Gas Transport Research Articles

Fractal Characterization on Three-Dimensional Tortuosity of Fault Tectonic

Faults, as a kind of fracture tectonics, play a role in reservoir closure or provide oil and gas transportation channels. The accurate understanding of the distribution characteristics of faults is significant for oil and gas exploration. The traditional fractal dimension for fault number (Df3) cannot comprehensively characterize the complexity and heterogeneity of fault network distribution. In this paper, a fractal characterization method on three-dimensional (3D) tortuosity of fault tectonics is proposed based on 3D seismic exploration. The methodology is described in detail to establish the model on the fractal dimension for the 3D tortuosity of fault tectonics. The results show the proposed method of estimation of the DT3 displaying high accuracy and rationality. Compared with the traditional fractal dimension Df3, the proposed DT3 can comprehensively characterize the fractal characteristics of faults network systems in the 3D space. This study achieves a breakthrough in the fractal characterization of the 3D tortuosity of fault tectonics. It is worth further study for establishing an analytical fractal equation based on the DT3 and oil or gas transfer, which can provide the theoretical foundation and technical support for oil and gas exploration.

Self-Supporting Microporous Layer for Polymer Electrolyte Fuel Cells Using Thin Carbon Mesh Gas Diffusion Layer

The gas diffusion layers (GDLs) used in polymer electrolyte fuel cells (PEFCs) are relatively thick compared with the electrocatalyst layer and electrolyte membrane. Reducing GDL thickness is expected to reduce gas transport resistance. Such thin GDLs with high electrical conductivity enable higher power density per volume of cell stacks. Here, thin MPL/GDLs are fabricated by printing a highly conductive microporous layer (MPL) on carbon meshes or carbon nanotube sheets as a substrate to create tailored self-supporting MPLs. The electrical conductivity was improved by optimizing the carbon black material used in the MPL, and carbon nanotubes were added to tailor a pore structure within the MPL. The thickness of the resulting MPL/GDLs is approaching 50 µm, which is not only thinner than the current MPL/GDL, but also results in lower gas transport resistance. The thinner MPL/GDLs are a promising solution to increase the power density of PEFC cell stacks.

Supercritical CO<sub>2</sub> treatment of mixed matrix membranes based on polyimides for improvement of their gas transport properties

Supercritical CO<sub>2</sub> treatment of mixed matrix membranes based on polyimides for improvement of their gas transport properties

Effective CO2 Thermocatalytic Hydrogenation with High Coke Resistance on Ni-CZ/Attapulgite Composite

Converting CO2 into methane is considered a promising and economically viable technology for global transportation and utilization of this greenhouse gas. This study involves the preparation of a Ni-CZ (CeO2-ZrO2)/ATP (attapulgite) catalyst through the co-precipitation and impregnation methods. XRD, SEM, TEM, N2 absorption-desorption isotherms, XPS, H2-TPR, CO2-TPD, TG/DSC, and Raman were adapted to characterize the obtained samples. Real-time GC was used to measure the catalytic performances and to intensively study the impact of Ni loading content and ATP to CZ ratio on the catalytic performance of the products. DRIFTs was used to monitor the interstitial radicals in the catalytic reactions and to deduce the catalytic mechanisms. The results indicate that the composite catalytic matrix composed of CZ assembled on ATP demonstrated higher CO2 methanation stability and better carbon deposition resistance ability than the single CZ or ATP as the carrier, which should be attributed to the improved specific surface area and pore volume of the ATP assembled matrix and the enhanced dispersibility of the CZ and Ni species. The adoption of CZ solid solutions improves the oxygen storage capability of the catalyst, thereby providing continued mobile O2− in the matrix and accelerating the molecular exchange rate in the catalytic reactions. The ideal loading quantity of nickel contents on the CZA matrix is 15%, as the CO2 conversion decreases at elevated temperatures when the Ni loading content reaches 20%. Among the tested samples, the 15Ni-0.8CZA sample showed the best catalytic performance of 75% CO2 conversion and 100% CH4 selectivity at 400 °C. After 50 h of stability tests, the CO2 conversion rate still remained 70.84%, and the CH4 selectivity obtained 97.46%. No obvious coke was detected according to the Raman spectra of the used catalyst. The in situ DRIFTS experiment showed that formate is the main intermediate of the CO2 hydrogenation reaction on the 15Ni-0.8CZA catalyst.

