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The high concentration of dissolved iron (Fe) in coastal waters triggers Lyngbya blooms in the Moreton Bay region of Southeast Queensland, Australia. Previous studies have provided a restricted understanding of how land-derived Fe is transported and then transformed into other forms (e.g., Fe oxides) before its release into the ocean. Here, a field investigation was conducted at a sandy beach on the northern end of Deception Bay, Queensland, Australia, focusing on porewater exchange and Fe transformation. This study revealed that tides provided a significant mechanism for driving the groundwater-seawater mixing in the intertidal area. Such forcing formed an upper saline plume (USP) with high dissolved oxygen (DO), creating a dynamic reaction zone for Fe oxidation and precipitation beneath the USP. The spatial distribution of Fe oxides highlighted a substantial Fe content in the subsurface, providing concrete evidence for the transformation of Fe from an aqueous state to a solid form. It also exhibited a low-permeable area that served as a geochemical barrier, absorbing chemical components like phosphate. These findings can assist in constructing a more accurate transport model that couples physical and geochemical processes to quantify the mechanisms driving Fe transformation in coastal areas and further deepen our comprehension of the hydrogeochemical functionalities in landocean connectivity via groundwater. [ABSTRACT FROM AUTHOR]

This study optimized the design of an aeration device for pond engineered recirculating aquaculture systems (RASs) whose application is aimed at increasing dissolved oxygen (DO) levels in RAS aquaculture practice. DO is a key factor in aquaculture productivity, and oxygenators are the power devices used for regulating its levels in aquaculture ponds. In this study, grass carp (Ctenopharyngodon idellus) aquaculture trials were conducted in a self-built RAS by using the new aeration device (NAD); the microporous and impeller aeration components were individually tested in terms of performance, and then combined for the orthogonal testing of their operating parameters in order to assess the NAD's oxygenation capacity. The test results show that the device effectively increased the dissolved oxygen levels in the RAS tank, enhanced the upper–lower water layer exchange and directional flow, and met the design and parameter selection requirements. Compared with the existing RAS oxygenation equipment, the NAD operated with the optimal parameters and increased the oxygen transfer rate in the pond water tank by 122%. [ABSTRACT FROM AUTHOR]

Water bodies are crucial components of urban ecology. The development of rapid and timely water-quality assessment tools using easily measured variables is essential for the health management of urban water bodies. In this study, we focused on the dissolved oxygen (DO) of Baiyangdian Lake using 251 sets of empirically measured water quality data and corresponding Sentinel-2 satellite images. Nine machine learning algorithms were then used to develop a rapid detection algorithm for the spatial distribution of the DO concentration in Baiyangdian Lake. This study successfully applied these methods to invert the DO concentration in Baiyangdian Lake during spring, summer, and autumn. The results indicated that extra tree regression (ETR) provided the most accurate and stable results for inverting the DO concentration among the nine machine learning methods. In contrast, AdaBoost regression (ABR), Bayesian ridge regression (BRR), and support vector machines (SVM) exhibit relatively poor regression performance and lack sensitivity to DO concentrations. Moreover, the DO concentration in Baiyangdian Lake ranged from approximately 0 to 12 mg/L, with notable spatiotemporal variations. The highest overall DO concentration was observed in the spring, particularly in the southern region. The DO concentration significantly decreased during summer compared to that in spring, with higher values in the southwestern area and lower values in the northern region. The DO concentration reached its lowest value in autumn, with slightly higher values in the southern region. This study focused on the estimation and inversion of DO concentrations in the water bodies of Baiyangdian Lake. By introducing and comparing the performances of commonly used machine learning models, a rapid estimation of the DO concentration was achieved, thereby overcoming the limitations of traditional water quality monitoring methods in DO inversion. It not only intuitively explained the temporal and spatial variation patterns of DO concentration but also laid a foundation for further in-depth exploration of the interactions between DO and other water quality parameters.

Dissolved oxygen (DO) concentration in aquatic systems plays a vital role in water aquaculture. An innovative approach that combines feature selection and ensemble learning to predict DO in aquatic ecosystems was proposed. Feature selection was first performed using Maximum Information Coefficient (MIC). Five machine learning algorithms were then employed to construct five hybrid-MIC models, including K-Nearest Neighbors (KNN), Backpropagation (BP) Neural Network, Long Short-Term Memory (LSTM), Kernel Ridge Regression (KRR), and Support Vector Regression (SVR). Finally, an ensemble-RF prediction model was built using Random Forests(RF). The main findings are as follows: (1) The MIC technique can effectively identify the key factors influencing DO. (2) The MIC significantly improves model performance. (3) The hybrid-MIC model was further improved by the ensemble-RF model, the average R2 and NSE were both as high as 0.99, and the average MAE and RMSE were decreased by 72 % and 64 %, respectively.

Dissolved oxygen (DO) is a critical factor in maintaining healthy aquatic ecosystems, including aquaculture ponds. Low DO levels can lead to hypoxia conditions, which are detrimental to fish health and productivity. To deal with this issue, we intend for a smart monitoring system that predicts hypoxia conditions due to low DO levels in aquaculture ponds. The proposed system collects water quality data using Internet of things (IoT) devices and segments it into different categories based on water quality parameters, with a particular focus on low DO levels. By detecting hypoxia conditions early, fish farmers can take corrective measures to prevent fish mortality and improve fish health. To achieve this, our proposed system uses a lightweight Spatially Shared Attention Long Short-Term Memory (SSA-LSTM) model that captures both temporal and spatial dependencies of DO content in water, enabling accurate prediction of hypoxia conditions. Our model outperforms traditional LSTM models and other existing state-of-the-art models, achieving 99.8% accuracy. The proposed system provides a reliable and efficient solution to monitor hypoxia conditions in aquaculture systems and help fish farmers make informed decisions for optimal fish health and productivity. [ABSTRACT FROM AUTHOR]

