Dissolved Oxygen Concentration Research Articles

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.

Multi-step ahead dissolved oxygen concentration prediction based on knowledge guided ensemble learning and explainable artificial intelligence

Accurate water quality prediction is crucial for effective environmental management and decision-making. However, previous studies have solely relied on historical data to simulate water quality, overlooking the potential discrepancies between predicted values and actual observations. Additionally, the opacity of machine learning models has posed challenges to the credibility of their predictions. Hence, considering the excellent nonlinear fitting ability of ensemble tree models, especially the Categorical Boosting (Catboost) model, this study proposes a knowledge-guided Catboost (KGCatboost) model for predicting the dissolved oxygen concentration, one of the vital water quality indicators, in 15 river sections of the Yangtze River Basin in Yunnan Province, China. Furthermore, to enhance the model’s interpretability, we employ the SHapley Additive exPlanations (SHAP) method to analyze the contributions of each input variable within the water body. The results demonstrate that on the test set of each dataset, the mean Nash-Sutcliffe Efficiency (NSE) value of KGCatboost is 0.874, which has improved by 0.34% and 3.07% compared to Catboost and eXtreme Gradient Boosting (Xgboost). In addition, this study reveals that pH has the most significant impact on DO concentrations. Specifically, as the pH increased, the DO concentration increased significantly. A regulatory mechanism has also been developed to alleviate the hazards caused by low DO concentrations. The KGCatboost model can provide valuable guidance for water resource management processes.

[Analysis of the Spatiotemporal Distribution of Algal Blooms and Its Driving Factors in Chaohu Lake Based on Multi-source Datasets].

Eutrophication and harmful algae blooms are one of the common ecological and environmental problems faced by freshwater lakes all over the world. As a typical inland freshwater lake, Chaohu Lake exhibits a high level of eutrophication and algae blooms year-round and shows a spatiotemporal difference in different regions of the lake. In order to understand the basic regularity of the development and outbreak of algal blooms in Chaohu Lake, the data from the comprehensive water observation platform and remote sensing were integrated to obtain the spatiotemporal distribution of algal blooms from 2015 to 2020. Then, an evaluation model based on Boosted Regression Trees (BRT) was constructed to quantitatively assess the importance and interactions of various environmental factors on algal blooms at different stages. The results indicated that:① The occurrence of algal blooms in Chaohu Lake exhibited significant seasonal variations, with the cyanobacteria beginning to recover in spring and bring about a light degree of algal blooms in the western and coastal areas of Chaohu Lake. The density of cyanobacteria reached its maximum in summer and autumn, accompanied by moderate and severe degrees of algal bloom outbreaks. ② During the non-outbreak period, the variation in the cyanobacteria density was greatly affected by physical and chemical factors, which explained 80.3% of the variance in the change in cyanobacteria density. The high concentrations of dissolved oxygen content in the water column and the weak alkalinity (7.2-7.6) and appropriate water temperature (about 3℃) provided a favorable environmental condition for the breeding and growth of cyanobacteria. In addition, the onset of algal blooms was closely related to the air temperature steadily passing through the threshold. According to the statistics, the date of first outbreak of algal blooms in Chaohu Lake was 11 days or so after the air temperature steadily remained above 7℃. ③ During the outbreak period, the occurrence of algal blooms was influenced by the combination of cyanobacterial biomass and meteorological conditions such as temperature, wind speed, and sunshine duration. The cumulative contribution ratio of the four factors was as high as 95%, and each factor had an optimal interval conductive to the outbreak of algal blooms. Furthermore, the results of multi-factor interaction analysis indicated a larger probability of the outbreak of algal blooms in Chaohu Lake under the combined effect of high cyanobacteria density, suitable temperature, and the breeze. This study analyzed and revealed the spatiotemporal characteristics and the dominant influencing factors of algal blooms in Chaohu Lake at different stages, which could provide the scientific basis for the prediction, early warning, and disposal of algal blooms under the context of climate change.

Structure and composition of microbial communities in the water column from Southern Gulf of Mexico and detection of putative hydrocarbon-degrading microorganisms.

