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Underwater optical wireless communication (UOWC) systems provide the potential to establish secure high-data-rate communication links in underwater environments. The uniqueness of oceanic impairments, such as absorption, scattering, oceanic turbulence, and air bubbles demands accurate statistical channel models based on empirical measurements for the development of UOWC systems adapted to different types of water and link conditions. Recently, generalized Gamma and a mixture of two generalized Gamma probability density functions (PDF) were proposed to describe the statistical behavior of small and large air bubbles, respectively, when considering several levels of particle-induced scattering. In this paper, we derive novel closed-form analytic expressions to compute the bit error rate (BER) and outage performance using both proposed PDFs for various scattering conditions. Furthermore, simple asymptotic expressions are obtained to determine the diversity order of each scenario. Monte Carlo simulation results verify the obtained theoretical expressions. Our results also reveal that UOWC systems present lower BER and outage performance under more turbid water cases with respect to the tap water case due to the higher diversity order and despite the significant increases in pathloss at short link distances. Particle-induced scattering provides an inherent mechanism of turbid waters to mitigate air bubble-induced fluctuations and light blockages. [ABSTRACT FROM AUTHOR]

Analyzing and breaking down nonstationary signals into their primary components is significant in various optical applications. In this work, we design a direct, localized, and mathematically rigorous method for nonstationary signals by employing a modulated short-time Fourier transform (MSTFT) that can be implemented efficiently using fast Fourier transform, subsequently isolating energy-concentrated sets through an approximate threshold process, allowing us to directly retrieve instantaneous frequencies and signal components by determining the maximum frequency within each set. MSTFT provides a new insight into the time-frequency analysis in multicomponent signal separation and can be extended to other time-frequency transforms. Beyond the analysis of the synthetic, we also perform real dual-comb ranging signals under turbid water, and the results show an approximate 1.5 dB improvement in peak signal-to-noise ratio, further demonstrating the effectiveness of our method in challenging conditions. [ABSTRACT FROM AUTHOR]

Abrasion occurs on the concrete surfaces of irrigation canals because of the flow of water and sand. Abrasion increases the roughness of the concrete surface. Increased roughness reduces water flow function. Underwater ultrasonic roughness measurements estimate roughness from the peak-to-peak strength of reflected underwater ultrasonic waves. However, the effect of turbid water flowing in a canal on peak-to-peak strength has been unclear. The objective of this study was to evaluate the effect of turbid water on underwater ultrasonic roughness measurements. The characteristics of the attenuation of the peak-to-peak strength of underwater ultrasonic waves were investigated as a function of the particle size and concentration of suspended solids. The suspended solids used in the experiments were kaolin with a particle size of 0.5 µm and alumina with particle sizes of 2, 4, and 8 µm. Peak-to-peak strength was measured as a function of the concentration of each particle size. The experimental results indicated that peak-to-peak strength was attenuated only with increasing concentrations of suspended solids and was not affected by particle size. A proposed correction equation for peak-to-peak strength took into consideration the concentration of suspended solids in turbid water. The coefficient of determination R2 between this equation and measured data was as high as 0.98. Using this correction equation, peak-to-peak strength without turbidity could be calculated from peak-to-peak strength measured in turbid water. It is thus possible to make effective use of underwater ultrasonic roughness measurements in turbid water. [ABSTRACT FROM AUTHOR]

The occurrence of high-concentration turbid water due to a large landslide in the upper reaches of the Tedori River Basin in Japan in May 2015 led to a rapid decline in the groundwater levels within the alluvial fan. However, factors other than turbid water, such as changes in precipitation patterns, can have a significant impact on groundwater levels but have not been thoroughly investigated. By analyzing the relationship between river water and groundwater levels, we found that by 2018, conditions had returned to those observed prior to the turbidity events. Regarding seepage, we found that approximately 24% of the Tedori River's discharge contributed to seepage before the turbidity event. In contrast, during the post-turbidity years, seepage decreased between 2015 and 2017 and returned to the pre-turbidity levels by 2018. Furthermore, by constructing a hydrological model and examining the contributions of turbidity and precipitation, we found that in 2015, turbidity contributed to 76% of the groundwater level changes, whereas precipitation accounted for 24%. In contrast, in 2016, turbidity contributed to 67%, while precipitation contributed to 33%. In essence, the first year was characterized by a significant contribution from turbidity, while precipitation also played a significant role in groundwater level fluctuations in the second year. [ABSTRACT FROM AUTHOR]

To enhance the long-term monitoring ability of multi-sourced remote sensing data, we needed to minimize the inter-mission biases between the data retrieved from different satellites. We evaluated three existing diffuse attenuation coefficients at 490 nm (Kd(490)) using nine independent datasets collected from the global oceans. The results indicate that the updated neural network-based four-band Kd(490) retrieval model (updated-NFKM) decreased the uncertainty by > 4% from the NASA official Kd(490) retrieval model (NOKM) and the inherent optical properties-based Kd(490) retrieval model (IOPK). Specifically, matchup analysis showed that the updated-NFKM model produced < 40% uncertainty in deriving Kd(490) from MODISA and SeaWiFS data, and the uncertainty of SeaWiFS-predicted Kd(490) was ~ 5% lower than the MODISA-predicted Kd(490). Using the updated-NFKM model, >80% of the global ocean had an uncertainty for Kd(490) estimates that were lower than 30%, while the model performed much better for the Western Pacific, Arctic Ocean, and Northern Atlantic compared to the Eastern Pacific, South Ocean, and Southern Atlantic. To enable a naturally smooth transition from SeaWiFS to MODISA-observed Kd(490) products, it was critical to cross-calibrate the inter-mission difference. The results show that using the cross-calibration models proposed in this study, the MODISA-predicted Kd(490) accounted for > 86% of the variations of SeaWiFS-predicted Kd(490), even though the empirical coefficients of the cross-calibration model had to be adjusted according to the time scales of the composite remote sensing data. Finally, we used the cross-calibration model to minimize the time series of SeaWiFS and MODISA-predicted Kd(490) data. Our results indicate that the Kd(490) values gradually increased in the low-latitude regions during past two decades. [ABSTRACT FROM AUTHOR]