Recent concern over energy use in wastewater treatment plants (WWTPs) has spurred research on enhancing efficiency and identifying energy-saving technologies. Treating one cubic meter of wastewater consumes at least 0.18 kWh of electricity. About 50% of the energy consumed during this process is attributed to aeration, which varies based on treatment quality and facility size. To harness energy savings in WWTPs, the transition from traditional controls to artificial intelligence (AI)-based strategies has been observed. Research in this area has demonstrated significant improvements to the efficiency of wastewater treatment. This contribution offers an extensive review of the literature from the past decade. It aims to contribute to the ongoing discourse on improving the efficiency and the sustainability of WWTPs. It covers conventional and advanced control strategies, with a particular emphasis on AI-based control utilizing algorithms such as neural networks and fuzzy logic. The review includes four key areas of wastewater treatment AI research as follows: parameter forecasting, performance analysis, modeling development, and process optimization. It also points out potential disadvantages of using AI controls in WWTPs as well as research gaps such as the limited translation of AI strategies from research to real-world implementation and the challenges associated with implementing AI models outside of simulation environments. [ABSTRACT FROM AUTHOR]

Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and deep learning for predicting river water quality. Initially, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to split the internal series of DO into numerous internal mode functions (IMFs). Subsequently, we employed multi-scale fuzzy entropy (MFE) to compute the entropy values for each IMF component. Time-varying filtered empirical mode decomposition (TVFEMD) is used to further extract features in high-frequency subsequences after linearly aggregating the high-frequency sequences. Finally, support vector machine (SVM) and long short-term memory (LSTM) neural networks are used to predict low- and high-frequency subsequences. Moreover, by comparing it with single models, models based on ‘single layer decomposition-prediction-ensemble’ and combination models using different methods, the feasibility of the proposed model in predicting water quality data for the Xinlian section of Fuhe River and the Chucha section of Ganjiang River was verified. As a result, the combined prediction approach developed in this work has improved generalizability and prediction accuracy, and it may be used to forecast water quality in complicated waters. HIGHLIGHTS Quadratic modal decomposition of water quality data to extract more informative features.; High-frequency and low-frequency sequences were separately predicted using the appropriate deep-learning models, respectively.; The model proposed in this study has more accurate point prediction and interval prediction results compared to other models.;

River pollution poses a significant concern for the people of India who heavily rely on river water for daily needs. This study focuses on assessing the status of river pollution in West Bengal, India, with a particular emphasis on a major canal (N22°39'08.75" E88°21'27.60") near Dakhineswar New Jetty, known for its Kali Temple and frequent ritual activities. The region's dependence on river water is evident in daily life. Analysis of three consecutive years (2016-2018) reveals alarming levels of Total Coliform, reaching up to 8.18 x 106 MPN/100ml during the premonsoon season. Disturbingly high concentrations of Lead (0.069 mg/L) and Mercury (0.008 mg/L) are recorded. Average Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) are 2.3 mg/L and 7.9 mg/L, respectively, during the premonsoon. The impact of wastewater on Bitter Gourd (Momordica charantia L.) manifests in detrimental changes to both morphological and biochemical parameters, including total chlorophyll and protein content. Low molecular weight peptide(s) (3.0-0.5 kDa) are extracted, purified, and analyzed through ion exchange resin column, ultra-filtration, and HPLC. HPLC analysis distinguishes the presence of peptides compared to the control, revealing adverse effects of wastewater. The antioxidant activity of low molecular weight peptide(s) is demonstrated through DPPH scavenging. Bioassays, such as the germination percentage of wastewater-treated Bitter Gourd seeds, indicate inhibition. The intensified impact of wastewater during the premonsoon season underscores the urgency of addressing river pollution in this region. [ABSTRACT FROM AUTHOR]

Lake Mead located in the Arizona–Nevada region of the Mohave Dessert is a unique and complex water system whose flow follows that of a warm monomictic lake. Although monomictic lakes experience thermal stratification for almost the entire year with a period of complete mixing, the lake on occasion deviates from this phenomenon, undergoing incomplete turnovers categorized with light stratifications every other year. The prolonged drought and growing anthropogenic activities have the potential to considerably impact the quality of the lake. Lake Mead and by extension the Boulder Basin receive cooler flow from the Colorado River and flow with varying temperatures from the Las Vegas Wash (LVW), which impacts its stratification and complete turnovers. This study analyzes four key water quality parameters (WQPs), namely, total dissolved solids (TDS), total suspended solids (TSS), temperature, and dissolved oxygen (DO), using statistical and spatial analyses to understand their variations in light of the lake stratifications and turnovers to further maintain its overall quality and sustainability. The study also evaluates the impacts of hydrological variables including in and out flows, storage, evaporation, and water surface elevation on the WQPs. The results produced from the analysis show significant levels of TDS, TSS, and temperature from the LVW and Las Vegas Bay regions compared with the Boulder Basin. LVW is the main channel for conveying effluents from several wastewater treatment facilities into the lake. We observed an increase in the levels of TDS, TSS, and temperature water quality in the epilimnion compared with the other layers of the lake. The metalimnion and the hypolimnion layer, however, showed reduced DO due to depletion by algal blooms. We observed statistically significant differences in the WQPs throughout various months, but not in the case for season and year, an indication of relatively consistent variability throughout each season and year. We also observed a no clear trend of influence of outflows and inflows on TDS, temperature, and DO. TSS concentrations in the lake, however, remained constant, irrespective of the inflows and outflows, possibly due to the settling of the sediments and the reservoir capacity.

This study optimized the design of an aeration device for pond engineered recirculating aquaculture systems (RASs) whose application is aimed at increasing dissolved oxygen (DO) levels in RAS aquaculture practice. DO is a key factor in aquaculture productivity, and oxygenators are the power devices used for regulating its levels in aquaculture ponds. In this study, grass carp (Ctenopharyngodon idellus) aquaculture trials were conducted in a self-built RAS by using the new aeration device (NAD); the microporous and impeller aeration components were individually tested in terms of performance, and then combined for the orthogonal testing of their operating parameters in order to assess the NAD’s oxygenation capacity. The test results show that the device effectively increased the dissolved oxygen levels in the RAS tank, enhanced the upper–lower water layer exchange and directional flow, and met the design and parameter selection requirements. Compared with the existing RAS oxygenation equipment, the NAD operated with the optimal parameters and increased the oxygen transfer rate in the pond water tank by 122%.

