Dissolved Oxygen Concentration Research Articles

Estimated dissolved oxygen in a river located in the south of Rio Grande do Sul

The present study aimed to estimate the dissolved oxygen concentrations in the Piratini River Watershed (in Brazilian Portuguese: BHRP), located in the northwest region of the State of Rio Grande do Sul. DO is a crucial parameter in assessing water quality, as it is essential for the maintenance of aquatic ecosystems. To estimate this, global linear regression models were applied, statistical tools capable of evaluating the effect that one or more variables exert on another. The analyzed data come from collections carried out by Environmental Protection Foundation (FEPAM) between the years 2005 and 2013, period that, due to the rigorous protocol followed and sampling periodicity, revealed a robust and consistent data set. For the selection of parameters, we sought those that had the smallest sampling failures in the historical data series, namely: Thermotolerant Coliforms, Total Phosphorus, Ammoniacal Nitrogen and Total Nitrogen, Turbidity, Total Solids and Hydrogenionic Potential. After applying the linear regressions, the best global models generated were able to explain 91.90% of the variations in dissolved oxygen in the BHRP, once all the analyzed parameters were incorporated, consolidating the applicability of the method. It is also conclude that, with the exception of Total Phosphorus, all training periods are within the limits tolerated by legislation.

Formation and removal mechanism of SiO2-MnO-Al2O3 inclusions in Al-killed 42CrMo4 wind turbine bearing steel forgings

SiO2-MnO-Al2O3 (SMA) inclusions were identified in the defective areas during the ultrasonic inspection of Al-killed 42CrMo4 wind turbine bearing steel forgings. To explore their formation mechanism, we systematically sampled 42CrMo4 steel through industrial experiments, analysing SMA inclusions’ morphology and composition at various stages. Thermodynamic calculations were also employed to assess the origin and formation of these inclusions. It was observed that these inclusions primarily formed during electric arc furnace tapping, where the mass ratio of SiO2 to MnO was close to 1, while the Al2O3 content varied. Despite undergoing the ladle furnace and vacuum degassing (VD) refining processes, these inclusions persisted in the molten steel. Following the conclusion of electric furnace smelting, the dissolved oxygen content in the molten steel could reach 193 ppm. During the tapping process, high oxidising steel comes into direct contact with silicon-manganese alloy. During this phase, the uneven distribution of Al content around the alloy results in the formation of SMA inclusions with varying Al2O3 content. Dynamic calculations suggest that in subsequent smelting processes, SMA inclusions with high Al2O3 content and large size are relatively easier to eliminate, while those with low Al2O3 content and small size are more likely to persist in the molten steel. During solidification, residual SMA inclusions agglomerate, forming larger sizes, consequently resulting in the ultrasonic inspection failure of 42CrMo4 steel forgings. After the process improvement, the SMA inclusions are greatly reduced and the effect is obvious.

Soft sensor for viable cell counting by measuring dynamic oxygen uptake rate

Regulatory authorities in biopharmaceutical industry emphasize process design by process understanding but applicable tools that are easy to implement are still missing. Soft sensors are a promising tool for the implementation of the Quality by Design (QbD) approach and Process Analytical Technology (PAT). In particular, the correlation between viable cell counting and oxygen consumption was investigated, but problems remained: Either the process had to be modified for excluding CO2 in pH control, or complex kLa models had to be set up for specific processes. In this work, a non-invasive soft sensor for simplified on-line cell counting based on dynamic oxygen uptake rate was developed with no need of special equipment. The dynamic oxygen uptake rates were determined by automated and periodic interruptions of gas supply in DASGIP® bioreactor systems, realized by a programmed Visual Basic script in the DASware® control software. With off-line cell counting, the two parameters were correlated based on linear regression and led to a robust model with a correlation coefficient of 0.92. Avoidance of oxygen starvation was achieved by gas flow reactivation at a certain minimum dissolved oxygen concentration. The soft sensor model was established in the exponential growth phase of a Chinese Hamster Ovary fed-batch process. Control studies showed no impact on cell growth by the discontinuous gas supply. This soft sensor is the first to be presented that does not require any specialized additional equipment as the methodology relies solely on the direct measurement of oxygen consumed by the cells in the bioreactor.