Experimental Study on Heat Recovery in a CaO/Ca(OH)2-Based Mechanical Fluidized Bed Thermochemical Energy Storage Reactor

Long-term storage of seasonally available solar energy and its provision to balance heating energy demand can contribute significantly to the sustainable use of energy resources. Thermochemical energy storage is a suitable process for this purpose, offering the possibility of loss-free long-term energy storing and heat supply. In order to develop suitable technical solutions for the use of this technology, novel reactor concepts and scientific questions regarding material and technology development are being investigated. In this publication, the energy storage process of a long-term energy storage system based on a ploughshare reactor is experimentally investigated under various technically relevant operating conditions. One specific aspect of this technology is related to the release of water vapour during the charging process. Therefore, this work focusses, in particular, on the possibility of technically utilizing the latent heat of the released water vapour in the range of 45 °C to 80 °C, which covers the operating requirements of common heating systems in households. The experiments have shown that the dehydration process enables the separation of two heat fluxes: the chemically bound energy for long-term storage and the physically (sensible and latent) stored energy for short-term applications. However, the limitation of gas transport was also identified as the most important influencing parameter for optimising the performance of the process.

Prediction of hydrate formation boundaries in pure water and salt/alcohol containing systems based on prior knowledge and artificial intelligence

As an efficient and clean energy source, natural gas plays a crucial role in optimizing the global energy consumption structure. However, the formation and accumulation of hydrates in pipelines during the exploitation and transportation of natural gas greatly jeopardize the production safety. Accurately predicting hydrate formation boundaries remains challenging due to the complex interation among various influencing factors. Although mechanistic models provide insights, they often fall short in accuracy. Conversely, machine learning models, while promising, may face limitations related to small sample sizes and a lack of physical interpretability. To overcome these limitations, this study proposed a physically guided neural network (PGNN) based on the Chen-Guo model, which integrated thermodynamic mechanisms with a neural network framework. This proposed model utilized pressure calculated from the Chen-Guo model as an input variable and incorporates physical inconsistencies into its loss function. A comparative analysis using literature data that PGNN achieved superior prediction accuracy, with an R2 value of 0.9768 for gas hydrate formation pressure prediction. The integration of thermodynamic mechanisms enhanced the prediction accuracy, as evidenced by an increase in the R2 value by 0.036, and a reduction in the MSE value by 5.56. Furthermore, the PGNN maintained a high level of prediction accuracy, ranging from 0.96 to 0.98, even with limited sample sizes, thus confirming its applicability and stability. This study validated the feasibility of PGNN for predicting gas hydrate formation conditions and offered insights into hydrate-based applications and hydrate management strategies.

A Thermal Model for a Hybrid Gas Bearing System in Oil-Free Turbochargers

Abstract With little drag friction, gas bearings operate at high rotor speed and high temperature and thus enable long operating life along with material damping for mechanical energy dissipation. However, most computational tools for modeling gas bearings are not accessible to bearing manufacturers or potential end-users, and the models are restricted to specific geometries and operating conditions or are missing important features, thus limiting their utility. This paper presents the thermal energy transport in a hybrid gas bearing for oil-free turbochargers with integrated heat and fluid flow models to produce a comprehensive thermo-hydrodynamic analysis predictive tool, and to design and evaluate air-lubricated gas bearings by predictions and experiments. An efficient algorithm couples the solution of the Reynolds equations and the thermal energy transport equations in the film between the rotor and a pad of the hybrid gas bearing, and updates the temperature-dependent viscosity and film thicknesses during the iterative process. The test repeats and averaged for each rotor speed from 2 krpm to 22 krpm at intervals of 2 krpm, for supply pressure varying along 25 psi to 100 psi and predicted film temperature matches well with the measured one. The parameters of interest in this analysis include the gas supply condition, bearing configuration, and the gas properties at high altitudes and high rotation speeds. The parametric study results show the density and kinematic viscosity are the most dominant properties of the gas lubrication for the film temperature.

