Landsat data reveal lake deoxygenation worldwide.

Dissolved oxygen (DO) is a fundamental requirement for the survival of aquatic organisms, which plays a crucial role in shaping the structure and functioning of aquatic ecosystems. However, the long-term DO change in global lakes remains unknown due to limited available data. To address this gap, we integrate Landsat data and geographic features to develop DO estimation models for global lakes using machine learning approaches. The results demonstrated that the trained random forest (RF) model has better performance (R2 = 0.72, and RMSE = 1.24 mg/L) than artificial neural network (ANN) (R2 = 0.66, and RMSE = 1.39 mg/L), support vector machine regression (SVR) (R2 = 0.62, and RMSE = 1.45 mg/L) and extreme gradient boosting (XGBoost) (R2 = 0.72, and RMSE = 1.29 mg/L). Then, we used the trained RF model to reveal the DO long-term (1984-2021) change in surface water (epilimnetic) of 351,236 global lakes with water area ≥ 0.1 km2. The results show that the average epilimnetic DO concentration of global lake was 9.72 ± 1.07 mg/L, with higher DO in the polar regions (latitude > 66.56 °) (10.87 ± 0.54 mg/L) and lower in the equatorial regions (-5 ° < latitude < 5 °) (6.29 ± 0.63 mg/L). We also find widespread deoxygenation in surface water of global lakes, with a rate of - 0.036 mg/L per decade. Meanwhile, the number of lakes and surface area that experiencing DO stress are continuously increasing, with rate of 39 and 212.85 km2, respectively. Our results offer a comprehensive dataset of DO variation spanning nearly 40 years, furnishing valuable insights for formulating effective management strategies, and enhancing the maintenance of the health of aquatic ecosystems.

Assessing the role of natural kelp forests in modifying seawater chemistry.

Climate change is causing widespread impacts on seawater pH through ocean acidification (OA). Kelp forests, in some locations can buffer the effects of OA through photosynthesis. However, the factors influencing this variation remain poorly understood. To address this gap, we conducted a literature review and field deployments of pH and dissolved oxygen (DO) loggers within four habitats: intact kelp forest, moderate kelp cover, sparse kelp cover and barrens at one site in Port Phillip Bay, a wind-wave dominated coastal embayment in Victoria, Australia. Additionally, a wave logger was placed directly in front of the intact kelp forest and barrens habitats. Most studies reported that kelp increased seawater pH and DO during the day, compared to controls without kelp. This effect was more pronounced in densely populated forests, particularly in shallow, sheltered conditions. Our field study was broadly consistent with these observations, with intact kelp habitat having higher seawater pH than habitats with less kelp or barrens and higher seawater DO compared to barrens, particularly in the afternoon and during calmer wave conditions. Although kelp forests can provide local refuges to biota from OA, the benefits are variable through time and may be reduced by declines in kelp density and increased wave exposure.

Impact of untreated tannery wastewater in the evolution of multidrug-resistant bacteria in Bangladesh.

The tannery industry produces one of the worst contaminants, and unsafe disposal in nearby waterbodies and landfills has become an imminent threat to public health, especially when the resulting multidrug-resistant bacteria and heavy metals enter community settings and animal food chains. In this study, we have collected 10 tannery wastewater (TWW) samples and 10 additional non-tannery wastewater (NTW) samples to compare the chemical oxygen demand (COD), pH, biological oxygen demand (BOD), dissolved oxygen (DO), total dissolved solids (TDS), chromium concentration, bacterial load, and antibiotic resistance profiles. While COD, pH, and chromium concentration data were previously published from our lab, this part of the study uncovers that TWW samples had a significantly higher bacterial load, compared to the non-tannery wastewater samples (5.89 × 104 and 9.38 × 103 cfu/mL, respectively), higher BOD and TDS values, and significantly lower DO values. The results showed that 53.4, 46.7, 40.0, and 40.0% of the TWW isolates were resistant to ceftriaxone, erythromycin, nalidixic acid, and azithromycin, respectively. On the other hand, 20.0, 30.0, 50.0, and 40.0% of the NTW isolates were resistant to the same antibiotics, respectively. These findings suggest that the TWW isolates were more resistant to antibiotics than the NTW isolates. Moreover, the TWW isolates exhibited higher multidrug resistance than the NTW isolates, 33.33, and 20.00%, respectively. Furthermore, spearman correlation analysis depicts that there is a negative correlation between BOD and bacterial load up to a certain level (r = - 0.7749, p = 0.0085). In addition, there is also a consistent negative correlation between COD and bacterial load (r = - 0.7112, p = 0.0252) and TDS and bacterial load (r = - 0.7621, p = 0.0104). These findings suggest that TWW could pose a significant risk to public health and the environment and highlight the importance of proper wastewater treatment in tannery industries.