Impacts of a newly formed lava delta on the marine environment: Lava-induced upwelling and abrupt chlorophyll depletion during the Tajogaite eruption (La Palma, 2021)

The 2021 Tajogaite eruption in La Palma (Canary Islands, Spain) emitted vast volumes of lava during 85 days, which reached the ocean in several occasions at the western flank of the island. Most of these flows merged to create a primary lava delta, covering an area of 48 ha, with an additional 30 ha underwater. Here we characterize the effects of the lava-seawater interaction on the surrounding marine environment. The area was sampled during two multidisciplinary oceanographic cruises: the first one comprised the days before the lava reached the ocean and after the first contact; and the second took place a month later, when the lava delta was already formed but still receiving lava inputs. Physical-chemical anomalies were found in the whole water column at different depths up to 300 m in all measured parameters, such as turbidity (+9 NTU), dissolved oxygen concentration (−17.17 μmol kg−1), pHT25 (−0.1), and chlorophyll-a concentration (−0.33 mg m−3). Surface temperature increased up to +2.3 °C (28.5 °C) and surface salinity showed increases and decreases of −1.01 and +0.70, respectively, in a radius of 4 km around the lava delta. In the water column, the heated waters experimented a lava-induced upwelling, bringing deeper, nutrient-rich waters to shallower depths; however, this feature did not trigger any phytoplankton bloom. In fact, integrated chlorophyll-a showed an abrupt decrease of −41 % in just two days and −69 % a month later, compared to prior conditions. The chlorophyll-a depletion reached a distance larger than 2.5 km (not delimited).

Numerical modeling and experiment for isothermal selective external oxidation behavior of Fe-Mn binary alloys

This paper described work undertaken to the selective external oxidation of Fe-Mn binary alloys during isothermal recrystallization annealing. The effect of different peak temperature, soaking time, dew point conditions on external oxidation of Fe-Mn alloys were investigated. Based on an image segmentation technology, external oxidation behavior was evaluated by the equivalent diameters and coverage fraction of external oxides. A numerical kinetics model of the coupled selective internal-external oxidation during isothermal annealing of Fe-Mn binary alloys was proposed. The model allowed the case of internal and external oxidation simultaneously occurred. Based on the assumptions of oxidation reaction at the steel/atmosphere interface reached an instantaneously local thermodynamic equilibrium, the coupled relationship was described by two interactions. The surface dissolved oxygen content could be controlled by both the annealing atmosphere and the coverage fraction of external oxides on the surface, while the diffusion of oxidant and oxygen inside the steel were blocked by the precipitation behavior of internal oxides. Therefore, the model achieved predict quantitatively the coverage of oxides on the external surface of the steel and the mass fraction profiles of Mn after annealing. The correctness of the calculation results was verified experimentally.

Evaluation of influence of dissolved oxygen on corrosion behaviors of FeCrW model alloys in 360 °C water

The dissolved oxygen in a coolant can affect the oxidation properties of structural materials. A desirable oxide phase formation is achieved by manipulating the oxygen level in the coolant, which can mitigate structural material degradation in nuclear power plants. Therefore, the role of dissolved oxygen in the corrosion of structural materials in aqueous environments needs to be understood. In this study, a short-term corrosion test (up to 300 h) of Ferritic/Martensitic steels (F/M steels; FeCrW model alloys), namely, Fe12Cr1W, Fe9Cr1W, and Fe9Cr, in stagnant water at 360 °C was performed in a pressurized autoclave with the dissolved oxygen concentration controlled to 1 ppm or a very low level (<1 ppm). The results of the corrosion tests showed that an increase in the oxygen level in the water elevated the corrosion potential, allowing the phase transition of iron oxide from magnetite (Fe3O4) to hematite (Fe2O3), whereas there was no significant correlation between the concentrations of the alloying elements Cr and W and the oxide growth rate. In addition, hematite was found to mitigate further oxide growth. Finally, a mechanism for the growth of the initial oxide layer was proposed based on the experimental results.

