A novel simulation model to assess the impact of Aerobic-Anoxic biostimulation mediated BTEX migration in a coupled Vadose-Phreatic subsurface system.

During CO2 storage in deep saline aquifers, low-permeability lenticular shale layers alter CO2 migration and affect dissolution trapping, but their impacts remain unclear. In this study, a two-dimensional radial numerical model coupling gas–brine two-phase flow and mass transfer is developed to simulate CO2 plume evolution and dissolution beneath discontinuous lenticular shale layers. In the model, lenticular shale interlayers are represented as discontinuous low-permeability barriers, and their geometry is characterized by radial length and vertical thickness. The blocking effect of lenticular shale layers induces bypass flow, promotes lateral plume spreading, and prolongs contact time between CO2 and brine, which increases dissolution during 250 to 1000 days of injection. When the permeability anisotropy ratio is 0.001, upward migration of CO2 is suppressed and a high-concentration retention zone forms beneath the lenticular shale layer. As the radial length of the lenticular shale layers increases from 150 to 250 m, the plume expands and the bypass-flow path lengthens, which strengthens lateral CO2 spreading and redistributes dissolved CO2 concentration. In contrast, varying the thickness of the lenticular shale layers from 6 to 10 m has a relatively limited influence on the extent of bypass flow and the morphology of the concentration field.

A synthesis on the spread of the tailing plumes resulting from the Fundão dam collapse along the Brazilian coast: Integrating evidence from multiple sources.

Lithospheric thickness controls asymmetric mantle plume spreading and metallogenesis in the Tarim Large Igneous Province–Central Asian Orogenic Belt System

River plumes are important parts of the land–ocean matter fluxes and provide the key stage of transformation of river discharge and river-borne matter in the sea. However, both the plume-sea mixing budget and the ultimate fate of continental discharge in the open sea remain poorly understood. In this study, we analyze the GLORYS12 ocean reanalysis data to assess structure and variability of the 10 largest river plumes in the World Ocean. We quantify the interrelated characteristics of plume-sea interaction, namely, (1) response of a river plume to variability of river discharge, (2) residence time of river water in a river plume, (3) mixing intensity at plume-sea frontal zone. Based on these characteristics, we distinguish three different types of large river plumes in relation to their horizontal advection and vertical mixing with ambient seawater. The opposite ends of this classification are illustrated by the Amazon plume, which is shallow and occupies wide area due to reduced vertical mixing, and the Changjiang and St. Lawrence plumes, which have deep penetration, albeit small area due to strong vertical mixing near the river mouths. Our results aim to contribute to our understanding of how river discharge merges with and alters ambient shelf and ocean waters. It provides new insights into the spreading and mixing patterns of river plumes formed by diverse river systems.

In contrast to CCUS/CCS, research on UHS in saline aquifers remains limited. Comparative analysis of H2 and CO2 migration offers a basis for transferring CCUS/CCS insights to UHS. Thus, to investigate how multiple factors affect H2 and CO2 migration in saline aquifers, this paper constructs various 3D models considering porosity, permeability, pressure, temperature, salinity, and capillary pressure. Numerical simulation results show that (1) H2 exhibits strong fingering and wide plume spread, with low solubility and weak residual retention. CO2 shows compact, stable plumes with high solubility and strong residual retention. (2) Low porosity enhances lateral migration and residual retention, especially for CO2. (3) Reduced vertical permeability (Kv) significantly suppresses the upward migration of CO2 and strengthens residual retention, whereas its effect on the H2 migration range is less than 5%. Low horizontal permeability (Kh) mainly restricts lateral spreading and only slightly increases residual retention, but the sensitivity of H2 is lower than that of CO2. (4) Increased pressure promotes the dissolution of H2 and CO2. The dissolved amount of H2 increased by approximately 16.15%, and CO2 by about 7.49%. The temperature rise increases the solubility of H2 and decreases that of CO2. H2 increased by approximately 15.56%, and CO2 decreased by about 13.82%. The increase in salinity inhibited the dissolution of the two gases. H2 and CO2 decreased by approximately 17.5% and 16.6%, respectively. Additionally, high salinity weakens the temperature sensitivity of gas solubility. (5) Ignoring capillary pressure underestimates residual retention. However, it is mainly reflected in an increase in the retention scale and does not change the trend of residual retention controlled by different variables. These insights provide a basis for applying CCUS/CCS experiences to UHS.

