Water Turbidity Research Articles (Page 1)

In 2023, an unprecedented drought and heat wave severely affected Amazon waters, leading to high mortality of fishes and river dolphins. Five of 10 lakes monitored had exceptionally high daytime water temperatures (over 37°C), with one large lake reaching up to 41°C in the entire approximately 2-meter-deep water column and up to 13°C of diel variation. Modeling showed that high solar radiation, reduced water depth and wind speed, and turbid waters were the main drivers of the high temperatures. This extreme heating of Amazon waters follows a long-term increase of 0.6°C/decade revealed by satellite estimates across the region's lakes between 1990 and 2023. With ongoing climate change, temperatures that approach or exceed thermal tolerances for aquatic life are likely to become more common in tropical aquatic systems.

Solar photocatalytic membranes (polyester membranes coated with TiO2-ZnO co-doped photocatalyst) were synthesized using aqueous heat attachment method. The membranes were characterized using Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray (EDX) spectroscopy, and X-ray Diffraction (XRD) to determine the morphology, composition, and crystal structure, respectively. The raw water was characterized to determine physicochemical and microbial properties. The membranes were used to treat synthetic water feed with E. coli and real water from Kesses Dam. The SEM images and EDX analysis showed the morphology and composition of membranes were TiO2-ZnO co-doped photocatalytic polyester/cotton. The XRD revealed the characteristic peaks of TiO2-ZnO plus membrane peaks. The coated membranes achieved an E. coli removal efficiency of 99.998%, with a log reduction of 4.6, for an initial concentration of 12 * 104CFU/100ml. The uncoated membranes achieved only 64.6% removal efficiency and a log reduction of 0.45. Additionally, the coated membranes reduced the turbidity of raw water from 16NTU to 0.7NTU, whereas uncoated membranes reduced turbidity to 4NTU. The coated membranes treated the raw water and attained the World Health Organization guidelines in terms of turbidity and E. coli. The study provides valuable insights into the development of solar-driven, photocatalytic membranes for point-of-use water treatment.

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health.

Underwater polarization imaging technology can effectively recover target information in scattering media. However, traditional methods require capturing two or more polarization images, resulting in complex imaging system structures. To address this limitation, this Letter proposes a novel, to the best of our knowledge, method to solve the perpendicular-polarized image from the parallel-polarized image, based on the generalized Malus' law. Then, an adaptive weighting mechanism based on the spatial distribution of the target light is introduced to mitigate calculation errors in the estimation of non-uniform target light intensity on orthogonal components. Moreover, an adaptive Gaussian low-pass filter was applied to estimate the backscattered light information, enabling effective separation of target light from backscattered light. Experimental results demonstrate that the proposed method significantly improves image quality for complex polarization targets in turbid water, surpassing the existing single-polarization descattering technique and some traditional methods. These advancements provide both theoretical insights and technical support for the development of compact underwater polarization imaging systems.

ABSTRACT This study investigated the physiological responses of phytoplankton to reduced light availability, a common feature of turbid coastal ecosystems, focusing on their acclimation and recovery under prolonged light exposure. Three irradiance treatments (low light [LL], medium light [ML] and high light [HL]) were administered for 15 hours, followed by a 3-hour recovery period at 10 μmol photons m−2 s−1. Exposure to prolonged HL significantly reduced the maximal photochemical efficiency of PSII (Fv/Fm) from 0.613 rel. units at initial incubation to 0.302 rel. units after one hour. The HL treatment showed significantly lower non-photochemical quenching (NPQ), indicating an impaired ability to dissipate excess light energy. However, despite this impairment, photoinhibition did not occur. Under HL treatments, a steady increase in photosynthetic capacity (rel.ETRmax) was observed after seven hours of incubation, suggesting that the phytoplankton fully utilized the available light for photosynthesis during the early stages of exposure. LL treatments maintained high photosynthetic efficiency (α) and displayed better overall photosynthetic performance compared to ML and HL treatments. Our findings highlight the significant photophysiological plasticity of turbid water phytoplankton, evidenced by their rapid recovery from light stress, a crucial trait for their survival and productivity in dynamic coastal environments.

This study explored the potential of residual aluminum silicate (Al2SiO5), a mining byproduct collected from Brazilian Lithium Company (CBL), as a flocculating agent in the treatment of clay particle-laden waters. The experiments evaluated the material's ability to aggregate particles and reduce water turbidity under different conditions, acting as an artificial zeolite. Results demonstrated the effectiveness of residual aluminum silicate in flocculation, outperforming the control treatment. It was observed that the flocculant's performance was influenced by the medium's pH, being more efficient under alkaline conditions. This work suggests that residual aluminum silicate is a promising candidate for water treatment, but highlights the need for further understanding of its action and optimization for large-scale applications.

