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The balance between sources and sinks of molecular oxygen in the oceans has greatly impacted the composition of Earth’s atmosphere since the evolution of oxygenic photosynthesis, thereby exerting key influence on Earth’s climate and the redox state of (sub)surface Earth. The canonical source and sink terms of the marine oxygen budget include photosynthesis, respiration, photorespiration, the Mehler reaction, and other smaller terms. However, recent advances in understanding cryptic oxygen cycling, namely the ubiquitous one-electron reduction of O₂ to superoxide by microorganisms outside the cell, remains unexplored as a potential player in global oxygen dynamics. Here we show that dark extracellular superoxide production by marine microbes represents a previously unconsidered global oxygen flux and sink comparable in magnitude to other key terms. We estimate that extracellular superoxide production represents a gross oxygen sink comprising about a third of marine gross oxygen production, and a net oxygen sink amounting to 15 to 50% of that. We further demonstrate that this total marine dark extracellular superoxide flux is consistent with concentrations of superoxide in marine environments. These findings underscore prolific marine sources of reactive oxygen species and a complex and dynamic oxygen cycle in which oxygen consumption and corresponding carbon oxidation are not necessarily confined to cell membranes or exclusively related to respiration. This revised model of the marine oxygen cycle will ultimately allow for greater reconciliation among estimates of primary production and respiration and a greatermechanistic understanding of redox cycling in the ocean.

In this study, a ratiometric optical fiber dissolved oxygen sensor based on dynamic quenching of fluorescence from a ruthenium complex is reported. Tris(4,7-diphenyl-1,10-phenanthrolin) ruthenium(II) dichloride complex (Ru(dpp)32+) is used as an oxygen-sensitive dye, and semiconductor nanomaterial CdSe/ZnS quantum dots (QDs) are used as a reference dye by mixing the two substances and coating it on the plastic optical fiber end to form a composite sensitive film. The linear relationship between the relative fluorescence intensity of the ruthenium complex and the oxygen concentration is described using the Stern–Volmer equation, and the ruthenium complex doping concentration in the sol-gel film is tuned. The sensor is tested in gaseous oxygen and aqueous solution. The experimental results indicate that the measurement of dissolved oxygen has a lower sensitivity in an aqueous environment than in a gaseous environment. This is due to the uneven distribution of oxygen in aqueous solution and the low solubility of oxygen in water, which results in a small contact area between the ruthenium complex and oxygen in solution, leading to a less-severe fluorescence quenching effect than that in gaseous oxygen. In detecting dissolved oxygen, the sensor has a good linear Stern–Volmer calibration plot from 0 to 18.25 mg/L, the linearity can reach 99.62%, and the sensitivity can reach 0.0310/[O2] unit. The salinity stability, repeatability, and temperature characteristics of the sensor are characterized. The dissolved oxygen sensor investigated in this research could be used in various marine monitoring and environmental protection applications.

Besides many efforts on the detection and quantification of reactive oxygen and nitrogen species (RONSs) in the aqueous media activated by the cold atmospheric plasma, to get a better insight into the dominant mechanism and reactive species in medical applications, a challenge still remains in monitoring the real-time evaluation of them. To this end, in the present work, relying on the photonic technology based on the time-resolved phosphorescence spectroscopy, real-time tracking of RONSs concentration in treated aqueous media is achieved by following the dissolved oxygen (DO) production/consumption. Using a photonic-based dissolved oxygen sensor, the dependence of real-time RONS concentration evaluation of plasma activated medium on plasma nozzle distance, non-thermal plasma jet exposure time, various culture media, and presence of cells is investigated. Analyzing the results, the activation parameters including the time of reaching maximum RONS concentration after treatment and defined activation parameter of the treated media for each case is measured and compared together. Moreover, employing the scavengers related to two involved ROSs, the dominant chemical reactions as well as ROS contributed in the DMEM medium is determined. As a promising result, the obtained correlation between the real-time DO level and viability and toxicity of the cancer cells, MCF-7 breast cancer cells, could enable us to exploit the present photonic setup as an alternative technique for the biological assessment.

Prediction of dissolved oxygen which is an important water quality (WQ) parameter is crucial for aquatic managers who have responsibility for the ecosystem health’s maintenance and for the management of reservoirs related to WQ. This study proposes a new ensemble method, Bayesian model averaging (BMA), for estimating hourly dissolved oxygen. The potential of the BMA was investigated and compared with five data-driven methods, extreme leaning machine (ELM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), classification and regression tree (CART), and multilinear regression (MLR), by considering hourly temperature, pH, and specific conductivity data as inputs. The methods were compared with respect to three statistics, root mean square errors (RMSE), Nash-Sutcliffe efficiency, and determination coefficient. Results based on two stations’ data indicated that the proposed method performed superior to the ELM, ANN, ANFIS, CART, and MLR in estimation of hourly dissolved oxygen; corresponding improvements obtained by BMA are about 5–8%, 13–12%, 7–9%, and 18–27% with respect to RMSE. The ELM also outperformed the other four methods (ANN, ANFIS, CART, and MLR), and the CART and MLR indicated the lowest estimation accuracy in both stations. Examination of various input combinations revealed that the most effective variable is water temperature while the specific conductivity has negligible effect on hourly dissolved oxygen.

