Search Results (193)

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

Coral reefs exposed to chronically turbid conditions challenge conventional assumptions about the optical environments required for reef persistence and productivity. This study investigates the utility of light absorption coefficients as indicators of optical water quality in Varadero Reef, an extreme coral ecosystem located in Cartagena Bay, Colombia. Field campaigns were conducted across three seasons (rainy, dry, and transitional) along a transect from fluvial to marine influence. Absorption coefficients at 440 nm were derived for particulate (a_p(440)) and chromophoric dissolved organic matter (a_CDOM(440)) to assess their contribution to underwater light attenuation. Average values across seasons show that a_p(440) reached 0.466 m⁻¹ in the rainy season (September 2021), 0.285 m⁻¹ in the dry season (February 2022), and 0.944 m⁻¹ in the transitional rainy season (June 2022). Meanwhile, mean a_CDOM(440) values were 0.368, 0.111, and 0.552 m⁻¹, respectively. These coefficients reflect the dominant influence of particulate absorption under turbid conditions and increasing a_CDOM(440) relevance during lower turbidity periods. Mean Secchi Disk Depth (ZSD) ranged from 0.6 m in the rainy season to 3.0 m in the dry season, aligning with variations in Kd PAR, which averaged 2.63 m⁻¹, 1.13 m⁻¹, and 1.08 m⁻¹ for the three campaigns. Chlorophyll-a concentrations at 1 m depth also varied significantly, with average values of 2.3, 2.7, and 6.2 μg L⁻¹, indicating phytoplankton biomass peaks associated with seasonal freshwater inputs. While particulate absorption limits light penetration, CDOM plays a potentially photoprotective role by attenuating UV radiation. The observed variability in these optical constituents reflects complex hydrodynamic and environmental gradients, providing insight into the mechanisms that sustain coral functionality under suboptimal light conditions. The absorption-based approach applied here, using standardized spectrophotometric methods, proved to be a reliable and reproducible tool for characterizing the spatial and temporal variability of IOPs. We propose integrating these indicators into monitoring frameworks as cost-effective, component-resolving tool for evaluating light regimes and ecological resilience in optically dynamic coastal systems. Full article

Understanding the spatiotemporal dynamics of water quality parameters and microbial communities in drinking water distribution systems (DWDS) and their interrelationships is critical for ensuring the safety of tap water supply. This study investigated the diurnal, monthly, and annual variation patterns of water quality and the stage-specific succession behaviors of microbial communities in a DWDS located in southeastern China. Results indicated that hydraulic shear stress during peak usage periods drove biofilm detachment and particle resuspension. This process led to significant diurnal fluctuations in total cell counts (TCC) and metal ions, with coefficients of variation ranging from 0.44 to 1.89. Monthly analyses revealed the synergistic risks of disinfection by-products (e.g., 24.5 μg/L of trichloromethane) under conditions of low chlorine residual (<0.2 mg/L) and high organic loading. Annual trends suggested seasonal coupling: winter pH reductions correlated with organic acid accumulation, while summer microbial blooms associated with chlorine decay and temperature increase. Nonlinear interactions indicated weakened metal–organic complexation but enhanced turbidity–sulfate adsorption, suggesting altered contaminant mobility in pipe scales. Microbial analysis demonstrated persistent dominance of oligotrophic Phreatobacter and prevalence of Pseudomonas in biofilms, highlighting hydrodynamic conditions, nutrient availability, and disinfection pressure as key drivers of community succession. These findings reveal DWDS complexity and inform targeted operational and microbial risk control strategies. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