Enhancing nitrogen removal by simultaneous nitritation and denitritation in a multi-cycle SBR with supplementation of solid carbon sources.

Simultaneous nitritation and denitritation have the potential to significantly improve nitrogen removal in sewage treatment processes. However, their application in low-strength sewage treatment systems presents challenges. This study explored the impact of four solid carbon sources (SCSs) on N-removal via nitrite in a multi-cycle SBR with biocarriers. Results showed that both N-removal efficiencies and nitrite accumulation rates increased with higher COD/N ratios, indicating that high COD/N ratios can improve the competition between denitrifiers and nitrite-oxidizing bacteria for nitrite, leading to stable simultaneous nitritation and denitritation. The supplementation of SCSs further enhanced this high-efficiency N-removal process, with polybutylene succinate (PBS) and polycaprolactone (PCL) showing greater increases in N-removal via nitrite than poly-hydroxybutyrate (PHB) and poly-hydroxyalkanoate (PHA). Moreover, PBS showed the most significant increase in denitrification efficiency in anoxic conditions, while PHA was the most effective external SCS at a moderate level of dissolved oxygen. These findings suggest that the incorporation of external SCSs can facilitate the simultaneous nitritation and denitrification process in multi-cycle SBRs, underscoring the importance of selecting an appropriate SCS for optimizing nitrogen removal in sewage treatment projects.

Electrochemical production of HO2- and O2 for sulfide removal from sewage.

In this study, a comparative analysis of two electrochemical methods for sulfide control in sewer networks was performed for the first time. In addition, the mechanism of sulfide control by HO2- was elucidated, and an analysis of the device operation and electrolyte selection was performed. The two-electron oxygen reduction reaction (2e--ORR) using untreated gas diffusion electrode (GDE) was superior to the hydrogen evolution reaction (HER) using stainless-steel mesh in terms of cell voltage, product formation, and sulfide suppression. The GDE maintained a stable HO2⁻ production capacity, achieving a concentration of 4566.6 ± 173.3 mg L⁻¹ with a current efficiency (CE) of 84.13 ± 3.5 %. During the electrolysis period, a stable dissolved oxygen (DO) level in sewage was consistently observed due to continuous in-situ oxygen production in anode. HO2- exhibited a notable increase in sewage pH (10.20 ± 0.01), effectively inhibiting the release of 99.93 % of sulfides. Moreover, the combined treatment of HO2- and DO significantly surpassed that of individual treatments. Seawater treated with cation exchange resin (CER) emerged as the most promising alternative to freshwater as the electrolyte. Overall, this study demonstrates that in-situ generation of HO2⁻ and oxygen is a more effective strategy for sulfide control in sewer systems.

Effect of dissolved oxygen on sulfur autotrophic denitrification and how to address it: An experimental and modelling work.