A common perception in limnology is that shallow lakes are homogeneously mixed owing to their small water volume. However, this perception is largely gained by downscaling knowledge from large lakes to their smaller counterparts. Here we show that shallow vegetated lakes (less than 0.6 m), in fact, undergo recurring daytime stratification and nocturnal mixing accompanied by extreme chemical variations during summer. Dense submerged vegetation effectively attenuates light and turbulence generating separation between warm surface waters and much colder bottom waters. Photosynthesis in surface waters produces oxygen accumulation and CO2 depletion, whereas respiration in dark bottom waters causes anoxia and CO2 accumulation. High daytime pH in surface waters promotes precipitation of CaCO3 which is re-dissolved in bottom waters. Nocturnal convective mixing re-introduces oxygen into bottom waters for aerobic respiration and regenerated inorganic carbon into surface waters, which supports intense photosynthesis. Our results reconfigure the basic understanding of local environmental gradients in shallow lakes, one of the most abundant freshwater habitats globally.

Dissolved oxygen (DO) is an important water quality monitoring parameter of great significance in aquaculture. Accurate prediction of dissolved oxygen can help farmers to take necessary measures in advance to ensure the healthy growth of cultured species. The characteristics of multivariate and long-term correlation of water quality time series in the traditional methods make it difficult to achieve the expected prediction accuracy. To solve this problem, we propose the combined prediction method LSTM-TCN (long short-term memory network and temporal convolutional network). After the preprocessing of time series, the LSTM extracts the features of the series in time dimension, and then combines with TCN to build the fusion prediction model. In this study, we have carried out the DO predictions of LSTM and TCN algorithms separately, followed by the analysis of DO prediction, based on CNN-LSTM and LSTM-TCN combined models. The effects of attention mechanism and window size of historical time on the prediction results were also investigated. The experimental results show that the combined method has high accuracy in dissolved oxygen prediction, and can capture better characteristics of historical data with increasing time window of the historical dissolved oxygen sequence. The LSTM-TCN method achieves better prediction performance, with evaluation index values of MAE = 0.236, MAPE = 3.10%, RMSE = 0.342, and R 2  = 0.94.

Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface. Coastal systems are hotspots of ecological, geochemical and economic activity, yet their dynamics are not accurately represented in global models. In this Review, Ward and colleagues assess the current state of coastal science and recommend approaches for including the coastal interface in predictive models.

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm. The proposed deep recurrent neural network (DRNN) model is compared with support vector machine (SVM) and artificial neural network (ANN) models, formerly shown to be robust AI algorithms. The Fanno Creek in Oregon (USA) is selected as a case study and daily values of water temperature, specific conductance, streamflow discharge, pH, and DO concentration are used as input variables to predict DO concentration for three different lead times (“t + 1,” “t + 3,” and “t + 7”). Based on Pearson’s correlation coefficient, several input variable combinations are formed and used for prediction. The model prediction performance is evaluated using various indices such as correlation coefficient, Nash–Sutcliffe efficiency, root mean square error, and mean absolute error. The results identify the DRNN model ( CC Testing = 0.97 , N S E Testing = 0.948 , RMSE Testing = 0.43 and MAE Testing = 0.25 ) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction.

Simple development of an electrochemiluminescence (ECL) immunosensor for convenient detection of tumor biomarker is of great significance for early cancer diagnosis, treatment evaluation, and improving patient survival rates and quality of life. In this work, an immunosensor is demonstrated based on an enhanced ECL signal boosted by nanochannel-confined Au nanomaterial, which enables sensitive detection of the tumor biomarker—carcinoembryonic antigen (CEA). Vertically-ordered mesoporous silica film (VMSF) with a nanochannel array and amine groups was rapidly grown on a simple and low-cost indium tin oxide (ITO) electrode using the electrochemically assisted self-assembly (EASA) method. Au nanomaterials were confined in situ on the VMSF through electrodeposition, which catalyzed both the conversion of dissolved oxygen (O2) to reactive oxygen species (ROS) and the oxidation of a luminol emitter and improved the electrode active surface. The ECL signal was enhanced fivefold after Au nanomaterial deposition. The recognitive interface was fabricated by covalent immobilization of the CEA antibody on the outer surface of the VMSF, followed with the blocking of non-specific binding sites. In the presence of CEA, the formed immunocomplex reduced the diffusion of the luminol emitter, resulting in the reduction of the ECL signal. Based on this mechanism, the constructed immunosensor was able to provide sensitive detection of CEA ranging from 1 pg·mL−1 to 100 ng·mL−1 with a low limit of detection (LOD, 0.37 pg·mL−1, S/N = 3). The developed immunosensor exhibited high selectivity and good stability. ECL determination of CEA in fetal bovine serum was achieved.

The accurate prediction of dissolved oxygen (DO) concentration in rivers is very important for the management of aquatic ecosystems, However, the hybrid model of ' modal decomposition + prediction ' for predicting the nonlinear change of dissolved oxygen in rivers is still insufficient. In this paper, a frequency division prediction framework based on the optimal ensemble of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed. The dissolved oxygen sequence was decomposed into multiple components by CEEMDAN, and the long short-term memory network (LSTM), support vector regression (SVR) and multi-layer perceptron (MLP) models were constructed to predict each component independently. An innovative grid search algorithm with constraints is constructed, and the advantages of each model are complemented by dynamic combination. The optimal ensemble scheme is obtained with the goal of minimizing the mean absolute error ( MAE ) of the training set. The empirical study of monitoring sections A and B in the Ganjiang River Basin shows that : in the prediction task, the prediction of the training set, the MAE of the integrated model is 18.6–35.5% lower than that of the ensemble model, the root mean square error ( RMSE ) is 22.1–22.8% lower, and the determination coefficient ( R2 ) reaches 0.954 and 0.972. In particular, the error accumulation of MAE in the 3-day prediction is 27.2–81.4% lower than that of the mixed model. This framework enables the modes of multi-component dissolved oxygen series prediction to be effectively aliasing, and provides an extensible technical path for the intelligent management of the basin.