Experimental study on the effects of co-firing mode and air staging on the ultra-low load combustion assisted by water electrolysis gas (HHO) in a pulverized coal furnace

Developing zero-carbon fuel (H2/NH3) co-firing technology with pulverized coal can improve the low-load flame instability and pollutant emissions of boilers during peak shaving. In this study, we propose to assist the low-load combustion of coal powder furnaces with the safer water electrolysis gas (HHO). To further optimize the combustion strategy, a one-dimensional furnace combustion system coupled with an HHO gas generation and transportation system was used to investigate the effects of injection methods and air staging on the flue gas emission and auxiliary combustion characteristics of the lignite load reduction process and ultra-low load. The results indicate that reducing the coal combustion load achieves carbon reduction and reduces actual CO2 emissions. The excess air coefficient increases, resulting in higher NOX and lower CO emissions. Air staging can control NOX and CO emissions during load shedding, with a 40.49 % reduction in NOX at 30 % load. Under ultra-low load, HHO-assisted combustion increases the oxygen concentration in the furnace, increasing NOX emissions, while SO2 decreases and then increases. However, the effect of HHO gas premixed mode (PM) on NOX generation is weaker than that of staged mode (SM). As the flow rate of HHO increases, HHO-SM promotes the conversion of CO to CO2 and reduces CO emissions, while CO emissions under PM remain at ∼10 ppm. Both HHO injection methods exhibit assisted combustion effects for ultra-low load operation. Due to the different effects of the two on the recirculation zone inside the combustion, the auxiliary combustion effect of PM is superior than that of SM. At 1800L/h HHO, the decrease in combustion instability coefficient (βT) of PM is 57.14 %, higher than that of SM. Air staging is beneficial for stable combustion under ultra-low load, but it can affect the auxiliary combustion of HHO gas. Under ultra-low load HHO co-firing conditions, 11%-OFA can also control NOX and CO emissions.

Anionic Ionomer: Released Surface-Immobilized Cations and an Established Hydrophobic Microenvironment for Efficient and Durable CO2-to-Ethylene Electrosynthesis at High Current over One Month.

Electrosynthesis of multicarbon products, such as C2H4, from CO2 reduction on copper (Cu) catalysts holds promise for achieving carbon neutrality. However, maintaining a steady high current-level C2H4 electrosynthesis still encounters challenges, arising from unstable alkalinity and carbonate precipitation caused by undesired ion migration at the cathode under a repulsive electric field. To address these issues, we propose a universal "charge release" concept by incorporating tiny amounts of an oppositely charged anionic ionomer (e.g., perfluorinated sulfonic acid, PFSA) into a cationic covalent organic framework on the Cu surface (cCOF/PFSA). This strategy effectively releases the hidden positive charge within the cCOF, enhancing surface immobilization of cations to impede both outward migration of generated OH- and inward migration of cations, inhibiting carbonate precipitation and creating a strong alkaline microenvironment. Meanwhile, the ionomer's hydrophobic chains create a hydrophobic environment within the cCOF, facilitating efficient gas transport. In situ characterizations and theoretical calculations demonstrate that the cCOF/PFSA catalyst establishes a hydrophobic strong alkaline microenvironment, optimizing the adsorption strength and configuration of *CO intermediates to promote the C2H4 formation. The optimized catalyst achieves a 70.5% Faradaic efficiency for C2H4 with a partial current density over 470 mA cm-2. Notably, it delivers a high single-pass carbon efficiency of 96.5% for CO2RR and sustains an exceptional stability over 760 h. When implemented in a large-area MEA electrolyzer and a 5-cell MEA stack, the system achieves an industrial current of 15 A and continuous C2H4 production exceeding 19 mL min-1, marking a significant step toward industrial feasibility in CO2RR-to-C2H4 conversion.