Introduction: Astambul Subdistrict in Banjar Regency is classified as a flood-prone area. Such regions tend to experience a decline in clean water quality. In Astambul, no mapping has been conducted regarding clean water quality based on water management and land conditions to improve clean water quality in flood-prone areas. This study aims to analyze the availability of clean water sources in flood-prone areas using geoelectrical methods and Geographic Information Systems (GIS). Methods: This study used a quantitative research design with a cross-sectional method to analyze clean water sources using geoelectric and Geographic Information System (GIS) methods. This study was conducted in five villages in Banjar Regency, South Kalimantan Province. Testing using the geoelectrical method was conducted at five locations in five villages, while the GIS method was used at 30 locations across the five villages. Results and Discussion: The geoelectric method showed that 4 villages had turbid water quality, and 1 village had very turbid water. The GIS (Geographic Information System) method indicated that the parameters for turbidity, iron (Fe), manganese (Mn), and coliforms did not meet the standards. The average values of Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), and turbidity are 8.03 mg/L, 179.27 mg/L, 0.22 mg/L, and 17.23 NTU, respectively. The average values for pH, iron (Fe), and manganese (Mn) are 6.44, 0.68 mg/L, and 21.02 mg/L, respectively. Conclusion: Based on Geoelectric and Geographic Information System analysis, the Astambul District area has clean water sources that are still below quality standards.

Due to spectral influence caused by turbidity, the accuracy of nitrate quantification using UV-vis spectroscopy remains challenging. This study proposes an integrated method combining UV-vis spectroscopy, difference spectrum analysis, and a hybrid prediction model to address this issue. By analyzing the linear relationship between the difference spectrum and turbidity, a novel turbidity compensation strategy-the Mixed Difference Nitrate Method (MDNM)-was developed. Subsequently, a hybrid prediction framework integrating linear regression and threshold-based waveband selection was employed to enhance modeling accuracy. Experimental results on both standard and natural water samples demonstrate that the method achieves high accuracy and generalization ability, with an R2 of 0.9982 and an RMSE of 0.2629 mg L-1 for standard samples, and an R2 of 0.9663 and an RMSE of 0.7835 mg L-1 for natural water samples. The proposed method offers a simple, effective, and low-cost strategy for nitrate detection in turbid water, with significant potential for practical environmental monitoring and water quality assessment.

Turbidity is a crucial indicator for evaluating water quality. This study obtained the long-term spatial distribution of water turbidity across Northeast China from 1985 to 2023. A combination of the geographically and temporally weighted regression (GTWR) model, the Lindeman, Merenda, and Gold (LMG) method, and statistical data analysis methods were employed to quantify the spatiotemporal impacts of driving factors on turbidity changes. The stepwise regression model was able to credibly estimate turbidity, achieving a low RMSE of 18.432 Nephelometric Turbidity Units (NTU). Temporal variations in turbidity showed that 69.90% of lakes exhibited a decreasing trend. Spatial variations revealed that lakes with significantly increased turbidity were predominantly concentrated in the Songnen and Sanjiang Plains, whereas lakes with lower turbidity were situated in the Eastern Mountains regions and Liaohe Plain. Temporal changes were closely associated with socioeconomic development and anthropogenic interventions implemented by governments on the aquatic environment. Vegetation coverage, precipitation, and elevation demonstrated significant contributions (exceeding 16.39%) to turbidity variations in the Lesser Khingan and Eastern Mountains regions, where natural factors played a more dominant role. In contrast, cropland area, wind speed, and impervious surface area showed higher contribution rates of above 14.00% in the Songnen, Sanjiang, and Liaohe Plains, where anthropogenic factors were dominant. These findings provide valuable insights for informed decision-making in water environmental management in Northeast China and facilitate the aquatic ecosystem sustainability under human activities and climate change.

