Turbidity Sensor Research Articles

From dishwasher to river: how to adapt a low-cost turbidimeter for water quality monitoring.

This study presents the process of design and development of a low-cost turbidimeter for monitoring water quality, facilitating rigorous spatial-temporal variability analysis within large-scale hydrological systems. We propose a low-cost optical turbidimeter, modifying the existent SEN0189 turbidity sensor, Arduino boards, and additional sensors for temperature compensation. We compared a low-cost system with high-tech sensors, modifying the original low-cost SEN0189 probe for enhanced environmental performance. The three-step methodological framework involved prototype development, compensation for environmental factors, and preparation for future field deployment. Calibration equations with a high coefficient of determination and a temperature correction equation were established. We made adaptations to overcome field deployment challenges, including a 3-D printed sensor case, defining the relationship between measurement uncertainty and energy consumption, and specifying field installation guidelines. In summary, this study presents a comprehensive approach to a low-cost optical turbidity system, demonstrating its potential for accurate and affordable field deployment. We aim to address the critical need for sustainable inland water management tools, making this system a valuable contribution to environmental monitoring practices. We also aim to inspire similar development of open-source monitoring systems within our community.

Automated Monitoring System for Rainwater Harvesting Tank at Telkom University

The use of ground tank constructed by Telkom University for Rainwater Harvesting (RWH), is limited to environmental maintenance due to concerns regarding the quality of water in the underground tank. Therefore, this research aims to develop a remote monitoring device that uses Internet of Things (IoT) technology to monitor the pH, water surface, submerged materials, and water clarity levels in ground tank. To achieve the requirements, pH, ultrasonic-based volume, Total Dissolved Solids (TDS), and Turbidity sensors were selected due to the IoT connectivity. The enabling device, namely the ESP 32 microcontroller and Blynk platform were installed on monitoring dashboard on a tablet computer with 4GB of RAM. The result showed that calibration of each sensor had good accuracy, except for the Turbidity sensor due unavailable materials. In conclusion, the RWH monitoring system is suitable for use.

Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models

Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.

Embedded IoT Design for Bioreactor Sensor Integration

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.

DEVELOPMENT OF WATER QUALITY MONITORING SYSTEM IN SHRIMP PONDS BASED ON INTERNET OF THINGS (IOT)

This type of research is research and development (R&D) which uses the waterfall development model. The aim of this research is to produce a water quality monitoring system in shrimp ponds based on the Internet of Things (IoT) and to find out the results of testing the development of the system using the ISO 25010 standard. This development involves the application of ESP32, Relay, Toa Siren, DS18b20 Temperature Sensor, pH Sensor Module 4502C, Water Salinity and Turbidity Sensor which allows users to get real-time pond water quality monitoring information via the Telegram application. The test results for the reliability aspect have met the fault tolerance sub-characteristic standards because the percentage of measurement error obtained for the temperature sensor was 0.10%, pH 0.54%, salinity 1.19%, and turbidity 1.00%, still within the permitted tolerance limits. Functionality suitability testing shows that this tool obtains a percentage of 100%, indicating that the system is suitable for use. This tool also meets the portability aspect because it can be used on various devices with different operating systems. Apart from that, the usability evaluation results from user responses are in the very good category with an average of >4.2.

Water Quality Monitoring and Control System for Tilapia Cultivation Based on Internet of Things

This research analyzes the quality of water for tilapia habitat which is a type of brackish water fish that is currently widely cultivated by pond farmers. This fish is the choice because of its flexibility regarding habitat. However, despite having flexibility in terms of habitat, each harvest of tilapia that lives in a different habitat will produce tilapia with different quantity and quality. Currently, many tilapia farmers still carry out the cultivation process using traditional methods using ponds. Kuala Kerto Village, Lapang District, North Aceh is one of the locations where many tilapia fish farmers use ponds as a habitat for this fish. Not infrequently, changes in natural conditions such as rain and floods have an impact on tilapia fish ponds in this village. Thus, crop yields are very varied, often even resulting in losses. One of the reasons for this is that there is still minimal use of technology in tilapia cultivation in this village. The design of a water quality monitoring and control system for IoT-based tilapia cultivation in this research was carried out to help the problems of tilapia pond farmers. Through this research, a tool was produced in the form of a prototype IoT device that can be used to monitor and control water quality in tilapia fish ponds. This device utilizes several sensors such as turbidity sensors, ammonia sensors, salinity sensors, pH sensors, and several other sensors as data takers which will later be transmitted and displayed via a web application. Research and development of this device uses the RD method, namely research and development.