ABSTRACT The spreading of solutes transported by groundwater is largely caused by the spatial variability of K , the hydraulic conductivity of aquifers. The longitudinal macrodispersivity α L quantifies the spreading of the solute plume along the mean flow direction. The dependence of α L on K is a key topic in the field of stochastic subsurface hydrology, a discipline which emerged in the second half of the last century. A major breakthrough was achieved by adopting the Lagrangian approach to transport. Within this framework, we present our contributions to research on longitudinal macrodispersivity over the last 50 years, ranging from theoretical aspects to field applications. After covering the basics, the first period derives the first-order approximation of logconductivity variance. The second explores the behaviour of α L in highly heterogeneous formations. The final period applies these findings to field experiments and offers practical recommendations.

Abstract Aerosol plumes emitted from ships can cause brightening of low clouds. The aerosol plume spreading rate controls what fraction of the cloud may experience brightening. Developing a deeper physical understanding of the mechanisms driving variations in spreading rate could inform the development of plume‐spreading parameterizations in global climate models, which may be relevant for assessing the feasibility of Marine Cloud Brightening. In this study, we employ large‐eddy simulations of two idealized precipitating stratocumulus cases to investigate the roles of collision‐coalescence, cloud droplet sedimentation, and droplet effective radius in the ship track and quantify their individual and combined effects on plume buoyancy anomalies and spreading rates. Our results indicate that cloud droplet sedimentation and collision‐coalescence are the primary mechanisms controlling buoyancy and horizontal spreading, whereas the influence of effective radius is negligible. Sensitivity tests indicate that mesoscale circulations can develop within the ship track even in the absence of precipitation suppression.

<div>Air pollution is a significant long-term public health issue, with on-road traffic emissions being a primary contributor, especially in urban areas. Remote emission sensing (RES) is an innovative method for large-scale monitoring of vehicle emissions. It not only enables accurate detection of pollutants from vehicles under real-world driving conditions but also offers actionable insights to optimize engine performance. The point sampling-based RES technique involves sampling the vehicle exhaust plume along the roadside with a sampling line and using exhaust analyzers. In this method, the sampling line is placed alongside the road for sample extraction. Thus, the sampling position and knowledge regarding the spread of the exhaust plumes are crucial. Other modern RES systems utilize laser absorption spectroscopy to measure the pollutants in vehicle exhaust. For accurate absorption measurements, the laser’s height must align with the height of the exhaust plume, and the absorption length must be known. In this work, we present a gas density Schlieren imaging sensor (GDSIS) system designed to visually capture, quantitatively analyze, and reconstruct the density fields of exhaust plumes from category L-vehicles. By analyzing the density fields, we can pinpoint the location of the highest density within the exhaust plume. This information indicates the ideal height for positioning sampling lines and lasers used in RES systems. Identifying this ideal height can enhance the efficiency and capture rate of RES systems while also helping to detect engine inefficiencies that can negatively affect performance and increase emissions. Moreover, emission patterns can inform engine calibration or maintenance schedules, which helps optimize fuel consumption and engine response. The performance of the GDSIS system in both laboratory settings with controlled gas flows and on the road with L-vehicles during emissions measurement campaigns is evaluated.</div>