Sulfur autotrophic denitrification (SAD) using elemental sulfur as the electron donor has aroused increasing interest of its application in treating secondary effluent from wastewater treatment plants (WWTPs). However, high influent dissolved oxygen (DO) in secondary effluent would limit the SAD process. This study examined the effect of different DO concentrations on SAD. Results revealed that both low (0-0.5 mg/L) and moderate (2.5-3.5 mg/L) DO concentrations would not harm the nitrate removal rate (NRR) (p > 0.05). However, high DO concentration (5.5-6.5 mg/L) significantly decreased the NRR (p < 0.05) through strong competition over the nitrate for electrons and cutting the relative abundance of sulfur-oxidizing bacteria (SOB). Both modeling and experimental results showed that applying internal reflux could serve as a strategy to mitigate the negative effect of high DO concentration, while keeping an appropriate ratio was crucial. When treating real membrane bioreactor (MBR) effluent with high DO concentration (5.5-6.5 mg/L), an internal reflux ratio of 0.5 boosted the NRR by 1.5 times. This study provided potential reference and strategy for dealing with high DO concentration wastewater by applying SAD technology.

Optimizing operation strategy to improve storage of intracellular carbon sources in anaerobic/oxic/anoxic system: Enhanced nitrogen removal by endogenous denitrification.

Endogenous denitrification (ED) can make full use of the carbon sources and avoid replenishment of it. However, strengthening the storage of intracellular carbon sources is an important factor in improving ED efficiency. In this study, employed batch experiments using real domestic wastewater in the anaerobic/oxic (A/O) process. The anaerobic and oxic processes were run for 4 h under ambient conditions with the dissolved oxygen (DO) concentrations in the oxic stage controlled at 0.5, 1.0, 1.5, and 3.0 mg/L, respectively. The results showed that the content of poly-ß-hydroxyalkanoates (PHA) reached its peak at 60 min (1.25 mmolC/L). And with DO concentrations of 1.5 mg/L, the contents of glycogen (Gly) were 27.74 mmolC/L. Subsequently, the AOA-SBR was established to investigate its effect on the long-term nitrogen removal performance of domestic wastewater by optimizing the anaerobic time and DO concentrations. The results showed that at an anaerobic time of 60 min and DO concentration of 1.5 mg/L, the storage of the intracellular carbon sources was highest and the total nitrogen (TN) removal efficiency increased to 82.12%. In addition, Candidatus Competibacter dominated gradually in the system as the strategy was optimized.

Activation of iron oxides through organic matter-induced dissolved oxygen penetration depth dynamics enhances iron-cycling driven ammonium oxidation in microaerobic granular sludge.

The iron redox cycle can enhance anammox in treating low-strength ammonia wastewater. However, maintaining an effective iron redox cycle and suppressing nitrite-oxidizing bacteria in a one-stage partial nitritation and anammox (PN/A) process poses challenges during long-term aeration. We proposed a novel and simple strategy to achieve an efficient iron redox cycle in an iron-mediated anoxic-microaerobic (A/O) process by controlling organic matter (OM) at medium-strength levels (30-110 mg COD/L) in microaerobic granular sludge (MGS)-dominated reactor. The developed A/O process consistently achieved >90 % OM removal and >75 % nitrogen removal. Medium-strength OM varied the penetration depths of dissolved oxygen (DO) in MGS, regulating redox conditions and promoting redox reactions across MGS layers, thus activating accumulated inert iron oxides. Ammonia-oxidizing bacteria (Nitrosomonas), iron-reducing bacteria (e.g., Ignavibacterium, Geobacter), and anammox bacteria (Ca. Kuenenia) coexisted harmoniously in MGS. This coexistence ensured high anammox and Feammox rates along with a robust iron redox cycle, thereby mitigating the adverse impacts of fluctuating DO and OM on one-stage PN/A process stability. The identification of iron reduction-associated genes within Ca. Kuenenia, Ignavibacterium, and Geobacter suggests their potential roles in supporting Feammox coupled in one-stage PN/A process. This study introduces an iron-cycle-driven A/O process as an energy-efficient alternative for simultaneous carbon and nitrogen removal from low-strength wastewater.