The influence of the oceanic medium on the underwater optical wireless communication (UOWC) highlights the need for an accurate mathematical channel characterization that models the properties of realistic oceanic environments, significantly optimizing the development of practical UOWC system designs. However, a need remains to provide a deeper insight into the behavior of air bubbles in turbid waters. This article presents an analysis and a statistical characterization of the received optical power fluctuations when considering different air bubble sizes and levels of particle-induced scattering. Experimental measurements were conducted in a water tank, manipulating water turbidity levels by dissolving a commercial antacid. Afterward, some evaluation metrics, such as the scintillation index, the average outage duration, and the coherence time, are calculated to provide an analytical framework of the air bubbles-induced fading in turbid environments. Additionally, the statistical behavior of small and large air bubbles are analytically described through the generalized Gamma distribution and a mixture of two generalized Gamma distributions, respectively, and validated with a coefficient of determination above 0.95 for all the considered turbid waters. Results demonstrate that water turbidity dramatically affects the stochastic behavior of the underwater channel in the presence of air bubbles, resulting in a significant reduction in light blockage due to the collection of scattered photons. As a result, the scintillation index and the average outage duration are noticeably reduced for more turbid water.

Chemical coagulants like alum, ferric salts, and polyacrylamide derivatives are helpful in water treatment. However, the long-term detrimental effects of chemical coagulants on humans and the environment require alternative research for natural coagulants. This study used novel leguminous (green beans (GB), pigeon pea (PP)), fruit seeds (Tamarind indica (TI), and date palm (DS)) as coagulants to remove turbidity. The seeds were powdered, and the crude active coagulants were extracted with distilled water and a 1 M NaCl solution. The result showed that PP's distilled water extract had the highest turbidity removal of 81.12%, while DS had the least performance of 62.54%. The NaCl extract of PP had the highest removal (94.62%), followed by TI (76.08%). This study found the optimum doses for GB, TI, PP, and DS to be 50, 40, 10, and 70 mL/L, with their optimum pH at 3, 1, 3, and 1, respectively. The FTIR spectra confirmed the existence of -OH, -NH, COOH, C = O, C–C, and C-H peaks, indicating the presence of protein-specific functional groups supporting their potential use as coagulants. Therefore, PP would have been used based on turbidity performance; however, due to their nutritional value, TI and DS are suitable seeds for the coagulation-flocculation treatment of turbid water because they are waste materials. [ABSTRACT FROM AUTHOR]

Underwater optical wireless communication (UOWC) systems provide the potential to establish secure high-data-rate communication links in underwater environments. The uniqueness of oceanic impairments, such as absorption, scattering, oceanic turbulence, and air bubbles demands accurate statistical channel models based on empirical measurements for the development of UOWC systems adapted to different types of water and link conditions. Recently, generalized Gamma and a mixture of two generalized Gamma probability density functions (PDF) were proposed to describe the statistical behavior of small and large air bubbles, respectively, when considering several levels of particle-induced scattering. In this paper, we derive novel closed-form analytic expressions to compute the bit error rate (BER) and outage performance using both proposed PDFs for various scattering conditions. Furthermore, simple asymptotic expressions are obtained to determine the diversity order of each scenario. Monte Carlo simulation results verify the obtained theoretical expressions. Our results also reveal that UOWC systems present lower BER and outage performance under more turbid water cases with respect to the tap water case due to the higher diversity order and despite the significant increases in pathloss at short link distances. Particle-induced scattering provides an inherent mechanism of turbid waters to mitigate air bubble-induced fluctuations and light blockages.

Analyzing and breaking down nonstationary signals into their primary components is significant in various optical applications. In this work, we design a direct, localized, and mathematically rigorous method for nonstationary signals by employing a modulated short-time Fourier transform (MSTFT) that can be implemented efficiently using fast Fourier transform, subsequently isolating energy-concentrated sets through an approximate threshold process, allowing us to directly retrieve instantaneous frequencies and signal components by determining the maximum frequency within each set. MSTFT provides a new insight into the time-frequency analysis in multicomponent signal separation and can be extended to other time-frequency transforms. Beyond the analysis of the synthetic, we also perform real dual-comb ranging signals under turbid water, and the results show an approximate 1.5 dB improvement in peak signal-to-noise ratio, further demonstrating the effectiveness of our method in challenging conditions.

Recently, ultraviolet-visible (UV-vis) absorption spectrometry has garnered considerable attention because it enables real-time and unpolluted detection of chemical oxygen demand (COD) and plays a crucial role in the early warning of emerging organic contaminants. However, the accuracy of detection is inevitably constrained by the co-absorption of organic pollutants and turbidity at UV wavelengths. To ensure accurate detection of COD, it is necessary to directly subtract the absorbance caused by turbidity from the overlaid spectrum using the principle of superposition. The absorbance of COD is confined to the UV range, whereas that of turbidity extends across the entire UV-vis spectrum. Therefore, based on its visible absorbance, the UV absorbance of turbidity can be predicted. In this way, the compensation for turbidity is achieved by subtracting the predicted absorbance from the overlaid spectrum. Herein, a straightforward yet robust exponential model was employed based on this principle to predict the corresponding absorbance of turbidity at UV wavelengths. The model was used to analyze the overlaid absorption spectra of synthetic water samples containing COD and turbidity. The partial least squares (PLS) method was employed to predict the COD concentrations in synthetic water samples based on the compensated spectra, and the corresponding root mean square error (RMSE) values were recorded. The results indicated that the processed spectra yielded a considerably lower RMSE value (9.51) than the unprocessed spectra (29.9). Furthermore, the exponential model outperformed existing turbidity compensation models, including the Lambert-Beer law-based model (RMSE = 12.53) and multiplescattering cluster method (RMSE = 79.34). Several wastewater samples were also analyzed to evaluate the applicability of the exponential model to natural water. UV analysis yielded undesirable results owing to filtration procedures. However, the consistency between the compensated spectra and filtered wastewater samples in the visible range demonstrated that the model is applicable to natural water. Therefore, this proposed method is advantageous for improving the accuracy of COD measurement in turbid water. [ABSTRACT FROM AUTHOR]