This study assessed the bacterioplankton community and its relationship with environmental variables, including total petroleum hydrocarbon (TPH) concentration, in the Yucatan shelf area of the Southern Gulf of Mexico. Beta diversity analyses based on 16S rRNA sequences indicated variations in the bacterioplankton community structure among sampling sites. PERMANOVA indicated that these variations could be mainly related to changes in depth (5 to 180 m), dissolved oxygen concentration (2.06 to 5.93 mg L-1), and chlorophyll-a concentration (0.184 to 7.65 mg m3). Moreover, SIMPER and one-way ANOVA analyses showed that the shifts in the relative abundances of Synechococcus and Prochlorococcus were related to changes in microbial community composition and chlorophyll-a values. Despite the low TPH content measured in the studied sites (0.01 to 0.86 μL L-1), putative hydrocarbon-degrading bacteria such as Alteromonas, Acinetobacter, Balneola, Erythrobacter, Oleibacter, Roseibacillus, and the MWH-UniP1 aquatic group were detected. The relatively high copy number of the alkB gene detected in the water column by qPCR and the enrichment of hydrocarbon-degrading bacteria obtained during lab crude oil tests exhibited the potential of bacterioplankton communities from the Yucatan shelf to respond to potential hydrocarbon impacts in this important area of the Gulf Mexico.

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.

Influence of Chloride Ion Concentration on Corrosion Behavior of WC–MgO Composite

The influence of chloride ion (Cl−) concentration on the corrosion mechanism of WC–MgO composites has been studied in this work. The results suggest that the corrosion resistance of WC–MgO composite decreases first and then increases with the increase in Cl− concentration. Solution conductivity and dissolved oxygen content are the main influence factors. The solution conductivity determines the charge transfer process, and the dissolved oxygen determines the cathodic oxygen absorption reaction. The corrosion characteristic is typical pitting corrosion. Meanwhile, the corrosion mechanism contains an oxidation process of the WC matrix and the dissolution destruction of the MgO toughening phase. The formation of the WO3 corrosion layer hinders the general corrosion to protect the inner material. However, the dissolution of MgO induces the initiation of pitting. The local alkaline caused by MgO dissolution promotes the dissolution of the WC matrix, which leads to the expansion of pitting.

Denitrification Performance and Microbiological Mechanisms Using Polyglycolic Acid as a Carbon Source

When treating municipal wastewater, nitrogen removal is often limited due to low C/N, which needs to be compensated for by additional carbon source injections. This study investigated the feasibility of using industrial-waste polyglycolic acid (PGA) as a carbon source for denitrification in an SBR to obtain an economical carbon source. The results revealed that an optimal denitrification performance in a methanol-fed activated sludge system was achieved with a PGA dosage of 1.2 mL/L, a pH of 7–8, and a dissolved-oxygen (DO) concentration of 3 ± 0.5 mg/L. Under these conditions, all quality parameters for effluent water met the required criteria [COD < 50 mg/L; TN < 15 mg/L; NH4+-N < 5(8) mg/L]. PGA enhanced the variety and richness of microbial communities, thereby markedly increasing the relative abundance of major phyla such as Proteobacteria and Bacteroidota and major genera such as Paracoccus and Dechloromonas. Furthermore, PGA upregulated the expression of nitrogen-metabolism-related genera, including amo, hao, nar, and nor, which improved the denitrification performance of the system. This study provides a reference for applying PGA as a carbon source for low-C/N-wastewater treatment and solid-waste utilization.

Sanitary and biological condition of open sources is a determining factor in the reconstruction of elements of water supply systems

The results of the analysis of monitoring data of the Kuibyshev reservoir, as a source of domestic and drinking water supply, for the period 2000-2018 are presented. It has been established that in summer, during the period of massive development of cyanobacteria, biological pollution of the reservoir is observed, the quality of water deteriorates in terms of smell, color, pH, and dissolved oxygen content. During the period of deterioration in the quality of water in the water supply source in large Volga cities, problems arise in purifying natural water to meet the requirements of the drinking water supply of the population. To solve the problem, recommendations have been developed to improve the technological scheme for purifying Volga water from organic and toxic substances.