Recent developments in environmental DNA (eDNA) analyses have facilitated non-invasive and cost-effective ecological monitoring. Based on eDNA of terrestrial species released into water upon contact, simultaneous detection of aquatic and terrestrial species is feasible. However, an efficient sampling design for terrestrial vertebrate eDNA in aquatic environments has not yet been established because DNA is rarely released into these environments. In this study, we targeted eDNA transported from land to rivers through surface runoff during rainfall in three rivers and one irrigation channel within the Kiyotake River system, Japan. We quantified the eDNA concentration of a specific terrestrial vertebrate (Bos taurus) using digital polymerase chain reaction (PCR) and examined the efficiency of using filter papers with different pore sizes (0.7 µm and 2.7 µm). We also assessed the influence of various environmental factors (e.g., rainfall characteristics described by the parameters of Gaussian distribution, water turbidity) on eDNA detection across different rainfall events. During the surveys, target DNA was detected in 42 out of 47 samples, suggesting the feasibility of consistently detecting terrestrial mammals from stormwater runoff. Overall, compared with the glass fiber filter with larger pore size, the smaller pore size filter captured more eDNA. The generalized linear mixed model revealed that prolonged rainfall duration, turbidity, and pH had a significant positive effect on eDNA concentration, whereas the distance from the assumed point of entry into the river to the sampling point had a significant negative effect. These results suggest that the runoff and transport of eDNA from terrestrial areas to rivers are enhanced under prolonged rainfall conditions, although eDNA degrades while transported along a longer watercourse by biochemical decomposition and sedimentation.

This research develops an Internet of Things (IoT)-based water quality monitoring system for freshwater fish farming that integrates several key sensors: DS18B20 for temperature measurement, turbidity sensor for water turbidity measurement, and ultrasonic sensor for water level monitoring. The system is equipped with two automatically controlled water pumps to maintain optimal conditions in the fish pond. The system works by reading water quality parameters in real-time using these sensors, then sending the data to the IoT platform via a WiFi network. The data is processed by an ESP32 microcontroller that will automatically activate the water pump when it detects a parameter change that exceeds a specified threshold and provides notifications to the farmer via a mobile application. Test results show that this system is able to automatically monitor water temperature and turbidity levels in pond water based on the temperature and turbidity data obtained

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and conditions. This study applies explainable machine learning to enhance insights into predicting direct filtration operations at the Ålesund WTP in Norway. Three baseline models (Multiple Linear Regression, Support Vector Regression, and K-Nearest Neighbour (KNN)) and three ensemble models (Random Forest (RF), Extra Trees (ET), and XGBoost) were optimised using the GridSearchCV algorithm and implemented on seven filter units to predict their filtered water turbidity. The results indicate that ML models can reliably predict filtered water turbidity in WTPs, with Extra Trees models achieving the highest predictive performance (R2 = 0.92). ET, RF, and KNN ranked as the three top-performing models using Alternative Technique for Order of Preference by Similarity to Ideal Solution (A-TOPSIS) ranking for the suite of algorithms used. The feature importance analysis ranked the filter runtime, flow rate, and bed level. SHAP interpretation of the best model provided actionable insights, revealing how operational adjustments during the ripening stage can help mitigate filter breakthroughs. These findings offer valuable guidance for plant operators and highlight the benefits of explainable machine learning in water quality management.

Abstract Worldwide, reefs are under significant pressure, and hence, understanding the consequences of natural and anthropogenically driven sediment influx to reef systems is crucial to planning future protection strategies. Most reef systems are associated with clear water settings, but reefs also evolved in turbid water environments stressed by high rates of sediment influx. Mixed carbonate–clastic environments have been considered unfavourable to reef‐building organisms. Currently, we lack generally applicable models for (i) reef growth under the stress of siliciclastic sediment influx and (ii) tools that diagnose ancient reefs that developed in sediment‐stressed environments. Case studies of sediment‐stressed reefs from the Devonian to the recent reviewed here demonstrate that reef organisms show the ability to survive, and even thrive, under clastic sediment influx. These case studies were selected based on (i) the presence of a mixed carbonate‐clastic matrix and (ii) the existence of a coral framework. For each example, the system was characterised in terms of sediment input, organism growth forms and the overall reef architecture. The host sediment from Cenozoic reefs is typically better described than that within Palaeozoic and Mesozoic communities. This may be due to the closer affinity between Cenozoic communities and recent species compared to more ancient systems. The same reasoning accounts for the paucity of data describing the internal structure of many fossil reefs, a feature also related to outcrop quality. Moreover, the juxtaposition of siliciclastic interbeds and ancient reefal bodies should not be taken as conclusive evidence that clastic influx was contemporaneous with the active growth stages of the framework organisms. Based on the data reviewed here, no relationship was identified between the nature of the reef builders, the character of the siliciclastic component and the reef structure. We suggest that this lack of understanding of mixed carbonate‐clastic reef systems significantly compromises potential forecasts of future reef development.

