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Ship emissions and coastal air pollution around China are expected to be alleviated with the gradual implementation of ship domestic emission control area (DECA) policies. However, a comprehensive post-assessment on the policy's effectiveness is still lacking. This study developed a series of high-spatiotemporal ship emission inventories around China from 2016 to 2019 based on an updated Ship Emission Inventory Model (SEIM v2.0) and analyzed the interannual changes in emissions under the influence of both ship activity increases and gradually promoted policies. In this model, NOx, SO2, PM and HC emissions from ships in China's inland rivers and the 200 Nm (nautical miles) coastal zone were estimated every day with a spatial resolution of 0.05∘×0.05∘ based on a combination of automatic identification system (AIS) data and the Ship Technical Specifications Database (STSD). The route restoration technology and classification of ocean-going vessels (OGVs), coastal vessels (CVs) and river vessels (RVs) has greatly improved our model in the spatial distribution of ship emissions. From 2016 to 2019, SO2 and PM emissions from ships decreased by 29.6 % and 26.4 %, respectively, while ship NOx emissions increased by 13.0 %. Although the DECA 1.0 policy was implemented in 2017, it was not until 2019 when DECA 2.0 came into effect that a significant emission reduction was achieved, e.g., a year-on-year decrease of 33.3 %, regarding SO2. Considering the potential emissions brought by the continuous growth of maritime trade, however, an even larger SO2 emission reduction effect of 39.8 % was achieved in these 4 years compared with the scenario without switching to cleaner fuel. Containers and bulk carriers are still the dominant contributors to ship emissions, and newly built, large ships and ships using clean fuel oil account for an increasingly large proportion of emission structures. A total of 4 years of consecutive daily ship emissions were presented for major ports, which reflects the influence of the step-by-step DECA policy on emissions in a timely manner and may provide useful references for port observation experiments and local policy making. In addition, the spatial distribution shows that a number of ships detoured outside the scope of DECA 2.0 in 2019, perhaps to save costs on more expensive low-sulfur oil, which would increase emissions in farther maritime areas. The multiyear ship emission inventory provides high-quality datasets for air-quality and dispersion modeling, as well as verifications for in situ observation experiments, which may also guide further ship emission control directions in China.

This paper demonstrates the development of a moving point source (MPS) model for simulating the atmospheric dispersion of pollutants emitted from ships under movement. The new model is integrated into the chemistry transport model EPISODE–CityChem v1.3. In the new model, ship parameters, especially speed and direction, are included to simulate the instantaneous ship positions and then the emission dispersion at different simulation time. The model was first applied to shipping emission dispersion modeling under simplified conditions, and the instantaneous and hourly averaged emission concentrations predicted by the MPS model and the commonly used line source (LS) and fixed point source (FPS) models were compared. The instantaneous calculations were quite different due to the different ways to treat the moving emission sources by different models. However, for the hourly averaged concentrations, the differences became smaller, especially for a large number of ships. The new model was applied to a real configuration from the seas around Singapore that included hundreds of ships, and their dispersion was simulated over a period of a few hours. The simulated results were compared to measured values at different locations, and it was found that reasonable emission concentrations were predicted by the moving point source model.

We developed a LOcal-scale High-resolution atmospheric DIspersion Model using Large-Eddy Simulation (LOHDIM-LES) for the safety assessment of nuclear facilities and emergency responses to accidenta...

In case of a major accident involving airborne emissions of harmful gases, a temporary portable meteorological station may be used to improve atmospheric dispersion modelling (ADM) for protection of people and the environment. While the meteorological station provides signal values in real time, ADM results for the future are of particular interest for planning purposes. It is possible to use the current measured value as a future input to the ADM but it is suboptimal. It is also possible to use model output statistics (MOS) to predict the future local weather information from numerical weather prediction (NWP) models, while available operational NWP models are in general too coarse to be used directly in fine-resolution ADM. MOS models are obtained through machine learning and the training data sets in most traditional uses of MOS are big, which is beneficial for modelling. We envision using MOS in an emergency and for a location of a temporary meteorological station. We use windowing for online data selection to explore its accuracy when the amount of available training data is very limited, which is expected in an emergency situation. We show that MOS for wind vector with 1 day of training data greatly improves on the numerical weather predictions and the persistence model, so its use in such an emergency would be advantageous.

Wind plays an important role in cases of unexpected radioactive pollutant dispersion, deciding distribution and concentration of the leaked substance. The accurate prediction of wind has been challenging in numerical weather prediction models, especially near the surface because of the complex interaction between turbulent flow and topographic effect. In this study, we investigated the characteristics of atmospheric dispersion of radioactive material (i.e. 137Cs) according to the simulated boundary layer around the HANARO research nuclear reactor in Korea using the Weather Research and Forecasting (WRF)-Mesoscale Model Interface (MMIF)-California Puff (CALPUFF) model system. We examined the impacts of orographic drag on wind field, stability calculation methods, and planetary boundary layer parameterizations on the dispersion of radioactive material under a radioactive leaking scenario. We found that inclusion of the orographic drag effect in the WRF model improved the wind prediction most significantly over the complex terrain area, leading the model system to estimate the radioactive concentration near the reactor more conservatively. We also emphasized the importance of the stability calculation method and employing the skillful boundary layer parameterization to ensure more accurate low atmospheric conditions, in order to simulate more feasible spatial distribution of the radioactive dispersion in leaking scenarios.

North Korea conducted the sixth underground nuclear test on September 3, 2017 at the Punggye-ri Nuclear Test Site (NTS). In contrast to the previous five nuclear tests, several induced earthquakes occurred around the NTS after the sixth nuclear test and this may have caused radioxenon leakages at the site. Considering these reported earthquakes, we performed atmospheric dispersion simulations on some radioxenon emission scenarios for this event using our Lagrangian Atmospheric Dose Assessment System (LADAS) model by employing the Unified Model (UM) based numerical weather prediction data produced by the Korea Meteorological Administration (KMA). To find out possible detection locations and times, we combined not only daily and weekly based delayed releases but also leakages after the reported earthquakes around the NTS to create emission scenarios. Our simulation results were generally in good agreement with the measured data of the Nuclear Safety and Security Commission and International Monitoring System (IMS) stations operated by the Comprehensive nuclear Test-Ban-Treaty Organization (CTBTO).

Atmospheric dispersion is a process that involves many uncertainties in model parameters, inputs and source parameters. In this article, we present an uncertainty management procedure for the heigh...