Physics-guided spatio–temporal neural network for predicting dissolved oxygen concentration in rivers

The prediction of river water quality is key in water resource management. Data-driven machine learning models have been widely used for predicting river water quality. However, these models seldom consider the physical mechanisms of water quality variation, which degrades the accuracy and stability of the prediction results. Hence, we develop a physics-guided spatio–temporal neural network (PGSTNN) model to predict a critical parameter for water quality assessment, i.e. dissolved oxygen. Physical information regarding spatio–temporal interactions in a hydrological network is explicitly considered to construct the architecture of PGSTNN. Two physical rules of dissolved oxygen variation (i.e. Henry’s law and power-scaling law) are established for the loss function of PGSTNN to guarantee the physical consistency of the prediction results. Experiments on the 2020–2021 water quality dataset in Atlanta, USA show that PGSTNN outperforms seven baseline neural network models in terms of prediction accuracy and stability. PGSTNN typically brings at least 10% accuracy (e.g. root mean square error and mean absolute error) improvement over the comparison methods. The proposed PGSTNN may not only improve the emergency response ability of water resource management, but also provide useful ideas for integrating scientific knowledge with machine learning.

Lysis Physiology of Pseudomonas aeruginosa Infected with ssRNA Phage PRR1.

The phage PRR1 belongs to the Leviviridae family, a group of ssRNA bacteriophages that infect Gram-negative bacteria. The variety of host cells is determined by the specificity of PRR1 to a pilus encoded by a broad host range of IncP-type plasmids that confer multiple types of antibiotic resistance to the host. Using P. aeruginosa strain PAO1 as a host, we analyzed the PRR1 infection cycle, focusing on cell lysis. PRR1 infection renders P. aeruginosa cells sensitive to lysozyme approximately 20 min before the start of a drop in suspension turbidity. At the same time, infected cells start to accumulate lipophilic anions. The on-line monitoring of the entire infection cycle showed that single-gene-mediated lysis strongly depends on the host cells' physiological state. The blockage of respiration or a reduction in the intracellular ATP concentration during the infection resulted in the inhibition of lysis. The same effect was observed when the synthesis of PRR1 lysis protein was induced in an E. coli expression system. In addition, lysis was strongly dependent on the level of aeration. Dissolved oxygen concentrations sufficient to support cell growth did not ensure efficient lysis, and a coupling between cell lysis initiation and aeration level was observed. However, the duration of the drop in suspension turbidity did not depend on the level of aeration.

Failure analysis of leaking firefighting pipeline used in nuclear power plant: Corrosion development process and key influence factors

A penetrating hole has been found in the inner wall of the firefighting pipeline (A106 steel) used for 20 years in a nuclear power plant of China, and the pipeline is filled with almost stagnant tap water before failure. The failed pipeline is taken from a vertically placed water outlet pipeline. The corrosion process and key influence factors of the failed firefighting pipeline were studied using optical microscopy (OM), scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results show that there exist two typical corrosion characteristics on the inner wall of the failed pipeline besides leakage hole, that is, corrosion nodulation and corrosion bulge. The corrosion nodulation, mainly consisting of a mixture of calcium and iron oxides, is the initiation position of perforation corrosion, and the iron-oxidizing bacteria (IOB) play a key role in the formation of corrosion nodulation. Then the sulfate-reducing bacteria (SRB) is involved in the propagation of corrosion nodulation when the dissolved oxygen concentration is low. The corrosion nodulation gradually become loose and form large bulge with extended service time. The corrosion pits under the corrosion bulge also develop gradually. Eventually, the inner wall of the pipeline is thinned and perforated.

Metabolic activity of Planktothrix rubescens and its consequences on oxygen dynamics in laboratory experiment: A stable isotope study.

Fluctuations in dissolved oxygen (DO) contents in natural waters can become intense during cyanobacteria blooms. In a reconnaissance study, we investigated DO concentrations and stable isotope dynamics during a laboratory experiment with the cyanobacterium Planktothrix rubescens in order to obtain insights into primary production under specific conditions. This observation was extended to sub-daily timescales with alternating light and dark phases. Dissolved oxygen concentrations and its isotopes (δ18ODO) ranged from 0.02 to 0.06 mmol · L-1 and from +9.6‰ to +23.4‰. The δ18ODO proved to be more sensitive than concentration measurements in response to metabolic variation and registered earlier shifts to dominance by respiration. Oxygen (O2) contents in the headspace and its isotopes (δ18OO2) ranged from 2.62 to 3.20 mmol · L-1 and from +9.8‰ to +21.9‰. Headspace samples showed less fluctuations in concentration and isotope trends because aquatic processes were hardly able to alter signals once the gas had reached the headspace. Headspace δ18OO2 values were corrected for gas-water equilibration and were determinedto be higher than the mean δ18OH2O of -8.7‰. This finding suggests that counteracting respiration was important even during the highest photosynthetic activity. Additionally, headspace analyses led to the definition of a fractionation factor for respiration (αR) of this cyanobacterium with a value of 0.980. This value confirms the one commonly used for cyanobacteria. Our findings may become important for the management of water bodies where decreases in DO are caused by cyanobacteria.

