Seawater Turbidity Research Articles

Sea Water Turbidity Variability and Relation to Tides and Environmental Factors in the Korean Coastal Region of the Yellow Sea

Sea Water Turbidity Variability and Relation to Tides and Environmental Factors in the Korean Coastal Region of the Yellow Sea

Eco-Friendly Superhydrophobic Modification of Low-Cost Multi-Layer Composite Mullite Base Tubular Ceramic Membrane for Water Desalination

This study aimed to investigate and develop a cost-effective and superhydrophobic ceramic membrane for direct contact membrane distillation (DCMD) applications. Two types of mullite-based composite membranes were prepared via extrusion and sintering techniques. To create a small and narrow pore diameter distribution on the membrane surface, the dip-coating technique with 1 µm alumina was employed. The hexadecyltrimethoxysilane eco-friendly grafting agent was adopted to modify low-cost multilayer mullite-based composite membranes, transforming them from hydrophilic to superhydrophobic. The prepared membranes were characterized via field emission scanning electron microscopy (FESEM), energy-dispersive spectrometry (EDS), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray diffraction (XRD), liquid entire pressure (LEP), contact angle, atomic force microscopy (AFM), porosity, and membrane permeability. The results of the prepared membranes validate the appropriateness of the material for membrane distillation applications. The optimized membrane, with a contact angle of 160° and LEP = 1.5 bar, was tested under DCMD using a 3.5 wt.% sodium chloride (NaCl) synthetic solution and Persian Gulf seawater as a feed. Based on the acquired results, an average permeate flux of 3.15 kg/(m2·h) and salt rejection (R%) of 99.62% were found for the 3.5 wt.% NaCl solution. Moreover, seawater desalination showed an average permeate flux of 2.37 kg/(m2·h) and salt rejection of 99.81% for a 20-h test without any pore wetting. Membrane distillation with a hydrophobic membrane decreased the turbidity of seawater by 93.13%.

Amazon River Plume in the Western Tropical North Atlantic

Measurements of temperature, salinity, and currents in the Amazon River plume over a section in the open ocean of the western tropical North Atlantic (38°48′ W) are considered. The measurements were carried out using an AML Base X CTD probe in the upper layer and a flow-through system that measures salinity, turbidity, and chlorophyll-a content in seawater while a vessel is on the way. The measurements were supplemented by velocity profiling using shipborne SADCP. Additionally, archived oceanographic data from the World Ocean Database (WOD18), data on satellite altimetry measurements (AVISO), and satellite salinity data from Aquarius and SMOS were used. It is shown that the width of the Amazon River plume is about 170–400 km and the depth of desalination is from 50 to 100 m. Surface salinity decreases compared to the background (36.1) by 0.25 in February and by more than 3.0 in September during the period of maximum development of the plume, which was determined from satellite measurements of surface salinity. Lagrangian modeling of the back-in-time advection of passive markers simulating freshwater particles was carried out. It was shown that the source of freshwater in the measurement area is discharge from the Amazon River. Amazon River freshwater covered a distance of 3300 km in 60–80 days. The estimate of freshwater transport in the plume was 0.02 Sv, which is one order of magnitude smaller than the mean river discharge.

Correlation between Turbidity and Inherent Optical Properties as an Initial Recognition for Backscattering Coefficient Estimation

Seawater turbidity is a common water quality indicator measured in situ and estimated from space on a regular basis. However, it is rarely correlated with the inherent optical properties of seawater, which convey information about seawater composition. In this study, we show a simple application of the turbidimeter’s weighting function in the estimation of the backscattering coefficient of a model inorganic suspension in seawater. First, we introduce a method to measure the instrument’s weighting function which describes the sensor’s angular response in terms of scattering angles. The determination of the sensor-specific weighting function led us to characterize its angular sensitivity to the presence of suspended particles. The highest sensitivity for the Seapoint turbidimeter is in the range of 114°–128° (containing 25% of the total signal). Next, we describe the correlations between turbidity and the scattering and backscattering coefficients on the example of the model of inorganic particle suspension using the calculations based on Mie theory. The correlations are analyzed for narrow size fractions of the particle size distribution of silica in the range of 0.59–190 µm. We established that there is a good linear correlation (characterized by the coefficient of determination r2 = 0.979) between the part of the scattering coefficient measured by the turbidimeter and the backscattering coefficient of all size fractions of the model inorganic suspension.