Saturated dissolved oxygen-driven high-rate and ultrastable partial nitrification in municipal wastewater.

Achieving stable and high-rate partial nitrification (PN) remains a worldwide technical conundrum in low-strength mainstream conditions. This study successfully achieved ultrarapid mainstream PN within 8 days under a saturated dissolved oxygen (DO) supply strategy, reaching a record-breaking PN rate of over 1.0 kg N m-3 d-1 treating municipal wastewater. Stable PN was maintained for over 200 days with an ultrahigh nitrite accumulation ratio of 98.5 ±â€¯0.9 %, resilient to seasonal fluctuations in temperature (16.0-25.6 °C) and load (NH4+-N, 40-80 mg N/L). Kinetics revealed a remarkable 159.1-fold increase in the maximum activity ratio of ammonia-oxidizing bacteria (AOB) to nitrite-oxidizing bacteria (NOB). The faster response of AOB to saturated DO stimulated its highest activity difference with NOB, contributing to the AOB (Nitrosomonas oligotropha) boom and the elimination of NOB groups (-99.9 %). Our results highlight the importance of promoting AOB rather than solely focusing on NOB suppression for initiating and stabilizing high-rate mainstream PN.

Efficient production of phenyllactic acid in Escherichia coli via metabolic engineering and fermentation optimization strategies.

Phenyllactic acid (PhLA), an important natural organic acid, can be used as a biopreservative, monomer of the novel polymeric material poly (phenyllactic acid), and raw material for various medicines. Herein, we achieved a high-level production of PhLA in Escherichia coli through the application of metabolic engineering and fermentation optimization strategies. First, the PhLA biosynthetic pathway was established in E. coli CGSC4510, and the phenylalanine biosynthetic pathway was disrupted to improve the carbon flux toward PhLA biosynthesis. Then, we increased the copy number of the key genes involved in the synthesis of the PhLA precursor phenylpyruvic acid. Concurrently, we disrupted the tryptophan biosynthetic pathway and enhanced the availability of phosphoenolpyruvate and erythrose 4-phosphate, thereby constructing the genetically engineered strain MG-P10. This strain was capable of producing 1.42 ± 0.02 g/L PhLA through shake flask fermentation. Furthermore, after optimizing the dissolved oxygen feedback feeding process and other conditions, the PhLA yield reached 52.89 ± 0.25 g/L in a 6 L fermenter. This study successfully utilized metabolic engineering and fermentation optimization strategies to lay a foundation for efficient PhLA production in E. coli as an industrial application.

Unveiling the temporal variability of gas transfer coefficients of streams based on high-frequency dissolved oxygen measurements.

Greenhouse gas (GHG) emissions from streams and rivers are important sources of global GHG emissions. As a crucial parameter for estimating GHG emissions, the gas transfer coefficient (expressed as K600 at water temperature of 20 °C) has uncertainties. This study proposed a new approach for estimating K600 based on high-frequency dissolved oxygen (DO) data and an ecosystem metabolism model. This approach combines the numerical solution method with the Markov Chain Monte Carlo analysis. This study was conducted in the Chaohu Lake watershed in Southeastern China, using high-frequency data collected from six streams from 2021 to 2023. This study found: (1) The numerical solution of K600 demonstrated distinct dynamic variability for all streams, ranging from 0 to 111.39 cm h-1 (2) Streams with higher discharge (>10 m3 s-1) exhibited significant seasonal differences in K600 values. The monthly average discharge and water temperature were the two factors that determined the variation in K600 values. (3) K600 was a major source of uncertainty in CO2 emission fluxes, with a relative contribution of 53.72%. An integrated K600 model of riverine gas exchange was developed at the watershed scale and validated using the observed DO change. Our study stressed that K600 dynamics can better represent areal change to reduce uncertainty in estimating GHG emissions.

