Search Results (109)

A VOC-contaminated shallow aquifer in an industrial site was investigated to evaluate its potential for natural attenuation. The shallow groundwater aquifer beneath the industrial site has been contaminated by dissolved volatile organic compounds (VOCs) such as trichloroethylene (TCE), cis-1,2-dichloroethylene (cis-DCE), and vinyl chloride (VC) for more than three decades. Monitoring and investigation were implemented during 2011–2024, aiming to propose future groundwater aquifer management strategies. This study included groundwater borehole investigation, well installation monitoring, hydraulic head measurements, slug tests, groundwater samplings, and microbial analyses. Microbial investigations identified the predominant group of microorganisms of Proteobacteria, indicating biodegradation potential, as demonstrated by the presence of cis-DCE and VC. BIOSCREEN was used to evaluate the process of natural attenuation, incorporating site-specific parameters. A two-layer groundwater flow model was developed using MODFLOW with hydraulic conductivities obtained from slug tests. The site has an average hydraulic head of 259.6 m amsl with a hydraulic gradient of 0.026, resulting in an average groundwater flow velocity of 11 m/y. Hydraulic conductivities were estimated during model calibration using the PEST pilot point technique. A reactive transport model, RT3D, was used to simulate dissolved TCE transport over 30 years, which can undergo sorption as well as biodegradation. Model calibration demonstrated a satisfactory fit between observed and simulated groundwater heads with a root mean square error of 0.08 m and a correlation coefficient (r) between measured and simulated heads of 0.81, confirming the validity of the hydraulic conductivity distribution. The TCE plume continuously degraded and gradually migrated southward, generating a cis-DCE plume. The concentrations in both plumes decreased toward the end of the simulation period at Source 1 (located upstream), while BIOSCREEN results confirmed ongoing natural attenuation primarily by biodegradation. The integrated MODFLOW-RT3D-BIOSCREEN approach effectively evaluated VOC attenuation and plume migration. However, future remediation strategies should consider enhanced bioremediation to accelerate contaminant degradation at Source 2 and ensure long-term groundwater quality. Full article

Efficient segmentation of smoke plumes is crucial for environmental monitoring and industrial safety. Existing models often face high computational demands and limited adaptability to diverse smoke appearances. To address these issues, we propose SmokeNet, a deep learning architecture integrating multiscale convolutions, multiview linear attention, and layer-specific loss functions. Specifically, multiscale convolutions capture diverse smoke shapes by employing varying kernel sizes optimized for different plume orientations. Subsequently, multiview linear attention emphasizes spatial and channel-wise features relevant to smoke segmentation tasks. Additionally, layer-specific loss functions promote consistent feature refinement across network layers, facilitating accurate and robust segmentation. SmokeNet achieves a segmentation accuracy of 72.74% mean Intersection over Union (mIoU) on our newly introduced quarry blast smoke dataset and maintains comparable performance on three benchmark smoke datasets, reaching up to 76.45% mIoU on the Smoke100k dataset. With a computational complexity of only 0.34 M parameters and 0.07 Giga Floating Point Operations (GFLOPs), SmokeNet is suitable for real-time applications. Evaluations conducted across these datasets demonstrate SmokeNet’s effectiveness and versatility in handling complex real-world scenarios. Full article

To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation. Full article

To achieve the precise quantification and real-time monitoring of CO₂ emissions from stationary sources, this study developed a Gaussian plume model-based dispersion framework incorporating emission characteristics. Critical factors affecting CO₂ dispersion were systematically analyzed, with model optimization conducted through plume rise height adjustments and reflection coefficient calibrations. MATLAB-based simulations on an industrial park case study demonstrated that wind speed, atmospheric stability, and effective release height constituted pivotal determinants for enhancing CO₂ dispersion modeling accuracy. Furthermore, the inverse estimation of source strength at emission terminals was implemented via particle swarm optimization, establishing both theoretical foundations and methodological frameworks for the precision monitoring and predictive dispersion analysis of stationary-source CO₂ emissions. Full article

