Search Results (14)

Seasonal thermal stratification in deep reservoirs easily causes bottom hypoxia and a sharp decrease in oxidation–reduction potential (ORP), leading to the pulsed release of internal phosphorus from sediments. Under climate warming, this has become a hot issue for sustainable reservoir eutrophication control. Taking the Quanmin Reservoir in Southwest China as the research object, this study combined high-resolution profile monitoring and a Box–Behnken response surface experiment to construct a semi-empirical model coupling redox threshold effect and Arrhenius kinetics. Results showed that during thermal stratification, the water body below 18 m formed a significant redox gradient, resulting in a 21-fold vertical difference in phosphorus concentration. The response surface experiment confirmed that ORP dominates phosphorus release, and the temperature (T) effect is strictly redox-dependent: warming only promotes phosphorus release under anaerobic conditions (−50 mV), with a 26% increase in release amount when temperature rises from 10 °C to 30 °C, while temperature has a negligible effect under aerobic conditions (+30 mV). Model fitting yielded an ORP critical threshold of −17.2 ± 4.8 mV and a normalized steepness of 0.033 mV⁻¹, indicating joint control by diffusion and reaction. Based on these results, a synergistic regulatory mechanism of redox threshold and temperature was proposed, providing a quantitative basis for reservoir eutrophication management under climate warming. Maintaining ORP above −17 mV through bottom aeration can effectively block internal phosphorus release from the redox threshold perspective, though practical in situ application is constrained by aeration-induced water mixing and microbial variations, and such precise redox control may save energy, supporting the sustainability of reservoir ecosystems and long-term water quality security. Full article

Complex climate change exacerbates variability in bottom oxygen availability, posing serious threats to aquatic ecosystems. This study investigates the interrelationships among meteorological, thermocline, oxycline variables, and the Kjeldahl nitrogen ratio in Lake Długie Wigierskie, Poland, using long-term monitoring data (2008–2022). Results show a decline in surface and bottom %saturation, but an increase in dissolved oxygen (DO) concentrations. Air temperature and Secchi depth primarily influenced surface oxygen dynamics, while wind speed drove bottom oxygen variability. Thermocline depth and thickness positively correlated with oxycline depth and hypoxic thickness, revealing that stable stratification restricts vertical mixing and shapes oxygen distribution. Air temperature significantly affected Schmidt Stability (SS), with warmer periods promoting stronger stratification, greater hypoxic thickness, and lower hypolimnetic oxygen minimum (HOM). Interestingly, DO levels and their variability showed significant correlation with the Kjeldahl nitrogen ratio (TKN/TN), suggesting that oxygen fluctuations may influence nitrogen cycling more strongly than average DO concentrations. These findings imply that warming may worsen bottom hypoxia by elevating respiration rates, thereby altering organic nitrogen mineralization. Overall, the study highlights the need for effective management strategies to alleviate hypoxia and protect water quality in deep lakes under climate change. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article

Rhizosphere hypoxia, caused by soil compaction and waterlogging, is a major constraint on agricultural productivity. It severely impairs crop growth and yield by inhibiting root aerobic respiration, disrupting energy metabolism, and altering the rhizosphere microecology. Micro-nano bubbles (MNBs) show significant potential for alleviating rhizosphere hypoxia due to their unique physicochemical properties, including large specific surface area, high oxygen dissolution efficiency, prolonged retention time, and negative surface charge. This paper systematically reviews the key characteristics of MNBs, particularly their enhanced mass transfer capacity and system stability, and outlines mainstream preparation methods such as cavitation, electrolysis, and membrane dispersion. And the multiple alleviation mechanisms of MNBs—including continuous oxygen release, improvement of soil pore structure, and regulation of rhizosphere microbial communities—are clarified. The combination of MNBs aeration and subsurface drip irrigation can increase soil aeration by 5%. When applied in soilless cultivation and conventional irrigation systems, MNBs enhance crop yield and nutrient use efficiency. For example, tomato yield can be increased by 12–44%. Furthermore, the integration of MNBs with water–fertilizer integration technology enables the synchronized supply of oxygen and nutrients, thereby optimizing the rhizosphere environment efficiently. This paper sorts out the empirical effects of MNBs in soilless cultivation and conventional irrigation, and provides directions for solving problems such as “insufficient oxygen supply to deep roots” and “reactive oxygen species (ROS) stress in sensitive crops”. Despite these significant advantages, the industrialization of MNBs still needs to overcome challenges including high equipment costs and insufficient precision in parameter control, so as to promote large-scale agricultural application and provide an innovative strategy for the management of rhizosphere hypoxia. Full article

