Atmospheric Dispersion Research Articles (Page 1)

The current Russian aggression against Ukraine leads to complex needs for and challenges to the expansion of the Ukrainian radiation monitoring network. The issue of preparedness and response in times of war, in particular during the seizure of the Zaporizhzhia Nuclear Power plant site, has greatly exacerbated the issue of developing a response strategy based not only on expert opinion on possible scenarios but also on actual measurements in the first hours of a potential release. In this context, the shortage of radiation monitoring stations has become a significant issue for operators, the public, and various organisations, including State Nuclear Regulatory Inspectorate of Ukraine (SNRIU) and State Scientific and Technical Center for Nuclear and Radiation Safety. To address this, proposals have been made to identify suitable locations for additional automatic radiation monitoring systems to complement the existing ones. The main environment for solving this problem became the RESTORATION project, conducted in 2023-2024 in collaboration with Norwegian Radiation and Nuclear Safety Authority SNRIU, and State Scientific and Technical Center for Nuclear and Radiation Safety. The main objective of activities is to identify the data needed to select appropriate locations for radiation monitoring stations using the European decision support system JRODOS. For this study, the reference severe accident scenario at Ukrainian nuclear facilities was selected in accordance with the IAEA Emergency Preparedness Category I. Such an event may cause significant impacts beyond the boundary of the monitoring zone and enables the assessment of how actual meteorological conditions influence the atmospheric dispersion of radionuclides. Meteorological data from a five year period were used for statistical calculations. Preliminary monitoring quantities were based on the data requirements of the JRODOS decision support system, which provided the necessary statistical outputs. The paper highlights the role of the radiation monitoring network during an emergency as well as the problem of its potential development in view of the challenges with regard to civil protection, analysis of radiological situations, dispersion modelling, and dose projection. An overview of the existing networks and hierarchy of monitoring data providers among the Ukrainian organisations is presented. Based on the Ukrainian experience, in particular the results of the RESTORATION project, a methodological approach and recommendations for expansion of the radiation monitoring network are presented.

ABSTRACT Radioactive leakage from nuclear facilities during severe accidents poses a significant threat to the environment and public safety. Rapid and accurate estimation of the source term is critical for radiation consequence assessment and emergency response. Forward methods rely on core radionuclide inventories but are limited by insufficient source information. Inversion methods, which utilize environmental measurements and atmospheric dispersion simulations, provide an essential alternative. However, inherent uncertainties in radionuclide transport models often result in biased estimates. To address this, a self-correction inversion method is employed and assessed in this study, introducing a correction coefficient matrix to mitigate errors in plume range and concentration values. The method is validated using wind tunnel experiments simulating three wind scenarios (NNW, W, SSE) that replicate the complex topography of a nuclear power plant site. The SWIFT-RIMPUFF model is employed for simulation and transport matrix construction. Results demonstrate that the self-correction inversion method significantly outperforms traditional approaches. Notably, for the SSE scenario, the traditional method overestimates the source term by 3.3 times the true value, whereas the self-correction method reduces the deviation to only 4%. These findings highlight the effectiveness of the self-correction method in improving source term estimation under complex conditions.

Objective: The main aim of this work is to analyze how environmental factors can influence the atmospheric dispersion of ammonia in different simulation scenarios of a possible accidental release, using the ALOHA simulator. Theoretical Framework: Ammonia is a hazardous product widely used in fertilizer. Production risks are necessary due to the risk of toxic vapor dispersion, explosions, and fires, requiring attention and care to prevent leaks from ammonia tanks. Using simulators with different contamination plume dispersion models enables the adoption of action strategies for accident simulations to mitigate the impacts of accidents, fatalities, environmental damage, and social effects. Method: Different atmospheric conditions, such as relative humidity, cloud cover, wind speed, and temperature, were considered to simulate the dispersion of a specific pollutant plume into the atmosphere, using the ALOHA simulator at a specific location. Two scenarios were simulated: one involving an instantaneous release of ammonia into the atmosphere and the other involving a storage tank explosion. Results and Discussion: It was observed in the study that in the instantaneous release model of the tank contents, cloud cover directly influenced the dispersion of the gas, while in the BLEVE model, the variation in the relative humidity of the atmosphere had a greater impact on the simulation, while cloud cover for this type of model was not significant. Research Implications: Demonstrate that fatal accidents can be prevented by using the ALOHA simulator for different scenarios on an industrial scale, in a real-world setting in Brazil. The simulation results enable safer and more predictive decision-making for the substance in question, which is highly reactive and explosive. Originality/Value: Considering the potential risk of ammonia use in different industrial sectors, this study sought to address the use of a free simulator to predict the effects of adverse situations, such as explosions, leaks, and releases of ammonia into the atmosphere, allowing for the development of emergency action plans to remedy and reduce the potential impacts generated in a given location.

