Water Turbidity Research Articles

Smart Aquaculture Design for Vannamei Shrimp Farming Based on Quality Function Development

In the fishery industry, Indonesia’s large water area has the potential for developing and cultivating fisheries such as vannamei shrimp. For this reason, aquaculture, particularly vannamei shrimp farming, can play a crucial role in Indonesia’s economy and food supply. However, challenges such as fluctuating water quality, disease outbreaks, turbidity levels, and irregular shrimp feeding schedules in ponds can affect the productivity and sustainability of shrimp farming. The smart aquaculture system integrates technologies, such as IoT-based sensors, automated feeding mechanisms, and real-time water quality monitoring to optimize the farming process. The research proposes a smart aquaculture design for vannamei shrimp farming based on the Quality Function Development (QFD) method. It starts by creating questionnaires to identify stakeholders’ level of interest. The questionnaire results are used as a reference for system redesign using the QFD method to improve the quality and quantity of shrimp harvest, cultivating effectively and efficiently and helping and facilitating the supervision of pond managers on pond water quality, feeding, and feed availability. The result highlights the application of QFD in creating a tailored, technology-driven solution that supports better decision-making, resource optimization, and improved shrimp health. The system reduces human error, enhances farm management, and promotes higher yields by providing real-time data and automation. The evaluation results show that the proposed design can achieve high stakeholder satisfaction. It also achieves better scores compared to the other two competitor’s designs.

Application of Thymus Species as Natural Coagulant for Elimination of Turbidity of Water and Its Comparison with the Conventional Coagulant

Introduction: The most important pollutant in surface water sources is turbidity. The aim of the study is to determine the most effective thyme plant extract as a natural coagulant to remove water turbidity and compare it with alum as a chemical coagulant.. Materials and Methods: In this study, plant extracts obtained from Thymus vulgaris (Tv), Thymus kotschyanus (Tk), and Thymus eriocalyx (Te) were used as natural coagulants for elimination of water turbidity. Then, the best of plant extract was confirmed by ninhydrin and FTIR analyses and the effect of simultaneous use of plant extract with chemical coagulant of aluminum sulfate (alum) was investigated in the hybrid ratio of 1:1, 2:1, and 1:2. Then, the effect of parameters include of pH, coagulant dose, turbidity removal efficiency, and the most suitable solvent for coagulant extraction were studied. Results: The results of this study showed that among the natural coagulants, Tv had a higher efficiency in elimination of water turbidity, and the highest amounts of turbidity elimination for Tv, alum, and Tv/alum hybrid coagulants was 94, 90, and 96, respectively. According to the results of this study, the most effective solvent for preparing plant extracts was 1M KCl solvent. Conclusion: The natural coagulant removes more turbidity from water than the chemical coagulant, and the simultaneous use of natural and chemical coagulants with a 1:1 ratio was more effective.

Wind and river effects on a coastal current in Chatham Sound, British Columbia

Chatham Sound is an ecologically and economically important semi-enclosed basin in the northeast Pacific which has received only limited oceanographic attention. Here we investigate the surface circulation in the southern Sound by analyzing hourly current fields obtained from a high-frequency (HF) radar array, installed there in 2017 and still in operation, as well as other hydrographic and mooring measurements that allow us to characterize subsurface features at several specific locations during 2018 and 2019, and satellite imagery processed to obtain a measure of suspended particulate matter at high spatial resolution. We find that the surface circulation is dominated by the plume of the Skeena River, which flows into the southeast corner of the Sound. On average, this plume forms a northward flowing coastal current in the eastern half of the Sound, and is turbid during the freshet. The plume width changes only marginally even though the Skeena’s flow changes seasonally by a factor of about 7. However, the depth of the plume and its salinity is dramatically different during the early summer freshet compared to values in winter. Fastest plume currents appear near the offshore edges of the plume, even though the plume depth is greatest near the coast. We show that these features are consistent with those predicted from an analytical 1-1/2 layer buoyant coastal current model. Winds can accelerate or retard the coastal current; when strong enough the flow is arrested and turbid plume water fills the southern Sound.