Abstract Satellite observations have enabled an important advance in the near‐real‐time quantification of the dynamic parameters of the volcanic plume spreading in the atmosphere. However, the link between these observations and the estimation of eruption source parameters, such as the mass eruption rate (MER), remains a scientific obstacle to be overcome. The previously developed methods to estimate the MER are less efficient for weak eruptions and/or occurring under strong wind conditions, which are the most frequent. Here, we update a 1‐D volcanic column model for the estimation of the MER based on satellite measurements of wind‐impacted plumes. The new model allows predicting the plume geometry as seen from space, and thus linking the source MER to the geometry far from the source. We find that the predictions mostly depend on the wind speed and the MER. We test the model using measurements made on GOES‐16 images during the 2021 eruption of La Soufrière, St Vincent, and find a good agreement between our MER estimates and those found in the literature (with a mean MER of −0.23). We finally test our ability in estimating the MER in near real‐time using the HOTVOLC system and Meteosat‐SEVIRI images of 10 paroxysms from Mt Etna. The GIS‐based tool integrated to HOTVOLC allows easier measurements of the plume growth and will provide a robust tool for a rapid interpretation of satellite data in terms of source conditions, which are necessary inputs for tephra dispersion models, such as those used by the Volcanic Ash Advisory Centers.

Subcutaneous depot formation and diffusion in autoinjector delivery: insights from high-speed synchrotron imaging.

Abstract Effective remediation of contaminated aquifers is often limited by the mixing among reaction partners. An effective method to enhance mixing and reach a faster groundwater remediation is engineered injection extraction (EIE). This technology consists of a sequence of 12-steps of injection and extraction of water through a system of four wells. In this work, we investigate, using Morris sensitivity analysis, which are the most relevant design parameters of EIE among the location of four injection-extraction wells and their pumping rate, in a two-dimensional domain. Moreover, we consider the effect of heterogeneous hydraulic conductivity fields in scenarios of increasing complexity. Our results show that the first steps of the remediation process are the most important ones. The value of the hydraulic conductivity is relevant in case of heterogeneous fields within the treated area, as it generates flow focusing, enhancing plume spreading and mixing. In the case of instantaneous, complete mixing-limited reactions, the sensitivity of the model parameters also depends on the critical mixing ratio, which controls the time needed for the complete degradation of a treatment solution injected in the middle of the EIE system.

Carbon Capture and Storage (CCS) is a critical strategy for reducing CO2 emissions from hard-to-abate sectors. Reliable and efficient reservoir simulation tools are essential for supporting the safe and effective deployment of CCS projects. This study presents a twofold contribution to CCS modeling in saline aquifers: (1) the validation of the Black Oil Model (BoM) as a computationally efficient alternative to compositional simulators, and (2) a systematic assessment of the impact of grid resolution on plume prediction accuracy. The BoM was benchmarked against three commercial compositional simulators—Eclipse E300, CMG-GEM, and TNavigator. The comparison focused on key aspects of CO2 storage operations, including plume evolution to assess containment and storage security, as well as injection safety and efficiency through pressure and saturation profile analysis, evaluated across both the injection and the post-closure monitoring phases. The BoM successfully reproduced plume extent and CO2 saturation distributions, with mean deviations of 3% during injection, 5% during post-closure, and an overall average of 4% across the entire project duration. Additionally, simulation times were reduced by a factor of four compared to compositional models. These results confirm the BoM’s practical utility as a robust and efficient tool for CO2 storage simulation. In parallel, the study investigated the influence of vertical and lateral grid resolutions/coarsening on the accuracy of CO2 modeling. Seven models were developed and evaluated using a hybrid qualitative–quantitative framework, consistent with the BoM validation methodology. Vertical resolution was found to be particularly critical during the monitoring phase. While a 5 m resolution proved adequate during injection, deviations in plume shape and magnitude during post-injection increased to an average of 15% compared to a fine 2 m vertical resolution model, highlighting the necessity of fine vertical discretization (≤2 m) to capture gravity-driven plume dynamics during the monitoring phase. Conversely, lateral grid resolution had a stronger effect during the injection phase. A lateral cell size of 150 m was required for accurate plume prediction, with 200 m remaining moderately acceptable for early-phase assessment and prospect ranking, whereas coarser lateral grids led to significant underestimation of plume spread and dissolution extent. These findings demonstrate that the BoM, when combined with informed grid resolution strategies, enables accurate and computationally efficient simulation of CO2 storage in saline aquifers. The study provides practical guidelines for fluid model selection and spatial discretization, offering critical input to subsurface experts involved in CCS project development, monitoring design, and regulatory compliance.