ABSTRACT Forest fires in the Himalayan region are recurring events during summer season, yet their impact on regional air quality remains poorly characterized. This study characterizes the spatio-temporal distribution of wildfire activity along the Himalayan belt and identifies the Central Himalayas as a major hotspot, accounting for roughly 50% of all detected fire pixels. We selected four significant fire events between 2019 and 2024 to assess their impact on trace gas concentrations. Compared to a baseline year of 2020, carbon monoxide concentrations rose by 28.8%–57.8%, while formaldehyde levels increased by 144.8%–200% during these fire events. Visible satellite imagery revealed extensive smoke plumes over Himalayan foothills in Nepal and adjacent Indian states, while atmospheric transport and dispersion modelling confirmed a dominant eastward transport of pollutants under prevailing summer winds. Our findings demonstrate the vulnerability of the Central Himalayan region to pre‑monsoon fires and the resulting degradation of regional air quality. This study highlights the need for enhanced fire and air quality monitoring across the Himalayan forests along with targeted mitigation strategies to protect environmental and human health.

Investigating anomalous radioxenon detections on the International Monitoring System related to a significant source in East Asia.

ABSTRACT The combined operation of multiple particulate matter (PM) emission sources in industrial and port areas creates major environmental threats and serious public health risks. Current methods of monitoring and predictive models lack sufficient capability to detect PM emission sources in real time. This study developed an integrated framework that uses Artificial Neural Networks (ANNs) and Computational Fluid Dynamics (CFD) to precisely locate PM emission sources in flat terrain. The CFD model was validated through experimental data analysis and the Monin-Obukhov similarity theory to precisely represent the particulate matter transport and atmospheric profiles. We created a simulation dataset containing 243 runs that tested different wind speed and direction combinations with variations in emission height and emission interval. The dataset served as training material for two deep learning models which used Long Short-Term Memory (LSTM) and a one-dimensional Convolutional Neural Network (CNN1D) to perform PM emission location classification. Both models achieved high accuracy levels with F1-scores above 0.95. The time needed to optimize hyperparameters proved the difference between models because LSTM required 4 h and 15 min and CNN1D needed 4 h and 43 min. This study proves that using CFD-generated data with ANN models allows reliable emission source localization which shows promise for environmental regulation, industrial accountability, and public health protection. The proposed framework represents a major breakthrough in real-time PM source localization in industrial and port environments.

Methodologies to assess the influence of chemistry on urban nitrogen dioxide concentrations using an atmospheric dispersion model under limited monitoring conditions

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1 + 1)-ES Sandpile model can effectively reconstruct source parameters, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications.