The Impact of Retrofitting Natural Gas-Fired Power Plants on Carbon Footprint: Converting from Open-Cycle Gas Turbine to Combined-Cycle Gas Turbine

Since retrofitting existing natural gas-fired (NGF) power plants is an essential strategy for enhancing their efficiency and controlling greenhouse gas emissions, this paper compares the carbon footprint of natural gas-fired power generation from an NGF power plant in Brazil (BR-NGF) with and without retrofitting. The former scenario entails retrofitting the BR-NGF power plant with combined-cycle gas turbine (CCGT) technology. In contrast, the latter involves continuing the BR-NGF power plant operation with open-cycle gas turbine (OCGT) technology. Our analysis considers the BR-NGF power plant’s life cycle (construction, operation, and decommissioning) and the natural gas’ life cycle (natural gas extraction and processing, liquefaction, liquefied natural gas transportation, regasification, and combustion). Moreover, it is based on data from primary and secondary sources, mainly the Ecoinvent database and the ReCiPe 2016 method. For OCGT, the results showed that the BR-NGF power plant and the natural gas life cycles are responsible for 620.87 gCO2eq./kWh and 178.58 gCO2eq./kWh, respectively. For CCGT, these values are 450.04 gCO2eq./kWh and 129.30 gCO2eq./kWh. Our findings highlight the relevance of the natural gas’ life cycle, signaling additional opportunities for reducing the overall carbon footprint of natural gas-fired power generation.

Sulfide stress corrosion cracking in L360QS pipelines: A comprehensive failure analysis and implications for natural gas transportation safety

This research investigates the severe weld failures in L360QS pipelines, which are integral to the infrastructure of high-pressure natural gas transportation systems. Even with strict compliance to engineering standards, two pivotal welds experienced sulfide stress corrosion cracking (SSCC) due to continuous exposure to hydrogen sulfide and stresses from construction activities. A comprehensive material analysis and stress simulation has been utilized, revealing a maximum stress concentration of 544.9 MPa that surpasses the yield strength of the pipeline material. This discovery prompts a reevaluation of current safety margins and underscores the urgent need for enhanced pipeline integrity management. Our findings, corroborated by stress distribution simulations, not only illuminate the complex interplay of material properties and environmental factors in SSCC but also provide a new perspective for the safe service of pipeline.

Exploring MOF-Derived CuO/rGO Heterostructures for Highly Efficient Room Temperature CO2 Sensors.

In response to the urgent need for advanced climate change mitigation tools, this study introduces an innovative CO2 gas sensor based on p-p-type heterostructures designed for effective operation at room temperature. This sensor represents a significant step forward, utilizing the synergistic effects of p-p heterojunctions to enhance the effective interfacial area, thereby improving sensitivity. The incorporation of CuO nanoparticles and rGO sheets also optimizes gas transport channels, enhancing the sensor's performance. Our CuO/rGO heterostructures, with 5 wt % rGO, have shown a notable maximum response of 39.6-500 ppm of CO2 at 25 °C, and a low detection limit of 2 ppm, indicating their potential as high-performance, room-temperature CO2 sensors. The prepared sensor demonstrates long-term stability, maintaining 98% of its initial performance over a 30-day period when tested at 1-day intervals. Additionally, the sensor remains stable under conditions of over 40% relative humidity. Furthermore, a first-principles study provides insights into the interaction mechanisms with CO2 molecules, enhancing our understanding of the sensor's operation. This research contributes to the development of CO2 monitoring solutions, offering a practical and cost-effective approach to environmental monitoring in the context of global climate change efforts.

A coarse-grained approach to modeling gas transport in swelling porous media

In many engineering applications, understanding gas adsorption and its induced swelling in nanoporous materials is crucial. In this study, we propose a novel coarse-grained molecular dynamics (CGMD) model with gas-gas, solid-solid, and gas-solid interactions explicitly controlled to achieve the coupling between gas transport and solid deformation at the microscale. The CGMD model has the capability to recover solid and gas properties, including density, Young's modulus of the solid, and viscosity of the gas to generate a broad range of swelling ratios relevant to nanostructures by using the innovative bead-spring chain networks. A comparison is made between gas transport through deformable and non-deformable nanochannels of varying sizes (35.4–123.9 nm), which is also compared with the macroscopic Hagen-Poiseuille equation. The proposed model has been further tested in a simplified nanoporous medium composed of four randomly distributed spherical solids. The Kozeny-Carman equation can generally describe the relationship between permeability and porosity, but small deviations are observed in the case of swelling porous media. Our results justify the effect of swelling on reducing gas permeability and provide a new approach to modeling gas transport in swelling porous media at the microscale within the framework of CGMD, with potential applications spanning nanofluidics, energy storage technologies, and environmental nanotechnology.