Dissolved oxygen (DO) plays a significant role in maintaining the health of aquatic ecosystems. In this study, we propose a model that incorporates multiple machine learning methods for predicting DO. Firstly, the maximum information coefficient (MIC) was utilized to identify the key drivers of DO. Afterward, the particle swarm optimization (PSO) algorithm was used to enhance the traditional support vector regression (SVR) model, optimizing the penalty factor (c) and the width of the Gaussian kernel function (g). This resulted in the development of a MIC-PSO-SVR-based DO prediction model. As a case study, we analyzed three points (G1, G2, and Z1) located in the mainstem and tributaries of the Shuikou Reservoir in Fujian, China. The original dataset, encompassing various time scales and sample sizes, underwent reclassification. Key factors influencing DO were determined by calculating the MIC values between DO and each monitoring factor. The main findings of this study are as follows: (1) By assessing the correlation between candidate factors and DO, the MIC effectively eliminated irrelevant variables with low correlation, thereby reducing the dataset size. Furthermore, it was observed that the fluctuations in MIC values for each variable stabilized when the sample size exceeded 4000. (2) The model’s performance exhibited improvement with the reduced dataset. For instance, the mean absolute error (MAE) and root mean square error (RMSE) of the hybrid MIC-PSO-SVR model decreased by approximately 66% and 49%, respectively, whereas the R2 and Nash-Sutcliffe efficiency (NSE) are as high as 0.98 and 0.95, showing superior performance compared to the unreduced PSO-SVR model. (3) More importantly, the model successfully predicted sudden hypoxia events in the Shuikou reservoir area from October 11 to November 8, 2021. Additionally, the MIC-PSO-SVR model accurately captured DO changes in the point G2 in front of the Shuikou dam during a hypoxic event in the Shuikou reservoir. The prediction errors during hypoxia were as low as 0.23 mg·L-1 and 0.31 mg·L-1 for 1 h and 24 h, respectively.

Lake Mead located in the Arizona–Nevada region of the Mohave Dessert is a unique and complex water system whose flow follows that of a warm monomictic lake. Although monomictic lakes experience thermal stratification for almost the entire year with a period of complete mixing, the lake on occasion deviates from this phenomenon, undergoing incomplete turnovers categorized with light stratifications every other year. The prolonged drought and growing anthropogenic activities have the potential to considerably impact the quality of the lake. Lake Mead and by extension the Boulder Basin receive cooler flow from the Colorado River and flow with varying temperatures from the Las Vegas Wash (LVW), which impacts its stratification and complete turnovers. This study analyzes four key water quality parameters (WQPs), namely, total dissolved solids (TDS), total suspended solids (TSS), temperature, and dissolved oxygen (DO), using statistical and spatial analyses to understand their variations in light of the lake stratifications and turnovers to further maintain its overall quality and sustainability. The study also evaluates the impacts of hydrological variables including in and out flows, storage, evaporation, and water surface elevation on the WQPs. The results produced from the analysis show significant levels of TDS, TSS, and temperature from the LVW and Las Vegas Bay regions compared with the Boulder Basin. LVW is the main channel for conveying effluents from several wastewater treatment facilities into the lake. We observed an increase in the levels of TDS, TSS, and temperature water quality in the epilimnion compared with the other layers of the lake. The metalimnion and the hypolimnion layer, however, showed reduced DO due to depletion by algal blooms. We observed statistically significant differences in the WQPs throughout various months, but not in the case for season and year, an indication of relatively consistent variability throughout each season and year. We also observed a no clear trend of influence of outflows and inflows on TDS, temperature, and DO. TSS concentrations in the lake, however, remained constant, irrespective of the inflows and outflows, possibly due to the settling of the sediments and the reservoir capacity. [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) is one of the most important indicators of water quality and an essential measure for the aquatic organisms and the local ecosystem. DO concentrations in karst spring flow involves complex hydrological processes because of the heterogeneous nature of the karst system. In this study, an interpretable and explainable model that integrates the shapley additive explanations (SHAP) algorithm with the long short‐term memory network model (LSTM) is proposed to evaluate the contributions of karst spring discharge, precipitation, water temperature, and specific conductance to DO concentrations in karst spring flow. The hybrid model can predict the temporal fluctuations of DO levels and provide a robust characterization of DO behaviours. To demonstrate the applicability of the proposed model, we adopted the hydrological and meteorological data from Barton Springs, situated within a highly karstified segment of the Edwards aquifer characterized by extensive fractures and openings. The optimal prediction performance is achieved with a 14‐day time step, which is considered as the effective response time between various hydrological processes and DO concentrations at Barton Springs. It reveals that the influence of karst spring discharge, precipitation, water temperature, and specific conductance in previous 14 days collectively contribute to the current DO concentration in karst spring flow. The SHAP values of input features provide both local and global explanations, demonstrating the magnitude and the direction of each feature's impact on DO levels in karst spring flow. In descending order, the contributions of various hydrological processes to DO are ranked as follows: precipitation, karst spring discharge, temperature, and specific conductance. Precipitation and discharge exhibit positive SHAP values, indicating that increases in these hydrological processes contribute to higher DO levels in the karst flow. Water temperature and specific conductance have negative SHAP values, suggesting that higher water temperature and specific conductance will lead to decreased DO levels in the karst flow. [ABSTRACT FROM AUTHOR]

In aquaculture, a paddlewheel aerator is conventionally used to increase dissolved oxygen (DO) in pond water. Occasionally, however, it is driven by a diesel generator, particularly when it is operated in remote areas where an electrical network connection is unavailable. Employing a diesel generator to power a paddlewheel aerator releases carbon monoxide (CO) and increases operational costs. To address these concerns, using a photovoltaic (PV) energy source is a promising option. Although a PV energy source can generate electricity without the need for fossil fuels, its generation is inevitably intermittent owing to weather conditions. This study used battery energy storage (BES) to provide additional energy support to a PV energy source in attempt to power a paddlewheel aerator uninterruptedly. The PV and BES systems were installed on a floating platform of the aerator. The PV and BES capacities were optimized considering techno-economic assessment and weight capacity restrictions. A comparative experiment was conducted over 21 days to investigate the proposed aerator performance compared with that of a standalone floating PV-powered paddlewheel aerator without BES. The results demonstrate that the proposed aerator offered a 10.91% superior DO performance compared with that obtained from the PV-powered paddlewheel aerator without BES.