Effect of low dissolved oxygen concentration on the tribocorrosion properties and ion release of Ti-13Nb-13Zr

Effect of low dissolved oxygen concentration on the tribocorrosion properties and ion release of Ti-13Nb-13Zr

Genesis of gray sandstone within the red beds in HLJ-DL uranium deposit, southwest Songliao Basin and its relationship with uranium mineralization

The search for uranium deposits in the red beds is currently a hotspot in China's sandstone-type uranium exploration efforts. The typical occurrence of uranium deposits is within gray sandstone layers amidst the red beds, where the distribution of these gray sand bodies dictates the presence of uranium deposits. Hence, delving into the genesis of gray sandstone within the red beds proves beneficial for both uranium exploration and unraveling the ore-forming mechanisms. However, there is currently significant controversy surrounding the genesis of gray sandstone in the red beds. This paper focuses on the typical uranium deposit in the red beds—the HLJ-DL uranium deposit in the southwestern part of the Songliao Basin. It investigates the genesis of gray sandstone within uranium-bearing rock series and its relationship with uranium mineralization. The lithology of representative drill cores, including the red sandstone for comparative study, is methodically examined, along with their elemental geochemistry as well as carbon and oxygen isotope characteristics. The petrological characteristics suggest that both the gray and red sandstone share similar compositions of clastic particles, indicating minimal influence of the source rock on the sandstone color. The gray sandstone displays comparable TOC content (averaging around 0.05 %) and δ18OPDB values (averaging around −16 ‰) to the red sandstone. However, it exhibits relatively higher δ13CPDB values (averaging −0.26 ‰), whereas the average δ13CPDB value for the red beds is −1.61 ‰. The data indicate that the gray sandstone is less affected by hydrothermal or hydrocarbon fluids. Compared to the red sandstone, which has relatively higher Cu/Al values (averaging 1.09) and Fe3+/Fe2+ ratios (averaging 3.37), the gray sandstone exhibits low Cu/Al values (averaging 0.44) and a low Fe3+/Fe2+ ratio (averaging 0.36), suggesting its formation in a relatively reducing environment. The reducing conditions necessary for the formation of gray sandstone may stem from the climate becoming relatively warm and humid, irrespective of whether it originates from the original sedimentary environment or is induced by the reducing pore water discharged from dark mudstones. The chemical weathering index of fine-grained sediments indicates that, the gray sandstones were commonly configured in relatively humid-warm climate with fairly low dissolved oxygen content in the bottom water. Additionally, the deposition of the uranium-bearing rock series experienced alternating climatic changes, rather than consistently being in a humid-warm climate. This resulted in the sand bodies with low TOC content, insufficient reducing potential, and uranium mineralization primarily occurring in the interior of the basin. Organic matter has a relatively weak control over uranium mineralization, whereas uranium mineralization shows a certain correlation with iron reduction. The ore-forming fluid was uranium-rich oxygenated atmospheric water. Dark-colored mudstones, formed under humid-warm climatic condition, contain relatively high TOC contents (avg. 0.43 %) and exhibit a strong reducing capacity. The reducing pore water in dark-colored mudstones excreted into the adjacent sandstones under compaction during the early diagenesis, is able to serve as a vital reducing agent in later-stage uranium mineralization. Additionally, certain dark-colored mudstones with high uranium content (up to 944 ppm) undergo significant uranium pre-enrichment, providing a partial uranium source for uranium mineralization.

The responses of pepper plants to nitrogen form and dissolved oxygen concentration of nutrient solution in hydroponics