The Geostationary Ocean Color Imager (GOCI) provided images at hourly intervals up to 8 times per day with a spatial resolution of 500 m from 2011 to 2021. However, in the typical sediment-laden turbid water of Hangzhou Bay, valid ocean color parameters in operational data products have been extensively missing due to failures in atmospheric correction (AC) and bio-optical retrieval procedures. In this study, the seasonal variations in chlorophyll a (Chl-a) concentrations in Hangzhou Bay derived using GOCI data in 2020 were presented. First, valid remote sensing reflectance data were obtained by transferring neighboring aerosol properties of less to more turbid water pixels. Then, we improved a regionally empirical Chl-a retrieval algorithm in extremely turbid waters using GOCI-derived surface reflectance and field Chl-a measurements and proposed a combined Chl-a retrieval scheme for both moderately and extremely turbid water in Hangzhou Bay. Finally, the seasonal variation in Chl-a was obtained by the GOCI, which was better than operational products and in good agreement with the buoy data. The method in this study can be effectively applied to the inversion of Chl-a concentration in Hangzhou Bay and adjacent sea areas. We also presented its seasonal variations, offering insight into the spatial and seasonal variation of Chl-a in Hangzhou Bay using the GOCI. [ABSTRACT FROM AUTHOR]

For the remote sensing of turbid waters, the atmospheric correction (AC) is a key issue. The "black pixel" assumption helps to solve the AC for turbid waters. It has proved to be inaccurate to regard all water pixels in the SWIR (Short Wave Infrared) band as black pixels. It is necessary to perform atmospheric correction in the visible bands after removing the radiation contributions of water in the SWIR band. Here, the modified ACZI (m-ACZI) algorithm was developed. The m-ACZI assumes the spatial homogeneity of aerosol types and employs the BPI (Black Pixel Index) and PIFs (Pseudo-Invariant Features) to identify the "black pixel". Then, the radiation contributions of waters in the SWIR band are removed to complete the atmospheric correction for turbid waters. The results showed that the m-ACZI had better performance than the SeaDAS (SeaWiFS Data Analysis System) -SWIR and the EXP (exponential extrapolation) algorithm in the visible band (sMAPE < 30.71%, RMSE < 0.0111 sr−1) and is similar to the DSF (Dark Spectrum Fitting) algorithm in floating algae waters. The m-ACZI algorithm is suitable for turbid inland waters. [ABSTRACT FROM AUTHOR]

Optical imaging usually shows significant differences in underwater environments with different characteristics. The general underwater imaging model ignores the influences of turbidity on the attenuation of light. For turbid water with different particle types and concentrations, we introduced background light correction and turbidity factors to express the influences. A parametric turbid underwater image generation model based on dark channel prior theory was proposed, in which, the transmission map changes with the turbidity while the image tone changes with the background light correction. Based on experiments, the high similarity between generated images and real images confirmed that our model can fit the imaging in different bodies of water. Thus, the model can be used to generate a greater diversity of samples of turbid underwater images. We also applied the model to turbid underwater image enhancement. The image quality evaluation results indicated that the model can enhance turbid underwater images well. [ABSTRACT FROM AUTHOR]

The Givetian Age witnessed the greatest expansion of stromatoporoid-coral reefs from low to higher latitudes of the Phanerozoic. Multi-proxy seawater surface temperature reconstruction suggests the establishment of a super-greenhouse climate as a major reason for reef expansion, yet many questions remain. This article presents the results of a rare earth element and yttrium (herein referred to as REY, derived from REE C Y) geochemical study as well as mineralogy and oxygen isotope values of two well-documented Middle Givetian reefal carbonate sections (Jiwozhai and Buzhai) of the Jiwozhai Formation of South China. The nearshore Jiwozhai patch reef succession displays greater biodiversity and more abundant coral than the marginal platform Upper Buzhai reef. Reefal and micritic carbonates of the Jiwozhai section are characterized by shale-like post-Archean Australian Shale (PAAS)-normalized REY patterns, by very weak negative Ce anomaly values (Ce/Ce* 0.80≥0.96; average D 0.89), slightly elevated Y/Ho values (28.9≥39.1; average D 34.1), and near-unity values of (Pr/Yb)N (average D 0.87), (Pr/Tb)N (average D 0.80), and (Tb/Yb)N (average D 1.09). Moreover, REY patterns of deposits of the Jiwozhai section differ markedly from those of modern seawater. The described geochemical aspects of the Jiwozhai section and the positive correlation of REY and Th contents displayed by the section point to a terrestrial siliciclastic contribution contemporaneous with reef-building. In contrast, REY patterns of the Upper Buzhai reef section samples are similar to those of modern seawater characterized by light rare earth element (LREE) depletion (average (Pr/Yb)N D 0.76), negative Ce anomalies (average Ce/Ce* D 0.88), and average super-chondritic Y/Ho ratios (average D 45.4)). Slightly positive Eu anomalies (Eu/Eu* D 0.93≥1.94; averageD1.36) of the Upper Buzhai reef section samples are attributed to the negligible effect of hydrothermal fluids. Middle REE (MREE) enrichment (average (Tb/Yb)N D 1.48) of Buzhai section carbonate samples and positive correlation of REY and Th suggest a riverine input. Combined with siliciclastic mineralogy, oxygen isotope values, and reef-building biota morphology of the studied two sections, we suggest that terrestrial nutrients delivered by rivers far outweighed upwelling as a source of nutrients supplied to the Givetian reef ecosystem of South China. Coral and stromatoporoid in tropic oceans thrived in turbid water containing abundant terrestrial sediment and the nutrient-laden water helped expand reef-builder habitats during the Givetian time. [ABSTRACT FROM AUTHOR]

Rural populations in Tanzania use unsafe drinking water from unimproved water sources which are inadequately treated using plant seed powder. The effectiveness of defatted crude seed extract (dCSE) of Moringa oleifera along with water filtration and solar disinfection (SODIS) for rural household water treatment was investigated. The performance of dCSE in turbidity and bacteria reduction was evaluated in 20 L buckets using river water with varied turbidities. Coagulation–flocculation processes were conducted using different dosages of 5% (Weight/Volume) dCSE. Optimum dosage for maximum turbid removal was established after 2–6 h of settling time. Optimum dosages of dCSE were 20, 30 and 80 mL/20 L for water with a turbidity of 150, 450 and 1,000 NTU with turbidity removal efficiencies of 98, 99 and 99%, respectively. The filtration process was able to remove about 66% of suspended solids from pretreated water by dCSE after coagulation/flocculation/sedimentation. SODIS of pretreated water in black painted and unpainted bottles removed 99 and 97.6%; 99.9 and 98.6% of total and fecal coliforms after 6 h of sunlight exposure, respectively. The pH and color of treated water were within Tanzania Standards. This study recommends dCSE, filtration and SODIS for use at household level for water treatment and storage using pots. HIGHLIGHTS The defatted crude seed extract (dCSE) can be used for turbid water treatment in Tanzania.; The elevated solar radiation and temperature between 12 noon and 3 p.m. disinfect water clarified by dCSE for drinking in rural communities.; The developed water treatment technology can directly apply at household level with limited formal technical knowledge.;