Internet of Things Based Water Quality Control System

Fish incubation, which involves the hatching of fish eggs, represents the process of embryogenesis until the embryo emerges from its protective shell. This intricate developmental journey is influenced by a combination of internal and external factors. External factors can be influenced by water quality that is not suitable for hatching fish eggs. Water quality, which includes temperature, dissolved oxygen, light intensity, salinity, and pH, is an important and limiting factor for living creatures that live in water, including chemical, biological, and physical factors. Poor water quality can prevent fish eggs from hatching and even cause death. The primary objective here is the creation of an Internet of Things (IoT)-based system designed for controlling water quality during fish incubation. This system is intended to aid fish farmers in effectively control the environmental conditions within their incubation ponds. The method used in designing IoT device is research and development, and it uses the Arduino Mega microcontroller as the main device to run pH sensors, turbidity sensors, oxygen sensors, temperature sensors, and relay modules. Water Quality Control System The fish incubation was successful. The average time needed to change the water temperature from 28.5 to 29 degrees Celsius is 4 minutes and 7 seconds; change the dissolved oxygen level from 11 to 11.5 is 3 minutes and 33 seconds; change the pH value of the water from 6.9 to 7.1; and maintain turbidity below 5 with an average time of 5 minutes and 58 seconds. Controlling water quality can speed up the fish egg incubation process for 8 hours.

Design and characterization of a novel turbidity sensor based on quadrature demodulation

Abstract Turbidity is regarded as a comprehensive indicator in water quality monitoring, and the turbidity sensor deployed in the water supply network can record the dynamic changes of water quality in time. However, the weak photoelectric signal from the photodetector contains a quantity of noise. In order to improve signal-to-noise ratio (SNR), a novel on-line turbidity sensor based on quadrature demodulation principle has been proposed in this paper. A near-infrared light-emitting diode (LED) with a wavelength of 860 nm was selected as a stable monochromatic light source, and a photodiode (PD) with an angle of 90° to the incident light from the LED was selected as the photodetector. Using signal modulation and demodulation technology, the weak photoelectric signal extraction, conversion, amplification and output of the turbidity sensor were realized through the effective integration. A corresponding test apparatus of the turbidity sensor was established and experimental results showed that within a 0~5 NTU measurement range, the turbidity sensor had good linearity and stability, the relative measurement error was within ±1% and the limit of detection could reach as low as 0.0049 NTU. The developed turbidity sensor has good detection performance and can meet the needs of low turbidity detection of drinking water.

Intercomparison of optical scattering turbidity sensors for a wide range of suspended sediment types and concentrations

To monitor the effects of rapid changes in climate and land use on sediment export from erodible environments, it is crucial to accurately quantify highly fluctuating suspended sediment concentrations (SSCs) in contrasted river systems that drain small to mesoscale catchments. To this end, we investigate the turbidity-based quantification of SSCs in the range of 0.05–100 g/L through laboratory experiments performed with 7 different types of turbidity sensors and sediments from 10 watersheds. We find that measurements of scattered light from multiple angles may allow for: (1) an extended monitoring range with SSCs up to 10–100 g/L, where enhanced uncertainty may occur near the transition in the effective operational ranges of the underlying signals (typically somewhere in the range of 1–10 g/L); and/or (2) a slightly reduced sensitivity to sediment properties. The specific turbidity of the investigated sensors is inversely related to particle diameter (D10) for SSCs up to 1–5 g/L. Backscatter and combined-signal sensors also show a dependency on sediment colour (CIE a∗), which becomes particularly prominent at SSCs above 10 g/L. We relate this increase in colour dependency with SSC to the expected effect of cumulative near-infrared light absorption associated with multiple scattering. We discuss covarying physical properties of naturally occurring river sediment that can dampen or enhance measurement sensitivity and result in turbidity-based SSC rating curves that may strongly differ in magnitude and form from curves derived for industrially prepared material that is often used for sensor calibration. Although the differences in SSC per sensor among sediment types are generally less than one order of magnitude, the systematic errors and uncertainties associated with high SSCs are typically greater than one order of magnitude and may disproportionally affect the quantification of sediment loads during large-magnitude flow events.

