Dissolved Oxygen Concentration Research Articles

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 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.

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 to catfish subjected to 12 hours of hypoxia (20% oxygen saturation; 1.8 mg O2/L; 27 °C) followed by 12 hours of recovery in normoxia to mimic 24-hours in a catfish aquaculture pond. Fish were sampled at 0-, 6-, 12-, 18-, and 24-hour time points, with the 6- and 12-hour 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 hours 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.

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.

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.

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.

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.