Decontamination Potential of Ultraviolet Type C Radiation in Water Treatment Systems: Targeting Microbial Inactivation

Access to safe water and sanitation is a critical global challenge, posing significant health risks worldwide due to waterborne diseases. This study investigates the efficacy of ultraviolet type C radiation as a disinfection method for improving water quality. The research elucidates UV-C’s mechanism of action, highlighting its ability to disrupt DNA and RNA replication, thereby inactivating pathogens. Furthermore, the study analyses the influence of key factors on UV-C disinfection effectiveness, including water turbidity and the presence of dissolved organic matter, which can attenuate UV-C penetration and reduce treatment efficiency. The experimental results demonstrate a substantial reduction in microbial content following UV-C treatment. River water samples exhibited a 57.143% reduction in microbial load, while well water samples showed a 50% reduction. Notably, Escherichia coli (E. coli) concentrations decreased significantly, with an 83.33% reduction in well water and a 62.5% reduction in borehole water. This study makes a novel contribution to the understanding of UV-C disinfection by identifying the presence of resistant organisms, including Adenoviruses, Bacterial spores, and the Protozoan Acanthamoeba, in water samples. This finding expands the scope of UV-C research beyond easily culturable bacteria. To address this challenge, future investigations should explore synergistic disinfection strategies, such as combining UV-C treatment with advanced oxidation processes. Optimising UV-C system designs and developing robust, real-time monitoring systems capable of detecting and quantifying known and emerging UV-resistant pathogens are crucial for ensuring comprehensive water decontamination.

Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.

Integration of geographic features and bathymetric inversion in the Yangtze River's Nantong Channel using gradient boosting machine algorithm with ZY-1E satellite and multibeam data

This study investigates the integration of geographic features from ZY-1E satellite data with advanced machine learning techniques to enhance water depth inversion in the Yangtze River's Nantong Channel. Utilizing the Gradient Boosting Machine (GBM) and its geospatially enhanced version, GBM-Lon./Lat., significant improvements in modeling precision were observed, as reflected by lower RMSE and higher R² values compared to traditional depth inversion methods. The research underscores the benefits of incorporating geospatial data, which allows for a more nuanced understanding of the hydrological dynamics and facilitates more accurate predictions in the turbid waters of the channel. Challenges such as atmospheric effects, water turbidity, and data acquisition issues under variable weather conditions were identified. The study proposes further optimization of these models to handle diverse environmental conditions and enhance the accuracy of bathymetric mapping. The integration of machine learning with remote sensing not only supports navigational safety and efficient waterway management but also contributes significantly to environmental monitoring and sustainable riverine infrastructure development.

Integrating deep learning techniques for effective river water quality monitoring and management

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality. The monitoring system was meticulously set up with strategically positioned sensors at various river locations, ensuring data collection at regular 5-min intervals. This data was then transmitted to a cloud-based web portal, facilitating storage and analysis. To assess water quality, we introduced a novel hybrid approach, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed CNN-LSTM model achieved a validation accuracy of 98.40%, surpassing the performance of other state-of-the-art methods. Notably, the practical application of this system includes real-time alerts, promptly notifying stakeholders when water quality parameters exceed predefined thresholds. This feature aids in making informed decisions in water resource management. The study's contributions lie in its effective river water quality monitoring system, which encompassing various parameters, and its potential to positively impact environmental conservation efforts by providing a valuable tool for informed decision-making and timely interventions.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Tracing pesticide dynamics: High resolution offers new insights to karst groundwater quality

Generally, karst aquifers and springs are highly susceptible to contamination due to the high permeability and, therefore, groundwater flow velocities. The often thin soil cover, accompanied by dolines, can lead to fast infiltration of precipitation water loaded with mobilized contaminants such as pesticides and their transformation products. To date, continuous, temporally highly resolved in-situ monitoring to decipher concentration dynamics for a broad range of pesticides is missing. Therefore, a transportable HPLC-HRMS/MS system (MS2field) was positioned at two karst study sites in the Swiss Jura. Water samples were collected and analyzed for pesticides and their transformation products in-situ every 20 min for 6 weeks in 2021 and 8 weeks in 2022. During the spraying season in 2021, six rain events at site 1 and three at site 2 in 2022 were captured. Concurrently, the water quality parameters electrical conductivity, pH, nitrate, turbidity, and water level, were monitored continuously at high temporal resolution. Further, bacterial cell counts were monitored via online flow cytometry.In 2021, several pesticides and pesticide transformation products were detected in peak concentrations after rain events, of which metamitron showed the highest concentration of up to 1000 ng/L. In one rain event, the Swiss federal and EU drinking water limit of 100 ng/L was exceeded for up to 38 h. Compared with highly frequent MS2field samples collected every 20 min, 42-hours composite samples severely underestimated peak concentrations for all compounds, especially for labile ones. Therefore, it was demonstrated that exceedences of the regulatory limit would have been missed if just composite sampling would have been conducted. Peak concentrations of pesticides coincided with peaks in nitrate concentration and bacterial cell counts following rain events. The correlation analysis showed strong correlations between the three analyzed contaminants (pesticides, nitrate and bacteria), and the proxy parameters electrical conductivity, and pH. The investigation of a second spring revealed similar dynamics indicating that these can be expected in other karst aquifers as well.