Mixing time, as a key parameter for evaluating ladle refining efficiency, has long attracted extensive attention from researchers. In typical experimental studies, salt solution tracers are introduced into ladle water models to assess the degree of mixing within the ladle. Previous studies have demonstrated that the volume of tracer can significantly influence the measured mixing time. However, the gas flow rates employed in these studies are generally relatively high, whereas, in industrial operations, especially during final composition adjustments, lower gas flow rates are often applied. To systematically investigate the effect of the salt solution tracer volume on the mixing efficiency in a ladle water model under asymmetrical gas stirring with a low gas flow rate, a 1:3-scaled water model was developed based on a 130-ton industrial ladle. The mixing behaviors corresponding to different tracer volumes were comprehensively analyzed. The results indicate that the relationship between tracer volume and mixing time is non-monotonic. As the tracer volume increases, the mixing time first decreases and then increases, reaching a minimum at 185 mL. When the tracer volume was small, the dimensionless concentration curves at Monitoring Point 4 exhibited two distinct patterns: A parabolic profile, which was when the tracer initially moved through the left and central regions and then slowly crossed the gas plume to reach the monitoring point. A sinusoidal profile, which was when the tracer predominantly circulated along the right side of the ladle. When the tracer volume exceeded 277 mL, the concentration curves at Monitoring Point 4 consistently exhibited a sinusoidal pattern. Compared with moderate gas flow conditions (8.3 L/min), the peak concentration at Monitoring Point 3 was significantly lower under a low gas flow (2.3 L/min), and the overall mixing time was longer, indicating reduced mixing efficiency. Based on the findings, a recommended tracer volume range of 185–277 mL is proposed for low gas flow conditions (2.3 L/min) to achieve accurate and efficient mixing time measurements with minimal disturbance to the flow field. It was also observed that when the tracer concentration was relatively low, the mixing behavior throughout the ladle became more uniform. Full article

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h⁻¹ from 188 platforms; 118 kg h⁻¹ from 104 platforms using mass balance methods; 284 kg h⁻¹ from 151 platforms using aircraft remote sensing; and 19,088 kg h⁻¹ from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies. Full article

High-temperature and high-pollution mobile sources are frequently encountered in industrial environments. Fixed-position exhaust outlets often fail to promptly remove heat and contaminants when these sources are in motion, leading to local accumulation and reduced indoor air quality. This study proposes a novel mobile exhaust system capable of tracking and dynamically aligning with moving emission sources to improve heat removal and cooling efficiency. Three configurations were evaluated: (1) a fixed exhaust outlet, (2) an exhaust vent moving synchronously with the heat source, and (3) a buoyancy-driven tracking exhaust outlet. Small-scale experiments and CFD simulations using dynamic mesh techniques were conducted. The results showed that the synchronous system reduced ambient temperature by an average of 0.25 to 2.3 °C compared to the fixed outlet, while the buoyancy-tracking system achieved an additional 0.15 to 2.5 °C reduction. The study also introduces a correlation between thermal plume inclination and the Archimedes number, providing a predictive basis for exhaust positioning. Given the similar dispersion patterns of heat and airborne pollutants, the proposed system holds promise for both thermal management and contaminant control in dynamic industrial environments. Furthermore, the system may offer critical advantages in emergency ventilation scenarios involving intense heat or hazardous pollutant outbreaks. Full article