This study presents the first environmental monitoring field campaign of a newly developed Tethered Air Blimp (TAB) system to investigate the microclimate over a complex terrain. The use of a tethered balloon in complex terrains such as mines and tailings ponds is novel and the focus of the present study. The TAB system was fully developed and launched at a mining facility in northern Canada in May 2018. This study describes the key design features, the sensor payload on board, calibration, and the observations made by the TAB system. The system measured meteorological conditions including components of wind velocity vector, temperature, relative humidity, and pressure over the first few tens of metres of the atmospheric boundary layer. The measurements were made at two primary locations in the facility: (i) near a tailings pond and (ii) in a mine pit. TAB measured the dynamics of the atmosphere at different diurnal times (e.g. day versus night) and locations (near a tailings pond versus inside the mine). Such dynamics include mean and turbulence statistics pertaining to flow momentum and energy, and they are crucial in the understanding of emission fluxes from the facility in future studies. In addition, TAB can provide boundary conditions and validation datasets to support mesoscale dispersion modelling or computational fluid dynamics simulations for various transport models.

Aviation is a basic necessity of our world, but its contribution to air pollution is considered significant. In this paper, the contribution of air traffic to air pollution levels in the area of the three larger airports of Greece is examined through the use of EDMS (Emission and Dispersion Modeling System), a regulatory model proposed by the US EPA (United States Environmental Protection Agency). To ensure a better understanding of air traffic contribution to air quality levels, the hourly aircraft movements along with the corresponding meteorological data for a whole year, 2009, were taken into account. During this year, air traffic peaked both in Greece as a total and in each of the three airports of this work. Airport emissions calculated by EDMS are found to be in good agreement with emissions monitored at Athens International Airport as well as with emission results and published data for International Zurich Airport. Concentration results have shown that PM10 and SO2 concentrations are well below the limit values, whereas NO2 concentrations exceeding limit value are expected in small areas under specific circumstances, when heavy air traffic coincides with meteorological conditions favoring air pollutant accumulation. Finally, the comparison of computational results with monitoring air quality data shows a good agreement, if other sources of air pollution are excluded.

We have estimated source term and analyzed processes of atmospheric dispersion against atmospheric discharge of radioactive materials due to the Fukushima Daiichi Nuclear Power Station (FDNPS) accident by atmospheric-dispersion calculation using the Worldwide version of System for Environmental Emergency Dose Information (WSPEEDI). On the basis of this experience, we developed an atmospheric-dispersion calculation method that can respond to various needs for dispersion prediction in a nuclear emergency and provide useful information for emergency-response planning. By this method, if a release point, such as a nuclear facility, is known, it is possible to immediately obtain the prediction results by applying provided source term (released radionuclides, release rate, and release period) to the database of dispersion-calculation results prepared in advance without specifying source term. With this function, it is easy to compare results by applying many kinds of source term with monitoring data, and to find out the optimum source term. By preparing a database by this calculation with past long-term meteorological data, we can immediately get dispersion-calculation results for various source term and meteorological conditions. This database is useful for pre-accident planning, such as optimization of a monitoring plan and understanding of events to be supposed in considering emergency countermeasures.

We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume and a stochastic model of the possible emissions. Satellite data are processed to give column loads where ash is detected and to give information on where we have high confidence that there is negligible ash. An atmospheric dispersion model is used to relate emissions and column loads. Gaussian distributions are assumed for the a priori emissions and for the errors in the satellite retrievals. The optimal emissions estimate is obtained by finding the peak of the a posteriori probability density under the constraint that the emissions are non-negative. We apply this inversion method within a framework designed for use during an eruption with the emission estimates (for any given emission time) being revised over time as more information becomes available. We demonstrate the approach for the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions. We apply the approach in two ways, using only the ash retrievals and using both the ash and clear sky retrievals. For Eyjafjallajökull we have compared with an independent dataset not used in the inversion and have found that the inversion-derived emissions lead to improved predictions.

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

In this study, we evaluated atmospheric particulate matter (PM) concentration predictions at a regional scale using a simplified Lagrangian particle dispersion modeling system and the Bayesian and multiplicative ratio correction optimization (Bayesian-RAT) method to improve the mixing ratio forecast of PM10 and PM2.5. We first examined the forecast performance of the LPD (i.e., the simplified FLEXPART model combined with the Bayesian-RAT method) by comparing the model predictions with the PM concentration observations from 95 observation stations in Xingtai city and its surrounding areas. The first 2 months (i.e., Oct. and Nov. 2017) of the study period represented the typical spin-up time period, and the analysis period was December 2017. The LPD forecast system was much better (correlation coefficient: R=0.64 vs. 0.48 and 0.67 vs. 0.50 for PM10 and PM2.5, respectively; root mean square error: RMSE = 74.98 vs. 105.96 μg/m3 for PM10 and 54.89 vs. 72.81 μg/m3 for PM2.5) than the pre-calibration results. We also compared the LPD forecasting model with other models (WRF-Chem and Camx) using data from monitoring stations in Xingtai, China, and the LPD forecasting model had higher accuracy than the other models. In particular, the RMSE scores for hourly PM10 concentrations were reduced by 36.51% and 42.21% compared to WRF-Chem and to Camx, respectively. The PM2.5 forecast results, as in the case of PM10, showed a better performance when applying the LPD model to the data from the monitoring stations. The RMSE was reduced by 26.44% and 18.47% relative to the WRF-Chem and Camx, respectively. The results confirm that there is much advantage of the LPD forecast system for predicting PM and may be for other pollutants.

The aim of this study is to investigate the impact of ten proposed plants along with three operating plants in Canakkale province of Turkey where the proposed plants are within very close proximity. The province has the highest capacity of the planned plants and the region is also of interest due to its history, tourism and agriculture potential. Current SO 2 pollution was assessed using ground observations and satellite retrievals where the impact of plants was better captured by satellite retrievals. Individual and cumulative impact from proposed and operating plants was simulated by CALPUFF for 2014. The study domain was 150 × 150 km 2 , with 1 × 1 km 2 cell size. The effect of changing meteorological inputs and domain size were investigated with simulations. Three cases were performed using meteorological inputs: from one surface and one radiosonde station (Case 1), 22 surface and one radiosonde station (Case 2), and 22 surface and two radiosonde stations (Case 3). Case 2 and 3 resulted in higher concentrations and showed larger affected regions than case 1 in all simulations. The cumulative impact of proposed plants indicated national annual and daily limit values were exceeded in Case 2 and 3. Hourly limit values were exceeded in all three cases. Simulations for two selected proposed plants were assessed for plant impact area given in environmental impact area reports. Results indicated the plant impact areas cannot be sufficient to determine the maximum SO 2 concentrations in some cases and using single meteorology station data cannot represent the study area, especially regions with complex terrain and land-sea interactions such as Canakkale province. Cumulative impact can be underestimated due to small size of plant impact areas not including other plants. Lastly satellite retrievals are better capturing the pollution than air quality monitoring stations which are strongly affected by meteorology.