Sustainable energy harvesting system for low-power underwater sensing devices

In marine scientific research, ocean monitoring is crucial where the battery powered sensor devices are placed under the water to collect different information like temperature, pressure, and turbidity in underwater sensor networks (UWSNs). Thus, keeping these devices active for longer periods is challenging. In the last decades, the piezoelectric transducer (PZT) material has been used widely for constructing more environmentally friendly energy harvesting systems. The PZT harvester offers a promising solution by eliminating the need for batteries for running devices in the future with less maintenance. The PZT harvester allows the system to generate higher voltage to run low-power devices. This paper designed and developed a new renewable energy harvester system using PZT transducers for running different types of underwater sensor devices like temperature, turbidity, and obstacle sensors. The proposed PZT-based energy harvester employs a two stage amplification model for generating higher voltage and current to run multiple devices. The sensing information collected from these sensors is transmitted to the cloud which is later utilized for analysis and decision making. Experiment results show the proposed PZT-based energy harvester can generate a voltage of 13 volts (V) and a current of 43.3 milliampere (mA) equivalent to 562 milliwatt (mW) which is very good to run multiple low-power underwater sensor devices.

IoT based Smart Aquaculture Monitoring System for Fish Tank Management

Aquaculture, the farming of aquatic organisms, plays a critical role in meeting the increasing global demand for seafood. Effective monitoring and management of aquaculture systems are essential for ensuring optimal conditions and maximizing productivity. This research presents a comprehensive aquaculture-based fish tank monitoring system designed to enhance operational efficiency and fish welfare. At the heart of the system is the Arduino Uno microcontroller, serving as the central processing unit to coordinate various functions. Key components include pH sensor for continuous monitoring of acidity levels, turbidity sensor for detecting suspended particles, and water level sensor for maintaining optical water levels within the tank. In the event of abnormal pH levels or high turbidity, the system triggers automated alerts, sending notifications to the designated owner through SMS and transmitting data to an IoT website for remote monitoring. To ensure consistent environmental conditions, two pump motors are utilized: one for automatic water replenishment in case of water level fluctuations and another for removing water in case of particle accumulation. Furthermore, the system integrates a servo motor mechanism for precise dispensing of fish feed at predetermined intervals, promoting healthy growth and nutrition. Control over pump motors and feeding schedules is facilitated through a user-friendly website interface, providing operators with seamless management capabilities.

Enhanced Hydroponic Farming with Crop Suitability Prediction

Hydroponics, a soil-less cultivation technique, offers efficient resource utilization and enhanced crop yields. However, precise water quality control is essential to ensure optimal nutrient absorption and successful crop growth. This research paper presents a comprehensive IoT-based water quality monitoring system specifically designed for hydroponics, incorporating pH, turbidity, and temperature sensors. The system can acquire data from sensors, data processing, and data visualization in real time. The sensor data is seamlessly transmitted from Arduino to ESP8266 and securely stored on the ThingSpeak cloud platform. Utilizing web scraping techniques, the collected data is extracted from ThingSpeak and seamlessly integrated into a user-friendly website. On the website, a machine learning (ML) model, trained on required data, automatically processes the realtime sensor readings and performs crop suitability predictions. The system enables continuous and automated monitoring of key water quality parameters, ensuring optimal conditions for hydroponic crop growth. The deployed ML model demonstrates remarkable performance in predicting water suitability for different crops. The model provides real-time predictions on the website by utilizing the data, empowering hydroponic farmers with data-driven insights to optimize crop selection based on water quality. To evaluate the system’s efficacy, extensive experimentation, and validation have been conducted using hydroponic setups with various crop types.

Assessment of suspended sediment export and dynamics using in‐line turbidity sensors and time series statistical models