Intelligent classification of water bodies with different turbidity levels based on Gaofen-1 multispectral imagery

The turbidity of water is crucial for the health of river and lake ecosystems, necessitating efficient monitoring for effective water management. Existing methods for studying water turbidity's spatial and temporal distribution rely mostly on measured data. There is limited research on the classification of water bodies with different turbidity levels. The main challenge lies in determining the boundaries of liquid water bodies at various turbidity levels, making it challenging to classify them accurately using traditional remote sensing image classification methods. This paper proposes and validates an intelligent turbidity classification method based on deep learning using GaoFen-1 multispectral remote sensing imagery. An adaptive threshold water extraction method based on the Normalized Difference Water Index is proposed to capture water boundaries more accurately to improve the accuracy of extracting nearshore water bodies. A semi-automatic semantic annotation method for water turbidity is introduced to reduce manual labeling costs. The paper applies mode filtering to address edge noise issues and establishes a high-quality training sample dataset. After comparing the accuracy of various neural network models, DeepLab V3+ is selected for intelligent turbidity classification. The results show high accuracy, with mean intersection over union (MIoU), mean F1 score (MF1), and overall accuracy (OA) reaching 94.73%, 97.29%, and 97.54%, respectively. The proposed method and experiments demonstrate the feasibility of intelligent classification of water bodies with different turbidity levels using deep learning networks. This provides a new approach for large-scale and efficient remote sensing water turbidity monitoring.

Methodological assessment for efficient collection of Schistosoma mansoni environmental DNA and improved schistosomiasis surveillance in tropical wetlands

Schistosomiasis, caused by trematodes of genus Schistosoma, is among the most seriously neglected tropical diseases. Although rapid surveillance of risk areas for Schistosoma transmission is vital to control schistosomiasis, the habitat and infection status of this parasite are difficult to assess. Environmental DNA (eDNA) analysis, involving the detection of extra-organismal DNA in water samples, facilitates cost-efficient and sensitive biomonitoring of aquatic environments and is a promising tool to identify Schistosoma habitat and infection risk areas. However, in tropical wetlands, highly turbid water causes filter clogging, thereby decreasing the filtration volume and increasing the risk of false negatives. Therefore, in this study, we aimed to conduct laboratory experiments and field surveys in Lake Victoria, Mbita, to determine the appropriate filter pore size for S. mansoni eDNA collection in terms of particle size and filtration volume. In the laboratory experiment, aquarium water was sequentially filtered using different pore size filters. Targeting >3 µm size fraction was found to be sufficient to capture S. mansoni eDNA particles, regardless of their life cycle stage (egg, miracidia, and cercaria). In the field surveys, GF/D (2.7 µm nominal pore size) filter yielded 2.5-times the filtration volume obtained with a smaller pore size filter and pre-filtration methods under the same time constraints. Moreover, a site-occupancy model was applied to the field detection results to estimate S. mansoni eDNA occurrence and detection probabilities and assess the number of water samples and PCR replicates necessary for efficient eDNA detection. Overall, this study reveals an effective method for S. mansoni eDNA detection in turbid water, facilitating the rapid and sensitive monitoring of its distribution and cost-effective identification of schistosomiasis transmission risk areas.

Long-term observations of the turbid outflow plume from the Russian River, California