Results from numerical simulations of tracer dispersion from a point source (an incinerator plant) over complex terrain are presented. Different modeling chains are used and model output compared with ground concentration measurements from the 2017 Bolzano Tracer EXperiment (BTEX) over the Eastern Italian Alps. In particular, meteorological simulations are run with the Weather Research and Forecasting (WRF) model to reproduce the flow field at sub-kilometer grid resolution (300 m). The simulations apply observational nudging of upper-air and surface meteorological observations, as well as an improved snow cover initialization, which allow to better characterize valley winds in the afternoon. In addition, the simulations are run by substituting the default closure constants of the Planetary Boundary Layer (PBL) scheme with a set of values specific for complex terrain applications. Meteorological results are evaluated comparing wind speed standard deviations with measurements from a SODAR instrumentation deployed during the experiment. The meteorological fields provide the input to two different dispersion models: the CALMET/CALPUFF puff-Gaussian model, representing the reference standard model often used in impact assessments, and the WSI/SPRAY-WEB Lagrangian particle model. Different parameterizations for the calculation of the dispersion coefficients, based either on surface layer scales or on the turbulent kinetic energy from the WRF PBL scheme, are tested. A statistical analysis of the results from all the dispersion models is performed to assess the simulation performance against tracer concentration measurements collected during the BTEX campaign.

A decision support system to aid in the risk evaluation of airborne animal diseases was developed for Ireland. The system's primary objective is to assist in risk evaluation of the airborne spread of Foot and Mouth Disease (FMD). The operational system was developed by Met Éireann – the Irish Meteorological Service and CVERA (Centre for Veterinary Epidemiology and Risk Analysis), in co-operation with NOAA-ARL (National Oceanic and Atmospheric Administration – Air Resources Laboratory) and ECMWF (European Centre for Medium-Range Weather Forecasts). The infrastructure largely relies on the HYSPLIT dispersion model driven by both ECMWF meteorological forecasts for longer range simulations, and HARMONIE-AROME meteorological forecasts, a high resolution local area meteorological model, ideal for shorter range national emissions. Following on from previous work by the Bureau of Meteorology, Australia as well as the Australian Department of Agriculture, Fisheries and Forestry, further modifications were made to the HYSPLIT source code to improve the model's characterisation of the Foot and Mouth Disease virus. FMD is a highly infectious disease among cloven hoofed animals that can transmit via airborne means. Biological characteristics related to temperature, humidity, lifespan as well as atmospheric washout were all incorporated either through new or existing functionality of the dispersion model. Combining the model dispersion capabilities of HYSPLIT with a virus emission model and GIS mapping software with farmland zoning, the disease dispersion system becomes a powerful analysis and decision support tool. This airborne animal disease atmospheric dispersion system helps improve emergency preparedness, as well as aid confinement and eradication strategies for relevant Irish authorities, during a disease outbreak.

Numerous uncertainty factors in dispersion models should be taken into account in order to improve the reliability of predictions. The ability of a mesoscale meteorological model to assimilate observational data is an efficient way to improve operational air quality model forecasts. In this study, local weather data assimilation based on a flux-adjusting surface data assimilation system (FASDAS) is introduced to a Gaussian atmospheric dispersion model for a period with reported stable meteorological conditions. After evaluating the vulnerabilities of FASDAS, a combined data assimilation method is proposed to simultaneously improve the model weather prediction and retrieve the representation of accurate concentration distributions for short-range dispersion modeling against a control run. The two main uncertainty parameters considered are the wind speed and direction. A twin experiment demonstrates that the combined technique effectively improves the distribution of simulated concentrations. Comparison between results before and after the implement of data assimilation demonstrates that discrepancies between the reference simulation and the model forecast are mitigated after introducing the combined method, with more than 70 % of the predictions within a factor of two of the measurements. The errors in wind predictions in the FASDAS influenced the dispersion calculations, and the implementation of wind data assimilation in conjunction with the FASDAS has an indirect effect on further alleviating pollutant transport modeling errors.

Installing new nuclear facilities require a serious consideration of safety measures throughout all stages. This work evaluates the radiological impact of the expected operations of potential Baiji nuclear power plant (NPP) in accord with the safety requirement’s achievement. GENII Environmental Dosimetry System is used to assess annual effective dose equivalent (AEDE) and cancer risks. GENII model configured according to ICRP 60 and 103 recommended dose conversion coefficients. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model configured to study spatial distribution of the exposure dose around the NPP according to distance and direction. Results showed that maximum AEDE was 0.5 μ Sv at the Baiji potential site, which was lower than the allowed dose limit by International Commission on Radiological Protection (ICRP) 60 and 103 recommendations. The cancer incidence and fatality risks were in the range of 1 in 107 people. The areas most influenced by the atmospheric emissions of radioactive discharges were Baiji and Tikrit cities at distances of 30–40 km south and south-east of Baiji site.

In recent years, the pollution caused by aviation has sharply been increasing as a result of growing the air traffic. In this study, the emission of pollutants caused by aircrafts’ operations during landing and takeoff cycles was calculated and the impact of meteorological parameters on emission along with its distribution at Imam Khomeini International Airport and Mehrabad Airport was also estimated. Moreover, the pollutants such as carbon monoxide, nitrogen oxides, hydrocarbon and sulphur dioxide were examined. The pollutant emission rate of each aircraft was estimated using the engines’ pollutant database of International Civil Aviation Organization. In order to evaluate the aircraft emission, the flights data of both airports, dated from 2011 to 2016, were used and their total emissions were also calculated. Finally, the atmospheric dispersion model system was used for modelling the dispersion of emissions of these two airports. The results indicated that the highest rate of the total emission at Mehrabad Airport was considered as 2718.47 ton/year in 2016. As for the Imam Khomeini International Airport, the highest rate of total emission was considered to be 4259.12 ton/year in 2015. Moreover, the dispersion of the pollutants along with their distribution at two airports was also modelled and explained, respectively. The results indicated that Mehrabad Airport through which more flights are operated and handled compared to Imam Khomeini International Airport produced lower emission. Moreover, eastern side of these airports suffered from more pollution because of most common wind direction from west to east at these airports.

Aiming at the dispersion of 131I in the regional scale, this paper proposes a dispersion trajectory simulation method for 131I based on the fully coupled online model of the Weather Research and Forecasting-Chemistry (WRF-Chem). The dry deposition, wet deposition and radioactive decay of 131I have been considered in the Eulerian advection-diffusion equation, which is the three important removal processes of 131I in the atmosphere. The atmospheric dispersion of 131I at the early stage in the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident (March 12th to 17th) was used to simulate to validate the method. Comparison between the 131I concentration of WRF-Chem modeling and the available observations data shows that the dispersion trajectory of 131I at the regional scale was simulated authentically by this method, which can explain the arrival and duration time of the measurements, indicating that the method can be used significantly as a decision support tool for the early nuclear emergency and evaluate the consequences of the accident.