Nowadays, industries and society are very concerned about pollution, well-being, health, air quality, and the possible negative effects of industrial emissions on a property’s surroundings. This gas dispersion is typically estimated with Gaussian Plume/Puff Models or software that uses these models with slight adjustments. The issue regarding these models is that they do not consider the surroundings’ particularities, for instance, when obstacles are present, and they require experimental data to adapt to specific scenarios. Therefore, the aim of this work is to validate the use of ANSYS Fluent^® 2022 R1 for modelling atmospheric gas dispersion. This validation is performed by comparing the ANSYS Fluent^® 2022 R1 findings to published experimental data, Gaussian Plume Models (GPM in this case corresponds to the application of the Gaussian Equation or Gaussian Fit, and does not correspond to a specific dispersion model), and ALOHA 5.4.7 software. A comparison between these three alternatives was not available in the literature. In terms of downwind dispersion, the findings of the three models are extremely comparable. However, ANSYS Fluent^® has a propensity to overestimate the concentration at higher heights. Validation using ANSYS Fluent^® in atmospheric gas dispersion applications enables confident results to be obtained in other scenarios. Differences in pollutant estimation between models are clear when studying more complex cases containing turbulence-inducing geometries. In these cases, CFD exhibits a more realistic description of the transport phenomena than the other models considered. The Prairie Grass Project is used as a tool to validate the CFD model, and to demonstrate its potential for more complex cases. Full article

As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources. Full article

The rapid expansion of the petrochemical industry has led to significant environmental issues, including groundwater and soil contamination from hydrocarbon spills. This study investigates the movement and dispersion of hydrocarbon contaminants in the Rey industrial area in Tehran (Iran) using a two-dimensional finite element model. The results indicate that the oil plume exhibits slow migration, primarily due to low soil permeability and high hydrocarbon viscosity, leading to localized contamination. High-density pollution zones, such as TORC and REY7, are characterized by persistent hydrocarbon accumulation with minimal lateral migration. The findings emphasize the limited effectiveness of natural attenuation alone, highlighting the need for targeted remediation measures in high-density zones to accelerate contamination reduction. This study provides insights into the dynamics of hydrocarbon pollution and supports the development of effective remediation strategies. Full article

Efficiently mapping hourly air quality at a fine scale (25 m) remains a computational challenge. This difficulty is heightened when aiming to accurately capture industrial plumes and time-varying traffic emissions. This paper presents a method for generating hourly pollutant concentration maps across an entire region for operational applications. Our approach assumes that concentration maps can be decomposed into three components: traffic concentrations, industrial concentrations and a residual “background” concentrations component. The background concentration is estimated using established fine-scale mapping methods involving ADMS-Urban dispersion simulations. Meanwhile, the traffic and industrial layers are derived using a KNN-based approach applied to a sample of hourly ADMS-Urban simulations. This method enhances the representation of industrial plumes and the temporal variability in traffic emissions while maintaining computational efficiency, making it suitable for the operational production of hourly air quality maps in the Hauts-de-France region (France). Full article

The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant potential to enhance the efficiency and effectiveness of data processing in the GST. This paper develops a method for detecting carbon dioxide (CO₂) emission hotspots using a convolutional neural network (CNN) with short-lived and co-emitted nitrogen dioxide (NO₂) as a proxy. To address the data gaps in model parameter training, we constructed a dataset comprising over 210,000 samples of NO₂ plumes and emissions based on atmospheric dispersion models. The trained model performed well on the test set, with most samples achieving an identification accuracy above 80% and more than half exceeding 94%. The trained model was also applied to the NO₂ column data from the TROPOspheric Monitoring Instrument (TROPOMI) for hotspot detection, and the detections were compared with the MEIC inventory. The results demonstrate that in high-emission areas, the proposed method successfully identifies emission hotspots with an average accuracy of over 80%, showing a high degree of consistency with the emission inventory. In areas with multiple observations from TROPOMI, we observed a high degree of consistency between high NO₂ emission areas and high CO₂ emission areas from the Global Open-Source Data Inventory for Anthropogenic CO₂ (ODIAC), indicating that high NO₂ emission hotspots can also indicate CO₂ emission hotspots. In the future, as hyperspectral and high spatial resolution remote sensing data for CO₂ and NO₂ continue to grow, our methods will play an increasingly important role in global data preprocessing and global emission estimation. Full article

