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Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants’ dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens’ concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed. Graphical Abstract

An algorithm utilizing four basic processes was described for chemical oil spill dispersion. Initial dispersion was calculated using a modified Delvigne equation adjusted to chemical dispersion, then the dispersion was distributed over the mixing depth, as predicted by the wave height. Then the droplets rise to the surface according to Stokes’ law. Oil on the surface, from the rising oil and that undispersed, is re-dispersed. The droplets in the water column are subject to coalescence as governed by the Smoluchowski equation. A loss is invoked to account for the production of small droplets that rise slowly and are not re-integrated with the main surface slick. The droplets become less dispersible as time proceeds because of increased viscosity through weathering, and by increased droplet size by coalescence. These droplets rise faster as time progresses because of the increased size. Closed form solutions were provided to allow practical limits of dispersibility given inputs of oil viscosity and wind speed. Discrete solutions were given to calculate the amount of oil in the water column at specified points of time. Regression equations were provided to estimate oil in the water column at a given time with the wind speed and oil viscosity. The models indicated that the most important factor related to the amount of dispersion, was the mixing depth of the sea as predicted from wind speed. The second most important factor was the viscosity of the starting oil. The algorithm predicted the maximum viscosity that would be dispersed given wind conditions. Simplified prediction equations were created using regression.

Ensemble forcasting,originally developed for weather prediction,is lately being extended to atmospheric dispersion applications,which is a new,effective methodology for improving the atmospheric dispersion numerical modeling.In March 2011,due to the massive 9.0 earthquakes and ensuing tsunami that struck off the northern coast of the island of Honshu,the Fukushima Nuclear Plant I had the substantial leak of radioactive materials into surrounding environment and atmosphere.To aim at the global dispersion modeling of atmospheric radionuclides from Fukushima Nuclear Accident,this paper presents two approaches of atmospheric dispersion forecasting：ensemble dispersion modeling（EDM） and deterministic dispersion modeling（DDM）,conducts the globally dispersion modeling cases for Fukushima nuclear accident,and analyzes and evaluates the simulation results using observation data.In this paper,EDM includes three different perturbation methods：meteorological perturbation method,turbulence perturbation method,and physical parameterization ensemble forecasting method.The simulation results show that the trajectories from EDM have a better performance,which is in better agreement with the atmospheric circulation and observation data; the spread from DDM is slower and not as far as EDM.Additionally,the results from EDM display a better performance in the modeling of transport from Japan to China East Sea on April 4.The reasons for these results are：the techniques of MET and TUR are performed by adding perturbations on mean wind and turbulent velocity,respectively; the various different flow fields will result in far spreading in horizontal and the simulation results closer to observation; PHY is performed by using different diffusion physical parameterizations and produces the perturbations on vertical wind,which results the spreading in smaller range and discontinuous in horizontal.Finally,the comparative analysis between modeling results and observation data shows that all cases results are in good agreement with trends of observed radionuclides surface concentration; however,the modeling surface concentration is smaller than observation,especially in DDM and PHY.Furthermore,the EDM results show that MET and TUR are of more evolutionary advantage than PHY in modeling of average and maximum concentration.Therefore,this study can serve as a reference to atmospheric dispersion and environmental emergency response（EER）.

In October 2017 unusual 106Ru detections across most of Europe prompted the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) to analyze the event in order to locate the origin and identify the magnitude of the release. This paper presents the inverse modeling techniques used during the event to achieve this goal. The method is based on a variational approach and consists of using air concentration measurements with the ldX long-range dispersion model included in the IRSN’s C3X operational platform. The method made it possible to quickly identify the southern Urals as the most likely geographical origin of the release. Despite uncertainties regarding the starting date of the release, calculations show that it potentially began on 23 September, while most of the release was emitted on 26 September. Among the nuclear plants identified in the southern Urals, the Mayak complex is that from which the dispersion of the 106Ru plume is most consistent with observations. The reconstructed 106Ru source term from Mayak is ∼250 TBq. In total, it was found that for 72% of the measurements simulated and observed air concentration agreed within a factor of 5. In addition, the simulated deposition of 106Ru agrees with the observed deposition. Outside the southern Urals, the simulations indicate that areas with highest deposition values are located in southern Scandinavia and southeastern Bulgaria and are explained by rainfall events occurring while the plume was passing over.

The most important anthropogenic sources of primary particulate matter (PM) in ambient air in Europe are exhaust and non-exhaust emissions from road traffic and combustion of solid biomass. There is convincing evidence that PM, almost regardless of source, has detrimental health effects. An important issue in health impact assessments is what metric, indicator and exposure-response function to use for different types of PM. The aim of this study is to describe sectorial contributions to PM exposure and related premature mortality for three Swedish cities: Gothenburg, Stockholm and Umea. Exposure is calculated with high spatial resolution using atmospheric dispersion models. Attributed premature mortality is calculated separately for the main local sources and the contribution from long-range transport (LRT), applying different relative risks. In general, the main part of the exposure is due to LRT, while for black carbon, the local sources are equally or more important. The major part of the premature deaths is in our assessment related to local emissions, with road traffic and residential wood combustion having the largest impact. This emphasizes the importance to resolve within-city concentration gradients when assessing exposure. It also implies that control actions on local PM emissions have a strong potential in abatement strategies.