The remote sensing inversion of the Suspended Sediment Concentration (SSC) at the Yellow River estuary is crucial for regional sediment management and the advancement of monitoring techniques for highly turbid waters. Traditional in situ methods and low-resolution imagery are no longer sufficient for high-accuracy studies. Using SSC data from the Longmen Hydrological Station (2019–2020) and Sentinel-2 imagery, multiple models were compared, and the random forest regression model was selected for its superior performance. A non-parametric regression model was developed based on optimal band combinations to estimate the SSC in high-sediment rivers. Results show that the model achieved a high coefficient of determination (R² = 0.94) and met accuracy requirements considering the maximum SSC, MAPE, and RMSE. The B4, B7, B8A, and B9 bands are highly sensitive to high-concentration sediment rivers. SSC exhibited significant seasonal and spatial variation, peaking above 30,000 mg/L in summer (July–September) and dropping below 1000 mg/L in winter, with a positive correlation with discharge. Spatially, the SSC was higher in the gorge section than in the main channel during the flood season and higher near the banks than in the river center during the dry season. Overall, the random forest model outperformed traditional methods in SSC prediction for sediment-laden rivers. Full article

Constructed wetlands are nature-based technologies widely used for the treatment of wastewater and contaminated surface water. This study evaluated the efficiency of free water surface (FWS) and horizontal subsurface flow (HSSF) constructed wetlands in reducing the turbidity of mine spoil rainwater using the w-C* model. Different hydraulic retention times (2, 4, and 6 days) were tested, and the influence of macrophyte type and substrate on the w parameter was investigated. Model calibration was performed based on correlation coefficients (R), coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE). The results indicated a 99% reduction in turbidity, with average values of R = 0.87 ± 0.05 (FWS) and 0.87 ± 0.03 (HSSF), and NSE of 0.76 ± 0.04 (FWS) and 0.74 ± 0.07 (HSSF), demonstrating good agreement between observed and predicted data. The settling rate (w) ranged from 0.16 to 0.40 m·d⁻¹ in FWS and from 0.20 to 0.70 m·d⁻¹ in HSSF, with the lowest value recorded in the control (0.09 m·d⁻¹). The best performances were observed in FWS-P with Pistia stratiotes (0.40 m·d⁻¹) and HSSF with Typha domingensis (0.70 m·d⁻¹), demonstrating that vegetation, combined with the use of medium-grain substrate (9.5–19.0 mm), enhances turbidity removal. The w-C* model proved to be a robust tool for describing the kinetics of suspended colloidal particle removal in constructed wetlands, providing valuable insights for optimizing hydraulic parameters and design criteria for full-scale application. Full article

Hypoxia in river estuaries poses significant ecological and water safety risks, yet long-term high-frequency monitoring data for comprehensive analysis remain scarce. This study investigates hypoxia dynamics in the Shenzhen River Estuary (southern China) using two-year high-frequency monitoring data. A hybrid anomaly detection method integrating wavelet analysis and temporal information entropy was developed to identify hypoxia events. The drivers of hypoxia were also identified with correlation coefficients and transfer entropy (TE). The results reveal frequent spring–summer hypoxia. Turbidity and total nitrogen (TN) exhibited significant negative correlations and time-lagged effects on dissolved oxygen (DO), where TE reaches a peak of 0.05 with lags of 36 and 72 h, respectively. Wastewater treatment plant (WWTP) loads, particularly suspended solids (SSs), showed a linear negative correlation with estuarine DO. Notably, the 2022 data showed minimal correlations (except SSs) due to high baseline pollution, whereas the post-remediation 2023 data revealed stronger linear linkages (especially r = −0.81 for SSs). The proposed “high-frequency localization–low-frequency assessment” detection method demonstrated robust accuracy in identifying hypoxia events, and mechanistic analysis corroborated the time-lagged pollutant impacts. These findings advance hypoxia identification frameworks and highlight the critical role of Turbidity and SSs in driving estuarine oxygen depletion, offering actionable insights for adaptive water quality management. Full article