New insights into estimation of bioavailable inorganic phosphorus in natural coastal seawater

Considering the impact of the high salinity and high turbidity of coastal seawater on phosphorus forms, a new method was proposed to determine bioavailable inorganic phosphorus (BIP). The phosphorus most relevant to eutrophication is BIP, and traditional analysis methods may underestimate the degree of eutrophication. In this study, a microelectrode of multigold (AuμE) was fabricated for direct voltammetric determination of BIP without filtration, and BIP environmental characteristics including distribution and correlation relationships with environmental factors in typical coastal seawater of Northern China were analyzed. The proposed AuμE showed a low detection limit of 0.03 μM. The surface and bottom BIP concentrations ranged from 1.00 to 2.13 and from 0.88 to 2.05 μM, respectively. BIP dominated the total P (TP) accounting for 48.5–67.5 % in the surface layer samples, and 32.6–92.7 % in the bottom layer samples, respectively. The concentrations of BIP were obviously higher than those of DIP, indicating that DIP may underestimate the probability of eutrophication occurring. And BIP was positively correlated with dissolved oxygen (DO) (P < 0.05). BIP may be a promising indicator of eutrophication potential in coastal areas with high salinity and high turbidity. The proposed reliable voltammetry method provides a new indicator for environmental assessment and represents a significant step in the comprehensive analysis of P species.

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

SummaryNowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.

Coverage Enhancement of Light-Emitting Diode Array in Underwater Internet of Things over Optical Channels

The construction of the underwater Internet of Things (UIoT) is crucial to marine resource development, environmental observation, and tactical surveillance. The underwater optical wireless communication (UOWC) system with its large bandwidth and wide coverage facilitates the high-capacity information interconnection within the UIoT networks over short and medium ranges. To enhance the coverage characteristics of the UOWC system, an optimized lemniscate-compensated layout of light-emitting diode (LED) array is proposed in this paper, which can ameliorate the received optical power and reliability at the receiving terminal. Compared with traditional circular and rectangular layouts, the received optical power and bit error rate (BER) performance of the proposed system are analyzed based on the Monte Carlo simulation method. The analysis results show that the proposed LED array achieves a smaller peak power deviation and mean square error of the received optical power under three typical seawater environments. Furthermore, the proposed LED-array scheme supports a better BER performance of the UOWC system. For example, in turbid seawater with a transmission depth of 9.5 m, the BER of the proposed LED array layout is 1 × 10−7, which is better than the BER of 3.5 × 10−6 and 1 × 10−4 under the other two traditional light source layouts.

Nitrate Measurement in Seawater Based on Environmental Correction Algorithm

The accuracy of the in-situ nitrate measurement in seawater is affected by many environmental factors, such as salinity, temperature, and turbidity. Along these lines, in this work, an environmental correction (including salinity, temperature, and turbidity) algorithm was proposed and experimentally verified, demonstrating a good prediction accuracy of the nitrate concentration. The orthogonal projections to latent structures (OPLS) algorithm was used for performing one-time turbidity and temperature correction of the acquired spectral data. After the environmental correction, the support vector machine (SVM) was used to establish the nitrate calculation model. The OPLS-SVM algorithm was systematically examined in spiked samples with four different groups of seawater matrices (the seawater samples from Western Pacific, Aoshan Bay of Qingdao, China, South China Sea, and Yellow Sea with addition of various nitrate concentrations and turbidity). From the extracted results, it was proven that the SVM calculation model with the OPLS correction algorithm could significantly improve the accuracy of the nitrate measurement. More specifically, a prediction performance with root mean square error (RMSE) of 0.22 μmol/L and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.999 was demonstrated when the temperature and turbidity ranges were 5-25 °C and 0-50 NTU (nephelometric turbidity unit), respectively. The introduced method is suitable for measuring the nitrate concentration in turbidity seawater with a high degree of accuracy, which is undoubtedly of great importance for performing in-situ nitrate measurement in complex seawater environments.

