Water Quality Monitoring Sensors Research Articles

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.

Developing a Semi-Automated Near-Coastal, Water Quality-Retrieval Process from Global Multi-Spectral Data: South-Eastern Australia

The estimation of water quality properties through satellite remote sensing relies on (1) the optical characteristics of the water body, (2) the resolutions (spatial, spectral, radiometric and temporal) of the sensor and (3) algorithm(s) applied. More than 80% of global water bodies fall under Case I (open ocean) waters, dominated by scattering and absorption associated with phytoplankton in the water column. Globally, previous studies show significant correlations between satellite-based retrieval methods and field measurements of absorbing and scattering constituents, while limited research from Australian coastal water bodies appears. This study presents a methodology to extract chlorophyll a properties from surface waters from near-coastal environments, within 2 km of coastline, in Tasmania, south-eastern Australia. We use general purpose, global, long-time series, multi-spectral satellite data, as opposed to ocean colour-specific sensor data. This approach may offer globally applicable tools for combining global satellite image archives with in situ field sensors for water quality monitoring. To enable applications from local to global scales, a cloud-based geospatial analysis workflow was developed and tested on several sites. This work represents the initial stage in developing a semi-automated near-coastal water-quality workflow using easily accessed, fully corrected global multi-spectral datasets alongside large-scale computation and delivery capabilities. Our results indicated a strong correlation between the in situ chlorophyll concentration data and blue-green band ratios from the multi-spectral sensor. In line with published research, environment-specific empirical models exhibited the highest correlations between in situ and satellite measurements, underscoring the importance of tailoring models to specific coastal waters. Our findings may provide the basis for developing this workflow for other sites in Australia. We acknowledge the use of general purpose multi-spectral data such as the Sentinel-2 and Landsat Series, their corrections and algorithms may not be as accurate and precise as ocean colour satellites. The data we are using are more readily accessible and also have true global coverage with global historic archives and regular, global collection will continue at least 10 years in the future. Regardless of sensor specifications, the retrieval method relies on localised algorithm calibration and validation using in situ measurements, which demonstrates close-to-realistic outputs. We hope this approach enables future applications to also consider these globally accessible and regularly updated datasets that are suited to coastal environments.

Long-Term Stability of Low-Cost IoT System for Monitoring Water Quality in Urban Rivers

Monitoring water quality in urban rivers is crucial for water resource management since point and non-point source pollution remain a major challenge. However, traditional water quality monitoring methods are costly and limited in frequency and spatial coverage. To optimize the monitoring, techniques such as modeling have been proposed. These methods rely on networks of low-cost multiprobes integrated with IoT networks to offer continuous real-time monitoring, with sufficient spatial coverage. But challenges persist in terms of data quality. Here, we propose a framework to verify the reliability and stability of low-cost sensors, focusing on the implementation of multiparameter probes embedding six sensors. Various tests have been developed to validate these sensors. First of all, a calibration check was carried out, indicating good accuracy. We then analyzed the influence of temperature. This revealed that for the conductivity and the oxygen sensors, a temperature compensation was required, and correction coefficients were identified. Temporal stability was verified in the laboratory and in the field (from 3 h to 3 months), which helped identify the frequency of maintenance procedures. To compensate for the sensor drift, weekly calibration and cleaning were required. This paper also explores the feasibility of LoRa technology for real-time data retrieval. However, with the LoRa gateways tested, the communication distance with the sensing device did not exceed 200 m. Based on these results, we propose a validation method to verify and to assure the performance of the low-cost sensors for water quality monitoring.

In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities

Water quality issues remain a major cause of global water insecurity, and real-time low-cost monitoring solutions are central to the remediation and management of water pollution. Optical sensors, based on fluorescence, absorbance, scattering and reflectance-based principles, provide effective water quality monitoring (WQM) solutions. However, substantial challenges remain to their wider adoption across scales and environments amid cost and calibration-related concerns. This review discusses the current and future challenges in optical water quality monitoring based on multi-peak fluorescence, full-spectrum absorbance, light-scattering and remotely sensed surface reflectance. We highlight that fluorescence-based sensors can detect relatively low concentrations of aromatic compounds (e.g., proteins and humic acids) and quantify and trace organic pollution (e.g., sewage or industrial effluents). Conversely, absorbance-based sensors (Ultraviolet-Visible-Infra-red, UV-VIS-IR) are suitable for monitoring a wider range of physiochemical variables (e.g., nitrate, dissolved organic carbon and turbidity). Despite being accurate under optimal conditions, measuring fluorescence and absorbance can be demanding in dynamic environments due to ambient temperature and turbidity effects. Scattering-based turbidity sensors provide a detailed understanding of sediment transport and, in conjunction, improve the accuracy of fluorescence and absorbance measurements. Recent advances in micro-sensing components such as mini-spectrometers and light emitting diodes (LEDs), and deep computing provide exciting prospects of in-situ full-spectrum analysis of fluorescence (excitation-emission matrices) and absorbance for improved understanding of interferants to reduce the signal-to-noise ratio, improve detection accuracies of existing pollutants, and enable detection of newer contaminants. We examine the applications combining in-situ spectroscopy and remotely sensed reflectance for scaling Optical WQM in large rivers, lakes and marine bodies to scale from point observations to large water bodies and monitor algal blooms, sediment load, water temperature and oil spills. Lastly, we provide an overview of future applications of optical techniques in detecting emerging contaminants in treated and natural waters. We advocate for greater synergy between industry, academia and public policy for effective pollution control and water management.