This study examined the feasibility of using two geographical features: magnetic vessel science and gravimetry, and monitoring the amount of carbon adsorbed in soil and subsurface layers. For the purpose of our study, we conducted field campaigns at various locations where we collected magnetotelluric gravity data by using of specialized equipment and integrated it with geological and environmental data analyses. Magnetotellurics measurements of soil electrical resistivity show a strong negative correlation with organic carbon concentration (e.g., S010 in situ has an electrical resistivity of 82.671 Ωm and organic carbon content of 3,786%). This made it possible to estimate the amount of carbon stored in the soil. Gravimetric changes below the ground were linked to higher levels of CO2 saturation (for example, site G003 had a 0.912 mGal change and a 25.621% CO2 saturation), which helped scientists figure out what kind of carbon storage reservoirs might be there. Using autoregressive integrated moving average (ARIMA) models and nonlinear regression techniques (Levenberg-Marquardt algorithm), time series analyses showed that it was possible to track the dynamics of CO2 injection (gravimetric anomalies went from 0.000 to 1.756 mGal when 0.623 Mt of CO2 was injected) and CO2 plume migration (resistivity went from 35.671 to 7.789 Ω•m as the plume spread to 2.16 km2). The findings contribute to developing effective climate change mitigation strategies, promoting sustainable land management practices, and informing carbon capture and storage initiatives.

ABSTRACT Thermaikos Gulf, located in the northeastern Mediterranean Sea, faces significant anthropogenic pressures and natural hazards, requiring reliable metocean forecasts for weather, ocean circulation, sea levels, waves, and hazard predictions, including pollutant transport, coastal floods, and freshwater discharges. The Wave4Us operational platform addresses these needs by providing high-resolution and specialised forecasts, accessible to local authorities, researchers, and the public. Additionally, on-demand predictions for marine pollution, coastal inundation, and heatwaves offer real-time insights to emergency responders and coastal authorities during hazardous events. This study presents the platform’s structure, modelling advancements, and predictive skill for specific hazards. Forecast efficiency is evaluated against satellite and field observations: (i) the simulated oil spill spreading is verified by satellite data; (ii) the modelled freshwater discharges are validated against field measurements (high correlation, RMSE < 10%); (iii) a pronounced river plume spreading is confirmed by ocean/tracer simulations and satellite imagery; (iv) the prediction of sea level, wave conditions, and coastal flooding under a severe low-pressure system is validated against measurements and documented events; (v) the marine heatwave predictions is confirmed by comparing simulated and satellite sea temperatures (error < 1%). These evaluations demonstrate the platform’s reliability in forecasting key environmental risks, aiding decision-making and response efforts in the Thermaikos Gulf region.

This work investigates the dynamics of flow, transport and mixing in subsurface porous media during an engineered injection–extraction (EIE) system. We perform laboratory bench-scale experiments mimicking an EIE system in an unconfined aquifer, and we explore the role of local dispersion on mixing enhancement. The experimental setup is equipped with four wells operated in a sequence, one at a time, creating transient flows and a fluctuating water table impacting the transport dynamics of an injected dye tracer plume. A high-resolution imaging technique is applied to monitor the spatial and temporal evolution of the plume concentration. The experiments are performed in porous media with fine and coarse grain sizes and considering two different sequences of injection and extraction. The plume spreading and mixing are quantified by computing the spatial moments and the plume area, respectively. The Okubo–Weiss parameter is calculated over the plume area to correlate mixing enhancement with changes in flow topology. The results indicate that the operation of EIE system significantly enhances mixing and spreading, particularly when the effective Okubo–Weiss parameter is higher. Furthermore, the mixing enhancement is larger in the experiments performed in the coarse porous media, indicating the importance of local dispersion as a factor for mixing enhancement in EIE systems.