Influence of water quality and seasonal variations on freshwater macroinvertebrate diversity and community structure in wastewater treatment ponds, Phetchaburi Province, Thailand

Wastewater originating from the Phetchaburi municipality undergoes treatment in a series of five distinct stages at the King’s Royally Initiated Leam Phak Bia Environmental Research and Development Project (LERD) in Phetchaburi province, Thailand. These stages involve a sedimentation (pond 1), three oxidation ponds (ponds 2 to 4), and a final stabilization pond (pond 5). These ponds serve as habitats for macroinvertebrates; consequently, their diversity and composition might be influenced by fluctuations in water quality and seasonal variations. The primary aim of this research was to analyze the diversity and species composition of macroinvertebrate communities concerning varying levels of organic contamination across the five wastewater treatment ponds at LERD. This investigation spanned three seasons: cold season (December 2019), rainy season (July 2020), and hot season (April 2021). The findings revealed that the diversity and species composition of macroinvertebrate communities displayed distinct alterations across multiple environmental gradients, especially identifying the significant influence of organic loading levels observed in ponds 1 to 5. The macroinvertebrate communities exhibited two distinct groupings, with the Chironomidae and Candonidae or ostracods prevailing prominently in ponds 1 and 2 (heterogenous environments). This prevalence was attributed to the high levels of detrital food and the robust resilience of chironomid larvae and ostracods to organic pollution, thriving even in environments characterized by low dissolved oxygen levels. Conversely, the prevalence of snails from the Thiaridae family in ponds 3 to 5 (homogenous environments) indicated improved water quality conditions, notably lower organic matter levels, and a higher dissolved oxygen content. In addition, the study identified seasonal variations in macroinvertebrates, likely influenced by the differing organic loading and environmental conditions. Thus, this research provided insights into the factors shaping macroinvertebrate communities in a wastewater treatment system.

A global four‐dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles

AbstractLack of a long‐term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC‐Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC‐Argo has shorter time series coverage and fewer profiles compared to Core‐Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core‐Argo and BGC‐Argo profiles and then developing a spatial, temporal and depth‐specific gridded DOC dataset, named G4D‐DOC. Validation results demonstrate that G4D‐DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D‐DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D‐DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D‐DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D‐DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.

Integrated Effects of Site Hydrology and Vegetation on Exchange Fluxes and Nutrient Cycling at a Coastal Terrestrial‐Aquatic Interface

AbstractThe complex interactions among soil, vegetation, and site hydrologic conditions driven by precipitation and tidal cycles control the biogeochemical transformations and bi‐directional exchange of carbon and nutrients across the terrestrial–aquatic interfaces (TAIs) in coastal regions. This study uses a highly mechanistic model, Advanced Terrestrial Simulator (ATS)‐PFLOTRAN, to explore how these interactions affect exchanges of materials and carbon and nitrogen cycling. We used a transect in the Chesapeake Bay region that spans zones of open water, coastal wetland, transition, and upland forest. We designed several simulation scenarios to parse the effects of the individual controlling factors and the sensitivity of carbon cycling to reaction rate parameters derived from laboratory experiments. Our simulations reveal an active zone for carbon cycling under the transition zones between the wetland and the upland. Evapotranspiration is found to enhance the exchange fluxes between the surface and subsurface domains, resulting in a higher dissolved oxygen concentration in the TAIs. The transport of organic carbon derived from plant leaves and roots provide an additional source of organic carbon needed for the aerobic respiration and denitrification processes in the TAIs. The variability in reaction rate parameters associated with microbial activities is also found to play a dominant role in controlling the heterogeneity and dynamics of the simulated redox conditions. This modeling‐focused exploratory study enabled us to better understand the complex interactions among soil, water and microbes that govern the hydro‐biogeochemical processes at the TAIs, which is an important step toward representing coastal ecosystems in larger‐scale Earth system models.

Effects of oxygen concentration on the corrosion behavior of high entropy alloy AlCoCrFeNi in simulated deep sea

In light of the low dissolved oxygen concentration in the deep sea, the corrosion mechanisms of the high entropy alloy (HEA) AlCoCrFeNi in artificial seawater with varying oxygen concentrations (2.0, 4.0, 7.0 mg/L) were studied. As the oxygen concentration decreases, the alloy's free corrosion potential decreases, and at 2.0 mg/L, the corrosion rate is 421 times higher than that at 7.0 mg/L. The corrosion form transforms from pitting to uniform corrosion. The primary reasons for this are the passivation film is thin under low oxygen concentration conditions, as well as the preferential dissolution of the alloy elements Al and Ni due to their high activity and “local acidizing” properties, respectively. In designing a super corrosion-resistant high entropy alloy for use in the deep sea, it is advisable to avoid the use of element Al and to add Ni with caution.