BackgroundThe presence of oxygen in the growth medium is absolutely essential for root development and the overall metabolic processes of plants. When plants do not have an adequate oxygen supply for respiration, they can experience a condition known as hypoxia. In order to investigate the impact of different nitrogen forms and varying oxygen levels in nutrient solutions on the growth, photosynthesis, and chlorophyll fluorescence parameters of bell pepper plants, a comprehensive study was conducted. The experiment was designed as a factorial experiment, considering two main factors: nitrogen forms (calcium nitrate and ammonium sulfate) with a fixed nitrogen concentration of 5 mM, and the oxygen levels of the nutrient solutions (ranging from 1.8 ± 0.2 to 5.3 ± 0.2 mg. L-1).ResultsThe study examined the effects of nitrogen (NH4+ and NO3−) application on various parameters of vegetative growth. The results demonstrated that the use of ammonium (NH4+) led to a reduction in the most measured parameters, including the fresh and dry mass of both the root and shoot, at low O2 concentrations of 1.8 ± 0.2; 2.6 ± 0.2 and 3.8 ± 0.2 mg. L-1. However, an interesting observation was made regarding the impact of oxygen levels on root growth in plants grown with nitrate (NO3−). Specifically, the highest levels of oxygen significantly increased root growth in NO3−-fed plants. Additionally, the application of NH4+ resulted in an increase in chlorophyll concentration in the leaves, particularly when combined with high oxygen levels in the nutrient solution. On the other hand, leaves of plants fed with NO3− exhibited higher photosynthetic rate (A), intrinsic water use efficiency (iWUE), and instantaneous carboxylation efficiency (A/Ci) compared to those fed with NH4+. Furthermore, it was found that NO3−-fed plants displayed the highest instantaneous carboxylation efficiency at oxygen levels of 3.8 and 5.3 mg. L-1, while the lowest efficiency was observed at oxygen levels of 1.8 and 2.6 mg. L-1. In contrast, NH4+-grown plants exhibited a higher maximal quantum yield of PSII photochemistry (Fv/Fm), as well as increased variable fluorescence (Fv) and maximum fluorescence (Fm), compared to NO3−-grown plants. Interestingly, the NO3−-fed plants showed an increase in Fv/Fm, Fv, and Fm with the elevation of oxygen concentration in the nutrient solution up to 5.3 mg. L-1.ConclusionThis study showed that, the growth and photosynthesis parameters in bell pepper plants are sensitive to oxygen stress in floating hydroponic culture. Therefore, the oxygen level in the nutrient solution must not be lower than 3.8 and 5.3 mg. L-1 in NH4+ and NO3− –supplied culture media or nutrient solutions, respectively.

Renural networks applied to self-purification models of a river in West Parana

Among the parameters that allow evaluating the degree of pollution of a river, one of the most efficient is dissolved oxygen concentration (DO), as it reflects the balance between the production and consumption of oxygen in aquatic ecosystems. Due to the dynamic characteristic of DO concentration, especially in rivers and wetlands, it is highly recommended to generate DO models periodically for aquatic ecosystems so that quality control measures can be optimized over a time horizon. To this end, implementation of different artificial intelligence (AI) techniques have been suggested in the relevant literature and among these techniques, neural networks (ANNs) have been successfully applied to water quality estimates. In this context, the present work describes the development of a model in Convolutional Neural Networks – CNN (Convolutional Neural Networks) with the objective of simulating the self-cleaning potential of the Rio Alegria, located in the municipality of Medianeira in the State of Paraná . The data used was the same as that collected by Schütz in 2014, so that it was possible to compare the performance of the CNN network with the performance of the Feed Forward Network (FFN), developed with the same set of data collected in 2014. The models were developed based on data on the quality of the river's water and the effluent that is incorporated into the watercourse throughout the studied interval. The model was named, CNN5. Tests and validations were carried out by varying the network architecture with cross-validation to estimate dissolved oxygen. Considering the results referring to the simulations carried out with the CNN5 model, where the simulated OD values are compared (from the combination of the weights that the network assigns to each input), with the results collected in the field and it can be concluded that a CNN network can be used to predict dissolved oxygen in the waters of a stretch of river, with an overall accuracy of 0.90, while FFN5 managed to achieve an accuracy of 77% for the same group of data. processing, a reduction from 12 hours to two minutes can be observed.

Co-migration behavior of toluene coupled with trichloroethylene and the response of the pristine groundwater ecosystems – A mesoscale indoor experiment

Experimental scale and sampling precision are the main factors limiting the accuracy of migration and transformation assessments of complex petroleum-based contaminants in groundwater. In this study, a mesoscale indoor aquifer device with high environmental fidelity and monitoring accuracy was constructed, in which dissolved toluene and trichloroethylene were used as typical contaminants in a 1.5-year contaminant migration experiment. The process was divided into five stages, namely, pristine, injection, accumulation, decrease, and recovery, and characteristics such as differences in contaminant migration, the responsiveness of environmental factors, and changes in microbial communities were investigated. The results demonstrated that the mutual dissolution properties of the contaminants increased the spread of the plume and confirmed that toluene possessed greater mobility and natural attenuation than trichloroethylene. Attenuation of the contaminant plume proceeded through aerobic degradation, nitrate reduction, and sulfate reduction phases, accompanied by negative feedback from characteristic ion concentrations, dissolved oxygen content, the oxidation-reduction potential and microbial community structure of the groundwater. This research evaluated the migration and transformation characteristics of typical petroleum-based pollutants, revealed the response mechanism of the ecosystem to pollutant, provided a theoretical basis for predicting pollutant migration and formulating control strategies.