This study investigates the atmospheric concentrations, spatiotemporal distribution, the associated health risks and the ozone formation potential of benzene, toluene, ethylbenzene and xylenes (BTEX) across 33 monitoring sites of Greece over a one-year period. Samples were collected using passive diffusive samplers and analyzed by gas chromatography–mass spectrometry (GC-MS). The highest BTEX concentrations were detected during winter and autumn, particularly in urban and industrial areas such as in the Attica and Thessaloniki regions, likely due to enhanced emissions from combustion-related activities and reduced atmospheric dispersion. Health risk assessment revealed that hazard quotient (HQ) values for all compounds were within the acceptable limits. However, lifetime cancer risk (LTCR) for benzene exceeded the recommended limits in multiple regions during the colder seasons, indicating notable public health concern. Source apportionment using diagnostic ratios suggested varying seasonal emission sources, with vehicular emissions prevailing in winter and marine or industrial emissions in summer. Xylenes and toluene exhibited the highest ozone formation potential (OFP), underscoring their role in secondary pollutant formation. These findings demonstrate the need for seasonally adaptive air quality strategies, especially in Mediterranean urban and semi-urban environments.

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO2, 20,500 tons of NOx, 4250 tons of SO2, 656 tons of PM10, and 603 tons of PM2.5, with anchorage activities contributing 497,000 tons of CO2, 7010 tons of NOx, 1520 tons of SO2, 232 tons of PM10, and 214 tons of PM2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies.

Initial 131I physicochemical composition ratios during the Fukushima Daiichi nuclear power station accident and impact of 131I re-emission process from surface deposition on air concentrations: three-dimensional atmospheric dispersion simulation approach.

Abstract. Fairbanks, Alaska, is a sub-Arctic city that frequently suffers from the non-attainment of national air quality standards in the wintertime due to the coincidence of weak atmospheric dispersion and increased local emissions. As part of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) campaign, we deployed a Chemical Analysis of Aerosol Online (CHARON) inlet coupled with a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF MS) and an Aerodyne high-resolution aerosol mass spectrometer (AMS) to measure organic aerosol (OA) and non-refractory submicron particulate matter (NR-PM1), respectively. We deployed a positive matrix factorization (PMF) analysis for the source identification of NR-PM1. The AMS analysis identified three primary factors: biomass burning, hydrocarbon-like, and cooking factors, which together accounted for 28 %, 38 %, and 11 % of the total OA, respectively. Additionally, a combined organic and inorganic PMF analysis revealed two further factors: one enriched in nitrates and another rich in sulfates of organic and inorganic origin. The PTRCHARON factorization could identify four primary sources from residential heating: one from oil combustion and three from wood combustion, categorized as low temperature, softwood, and hardwood. Collectively, all residential heating factors accounted for 79 % of the total OA. Cooking and road transport were also recognized as primary contributors to the overall emission profile provided by PTRCHARON. All PMF analyses could apportion a single oxygenated secondary organic factor. These results demonstrate the complementarity of the two instruments and their ability to describe the complex chemical composition of PM1 and related sources. This work further demonstrates the capability of PTRCHARON to provide both qualitative and quantitative information, offering a comprehensive understanding of the OA sources. Such insights into the sources of submicron aerosols can ultimately assist environmental regulators and citizens in improving the air quality in Fairbanks and in rapidly urbanizing regional sub-Arctic areas.

ABSTRACT Rapid and accurate prediction of atmospheric radionuclide dispersion during nuclear power plant (NPP) accidents is essential for effective emergency response planning and radiation exposure minimization, but remains challenging for complex local-scale scenarios. Accelerated by graphics processing unit (GPU), the Quick Environment Simulation (GPU-QES) is a three-dimensional Lagrangian particle dispersion model with a building-effect module tailored for high-resolution atmospheric dispersion modeling, making it suitable for radiological emergency response. However, its performance in local-scale scenarios involving both complex terrain and high-density buildings has not yet been systematically validated. In this study, GPU-QES was comprehensively evaluated against four local-scale wind tunnel experiments. The simulation results were compared with measurements of two-dimensional ground-level wind and concentration fields, as well as vertical profiles of wind speed and pollutant concentration. The results demonstrate that GPU-QES consistently produces accurate three-dimensional wind fields across all four wind directions, reliably capturing both ground-level wind and vertical profiles. In addition, with GPU acceleration, it completes the computation of 90 million grid points within just 12 seconds under each wind condition, highlighting its remarkable efficiency. The model accurately reproduced the high concentration values observations in the building area, achieving a minimum absolute fractional bias of 0.04 and a normalized mean square error of 0.12, respectively. For axial concentration distributions, over 82% and 90% of simulated values fall within a factor of 2 and 5 of measurements, respectively. An optimal parameter configuration was also identified to balance accuracy and computational efficiency, providing valuable guidance for modeling complex NPP scenarios.