Understanding multi-scale heterogeneity in porous media has become increasingly critical as the world transitions from fossil fuel production to geological storage of CO2 and H2 for climate change mitigation. This commentary examines why small-scale heterogeneities have taken on a heightened importance in modeling subsurface fluid migration. We identify three key factors: increased public scrutiny and stricter permitting requirements for storage projects, different risk tolerances requiring long-term monitoring, and distinct flow physics compared to traditional oil and gas extraction. Drawing from current research, we demonstrate how current models consistently underestimate CO2 plume spread, likely due to inadequate representation of small-scale heterogeneities, which will also heavily impact H2 storage in porous rocks. We review the current state of research on incorporating small-scale heterogeneities into field scale models, discuss relevant spatial scales for both CO2 and H2 storage applications, and highlight promising directions for future research in this critical area.

This study explores a method of detecting smoke plumes effectively as the early sign of a forest fire. Convolutional neural networks (CNNs) have been widely used for forest fire detection; however, they have not been customized or optimized for smoke characteristics. This paper proposes a CNN-based forest smoke detection model featuring novel backbone architecture that can increase detection accuracy and reduce computational load. Since the proposed backbone detects the plume of smoke through different views using kernels of varying sizes, it can better detect smoke plumes of different sizes. By decomposing the traditional square kernel convolution into a depth-wise convolution of the coordinate kernel, it can not only better extract the features of the smoke plume spreading along the vertical dimension but also reduce the computational load. An attention mechanism was applied to allow the model to focus on important information while suppressing less relevant information. The experimental results show that our model outperforms other popular ones by achieving detection accuracy of up to 52.9 average precision (AP) and significantly reduces the number of parameters and giga floating-point operations (GFLOPs) compared to the popular models.

Abstract Cloud responses to surface‐based sources of aerosol perturbation partially depend on how turbulent transport of the aerosol to cloud base affects the spatial and temporal distribution of aerosol. Here, scenarios of plume injection below a marine stratocumulus cloud are modeled using large eddy simulations coupled to a prognostic bulk aerosol and cloud microphysics scheme. Both passive plumes, consisting of an inert tracer, and active plumes are investigated, where the latter are representative of saltwater droplet plumes such as have been proposed for marine cloud brightening. Passive plume scenarios show higher in‐plume cloud brightness (relative to out‐of‐plume) due to the predominant transport of the passive plume tracer from the near‐surface to the cloud layer within updrafts. These updrafts rise into brighter areas within the cloud deck, even in the absence of an aerosol perturbation associated with an active plume. Comparing albedo at in‐plume to out‐of‐plume locations associates the inert plume with the brightest cloud locations, without any causal effect of the plume on the cloud. Numerical sensitivities are first assessed to establish a suitable model configuration. Then sensitivity to particle injection rate is investigated. Trade‐offs are identified between the number of injected particles and the suppressive effect of droplet evaporation on plume loft and spread. Furthermore, as the near‐field in‐plume brightening effect does not depend significantly on injection rate given a suitable definition of perturbed versus unperturbed regions of the flow, plume area is a key controlling factor on the overall cloud brightening effect of an aerosol perturbation.

Based on machine learning (ML) methods, a new problem-solving procedure is proposed here to enhance river-plume research, including data collection, data-mining processes, and characteristic analyses. A plume shape recognition model is developed, structured on convolutional neural networks (CNNs), and is named “PlumeCatcher.” This model is shown to extract various river-plume features from satellite images. The random forests (RFs) data-mining method was applied to investigate the significance of the controller on plume development, including dynamic factors such as wind, current, water discharge, and tides, all of which are potential factors that can influence plume properties on daily, seasonal, and annual timescales. Focusing on the Magdalena River in Columbia (South America), the results of the methodology indicate that wind is the factor with maximum sensitivity dominating plume movement starting from the river mouth. Moreover, the plume features including area, direction, and spreading range are varied from short timescales (tidal cycle) to long timescales (yearly) and demonstrate the dynamic nature of plume spreading. ML methods provide an effective and convenient way to map plume features and extract and analyze their properties, which is beneficial for future scientific analyses.