Integrated mRNA-seq and miRNA-seq analysis reveals the miRNAs participating in the response and metabolic remodeling processes of Trachinotus blochii to different reoxygenation modes after hypoxia

Hypoxia has become a common phenomenon in aquatic environments. Golden pompano (Trachinotus blochii) are sensitive to fluctuations in dissolved oxygen concentration and are seriously affected by hypoxic stress. When facing hypoxia in aquaculture areas, it may be effective to alleviate the adverse effects of low oxygen by increasing dissolved oxygen through physical and chemical means. However, it is not known whether different reoxygenation rates have different physiological consequences. In this study, we utilized RNA-seq and miRNA-seq to investigate the effects of various hypoxia and reoxygenation treatments: normoxia, hypoxia, rapid reoxygenation (RRG; dissolved oxygen [DO] recovery within 10 mins), and gradual reoxygenation (GRG; DO recovery within 3 h). Under hypoxic conditions, the miR-130b-5p∼IDH3 pair was involved in the regulation of the TCA cycle and activation of HIF-1α expression in response to insufficient oxygen levels. Additionally, in RRG, under the regulation of the miR-132–5p∼PGAM, miR-8485∼LIPA, and miR-212–5p∼GAPDHS pairs, the energy supply came to increase relative to hypoxia. In GRG, the miR-130b-5p∼IDH3 pair regulated metabolism, which returned to the normal level. These findings provide new data for clarifying the metabolic regulatory pathways of golden pompano during hypoxia and reoxygenation processes.

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

Treatment of Wastewaters with Different Ammonium Concentrations in a Sequencing Batch Reactor by Nitritation and Denitritation Processes

In the treatment of industrial and municipal wastewaters, biological nitrification and denitrification processes have been widely applied methods. However, in recent years, experimental studies have been carried out on nitritation and denitritation processes in order to reduce the cost of wastewater treatment. In this study, the removal of synthetic wastewaters containing ammonium at various concentrations through nitritation and denitritation processes was investigated in a sequence batch reactor (SBR). The SBR was operated at a low dissolved oxygen concentration of 0.7±0.2 mg/L, while the initial pH level and temperature were kept constant at 7.5 and 25 ℃, respectively, which were below the defined optimal level for nitritation. For the initial concentrations of 100, 150, and 200 mg NH4-N/L, 91, 142, and 180 mg NOx-N were produced, respectively with the specific nitritation rates of 0.69, 0.95, and 0.81 mg NH4-N/mg MLVSS.hour. Considering the operating conditions of nitritation, approximately NO2-N/NOx-N ratio of 80% could be considered as successful with the combined effect of DO limitation and NH3 inhibition on the nitrite oxidizing bacteria. During the first days of nitritation and denitritation processes, change of nitrogen concentrations was negligible because the adaptation of organisms to the oxic and anoxic conditions. After completing nitritation stage, denitritation was carried out by using methanol as an electron donor for the initial concentrations of 91, 142, and 180 mg/L of NOx-N in 96, 120 and 264 hours of reaction times, respectively.

Forecasting fish mortality from water and air quality data using deep learning models.

The high rate of aquatic mortality incidents recorded in Taiwan and worldwide is creating an urgent demand for more accurate fish mortality prediction. Present study innovatively integrated air and water quality data to measure water quality degradation, and utilized deep learning methods to predict accidental fish mortality from the data. Keras library was used to build multilayer perceptron and long short-term memory models for training purposes, and the models' accuracies in fish mortality prediction were compared with that of the naïve Bayesian classifier. Environmental data from the 5 days before a fish mortality event proved to be the most important data for effective model training. Multilayer perceptron model reached an accuracy of 93.4%, with a loss function of 0.01, when meteorological and water quality data were jointly considered. It was found that meteorological conditions were not the sole contributors to fish mortality. Predicted fish mortality rate of 4.7% closely corresponded to the true number of fish mortality events during the study period, that is, four. A significant surge in fish mortality, from 20% to 50%, was noted when the river pollution index increased from 5.36 to 6.5. Moreover, the probability of fish mortality increased when the concentration of dissolved oxygen dropped below 2mg/L. To mitigate fish mortality, ammonia nitrogen concentrations should be capped at 5mg/L. Dissolved oxygen concentration was found to be the paramount factor influencing fish mortality, followed by the river pollution index and meteorological data. Results of the present study are expected to aid progress toward achieving the Sustainable Development Goals and to increase the profitability of water resources.

Hypothalamic transcriptome response to simulated diel earthen pond hypoxia cycles in channel catfish (Ictalurus punctatus).