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semi-empirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events.

Preliminary examination of influence of Chlorophyll, Total Suspended Material, and Turbidity on Satellite Derived-Bathymetry estimation in coastal turbid water

The satellite-Derived Bathymetry (SDB) has been researched for almost the past five decades, but the application of SDB for operational use is still in its initial stages. The higher concentration of suspended material is a major barrier in assessing bathymetry accurately with remote sensing data. The influence of various optically active substances (OAS) needs to be quantified to develop a more robust SDB algorithm. The goal of the study is to improve the accuracy of SDB by understanding the spatial distribution of Chlorophyll (Chl), Total Suspended Material (TSM), and Turbidity in coastal water. A preliminary investigation using the numerical analysis between Landsat-7 & 8 spectral bands, OAS parameters, and bathymetry is presented in this paper. SDB has been derived using three machine learning algorithms; Linear, Random Forest (RF), and Support Vector Machine (SVM) Regression. The resulting SDB residuals have been analyzed in relation to Chlorophyll, Turbidity, and TSM in coastal seawater. The study found that most of the erroneous SDB residuals and extreme values were distributed in high or medium concentrated OAS regions. The result of the study may further enhance SDB estimation accuracy by considering the influence of the above three OAS in coastal waters.

Nitrate Measurement in Seawater with a Turbidity Correction Algorithm based on Ultraviolet Spectrophotometry

Turbidity usually has a great impact on the actual measurement of the value of nitrate concentrations in the seawater. In this study, a new turbidity correction approach for nitrate concentrations measurement in the seawater based on ultraviolet absorption spectroscopy is presented. Experiments were carried out to prove the improvement of the turbidity correction method with ultraviolet spectroscopy. The orthogonal projections to latent structures (O-PLS) algorithm was chosen to correct the additional information in absorbance caused by turbidity. The experimental results show that the influence of turbidity could be excellently removed. Compared with the standard normalized variate (SNV) transformation, the O-PLS algorithm could better reduce the influence of turbidity on the nitrate absorbance and improve the measurement accuracy in turbid seawater. The linear correlation coefficient (R2) of the prediction results in test samples is 0.9999 and RMSEP is 0.1886 μmol/L.

Bacterial taxonomic and functional profiles from Bohai Sea to northern Yellow Sea.

Microbial distribution patterns are the result of a combination of biotic and abiotic factors, which are the core issues in microbial ecology research. To better understand the biogeographic pattern of bacteria in water environments from the Bohai Sea to the northern Yellow Sea, the effects of environmental factors, and spatial distance on the structure of bacterial communities in marine water were investigated using high-throughput sequencing technology based on 16S rRNA genes. The results showed that Proteobacteria, Bacteroidetes, Actinobacteri, Desulfobacterota, and Bdellovibrionota were the dominant phyla in the study area. A clear spatial pattern in the bacterial community was observed, and environmental factors, including salinity, nutrient concentration, carbon content, total phosphorus, dissolved oxygen, and seawater turbidity emerged as the central environmental factors regulating the variation in bacterial communities. In addition, the study provides direct evidence of the existence of dispersal limitation in this strongly connected marine ecological system. Therefore, these results revealed that the variation in bacterial community characteristics was attributed to environmental selection, accompanied by the regulation of stochastic diffusion. The network analysis demonstrated a nonrandom co-occurrence pattern in the microbial communities with distinct spatial distribution characteristics. It is implied that the biogeography patterns of bacterial community may also be associated with the characteristics of co-occurrence characterize among bacterial species. Furthermore, the PICRUSt analysis indicated a clear spatial distribution of functional characteristics in bacterial communities. This functional variation was significantly modulated by the environmental characteristics of seawater but uncoupled from the taxonomic characteristics of bacterial communities (e.g., diversity characteristics, community structure, and co-occurrence relationships). Together, this findings represent a significant advance in linking seawater to the mechanisms underlying bacterial biogeographic patterns and community assembly, co-occurrence patterns, and ecological functions, providing new insights for identifying the microbial ecology as well as the biogeochemical cycle in the marine environment.