Effects of hypoxia on the reproductive endocrine axis of the pejerrey (Odontesthes bonariensis).

Recently, hypoxic areas have been identified in water bodies of the Pampas region due to human activity. The objective of this work was to study the effect of low concentrations of dissolved oxygen (hypoxia) on the reproductive endocrine axis of a pampas fish (Odontesthes bonariensis). Groups of 8 males and 8 females were subjected to severe hypoxia (2-3 mg l-1) and normoxia (7-9 mg l-1) in 3000 l tanks by duplicate during the reproductive season (spring). After 21 days, 4 males and 4 females from each tank were sacrificed, and blood was drawn to measure estradiol (E2) and testosterone (T). The brain, pituitary gland and a portion of the gonads were extracted and processed to measure the expression of: gnrh1, cyp19a1b, fshß, lhß, fshr, lhcgr and cyp19a1a. From the second experimental week, no spawning was found in the hypoxic females, while at the end of the treatment period no male released sperm. Fish under hypoxic conditions showed signs of gonadal regression, reduction of GSI and plasma levels of sex steroids. Furthermore, the expression of gnrh1 in both sexes, cyp19a1b and fshr in males and only fshß and cyp19a1a in females decreased in comparison with normoxic fish. After 40 days under normal conditions, signs of reproductive recovery were observed in the treated fish. The results obtained demonstrated that hypoxia generated an inhibition of some components of the pejerrey's reproductive endocrine axis, but the effect was reversible.

Genome-resolved metagenomics reveals the nitrifiers enrichment and species succession in activated sludge under extremely low dissolved oxygen.

Nitrification, a process carried out by aerobic microorganisms that oxidizes ammonia to nitrate via nitrite, is an indispensable step in wastewater nitrogen removal. To facilitate energy and carbon savings, applying low dissolved oxygen (DO) is suggested to shortcut the conventional biological nitrogen removal pathway, however, the impact of low DO on nitrifying communities within activated sludge is not fully understood. This study used genome-resolved metagenomics to compare nitrifying communities under extremely low- and high-DO. Two bioreactors were parallelly operated to perform nitrification and DO was respectively provided by limited gas-liquid mass transfer from the atmosphere (AN reactor, DO < 0.1 mg/L) and by sufficient aeration (AE reactor, DO > 5.0 mg/L). Low DO was thought to limit nitrifiers growth; however, we demonstrated that complete nitrification could still be achieved under the extremely low-DO conditions, but with no nitrite accumulation observed. Kinetic analysis showed that after long-term exposure to low DO, nitrifiers had a higher oxygen affinity constant and could maintain a relatively high nitrification rate, particularly at low levels of DO (<0.2 mg/L). Community-level gene analysis indicated that low DO promoted enrichment of nitrifiers (the genera Nitrosomonas and Nitrospira, increased by 2.3- to 4.3-fold), and also harbored with 2.3 to 5.3 times higher of nitrification functional genes. Moreover, 46 high-quality (>90 % completeness and <5 % contamination) with 3 most abundant medium-quality metagenome-assembled genomes (MAGs) were retrieved using binning methods. Genome-level phylogenetic analysis revealed the species succession within nitrifying populations. Surprisingly, compared to DO-rich conditions, low-DO conditions were found to efficiently suppressed the ordinary heterotrophic microorganisms (e.g., the families Anaerolineales, Phycisphaerales, and Chitinophagales), but selected for the specific candidate denitrifiers (within phylum Bacteroidota). This study provides new microbial insights to demonstrate that low-DO favors the enrichment of autotrophic nitrifiers over heterotrophs with species-level successions, which would facilitate the optimization of energy and carbon management in wastewater treatment.

IoT-enabled effective real-time water quality monitoring method for aquaculture.

