Turbidity Sensor Research Articles

Water Turbidity and Suspended Particulate Matter Concentration at Dredged Material Dumping Sites in the Southern Baltic.

Dredged material dumping is an activity that causes some of the greatest changes in coastal waters. It results in the need to regularly monitor the properties of seawater related to water quality. In this study, we present the first wide-ranging attempt to correlate seawater turbidity and suspended particulate matter (SPM) concentrations within dumping sites and adjacent waters on the basis of in situ measurements. In the years 2019–2020, we examined four dumping sites, namely Darłowo, Gdynia, Gdańsk, and DCT, located in Polish coastal waters of the Baltic Sea, in the course of four measurement campaigns conducted in the spring, summer, autumn, and winter. The measurements were conducted using a turbidity sensor to determine the turbidity, in formazin turbidity units (FTU), a spectrophotometer to determine the concentrations of nutrients (total phosphorus (P-tot), phosphate phosphorus (P-PO4−3), total nitrogen (N-tot), ammonium nitrogen (N-NH4+), and nitrate nitrogen (N-NO3−)), as well as glass microfiber filters to determine the concentrations of SPM. The analysis of the relationship between the turbidity and SPM within the dumping sites in comparison to reference points showed that the dumping sites are very complex waters and that the issue must be approached locally. The highest turbidity values were registered in the spring, and they correlated linearly with the SPM concentrations (R2 = 0.69). Moreover, we performed a statistical cluster analysis to illustrate the similarities between sampling points in the four dumpsites based on nutrient concentrations. We conclude that the influence of the dumping sites on the local bio-optical and chemical properties significantly exceeds their borders and spreads to the adjacent waters. Nutrient concentrations in many cases exceeded the legal policy limits.

Laboratory Experiments to Assess the Effect of Chlorella on Turbidity Estimation

Turbidity is an important parameter in monitoring water quality, and thus attracts significant attention. Changes in the various components and constituent elements of water directly affect turbidity measurements. The turbidity of water is generally quantified by measuring the absorbance or scattering characteristics of substances suspended in it. The complex composition of environmental water bodies complicates the determination of factors influencing their turbidity. Controlled experiments that quantitatively analyze the effect of a single factor on the measurement of turbidity in the laboratory are an important means to improve the accuracy of turbidity assessment. Properties of suspended materials in a water column that may affect its measured turbidity include the concentration of algae, particle size, and the color of soluble substances, etc. The laboratory experiments reported here used Chlorella as an example to investigate the effect of algal concentration on turbidity measurement. The results are as follows. When the turbidity is low (100 NTU), the average relative error between the theoretical and practical absorbance is about 37.52%, which decreases to 19.20% at 100–200 NTU and 5.16% at 200–400 NTU. The characteristic spectral bands sensitive to turbidity (680 nm) and Chlorella (240 nm) were selected, and the theoretical and practical turbidity results were analyzed. The average relative errors of mixed liquids of less than 100, 100–200, and 200–400 NTU are 65.07%, 34.18%, and 3.95%, respectively. Therefore, the concentration of Chlorella significantly affects the measured turbidity, and results in a more complex effect at low turbidity (<100 NTU). Combining the analysis of absorbance peak values and characteristic spectral bands, we can assess the turbidity changes in different components, and through calibration, information regarding the concentration and variation of different components in water bodies can be obtained. The results of this research could improve the accuracy of on-site measurement of the concentrations of different components suspended in water, and also facilitate the development of new turbidity sensors.

Pathways for sediment transport and retention in a vegetated, mid‐channel island: Connecting sediment dynamics to morphology in Meinmahla Island, Ayeyarwady Delta, Myanmar