Changes in nearshore water quality directly influence ecosystem stability and the sustainability of aquaculture production. Among these factors, rapid fluctuations in dissolved oxygen (DO) can compromise the physiological functions of aquatic organisms, often leading to mass mortality events and significant economic losses. To enhance the predictive capability of DO in marine ranching areas, this study evaluates multiple forecasting approaches, including AutoARIMA, XGBoost, BlockRNN-LSTM, BlockRNN-GRU, TCN, Transformer, and an ensemble model that integrates these methods. Using hourly DO observations from coastal buoys, we performed multi-step rolling forecasts and systematically assessed model performance across multiple evaluation metrics (MAPE, RMSE, and R²), complemented by residual and error distribution analyses. The results show that the ensemble model, based on deep learning techniques, consistently outperforms individual models, achieving higher forecast robustness and more effective variance control, with MAPE values maintained below 4% across all three buoys. Building upon these findings, we further developed and deployed a DO forecasting and early-warning system centered on the ensemble framework. This system enables end-to-end functionality, including automatic data acquisition, real-time prediction, hypoxia risk identification, and alert dissemination. It has already been applied in marine ranching operations, providing 1–3 day forecasts of DO dynamics, facilitating the early detection of hypoxia risks, and significantly improving the scientific support and responsiveness of aquaculture management. Full article

Although the sediment–water interface of deep and large reservoirs is recognized as a dominant source of internal phosphorus (P) loading, the quantitative hierarchy of environmental drivers and their interaction thresholds remains poorly resolved. Here, we integrate 512 studies to provide the first process-based synthesis that partitions P release fluxes among temperature, pH, dissolved oxygen, salinity, sediment properties, and microbial activity across canyon, valley, and plain-type reservoirs. By deriving standardized effect sizes from 61 data-rich papers, we show that (i) a 1 °C rise in bottom-water temperature increases soluble reactive P (SRP) flux by 12.4% (95% CI: 10.8–14.0%), with sensitivity 28% lower in Alpine oligotrophic systems and 20% higher in warm monomictic basins; (ii) a single-unit pH shift—whether acid or alkaline—stimulates P release through distinct desorption pathways,; and (iii) each 1 mg L⁻¹ drop in dissolved oxygen amplifies release by 31% (25–37%). Critically, we demonstrate that these drivers rarely act independently: multi-factor laboratory and in situ analyses reveal that simultaneous hypoxia and warming can triple the release rate predicted from single-factor models. We further identify that >75% of measurements originate from dam-proximal zones, creating spatial blind spots that currently limit global P-load forecasts to ±50% uncertainty. To close this gap, we advocate coupled metagenomic–geochemical observatories that link gene expression (phoD, ppk, pqqC) to real-time SRP fluxes. The review advances beyond the existing literature by (1) establishing the first quantitative, globally transferable framework for temperature-, DO-, and pH-based management levers; (2) exposing the overlooked role of regional climate in modulating temperature sensitivity; and (3) providing a research agenda that reduces forecasting uncertainty to <20% within two years. Full article

Cervical cancer represents a significant global health challenge. Photodynamic therapy (PDT) appears to be a promising, minimally invasive alternative to standard treatments. However, the clinical efficacy of PDT is sometimes limited by the low solubility and aggregation of photosensitizers, their non-selective distribution in the body, hypoxia in the tumor microenvironment, and limited light penetration. Recent advances in nanoparticle and nanocomposite platforms have addressed these challenges by integrating multiple functional components into a single delivery system. By encapsulating or conjugating photosensitizers in biodegradable matrices, such as mesoporous silica, organometallic structures and core–shell construct nanocarriers increase stability in water and extend circulation time, enabling both passive and active targeting through ligand decoration. Up-conversion and dual-wavelength responsive cores facilitate deep light conversion in tissues, while simultaneous delivery of hypoxia-modulating agents alleviates oxygen deprivation to sustain reactive oxygen species generation. Controllable “motor-cargo” constructs and surface modifications improve intratumoral diffusion, while aggregation-induced emission dyes and plasmonic elements support real-time imaging and quantitative monitoring of therapeutic response. Together, these multifunctional nanosystems have demonstrated potent cytotoxicity in vitro and significant tumor suppression in vivo in mouse models of cervical cancer. Combining targeted delivery, controlled release, hypoxia mitigation, and image guidance, engineered nanoparticles provide a versatile and powerful platform to overcome the current limitations of PDT and pave the way toward more effective, patient-specific treatments for cervical malignancies. Our review of the literature summarizes studies on nanoparticles and nanocomposites used in PDT monotherapy for cervical cancer, published between 2023 and July 2025. Full article

Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) for feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model to predict TN concentrations in the Dongjiang River in China. A dataset of 28,922 time-series data points was collected from seven sampling sites along the Dongjiang River, spanning from November 2020 to February 2023. The DCC method identified conductivity, Permanganate Index (COD_Mn), and total phosphorus as the most significant predictors for TN levels. The At-CBiLSTM model, optimized with a time step of three, outperformed other models, including standalone Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Neural Network LSTM (CNN-LSTM), and Attention-LSTM variants, achieving excellent performance with the following metrics: mean absolute error (MAE) = 0.032, mean squared error (MSE) = 0.005, mean absolute percentage error (MAPE) = 0.218, and root mean squared error (RMSE) = 0.045. Importantly, increasing the number of input features beyond three variables led to a decline in model accuracy, underscoring the importance of DCC-driven feature selection. The results highlight that combining DCC with deep learning models, particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies and improves prediction accuracy. This approach provides a solid foundation for real-time water quality monitoring and can inform targeted pollution control strategies in river ecosystems. Full article

Predicting the dissolved oxygen concentration and identifying its driving factors are essential for improved prevention and management of anoxia in estuaries. However, complex hydrodynamic conditions and the limitations in traditional methods result in challenges in the identification of the driving factors for the low dissolved oxygen (DO) phenomenon. The objective of our study is to develop a robust deep learning model using four-year in situ data collected from an automatic water quality monitoring station (AWQMS) in an estuary, for accurate identification and quantification of the driving factors influencing DO levels. Mitigations in hypoxia were observed during the initial two years, but a subsequent decline in DO concentrations was witnessed recently. The periodicity of DO concentrations in the Pearl River Estuary reduced with the increase in the hypoxic intensity. Maximal information coefficient (MIC) and extreme gradient boosting (XGBoost) were employed to determine the significance of input variables, which were subsequently validated by using the long- and short-term memory networks (LSTMs). The driving factors contributing to the hypoxia problem were shown as temperature, pH, conductivity, and NH₄⁺-N concentrations. Notably, the evaluation index values of the hybrid model are MAPE = 0.0887 and R² = 0.9208, which have been improved compared with the LSTM model by about 99.34% in MAPE reduction and 16.56% in R² improvement, indicating that the MixUp-LSTM model was capable of effectively capturing nonlinear relationships between DO and other water quality indicators. Based on existing literature, three traditional statistical methods and four machine learning models were also performed to compare with the proposed MixUp-LSTM model, which outperformed other models in terms of prediction accuracy and robustness. Overall, the successful identification of the driving factors for the deoxygenation phenomenon would have important implications for the governance and regulation of low DO in estuaries. Full article

In direct seeding, hypoxia is a major stress faced by rice plants. Therefore, dissecting the response mechanism of rice to hypoxia stress and the molecular regulatory network is critical to the development of hypoxia-tolerant rice varieties and direct seeding of rice. This review summarizes the morphological, physiological, and ecological changes in rice under hypoxia stress, the discovery of hypoxia-tolerant and germination-related genes/QTLs, and the latest research on candidate genes, and explores the linkage of hypoxia tolerance genes and their distribution in indica and japonica rice through population variance analysis and haplotype network analysis. Among the candidate genes, OsMAP1 is a typical gene located on the MAPK cascade reaction for indica–japonica divergence; MHZ6 is involved in both the MAPK signaling and phytohormone transduction pathway. MHZ6 has three major haplotypes and one rare haplotype, with Hap3 being dominated by indica rice varieties, and promotes internode elongation in deep-water rice by activating the SD1 gene. OsAmy3D and Adh1 have similar indica–japonica varietal differentiation, and are mainly present in indica varieties. There are three high-frequency haplotypes of OsTPP7, namely Hap1 (n = 1109), Hap2 (n = 1349), and Hap3 (n = 217); Hap2 is more frequent in japonica, and the genetic background of OsTPP7 was derived from the japonica rice subpopulation. Further artificial selection, natural domestication, and other means to identify more resistance mechanisms of this gene may facilitate future research to breed superior rice cultivars. Finally, this study discusses the application of rice hypoxia-tolerant germplasm in future breeding research. Full article

The Mar Menor is a coastal lagoon of great socio-ecological and environmental value; in recent years, different localized episodes of hypoxia and eutrophication have modified the quality of its waters. The episodes are due to a drop in dissolved oxygen levels below 4 mg/L in some parts of the lagoon and a rise in chlorophyll a to over 1.8 mg/L. Considering that monitoring the Mar Menor and its watershed is essential to understand the environmental dynamics that cause these dramatic episodes, in recent years, efforts have focused on carrying out periodic measurements of different biophysical parameters of the water. Taking advantage of the data collected and the versatility offered by neural networks, this paper evaluates the performance of a dozen advanced neural networks oriented to time series forecasted for the estimation of dissolved oxygen and chlorophyll a parameters. The data used are obtained in the water body by means of sensors carried by a multiparameter oceanographic probe and two agro-climatic stations located near the Mar Menor. For the dissolved oxygen forecast, the models based on the Time2Vec architecture, accompanied by BiLSTM and Transformer, offer an R2 greater than 0.95. In the case of chlorophyll a, three models offer an R2 above 0.92. These metrics are corroborated by forecasting these two parameters for the first time step out of the data set used. Given the satisfactory results obtained, this work is integrated as a new biophysical parameter forecast component in the monitoring platform of the Mar Menor Observatory developed by IMIDA. The results demonstrate that it is feasible to forecast the concentration of chlorophyll a and dissolved oxygen using neural networks specialized in time series forecasts. Full article