HEAT TRANSFER ENHANCEMENT AND SCALE FORMATION: EXPERIMENTAL STUDIES IN FALLING FILM EVAPORATORS USING COPPER METAL FOAM

Performance enhancement of horizontal tube falling film evaporators using thin metal foam was studied. A copper foam was brazed over the outer surface of a copper horizontal tube and used in the falling film evaporator of a desalination system. It was observed that the distillate production and heat transfer rate was enhanced by more than twice with the modified tube. Effect of feed rate, feed temperature, and subcooling on distillate production was studied for the copper foam-wrapped heat exchanger tube evaporator. A metal foam-wrapped heat exchanger tube was also used to study the effect of scale formation. The effects of temperature, flow rate, and seawater turbidity on scale formation was studied. Total scale deposit weight was observed after every 16 h of operation for a total run time of 128 h. It was observed that scale formation reduces with an increase in flow rate and increases with seawater temperature. It was also observed that scale formation is lower when the system is operated with seawater of low turbidity. The heat transfer enhancement surface promotes scale formation due to the increase in metal surface area in contact with seawater as in the copper foam-wrapped tube. Energy-dispersive X-ray spectroscopy and scanning electron microscopy were applied to investigate the mineralogy and microstructure of the deposits.

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.

SEA WATER TURBIDITY ANALYSIS FROM SENTINEL-2 IMAGES: ATMOSPHERIC CORRECTION AND BANDS CORRELATION

Abstract. Turbidity is a visual property of water, related to the presence of suspended particles in waters. This parameter is measured in different water quality monitoring programmes as it can determine negative environmental effects both on the biotic and abiotic marine ecosystem. Traditional methods, e.g., in situ monitoring, offer high accuracy but provide sparse information in space and time. On the other hand, Earth Observation (EO) techniques have the potential to provide a comprehensive, fast and inexpensive monitoring system to observe the biophysical and biochemical conditions of water bodies. In the present work, a method for seawater turbidity retrieval from Sentinel-2 multispectral optical images, freely available within the EU Copernicus programme, is presented. The study explores different atmospheric correction methods available in open source software (QGIS, GRASS GIS and SNAP), in order to convert Level-1C (L1C) Top-Of-Atmosphere (TOA) images to Level-2A (L2A) Bottom-Of-Atmosphere (BOA), when the latter is not directly available. Once the proper method for atmospheric correction was identified and applied, the correlation between the in situ dataset and the individual bands known to be most sensitive to water turbidity, i.e., blue (B2), green (B3), red (B4) and near infrared (B8 and B8A) bands, were investigated and a linear regression model between selected band values and turbidity was identified.

A Novel Machine Learning Method for Accelerated Modeling of the Downwelling Irradiance Field in the Upper Ocean

The downwelling irradiance is a crucial part of the air-sea heat flux and a major energy source for the photosynthesis processes in the upper ocean. The modeling of the irradiance field is often time-consuming in conventional Monte Carlo (MC) simulations. We propose an accelerated model based on machine learning to significantly reduce the computational cost. By introducing a generalized beam spreading function, we transform the raw data generated with the MC simulation into training data of reduced dimensions, which is then used to develop an artificial neural network. To validate the machine learning model, we use the MC model to simulate the irradiance field under a broadband wave field. For both clear and turbid seawater, the irradiance field predicted by the machine learning model agrees well with the MC simulation result. Compared with the MC simulation that is computationally expensive, our machine learning model is O(1,000) times faster.