The Geostationary Ocean Color Imager (GOCI) provided images at hourly intervals up to 8 times per day with a spatial resolution of 500 m from 2011 to 2021. However, in the typical sediment-laden turbid water of Hangzhou Bay, valid ocean color parameters in operational data products have been extensively missing due to failures in atmospheric correction (AC) and bio-optical retrieval procedures. In this study, the seasonal variations in chlorophyll a (Chl-a) concentrations in Hangzhou Bay derived using GOCI data in 2020 were presented. First, valid remote sensing reflectance data were obtained by transferring neighboring aerosol properties of less to more turbid water pixels. Then, we improved a regionally empirical Chl-a retrieval algorithm in extremely turbid waters using GOCI-derived surface reflectance and field Chl-a measurements and proposed a combined Chl-a retrieval scheme for both moderately and extremely turbid water in Hangzhou Bay. Finally, the seasonal variation in Chl-a was obtained by the GOCI, which was better than operational products and in good agreement with the buoy data. The method in this study can be effectively applied to the inversion of Chl-a concentration in Hangzhou Bay and adjacent sea areas. We also presented its seasonal variations, offering insight into the spatial and seasonal variation of Chl-a in Hangzhou Bay using the GOCI.

For the remote sensing of turbid waters, the atmospheric correction (AC) is a key issue. The “black pixel” assumption helps to solve the AC for turbid waters. It has proved to be inaccurate to regard all water pixels in the SWIR (Short Wave Infrared) band as black pixels. It is necessary to perform atmospheric correction in the visible bands after removing the radiation contributions of water in the SWIR band. Here, the modified ACZI (m-ACZI) algorithm was developed. The m-ACZI assumes the spatial homogeneity of aerosol types and employs the BPI (Black Pixel Index) and PIFs (Pseudo-Invariant Features) to identify the “black pixel”. Then, the radiation contributions of waters in the SWIR band are removed to complete the atmospheric correction for turbid waters. The results showed that the m-ACZI had better performance than the SeaDAS (SeaWiFS Data Analysis System) -SWIR and the EXP (exponential extrapolation) algorithm in the visible band (sMAPE < 30.71%, RMSE < 0.0111 sr−1) and is similar to the DSF (Dark Spectrum Fitting) algorithm in floating algae waters. The m-ACZI algorithm is suitable for turbid inland waters.

Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future. [ABSTRACT FROM AUTHOR]

The Amazon Basin is the largest on the planet, and its aquatic ecosystems affect and are affected by the Earth’s processes. Specifically, Amazon aquatic ecosystems have been subjected to severe anthropogenic impacts due to deforestation, mining, dam construction, and widespread agribusiness expansion. Therefore, the monitoring of these impacts has become crucial for conservation plans and environmental legislation enforcement. However, its continental dimensions, the high variability of Amazonian water mass constituents, and cloud cover frequency impose a challenge for developing accurate satellite algorithms for water quality retrieval such as chlorophyll-a concentration (Chl-a), which is a proxy for the trophic state. This study presents the first application of the hybrid semi-analytical algorithm (HSAA) for Chl-a retrieval using a Sentinel-3 OLCI sensor over five Amazonian floodplain lakes. Inherent and apparent optical properties (IOPs and AOPs), as well as limnological data, were collected at 94 sampling stations during four field campaigns along hydrological years spanning from 2015 to 2017 and used to parameterize the hybrid SAA to retrieve Chl-a in highly turbid Amazonian waters. We implemented a re-parametrizing approach, called the generalized stacked constraints model to the Amazonian waters (GSCMLAFW), and used it to decompose the total absorption αt(λ) into the absorption coefficients of detritus, CDOM, and phytoplankton (αphy(λ)). The estimated GSCMLAFWαphy(λ) achieved errors lower than 24% at the visible bands and 70% at NIR. The performance of HSAA-based Chl-a retrieval was validated with in situ measurements of Chl-a concentration, and then it was compared to literature Chl-a algorithms. The results showed a smaller mean absolute percentage error (MAPE) for HSAA Chl-a retrieval (36.93%) than empirical Rrs models (73.39%) using a 3-band algorithm, which confirms the better performance of the semi-analytical approach. Last, the calibrated HSAA model was used to estimate the Chl-a concentration in OLCI images acquired during 2017 and 2019 field campaigns, and the results demonstrated reasonable errors (MAPE = 57%) and indicated the potential of OLCI bands for Chl-a estimation. Therefore, the outcomes of this study support the advance of semi-analytical models in highly turbid waters and highlight the importance of re-parameterization with GSCM and the applicability of HSAA in Sentinel-3 OLCI data.

Underwater collaborative work between small mobile platforms (SMP) such as divers, litter submarine or AUV, requires a high data rate communication system which is compact, reliable, affordable and eye-safe. The high bandwidth of LED-based underwater wireless optical communication (UWOC) is an advantage. Artificial, emulated as well as simulated, turbid underwater channel has been studied recently, but no practical nature water. To fully meet the requirements of challenging turbid underwater channel scenarios for SMP, we demonstrated a green LED-based UWOC system up to a bandwidth of 3.4 MHz in a highly-turbid nature lake water. Through a contrast wireless optical communication (WOC) experiment in air, turbid water channel is observed to compensates the receiving signal amplitude attenuates after propagating a longer attenuation length due to multiple-scattered light caused diffusion, and thus to be more like a single-input multi-output (SIMO) system. From our experimental observation, the SIMO channel model could be employed not only in turbulent UWOC, but also in highly turbid UWOC. Moreover, turbid water channel is observed a “frequency selection” effect, thus LED with lower threshold voltage at higher frequency would optimize bandwidth and link range, and a robust pre-code for interference cancellation would be a well-direct choice to constructing different UWOC system in highly turbid water for SMP. Our finding will provide a new reference complementing the current LED-based UWOC systems in realistic water environment.