In a complex environment such as an urban area, accurate prediction of the atmospheric dispersion of airborne harmful materials such as radioactive substances is necessary for establishing response actions and assessing risk or damage. Given the variety of urban atmospheric dispersion models available, evaluation and inter-comparison exercises are vital for assessing quantitatively and qualitatively their capabilities and differences. To that end, the European Commission/Directorate General Joint Research Centre with support from the European Commission/Directorate General-Migration and Home Affairs, and with the contribution of the U.S. Defense Threat Reduction Agency, launched the Urban Dispersion INternational Evaluation Exercise (UDINEE) project. Within UDINEE, nine atmospheric dispersion models are evaluated and intercompared. Sulphur hexafluoride concentrations from puffs released near the ground during the Joint Urban 2003 (JU2003) field experiment are used in UDINEE to evaluate atmospheric dispersion models. The JU2003 experiment is chosen because UDINEE aims at the better understanding of modelling capabilities for radiological dispersal devices in urban areas, and the neutrally-buoyant puff releases performed in the JU2003 experiment are the closest scenario to this purpose. The present study evaluates the capability of models at simulating the presence and concentration levels of the tracer at sampling locations. The fraction of predicted concentrations and time-integrated concentrations within a factor-of-two of observations are less than 0.36 and 0.4 respectively. The analysis reveals an improvement in the performance of models by using time-varying inflow conditions. Since the simulation of the dispersion of puff release is particularly challenging, the results of UDINEE could constitute a benchmark for future model developments.

In this work, protective actions have been studied assuming a hypothetical severe accident of the Bushehr nuclear power plant at different meteorological conditions. Simulations of the atmospheric dispersion of accidental airborne releases were performed using the RASCAL code. Total effective dose equivalent (TEDE) and thyroid dose received by members of the public living within a radius of 40 km around the reactor site were calculated for various atmospheric stability classes and weather conditions. According to the results of the dose assessment and by following the protective action guide of the Environmental Protection Agency (EPA), the critical zone and appropriate protective actions were determined depending on various metrological conditions. It was found that, for atmospheric stability class F and calm weather conditions, the maximum distance from the site of release for which TEDE is greater than the corresponding dose limit and for which sheltering or evacuation response actions are required, is 11 km. For the same weather conditions, the corresponding maximum distance for which iodine prophylaxis is required is 32 km. Based on the present simulations, it can be concluded that the metrological condition has a great influence on the radionuclide atmospheric dispersion and, consequently, on the critical zone where protective actions are required after the assumed accident condition.

Atmospheric dispersion models are crucial for nuclear risk assessment and emergency response systems since they rapidly predict air concentrations and deposition of released radionuclides, providing a basis for dose estimations and countermeasure strategies. Atmospheric dispersion models are associated with relatively large and often unknown uncertainties that are mostly attributed to meteorology, source terms and parametrisation of the dispersion model. By developing methods that can provide reliable uncertainty ranges for model outputs, decision makers have an improved basis for handling nuclear emergency situations. In the present work, model skill of the Severe Nuclear Accident Programme (SNAP) model was quantified by employing an ensemble method in which 51 meteorological realisations from a numerical weather prediction model were combined with 9 source term descriptions for the accidental 137Cs releases from Fukushima Daiichi Nuclear Power Plant during 14th-17th March 2011. The meteorological forecast was compared to observations of wind speed from 30 meteorological stations. The 459 dispersion realisations were compared with hourly observations of activity concentrations from 100 air filter stations. Exclusive use of deterministic meteorology resulted in most members of the dispersion ensemble showing too low concentration values, however this was mitigated by applying ensemble meteorology. Ensemble predictions, including both the meteorological and source term ensemble, show an overall higher prediction skill compared to individual meteorology and source term runs, with true predictive rate accuracy increasing from 30%-50% to 70%-90%, with a decrease in positive predictive rate accuracy from 75%-80% to 65%-75%. Skill scores and other ensemble indicators also showed improvements in using ensembles of source terms and meteorology. From the present study on the Fukushima accident there are strong indications that ensemble predictions improve the basis for decision making in the early phase after a nuclear accident, which emphasises the importance of including ensemble prediction in nuclear preparedness tools of the future.

With the increasing usage of renewable energy systems to meet the climate agreement aims accurate predictions of the possible amount of energy production stemming from renewable energy systems are needed. The need for such predictions and their uncertainty is manifold: to estimate the load on the power grid, to take measures in case of too much/not enough renewable energy with reduced nuclear energy availability, rescheduling/adjusting of energy production, maintenance, trading, and more. Furthermore, TSOs and energy providers need the information as finegrained, spatially and temporarily, as possible, on third level hub or even on solar farm / wind turbine level for a comparatively large area.These needs pose a challenge to numerical weather prediction (NWP) post-processing methods. Typically, one uses selected NWP fields aswell as observations, if available, as input in post-processing methods. Here, we combine two post-processing methods namely a neural network and random forest approach with the Flex_extract algorithm. Flex_extract is the pre-processing algorithm for the langrangian particle dispersion model FLEXPART and the trajectory model FLEXTRA. Flex_extract uses the three-dimensional wind fields of the NWP model and calculates additionally the instantaneous surfaces fluxes. Thus, coupling Flex_extract with a machine learning post-processing algorithm enables the usage of native NWP fields with a higher vertical accuracy than pressure levels. To generate an ensmeble in post-processing from deterministic sources different tools are available. Here, we will apply the Schaake Shuffle. In this study a neural network and random forest approach for probabilistic forecasting with a high horizontal grid resolution (1 km ) as well as a high temporal forecasting frequency of wind speed and global horizontal irradiance for Austria will be presented. Evaluation will be carried out against gridded analysis fields and observations.