Commercial culture of channel catfish (Ictalurus punctatus) occurs in earthen ponds that are characterized by diel swings in dissolved oxygen concentration that can fall to severe levels of hypoxia, which can suppress appetite and lead to suboptimal growth. Given the significance of the hypothalamus in regulating these processes in other fishes, an investigation into the hypothalamus transcriptome was conducted to identify specific genes and expression patterns responding to hypoxia. Channel catfish in normoxic water were compared with catfish subjected to 12 h of hypoxia (20% oxygen saturation; 1.8 mg O2/L; 27°C) followed by 12 h of recovery in normoxia to mimic 24 h in a catfish aquaculture pond. Fish were sampled at 0-, 6-, 12-, 18-, and 24-h timepoints, with the 6- and 12-h samplings occurring during hypoxia. A total of 190 genes were differentially expressed during the experiment, with most occurring during hypoxia and returning to baseline values within 6 h of normoxia. Differentially expressed genes were sorted by function into Gene Ontology biological processes and revealed that most were categorized as "response to hypoxia," "sprouting angiogenesis," and "cellular response to xenobiotic stimulus." The patterns of gene expression reported here suggest that transcriptome responses to hypoxia are broad and quickly reversibly with the onset of normoxia. Although no genes commonly reported to modulate appetite were found to be differentially expressed in this experiment, several candidates were identified for future studies investigating the interplay between hypoxia and appetite in channel catfish, including adm, igfbp1a, igfbp7, and stc2b.NEW & NOTEWORTHY Channel catfish are an economically important species that experience diel episodic periods of hypoxia that can reduce appetite. This is the first study to investigate their transcriptome from the hypothalamus in a simulated 24-h span in a commercial catfish pond, with 12 h of hypoxia and 12 h of normoxia. The research revealed functional groups of genes relating to hypoxia, angiogenesis, and glycolysis as well as individual target genes possibly involved in appetite regulation.

Effects of dissolved oxygen on the corrosion-related unidentified deposit formed of 304 SS in the flow accelerated zone under the simulated secondary water chemistry

Effects of dissolved oxygen (DO) on the deposition characteristics of corrosion-related unidentified deposit (CRUD) were investigated by using 304 stainless steel micro-orifices. The main results indicate that the change of DO concentration resulted in a change of the composition of the outermost deposition layer. The DO concentration amplifies/diminishes the porosity in CRUD effect by changing the rate of ion transport. Meanwhile, the electrokinetic effect is weakened by the decrease of the thickness of the electric double layer, which is caused by the dissolution of Cr ions increased by increasing the DO concentration. Meanwhile, the chromium ion concentration increases with the dissolved oxygen concentration, which leads to a decrease in the thickness of the electric double layer, weakening the electrodynamic effect. The above results in the thickness and width of CRUD in the flow acceleration zone vary in varying degrees. This suggests that the deposition process of CRUD occurs under the mechanism of mass transfer and electrokinetic mechanism.

Salinity decreases methane concentrations in Chinese lakes

Lakes are important sources of methane (CH4), and understanding the influence of environmental factors on CH4 concentration in lake water is crucial for accurately assessing CH4 emission from lakes. In this study, we investigated CH4 concentration in two connected Tibetan Plateau lakes, Lake Keluke (an open freshwater lake) and Lake Tuosu (a closed saline lake), through in-situ continuous measurements taken in different months from 2021 to 2023. The results show substantial spatial and seasonal variations in CH4 concentrations in the two lakes, while the CH4 concentrations in Lake Keluke are consistently higher than those in Lake Tuosu for each month. Despite sharing similar environmental conditions due to connected (e.g. pH, water temperature, dissolved oxygen content, and total organic carbon content), the critical difference between the two lakes is their salinity. This implies that salinity is the critical factor contributing to the decrease in CH4 concentrations in Lake Tuosu, possibly due to the changes in microbial species between freshwater and brackish/saline lakes. Additionally, to further validate the effect of salinity on CH4 concentrations in lake water, we compared the CH4 concentrations of 33 lakes (including 5 saline lakes and 28 freshwater lakes) from the Tibetan Plateau, Chinese Loess Plateau, and Yangtze Plain, and found that saline lakes consistently exhibit lower CH4 concentrations (avg. 0.08 μmol/L), while freshwater lakes generally display higher CH4 concentrations (avg. 1.25 μmol/L) with considerable fluctuations. Consequently, freshwater and saline lakes exhibit distinct CH4 emissions, which could be used for more accurate estimation of global CH4 emission from lakes.