Quantifying the concentration of suspended particulate matter (CSPM) is necessary for the evaluation of the ecological processes, matter transfer, and environments in lakes. Traditional monitoring via the use of field measurements lacks the spatial coverage necessary for detailed analysis in large-area lakes. In contrast, the macroscopic, real-time monitoring of CSPM can be achieved using data from remote-sensing satellites. Due to the major limitations of the existing analytical and semi-analytical algorithms, namely the lake locations and limited data, regression algorithms have become important tools for the analysis of the CSPM values in lakes. The purpose of this study is the evaluation of both parametric and nonparametric regression algorithms for CSPM estimation based on the simulation of Gaofen-1 (GF-1) wide field-of-view (WFV) satellites for the imaging of turbid water. A total of 71 samples collected during four cruises were analyzed to determine the spectral reflectance and CSPM in Poyang Lake. The results indicate that the spectral ratio (SR) parametric regression model and the extreme learning machine (ELM) nonparametric regression model based on in situ SPM and spectral data perform relatively better than the other investigated models. A SR fitting model using band3 (λ = 660 nm)/band2 (λ = 555 nm) of the GF-1 WFV sensor was established to estimate the CSPM value. The evaluation results indicate that the SR model achieved a high retrieval accuracy (R2 = 0.901, RMSE = 8.22 mg·L−1); this is because the model was more sensitive and could effectively weaken, and even partially eliminate, the effects of external factors. The ELM model achieved a higher retrieval accuracy (R2 = 0.903, RMSE = 8.05 mg·L−1) than the other neural network-based algorithms, namely the backpropagation (BP), radial basis function (RBF), and cascade-forward neural network (CFNN) algorithms. The ELM model is well suited for both low and high turbidity, and has a robust ability to accurately retrieve the CSPM value. In Poyang Lake, the CSPM values were found to have a spatial characteristic and were higher in the northern waters than in the southern waters. Overall, this study demonstrates the applicability of GF-1 WFV sensors with an advanced spatial resolution and an increased spectral sensitivity for monitoring the CSPM values in high-turbidity water, and the ELM model is recommended for application to GF-1 images to satisfactorily produce quantitative CSPM maps. [ABSTRACT FROM AUTHOR]

The Yellow River is globally recognized for its significant sediment load, primarily attributed to its passage through the Loess Plateau. Notably, effective soil erosion control measures have led to a substantial decrease in sediment transport since the 1950s. However, a lack of comprehensive and detailed data impedes understanding of long-term spatiotemporal changes in suspended sediment concentration (SSC). To address this gap, this study utilizes in-situ daily data from 12 hydrological stations (SSC range: 0.41–1.08 × 106 mg/L) to develop a high-precision SSC model for Landsat series sensors (R2 > 0.86, root mean square error (RMSE) < 1105.74 mg/L, and mean absolute percent difference (MAPD) < 39.67%). Significant spatial variabilities in SSC are observed within the Yellow River and its main tributaries. Temporally, 97.39% of the investigated river sections (N = 480) exhibited a decreasing trend from 1986 to 2022, of which 50% are statistically significant (p < 0.05). Generally, the SSC was higher in summer and fall (2998.16 mg/L) with higher water discharge compared to winter and spring (1126.25 mg/L), although the seasonal variability weakened during the 1980s–2020s. Analysis of suspended sediment flux identified the Huangfu, Kuye, and Fen Rivers as major sediment sources for the Yellow River, while suspended sediment deposition/erosion varies across different sections. Driver analyses revealed that human sediment control projects are the primary contributors to the decrease in SSC, whereas natural factors predominantly influence intra-annual SSC variability across different years. This study is important for understanding the spatiotemporal variations in SSC within the Yellow River and its tributaries, monitoring sediment transport dynamics in global rivers and providing scientific references for watershed ecology and water resource management. • A Landsat-based algorithm was developed for estimating SSC in the turbid Yellow River. • The spatiotemporal variations of SSC over four decades were presented for the first time. • SSC exhibited spatial heterogeneity and a declining trend since the 1980s. • The decline in SSC is primarily attributed to conservation policies on the Loess Plateau. • The transport flux of SSC revealed sediment deposition or erosion in various river sections. [ABSTRACT FROM AUTHOR]

Sedimentation tanks have a vital role in the overall efficiency of solid particles removal in treatment units. Therefore, an in-depth study these tanks is necessary to ensure high quality of water and increasing the system efficiency. In this work, an experimental rectangular sedimentation tank has been operated with and without a baffle to investigate the system behaviour and effectiveness for the reduction of solid particles. Turbid water was prepared using clay, which was collected from the water treatment plant of Al Maqal Port (Iraq), mixed with clear water in a plastic supply tank. Raw and outflow samples were tested against turbidity after plotting a calibration curve between inflow suspended solids versus their corresponding turbidity values. The key objective was to assess the impact of different flow rates, particle concentrations, heights and positions of the baffle on the system efficiency. Findings showed that the tank performance was enhanced significantly (p < 0.05) with the use of a baffle placed at a distance of 0.15 of tank length with height equal to 0.2 of tank depth. Higher removal efficiency (91%) was recorded at a lower flow rate (0.015 dm³·s-1) and higher concentration (1250 mg·dm-3), as the treatment efficiency enhanced by 34% compared with the operation without a baffle. Placing the baffle in the middle of the sedimentation tank produced the worst results. System efficiency for solids removal reduced with increasing baffle height. Further research is required to evaluate the efficiency of an inclined baffle. [ABSTRACT FROM AUTHOR]

Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.