AbstractVegetated mid‐channel islands play an important though poorly understood role in the sediment dynamics and morphology of tide‐dominated deltas. Meinmahla Island is a mangrove‐forest preserve at the mouth of the Bogale distributary channel, in the Ayeyarwady Delta, Myanmar. In this relatively unaltered mid‐channel island, sediment dynamics can be directly connected to morphology. Field measurements from 2017 to 2019 provide insight into the pathways for sediment transport and resulting morphological evolution. Water depth, salinity and turbidity were monitored semi‐continuously, and velocity profilers with turbidity and salinity sensors were deployed seasonally in single‐entrance (dead‐end/blind) and multi‐entrance tidal channels of the island. The morphological evolution was evaluated using grain size, 210Pb geochronology, remote sensing and channel surveys. The data show that ebb‐dominant, single‐entrance channels along the island exterior import sediment year‐round to the land surface. However, these exterior channels do not deliver enough sediment to maintain the observed ca 0.8 cm/yr accretion rate, and most of the sediment import occurs via interior, multi‐entrance channels. Interior channels retain water masses that are physically distinct from the water in the Bogale distributary, and estuarine processes at the tidal‐channel mouths import sediment into the island. Sediment is sourced to the island from upriver in the wet season and from the Gulf of Mottoma in the dry season, as the location of the estuary shifts seasonally within the Bogale distributary. The salinity and biogeochemistry of the distributary water are affected by interactions with sediment and groundwater in the island interior. The largest interior channels have remained remarkably stable while the island has aggraded and prograded over decadal timescales. However, the studied multi‐entrance channel is responding to a drainage‐network change by narrowing and shoaling. Overall, mid‐channel islands trap sediment and associated nutrients at the river–ocean interface, and these resilient landscape features evolve in response to changes in drainage‐network connectivity.

Sistem Monitoring Debit dan Tingkat Kekeruhan Air Pada Kolam KOI Berbasis Mikrokontroler

This system aims to make it easier for owners to monitor the condition of the koi pond, and minimize the death of koi fish, and can maintain water quality and maximize koi growth and development. Therefore, a system was created that can monitor the discharge and the level of turbidity of water in the koi pond. This system consists of Arduino Uno, NodeMCu, Waterflow Sensor, Turbidity Sensor and buzzer. This system can determine the condition of the pump, whether the pump is running maximally, the signal is not optimal or the pump off based on the water flow produced by the pump, which is then read by the waterflow sensor. When the water discharge > 40 L/hour then the pump turns on maximum, when 40 L/hour > discharge > 0, then the pump flame is not maximal, and when there is no water discharge produced by the pump then the pump is off, if the water turbidity value > 400 NTU, then the water is in a cloudy state and the buzzer as an alarm so that the owner checks the filter and the pool drain will sound, and later the data read by the sensor will be saved to the firebase and will be displayed on the mobile application, and tegram will send a notification in the form of a short message , when the pump is not running optimally, the pump is off, and when the water is cloudy.

A low cost UV-IR dual wavelength optical sensor with Chirp modulation for in-situ chemical oxygen demand measurements

Chemical oxygen demand (COD) is one of the most representative indicators in water quality monitoring. Among numerous methodologies of COD measurement, optical method has received increasing interests as it can avoid secondary contamination and the frequent replacement of electrodes. However, different with the evolving progress of Infrared (IR) based turbidity sensor, the optical measurements of COD with ultraviolet (UV) absorption still rely on the complex high-end hardware architecture to achieve high accuracy, which prevent its wide deployment in the large scale monitoring of fresh waters. Nonetheless, the essential reason is the weak and in-stable UV signals after absorption. This motivates numerous method in optic domain to enhance the Signal-to-Noise Ratios (SNR) of UV signal, which, as a trade off, have to involve the complex and precise hardware architecture. This paper chooses to ease this challenge from the digital domain, where the Chirp modulation has been applied to both UV and IR signals to form a UV-IR dual wavelength COD measurement system. With Chirp modulated signals, the effective SNRs have been effectively improved due to the convolution gain, while the cross interference of dual-wavelength has been avoided as well with the higher time resolution. Benefited by this simple architecture, a low cost, low power, and high-precision COD sensor has be designed to support the large scale in-Situ deployment.