Understanding the mechanisms that spread freshwater away from small river systems and form turbid, low-salinity coastal plumes is crucial for assessing water quality in coastal waters. We present an analysis of 15 years (January 2004 to December 2018) of daily MODIS Aqua satellite data and in situ instrument data on the turbid freshwater plume that forms off the Russian River (California, USA), a prototypical Mediterranean-climate, small mountainous river system (SMRS). We present per-pixel statistical metrics and regression analyses to identify and quantify the controls on the extent and configuration of the plume exerted by river discharge, waves, winds, and tides. While freshwater outflow exhibits a persistent signal in nearshore waters, a large-scale plume only extends offshore into coastal waters during high river flow, when plume turbidity can be detected more than 10 km offshore from the river mouth. Our results show times when wave radiation stress exceeds outflow inertia, confining the plume within the surf zone and leading to an absence of detectable plume turbidity in coastal waters. Although tidal currents significantly influence the plume near the inlet, wind forcing is the primary control on plume shape and extent in coastal waters, deflecting the turbid outflow more than 30 km upcoast or downcoast of the river mouth with respective wind directions. Coriolis forcing is also significant and observed most clearly during periods of high river discharge and low wind forcing. In addition to introducing novel remote sensing methodology for SMRS plume analyses, these findings highlight the complex interplay of forcing related to tides, river discharge, winds, and waves in shaping the behavior of SMRS plumes. New insights include the impact of tides on larger discharges, the role of Coriolis forcing in SMRS plumes, and the effect of cross-shore winds on plume compression. Further, by considering the Russian River as a model for SMRS, this study can be used to ground-truth existing numerical models of small river plumes and to contribute to understanding critical for managing coastal water quality and nearshore ecosystems.

Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively higher fish density and water turbidity in intensive RAS culture produce major challenges in acquiring high-quality underwater image data. Additionally, the manual image annotation involved in model training can be subjective, time-consuming, and labor-intensive. Therefore, the presented study aimed to simulate fish schooling behavior for RAS conditions and investigate the feasibility of using computer-simulated virtual images to train a robust fish detection model. Additionally, to expedite the model training and automate the virtual image annotation, a process flow was developed. The 'virtual model' performances were compared with models trained on real-world images and combinations of real and virtual images. The results of the study indicate that the virtual model trained solely with computer-simulated images could not perform satisfactorily (mAP = 62.8%, F1 score = 0.61) to detect fish in a real RAS environment; however, replacing a small number of the virtual images with real images in the training dataset significantly improved the model's performance. The M6 mixed model trained with 630 virtual and 70 real images (virtual-to-real image ratio: 90:10) achieved mAP and F1 scores of 91.8% and 0.87, respectively. Furthermore, the training time cost for the M6 model was seven times shorter than that for the 'real model'. Overall, the virtual simulation approach exhibited great promise in rapidly training a reliable fish detection model for RAS operations.

IMPROVEMENT OF OPERATION AND RESEARCH METHODOLOGY OF THE HYDRAULIC FLAKE FORMATION CHAMBER FOR DRINKING WATER SUPPLY

Ukraine’s surface water sources are unsatisfactory due to pollution caused by industrial and agricultural activities and the Russian invasion. As the natural water quality deteriorates, the treatment plant’s efficiency in preparing drinking water for the population’s needs decreases. The hydraulic flake formation chamber allows for physicochemical processes that produce large and solid flakes of metal hydroxides with impurities that quickly settle and are then removed from the water. This study aims to develop a methodology for experimental research on the new design of a swirl-vortex flake formation chamber that intensifies the water purification process in the domestic and drinking water supply systems. The proposed design of the swirl-vortex chamber has dimensions of 0.75×2.0 m and a height of 0.65 m. Due to the installation in the first section of 4 fittings with nozzles, two pieces on each pipe, and in the second section, six fittings with nozzles, three pieces on each pipe with a diameter of 6–10 mm, it is possible to increase the effect of water illumination. The authors performed hydraulic modelling following the Reynolds criterion (Re) and the Froude criterion (Fr). We conducted experiments using artificial turbidity water. White-blue clay was used as a turbidant because it contains more small particles than the white-green one. We decided to model flake formation chambers according to the Froude criterion (Fr). Modelling according to the Reynolds criterion leads to such large velocity gradients that it simply does not make sense to talk about flake formation processes. We carried out the second series of experiments to obtain a qualitative assessment of the swirl-vortex chamber’s work in forming flakes by studying the deposition of flakes at different velocity gradients and angles of inclination of the nozzles. The best effect of lightening the water in the flake formation chamber occurs at a nozzle inclination angle of 45° to the bottom. The best effect of water clarification happens at a velocity gradient of about 60 s-1 and the water residence time in the flake formation chamber of 200–300 s. Keywords: water treatment, intensification, flake formation chamber, drinking water supply.

Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing

ABSTRACT Machine learning models for water quality prediction often face challenges due to insufficient data and uneven spatial-temporal distributions. To address these issues, we introduce a framework combining machine learning, numerical modeling, and remote sensing imagery to predict coastal water turbidity, a key water quality proxy. This approach was tested in the Great Lakes region, specifically Cleveland Harbor, Lake Erie. We trained models using observed and synthetic data from 3D numerical models and tested them against in situ and remote sensing data from PlanetLabs' Dove satellites. High-resolution (HR) data improved prediction accuracy, with RMSE values of 0.154 and 0.146 log10(FNU) and R2 values of 0.92 and 0.93 for validation and test datasets, respectively. Our study highlights the importance of unified turbidity measures for data comparability. The machine learning model demonstrated skill in predicting turbidity through transfer learning, indicating applicability in diverse, data-scarce regions. This approach can enhance decision support systems for coastal environments by providing accurate, timely predictions of water quality variables. Our methodology offers robust strategies for turbidity and water quality monitoring and holds significant potential for improving input data quality for numerical models and developing predictive models from remote sensing data.

Assessment of poly(diallyl dimethyl ammonium chloride) and lime for surface water treatment (pond, river, and canal water): seasonal variations and correlation analyses.

The present study deals with the assessment of different physicochemical parameters (pH, electrical conductivity (E.C.), turbidity, total dissolved solids (TDS), and dissolved oxygen) in different surface water such as pond, river, and canal water in four different seasons, viz. March, June, September, and December 2023. The research endeavors to assess the impact of a cationic polyelectrolyte, specifically poly(diallyl dimethyl ammonium chloride) (PDADMAC), utilized as a coagulation aid in conjunction with lime for water treatment. Employing a conventional jar test apparatus, turbidity removal from diverse water samples is examined. Furthermore, the samples undergo characterization utilizing X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The study also conducts correlation analyses on various parameters such as electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity of raw water, polyelectrolyte dosage, and percentage of turbidity removal across different water sources. Utilizing the Statistical Package for Social Science (SPSS) software, these analyses aim to establish robust relationships among initial turbidity, temperature, percentage of turbidity removal, dosage of coagulant aid, electrical conductivity, and total dissolved solids (TDS) in pond water, river water, and canal water. A strong positive correlation could be found between the percentage of turbidity removal and the value of initial turbidity of all surface water. However, a negative correlation could be observed between the polyelectrolyte dosage and raw water's turbidity. By elucidating these correlations, the study contributes to a deeper understanding of the effectiveness of PDADMAC and lime in water treatment processes across diverse environmental conditions. This research enhances our comprehension of surface water treatment methodologies and provides valuable insights for optimizing water treatment strategies to address the challenges posed by varying water sources and seasonal fluctuations.

Performance evaluation and factors influencing a drinking water treatment plant, Shire Indassilassie, Ethiopia

This research assesses the efficacy of the water treatment plant in Shire Indassilassie, Tigray, Ethiopia, focusing on the slow sand filtration system. Despite the inclusion of horizontal roughing filtration and a sedimentation tank, the water treatment plant struggles with an inadequate and turbid water supply. This study evaluates the water treatment plant’s performance through field evaluation, interviews, and laboratory experiments, concentrating on major treatment processes to identify performance-limiting factors. The water source had an average turbidity of 29.70 ± 1.03 NTU in the dry season and 94.27 ± 16.71 NTU in the summer. Results indicate that horizontal roughing filtration, sedimentation tank, and slow sand filtration achieve average turbidity removal efficiencies of 42.02% ± 4.00%, 5.67% ± 3.15%, and 83.51% ± 8.75%, respectively, during the dry season. In the summer or wet season, these treatment units achieve efficiencies of 40.06% ± 3.26%, 28.17% ± 4.62%, and 77.07% ± 4.61%, respectively. Additionally, the average run times for slow sand filtration are 22 days in the dry season and 13 days in the summer. Total and fecal coliform removals were effective, except in the sedimentation tank. A one-way ANOVA confirms a significant difference in turbidity among treatment units. Factors such as fine filter sand particles causing faster clogging, pipe misconnections, inadequate cleaning practices, irregular filtration rates, air binding due to a lack of backfilling at startup, and a lack of periodic monitoring and emergency response lead to suboptimal turbidity removal and shorter slow sand filtration run times. The study underscores the need for corrective measures to address these issues, enhancing water treatment plant sustainability in the region.