Hydrocarbon exploitation in Argentina is a strategic sector for the national economy, but also a significant source of atmospheric emissions. In the context of climate change, energy transition, and increasing health risks, robust evidence is needed to characterize pollutant dynamics in hydrocarbon basins. This study modeled the atmospheric dispersion of CO (carbon monoxide), CH4 (methane), SO2 (sulfur dioxide), and HCHO (formaldehyde) around oil wells by integrating satellite imagery with meteorological data. The study covered Argentina’s main hydrocarbon basins, applying generalized additive mixed models (GAMM) to assess relationships between pollutants, climatic variables, and basin locations. Results showed that CO and SO2 peaked in the Cuyana basin, influenced by outdated infrastructure, flaring, and atmospheric stability, reaching maxima in spring (CO > 30,000 µmol·m−2) and winter (SO2 = 2760 µmol·m−2). HCHO levels were elevated in Cuyana and Neuquina, during warmer months (> 170 µmol·m−2). CH4 displayed a more uniform distribution (~1800 ppb), with slightly higher values in Cuyana due to temperature and pressure. By combining high-resolution satellite observations with climate data, this study makes a novel and outstanding contribution by providing the first integrated assessment of pollutant dynamics across Argentina’s oil basins, offering actionable benchmarks for emission reduction, infrastructure modernization, and alignment with sustainability commitments.

Numerical simulation of evaporation and dispersion of tritium released in Hangzhou Bay.

ABSTRACT We developed a framework for rapid monitoring of radioactive plumes in the vicinity of nuclear facilities based on a quick and practical high-resolution atmospheric dispersion simulation method that combines a large-eddy simulation (LES) model pre-simulation database (pre-sim DB) of wind conditions and onsite meteorological observation results, as proposed by the previous study. However, this framework was not quantitatively demonstrated using measurement data. In this study, we evaluated the performance of the wind condition reproduction and plume dispersion analysis methods. Air dose rates observed at monitoring posts around the stack were compared with the values reproduced by the method using the pre-sim DB, and the reproducibility of both air dose rate and flow field was discussed. The pre-sim DB-based method successfully captured the temporal variation of air dose rates at the monitoring posts, though it tended to overestimate the peak values. Particularly when the vertical wind shear was pronounced, the method using the pre-sim DB could cause significant errors. This is likely because the method relies on wind conditions from a single observation point, which inherently limits its ability to represent vertical wind shear within the pre-sim DB. Despite these limitations, particularly in reproducing complex wind fields, the method utilizing the pre-sim DB offers a valuable and practical tool for rapid dose rate simulation due to its lower computational cost compared to unsteady simulations using an LES model.

Abstract Laboratory and field assessments of low-cost sensors (LCS) are essential for ensuring the accuracy of PM2.5 measurements collected by citizens in air quality campaigns. Evaluation of Sensirion SPS30 (LCS SPS30) in controlled laboratory setting showed a coefficient of determination (R2 ) ranging from 0.81−0.99 and a root mean square error (RMSE) from 0.81−61.72 µg m−3, at average concentration of 21.5 µg m−3. In contrast, co-location assessment at an average concentration of 9 µg m−3 resulted in R2 of 0.5 and a RMSE of 6.82 µg m−3. The results demonstrated that the sensor met micro-environmental monitoring standards (accuracy < 25 %) and US EPA performance criteria (RMSE ≤ 7 µg m−3, R2 > 0.7) only at relative humidity (RH) levels below 60 %, emphasising its strong sensitivity to RH and the need for RH-dependent data corrections. The observed underestimation or overestimation of PM2.5 readings was primarily attributed to variations in particle composition and concentration. Despite accuracy variations, LCSs can effectively capture spatiotemporal urban air quality patterns and identify pollution hotspots in community monitoring, particularly in low-pollution environments. In a citizen-led PM2.5 monitoring campaign in Maribor, Slovenia, the lowest concentrations were recorded at 15:00 (2.9 µg m−3), while the highest occurred during the morning rush-hour (4.8 µg m−3), likely attributed to the planetary boundary layer’s impact on atmospheric particulate dispersion. Spatial analysis revealed that hotspots clustered near intersections, where vehicle waiting time is the longest.