Rancang Bangun Sistem Monitoring Pengolahan Limbah Cair Tahu Di Kabupaten Purbalingga Berbasis Internet of Things

In the production of tofu, it usually produces liquid waste which can cause environmental pollution when no processing is carried out which will have an impact on local residents. Disposal of liquid waste from the production of tofu without going through processing will result in air pollution in the form of a pungent odor and too low oxygen and pollution in the water which will become dirty. Liquid waste generated from the production of tofu contains organic substances that cause microbial growth in the water which results in decreased oxygen levels in the water and the water becomes cloudy. Based on these problems, a monitoring system for turbidity and acid-base levels of the tofu industrial wastewater in Purbalingga Regency based on the Internet of Things was designed by utilizing the NodeMCU ESP8266 which is connected to a Turbidity sensor, pH sensor, DS18B20 sensor, LCD 16x2 I2C, relay, and a water pump that will connected to the Telegram Bot for monitoring and control. Telegram bots can work when Telegram and the network are turned on and connected to the internet. From the results of testing the system produces tools that are in accordance with what the author expects. All sensors and relays can work well on the waste onjek, the water pump also works well so that it can carry out waste disposal when the turbidity level has reached less than 35 which is carried out after the decomposition process is complete. The system can also send notifications to the Telegram Bot when the pump is on and when commands are requested via the gadget, resulting in the conclusion that the system works in real time and must be connected to the internet network.

Embedded system in handheld water turbidity meter for smallholders

The periodical assessment of water quality in response to increased pollution has become increasingly crucial. Water turbidity, which indicates the growth of pathogenic microorganisms in water, is a well-known water quality indicator. In fish farming, water turbidity affects fish health. Commercially available tools for estimating water turbidity are expensive, which is an obstacle for small-scale farmers. In this study, a low-cost (<20 USD) embedded meter that allows near real-time assessment of water turbidity was developed. The developed tool was integrated into an Android-based mobile application and comprised a turbidity sensor, RGB sensor, LED as the light source, and electronic components. Light source uniformity is essential for obtaining reliable measurement results and instrument replication. Uniformity is performed by calculating the luminance index from the RGB sensor value and then determining the light intensity. Moreover, two developed tools were tested by comparing their results against those of a commercial turbidimeter (Eutech TN-100). The comparison indicated a strong coefficient of determination (0.96 and 0.98), with root-mean-square error (RMSE) values of 30.48 and 22.08, in the turbidity evaluation range of 300–500 NTU. However, upon measuring samples with turbidity levels of 0–300 NTU, the developed tools showed better performance based on the RMSE results of 20.63 and 11.87. Thus, the developed meter is proven useful for smallholders to manage water resources and fish farming activities.

An innovative approach to correct data from in-situ turbidity sensors for surface water monitoring

In-situ water quality sensors yield continuous, high-frequency measurements that are essential for long-term monitoring. Optical sensors, such as turbidity sensors, are prone to lens obstruction and ultimately, anomalous measurements. In this study, we developed a novel approach to detect, remove, and replace these anomalies. A free, open-source, software package implementing this approach is provided. Anomaly detection and removal is completely automated, though manual verification is recommended. Sections of missing data are filled by one of two methods, depending on the size of each section. This approach was tested on turbidity measurements from sensors deployed across southern Ontario. Automated detection performance was consistent across all test datasets, with most (>95%) synthetically introduced anomalies removed in all but one dataset. Gap-filling provided accurate estimates for smaller gaps, while performance on larger gaps varied. Overall, this approach as implemented in the provided software can greatly assist with data management in long-term monitoring programs.

Open-source Internet of Things remote aquatic environmental sensing

This paper details the physical and hardware design of a flexible open-source IoT (Internet of Things) platform for environmental sensing. The application is a remote water quality monitoring buoy that can be deployed in calm, shallow near-shore aquatic environments with fresh or brackish water. The system’s development has been informed by experience through conducting multiple actual water quality studies over a prolonged period. The system runs an Arduino Mega 2560 microcontroller using off-the-shelf Adafruit lux and temperature sensors. A light attenuation turbidity sensor is adapted and integrated into the design. A TinySine 3G GSM module transmits data to a server that is displayed via a ThingsBoard IoT dashboard. The system is stable over time, provides reliable remote sensor readings, has low energy consumption, and is powered by renewable energy (up-cycled batteries). The hardware aspires to be general-purpose so that future environmental monitoring applications can repurpose the electronics by adding new compatible sensors and modifying the physical design to match the requirements.