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In this study, Aspergillus niger was isolated from the solid waste generated during mushroom fermentation and was employed as an eco-friendly bioflocculant for the treatment of high-turbidity water. A comprehensive evaluation of the flocculation performance and underlying mechanisms of A. niger was conducted. Characterization of its physical and chemical structures was performed, and parameters, such as adsorbent dosage, culture time, pH, and agitation, were investigated to optimize turbidity removal. In addition, kinetic, thermodynamic, and isotherm analyses were carried out. Under optimized conditions, an adsorbent dosage of 5.25 g/L, a culture time of four days, and a pH range of 4.0–10.0, a remarkable turbidity removal rate of 98% was achieved. Moreover, A. niger manifested a high adsorption capacity for kaolin at 318 K. The kinetics and isotherm processes were well-described by the pseudo-first-order and the Langmuir models, respectively. Advanced analytical techniques, including zeta potential, scanning electron microscopy, X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and density functional theory analyses, revealed that turbid colloidal particles were eradicated through the synergy of physisorption, charge neutralization, van der Waals interactions, and the functional groups (amino, carboxylic, and hydroxyl) present in the fungal structure. These findings suggest that A. niger reaches adsorption equilibrium rapidly and is effective for treating turbid water across a broad pH range. [Display omitted] • Aspergillus niger was successfully isolated from fermented mushroom solid wastes. • Aspergillus niger exhibited excellent turbidity removal rate in wide pH and short cultural time. • Aspergillus niger can quickly reach adsorption equilibrium in flocculation process. • Physisorption and Van der Waals' interactions synergetic contributed to turbidity removal. • Aspergillus niger bioflocculants avoided residuals compared with traditional flocculants. [ABSTRACT FROM AUTHOR]

Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrated that this improved method can identify the spatial positions and shapes of clouds more realistically, and more accurate pixels of turbid waters were retained. This improved method can be effectively applied in typical turbid coastal waters. It has potential to be used in cloud masking procedures of spaceborne ocean color sensors without short-wave infrared bands.

Research on the consistency of suspended particulate matter (SPM) concentration retrieved from multisource satellite sensors can serve as long-time monitoring of water quality. To explore the influence of the atmospheric correction (AC) algorithm and the retrieval model on the consistency of the SPM concentration values, Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Imager (MSI) images acquired on the same day are used to compare the remote sensing reflectance (Rrs) SPM retrieval values in two high-turbidity lakes. An SPM retrieval model for Shengjin Lake is established based on field measurements and applied to OLI and MSI images: two SPM concentration products are highly consistent (R2 = 0.93, Root Mean Squared Error (RMSE) = 20.67 mg/L, Mean Absolute Percentage Error (MAPE) = 6.59%), and the desired results are also obtained in Chaohu Lake. Among the four AC algorithms (Management Unit of the North Seas Mathematical Models (MUMM), Atmospheric Correction for OLI’lite’(ACOLITE), Second Simulation of Satellite Signal in the Solar Spectrum (6S), Landsat 8 Surface Reflectance Code & Sen2cor (LaSRC & Sen2cor)), the two Rrs products, as well as the final SPM concentration products retrieved from OLI and MSI images, have the best consistency when using the MUMM algorithm in SeaWIFS Data Analyst System (SeaDAS) software. The consistency of SPM concentration values retrieved from OLI and MSI images using the same model or same form of models is significantly better than that retrieved by applying the optimal models with different forms.

The current generation of geostationary weather satellite instruments, such as the Advanced Baseline Imagers (ABIs) on board the US NOAA GOES 16 and 17 satellites and the Advanced Himawari Imagers (AHIs) on board the Japanese Himawari-8/9 satellites, have six channels located in the visible to shortwave IR (SWIR) spectral range. These instruments can acquire images over both land and water surfaces at spatial resolutions between 0.5 and 2 km and with a repeating cycle between 5 and 30 min depending on the mode of operation. The imaging data from these instruments have clearly demonstrated the capability in detecting sediment movements over coastal waters and major chlorophyll blooms over deeper oceans. At present, no operational ocean color data products have been produced from ABI data. Ocean color data products have been operationally generated from AHI data at the Japan Space Agency, but the spatial coverage of the products over very turbid coastal waters are sometimes lacking. In this article, we describe atmospheric correction algorithms for retrieving water leaving reflectances from ABI and AHI data using spectrum-matching techniques. In order to estimate aerosol models and optical depths, we match simultaneously the satellite-measured top of atmosphere (TOA) reflectances on the pixel by pixel basis for three channels centered near 0.86, 1.61, and 2.25 μm (or any combinations of two channels among the three channels) with theoretically simulated TOA reflectances. We demonstrate that water leaving reflectance retrievals can be made from ABI and AHI data with our algorithms over turbid case two waters. Our spectrum-matching algorithms, if implemented onto operational computing facilities, can be complimentary to present operational ocean versions of atmospheric correction algorithms that are mostly developed based on the SeaWiFS type of two-band ratio algorithm.

The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by relying on limited in situ water depth data to extract and calibrate bathymetry from a QuickBird satellite image acquired on a highly dynamic coastal environment. The image was processed using the log-band ratio and optimal band ratio analysis (OBRA) methods. Preprocessing steps included the conversion of the raw satellite image into top of atmosphere reflectance, spatial filtering, land and water classification, the determination of the optimal OBRA spectral band pairs, and the estimation of relative SDB. Furthermore, calibration and vertical referencing were performed via in situ bathymetry acquired in November 2007. The relative bathymetry obtained from different band ratios were vertically referenced to the local datum using in situ water depth in order to obtain absolute SDB. The coefficient of determination (R2) and vertical root mean square error (RMSE) were computed for each method. A strong correlation with in situ field bathymetry was observed for both methods, with R2 = 0.8682 and RMSE = 0.518 m for the log-band ratio method and R2 = 0.8927−0.9108 and RMSE = 0.35 m for the OBRA method. This indicated a high degree of confidence of the SDB results obtained for the study area, with a high performance of the OBRA method for SDB mapping in turbid water.

Abstract: Riverine systems are inherently dendritic in nature, and turbid water may not necessarily flow equally throughout an entire catchment. Tributaries have the potential to serve as refugia from temporarily turbid water in the main stem. As ayu (Plecoglossus altivelis) are one of the important species for the inland fisheries industry and recreational fishing, their behaviour in a branched river network is a primary concern. We released ayu with radio‐transmitting tags into two experimental streams that joined at a downstream pool. Turbid water was released into one of the streams, and we identified whether avoidance movement of ayu between the two experimental streams was triggered by flowing turbid water. The suspended solid concentration elevated rapidly after adding the turbid water and was maintained at more than 400 mg/L for 3 hr. Avoidance movement of ayu was triggered when the suspended solid concentration exceeded approximately 200 mg/L. Then, most ayu moved from the turbid stream to the nonturbid one, leading to a difference in ayu density between the two streams. Therefore, we demonstrated that turbid water induced interstream movement of a commercially important ayu as refuge behaviour, suggesting that river connectivity is important for the persistence of the ayu. [ABSTRACT FROM AUTHOR]