The ESTE system is running in nuclear crisis centers at various levels of emergency preparedness and response in Slovakia, the Czech Republic, Austria, Bulgaria, and Iran (at the Bushehr Nuclear Power Plant, monitored by the International Atomic Energy Agency (IAEA)). ESTE is a decision-support system that runs 24/7 and serves the crisis staff to propose actions to protect inhabitants against radiation in case of a nuclear accident. ESTE is also applicable as a decision-support system in case of a malicious act with a radioactive dispersal device in an urban or industrial environment. The dispersion models implemented in ESTE are the Lagrangian particle model (LPM) and the Puff trajectory model (PTM). We describe model approaches as implemented in ESTE. The PTM is applied in ESTE for the dispersion calculation near the point of release, up to 100 km from the point of a nuclear accident. The LPM for general atmospheric transport is applied for short-range, meso-scale and large-scale dispersion, up to dispersion on the global scale. Additionally, a specific micro-scale implementation of the LPM is applied for urban scale dispersion modeling. The dispersion models of ESTE are joined with radiological-consequences models to calculate a complete spectrum of radiological parameters—effective doses, committed doses, and dose rates by various irradiation pathways and by various radionuclides. Finally, radiation protective measures, like sheltering, iodine prophylaxis, or evacuation, evaluated on the base of predicted radiological impacts, are proposed. The dispersion and radiological models of the state-of-the-art ESTE systems are described. The results of specific analyses, like the number of particles applied, the initial spatial distribution of the source, and the height of the bottom reference layer, are presented and discussed.

COVID-19 is now a pandemic and the knowledge gap on SARS-CoV-2, i.e., the COVID-19 disease agent, dispersion persists. The US Centers for Disease Control and Prevention suggests fomites may not be the main route through which the novel coronavirus spreads. Supporting the same view, the latest the World Health Organization report recommends wearing masks for every individual in public, highlighting the transmission through the air. In the current study AERMOD, one of the most validated and tested models suggested by the USEPA, is used to model SARS-CoV-2-laden PM10 in a hypothetical outdoor environment. Multiple scenarios including particle size, wind speed, source height variations as well as and combined scenarios were modeled to estimated how exposure risk changes with the above-mentioned variables. The results reveal that wind speed majorly narrows infectious plume rather than transferring the peak concentration. The particle size variation indicated that small particles, i.e.,0.01 − 2.5 μm, could reach more than 9 m away from the source in concentration range of 10 − 20 (μg/m3). On the other hand, source height contributes to peak plume shift rather than dispersing the infected particles. This idea was further studies by using combined scenarios which indicated height difference can impact peak plume displacement rather than wind speed. In the worst-case scenario, the results indicate that the virus-laden particles can travel outdoors more than 8 m away from an infected source. The video output of the model results clearly shows the dynamic of viral peak shifts in several scenarios. The results also indicate that in specific conditions the airborne SARS-CoV-2 can be transported to 9 m away from the source. These findings can be useful for individuals as well as decision-makers to mitigated exposure risk in real-world conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s40201-020-00602-9.

Estimating and quantifying in an accurate way, the distribution of pollutant concentrations plays a crucial role in the emergency response management in major nuclear accidents. As in many areas of the environmental studies, air quality models are used to simulate atmospheric dispersion phenomena, although they lack accuracy due to the uncertainty in source term and meteorological fields. Even though the ability of a mesoscale meteorological model to assimilate observational data is an efficient way to improve operational air quality model forecasts, errors still occur in the flux-adjusting surface data assimilation system (FASDAS) algorithm when predicting the wind components, a factor that influences directly the plume shape, concentration distribution, and ground deposition of radionuclides. Thus, operational models should carefully handle this meteorological parameter with both accuracy and efficiency. In response to this problem, our newly proposed combined data assimilation scheme is used to combine deep learning and meteorological data assimilation. In particular, a correction of FASDAS real-time wind predictions method is proposed using an artificial neural network (ANN). This alternative wind forecast method aims to train the ANN in a manner that it can predict wind speed and direction efficiently, correcting, therefore, the errors introduced with the relaxation assimilation methods. The combined system rapidly corrects the errors in the wind data, thereby significantly improves the Gaussian dispersion model results.

The role of atmospheric dispersion models is becoming increasingly relevant to assess air pollution urban population exposure for epidemiological studies. Estimating urban air quality is challenging, because of the intrinsic characteristics of cities atmospheric structure, such as high density of primary emissions and presence of local dispersion processes, that produce strong concentration gradients. Therefore, very high spatial resolution simulations may often be required to improve the accuracy of estimations. The objective of this study is developing a microscale hybrid modelling system (HMS) to carry out, in a reasonable computational time, long-term simulations providing hourly concentration fields at building-resolving scale in extended urban areas in order to calculate annual indicators to evaluate exposure. The proposed system couples two atmospheric dispersion models suited for different scales: a Eulerian chemical transport model, FARM (Flexible Air quality Regional Model), accounting for dispersion phenomena due to regional and local emission sources, and a Lagrangian particle micro-scale dispersion model, PMSS (Parallel Micro Swift Spray), used to compute concentrations induced by vehicular traffic inside the city. The HMS has been applied on 12 × 12 km2 domain in Rome with a horizontal resolution of 4 m for calculating NO2 and PM10 concentrations for all year 2015. This study has been carried out in the frame of project BEEP (Big data in Environmental and occupational Epidemiology), that is an Italian research project in epidemiological field. Results show that the combined use of the two models reproduces the spatial and temporal variability of the observed atmospheric pollutants with a good agreement. The statistical analysis performed on daily average concentrations proves that the HMS suits the standard acceptance criteria for urban dispersion model evaluation, with a FAC2 of 0.92 and 0.80 and a Fractional Bias of −0.03 and −0.2 for NO2 and PM10 respectively. Furthermore, the implementation of an innovative kernel method to calculate concentrations in PMSS has made possible to reduce the computational time by 80%, leading to an average computational time of 3 h per simulated day on an HPC (High Performance Computing) system with 180 cores.

The spatial patterns of the spreading of the COVID19 indicate the possibility of airborne transmission of the coronavirus. As the cough-jet of an infected person is ejected as a plume of infected viral aerosols into the atmosphere, the conditions in the local atmospheric boundary layer together dictate the fate of the infected plume. For the first time - a high-fidelity numerical simulation study - using Weather-Research-Forecast model coupled with the Lagrangian Hybrid Single-Particle Lagrangian Integrated Trajectory model (WRF-HYSPLIT) model has been conducted to track the infected aerosol plume in real-time during March 9-April 6, 2020, in New York City, the epicenter of the coronavirus in the USA for comparing the morning, afternoon and evening release. Atmospheric stability regimes that result in low wind speeds, low level turbulence and cool moist ground conditions favor the transmission of the disease through turbulence energy-containing large-scale horizontal “rolls” and vertical thermal “updrafts” and “downdrafts”. Further, the wind direction is an important factor that dictates the direction of the transport. From the initial time of release, the virus can spread up to 30 min in the air, covering a 200-m radius at a time, moving 1–2 km from the original source., Highlights • A real-time,high-fidelity simulation of blob of virus filled cough-jet released in the atmosphere has been conducted. • Air-borne is a possible pathway for the transmission of COVID19/. • Atmospheric stability regimes determine the transmission or dilution of the virus blob. • Cough plume with virus aerosols can survive for 30 min and spreading through 1–2 km distance in air before diluting. • The wind direction is an important factor that dictates the direction of the transport from the source.