Hazardous gas leakage in the petrochemical industry frequently results in major incidents. A significant challenge arises due to the limitations of the current gas plume target feature extraction and identification techniques, which reduce the automated detection capabilities of remote monitoring systems. To address this, we propose BBGFA-YOLO, a real-time detection method leveraging background information and an improved YOLO network. This approach is designed specifically for the infrared imaging of gas plume targets, fulfilling the requirements of visual remote monitoring for hazardous gas leaks. We introduce a synthetic image colorization method based on background estimation, which leverages background estimation techniques to integrate motion features from gas plumes within the synthesized images. The resulting dataset can be directly employed by existing target detection networks. Furthermore, we introduce the MSDC-AEM, an attention enhancement module based on multi-scale deformable convolution, designed to enhance the network’s perception of gas plume features. Additionally, we incorporate an improved C2f-WTConv module, utilizing wavelet convolution, within the neck stage of the YOLO network. This modification strengthens the network’s capacity to learn deep gas plume features. Finally, to further optimize the network performance, we pre-train the network using a large-scale smoke detection dataset that includes reference background information. The experimental results, based on our self-acquired gas plume dataset, demonstrate a significant improvement in detection accuracy with the BBGFA-YOLO method, specifically achieving an increase in the average precision (AP50) from 74.2% to 96.2%. This research makes a substantial contribution to industrial hazardous gas leak detection technology, automated alarm systems, and the development of advanced monitoring equipment. Full article

Uncooled infrared imaging systems have significant potential in industrial hazardous gas leak detection. However, the use of narrowband filters to match gas spectral absorption peaks leads to a low level of incident energy captured by uncooled infrared cameras. This results in a mixture of fixed pattern noise and Gaussian noise, while existing denoising methods for uncooled infrared images struggle to effectively address this mixed noise, severely hindering the extraction and identification of actual gas leak plumes. This paper presents a UNet-structured dual-encoder denoising network specifically designed for narrowband uncooled infrared images. Based on the distinct characteristics of Gaussian random noise and row–column stripe noise, we developed a basic scale residual attention (BSRA) encoder and an enlarged scale residual attention (ESRA) encoder. These two encoder branches perform noise perception and encoding across different receptive fields, allowing for the fusion of noise features from both scales. The combined features are then input into the decoder for reconstruction, resulting in high-quality infrared images. Experimental results demonstrate that our method effectively denoises composite noise, achieving the best results according to both objective metrics and subjective evaluations. This research method significantly enhances the signal-to-noise ratio of narrowband uncooled infrared images, demonstrating substantial application potential in fields such as industrial hazardous gas detection, remote sensing imaging, and medical imaging. Full article

Houston, Texas, with its large-scale industrial activities, serves as a national hub for petrochemical processing and chemical feedstock production, making it a unique emission region for volatile organic compounds (VOCs) and production-related emissions. These emissions can be associated with industrial activities, including solvent usage and production to manufacture consumer products such as volatile chemical products. To support the Houston-based Dept. of Energy’s Atmospheric Measurement Radiation program-led Tracking Aerosol Convection ExpeRiment (TRACER) projects, VOCs were measured at the San Jacinto Battleground State Historic Site during September 2021 and 2022. The observed VOC mixing ratios reveal unique emission signatures for select VOCs, including benzene, toluene, acetone, and isoprene. Routine nighttime enhancements of these compounds exceeded the urban background, with mixing ratios increasing by up to 20 ppbv per hour and persisting for up to 6 h, suggesting that emissions from local industrial activities near the Houston Ship Channel (HSC) are impacting the site. For example, mixing ratios exceeding 15 ppbv for at least one VOC were observed on 58% of nights (n = 32 nights), with 19 nights (~35%) having two or more VOCs with mixing ratios above 15 ppbv. For select peak emission events, the NOAA dispersion model estimated plume transport across parts of the urban system, suggesting that VOCs from the HSC may impact local air quality. This study highlights the importance of VOC-related emissions from industrial production and supply chains in contributing to total VOC emissions in urban areas like Houston, Texas. Full article