Feasibility of using Pleurozium schreberi as a biomonitor to study antiozonant dispersion: A case study in Southern Quebec.

Abstract We present XRISM Resolve observations centered on Hydra-A, a redshift z = 0.054 brightest cluster galaxy, which hosts one of the largest and most powerful FR-I radio sources in the nearby Universe. We examine the effects of its high jet power on the velocity structure of the cluster’s hot atmosphere. Hydra-A’s central radio jets have inflated X-ray cavities with energies upward of 1061 erg. They reach altitudes of 225 kpc from the cluster center, well beyond the atmosphere’s central cooling region. Resolve’s 3′× 3′ field of view covers 190 × 190 kpc, which encompasses most of the cooling volume. We find a one-dimensional atmospheric velocity dispersion across the volume of 164 ± 10 km s−1. The fraction in isotropic turbulence or unresolved bulk velocity is unknown. Assuming pure isotropic turbulence, the turbulent kinetic energy is 2.5% of the thermal energy radiated away over the cooling timescale, implying that kinetic energy must be supplied continually to offset cooling. While Hydra-A’s radio jets are powerful enough to supply kinetic energy to the atmosphere at the observed level, turbulent dissipation alone would struggle to offset cooling throughout the cooling volume. The central galaxy’s radial velocity is similar to the atmospheric velocity, with an offset of −37 ± 23 km s−1.

Ambient air pollution exposure during and before the pregnancy could result in adverse birth outcomes. This study uses data from women undergoing in vitro fertilization (IVF) data to investigate the associations between ambient air pollution exposure and adverse birth outcomes. This study analyses the associations between adverse birth outcomes, namely low birth weight (LBW), small for gestational age (SGA), and preterm birth and daily mean air pollution exposure during each of four IVF windows. The air pollutants considered were particulate matter with an aerodynamic diameter of less than 10 µm (PM10) and 2.5 µm (PM2.5), as well as nitrogen dioxide (NO2), which were estimated using the Atmospheric Dispersion Modelling System (ADMS-Urban). This data was linked to the IVF patients' postcode providing estimates of exposure to air pollutants. Logistic regression models were used to quantify the associations between air pollution exposure and adverse birth outcomes, and conditioning confounding factors. A subgroup analysis was conducted to investigate the differences in the effects of ambient air pollution exposure on the ICSI and IVF groups. From January 2010 to May 2018, there are 2069 babies were able to be included in this study. We found no significant associations between air pollution exposure and the risk of adverse birth outcomes during window 1(85 days before oocyte retrieval) and 2 (14 days after gonadotrophin medication). With 1 µg⋅m-3 increase in PM10 concentration during window 3 (14 days after embryo transfer) and 4 (embryo transfer to delivery) led to a 5% (95% CI: 1.05-1.06) and 10% (95% CI: 1.01-1.21) increase in the odds of preterm birth, but not other outcomes. In window 3, every 1 µg⋅m-3 increase in NO2 concentrations resulted in a 2% (95% CI: 1.00 - 1.04) increase in the odds of LBW and a 3% (95% CI: 1.00 -1.05) increase in the odds of SGA but showed no effect for preterm birth. The results of the subgroup analysis suggest that the air pollution exposure may have a greater impact on the IVF group compared to the ICSI group. The results suggest that exposure to air pollution during the very early stage of pregnancy (14 days after conception) may represent the most critical window of susceptibility to an increased risk of adverse birth outcomes.