A limit of detection of 200 CFU/mL of Salmonella typhi spiked in various sample matrices were achieved in 30 min. The sample matrices were raw/unprocessed milk, commercially available milk, juice from packed bottles, fresh juice from carts, potable water, turbid water and calf serum. The complete protocol comprised of three steps: (a) cell lysis (b) nucleic acid amplification and (c) an in situ optical detection. The cell lysis was carried out using a simple heating based protocol, while the loop-mediated isothermal amplification of DNA was carried out by an in-house designed and fabricated system. The developed system consists of an aluminum block fitted with two cartridge heaters along with a thermocouple. The system was coupled to a light source and spectrometer for a simultaneous in situ detection. Primers specific for STY2879 gene were used to amplify the nucleic acid sequence, isolated from S. typhi cells. The protocol involves 15 min of cell lysis and DNA isolation followed by 15 min for isothermal amplification and simultaneous detection. No cross-reactivity of the primers were observed at 106 CFU/mL of Escherichia coli, Vibrio cholerae, Salmonella typhimurium, Salmonella paratyphi A, Pseudomonas aeruginosa, Bacillus cereus, Lysteria monocytogenes, Clostridium botulinum, Staphylococcus aureus and Salmonella havana. In addition, the system was able to detect S. typhi of 200 CFU/mL in a concoction of 106 CFU/mL of E. coli, 106 CFU/mL of V. cholerae, and 106 CFU/mL of hepatocyte-derived cellular carcinoma HUH7 cells. The proposed rapid diagnostic system shows a promising future in the field of food and medical diagnostics. [ABSTRACT FROM AUTHOR]

Mesophotic reefs (30-150 m depths) have recently become recognized as species-rich marine ecosystems, even in areas showing suboptimal conditions, like turbid water with sediment resuspension. The present study reports a qualitative survey focused on scleractinian coral and fish assemblages in a marginal reef (northeastern Brazil, southwestern Atlantic) at lower mesophotic depth. Data collection was conducted in the summer of 2016 by scuba diving at 35-37 m depths. The occurrence of scleractinian reef corals Siderastrea stellata and Montastraea cavernosa suggests that these two species can be important reef-builders in mesophotic ecosystems in northeastern Brazil. The fish assemblage was characterized by 33 observed species, representing a large variety of trophic categories. The compositions of coral and fish species showed similarities with those of shallow-water reefs in the area. This may become relevant in understanding the connectivity between shallow and deep populations in the light of mesophotic reefs serving as refugia. [ABSTRACT FROM AUTHOR]

Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L−1 and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L−1 and less than 600 NTU, respectively and used rather than using whole dataset (R2 = 0.93 versus 0.88 for turbidity and R2 = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R2 = 0.66) was better than with the MR approach (R2 = 0.58), as expected due to the nonlinear nature of the transformation model.

The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging SpectroRadiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains.

This study presents a comprehensive assessment of the performance of the commonly used atmospheric correction models (NIR, SWIR, NIR-SWIR and FM) and ocean color products (OC3 and OC2) derived from MODIS images over the Arabian Gulf, Sea of Oman, and Arabian Sea. The considered atmospheric correction models have been used to derive MODIS normalized water-leaving radiances (nL w ), which are compared to in situ water nL w (λ) data collected at different locations by Masdar Institute, United Arab of Emirates, and from AERONET-OC (the ocean color component of the Aerosol Robotic Network) database. From this comparison, the NIR model has been found to be the best performing model among the considered atmospheric correction models, which in turn shows disparity, especially at short wavelengths (400–500 nm) under high aerosol optical depth conditions (AOT (869) > 0.3) and over turbid waters. To reduce the error induced by these factors, a modified model taking into consideration the atmospheric and water turbidity conditions has been proposed. A turbidity index was used to identify the turbid water and a threshold of AOT (869) = 0.3 was used to identify the dusty atmosphere. Despite improved results in the MODIS nL w (λ) using the proposed approach, Chl- a models (OC3 and OC2) show low performance when compared to the in situ Chl- a measurements collected during several field campaigns organized by local, regional and international organizations. This discrepancy might be caused by the improper parametrization of these models or/and the improper selection of bands. Thus, an adaptive power fit algorithm (R 2 = 0.95) has been proposed to improve the estimation of Chl- a concentration from 0.07 to 10 mg/m 3 by using a new blue/red MODIS band ratio of (443,488)/645 instead of the default band ratio used for OC3(443,488)/547. The selection of this new band ratio (443,488)/645 has been based on using band 645 nm which has been found to represent both water turbidity and algal absorption. [ABSTRACT FROM AUTHOR]

Light availability for photosynthetically active radiation (PAR) is one of the major factors governing photosynthesis in aquatic ecosystems. Conventional measurements of light attenuation in the PAR domain (K d (PAR)) is representative for only small areas of water body. Remotely sensed optical imagery can be utilized to monitor K d (PAR) in large areas of water bodies, based on the relationship between water leaving radiance and K d (PAR). In this study, six field surveys were conducted over 20 lakes (or reservoirs) across Northeast China from April to September 2015. In order to derive the K d (PAR) at regional scale, the Landsat/TM/ETM+/OLI and the MODIS daily surface reflectance data (MOD09GA ∼500 m, Bands 1–7) were used to establish empirical inversion models. Through multiple stepwise regression analysis, the band difference (Red-Blue) and band ratio (NIR/Red) were used in Landsat imagery modeling, and the band difference (Red-Blue) and ratio (Red/Blue) were used in MODIS imagery modeling. The accuracy of the two models was evaluated by 10-fold cross-validation in ten times. The results indicate that the models performed well for both Landsat (R 2 = 0.83, RMSE = 0.95, and MRE = 0.33), and MODIS (R 2 = 0.86, RMSE = 0.91, and MRE = 0.19) imagery. However, the K d (PAR) derived by MODIS is slightly higher than that estimated by Landsat (slope = 1.203 and R 2 = 0.972). Consistency of model performance between the MODIS daily (MYD09G A) and the 8-Day composite reflectance (MYD09A1) data was verified by K d (PAR) estimations and regression analysis (slope = 1.044 and R 2 = 0.966). Finally, the spatial and temporal distribution of K d (PAR) in Northeast China indicated that specific geographical characteristics as well as meteorological alterations can influence K d (PAR) calibrations. Specifically, we have revealed that the wind speed and algal bloom are the major determinants of K d (PAR) in Lake Hulun (2050 km 2 ) and Xingkai (4412 km 2 ). [ABSTRACT FROM AUTHOR]