AbstractThe Coln is an ecologically sensitive river in a limestone dominated catchment with no major tributaries. Three in‐line turbidity sensors were installed to monitor changes in the dynamics of suspended sediment transport from headwaters to the confluence. The aims were to (i) provide estimates of yield (t km−2 year−1) and likely drivers of suspended sediment over ~3 years and (ii) assess turbidity dynamics during storm events in different parts of the catchment. In addition, the sensor installation allowed a novel wavelet analysis based on identifying groups of turbidity peaks to estimate transport times of suspended sediment through the catchment. Yearly suspended sediment yields calculated for the upper catchment were typically less than 4 t ha−1 year−1 being similar to other UK limestone or chalk‐based rivers. Time series autoregressive integrated moving average models including explanatory variable regression modelling indicated that river discharge, groundwater level and water temperature were all significant predictors of turbidity levels throughout the year. However, high model residuals demonstrate that the models failed to capture random turbidity events. Five parts of the time series data were used to examine sediment dynamics. Plots of scaled discharge verses turbidity demonstrated that in the upper catchment, after initial suspended sediment generation, sediment quickly became limited. In the lower catchment, hysteresis analysis suggested that sediment dilution occurred, due to increasing base flow. The novel wavelet analysis demonstrated that during winter ‘sediment events’ identified as groups of turbidity peaks, took ~18 h to pass from the first sensor in the upper catchment to the second sensor (10.3 km downstream of sensor 1) and 24 h to the third sensor (23.3 km from sensor 1). The work demonstrates the potential for using multiple turbidity sensors and time series statistical techniques in developing greater understanding of suspended sediment dynamics and associated poor water quality in ecologically sensitive rivers.

Application of High-Frequency Intelligent Sensing Network in Monitoring and Early Warning of Water Quality Dynamic Change

By implementing a high-frequency intelligent network of sensors, this work explores continuous monitoring and alerting for dynamic changes in water quality. Life depends on water, yet pollution is a greater menace. For this reason, precautions and careful observation are necessary. Typically, the focus on conventional water quality system monitoring is too much on data collection and needs more on analysis and extraction, limiting its capacity to offer thorough solutions. Making informed decisions becomes more complicated when there are discrepancies like damaged data, loss from power outages, or transmission issues. The proposed High-Frequency Intelligent Sensing Network (HFISN) monitoring system uses cloud computing, IoT and Big data technologies for intelligent sensing. Researchers developed it to address various challenges. Researchers recommend Nephelometric Turbidity Unit (NTU) Sensor installation to enhance the system’s performance and facilitate better monitoring of sedimentation, particle issues, and water purity. This sensor makes it possible to make more informed decisions by expanding the platform’s dataset. The solution not only resolves data cleaning and analysis issues but also includes intelligent early-warning capabilities for timely alerts. Quantum Cloud (QC) technology is employed to enhance security and accessibility. Test findings confirm its robustness with extra features and a built-in turbidity sensor. Because the platform ensures data accuracy and dependability, it provides decision-makers with a solid foundation to protect water resources.

Classification of water quality based on dissolved solids and turbidity parameters with the utilization of total dissolved solids sensor and turbidity sensor

Clean water quality is essential for public health, but its scarcity is increasing amid population growth and industrialization. Monitoring turbidity and total dissolved solids (TDS) is essential to determine the quality of clean water. This study addresses the urgent need for accurate and reliable water quality monitoring to test the applicability of TDS and turbidity sensors in taking measurements, aiming to develop efficient monitoring solutions for public health and sustainable water management. The TDS sensor operates according to the principle of electrical conductivity, with a range of 0 to 1000 ppm and an accuracy of ±10%. The turbidity sensor detects water turbidity by determining the level of turbidity particles. The ESP32 microcontroller integrates Wi-Fi and USB capabilities. The hardware and software design ensures accurate sensor readings, which are critical to successful water quality measurement and monitoring. The test results show satisfactory accuracy of the TDS sensor with an average error of 0.09% and good accuracy of the turbidity sensor with an average error of about 1.536%. Concerning the above two parameters, in this study, among 15 water samples, seven were clean, meeting the standard, while eight water samples were dirty, exceeding the limit, making them unsafe for human consumption.

PENGEMBANGAN SISTEM TERINTEGRASI BERBASIS IOT (INTERNET OF THINGS) UNTUK MONITORING DAN KONTROL KUALITAS AIR PADA BUDIDAYA TAMBAK UDANG DARATAN