Arsenic in the Tibetan Plateau’s geothermal systems: a detailed analysis of forms, sources, and geochemical behaviors

In high-altitude tectonic regions, significant geothermal activity influences groundwater arsenic levels, presenting crucial resource and environmental challenges. The present study examines the Gonghe-Guide Basin located in the northeastern region of the Qinghai-Tibet Plateau. The study employs a comprehensive approach encompassing field sampling, hydrochemical analysis, thermodynamic modeling, and statistical analysis to ascertain the composition and origins of arsenic in geothermal groundwater. The research data indicates that the geothermal groundwater in the area displays weak alkalinity and medium to high mineralization, with the principal hydrochemical types being SO4−Cl·Na and Cl·Na. The concentration of arsenic has a notable inverse relationship with Cl− and a positive correlation with water temperature and DO. According to thermodynamic calculations, the most common kind of arsenic is As5+. The hydrochemical properties of the research area are shaped by rock weathering, evaporative concentration, and ion-exchange adsorption working together. These factors contribute to the favorable circumstances for the formation and migration of arsenic throughout the environment. Notably, the ion-exchange between sodium ions and calcium and magnesium ions significantly impacts the arsenic concentration. This study marks the first discovery of a unique arsenic contamination pattern in geothermal groundwater within the Gonghe-Guide Basin in the northeastern Tibetan Plateau, revealing a positive correlation between arsenic levels, water temperature, and dissolved oxygen content. This provides a new perspective on understanding arsenic pollution in geothermal groundwater in high-altitude regions.

Ecological condition of marine waters in the area of dredging operations of the kashagan field, Kazakhstan

To determine the hydrophysical and hydrochemical parameters of sea water and their changes during dredging works in the north‐eastern part of the Caspian Sea along the Western Approach Route and near/inside block D, islands EPC‐2, EPC‐3, EPC‐4 and island A of the Kashagan field.Measurement of hydrophysical parameters: salinity, temperature, turbidity, dissolved oxygen concentration, electrical conductivity, pH of seawater was carried out using a portable multi‐parameter analyser "Horiba‐U53" directly in the water column. Water transparency was determined using a Secchi disc. The depth of the water layer was determined using a sounder. Hydrochemical investigations were carried out for seven parameters, with all components sampled in the near‐surface and benthic horizons. Water samples were taken using probes or bathometers: BM‐48, GR‐18 or their analogues.Hydrochemical analyses of seawater samples for: biogens (NO2, NO3, NH4, Ntotal, and Ptotal), synthetic surfactants, phenols, hydrocarbons including polyaromatic hydrocarbons (PAHS), heavy metals, chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). These were carried out in laboratory conditions using analytical equipment according to scientifically accepted methods.The data obtained as a result of the research allow us to draw the following conclusion: changes in the amount of suspended sediment in sea water lead to changes in physical and chemical properties of water (increase in turbidity, disturbance of sediment composition and properties), changes in biotopes, deterioration of oxygen regime, optical properties of water and temperature regime.

Advances in aeration and wastewater treatment in shrimp farming: emerging trends, current challenges, and future perspectives

Abstract The shrimp aquaculture industry has recently witnessed dramatic growth. Shrimp farming has gradually shifted from extensive to intensive or super-intensive models. However, the intensification of shrimp aquaculture is associated with energy security and environmental issues. The aeration system requires high energy demand to run mechanical aerators to maintain dissolved oxygen concentration in cultured ponds. Besides, intensive shrimp farms usually produce wastewater with high pollutant concentrations that may jeopardize the ecosystem when discharged. In an attempt to minimize the impacts of these problems, rigorous technological approaches have been carried out. This review provides recent advanced technologies employed to improve aeration and wastewater management. Moreover, this paper also introduces a sustainable energy model being studied and developed for aeration and wastewater treatment at shrimp farms.