Supersaturation of dissolved oxygen (DO) and total dissolved gas (TDG) is generated by high dam discharge, excess oxygen production in photosynthesis and increasing temperature in water, which may directly lead to fish suffering from ‘gas bubble disease’ or death. In this paper, under a series of experimental aeration conditions in standing water, it was concluded that aeration had a positively promoting effect on releases of supersaturated DO and TDG, while aeration aperture and aeration depth had inhibitory effects on them. For single factor analysis, aeration had the greatest effect on the release of DO and TDG, the second effect on DO was that of aeration depth and the smallest effect was that of aeration aperture, but the second effect on TDG was that of aeration aperture and the smallest effect was that of aeration depth. Most importantly, the release coefficient of DO was greater than that of TDG, and a quantitative relationship between the release coefficient of DO and TDG and aeration conditions, respectively, was established. An exponential function relationship of the release coefficients of DO and TDG was also established. The results of the research have important guiding significance and theoretical value for reducing the harm caused by supersaturated DO and TDG. HIGHLIGHTS The relationships between dissolved oxygen (DO) and total dissolved gas (TDG) and aeration, aeration depth and aeration aperture were established respectively.; A quantitative relationship between release coefficient (DO/TDG) and aeration conditions was established.; The root mean square error and the absolute average error were used for error analysis to judge the applicability of the equation. These equations were within 10%.; The relationship between dissolved oxygen (DO) and total dissolved gas (TDG) was established.;

An accurate investigation of bio-physical and chemical parameters as proxy of in situ water quality conditions in the Himalayan region is highly challenging owing to cumbersome, strenuous, and physically exhausting sampling exercises at high altitude locations. The upper stretches of Yamuna River in the Himachal Pradesh are typical examples of such sampling locations that have rarely been examined in the past studies. A widely accepted and recognized QUAL 2Kw model is applied for estimating the water quality parameters on the upper segment of the Yamuna River from Paonta Sahib to Cullackpur. These water quality indicators mainly included electric conductivity, pH, dissolved oxygen, temperature, carbonaceous biological oxygen demand (CBOD), inorganic suspended solids, total nitrogen, total phosphorus, and alkalinity, which were systematically investigated for predicting the spatio-temporal trends during the year 2018. A total of 12 distantly located river sites were identified for sample collection and data validation using QUAL 2Kw model. The present investigation attempts to reveal long-term degraded impact of untreated wastewater and biased agricultural practices on the water quality conditions over the upper stretches of Yamuna River. The QUAL 2Kw-derived values for selected variables were inter-compared with in situ values, and any deviation from measured values was ascertained based on meaningful statistical measures. The lower error of RMSE, MRE, and BIAS, corresponding to < 15%, ± 10%., ± 20%, and ~ 1 slope evidently indicated better matchup of values, wherein, higher slope correlation coefficient (R2) of ~ 90% indicated the robust performance of the QUAL 2Kw algorithm in accurately predicting the chosen variables. A comparative assessment of QUAL 2Kw and WASP has been performed to justify aptness of water quality model in scenarios of lean flow. [ABSTRACT FROM AUTHOR]

The current paper deals with the performance evaluation of the application of three soft computing algorithms such as adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural network (BPNN), and deep neural network (DNN) in predicting oxygen aeration efficiency (OAE20) of the gabion spillways. Besides, classical equations, namely multivariate linear and nonlinear regressions (MVLR and MVNLR), including previous studies, were also employed in predicting OAE20 of the gabion spillways. The analysis of results showed that the DNN demonstrated relatively lower error values (root mean square error, RMSE = 0.03465; mean square error, MSE = 0.00121; mean absolute error, MAE = 0.02721) and the highest value of correlation coefficient, CC = 0.9757, performed the best in predicting OAE20 of the gabion spillways; however, other applied models, such as ANFIS, BPNN, MVLR, and MVNLR, were giving comparable results evaluated to statistical appraisal metrics of the relative significance of input parameters based on sensitivity investigation, the porosity (n) of gabion materials was observed to be the most critical parameter, and gabion height (P) had the least impact over OAE20 of the spillways. HIGHLIGHTS An experimental study of the aeration performance of gabion spillways was studied.; Soft computing techniques have been used to evaluate the aeration performance of the gabion spillways using an experimental dataset.; DNN was found to be outperforming the model; however, the proposed ANFIS and BPNN models were performing well.; The sensitivity study suggested that the input parameter, i.e. porosity, was the most sensitive parameter.;

• A eutrophic lake was shown to release more methane than an oligotrophic lake. • Methane production from bottom sediment increases with lower dissolved oxygen. • Strong stratification suppresses methane flux from the anoxic to aerobic layer. • A new equation to estimate methane production was developed. • The new equation was validated by using a one-dimensional methane model. Clarifying the role of sulfate and dissolved oxygen (DO) in methane production may allow for precise and accurate modeling of methane emissions in eutrophic lakes. We conducted field observations of sulfate, methane, and DO concentrations in Lake Abashiri, a typical brackish and eutrophic lake in a cold region, to develop a DO-based method for quantitively estimating methane production in a eutrophic lake and analyzed the results. We found that sulfate concentrations decreased rapidly from 900.0 mg/L in water overlying the sediments to nearly 0.0 mg/L in the bottom sediment. Methane production was almost uniform across sediment depths of 0.05 to 0.25 m, ranging from 1400 to 1800 µmol/m2/day. Also, methane production was found to be a function of DO concentrations in water overlying the bottom and could be modeled by a logistic function: constant production at 1,400 µmol/m2/day for DO concentrations of 0.0 to 3.0 mg/L, rapidly decreasing to 0 µmol/m2/day for DO concentrations of 3.0 to 6.0 mg/L. This methane model was verified using a simple one-dimensional numerical model that showed good agreement with field observations. Our results thus suggest that the proposed methane model reduces uncertainty in estimating methane production in a eutrophic lake. [Display omitted] [ABSTRACT FROM AUTHOR]

