Search Results (91)

The operation of medical cyclotrons for PET radiopharmaceutical production presents significant radiological and environmental challenges that require systematic risk assessment and evidence-based mitigation strategies. In this study, an integrated framework combining Failure Mode and Effects Analysis (FMEA) with a quantitative Defense Effectiveness Factor (DEF) approach to evaluate and reduce residual risk in a real urban cyclotron facility. High-criticality failure modes (Risk Priority Number $\ge 120$) affecting HVAC systems, stack exhaust, and power supply were identified and validated through a Delphi expert consensus process. These modes were addressed with multi-layered defense-in-depth strategies: redundant systems (occurrence reduction, 60–80% effectiveness), real-time monitoring (detection reduction, 40–50% effectiveness), and design robustness (severity reduction, 70–85% effectiveness). The combined DEF yielded a 96–97% risk reduction. One-way sensitivity analysis confirmed the robustness of these results, with residual annual effective dose to the representative person remaining between 50–88 μSv/year (well below the IAEA 1 mSv/year public dose constraint) even under pessimistic scenarios. Primary exposure pathways were inhalation and cloud gamma from ¹⁸F and ⁴¹Ar during the early-morning production window, while secondary pathways were negligible due to the short half-lives of the radionuclides. These findings demonstrate that the integration of FMEA with DEF-based defense-in-depth and Gaussian plume modeling provides a transparent, robust, and regulatory-compliant framework for managing radioactive atmospheric emissions in urban medical cyclotron facilities. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and $R^2$ values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

Air pollution from industrial point sources remains a major concern in urban environments, highlighting the need for accessible tools that support both education and preliminary environmental assessment. This study presents the development and intercomparison of an open-source, QGIS-based geospatial model for simulating atmospheric pollutant dispersion from fixed point sources using the Gaussian plume formulation. The model integrates emission parameters, meteorological conditions, and terrain data within a fully spatial workflow implemented through the QGIS graphical modeler, enabling the generation of ground-level concentration fields without advanced programming expertise. Dispersion is simulated with and without inclusion of a ground reflection term, allowing comparative analysis of boundary condition effects. The model was applied to a representative urban industrial source at the National University of Science and Technology POLITEHNICA Bucharest, using CO₂ emissions treated as a passive tracer. Model outputs were evaluated through descriptive statistics and quantitative comparison with two established open-source Gaussian plume implementations developed in Python. Ground reflection leads to an increase of approximately 60% in modeled near-surface concentrations, particularly in the upper tail of the distribution, underscoring its importance for screening-level exposure assessment. The proposed model provides a transparent, reproducible, and user-friendly framework suitable for teaching activities, rapid screening analyses, and exploratory air quality assessments. Full article

Air transport, acknowledged as the safest and most efficient mode for long-haul travel, is confronted with diverse challenges aimed at improving its environmental performance. A notable aspect of this effort involves the formation of contrails, arising from the emission of water vapor and condensation nuclei in a cold, ice-supersaturated atmosphere, which represents one of the most difficult-to-predict yet physically quantifiable environmental impacts of air traffic. Adopting the bottom-up principle to evaluate individual contrails for trajectory optimization introduces uncertainties in calculating the radiative forcing of contrails and modeling their life cycle. Former studies for modeling the microphysical life cycle of individual contrails based on a 2D Gaussian plume model could be validated with a photographic contrail tracking method in the mid-latitudes. However, contrails rarely form individually over Central Europe; rather, they form as an accumulation behind many aircraft flying through an ice-supersaturated region. For this reason, the 3D Gaussian plume model has been extended for the co-existence of several contrails. The greater the overlap of the contrails, the greater the competition in ice supersaturation between the contrails and therefore the greater the reduction in lifetime compared to single contrails. Furthermore, with increasing overlap, the number density of ice crystals increases, resulting in smaller ice crystals with shorter lifetimes. The overlap effect is also reflected in the angle between non-parallel contrails. The results can be used for further studies on the optical properties of real co-existing contrails. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article