The CALMET-RIMPUFF is composed of the California Meteorological Model and the Riso Mesoscale PUFF model, which provides refined atmospheric dispersion modeling for nuclear emergency response. Because the performance of an atmospheric dispersion model can be case-sensitive, a multi-scenario validation is important to understand a model's behavior and limits. In this study, a multi-scenario validation of CALMET-RIMPUFF was performed based on six wind tunnel experiments simulating a real China's nuclear powerplant site with complex topographies and dense buildings. The CALMET-RIMPUFF simulations were compared with the measurements of the vertical wind profiles, 2D ground wind and concentration fields, both qualitatively and quantitatively. The results demonstrate that the CALMET-RIMPUFF can simulate the ground-level wind with acceptable accuracies. For vertical wind profiles, the accuracies show high dependencies on the local topography and building layout. The simulated ground concentrations generally agree well with the measurements, though the plume axis showed slight discrepancies from the measurements in three cases. Because the CALMET-RIMPUFF lacks a building effect module, it shows noticeable discrepancies in the building area. However, such discrepancies do not propagate to the downwind mountainous and sea areas, which the accuracies are quite satisfactory. Thus, the CALMET-RIMPUFF is capable for local-scale modeling at this site.

Predicting boundary layer clouds is important for the accurate modeling of pollutant dispersion. Higher resolution mesoscale models would be expected to produce better forecasts of cloud properties that affect dispersion. Using ceilometer observations, we assess the skill of two operational mesoscale models (RAMS and WRF) to forecast cloud base altitude and cloud fraction at the Savannah River Site in the southeastern US during the springtime. Verifications were performed at small spatial and temporal scales necessary for dispersion modeling. Both models were unreliable with a 50% (RAMS) and a 46% (WRF) rate of predicting clouds observed by the ceilometer which led to low cloud fraction predictions. Results indicated that WRF better predicted daytime cloud bases from convection that occurred frequently later in the period and RAMS better predicted nighttime cloud bases. Using root mean squared error (RMSE) to score the forecast periods also highlighted this diurnal dichotomy, with WRF scores better during the day and RAMS scores better at night. Analysis of forecast errors revealed divergent model cloud base biases&mdash, WRF low and RAMS high. A hybrid solution which weighs more heavily the RAMS nighttime forecasts and WRF daytime forecasts will likely provide the best prediction of cloud properties for dispersion.

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NOx), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM2.5) concentration below the apartment building height. Atmospheric NOx dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NOx concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

A wide variability of odour impact criteria is found around the world. The objective of this research work was to evaluate the influence of the uncertainties related to some individual stages of odour impact assessment in the application of regulatory criteria. The evaluation procedure was established by following the guidelines of the Northern Italian regions. A wastewater treatment plant located in Northern Italy was considered as a case study. Odour dispersion modelling was carried out with the CALPUFF model. The study focused on two phases of the assessment. The first phase was the selection of the meteorology datasets. For low odour concentration thresholds (CT = 1 OU m&minus, 3), the results showed that two different years (2018 and 2019) provided similar patterns of the separation distances. The difference between the two years tended to increase by increasing the value of the concentration threshold (CT = 3 OU m&minus, 3 and CT = 5 OU m&minus, 3). The second phase of the assessment was the selection of the open field correction method for wind velocity used in the calculation of odour emission rates (OERs). Three different relationships were considered: the power law, the logarithmic law and the Deaves&ndash, Harris (D&ndash, H) law. The results showed that OERs and separation distances varied depending on the selected method. Taking the power law as the reference, the average variability of the separation distances was between &minus, 7% (D&ndash, H law) and +10% (logarithmic law). Higher variability (up to 25%) was found for single transport distances. The present study provides knowledge towards a better alignment of the concept of the odour impact criteria.

Volcanic ash can interact with the earth system on many temporal and spatial scales and is a significant hazard to aircraft. In the event of a volcanic eruption, fast and robust decisions need to be made by aviation authorities about which routes are safe to operate. Such decisions take into account forecasts of ash location issued by Volcanic Ash Advisory Centers (VAACs) which are informed by simulations from Volcanic Ash Transport and Dispersion (VATD) models. The estimation of the time-evolving vertical distribution of ash emissions for use in VATD simulations in real time is difficult which can lead to large uncertainty in these forecasts. This study presents a method for constraining the ash emission estimates by combining an inversion modeling technique with an ensemble of meteorological forecasts, resulting in an ensemble of ash emission estimates. These estimates of ash emissions can be used to produce a robust ash forecast consistent with observations. This new ensemble approach is applied to the 2011 eruption of the Icelandic volcano Gr&iacute, msv&ouml, tn. The resulting emission profiles each have a similar temporal evolution but there are differences in the magnitude of ash emitted at different heights. For this eruption, the impact of precipitation uncertainty (and the associated wet deposition of ash) on the estimate of the total amount of ash emitted is larger than the impact of the uncertainty in the wind fields. Despite the differences that are dominated by wet deposition uncertainty, the ensemble inversion provides confidence that the reduction of the unconstrained emissions (a priori), particularly above 4 km, is robust across all members. In this case, the use of posterior emission profiles greatly reduces the magnitude and extent of the forecast ash cloud. The ensemble of posterior emission profiles gives a range of ash column loadings much closer in agreement with a set of independent satellite retrievals in comparison to the a priori emissions. Furthermore, airspace containing volcanic ash concentrations deemed to be associated with the highest risk (likelihood of exceeding a high concentration threshold) to aviation are reduced by over 85%. Such improvements could have large implications in emergency response situations. Future research will focus on quantifying the impact of uncertainty in precipitation forecasts on wet deposition in other eruptions and developing an inversion system that makes use of the state-of-the-art meteorological ensembles which has the potential to be used in an operational setting.

The JRODOS nuclear emergency response system was applied for estimation of risks caused by transboundary transport of possible accidental release from the newly designed units 3&4 of Khmelnitsky NPP. The total effective potential dose for 1-year children, received during 1 year after release from all pathways was calculated and used as measure of impact. About 4000 calculations of the dispersion scenarios were conducted using JRODOS atmospheric dispersion model and food dose model, with the start times of the releases falling within the time period of June-November, 2018. The numerical weather prediction data of WRF-Ukraine forecasting system operating in the Ukrainian Hydrometeorological Center were used to drive the JRODOS simulations. For each dispersion scenario maximum dose reached at the territory of every considered neighbor country (Belarus, Poland, Slovakia, Hungary, Romania, Moldova) was calculated. The simulated series of effective doses were fitted with GEV distribution. Taking into account probability of the accident the total probability of effective dose exceeding 1 mSv in at least one of neighboring countries is 9.74E − 07 yr-1, which corresponds to the frequency of occurrence of such event 1 time in 1 million years.

