensor frameworks sent in water bodies utilizing independent vehicles, for example, marine robots and flying vehicles to widen the information inclusion in existence. Further, smart calculations could be utilized for normalized information examination what's more, determining. This paper presents an exhaustive survey of the sensors, sending and examination advancements for WQM. An organization of arranged water bodies could improve the worldwide information interoperability and empower WQM at worldwide scale to address worldwide difficulties connected with food, drinking water, and wellbeing.

Real Time Water Pollution Monitoring RC Boat Using IOT

Nowadays drinking water is the most precious and valuable for all the human beings, drinking water utilities faces new challenges in real-time operation. This challenge occurred because of limited water resources growing population, ageing infrastructure etc. Hence therefore there is a need of better methodologies for monitoring the water quality. Traditional methods of water quality involve the manual collection of water sample at different locations, followed by laboratory analytical techniques in order the character the water quality. Such approaches take longer time and no longer to be considered efficient. Although the current methodologies analysis the physical, chemical and biological agents, it has several drawbacks: a) poor spatiotemporal coverage b) it is labor intensive and high cost(labor, operation; and equipment) c)the lack of real time water quality information to enable critical decisions for public health protection. Therefore, there is a need for continuous online water quality monitoring. The online water monitoring technologies have made a significant progress for source water surveillance and water plant operation. The use of their technologies having high cost associated with installation and calibration of a large distributed array of monitoring sensors. The algorithm proposed on the new technology must be suitable for particular area and for large system is not suitable.