Water management is a significant challenge, stimulating synergies between scientists and practitioners to create new tools and approaches to streamline decision making in this field. The assessment and monitoring of freshwater quality in surface water bodies are crucial for sustainable and safe water management. The main objectives of this study were to analyze the characteristics and properties of Chirita lake, assess seasonal variations in water quality, determine compliance with national environmental legislation, and perform a comparison with monitoring systems in other European lakes. The study used data that determined water quality indicators for a five-year period, from 2020 to 2024, considering temperature, turbidity, pH, conductivity, alkalinity, hardness, organic matter, nitrates, nitrites, ammonium, and chlorides. The statistical analysis technique based on the Pearson correlation coefficient was used to evaluate the seasonal correlations of water quality parameters in Chirita lake and to extract the essential parameters for assessing seasonal variations in river water quality. The results obtained indicated that the indicators considered important for water quality variation in one season may not be important in another season, except for organic matter and conductivity, which showed a significant contribution to water quality variation throughout the four seasons. This study demonstrated that lake water is classified as first class, according to national regulations. These results provide valuable support for local authorities to develop effective strategies for water quality management and the prevention of eutrophication processes in reservoirs. Full article

Nutrients function as essential biological substrates for coastal phytoplankton growth and serve as pivotal indicators in marine environmental monitoring. The intensification of land-based nutrient sources inputs has exacerbated eutrophication in Chinese coastal water, while mechanistic understanding of differential self-purification processes among distinct land-based source nutrients (river source, domestic source, aquaculture source, and industrial source) remains limited, constraining accurate assessment of bay’s self-purification capacity. This study conducted incubation experiments in Tieshan Bay (TSB) during Summer (June 2023) and winter (January 2024), systematically analyzing the self-purification process of nutrients and associated environmental drivers. Distinct source-specific patterns emerged: river inputs exhibited maximal dissolved inorganic nitrogen (DIN) 1.390 ± 0.74 mg/L, whereas industrial discharges showed peak dissolved inorganic phosphorus (DIP) 4.88 ± 1.45 mg/L. Chlorophyll a (Chl-a) concentrations varied markedly across sources, ranging from 34.97 ± 23.37 μg/L (domestic source) to 86.63 ± 77.08 μg/L (river source). First-order kinetics demonstrated significant source differentiation (p < 0.05). River-derived DIN exhibited the highest attenuation coefficient (−0.3244 ± 0.17 d⁻¹), contrasting with industrial-sourced DIP showing maximum depletion (−0.4332 ± 0.20 d⁻¹). Correlation analysis indicated that summer was significantly associated with the impacts of three key control factors pH, dissolved oxygen, and turbidity on nutrient dynamics (p < 0.05), whereas winter exhibited a stronger dependence on salinity. These parameters collectively may modulate microbial degradation pathways and particulate matter adsorption capacities. These findings establish quantitative thresholds for coastal nutrient buffering mechanisms, highlighting the necessity for source-specific eutrophication mitigation frameworks. The differential self-purification efficiencies underscore the importance of calibrating pollution control strategies according to both anthropogenic discharge characteristics and regional hydrochemical resilience, which is of key importance for ensuring the traceability and control of land-based sources of pollution into the sea and the scientific utilization of the self-purification capacity of the bay water body. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

This study examined a sediment plume from Australia’s largest river, The River Murray, which was produced during a major flood event in 2022–2023. This flood resulted from successive La Niña events, causing high rainfall across the Murray–Darling Basin and ultimately leading to a significant riverine flow through South Australia. The flood was characterised by a significant increase in riverine discharge rates, reaching a peak of 1305 m³/s through the Lower Lakes barrage system from November 2022 to February 2023. The water quality anomaly within the coastal region (<~150 km offshore) was effectively quantified and mapped utilising the diffuse attenuation coefficient at 490 nm (Kd490) from products derived from MODIS Aqua Ocean Color satellite imagery. The sediment plume expanded and intensified alongside the increased riverine discharge rates, which reached a maximum spatial extent of 13,681 km². The plume typically pooled near the river’s mouth within the northern corner of Long Bay, before migrating persistently westward around the Fleurieu Peninsula through Backstairs Passage into Gulf St Vincent, occasionally exhibiting brief eastward migration periods. The plume gradually subsided by late March 2023, several weeks after riverine discharge rates returned to pre-flood levels, indicating a lag in attenuation. The assessment of the relationship and accuracy between the Kd490 product and the surface-most in situ turbidity, measured using conductivity, temperature, and depth (CTD) casts, revealed a robust positive linear correlation (R² = 0.85) during a period of high riverine discharge, despite temporal and spatial discrepancies between the two datasets. The riverine discharge emerged as an important factor controlling the spatial extent and intensities of the surface sediment plume, while surface winds also exerted an influence, particularly during higher wind velocity events, as part of a broader interplay with other drivers. Full article