Implementation and Calibration of an IoT Light Attenuation Turbidity Sensor

Turbidity is an important characteristic of water quality that can indicate the presence of undesirable suspended particulate matter. Having access to an inexpensive and effective turbidity sensor unlocks numerous Internet of Things (IoT) possibilities for remote environmental monitoring. Optical light attenuation turbidity sensors operate on the premise of detecting signal degradation from a light source due to scattering from particles in a solution. This approach is technologically unpretentious and only requires a handful of inexpensive electronic components to construct. However, while this method is touted as “simple”, a significant challenge lies in sensor calibration. That is, converting an analogue signal into a meaningful and accurate digital reading in a known turbidity measurement standard (e.g., Nephelometric Turbidity Units (NTU)). This paper presents an IoT light attenuation turbidity sensor design and explores the calibration process to determine the sensor's range and accuracy. Sensor calibration is undertaken using Formazin turbidity standards and is cross-checked against a commercial turbidimeter. We provide a step-by-step procedure for determining the correct signal strength to use and a functional form for the sensor response to the Formazin standard. Finally, we specify an estimate of the accuracy of the sensor and suggest the next steps in the proposed turbidity sensor's development. Results indicate that the sensor achieves within 2%-10% of accuracy at higher ranges (100–4000 NTU), but its performance becomes significantly less reliable in low NTU ranges (< 100 NTU) where the error rate increases to 26%. The turbidity sensor is used as part of an IoT remote aquatic environmental monitoring platform.

Automation of Microcontroller-Based Control System for Ph, Temperature, and Turbidity of Aquarium Water

This research is an experimental study to know the effectiveness of the control system for pH, temperature, and turbidity of aquarium water in ornamental fish cultivation. The method used is by assembling an automation tool for controlling the pH, temperature, and turbidity of the aquarium water based on a microcontroller, which is then compared with an aquarium without a control system. This tool is designed using the Arduino Uno as a system controller, the pH sensor E-201-C as a measuring tool for pH levels with a solenoid valve to drain pH up and pH down as a stabilizer for water pH levels, the DS18B20 sensor as a temperature measurement tool by utilizing a heater and DC fan as a water temperature stabilizer, and the turbidity sensor as a turbidity measurement tool with a clean water replacement system as a water turbidity stabilizer. Based on the experiments carried out, it was found that there were significant differences between aquariums using a control system automation tool compared to aquariums without a control system tool. This means that the system that is made is effective and proven to be able to maintain the environmental conditions of ornamental fish with measurement results of pH ranging from 7–7.5, the temperature of 24°–27°C, and water turbidity of 10 NTU.

Rancang Bangun Sistem Monitoring Ketinggian Air, Suhu Dan Kekeruahan Air Pada Budidaya Ikan Lele

&#x0D; Cultivating catfish is one of the popular fishery farming activities in Indonesia. Catfish is one of the many fish chosen for cultivation because it has a fast growth rate and can live in various air conditions. However, there are several obstacles or loss factors if not addressed quickly, such as if the air level occurs, changes in temperature conditions and also the level of air turbidity which is also not released is always reviewed regularly. This study uses a prototype method that uses system development from pre-existing systems, this writing covers the design and manufacture of hardware and software with the right detection development algorithm. Aims to produce or create a microcontroller-based system that has the ability to detect air level, temperature and air turbidity in ponds with the hope of being able to minimize the risk of losses caused by air conditions in catfish ponds. The system is equipped with an air level detector using an ultrasonic sensor, and is equipped with a DS18B20 sensor which can detect air temperature and a turbidity sensor which is used to detect the level of air turbidity and of course this system is also equipped with a microcontroller to process data from sensors used as input. The output of the air level monitoring system is an LED with red, yellow and green colors, as well as a buzzer as a marker of air level in catfish ponds. The monitoring system for temperature and also turbidity in water pools is a notification system using an I2C LCD that displays readings of sensor values. The monitoring tool has been successfully tested and is expected to be useful for increasing catfish farming results.&#x0D; &#x0D; Keywords: Arduino Nano, DS18B20 Sensor, Turbidity Sensor

Smart Water Quality Monitoring System using IOT and Electronic Sensors

Abstract: Water plays an Important role in our day to day life, our body consists of 70% of water, and the major diseases are caused due to the bad quality of water. How much ever we filter the water their might be few impurities in it, so we are using Internet of Things technology and sensors to measure the quality of water. Internet Of Things (IOT) is used in different area of research for monitoring and collecting data on a particular project. Smart Water Quality Monitoring System is necessary nowadays due to increase in pollution and otherindustrial waste which is being dumped in the water bodies, which makes water polluted even after purifying it. Around 40% of the death’s are caused due to water pollution around the world. With thehelp of smart water quality monitoring system we can find out if the water is pure or impure at an affordable cost. Keywords: Internet of Things (IOT),Temperature Sensor, Turbidity Sensor, Quality of water, arduino board, Smart water monitoring system, Wireless network.