Aquaculture is growing industry from the perspective of sustainable food fulfillment and county's economic development. Technology oriented aquafarming is the solution for effective water quality monitoring and high yield production. Internet of Things (IoT) integrated aquaculture can cater to such requirements. This research article introduces a comprehensive method aimed at seamlessly incorporate IoT sensors into aquafarming environments, utilizing Arduino boards and communication modules. The proposed method measures accurate water quality parameters, such as temperature, pH levels, and Dissolved Oxygen (DO), which are essential for maintaining optimal conditions for suitable aquaculture environment. This method enables the real-time collection of critical data points that are essential prevent fish diseases and mortality with low human intervention and maintenance cost. The key contributions of the methodology are mentioned below.•Design and development of a compact and efficient Printed Circuit Board (PCB) to achieve accurate sensor data readings and reliable communication in an aqua environment.•Prevent fish disease and mortality rate through data-driven decision incorporating correlation of DO, pH, and temperature sensor data.•Conducted instrument calibration checks and cross-validated automated system data with manual observations through repeatability tests to ensure precise measurements of sensor parameters.

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks.

Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (DO) concentrations. Despite advancements, challenges persist regarding accuracy, scalability, and adaptability of data-driven models to diverse environmental conditions. Previous studies primarily employed singular models or basic combinations of machine learning techniques, lacking advanced integration of adaptive mechanisms to process complex and evolving datasets. The current study introduces innovative hybrid models that integrate temporal pattern attention (TPA) mechanisms with advanced neural networks, including feed-forward neural networks (FFNNs) and long short-term memory networks (LSTMs). This approach leverages the synergistic strengths of individual models, significantly enhancing the accuracy of DO predictions. The models were rigorously tested against water quality data obtained from two distinct riverine environments, the Illinois River (ILL) and Des Plaines River (DP). Daily measured water quality data, including DO, chlorophyll-a, nitrate plus nitrite, water temperature, specific conductance, and pH, from 2017 to 2024 provided a robust foundation for comprehensive analysis of DO dynamics in these rivers. We conducted 10 scenarios with different model inputs, wherein the hybrid TPACWRNN-LSTM-10 model particularly excelled, achieving coefficient of determination values of 0.993 and 0.965, and root mean squared errors of 0.241 mg/L and 0.450 mg/L for DO predictions at the ILL and DP stations, respectively. The model's reliability was further confirmed by Willmott's index values of 0.998 and 0.992 and Nash-Sutcliffe efficiency values of 0.990 and 0.961 at the ILL and DP stations, respectively. Additionally, Shapley additive explanations (SHAP) values were utilized to interpret each predictor's contribution, revealing key drivers of DO predictions. We believe the novel hybrid modeling approach presented in this study could benefit utilities and water resource management systems for predicting water quality in complex systems.

Realtime RONS monitoring of cold plasma-activated aqueous media based on time-resolved phosphorescence spectroscopy.

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 [Formula: see text] 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.

Assessment and optimization of wastewater treatment plant in terms of effluent quality, energy footprint, and greenhouse gas emissions: An integrated modeling approach.

Wastewater treatment plants (WWTPs) can benefit from utilizing digital technologies to reduce greenhouse gas (GHG) emissions and to comply with effluent quality standards. In this study, the GHG emissions and electricity consumption of a WWTP were evaluated via computer simulation by varying the dissolved oxygen (DO), mixed liquor recirculation (MLR), and return activated sludge (RAS) parameters. Three different measures, namely, effluent water quality, GHG emissions, and energy consumption, were combined as water-energy-carbon coupling index (WECCI) to compare the effects of the parameters on WWTPs, and the optimal operating condition was determined. The initial conditions of the A2O process were set to 4.0 mg/L of DO, 100 % MLR, and 90.7 % RAS. Eighty scenarios with various DO, MLR, and RAS were simulated under steady-state condition to optimize the biological treatment process. The optimal operating conditions were found to be 1.5 mg/L of DO, 190 % MLR, and 90.9 % RAS, which had the highest WECCI of 2.40 when compared to the WECCI of the initial condition (1.07). This optimal condition simultaneously reduced GHG emissions by 1348 kg CO2-eq/d and energy consumption by 11.64 MWh/d. This implies that controlling DO, MLR, and RAS through sensors, valves, and pumps offers a promising approach to operating WWTPs with reduced electricity consumption and GHG emissions while attaining effluent quality standards. Additionally, the nitrous oxide stripping rate exhibited linear relationships with the effluent total ammonia and nitrite concentrations in the aerobic reactor, suggesting that monitoring dissolved nitrogen compounds in the effluent and reactor could be a viable strategy to control MLR and DO in the biological reactor. The digital-based assessment and optimization tools developed in this study are expected to hold promise for application in broader environmental management efforts.