Lake Kasumigaura, one of Japan’s largest lakes, presents significant challenges for remote sensing due to its eutrophic conditions and complex optical properties. Although the Global Change Observation Mission-Climate (GCOM-C)/Second-generation Global Imager (SGLI)-derived inherent optical properties (IOPs) offer water quality monitoring potential, their performance in such turbid inland waters remains inadequately validated. This study evaluated five established IOP retrieval algorithms, including the quasi-analytical algorithm (QAA_V6), Garver–Siegel–Maritorena (GSM), generalized IOP (GIOP-DC), Plymouth Marine Laboratory (PML), and linear matrix inversion (LMI), using measured remote sensing reflectance (Rrs) and corresponding IOPs between 2017–2018. The results demonstrated that the QAA had the highest performance for retrieving absorption of particles (a_p) with a Pearson correlation (r) = 0.98, phytoplankton (a_ph) with r = 0.97, and non-algal particles (a_nap) with r = 0.85. In contrast, the GSM algorithm exhibited the best accuracy for estimating absorption by colored dissolved organic matter (a_CDOM), with r = 0.87, along with the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE). Additionally, a strong correlation (r = 0.81) was observed between SGLI satellite-derived remote-sensing reflectance (R_rs) and in situ measurements. Notably, a high correlation was observed between the a_ph (443 nm) and the chlorophyll a (Chl-a) concentration (r = 0.84), as well as between the backscattering coefficient (b_bp) at 443 nm and inorganic suspended solids (r = 0.64), confirming that IOPs are reliable water quality assessment indicators. Furthermore, the use of IOPs as variables for estimating water quality parameters such as Chl-a and suspended solids showed better performance compared to empirical methods. Full article

Cyanobacteria harmful blooms (Cyano-HABs) have become a globally critical environmental issue, threatening freshwater ecosystems by degrading water quality and posing risks to human and aquatic life. Chlorophyll-a (Chl-a), a key biomarker of bloom intensity, offers crucial insights into algal bloom dynamics. However, predicting Chl-a concentrations remains challenging due to the complex interactions between various environmental factors. This study utilizes machine learning (ML) models to predict Chl-a concentrations, focusing on Lake Taihu in China, a large eutrophic lake that serves as an example of numerous freshwater lakes suffering from Cyano-HABs. The research leverages nine critical water quality parameters—water temperature, pH, dissolved oxygen, turbidity, electrical conductivity permanganate index, ammonia nitrogen, total phosphorus, and total nitrogen—to develop an ensemble ML model using XGBoost, known for its ability to handle nonlinear relationships and integrate multiple variables. The XGBoost model achieved superior predictive accuracy with an R² value of 0.78 and RMSE of 8.97 mg/m³ on the test set, outperforming traditional models like linear regression, decision trees, multi-layer perceptrons, support vector regression, and random forests. Feature importance analysis identified electrical conductivity, turbidity, and water temperature as the most significant predictors of Chl-a levels. This study further enhances model interpretability through Pearson correlation analysis, which quantifies the relationships between Chl-a concentrations and other water quality factors. Additionally, we employed principal component analysis (PCA), mutual information, Spearman rank correlation coefficients, and SHAP models to analyze feature importance and model interpretability in ML. The model’s robustness was tested across multiple monitoring sites in Lake Taihu, demonstrating its potential for broader application in other eutrophic lakes facing similar environmental challenges. By providing a reliable tool for forecasting Chl-a concentrations, this research contributes to the development of early warning systems that can help mitigate the impacts of Cyano-HABs, aiding in more effective water resource management. Full article