Sistem Monitoring Kualitas Air Layak Pakai Menggunakan Arduino Uno

In this design a tool is made to monitor the quality of water suitable for use using Arduino Uno. This design uses a pH meter sensor which functions to monitor acid levels in water and a turbidity sensor which is used to see the level of water turbidity. The use of these two sensors is based on the suitability of the water for consumption based on the acid level and the level of turbidity of the water. The results obtained in the design show that Arduino can read the acid levels in the water and see the level of turbidity of the water well so that the designed tools can be used directly to monitor the quality of water suitable for use.

A hands-free stool sampling system for monitoring intestinal health and disease

Analysis of stool offers simple, non-invasive monitoring for many gastrointestinal (GI) diseases and access to the gut microbiome, however adherence to stool sampling protocols remains a major challenge because of the prevalent dislike of handling one’s feces. We present a technology that enables individual stool specimen collection from toilet wastewater for fecal protein and molecular assay. Human stool specimens and a benchtop test platform integrated with a commercial toilet were used to demonstrate reliable specimen collection over a wide range of stool consistencies by solid/liquid separation followed by spray-erosion. The obtained fecal suspensions were used to perform occult blood tests for GI cancer screening and for microbiome 16S rRNA analysis. Using occult blood home test kits, we found overall 90% agreement with standard sampling, 96% sensitivity and 86% specificity. Microbiome analysis revealed no significant difference in within-sample species diversity compared to standard sampling and specimen cross-contamination was below the detection limit of the assay. Furthermore, we report on the use of an analogue turbidity sensor to assess in real time loose stools for tracking of diarrhea. Implementation of this technology in residential settings will improve the quality of GI healthcare by facilitating increased adherence to routine stool monitoring.

Open-source, low-cost, in-situ turbidity sensor for river network monitoring

Fine sediment transport in rivers is important for catchment nutrient fluxes, global biogeochemical cycles, water quality and pollution in riverine, coastal and marine ecosystems. Monitoring of suspended sediment in rivers with current sensors is challenging and expensive and most monitoring setups are restricted to few single site measurements. To better understand the spatial heterogeneity of fine sediment sources and transport in river networks there is a need for new smart water turbidity sensing that is multi-site, accurate and affordable. In this work, we have created such a sensor, which detects scattered light from an LED source using two detectors in a control volume, and can be placed in a river. We compare several replicates of our sensor to different commercial turbidity probes in a mixing tank experiment using two sediment types over a wide range of typical concentrations observed in rivers. Our results show that we can achieve precise and reproducible turbidity measurements in the 0–4000 NTU or 0–16g/L range. Our sensor can also be used directly as a suspended sediment sensor and bypass an unnecessary calibration to Formazin. The developed turbidity sensor is much cheaper than existing options of comparable quality and is especially intended for distributed sensing across river networks.

Influence of Chemoautotrophic Organic Carbon on Sediment and Its Infauna in the Vicinity of the Rainbow Vent Field