Water quality is very important for the sustainability of a shrimp farm, starting from the quantity and quality of shrimp harvested is very influential on the good quality of the pond water. The water quality in question is parameters such as water turbidity, a lot or clean of dirt in the pool, a pH value that suits your needs and ammonia content in the pond which is usually produced from shrimp manure. This research designed a water quality monitoring and control tool in shrimp ponds that is integrated automatically through an IoT-based system (Internet of Things) using the thingspeak application as a server and telegram bots as controls. Based on the results of tests that have been carried out, the tool has been able to determine the quality of water correctly. The error percentage was 0.01% for turbidity sensors, 0.05% for TDS sensors, 0.01% for pH sensors and 0.12% for ammonia gas sensors. Based on the results of direct data collection on shrimp ponds for 6 hours, the average value of water turbidity was 3.06 NTU, the lowest was 1.07 NTU and the highest was 5.99 NTU, for the average value of TDS content was 170.98 ppm, 150.00 ppm was the lowest and 204.12 ppm was the highest, for the average pH content was 7.80, the lowest was 7.50 and the highest was 8.74, and the average value of ammonia content was 0.004 ppm, 0.0007 ppm is the lowest and 0.0113 is the highest.

A Compact, Low-Cost, and Low-Power Turbidity Sensor for Continuous In Situ Stormwater Monitoring.

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist, their high cost prohibits their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor that is designed specifically for long-term stormwater in-field monitoring. Its low cost (USD 23.50) enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. By testing the sensor in the lab with standard turbidity solutions, the proposed low-cost turbidity sensor demonstrated a strong linear correlation between a low-cost sensor and a commercial hand-held turbidimeter. In the field, the low-cost sensor measurements were statistically significantly correlated to a standard high-cost commercial turbidity sensor. Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing that both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the GreenSpan sensor.

Green Approach for Water Treatment

The common first step for water treatment is removing the particulate impurities by coagulation and flocculation. The conventional treatment uses polyvalent salts such as aluminum sulfate, iron (III) chloride, and synthetic polymers as coagulating agents which leads to potential toxicity. The purpose of this study is to identify environmentally friendly natural products as coagulant agents for water treatment. Four plant seeds (Amaranth, chia, quinoa, and wheat germ), corn cob, orange peel, and avocado peel were tested. Turbidity measurements were conducted with a wireless, portable turbidity sensor. Data was collected on a smartphone. The performance of the turbidity sensor was evaluated by preparation of a calibration curve (r2 = 0.9928). Results show that all four plant seeds and avocado peel demonstrated the ability to decrease the turbidity of the water sample. Among the four seeds that were studied, Amaranth and chia were identified as the top two natural coagulant agents. Inter-day and Intra-day studies were performed for the turbidity measurements. The relative standard deviation (RSD) was found to be less than 10%, indicating the method has good precision. The method can serve as a point-of-use water treatment in remote areas of developing countries with limited resources.

2D Hybrid Perovskite Sensors for Environmental and Healthcare Monitoring.

Layered perovskites, a novel class of two-dimensional (2D) layered materials, exhibit versatile photophysical properties of great interest in photovoltaics and optoelectronics. However, their instability to environmental factors, particularly water, has limited their utility. In this study, we introduce an innovative solution to the problem by leveraging the unique properties of natural beeswax as a protective coating of 2D-fluorinated phenylethylammonium lead iodide perovskite. These photodetectors show outstanding figures of merit, such as a responsivity of >2200 A/W and a detectivity of 2.4 × 1018 Jones. The hydrophobic nature of beeswax endows the 2D perovskite sensors with an unprecedented resilience to prolonged immersion in contaminated water, and it increases the lifespan of devices to a period longer than one year. At the same time, the biocompatibility of the beeswax and its self-cleaning properties make it possible to use the very same turbidity sensors for healthcare in photoplethysmography and monitor the human heartbeat with clear systolic and diastolic signatures. Beeswax-enabled multipurpose optoelectronics paves the way to sustainable electronics by ultimately reducing the need for multiple components.

Study On Implementation Of Watergate Control System From Manual To Automatic Based Arduino Nano ATmega328

The system for processing river water into clean water by operating sluice gates which are controlled manually by operators often causes problems (human error), so a system must be created that is capable of controlling sluice gates automatically. The turbidity sensor detects water turbidity which is then accepted by the Arduino nano as the basis for controlling the water gate. In this research, data collection techniques were carried out using interviews with resource persons (operators), observation and literature study. After the automatic sluice control system based on the Arduino nano ATmega328 was implemented, the results obtained were: When the water conditions are muddy and murky, relay one is "on", relay two is "off" (the sluice gate is closed), while when the water conditions are normal and clear then the relay two "on" relays and one "off" relay (open floodgate). In this way, we no longer just have to rely on the operator to control the sluice gate, so that the water treatment process can be more optimal and efficient