Cr (VI) is hazardous to humans and our environment. Leersia hexandra Swartz (L. hexandra) is the first wet chromium hyperaccumulator found in China. This study constructed the L. hexandra constructed wetland-microbial fuel cell (CW-MFC) system to treat Cr (VI) wastewater. It also determined the effects of different dissolved oxygen (DO) concentrations on power generation, pollutant removal, and Cr (VI) reduction. Cathode aeration promoted the voltage output and pollutant removal of the L. hexandra CW-MFC when the DO concentration was 4.5 mg·L−1: the highest voltage was 520 mV, the chemical oxygen demand (COD) removal rate was 93.73%, and the Cr (VI) removal rate was 97.77%. Moreover, the increase in the DO concentration improved the absorption of heavy metal Cr by the substrate and L. hexandra, and promoted the transformation from Cr (VI) to Cr (III). Chromium mostly exists as a residue with low toxicity and low mobility in L. hexandra and the substrate. This proves that the increased DO concentration promotes the redox reaction in the system and plants, reducing Cr (VI) to Cr (III). At the same time, the key micro-organism Geobacter that enhances the performance of the system and Cr (VI) reduction was found. The research results can provide a reference for the subsequent CW-MFC treatment of actual Cr-containing wastewater. [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) is one of most important factors which affect wide range physiologic features of fish, including immune responses and intestinal bacterial community. However, the underlying mechanisms remain enigmatic. To address this question, the intestinal bacterial community compositions and the immune features of Atlantic salmon (Salmo salar) grown in recirculating aquaculture systems (RAS) were characterized. Fish were reared under different DO saturation levels, e.g., 200% saturation named high group (H), 100% saturation named control group (CK), and 60% saturation named lower group (L). Large variations in the operational taxonomic units (OTUs) frequency distribution for the intestinal bacterial community of Atlantic salmon were observed. The intestinal bacterial community of all groups was dominated mainly by three phyla, e.g., Proteobacteria, Firmicutes, and Bacteroidetes. Interestingly, Acinetobacter baumannii, an opportunistic pathogen of salmon was increased significantly in L group. We further monitored the immunity features of fish under different DO levels. The results show that leucocyte number, cortisol level, the expressions of interleukin-1β (IL-1β), Toll-like receptor 4 (TLR4), and nucleotide-binding oligomerization domain like protein 2 (NOD2) were higher at significant levels in the L group than those in the other two groups. TLR4 and NOD2 are usually related with the bacterial infections; therefore, it is reasonable to believe that the stronger immune responses observed in the L group might be related with the higher abundance of A. baumannii in the intestine of Atlantic salmon. Overall, these findings demonstrated that low DO level may induce stronger immunity responses in Atlantic salmon. [ABSTRACT FROM AUTHOR]

Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.

While fjords often have low oxygen concentrations in their deep waters, this research identified seasonal, near-surface hypoxia (≤ 2 mL L-1 or 2.9 mg L-1) through a year-long monthly time series in Clayoquot Sound, British Columbia. Temperature, salinity, and oxygen data were collected monthly in the upper 50 m at three stations in Herbert Inlet from June 2020 to July 2021, marking the first time series of its kind in a Clayoquot Sound fjord. Hypoxic conditions were shallowest (minimum depth of 12 m) and most intensified in summer; near-surface hypoxia was recorded at one or more stations in all months except in winter. Considering that many local marine species, including wild Pacific salmon, experience adverse effects at oxygen concentrations much higher than the hypoxic threshold, we note that 50 to 100% of the upper 50 m of Herbert Inlet consistently presented low oxygen concentrations (defined here as a guideline as ≤ 4.9 mL L-1 or 6.9 mg L-1) during the 14-month study period. Previous observations collected sporadically since May 1959 confirmed the presence of hypoxic conditions in the past. These findings suggest that long-term, multidisciplinary studies are needed to understand and predict the impact of hypoxia and deoxygenation on wild salmon stocks as climate changes.

Gamak Bay is frequently affected by hypoxia in summer. Therefore, this study aimed to examine the effect of hypoxia on the mesozooplankton community of this region. Mesozooplankton samples were obtained biweekly (28 times) at a hypoxic station (inner bay) and a normoxic station (middle bay) using a conical net (mesh size 200 μm; mouth diameter 30 cm) from April 2020 to April 2021. In the inner bay, hypoxia was observed a total of five times between early June and late September, whereas no instances of hypoxia were detected in the middle bay. During the hypoxic period, mesozooplankton abundance was higher in the inner bay than the middle bay (p < 0.05). During the normoxic period, there was no difference between the inner bay and the middle bay (p > 0.05). The dominant species in the hypoxic period were three species of cladocerans (Pleopis polyphemoides, Pseudoevadne tergestina, Penilia avirostris) and the copepod Acartia sinjiensis. The abundance of cladocerans was higher in the inner bay, and that of A. sinjiensis was higher in the middle bay (p < 0.05). In addition to water temperature, salinity, and size-fractionated Chl-a concentration, mesozooplankton abundance was also significantly correlated with the bottom DO concentration. Collectively, our findings suggest that hypoxia can affect mesozooplankton abundance and occurrence times.

The oxygen minimum zone has a significant effect on primary production, marine biodiversity, food web structure, and marine biogeochemical cycle. The Arabian Sea oxygen minimum zone (ASOMZ) is one of the largest and most extreme oxygen minimum zones in the world, with a positional decoupling from the region of phytoplankton blooms. The core of the ASOMZ is located to the east of the high primary production region in the western Arabian Sea. In this study, a coupled physical–biogeochemical numerical model was used to quantify the impact of ocean circulation and settling of particulate organic matters (POMs) on the decoupling of the ASOMZ. Model results demonstrate that the increased (decreased) dissolved oxygen replenishment in the western (central) Arabian Sea is responsible for decoupling. The oxygen-rich intermediate water (200–1,000 m) from the southern Arabian Sea enters the Arabian Sea along the west coast and hardly reaches the central Arabian Sea, resulting in a significant oxygen replenishment in the western Arabian Sea high-productivity region (Gulf of Aden) but only a minor contribution in the central Arabian Sea. Besides that, the POMs that are remineralized to consume central Arabian Sea dissolved oxygen comprises not only local productivity in winter bloom but also the transport from the western Arabian Sea high-productivity region (Oman coast) in summer bloom. More dissolved oxygen replenishment in the western Arabian Sea, and higher dissolved oxygen consumption and fewer dissolved oxygen replenishment in the central Arabian Sea could contribute to the decoupling of the ASOMZ and phytoplankton productive zone.