Non-invasive real-time imaging through turbid water based on coherent detection

A physics-enhanced deep-learning model for estimating turbid shallow water depth from SAR images

Access to clean water remains a global challenge, particularly in areas where populations rely on surface water. These water sources must be treated. Coagulation with chemicals causes environmental problems and adverse effects on human health. Natural coagulants obtained from papaya (Carica papaya) waste are presented as an alternative that is safe for human health, non-polluting, and biodegradable. The effectiveness of these natural coagulants is compared to that of aluminum sulfate using jar tests and synthetic and natural surface water, with statistical tools to model treatment processes. All coagulants have competitive results, reaching turbidity remotion levels above 90%. However, in equivalent tested ranges, natural coagulants require lower dosages and perform better with high initial water turbidity due to their polymeric bridging mechanisms and adsorption processes through the action of their functional groups, as detected by FTIR analysis. Additional testing with contaminated water from the Valsequillo dam confirms the use of these coagulants to treat water, with papaya seed coagulant yielding the best results and requiring lower doses, making it a competitive alternative. It can be concluded that papaya-based coagulants obtained from waste can be used as an eco-friendly alternative to aluminum sulfate in physicochemical treatments to purify surface water for human consumption.

Water quality that is not optimally maintained can have a negative impact on the health of ornamental fish, especially Bluerim betta fish that require an aquarium environment with a certain level of acidity and turbidity of the water. The problems that are often faced by ornamental fish hobbyists are delays in changing water and difficulties in monitoring water conditions manually. This study aims to design and build an automatic water replacement and control system in betta fish aquariums with the ability to detect turbidity levels and water pH in real-time. The system uses a pH-SEN0161 sensor to measure acidity, a turbidity-SEN0189 sensor to detect turbidity in NTU units, and an SRF05 ultrasonic sensor to measure water level. The software was developed using the Arduino IDE and implemented on the Arduino ATMega2560 microcontroller as well as the NodeMCU ESP8266 for data processing and automatic control. The test was carried out for 30 days with an ideal pH standard between 6–7 and a turbidity value below 400 NTU. The test results show that the system can work optimally in replacing and controlling the water conditions of the Bluerim betta fish aquarium, thus supporting the quality of life of the fish effectively and efficiently.

First high temporal resolution retrievals of AOD over shallow and turbid coastal waters for Himawari-8

Abstract Exposure to dye‐laden wastewater poses a potential threat to human health and the well‐being of all living organisms. The present study proposes a dual‐purpose water treatment and waste management approach by utilizing palm tree leaves to synthesize a hydrogel‐based composite for dye adsorption, subsequently incorporating the pollutant‐laden material into concrete structures. Characterization of the adsorbent was done using SEM, FTIR, and swelling studies. The adsorption performance was evaluated for the removal of Crystal Violet and Rhodamine B dyes from aqueous solutions at variable operational parameters. Maximum dye removal efficiency, reaching 86.9% for RhB, was observed for the composite synthesized with 5% Palm tree leaf, 5% gum tragacanth, and 1.4 × 10−2 mol/L acrylamide concentration. Adsorption results indicated a superior fit to the Langmuir model for both dyes, with high correlation coefficients (R2 > 0.99 for CV and R2 > 0.96 for RhB). Kitchen wastewater treatment studies revealed improvements in water quality parameters, including pH, conductivity, and turbidity. In a novel integration of waste reuse, a concrete block designed using dye‐saturated composite material exhibited 7.17 N/mm2 compressive strength. These findings underscore the potential of palm tree leaves as a viable material for fabricating wastewater treatment composites and sequestration by embedding exhausted adsorbents into concrete blocks, thereby achieving environmental remediation and waste valorization.

To address the challenges of low target contrast and severe scattering noise in turbid media imaging, this paper proposes an underwater active imaging method that integrates range-gated and polarization-difference joint modulation. By establishing a physical model of spatio-polarimetric joint modulation, we systematically analyze the differences in both depolarization characteristics and time-delay properties between target-reflected light and backscattered light. A dual mechanism combining time gating for primary scattering suppression and polarization difference for residual noise elimination is utilized to achieve high-precision separation and enhancement of the target signal. Through a combination of MATLAB simulations and experimental studies, using fat emulsion solutions to emulate turbid water and a 450 nm laser as the light source, key parameters including the medium absorption coefficient and scattering intensity were adjusted to identify system-sensitive parameters and optimization strategies. Results demonstrate that the proposed polarization-difference range-gated imaging method outperforms conventional imaging approaches in both image signal-to-noise ratio and target contrast, confirming its effectiveness and advancement for target detection in turbid media. This work provides a new perspective for high-resolution optical imaging in complex underwater environments.