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.

MOF-5 fortified fiber optic plasmonic absorption-based Pb(ii) ion sensor for rapid water quality monitoring

MOF-5 fortified fiber optic plasmonic absorption-based Pb(<scp>ii</scp>) ion sensor for rapid water quality monitoring

Utilization of tds sensors for water quality monitoring and water filtering of carp pools using IoT

Carp is a type of consumption fish that is much favored by people in Indonesia, because Carp has a lot of protein and meat with good taste. In addition, Carp can be cultivated using tarpaulin ponds, because it saves land and land rental costs. In addition, Carp can be harvested in just 7 months. According to carp cultivators, market demand tends to increase every day, making the need for carp increase and causing Indonesian people to cultivate carp. In this case, it is necessary to apply technology that can help the community in the field of aquaculture, which can be controlled remotely and can monitor the condition of the water in the ponds. The purpose of this study is to find out how pool water filtration works and monitor water conditions using a Total Dissolved Solids (TDS) sensor. The selection of sensor inputs and set point values must be in accordance with the water quality conditions in the Carp pond. The use of the TDS sensor here functions as a detector for turbidity values in Carp ponds that have been set on the NodeMCU ESP32, a submersible water pump as output and the Blynk application as monitoring. The method used, Literature Study, Research Design, Field Study, Functional Testing, Data Processing, Analysis. Because it uses Internet of Thing (IoT) technology in this study, the monitoring and filtering control process can be done anywhere and anytime as long as it is connected to the internet network. From the results of testing the tool on 3 types of water, namely mineral water, clean water, and turbid water, the results for turbid water with 400–600 ppm turned out to be 479 ppm, the value is in the middle between 478 ppm mineral water and 480 ppm clean water. So it can be concluded that this tool works well and can be used in Carp ponds

Optical fiber sensors for water and air quality monitoring: a review

Optical fiber sensors for water and air quality monitoring: a review

Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique.

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.

Research Trends in Smart Cost-Effective Water Quality Monitoring and Modeling: Special Focus on Artificial Intelligence

Numerous conventional methods are available for analyzing various water quality parameters to determine the water quality index. However, ongoing surveillance is necessary for large bodies of water. A water quality monitoring system supports a robust surface and groundwater ecosystem. Various tactics are used to improve aquatic habitats: identification of the precise chemical pollutants released into the aquatic environment; advancements in assessing ecological effects; and working on ways to enhance water quality through informing the public, communities, businesses, etc. In order to save the marine ecosystem and those who entirely depend on these enormous bodies of water, it is also crucial to continuously handle many data sets of water quality metrics. To predict the water quality index, this review paper provides an overview of water quality monitoring, the modeling and numerous sensors employed, and various artificial intelligence approaches. Various water quality models were proposed to assess pH, a few components, and alkalinity. Additionally, handling raw information for surface and groundwater quality metrics was studied using artificial intelligence techniques like neural networks.

Inferring drivers of nitrate and sediment event dynamics from hysteresis metrics for two large agricultural watersheds

AbstractExcess nitrate and sediment, mobilized by precipitation events and transported into surface waters, is a global water quality challenge. Recent advances in high‐frequency in situ water quality monitoring sensors have created opportunities to investigate constituent concentration dynamics during short‐term hydrological changes. In this study, we characterized the event‐scale variability of nitrate () and turbidity (a surrogate for sediment transport) in two large agricultural watersheds of the Upper Mississippi River Basin using hysteresis loop characteristics to determine sources and dominant transport mechanisms. We then applied factor analysis to detect variable groupings and thus determine controls on nitrate and sediment dynamics. We observed consistent counterclockwise hysteresis patterns between the two watersheds. This was indicative of distal contributions and/or late‐event mobilization and flushing, which was controlled by the characteristics of the event hydrology (such as, event duration and magnitude of event discharge). However, turbidity hysteresis loops indicated different sediment delivery behaviours between the two watersheds. The smaller watershed with more diverse land use demonstrated consistent clockwise hysteresis patterns indicating early event flushing or rapidly responding pathways. The time lag between turbidity and discharge peaks was identified as a driver of hysteresis magnitude for the smaller site. In contrast, the larger and more agricultural watershed showed variability between dilution versus flushing as well as delivery pathways between events. The hysteresis magnitude was driven by the event peak discharge and discharge range demonstrating an increase in stream power with scale influenced sediment dynamics at the larger site that switched behaviour. This result is critical for watershed water quality management, especially in the context of a changing climate and further underscores the utility of high‐frequency sensors monitoring data to offer deep insights into hydrological processes controls on contaminant transport and delivery.