DEVELOPMENT OF A METHOD FOR MAPPING THE BOTTOM OF SHALLOW RESERVOIRS BY LASER BATHYMETRY

The article is devoted to the development of methods for mapping the bottom of shallow reservoirs by laser bathymetry. It is noted that the main factors influencing the depth of laser bathymetric studies are the correct choice of the emitter wavelength, turbidity of sea water, especially in coastal zones and undulation of the sea surface. The problem of developing a method for mapping the bottom relief of shallow-water reservoirs is formulated and solved. A laser installed on board the aircraft is used as an emitter. It is noted that one of the main problems in laser bathymetry is the issue of modeling the seabed. While the surface of various reservoirs is currently modeled by various continuous functions, such as Hermite polynomials, B-splines, etc., modeling of reflection from the bottom of a reservoir is mainly limited to a diffuse model, i.e. The bottom is considered as a Lambert reflector Based on the geometric representation of the course of optical rays in laser bathymetry and the analysis of known measurement results, a step model of the bottom of the reservoir is proposed, the height and width of the steps of which vary widely. Based on the wellknown method of mapping the bottom of a shallow reservoir from one point, as well as the proposed step model of the bottom of the reservoir, a method for mapping the relief of the bottom irregularity has been developed, according to which measurements are carried out at two spatial points, with respect to which the difference in the depth of the reservoir should be determined. At the same time, measurements are carried out using a laser beam of constant power and at constant nadir angles, model studies have shown that the measurement error of the proposed method is within ±4.5%.

Pretreatment approach for the high turbid seawater using decanter centrifuge-microfiltration hybrid system

Pretreatment approach for the high turbid seawater using decanter centrifuge-microfiltration hybrid system

3D surface measurement of static and dynamic objects in underwater environments based on the propagation and projection of CW sinusoidal signal at 532 nm

This work carries out both theoretical and experimental studies on three dimensional surface measurement of objects in underwater environments based on the use of the projection of continuous-wave laser-beam-based monochromatic structured light. The mathematical analysis and description of the generation and propagation of sinusoidal fringe patterns in air and underwater environments are presented, which provides the physical basis for the proposed method of measuring the underwater objects. In the experimental investigation we completed the in-door measurements of both static and dynamic objects located in a tank filled with real turbid seawater or lake water. Experimental results have indicated that the proposed approach is capable of yielding high-accuracy reconstructions for underwater 3D imaging of both static and dynamic objects, which shows the possibility of building a portable instrument based on the optical design and measuring technique presented in this work for carrying out high-accuracy in-situ underwater measurement.

Influence of several stressful factors on the condition of seagrasses at Sabana–Camagüey​ Archipelago, Cuba

The influence of three potential anthropogenic stressful factors (coastal pollution from the mainland, the causeway Caibarién–Santa María and fishing with bottom-trawling nets) on the condition of seagrasses was assessed at 12 sites in two bays of Sabana–Camagüey​ archipelago, between 2010 and 2013. As for indicators of the condition of the seagrass ecosystem, the structure of the macrophyte community, marine angiosperm’s shoot density, and shoot length of marine angiosperms were analyzed. Abiotic variables as depth, turbidity, temperature, pH, dissolved oxygen, oxygen saturation, and chemical oxygen demand of seawater, were evaluated. The relationships of the three potential anthropogenic stressful factors and the abiotic factors (predictor variables) with the biological response variables were analyzed. Seagrass meadows in better condition, with higher values of cover and density of Thalassia testudinum, and lower values of depth, turbidity, salinity, chemical oxygen demand, were found at sites out of the bays and furthermost from the causeway and mainland. Areas without vegetation or with lower values of cover and density of T. testudinum, and higher cover of macroalgae and pioneer species of marine angiosperms as Syringodium filiforme and Halodule wrightii, were found in both bays. Turbidity and depth were the abiotic factors with the stronger negative influence on the structure of the seagrass community. Chemical oxygen demand and salinity also showed negative influences. Coastal pollution seems to be the anthropogenic factor that most influences the condition of seagrasses. No recovery of seagrasses was observed in areas where bottom-trawling fisheries had ceased. Distance to the causeway was not correlated with the condition of seagrass, but the potential for a negative influence on circulation persists. Seagrasses in the studied bays remain affected by the above-mentioned predictor abiotic factors, whose effects may be reinforced by the three anthropogenic stressful factors, acting synergistically with the bays’ oceanographic characteristics.