Downwind odor characteristics can be very different depending upon the size of the upwind point-source, interim topography, and wind conditions. At one extreme, the downwind odor plume from a relatively large, confined animal feeding operation (CAFO), located on a flat open plain and under stable, near-straight-line wind conditions can be rather broad, sustained and predictable relative to a fixed receptor site downwind. In contrast, the plume from a small point-source (e.g., a roof vent stack) located on irregular topography and under rapidly shifting wind conditions can be intermittent and fleeting. These transient odor events can be surprisingly intense and offensive, in spite of their fleeting occurrence and perception. This work reports on efforts to optimize an environmental odor sampling strategy, which is optimized for the challenges of (1) sampling of such transient odor 'spikes' and (2) the prioritization of odors/odorants from multiple, closely co-located point-sources, under such transient event conditions. Protocol refinement has emerged by way of 2 environmental odor assessment projects which have been undertaken on behalf of the Missouri Department of Natural Resources. The challenge of transient odor events has been mitigated utilizing rapid, odor cued whole-air grab capture sampling into metalized-FEP gas sampling bags, followed by immediate adsorption transfer onto SPME fibers or sorbent tubes for stabilization during the shipment and storage interval between collection and final analysis. Initial results demonstrated approximately 11 fold increases in target odorant yields for 900 mL sorbent tube transfers from 2-3 second 'burst' odor event bag-captures, as compared to equivalent direct collections at the same downwind receptor location but during perceived (stable) odor 'lull' periods. Results-to-date targeting refinement and field trials of this integrated environmental odor assessment strategy are presented. Preliminary application targeting general odor sampling and point-source differentiation utilizing tracer gases is also presented.

Power plant’s site selection is a complex task and involves through analyses of multi-disciplinary processes which are interlinked with each other. The site selection for nuclear power plants additionally requires an assessment of radiation doses to the environment and public during normal operation and in the case of an accident. This demands the problem of radioactive particles’ dispersion in atmosphere to be analysed in real time for a comprehensive set of radioactive release scenarios in prevailing meteorological conditions in the plant surroundings. In this study, a local scale atmospheric dispersion problem, considering a hypothetical accidental release (1 Bq s−1 of I-131) from a nuclear power plant is simulated with a combination of weather forecasting and particle dispersion codes on a multiprocessor computer system. The meteorological parameters are predicted with a weather research and forecasting (WRF) model and used in Lagrangian particle dispersion model based code FLEXPART to calculate the trajectory of released particles, and thereby, the estimation of spatial I-131 dose distribution. The concentration of particles and radiation doses were calculated for release heights of 10, 57, and 107 m and found in a reasonable agreement with the observed data and better than an earlier investigation done with regional atmospheric modelling system (RAMS) code. A comparison between the results of WRF and RAMS for various meteorological parameters revealed that better space–time predictions of wind speeds and directions by WRF had a profound effect on tracing the trajectories of particles and thereby the spatial dose distribution. The particles followed the changes in wind direction predicted by WRF that were known to prevail in the region.

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused chaos to air traffic across Europe. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. Here we present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned.Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep atmospheric convection and parameterizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Further, atmospheric dispersion models are driven by 3-dimensional meteorological data from Numerical Weather Prediction (NWP) models and the Met Office’s upper air wind field data is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event affecting their region.

Poor knowledge of dispersion model source parameters related to quantities such as the total fine ash mass emission rate, its effective spatial distribution, and particle size distribution makes the provision of quantitative forecasts of volcanic ash a difficult problem. To ameliorate this problem, we make use of satellite-retrieved mass load data from 14 eruption case studies to estimate fine ash mass emission rates and other source parameters by an inverse modelling procedure, which requires multidimensional sampling of several thousand trial simulations with different values of source parameters. We then estimate the dependence of these optimal source parameters on eruption height. We show that using these empirical relationships in a data assimilation procedure leads to substantial improvements to the forecasts of ash mass loads, with the use of empirical relationships between parameters and eruption height having the added advantage of computational efficiency because of dimensional reduction. In addition, the use of empirical relationships, which encode information in satellite retrievals from past case studies, implies that quantitative forecasts can still be issued even when satellite retrievals of mass load are not available in real time due to cloud cover or other reasons, making it especially useful for operations in the tropics where ice and water clouds are ubiquitous.

The intent of minimizing the impact of the large amount of radioactive material potentially released into the atmosphere in a nuclear event implies preparedness activities. In the early phase and in absence of field observations, countermeasures would largely rely on a previous characterization of the transport and dispersion of radioactive particles and the potential levels of radioactive contamination. This study presents a methodology to estimate the atmospheric transport, dispersion and ground deposition patterns of radioactive particles. The methodology starts identifying the main airflow directions by means of the air mass trajectories calculated by the HYSPLIT model, and, secondly, the dispersion and the ground deposition characteristics associated with each airflow pattern by running the RIMPUFF atmospheric dispersion model. From the basis of these results, different products can be obtained, such as the most probable transport direction, spatial probability distribution of deposition and the geographical probability distribution of deposition above certain predefined threshold. The method is trained on the HYSPLIT trajectories and RIMPUFF simulations during five consecutive years (2012–2016) at the Almaraz Nuclear Power Plant, in Spain. 3644 forward air mass trajectories were calculated (at 00 and 12 UTC, and with duration of 36 h). Eight airflow patterns were identified, and within each pattern, the persistent days, i.e. those days in which trajectories at 00 and 12 UTC grouped into the same airflow pattern, were extracted to simulate the atmospheric dispersion and ground deposition following a hypothetical ISLOCA accident sequence of 35 h. In total, 833 simulations were carried out, in which ground contamination was estimated at cell level on a non-homogeneous geographical grid spacing up to 800 km from Almaraz. The corresponding outcomes show a large variability in the area covered and in deposition values between airflow patterns, which provide comprehensive and oriented information and resources to decision makers to emergency management., Highlights • Methodology to enhance the preparedness to N/R emergencies is presented. • Modelling results are used to estimate the most vulnerable areas to radioactive contamination. • The HYSPLIT forward trajectories and RIMPUFF dispersion simulations are combined. • The method is trained at the Almaraz NPP for five years (2012–2016).