Establishment of an antifouling performance index derived from the assessment of biofouling on typical marine sensor materials

Marine biofouling, known as the unwanted accumulation of living organisms on submerged surfaces, is one of the main factors affecting the operation, maintenance and data quality of water quality monitoring sensors. This can be a significant challenge for marine deployed infrastructure and sensors in water. When organisms attach to the mooring lines or other submerged surfaces of the sensor, they can interfere with the sensor's operation and accuracy. They can also add weight and drag to the mooring system, making it more difficult to maintain the desired position of the sensor. This increases the cost of ownership to the point where it becomes prohibitively expensive to maintain operational sensor networks and infrastructures.Furthermore, the analysis and quantification of biofouling is extremely complex as it is based on biochemical methods such as the analysis of pigments such as chlorophyll-a as a direct indicator of the biomass of photosynthetic organisms, dry weight, carbohydrate analysis and protein analysis among others.In this context, this study has developed a method to estimate biofouling quickly and accurately on different submerged materials used in the marine industry and specifically in sensor manufacturing like copper, titanium, fiberglass composite, different types of polyoxymethylene (POMC, POMH), polyethylene terephthalate glycol (PETG) and 316L-stainless steel. To do this, in situ images of fouling organisms were collected with a conventional camera and image processing algorithms and machine learning models trained were used to construct a biofouling growth model. The algorithms and models were implemented with Fiji-based Weka Segmentation software. A supervised clustering model was used to identify three types of fouling to quantify fouling on panels of different materials submerged in seawater over time. This method is easy, fast and cost-effective to classify biofouling in a more accessible and holistic way that could be useful for engineering applications.

Nanoflowers on Microporous Graphene Electrodes as a Highly Sensitive and Low-Cost As(III) Electrochemical Sensor for Water Quality Monitoring

In this work, we report a low-cost and highly sensitive electrochemical sensor for detecting As(III) in water. The sensor uses a 3D microporous graphene electrode with nanoflowers, which enriches the reactive surface area and thus enhances its sensitivity. The detection range achieved was 1-50 ppb, meeting the US-EPA cutoff criteria of 10 ppb. The sensor works by trapping As(III) ions using the interlayer dipole between Ni and graphene, reducing As(III), and transferring electrons to the nanoflowers. The nanoflowers then exchange charges with the graphene layer, producing a measurable current. Interference by other ions, such as Pb(II) and Cd(II), was found to be negligible. The proposed method has potential for use as a portable field sensor for monitoring water quality to control hazardous As(III) in human life.

Application of 3D Printing Technology in Sensor Development for Water Quality Monitoring.

The development of sensors for water quality monitoring is crucial to protect water quality, aquatic biota and human health. Traditional sensor manufacturing methods have significant drawbacks, such as low fabrication freedom, limited material choice and expensive manufacturing cost. As a possible alternative method, 3D printing technologies are increasingly popular in sensor development due to their high versatility, fast fabrication/modification, powerful processing of different materials and ease of incorporation with other sensor systems. Surprisingly, a systematic review examining the application of 3D printing technology in water monitoring sensors has not yet been conducted. Here, we summarized the development history, market share and advantages/disadvantages of typical 3D printing techniques. Specifically focused on the 3D-printed sensor for water quality monitoring, we then reviewed the applications of 3D printing in the development of sensors' supporting platform, cell, sensing electrode as well as all-3D-printed sensors. The fabrication materials and processing, and the sensor's performances regarding detected parameters, response time and detection limit/sensitivity, were also compared and analyzed. Finally, the current drawbacks of 3D-printed water sensors and potential directions for future study were discussed. This review will substantially promote the understanding of 3D printing technology used in water sensor development and benefit the protection of water resources.