An integrated deep learning approach for modeling dissolved oxygen concentration at coastal inlets based on hydro-climatic parameters.

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.

The potential linkage between sediment oxygen demand and microbes and its contribution to the dissolved oxygen depletion in the Gan River.

The role of sediment oxygen demand (SOD) in causing dissolved oxygen (DO) depletion is widely acknowledged, with previous studies mainly focusing on chemical and biological SOD separately. However, the relationship between the putative functions of sediment microbes and SOD, and their impact on DO depletion in overlying water, remains unclear. In this study, DO depletion was observed in the downstream of the Gan River during the summer. Sediments were sampled from three downstream sites (YZ, Down1, and Down2) and one upstream site (CK) as a control. Aquatic physicochemical parameters and SOD levels were measured, and microbial functions were inferred from taxonomic genes through analyses of the 16S rRNA gene. The results showed that DO depletion sites exhibited a higher SOD rate compared to CK. The microbial community structure was influenced by the spatial variation of Proteobacteria, Chloroflexi, and Bacteroidota, with total organic carbon (TOC) content acting as a significant environmental driver. A negative correlation was observed between microbial diversity and DO concentration (p < 0.05). Aerobic microbes were more abundant in DO depletion sites, particularly Proteobacteria. Microbes involved in various biogeochemical cycles, such as carbon (methane oxidation, methanotrophs, and methylotrophs), nitrogen (nitrification and denitrification), sulfur (sulfide and sulfur compound oxidation), and manganese cycles (manganese oxidation), exhibited higher abundance in DO depletion sites, except for the iron cycle (iron oxidation). These processes were negatively correlated with DO concentration and positively with SOD (p < 0.05). Overall, the results highlight that aerobic bacteria's metabolic processes consume oxygen, increasing the SOD rate and contributing to DO depletion in the overlying water. Additionally, the study underscores the importance of targeting the removal of in situ microbial molecular mechanisms associated with toxic H2S and CH4 to support reoxygenation efforts in rehabilitating DO depletion sites in the Gan River, aiding in identifying factors controlling DO consumption and offering practical value for the river's restoration and management.

Completely noninvasive multi-analyte monitoring system for cell culture processes.

Although online monitoring of dissolved O2, pH, and dissolved CO2 is critical in bioprocesses, nearly all existing technologies require some level of direct contact with the cell culture environment, posing risks of contamination. This study addresses the need for an accurate, and completely noninvasive technique for simultaneous measurement of these analytes. A "non-contact" technique for simultaneous monitoring of dissolved O2, pH, and dissolved CO2 was developed. Instead of direct contact with the culture media, the measurements were made through permeable membranes via either a sampling port in the culture vessel wall or a flow cell. The efficacy of the "non-contact" technique was validated in Escherichia coli (E.coli), Chinese hamster ovary (CHO) culture processes, and dynamic environments created by sparging gases in cell culture medium. The measurements obtained through the developed techniques were comparable to those obtained through control methods. The noninvasive monitoring system can offer accurate, and contamination-minimized monitoring of critical process parameters including dissolved O2, pH, and dissolved CO2. These advancements will enhance the control and optimization of cell culture processes, promising improved cell culture performance.