Insects are the most evolutionarily and ecologically successful group of living animals, being present in almost all possible mainland habitats; however, they are virtually absent in the ocean, which constitutes more than 99% of the Earth’s biosphere. Only a few insect species can be found in the sea but they remain at the surface, in salt marshes, estuaries, or shallow waters. Remarkably, a group of 13 species manages to endure long immersion periods in the open sea, as well as deep dives, i.e., seal lice. Sucking lice (Phthiraptera: Anoplura) are ectoparasites of mammals, living while attached to the hosts’ skin, into their fur, or among their hairs. Among them, the family Echinophthiriidae is peculiar because it infests amphibious hosts, such as pinnipeds and otters, who make deep dives and spend from weeks to months in the open sea. During the evolutionary transition of pinnipeds from land to the ocean, echinophthiriid lice had to manage the gradual change to an amphibian lifestyle along with their hosts, some of which may spend more than 80% of the time submerged and performing extreme dives, some beyond 2000 m under the surface. These obligate and permanent ectoparasites have adapted to cope with hypoxia, high salinity, low temperature, and, in particular, conditions of huge hydrostatic pressures. We will discuss some of these adaptations allowing seal lice to cope with their hosts’ amphibious habits and how they can help us understand why insects are so rare in the ocean. Full article

Harmful algal blooms (HABs), eutrophication, and internal pollutant sources from sediment, represent serious problems for public health, water quality, and ecological restoration worldwide. Previous studies have indicated that Modified Local Soil (MLS) technology is an efficient and cost-effective method to flocculate the HABs from water and settle them onto sediment. Additionally, MLS capping treatment can reduce the resuspension of algae flocs from the sediment, and convert the algal cells, along with any excessive nutrients in-situ into fertilisers for the restoration of submerged macrophytes in shallow water systems. Furthermore, the capping treatment using oxygen nanobubble-MLS materials can also mitigate sediment anoxia, causing a reduction in the release of internal pollutants, such as nutrients and greenhouse gases. This paper reviews and quantifies the main features of MLS by investigating the effect of MLS treatment in five pilot-scale whole-pond field experiments carried out in Lake Tai, South China, and in Cetian Reservoir in Datong city, North China. Data obtained from field monitoring showed that the algae-dominated waters transform into a macrophyte-dominated state within four months of MLS treatment in shallow water systems. The sediment-water nutrient fluxes were substantially reduced, whilst water quality (TN, TP, and transparency) and biodiversity were significantly improved in the treatment ponds, compared to the control ponds within a duration ranging from one day to three years. The sediment anoxia remediation effect by oxygen nanobubble-MLS treatment may further contribute to deep water hypoxia remediation and eutrophication control. Combined with the integrated management of external loads control, MLS technology can provide an environmentally friendly geo-engineering method to accelerate ecological restoration and control eutrophication. Full article

Using a validated coupled hydrodynamic-tracer transport model, this study quantified the mean residence time in Jamaica Bay, a highly eutrophic lagoonal estuary in New York City. The Bay is a well-mixed to partially-stratified estuary with heavily-dredged bathymetry and substantial wastewater treatment plant effluent inputs that lead to seasonal hypoxia in some poorly-flushed deep-water basins. Residence time was computed for Jamaica Bay and its largest isolated deep basin, Grassy Bay. The response of residence time to freshwater discharge and wind forcing during summer 2015 was also investigated. The model results showed that the mean residence time, which represents the time required to flush out 63% of tracers released into the region of interest, was 17.9 days in Jamaica Bay and 10.7 days in Grassy Bay. The results also showed that some regions in Jamaica Bay retained their tracers much longer than the calculated residence time and, thus, are potentially prone to water quality problems. Model experiments demonstrated that summertime wind forcing caused a small increase in residence time, whereas freshwater discharge substantially reduced residence time. Freshwater inputs were shown to strongly enhance the two-layer estuarine gravitational circulation and vertical shear, which likely reduced residence time by enhancing shear dispersion. Due to the Bay’s small, highly-urbanized watershed, freshwater inputs are largely derived from the municipal water supply, which is fairly uniform year-round. This water helps to promote bay flushing, yet also carries a high nitrogen load from wastewater treatment. Lastly, the tidal prism method was used to create a simple calibrated model of residence time using the geometry of the study area and the tidal range and period. Full article