Hydrothermal vents have been recognised as isolated islands of productivity in the deep sea, but very little is known about the transport and export of chemoautotrophic produced organic carbon to vent surrounding habitats. Here, we investigate vent carbon export and its influence on benthic food webs in sediments at the Rainbow (RB) vent field at the Mid Atlantic Ridge in ~2200 m water depth. Two sites were considered along the dispersal direction of the RB vent plume for sample collection: the close vicinity at about ~30-100 m (“near-vent”); and at 4 km distance (“off-vent”) to the nearest venting area. At both sites, box corers were used to sample sediments and their fauna. A turbidity sensor mounted on a CTD/Rosette provided data on the presence of a vent fluid plume in the water column. Niskin bottles were used to sample suspended particulate organic matter (SPOM) from the plume, as well as from surface waters at 75 m depth. SPOM, sediments and fauna were analysed for nitrogen and carbon stable isotopes. Carbon derived from in situ chemoautotrophy, characterised by depleted δ13C, was taken up by infauna close to the venting area as the main nutrition source, while fauna at the off-vent site showed less depleted δ13C with a signature more typical for photosynthetic- derived material. Nematodes were the most abundant faunal taxon. Their abundance and biomass were variable and not different at the two studied sites. In situ derived organic matter was traced back in the food web in the close vicinity of the venting area. The connectivity of vent and non-vent habitats should be taken appropriately into account when designing future spatial management plans with regard to deep-seabed mining at hydrothermal vents.

Smart Aquarium with IoT based as Monitoring in Fish Farming

The development of science and technology has developed rapidly at this time and will have a positive impact to facilitate human activities, including aquarium ornamental fish hobbyists. The problems encountered are, ornamental fish sellers who have difficulty monitoring all aquarium conditions and feeding and for people who are very busy and even indifferent to monitoring and feeding their ornamental fish. This tool has a water heater as well as a water temperature and pH sensor that can directly monitor the conditions in the aquarium. Arduino Uno is the mainboard that is used to control all sensors with Internet of Things (IoT) including : water temperature sensors, water turbidity sensors, water level sensors, water pH sensors, automatic lights, water heaters that will automatically turn on when the temperature is low and will automatically turn off when they reach the specified temperature and ESP8266-01 which functions to communicate between the Board and the smartphone, as well as the components of the tool and its uses are in accordance with requests from users obtained through Requirement Elicitation. Through the BlackBox trial, it includes low temperature testing of the water which makes the water heater automatically turn on and the water heater turns off when it reaches the specified temperature, monitoring water pH, water temperature, water level, water turbidity, on &amp; off lights and feeding fish manually via a smartphone. And for the results of the Smart Aquarium trial, it can be operated with an Android smartphone, at least Android OS 4.3 and above.

Monitoring of Anthropogenic Sediment Plumes in the Clarion-Clipperton Zone, NE Equatorial Pacific Ocean

The abyssal seafloor in the Clarion-Clipperton Zone (CCZ) in the NE Pacific hosts the largest abundance of polymetallic nodules in the deep sea and is being targeted as an area for potential deep-sea mining. During nodule mining, seafloor sediment will be brought into suspension by mining equipment, resulting in the formation of sediment plumes, which will affect benthic and pelagic life not naturally adapted to any major sediment transport and deposition events. To improve our understanding of sediment plume dispersion and to support the development of plume dispersion models in this specific deep-sea area, we conducted a small-scale, 12-hour disturbance experiment in the German exploration contract area in the CCZ using a chain dredge. Sediment plume dispersion and deposition was monitored using an array of optical and acoustic turbidity sensors and current meters placed on platforms on the seafloor, and by visual inspection of the seafloor before and after dredge deployment. We found that seafloor imagery could be used to qualitatively visualise the redeposited sediment up to a distance of 100 m from the source, and that sensors recording optical and acoustic backscatter are sensitive and adequate tools to monitor the horizontal and vertical dispersion of the generated sediment plume. Optical backscatter signals could be converted into absolute mass concentration of suspended sediment to provide quantitative data on sediment dispersion. Vertical profiles of acoustic backscatter recorded by current profilers provided qualitative insight into the vertical extent of the sediment plume. Our monitoring setup proved to be very useful for the monitoring of this small-scale experiment and can be seen as an exemplary strategy for monitoring studies of future, upscaled mining trials. We recommend that such larger trials include the use of AUVs for repeated seafloor imaging and water column plume mapping (optical and acoustical), as well as the use of in-situ particle size sensors and/or particle cameras to better constrain the effect of suspended particle aggregation on optical and acoustic backscatter signals.

Use of wireless sensor network system based on water level, rain, conductivity, oil and turbidity sensors to monitor the storm sewerage

Use of wireless sensor network system based on water level, rain, conductivity, oil and turbidity sensors to monitor the storm sewerage