Accurate quantification of methane (CH₄) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH₄ dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies. Full article

To achieve more precise and regionally adaptive emission control, this study develops a dual-control framework that simultaneously constrains both total carbon emissions and pollutant concentration levels. Regional environmental heterogeneity is incorporated into the dispatch of generating units to balance emission reduction and operational efficiency. Based on this concept, a regional carbon emission allowance allocation model is constructed by integrating ecological pollutant concentration thresholds. A multi-source Gaussian plume dispersion model is further developed to characterize the spatial and temporal distribution of pollutants from coal-fired power units. These pollutant concentration constraints are embedded into an environmental–economic dispatch model of a coupled electricity–hydrogen–carbon system supported by hybrid storage. By optimizing resource use and minimizing environmental damage at the energy-supply stage, the proposed model provides a low-carbon foundation for the entire industrial production cycle. This approach aligns with the sustainable development paradigm by integrating precision environmental management with circular economy principles. Simulation results reveal that incorporating pollutant concentration control can effectively reduce localized environmental pressure while maintaining overall system economy, highlighting the importance of region-specific environmental capacity in enhancing the overall environmental friendliness of the industrial chain. Full article

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages. Full article

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article

Carbon dioxide (CO₂) is a non-toxic asphyxiant gas that, once released, can pose severe risks, including suffocation, poisoning, frostbite, and even death. As a critical component of carbon capture, utilization, and storage (CCUS) technology, CO₂ pipeline transportation requires reliable leakage detection and precise localization to safeguard the environment, ensure pipeline operational safety, and support emergency response strategies. This study proposes an inversion model that integrates wireless sensor networks (WSNs) with the Gaussian plume model for CO₂ pipeline leakage monitoring. The WSN is employed to collect real-time CO₂ concentration data and environmental parameters around the pipeline, while the Gaussian plume model is used to simulate and invert the dispersion process, enabling both leak source localization and emission rate estimation. Simulation results demonstrate that the proposed model achieves a source localization error of 12.5% and an emission rate error of 3.5%. Field experiments further confirm the model’s applicability, with predicted concentrations closely matching the measurements, yielding an error range of 3.5–14.7%. These findings indicate that the model satisfies engineering accuracy requirements and provides a technical foundation for emergency response following CO₂ pipeline leakage. Full article

This study analyzed the radiation dose rate in air, water and soil following a simulated Large Break LOCA (LBLOCA) accident in a Pressurized Water Reactor (PWR) nuclear power plant with a point-source release of radionuclides into the atmosphere. AERMOD and RESRAD-BIOTA 1.8 codes were used, with meteorological data processed by AERMET and terrain elevation data generated using AERMAP. AERMOD performed dispersion calculations using Gaussian and bi-Gaussian models. The simulations identified atmospheric stability classes C and F, which, combined with other external factors, directly influenced the dose rates and the distances reached by the radioactive plume. The dose rate analysis, based on calculated concentrations in the air, water and soil, indicated that, in this scenario, the potential release of radioactive material does not pose a threat to the population. The adopted methodology proved effective in mapping the behavior of the radioactive plume across the three media, providing accurate and reliable results for use in safety assessments and emergency response planning. Full article

This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation system—captured plume images for analysis. To detect abnormal flame phenomena in the plume, a Gaussian support vector machine (SVM) model was developed using image features that were derived from both color and gradient information. Six representative frames containing visible flames were selected from a single test failure video. These images were segmented in the YCbCr color space using the k-means clustering algorithm to distinguish flame and non-flame pixels. A 10-dimensional feature vector was constructed for each pixel and then reduced to five dimensions using the Maximum Relevance Minimum Redundancy (mRMR) method. The reduced vectors were used to train the Gaussian SVM model. The model achieved a 97.6% detection accuracy despite being trained on a limited dataset. It has been successfully applied in multiple subsequent engine tests, and it has proven effective in detecting ablation-related anomalies. By combining real-world sensor data acquisition with intelligent image-based analysis, this work enhances the monitoring capabilities in rocket engine development. Full article

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article