High-Order Models for Gas Transport in Multiscale Coal Rock.

The coal reservoirs exhibit great heterogeneity and strong anisotropy in multiscale pore/fracture structures. Developing highly accurate multiscale models for real-time prediction of microgaseous flow in complicated porous media with pronounced contrast in transport coefficients is crucial but not yet available, which is time-consuming, expensive, and even computationally impossible. In this study, a multiscale approximate solution of the gas flow and pressure field is derived in this paper for predicting coalbed methane (CBM) transport in macro-microscopic two-scale porous media of typical coal rocks in Guizhou Province, China, and the detailed finite element algorithm is established. The multiscale approximate solutions are constructed from the first- and second-order auxiliary cell functions and low- or high-order derivatives of the homogenized pressure field, which can accurately approximate the solution of the original problem to a certain extent. The numerical accuracy and validity of the multiscale approximate solutions under various working conditions are analyzed in detail by comparing and verifying the results with the direct numerical simulation results of fine-mesh high-precision finite elements. The results show that the homogenized approximate solution can only roughly capture the characteristics of the macroscopic pressure evolution, the first-order approximate solution can partially reproduce the macro-microscopic pressure oscillation phenomenon, and the second-order approximate solution can accurately predict the pressure profile and its evolution law with time in porous media with macro-micro configuration under various complex conditions. The second- and high-order two-scale approximate solutions constructed in this paper exhibit high numerical accuracy and excellent computational efficiency. We also develop multiscale approximate solutions for predicting microgaseous flow at a low reservoir pressure where the slippage effects are very pronounced. Potential applications of our high-order models to the coal reservoirs with a hierarchical matrix and fractures are discussed. The developed multiscale models exhibit excellent applicability to investigating the crossflow effects and coupling mechanisms between the matrix and cleats or fractures with multiple configurations in the CBM and shale gas reservoirs, which is crucial for the effective implementation of hydraulic fracturing technology. This study provides a fundamental understanding of gas transport in multiscale matrix-fracture networks, which helps to improve the optimization design of hydraulic fracturing in tight reservoirs.

3D Printing as a Strategy to Scale-Up Biohybrid Hydrogels for T Cell Manufacture.

The emergence of cellular immunotherapy treatments is introducing more efficient strategies to combat cancer as well as autoimmune and infectious diseases. However, the cellular manufacturing procedures associated with these therapies remain costly and time-consuming, thus limiting their applicability. Recently, lymph-node-inspired PEG-heparin hydrogels have been demonstrated to improve primary human T cell culture at the laboratory scale. To go one step further in their clinical applicability, we assessed their scalability, which was successfully achieved by 3D printing. Thus, we were able to improve primary human T cell infiltration in the biohybrid PEG-heparin hydrogels, as well as increase nutrient, waste, and gas transport, resulting in higher primary human T cell proliferation rates while maintaining the phenotype. Thus, we moved one step further toward meeting the requirements needed to improve the manufacture of the cellular products used in cellular immunotherapies.

Micro–Nano 3D CT Scanning to Assess the Impact of Microparameters of Volcanic Reservoirs on Gas Migration

Volcanic rock reservoirs for oil and gas are known worldwide for their considerable heterogeneity. Micropores and fractures play vital roles in the storage and transportation of natural gas. Samples from volcanic reservoirs in Songliao Basin, CS1 and W21, belonging to the Changling fault depression and the Wangfu fault depression, respectively, have similar lithology. This study employs micro–nano CT scanning technology to systematically identify the key parameters and transport capacities of natural gas within volcanic reservoirs. Using Avizo 2020.1software, a 3D digital representation of rock core was reconstructed to model pore distribution, connectivity, pore–throat networks, and fractures. These models are then analyzed to evaluate pore/throat structures and fractures alongside microscopic parameters. The relationship between micropore–throat structure parameters and permeability was investigated by microscale gas flow simulations and Pearson correlation analyses. The results showed that the CS1 sample significantly exceeded the W21 sample in terms of pore connectivity and permeability, with connected pore volume, throat count, and specific surface area being more than double that of the W21 sample. Pore–throat parameters are decisive for natural gas storage and transport. Additionally, based on seepage simulation and the pore–throat model, the specific influence of pore–throat structure parameters on permeability in volcanic reservoirs was quantified. In areas with well–developed fractures, gas seepage pathways mainly follow fractures, significantly improving gas flow efficiency. In areas with fewer fractures, throat radius has the most significant impact on permeability, followed by pore radius and throat length.