In this study, we aimed to synthesize magnetically well-dispersed nanosensors for detecting dissolved oxygen (DO) in water, and explore their biological applications. Firstly, we synthesized two kinds of magnetic nanoparticle with average sizes of approximately 82 nm by one-step emulsion polymerization: polystyrene magnetic nanoparticles (Fe3O4@Os1-PS) and polymethylmethacrylate magnetic nanoparticles (Fe3O4@Os1-PMMA). Both types of nanoparticle present good dispersibility and fluorescence stability. The nanoparticles could be used as oxygen sensors that exhibited a high DO-sensitivity response in the range 0-39.30 mg/L, with a strong linear relationship. The nanoparticles have good magnetic properties, and so they could be recycled by magnet for further use. Recovered Fe3O4@Os1-PS still presented high stability after continued use in oxygen sensing for one month. Furthermore, Fe3O4@Os1-PS was employed for detecting the bacterial oxygen consumption of Escherichia coli (E-coli) to monitor the metabolism of bacteria. The results show that Fe3O4@Os1-PS provide high biocompatibility and non-toxicity. Polystyrene magnetic nanoparticles therefore present significant potential for application in biological oxygen sensing. [ABSTRACT FROM AUTHOR]

The response of marine ammonia-oxidizing archaea (AOA) to environmental stress is reflected in changes in their membrane lipid composition, particularly the unique isoprenoid glycerol dialkyl glycerol tetraethers (iGDGTs). However, the influence of dissolved oxygen (DO) on the composition of iGDGTs in the ocean remains unclear. This study aims to investigate the link between DO levels and the fractional abundances of iGDGTs in the East China Sea (ECS) to establish a redox proxy. Results suggested that the absolute abundances of iGDGTs were influenced by Thaumarchaeota biomass when DO concentrations exceeded 2 mg/L. Vertical distributions of iGDGTs through the water column suggested their transport from bottom waters to sediments. Increasing proportions of iGDGT-0 and the sum of iGDGT-1, iGDGT-2 and iGDGT-3 from suspended particulate matters to surface sediments indicated their preferential preservation. DO concentrations (2–6 mg/L) in the water column showed a significant positive correlation with the relative abundance of crenarchaeol (cren%) but a negative correlation with iGDGT-0%, suggesting insufficient DO levels to promote AOA cyclization. However, bottom DO concentrations exhibited significant negative correlations with both cren% and iGDGT-0% in surface sediments, attributed to enhanced cyclization of iGDGTs and Euryarchaeota abundance within more reducing sediments, respectively. The consistent relationship between iGDGT-0% and DO in both water column and sediments enabled iGDGT-0% to be a potential redox proxy. Temporal variations in iGDGT-0% in the ECS in recent decades aligned well with in-situ DO monitoring data, further validating iGDGT-0% as a promising redox proxy. • Oxygen impact on iGDGTs in the East China Sea was studied to develop a redox proxy. • Thaumarchaeota determined the abundance of iGDGTs when oxygen was available. • Sediment iGDGTs showed preferential preservation of iGDGT-0, −1, −2, −3. • Correlations between iGDGT-0% and DO in water/sediments enabled it as a redox proxy. • iGDGT-0% was used to reconstruct DO changes in the East China Sea in past decades. [ABSTRACT FROM AUTHOR]

• A highly accurate and robust DO prediction model was developed by exploring the combination of feature selection and ensemble learning. • The effects of different times and sample sizes on the screening of MIC features were thoroughly considered to more scientifically use MIC to screen for key drivers affecting DO. • The ensemble-RF model was able to capture and simulate the trend of DO well during the model construction period, both during the period of steady change and during the period of sudden hypoxia. Dissolved oxygen (DO) concentration in aquatic systems plays a vital role in water aquaculture. An innovative approach that combines feature selection and ensemble learning to predict DO in aquatic ecosystems was proposed. Feature selection was first performed using Maximum Information Coefficient (MIC). Five machine learning algorithms were then employed to construct five hybrid-MIC models, including K-Nearest Neighbors (KNN), Backpropagation (BP) Neural Network, Long Short-Term Memory (LSTM), Kernel Ridge Regression (KRR), and Support Vector Regression (SVR). Finally, an ensemble-RF prediction model was built using Random Forests(RF). The main findings are as follows: (1) The MIC technique can effectively identify the key factors influencing DO. (2) The MIC significantly improves model performance. (3) The hybrid-MIC model was further improved by the ensemble-RF model, the average R2 and NSE were both as high as 0.99, and the average MAE and RMSE were decreased by 72 % and 64 %, respectively. [ABSTRACT FROM AUTHOR]