Physical-chemical characterisation of an alum-based water treatment sludge in different raw water turbidity scenarios

Characterisation of the water treatment sludge (WTS) generated in drinking water treatment plants (DWTPs) is crucial to define alternatives for its adequate management, including potential reuse options. To define these alternatives, it is necessary to evaluate rainfall seasonality effect on WTS production and its physical and chemical characteristics. This study assessed the production and characterisation of four types of alum-based WTS. The WTS was generated in a pilot-scale system from different raw water turbidities (i.e., low: <5 NTU, medium: 5–10 NTU, high: ≥10 NTU, and very high turbidity: ∼300 NTU) and coagulant doses. To estimate WTS production, mathematical models based on variables such as raw water turbidity, coagulant dosage, and organic matter removed were used. The WTS characterisations included physical (solids and particle size distribution), chemical (metallic oxides, pH, mineral phases), and surface properties (functional groups and zero-charge point pH). The modified Kawamura model presented the best fit (R2 = 1.0, RMSE = 0.1062 and the lower Akaike Information Criterion) for the estimation of WTS production, indicating that at the DWTPs, it is possible to make sludge production projections using only two simple variables: coagulant dose and the raw water turbidity. The four types of WTS consist mainly of amorphous materials (45–65 %), featuring some mineral phases and exhibiting high contents of Al (Al2O3: 30–34 %), Si (SiO2: 21–26 %) and Fe (Fe2O3: 11–13 %). Nevertheless, very high turbidity WTS shows variations in its characteristics, notably a heightened content of clays. As a result of the high concentrations of Al and Fe, the WTS has the potential to be used as coagulants or for the recovery of coagulants, especially low turbidity WTS, which is produced from water with low turbidity and organic matter. The presence of aluminium–silicate clays and the surface functional groups of the silica network suggest that WTS, particularly very high turbidity WTS, also has the potential to be raw materials for generating adsorbents. The potential applications of WTS in coagulation and adsorption can be leveraged in wastewater treatment, promoting the circular economy in the water sector.

Application of gradient boosting machine in satellite-derived bathymetry using Sentinel-2 data for accurate water depth estimation in coastal environments

Accurate water depth estimation is crucial in coastal environmental management, resource exploration, and ecological protection. Traditional water depth measurement methods are often time-consuming and costly, especially in vast sea areas where their application is limited. However, with the rapid development of remote sensing technology, particularly the widespread use of high-resolution satellite imagery, water depth remote sensing has emerged as a more efficient, economical, and widely applicable solution. In this study, we utilized Sentinel-2 satellite data and applied various algorithms to accurately estimate water depth in the Nanshan Port area. The results showed that the Gradient Boosting Machine (GBM) model excelled in monitoring shallow water and coastal environments, effectively addressing challenges such as light attenuation and water scattering in turbid waters. Compared to traditional methods, GBM-generated predictions were smoother and more detailed. This study not only demonstrates the significant potential of satellite remote sensing for water depth measurement but also points to future directions for algorithm optimization and the integration of remote sensing technologies. It is expected to bring revolutionary progress to oceanic scientific research and coastal management.

Growth rates of five coral species across a strong environmental gradient in the Colombian Caribbean

Coral calcification is critical for reef growth and highly dependent on environmental conditions. Yet, little is known about how corals calcify under sub-optimal conditions (e.g., turbid waters, high nutrients, sedimentation) or coral growth in understudied regions such as the Colombian Caribbean. We therefore assessed the calcification and linear extension rates of five coral species across an inshore-to-offshore gradient in the Colombian Caribbean. A suite of environmental variables (temperature, light intensity, visibility, pH, nutrients) measured during the rainy season (May – November 2022) demonstrated more sub-optimal conditions inshore compared to offshore. Across all species, calcification rates were 59% and 37% lower inshore compared to the offshore and midshore sites, respectively. Across all sites, massive corals calcified up to 92% more than branching species but were more susceptible to heat stress and sub-optimal inshore conditions. However, branching species had reduced survival due to extreme climatic events (i.e., bleaching, hurricanes). A comparison with published rates for the wider Caribbean revealed that massive species in the Colombian Caribbean grow up to 11 times more than those in the wider Caribbean while branching species generally have similar growth rates, but this finding may have been influenced by fragment size and/or heat stress. Our findings indicate that present-day environmental conditions, coupled with more frequent extreme climatic events, will favor massive over branching species in midshore areas of the Colombian Caribbean. This suggests a possible shift towards faster calcifying massive species in future coral communities, possibly exacerbating the ongoing regional decline in branching species over the last decades.