Enhancing docosahexaenoic acid production from Schizochytrium sp. by using waste Pichia pastoris as nitrogen source based on two-stage feeding control

To reduce the cost of docosahexaenoic acid (DHA) production from Schizochytrium sp., the waste Pichia pastoris was successfully used as an alternative nitrogen source to achieve high-density cultivation during the cell growth phase. However, due to the high oxygen consumption feature when implementing high-density cultivation, the control of both the nitrogen source and dissolved oxygen concentration (DO) at each sufficient level was impossible; thus, two realistic control strategies, including “DO sufficiency-nitrogen limitation” and “DO limitation-nitrogen sufficiency”, were proposed. When using the strategy of “DO sufficiency-nitrogen limitation”, the lowest maintenance coefficient of glucose (12.3 mg/g/h vs. 17.0 mg/g/h) and the highest activities of related enzymes in DHA biosynthetic routes were simultaneously obtained; thus, a maximum DHA concentration of 12.8 ± 1.2 g/L was achieved, which was 1.58-fold greater than that of the control group. Overall, two-stage feeding control for alternative nitrogen sources is an efficient strategy to industrial DHA fermentation.

Recent changes of the dissolved oxygen distribution in the deep convection cell of the southern Adriatic Sea

The dynamics of dissolved oxygen in the ocean are of crucial importance for understanding marine ecosystems, with influences ranging from exchange with the atmosphere to biological processes and ocean circulation. In this study, we focus on the southern Adriatic Sea, an essential component of the Eastern Mediterranean “conveyor belt”, to investigate long-term oxygen dynamics and its driving factors. We use cross-platform datasets from 2013 to 2020, including remote sensing data, model reanalysis and in-situ observations from Argo floats, ocean gliders and ship-based measurements. Our analysis investigate the interplay of physical, biological and atmospheric forcing that drive oxygen variability. The distribution of dissolved oxygen in the southern Adriatic Sea is influenced by vertical mixing, advection of water masses and ecosystem dynamics. In the surface layer, the variability of dissolved oxygen is triggered by annual primary production and deep convection events. The dynamics in the intermediate and the deep layers are instead primarily influenced by physical processes, such as the vertical mixing and the water masses inflow from the adjacent sub-basins, which is driven by the periodic reversals of northern Ionian Gyre circulation. In particular our study reveals that the water masses advective dynamics driving the increase and decrease of dissolved oxygen have drastically changed in recent years. The highest dissolved oxygen concentrations are currently observed during the northern Ionian Gyre anticyclonic phase, while they have been previously documented during the cyclonic phase. This change appears to be connected with the significant increase in salinity observed in the southern Adriatic Sea in the same period and contributes to a better understanding of the processes that determine oxygen distribution in the Eastern Mediterranean basin.

A hybrid deep learning approach to predict hourly riverine nitrate concentrations using routine monitored data

With high-frequency data of nitrate (NO3–N) concentrations in waters becoming increasingly important for understanding of watershed system behaviors and ecosystem managements, the accurate and economic acquisition of high-frequency NO3–N concentration data has become a key point. This study attempted to use coupled deep learning neural networks and routine monitored data to predict hourly NO3–N concentrations in a river. The hourly NO3–N concentration at the outlet of the Oyster River watershed in New Hampshire, USA, was predicted through neural networks with a hybrid model architecture coupling the Convolutional Neural Networks and the Long Short-Term Memory model (CNN-LSTM). The routine monitored data (the river depth, water temperature, air temperature, precipitation, specific conductivity, pH and dissolved oxygen concentrations) for model training were collected from a nested high-frequency monitoring network, while the high-frequency NO3–N concentration data obtained at the outlet were not included as inputs. The whole dataset was separated into training, validation, and testing processes according to the ratio of 5:3:2, respectively. The hybrid CNN-LSTM model with different input lengths (1d, 3d, 7d, 15d, 30d) displayed comparable even better performance than other studies with lower frequencies, showing mean values of the Nash-Sutcliffe Efficiency 0.60–0.83. Models with shorter input lengths demonstrated both the higher modeling accuracy and stability. The water level, water temperature and pH values at monitoring sites were main controlling factors for forecasting performances. This study provided a new insight of using deep learning networks with a coupled architecture and routine monitored data for high-frequency riverine NO3–N concentration forecasting and suggestions about strategies about variable and input length selection during preprocessing of input data.