• Identifying abrupt depletion of dissolved oxygen effectively prevents biological catastrophic events in freshwater lakes. • Coupling model of convolution neural network (CNN) and BiLSTM with attention mechanism were developed for lake DO prediction. • AC-BiLSTM model outperforms BiLSTM and CNN-BiLSTM models in DO prediction accuracy and ability of capturing dependencies. • Universal importance of TN and TP to DO reveals point source pollution is critical cause of DO depletion. • NTU importance indicates flow rate changes brought by tributaries increase self-purification capacity of monitoring site. Depletion of dissolved oxygen (DO) is a significant incentive for biological catastrophic events in freshwater lakes. Although predicting the DO concentrations in lakes with high-frequency real-time data to prevent hypoxic events is effective, few related experimental studies were made. In this study, a short-term predicting model was developed for DO concentrations in three problematic areas in China's Chaohu Lake. To predict the DO concentrations at these representative sites, which coincide with biological abnormal death areas, water quality indicators at the three sampling sites and hydrometeorological features were adopted as input variables. The monitoring data were collected every 4 h between 2020 and 2023 and applied separately to train and test the model at a ratio of 8:2. A new AC-BiLSTM coupling model of the convolution neural network (CNN) and the bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) was proposed to tackle characteristics of discontinuous dynamic change of DO concentrations in long time series. Compared with the BiLSTM and CNN-BiLSTM models, the AC-BiLSTM showed better performance in the evaluation criteria of MSE, MAE, and R2 and a stronger ability to capture global dependency relationships. Although the prediction accuracy of hypoxic events was slightly worse, the general time series characteristics of abrupt DO depletion were captured. Water temperature regularly affects DO concentrations due to its periodic variations. The high correlation and the universal importance of total nitrogen (TN) and total phosphorus (TP) with DO reveals that point source pollution are critical cause of DO depletion in the freshwater lake. The importance of NTU at the Zhong Miao Station indicates the self-purification capacity of the lake is affected by the flow rate changes brought by the tributaries. Calculating linear correlations of variables in conjunction with a permutation variable importance analysis enhanced the interpretability of the proposed model results. This study demonstrates that the AC-BiLSTM model can complete the task of short-term prediction of DO concentration of lakes and reveal its response features of timing and magnitude of abrupt DO depletion. [Display omitted] [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this phenomenon, predicting and modeling DO variation is a challenging process. Accordingly, this study introduces an innovative Deep Learning Neural Network (DLNN) methodology to model and predict DO concentrations for the Egyptian Rashid coastal inlet, leveraging field-recorded WQ and hydroclimatic datasets. Initially, statistical and exploratory data analyses are performed to provide a thorough understanding of the relationship between DO fluctuations and associated WQ and hydroclimatic stressors. As an initial step towards developing an effective DO predictive model, conventional Machine Learning (ML) approaches such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR) are employed. Subsequently, a DLNN approach is utilized to validate the prediction capabilities of the investigated conventional ML approaches. Finally, a sensitivity analysis is conducted to evaluate the impact of WQ and hydroclimatic parameters on predicted DO. The outcomes demonstrate that DLNN significantly improves DO prediction accuracy by 4% compared to the best-performing ML approach, achieving a Correlation Coefficient of 0.95 with a root mean square error (RMSE) of 0.42 mg/l. Solar radiation (SR), pH, water levels (WL), and atmospheric pressure (P) emerge as the most significant hydroclimatic parameters influencing DO fluctuations. Ultimately, the developed models could serve as effective indicators for coastal authorities to monitor DO changes resulting from accelerated climate change along the Egyptian coast. [Display omitted] • Climate change significantly affects dissolved oxygen at coastal inlets. • Comparative study confirms DLNN's superior prediction capabilities over ML models. • Innovative DLNN model enhances DO prediction accuracy by 4% over top ML approaches. • Key hydroclimatic factors affecting DO include SR, pH, AP, and water levels. • Developed models serve as effective tools for coastal authorities to monitor climate impacts. [ABSTRACT FROM AUTHOR]

Water bodies are crucial components of urban ecology. The development of rapid and timely water-quality assessment tools using easily measured variables is essential for the health management of urban water bodies. In this study, we focused on the dissolved oxygen (DO) of Baiyangdian Lake using 251 sets of empirically measured water quality data and corresponding Sentinel-2 satellite images. Nine machine learning algorithms were then used to develop a rapid detection algorithm for the spatial distribution of the DO concentration in Baiyangdian Lake. This study successfully applied these methods to invert the DO concentration in Baiyangdian Lake during spring, summer, and autumn. The results indicated that extra tree regression (ETR) provided the most accurate and stable results for inverting the DO concentration among the nine machine learning methods. In contrast, AdaBoost regression (ABR), Bayesian ridge regression (BRR), and support vector machines (SVM) exhibit relatively poor regression performance and lack sensitivity to DO concentrations. Moreover, the DO concentration in Baiyangdian Lake ranged from approximately 0 to 12 mg/L, with notable spatiotemporal variations. The highest overall DO concentration was observed in the spring, particularly in the southern region. The DO concentration significantly decreased during summer compared to that in spring, with higher values in the southwestern area and lower values in the northern region. The DO concentration reached its lowest value in autumn, with slightly higher values in the southern region. This study focused on the estimation and inversion of DO concentrations in the water bodies of Baiyangdian Lake. By introducing and comparing the performances of commonly used machine learning models, a rapid estimation of the DO concentration was achieved, thereby overcoming the limitations of traditional water quality monitoring methods in DO inversion. It not only intuitively explained the temporal and spatial variation patterns of DO concentration but also laid a foundation for further in-depth exploration of the interactions between DO and other water quality parameters. • Nine machine learning methods were employed to train and predict Dissolved Oxygen (DO) concentration in Baiyangdian Lake. • Extra Tree Regression (ETR) yielded the best results for inverting DO concentration in Baiyangdian Lake. • AdaBoost Regression (ABR), Bayesian Ridge Regression (BRR), and Support Vector Machine (SVM) showed subpar regression result. • The DO concentration in Baiyangdian Lake ranges from approximately 0 to 12 mg/L. • The DO concentration in Baiyangdian Lake exhibits obvious spatiotemporal differentiation characteristics. [ABSTRACT FROM AUTHOR]

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 μmol/kg to 0.99 and 4.01 μmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region. • The accuracies of DO retrieval obtained from the ML methods at different depths are analyzed. • Global DO spatiotemporal distributions obtained from the ML methods are exhibited. • The importance of input attributes for subsurface DO retrieval is determined. • The superiority of ML methods for subsurface DO retrieval is discussed. [ABSTRACT FROM AUTHOR]