IOT Based Real Time River Water Quality Monitoring and Control System

Water quality monitoring systems currently in use are manual and involve tedious processes that are time intensive. This research suggests a system with sensors for water quality monitoring. Access to real-time data may be obtained through remote monitoring and the Internet of Things. A wireless sensor network (WSN) contains a micro-controller for data processing, a mechanism for communicating between and inside nodes and many (IoT). Using Spark flow analysis with Spark MLlib, deep on it. The agent will receive a warning SMS automatically if the detected value exceeds the threshold. Our plan to develop a high- frequency, high-mobility, low-power water monitoring system makes it special. As a result, the Bangladeshi people will find our proposed approach highly useful in raising awareness of and putting an end to water pollution.

Determining the Dose-Response Curve of Exoelectrogens: A Microscale Microbial Fuel Cell Biosensor for Water Toxicity Monitoring.

Nowadays, the development of real-time water quality monitoring sensors is critical. However, traditional water monitoring technologies, such as enzyme-linked immunosorbent assay (ELISA), liquid chromatography, mass spectroscopy, luminescence screening, surface plasma resonance (SPR), and analysis of living bioindicators, are either time consuming or require expensive equipment and special laboratories. Because of the low cost, self-sustainability, direct current output and real-time response, microbial fuel cells (MFCs) have been implemented as biosensors for water toxicity monitoring. In this paper, we report a microscale MFC biosensor to study the dose–response curve of exoelectrogen to toxic compounds in water. The microscale MFC biosensor has an anode chamber volume of 200 μL, which requires less sample consumption for water toxicity monitoring compared with macroscale or mesoscale MFC biosensors. For the first time, the MFC biosensor is exposed to a large formaldehyde concentration range of more than 3 orders of magnitudes, from a low concentration of 1 × 10−6 g/L to a high concentration of 3 × 10−3 g/L in water, while prior studies investigated limited formaldehyde concentration ranges, such as a small concentration range of 1 × 10−4 g/L to 2 × 10−3 g/L or only one high concentration of 0.1 g/L. As a result, for the first time, a sigmoid dose–response relationship of normalized dose–response versus formaldehyde concentration in water is observed, in agreement with traditional toxicology dose–response curve obtained by other measurement techniques. The biosensor has potential applications in determining dose–response curves for toxic compounds and detecting toxic compounds in water.

Identification of Suitable Locations in a Small Water Supply Network for the Placement of Water Quality Sensors Based on Different Criteria under Demand-Driven Conditions

Drinking water quality monitoring in real time is of utmost importance to ensure public health. Although water utilities, following the related legislative framework, monitor drinking water quality through samplings, the likelihood of detecting contaminants in consumers’ taps is low, depending on the scale of the monitoring programme. Additionally, even if the monitoring frequency is high, there is a time delay since sampling and analysis processes take some time. The selection of suitable locations for the installation of online water quality sensors is a hard task for a water utility due to the complexity of the water distribution system, the limitations of certain network junctions which are not easily accessible, and the computational burden involved. This topic has been extensively studied in recent years and sophisticated methods have been developed using optimization techniques. However, small water utilities do not have the means to implement such tools. This paper applies a methodology to identify the suitable junctions for the installation of online water quality sensors based on different objectives and under demand-driven conditions. This paper utilizes the hydraulic simulation model of a standard network to set up the water quality simulation model. A thorough analysis of various contamination scenarios takes place with different injection nodes and at different starting injection times for 24 h. The latter relates to the contaminant’s spread due to varying water demand. After a thorough analysis of 816 scenarios, a prioritized list of the most suitable nodes for the installation of the sensors is available for each optimization objective. Comparing the prioritized list of nodes achieved from each single or multi-objective function, the detection probability is almost the same. The analysis revealed that, due to varying water demand conditions, the ranking of the proposed nodes suitable for the installation of water quality monitoring sensors differs. Thus, varying hourly water demand should be part of analyses seeking to get reliable results.

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.

IMPLEMENTASI SISTEM PEMANTAUAN KUALITAS AIR DENGAN IOT DI PLANT FACTORY KEBUN PERCOBAAN FAKULTAS PERTANIAN UNIVERSITAS UDAYANA

Plant Factory is a technological concept that forms an ideal environment for plant growth. Water quality has several factors including pH value, water nutrient level (ppm), water temperature and others, plants require a pH value of 5.5 to 6. Differences in pH values and plant nutrients affect plant growth. Lettuce Cultivation has different nutrient requirements for water where plants need around 1000-2000 ppm and 20-25°C in order to produce good growth. Therefore we need a system that can regulate and monitor water quality that can be monitored anywhere. Based on system testing that has been carried out for 30 days, it is found that all components used function as expected, the water quality monitoring sensor is able to detect the value of pH, TDS, water temperature with an average difference with measuring instruments below 2%. After being tested for 30 days from planting to harvesting, the tool can still function properly with indicators that this tool can record data for 30 days and store all data in the database accurately.