Decoupling and predicting natural gas deviation factor using machine learning methods

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

Numerical simulation of hydrate flow in gas-dominated undulating pipes considering nucleation and deposition behaviors

The distribution and deposition of hydrates in deep-water gas transportation pipelines are crucial for safety. Current simulations of hydrate flow based on the population balance model have predominantly focused on water-dominant systems and neglect the nucleation and deposition of hydrates. By establishing a model for a hydrate flow in a gas-dominant system that considers the uneven distribution of the liquid film on the wall, hydrate nucleation, gas–liquid mass transfer, particle adhesion and aggregation, and dynamic deposition, we achieved simulation of the entire process of hydrate formation, aggregation, breakage, and deposition. Simulations in undulating pipelines were carried out to investigate the effects of gas velocity, liquid injection, pressure, and pipeline structure on distribution patterns. The results showed increasing the gas velocity enhanced the dispersion of particles and increasing the pressure increased the rate of aggregation. The formation of blocky aggregates posed significant risk in the rear section of the lower-bend pipe.

Methane concentration in the heartwood of living trees in a cold temperate mountain forest: variation, transport and emission.

Forest soils are the largest terrestrial sink of methane (CH4), but CH4 produced in tree trunks by methanogenic archaea and emitted into the atmosphere can significantly offset CH4 oxidation in the soil. However, our mechanistic understanding of CH4 accumulation in tree trunks, in relation to CH4 emission from the trunk surface, is still limited. We characterized temporal variations in the molar fraction of CH4 in the heartwood of trees ([CH4]HW) of four different species in a mountain forest and addressed the relationship between [CH4]HW and emission from the surface of the trunk (${F}_{CH_4}$), in connection with the characteristics of the wood. [CH4]HW measurements were made monthly for 15 months using gas-porous tubes permanently inserted into the trunk. [CH4]HW were above ambient CH4 molar fraction for all trees, lower than 100p.p.m. for seven trees, higher for the nine other trees and greater than 200,000p.p.m. (>20%) for two of these nine trees. [CH4]HW varied monthly but were not primarily determined by trunk temperature. Heartwood diffusive resistance for CH4 was variable between trees, not only due to heartwood characteristics but probably also related to source location. ${F}_{CH_4}$were weakly correlated with [CH4]HW measured a few days after. The resulting apparent diffusion coefficient was also variable between trees suggesting variations in the size and location of the CH4 production sites as well as resistance to gas transport within the trunk. Our results highlight the challenges that must be overcome before CH4 emissions can be simulated at the tree level.

Distribution and control mechanism of pCO2 and water-air CO2 efflux in the Pearl River Estuary.

Estuaries are generally considered to be important sources of atmospheric CO2. However, the differences between estuaries, and inadequate observations of partial pressure of CO2 in estuarine water (pCO2water) hamper global estuarine CO2 budgeting. In this study, the longitudinal distribution of CO2 in the waters of Modaomen (MSE) and Lingdingyang (LSE), two sub-estuaries of the Pearl River Estuary (PRE), and its influencing mechanism are studied. The change in the distribution of pCO2water along the distance from the upstream estuary to the ocean between LSE and MSE was significantly different. pCO2water at the LSE ranges from 238 to 7267 µatm, whereas the MSE ranges from 406 to 3078 µatm. Stronger microbial respiration and relatively long water retention times were the main influences that led to higher pCO2water at LSE than at MSE. Seasonally, the increase of soil CO2 into the water in the upstream basin caused by precipitation is the potential influencing factor that the water pCO2water in the flood season is higher than in the dry season. PRE was a net source of atmospheric CO2 with an average annual water-air flux of 41.2 ± 33.3mmolm-2day-1. Our results suggest that the differences in longitudinal gradients of pCO2water between estuaries in the same region and the effects of different gas transport velocity models on CO2 emission estimates need to be considered in estuarine CO2 emission budgeting.