Due to the pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) commonly known as COVID-19, a nationwide lockdown came into effect in India from 24th March midnight, 2020, which slowed down the movement of vehicles, operation of industries etc. Due to this reason a drastic change in the environment occurred and it caused the reduction of pollution level in the environment. The study mainly focuses on the positive impacts of lockdown in India. Studies have shown that after the implementation of lockdown, the level of the various major air pollution constituents of such as particulate matter (PM2.5, PM10, etc.), NO2, CO and ozone etc. in the air has reduced substantially. The air quality index data have clearly shown the difference in the level of air pollution between the year 2019 and 2020. The study was mainly focused on the comparison of air quality parameters such as particulate matter, NO2, CO and ozone etc. as well as the water pollution parameters such as Biochemical oxygen demand (BOD), chemical oxygen demand (COD) and dissolved oxygen (DO) for the River Yamuna and it was observed that there was substantial amount of positive change in pollutants after lockdown compared to what was before lockdown. The observations are carried out in a time frame manner such as before lockdown and after lockdown and results have been found with a huge difference in reduction of air pollution as well as water pollution. [ABSTRACT FROM AUTHOR]

High-accuracy ship-based observations were conducted at 80°E in the Indian Ocean. Salinity below the mixed layer in 2019 was observed to be lower than that in 1995. This decrease in salinity was mainly attributed to anomalous advection associated with one of the strongest positive Indian Ocean dipole (pIOD) events in 2019 through analysis of the gridded time series of the salinity distributions based on the Argo float array. Increases and decreases in nitrate and dissolved inorganic carbon (DIC) and dissolved oxygen (DO), respectively, were also detected on the isopycnal surfaces where decreases in salinity were observed, suggesting that the anomalous upwelling and westward advection associated with the pIOD in the eastern part of the equatorial region resulted in low-salinity, low-oxygen, and nutrient-rich waters in the central off-equatorial region of the Indian Ocean. However, downward isopycnal heaving, which was also associated with the pIOD, was too strong to have increased nitrate below the mixed layers, and thus might have suppressed biological activity. The heaving also affected the DIC and DO distributions, and the effect of interannual changes such as those associated with the Indian Ocean dipole is essential to estimating changes in anthropogenic carbon storage. This research represents a case study, based on only two occupations; therefore, an assessment utilizing more intensive observations and more realistic numerical simulations is necessary in the future.

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Context: The lack of dissolved oxygen (DO) in water bodies has become a serious problem for several ecosystems due to drastic changes in their climatic, biological, and physical conditions. The presence of microorganisms, organic matter, and clays, which are carried by landslides and rainwater, affect the process of restoration and transfer of DO in water, thus making it necessary to study how these factors affect the process of DO transfer in water bodies. Method: This work is focused on studying the influence of clays during the DO transfer process in water bodies. To this effect, samples of clays from the region of Paipa, Boyacá, were added in 1, 3 and 5% by weight into the samples of clean water. The aeration process was performed using a constant flow diffusion system. The tests were carried out until the saturation of dissolved oxygen in each of the water samples was obtained. Results: The results show that the presence of clays during the transfer of DO have little influence on bodies of water. However, variations were observed in time intervals of

Sungai Siak merupakan sungai yang menjadi sumber air bagi masyarakat bahkan hewan dan tumbuhan untuk memenuhi kebutuhan sebagai kelangsungan hidup, sehingga kualtias air sungai ini perlu dijaga dan dipelihara. Analisa kapasitas muatan sumber polusi pada air sungai merupakan proses yang rumit sehingga dapat direpresentasikan dengan pendekatan model Qual2Kw. Model ini dapat mendeskripsikan hasil parameter kualitas air yang mendekati angka aktual setelah melakukan kalibrasi. Parameter DO pada skenario 1 dimana tidak menginput debit limbah atau sumber pencemaran maka segmen Tandun merupakan nilai parameter terendah yaitu 4,49 mg/l dan tidak memenuhi standar kualitas air baku mutu kelas I, sedangkan untuk ammonia (NH4) tertinggi berada pada segmen Teluk Ketari yaitu 0,43 mg/l dan nilai ini masih memenuhi standar kualitias air baku mutu kelas I. Skenario 2 dengan menginput debit limbah maka parameter DO hanya pada segmen Petapahan yang memenuhi buku mutu dengan nilai 6,20 mg/l sedangkan untuk NH4 nilai tertinggi berada pada segmen Pantai Cermin dengan nilai 0,04 mg/l dimana angka ini masih memenuhi baku mutu.

Cr (VI) is hazardous to humans and our environment. Leersia hexandra Swartz (L. hexandra) is the first wet chromium hyperaccumulator found in China. This study constructed the L. hexandra constructed wetland-microbial fuel cell (CW-MFC) system to treat Cr (VI) wastewater. It also determined the effects of different dissolved oxygen (DO) concentrations on power generation, pollutant removal, and Cr (VI) reduction. Cathode aeration promoted the voltage output and pollutant removal of the L. hexandra CW-MFC when the DO concentration was 4.5 mg·L−1: the highest voltage was 520 mV, the chemical oxygen demand (COD) removal rate was 93.73%, and the Cr (VI) removal rate was 97.77%. Moreover, the increase in the DO concentration improved the absorption of heavy metal Cr by the substrate and L. hexandra, and promoted the transformation from Cr (VI) to Cr (III). Chromium mostly exists as a residue with low toxicity and low mobility in L. hexandra and the substrate. This proves that the increased DO concentration promotes the redox reaction in the system and plants, reducing Cr (VI) to Cr (III). At the same time, the key micro-organism Geobacter that enhances the performance of the system and Cr (VI) reduction was found. The research results can provide a reference for the subsequent CW-MFC treatment of actual Cr-containing wastewater.

[Display omitted] In this study, a set of novel double-layer-packed sequencing batch biofilm reactors (SBBRs) were constructed to treat secondary effluent with a low carbon-to-nitrogen ratio (C/N = 3.0). All the reactors were operated under identical conditions except dissolved oxygen (DO) in the aerobic section, which was controlled at seven different levels (0.5, 1.0, 1.5, 2.0, 2.5, 3.5 and 4.5 mg/L). It was found that the aerobic section contributed most to performance at an optimal DO of 2.0 mg/L. The change in DO accounted for the varied bacterial communities, which resulted in different performances. Both bacterial community structure and potential functions displayed close associations with performance implying nutrients were mainly removed by simultaneous nitrification-endogenous denitrification and phosphorus removal (SNEDPR) process. Moreover, the proliferation of nonfunctional nitrifiers/denitrifiers led to the inhibition of denitrification resulting in a lower removal of total dissolved nitrogen and phosphorus. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)