The largest concern on the cesium-137 (137Cs) deposition and its soil contamination due to the emission from the Fukushima Daiichi Nuclear Power Plant (NPP) showed up after a massive quake on March 11, 2011. Cesium-137 (137Cs) with a half-life of 30.1 y causes the largest concerns because of its deleterious effect on agriculture and stock farming, and, thus, human life for decades. Removal of 137Cs contaminated soils or land use limitations in areas where removal is not possible is, therefore, an urgent issue. A challenge lies in the fact that estimates of 137Cs emissions from the Fukushima NPP are extremely uncertain, therefore, the distribution of 137Cs in the environment is poorly constrained. Here, we estimate total 137Cs deposition by integrating daily observations of 137Cs deposition in each prefecture in Japan with relative deposition distribution patterns from a Lagrangian particle dispersion model, FLEXPART. We show that 137Cs strongly contaminated the soils in large areas of eastern and northeastern Japan, whereas western Japan was sheltered by mountain ranges. The soils around Fukushima NPP and neighboring prefectures have been extensively contaminated with depositions of more than 100,000 and 10,000 MBq km-2, respectively. Total 137Cs depositions over two domains: (i) the Japan Islands and the surrounding ocean (130–150 °E and 30–46 °N) and, (ii) the Japan Islands, were estimated to be more than 5.6 and 1.0 PBq, respectively. We hope our 137Cs deposition maps will help to coordinate decontamination efforts and plan regulatory measures in Japan.

For more than fifty years, atmospheric dispersion predictions based on the joint use of a Gaussian plume model and wind tunnel experiments have been applied in both Japan and the U.K. for the evaluation of public radiation exposure in nuclear safety analysis. The effective source height used in the Gaussian model is determined from ground-level concentration data obtained by a wind tunnel experiment using a scaled terrain and site model. In the present paper, the concentrations calculated by this method are compared with data observed over complex terrain in the field, under a number of meteorological conditions. Good agreement was confirmed in near-neutral and unstable stabilities. However, it was found to be necessary to reduce the effective source height by 50% in order to achieve a conservative estimation of the field observations in a stable atmosphere.

The requirement to forecast volcanic ash concentrations was amplified as a response to the 2010 Eyjafjallajökull eruption when ash safety limits for aviation were introduced in the European area. The ability to provide accurate quantitative forecasts relies to a large extent on the source term which is the emissions of ash as a function of time and height. This study presents source term estimations of the ash emissions from the Eyjafjallajökull eruption derived with an inversion algorithm which constrains modeled ash emissions with satellite observations of volcanic ash. The algorithm is tested with input from two different dispersion models, run on three different meteorological input data sets. The results are robust to which dispersion model and meteorological data are used. Modeled ash concentrations are compared quantitatively to independent measurements from three different research aircraft and one surface measurement station. These comparisons show that the models perform reasonably well in simulating the ash concentrations, and simulations using the source term obtained from the inversion are in overall better agreement with the observations (rank correlation = 0.55, Figure of Merit in Time (FMT) = 25–46%) than simulations using simplified source terms (rank correlation = 0.21, FMT = 20–35%). The vertical structures of the modeled ash clouds mostly agree with lidar observations, and the modeled ash particle size distributions agree reasonably well with observed size distributions. There are occasionally large differences between simulations but the model mean usually outperforms any individual model. The results emphasize the benefits of using an ensemble‐based forecast for improved quantification of uncertainties in future ash crises. Key Points The source parameters of volcanic emissions are crucial for ash forecasts The source parameters can be substantially improved by assimilating observations The simulated ash transport is improved with new source term estimates

Maritime emissions contribute significantly to global pollution, necessitating accurate and efficient monitoring methods. Traditional methods for tracking ship emissions often face limitations in real-time data accuracy, with wind measurement being a critical yet challenging aspect. This paper introduces an innovative mission planner module for unmanned aerial vehicles (UAVs) that leverages onboard wind sensing capabilities to enhance maritime emission monitoring. The module’s primary objective is to assist operators in making informed decisions by providing real-time wind data overlays, thus optimizing flight paths and data collection efficiency. Our experimental setup involves the testing of the module in simulated maritime environments, demonstrating its efficacy in varying wind conditions. The real-time wind data overlays provided by the module enable UAV operators to adjust their flight paths dynamically, reducing unnecessary power expenditure and mitigating the risks associated with low-battery scenarios, especially in challenging maritime conditions. This paper presents the implementation of real-time wind data overlays on an open-source state-of-the-art mission planner as a C# plugin that is seamlessly integrated into the user interface. The factors that affect performance, in terms of communication overheads and real-time operation, are identified and discussed. The operation of the module is evaluated in terms of functional integration and real-time visual representation of wind measurements, and the enhanced situational awareness that it can offer to mission controllers is demonstrated. Beyond presenting a novel application of UAV technology in environmental monitoring, we also provide an extensive discussion of how this work will be extended in the context of complete aerial environmental inspection missions and the future directions in research within the field that can potentially lead to the modernization of maritime emission monitoring practices.

Carbon capture and storage is considered an effective mitigation strategy to reduce the most challenging emissions from heavy industries and gas processing. The safe transport of carbon dioxide via pipelines is an important aspect for developing large-scale Carbon Capture and Storage projects. Dispersion modeling for heavy gas such as carbon dioxide is considerably different from natural gas. The set up for modeling simulations is more challenging than conventional natural gas pipeline for several reasons, such as the differences in thermodynamics that must be considered. Moreover, when the carbon dioxide is transported in dense or liquid phase, the rapid phase changing, and possible consequent formation of solids should be considered. Finally, the equation of state required for accurate prediction of parameters is generally different than the ones applicable for natural gas. The main scope of this comprehensive review is to identify the most important parameters, critical events, suitable models, and identification of dispersion modeling issues. An extensive literature review of experiments conducted in the last ten years has been developed, experimental data, integral and simplified model, as well as CFD modeling issues has been identified and reported in the work proposed to highlight the advances and the gaps that could need further research activities.