Vegetation in the urban environment has various impacts on microclimate. While optimal strategies for investigating these impacts are the subject of ongoing research, most approach rely on Computational Fluid Dynamics (CFD) simulations. We evaluate mean wind and turbulence fields simulated using the fast-running Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System, a simplified CFD tool that resolves buildings and vegetation. We use QUIC to investigate the role of deciduous trees in modifying the airflow of a real neighbourhood in Bologna, Italy by running large ensembles of simulations per case study, obtained by varying the input wind direction. This approach can minimise intrinsic model uncertainty as well as uncertainties associated with real environments. Model validation is performed using measurements from an experimental field campaign focused on a vegetated urban street canyon in Bologna. Ensemble simulation results show good agreement with the experimental data for various conditions (i.e., simulation ensembles overlap experimental variability in most cases). The role of trees is investigated by comparing simulations with and without trees. Trees are found to reduce airflow by constraining local circulation and reducing turbulence intensity. Finally, the combined effect of building morphology and vegetation is investigated by adopting a formalism to represent the presence of vegetation using area-fraction coefficients. An area-fraction coefficient threshold of 0.225 has been identified that separates flow behaviours and is present in cases with and without vegetation. Below this threshold, a constant wind and turbulence regime exists, while above the threshold, winds and turbulence vary with area-fraction coefficient.

During the time period December 2019 – January 2020, a lot of biomass burning has been ongoing in the Southern Hemisphere. This led to a large amount of fine dust being emitted and transported into the atmosphere of the Southern Hemisphere. With the dispersion model FLEXPART, we will simulate these fires, using the CAMS Global Fire Assimilation System (GFAS) which assimilates fire radiative power (FRP) observations from satellite-based sensors in order to reproduce daily estimates of biomass burning emissions. Also information on the injection height is available.Aerosol fluxes and sources in Antarctica and its closely associated Southern Ocean are poorly constrained, in particular the particle chemistry. A detailed understanding of present-day atmospheric transport pathways of particles and of volatile organic compounds (VOC) from source to deposition in Antarctica remains essential to document biogeochemical cycles and the relative importance of natural and anthropogenic compounds. Within the CHASE project (chase.meteo.be), the Royal Meteorological Institute of Belgium, Ghent University and the Université Libre de Bruxelles are doing research at the Belgian research station Princess Elisabeth (71.9°S, 23.3°E; East Antarctica, Dronning Maud Land) on the physical-chemical composition of both atmospheric particles, particles in surface snow particles as well as of VOCs. Since 2018, samples are taken both near the Belgian research station Princess Elisabeth (active sampling with pumps) and on a transect to the coast (passive samplers and surface snow samples). In this contribution we will thoroughly investigate the atmospheric transport pathways of the recent biomass burning plumes, and in particular to what extent parts of these plumes have reached Antarctica. The measured chemical signatures of atmospheric particles and VOCs will help to constrain the simulations of the dispersion model.

In the framework of the European project CONFIDENCE, Work Package 1 (WP1) focused on the uncertainties in the pre- and early phase of a radiological emergency, when environmental observations are not available and the assessment of the environmental and health impact of the accident largely relies on atmospheric dispersion modelling. The latter is subject to large uncertainties coming from, in particular, meteorological and release data. In WP1, several case studies were identified, including hypothetical accident scenarios in Europe and the Fukushima accident, for which participants propagated input uncertainties through their atmospheric dispersion and subsequent dose models. This resulted in several ensembles of results (consisting of tens to hundreds of simulations) that were compared to each other and to radiological observations (in the Fukushima case). These ensembles were analysed in order to answer questions such as: among meteorology, source term and model-related uncertainties, which are the predominant ones? Are uncertainty assessments very different between the participants and can this inter-ensemble variability be explained? What are the optimal ways of characterizing and presenting the uncertainties? Is the ensemble modelling sufficient to encompass the observations, or are there sources of uncertainty not (sufficiently) taken into account? This paper describes the case studies of WP1 and presents some illustrations of the results, with a summary of the main findings.

Several European urban areas are characterised by low air quality due to high local emission per unit surface. A further key feature can be related to the pollutant load due to adverse local meteo-climatic conditions. This study aims to compare the two urban agglomerations of Torino and Lyon – located on opposite sides of the Alps and characterised by similar size and population – to enlighten the role of meteorology on local pollutant dispersion. The assessment of air quality has been developed by monitoring network data, emissions analysis and the SIRANE urban dispersion model. Although the two agglomerations have similar NOX and PM10 emissions, the simulation results show higher ground level concentrations in Torino. To quantify the effect of meteorology on this excess of concentrations, we run simulations in Torino imposing the meteorological conditions of Lyon and vice versa. This implies an overall reduction of ground level concentrations in the city centre of Torino between 20% and 40% (analogously, Lyon concentrations increase by a similar amount). These results show the peculiar difficulties faced by Po valley's cities in maintaining pollution levels below regulatory thresholds and highlight the need of systemic policies and site-specific mitigation to reduce air pollution health risks.

During the pre-release and early phase of an accidental release of radionuclides into the atmosphere there are few or no measurements, and dispersion models are used to assess the consequences and assist in determining appropriate countermeasures. However, uncertainties are high during this early phase and it is important to characterise these uncertainties and, if possible, include them in any dispersion modelling. In this paper we examine three sources of uncertainty in dispersion modelling; uncertainty in the source term, uncertainty in the meteorological information used to drive the dispersion model and intrinsic uncertainty within the dispersion model. We also explore the possibility of ranking these uncertainties dependent on their impact on the dispersion model outputs.

People spend major part of their time inside places such as homes and offices, so it is very important to know the indoor and outdoor pollution in this type of studies. The atmospheric dispersion model WRF/Chem is used to know the outdoor pollution and meteorological conditions with high spatial (1 km) and temporal (1-hour) resolution and the building energy model EnergyPlus to simulate the indoor contaminants. EnergyPlus model is used to investigate the dynamic behaviour of pollutants with a single package using a multizone approach. 2016 year is used for the simulations with hourly outputs. Outdoor and indoor pollutions are linked to through the simulated infiltration process. The evaluation of outdoor, indoor air quality and human health effects was carried out considering different exposure profiles, for people working and living in an office and house located in the same building in the Madrid city center. The study takes into account different ventilation modes in the building and indoor emission scenarios (oven for heating, cooking, photocopy machine, smoke cigarettes). Health impact assessment considered mortality and hospital admissions, associated with exposure to PM2.5 and NO2 taking into account the differences between the exposure profiles, which have been used to describe the time activity patterns of the people. The health impacts of emitting sources are highest in the warm months due to the operation of the air conditioning system. The health impact of indoor emission sources is higher than the outdoor pollution. People in the zone where the emitting sources are located would experience a mortality and morbidity of 2.5 times more than in the non-emitting zones.