Biomanipulation by fish removal has been used in many shallow lakes as a method to improve lake water quality. Here, we present and analyse 30 years of chemical and biological data from the shallow and 16 ha large Lake Væng, Denmark, which has been biomanipulated twice with a 20-year interval by removing roach (Rutilus rutilus) and bream (Abramis brama). After both biomanipulations, Lake Væng shifted from a turbid, phytoplankton-dominated state to a clear, water macrophyte-dominated state. Chlorophyll a was reduced from 60-80 μg·L-1 to 10-30 μg·L-1 and the coverage of submerged macrophytes, dominated by Elodea canadensis, increased from <0.1% to 70%-80%. Mean summer total phosphorus was reduced from about 0.12 to 0.07 mg·L-1 and total nitrogen decreased from 1.0 to 0.4 mg·L-1. On a seasonal scale, phosphorus and chlorophyll concentrations changed from a summer maximum during turbid conditions to a winter maximum under clear conditions. The future of Lake Væng is uncertain and a relatively high phosphorus loading via the groundwater, and the accumulation of a mobile P pool in the sediment make it likely that the lake eventually will return to turbid conditions. Repeated fish removals might be a relevant management strategy to apply in shallow lakes with a relatively high external nutrient loading. [ABSTRACT FROM AUTHOR]

Recently, ultraviolet-visible (UV-vis) absorption spectrometry has garnered considerable attention because it enables real-time and unpolluted detection of chemical oxygen demand (COD) and plays a crucial role in the early warning of emerging organic contaminants. However, the accuracy of detection is inevitably constrained by the co-absorption of organic pollutants and turbidity at UV wavelengths. To ensure accurate detection of COD, it is necessary to directly subtract the absorbance caused by turbidity from the overlaid spectrum using the principle of superposition. The absorbance of COD is confined to the UV range, whereas that of turbidity extends across the entire UV-vis spectrum. Therefore, based on its visible absorbance, the UV absorbance of turbidity can be predicted. In this way, the compensation for turbidity is achieved by subtracting the predicted absorbance from the overlaid spectrum. Herein, a straightforward yet robust exponential model was employed based on this principle to predict the corresponding absorbance of turbidity at UV wavelengths. The model was used to analyze the overlaid absorption spectra of synthetic water samples containing COD and turbidity. The partial least squares (PLS) method was employed to predict the COD concentrations in synthetic water samples based on the compensated spectra, and the corresponding root mean square error (RMSE) values were recorded. The results indicated that the processed spectra yielded a considerably lower RMSE value (9.51) than the unprocessed spectra (29.9). Furthermore, the exponential model outperformed existing turbidity compensation models, including the Lambert-Beer law-based model (RMSE = 12.53) and multiple-scattering cluster method (RMSE = 79.34). Several wastewater samples were also analyzed to evaluate the applicability of the exponential model to natural water. UV analysis yielded undesirable results owing to filtration procedures. However, the consistency between the compensated spectra and filtered wastewater samples in the visible range demonstrated that the model is applicable to natural water. Therefore, this proposed method is advantageous for improving the accuracy of COD measurement in turbid water., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Xiang, Xiong, Feng, Zhang and Li.)

Water depth, substrate type and wave exposure are major environmental factors influencing the growth and distribution of submerged macrophytes. However, we lack knowledge on the ecological reactions of submerged macrophytes to those factors during an entire growth period in a eutrophic lake with a Secchi depth of less than 50 cm. We investigated the interactive effects of water depth (60, 120 and 200 cm), waves (exposed vs. sheltered) and substrate nutrient level (fertile sludge vs. brown clay) on the survival, growth and morphology of Vallisneria natans using a new type of rhizotron in a 252-day field experiment. Plant length, leaf number, ramet number, root length, root number and biomass generally decreased with increased water depth at both wave conditions and in both substrates. When exposed to wave, the biomass of V. natans in water at 200 cm depth rapidly declined in both substrates. When sheltered to wave, aboveground biomass at 60 cm water depth first declined to zero with many buried rhizomes remained. The tuber germination rate decreased with increasing water depth during the second year of germination. No plants sprouted at 200 cm water depth except at the case of exposed wave and sludge substrate. Ramet number was influenced most by water depth, followed by aboveground biomass, leaf number, plant length, and survival rate. Wave shelter and brown clay enhanced the impacting strength of the water depth. Wave exposure exhibited no negative influence on survival percentage, whereas sludge had a positive influence on plant survival in deep water after overwintering. Waves negatively affected the rate of increase of plant growth in sludge but positively in clay. The positive joint effects of wave exposure and low-nutrient substrate were mainly on plant length and biomass. Water depth had a negative influence that predominated over substrate and waves regarding plant survival and growth. Moreover, this negative influence may be aggravated by an increasing risk of submersed macrophyte loss caused by decreased belowground growth under high water levels, high wave exposure and high-nutrient conditions. Decreasing the water depth could be an useful measure for submerged macrophyte restoration in freshwater habitats, even in turbid eutrophic water. [ABSTRACT FROM AUTHOR]

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

The massive development of floating plants in floodplain lakes and wetlands in the upper Middle Paraná river in the La Plata basin is environmentally and socioeconomically important. Every year aquatic plant detachments drift downstream arriving in small amounts to the Río de la Plata, but huge temporary invasions have been observed every 10 or 15 years associated to massive floods. From late December 2015, heavy rains driven by a strong El Niño increased river levels, provoking a large temporary invasion of aquatic plants from January to May 2016. This event caused significant disruption of human activities via clogging of drinking water intakes in the estuary, blocking of ports and marinas and introducing dangerous animals from faraway wetlands into the city. In this study, we developed a scheme to map floating vegetation in turbid waters using high-resolution imagery, like Sentinel-2/SMI (MultiSpectral Imager), Landsat-8/OLI (Operational Land Imager), and Aqua/MODIS (MODerate resolution Imager Spectroradiometer)-250 m. A combination of the Floating Algal Index (that make use of the strong signal in the NIR part of the spectrum), plus conditions set on the RED band (to avoid misclassifying highly turbid waters) and on the CIE La*b* color space coordinates (to confirm the visually “green” pixels as floating vegetation) were used. A time-series of multisensor high resolution imagery was analyzed to study the temporal variability, covered area and distribution of the unusual floating macroalgae invasion that started in January 2016 in the Río de la Plata estuary.