A dispersion modelling system SILAM and its evaluation against ETEX data

The environmental impact of systems managing large (kg) tritium amount represents a public scrutiny issue for the next coming fusion facilities as ITER and DEMO. Furthermore, potentially new dose limits imposed by international regulations (ICRP) shall impact next coming devices designs and the overall costs of fusion technology deployment. Refined environmental tritium dose impact assessment schemes are then overwhelming. Detailed assessments can be procured from the knowledge of the real boundary conditions of the primary tritium discharge phase into atmosphere (low levels) and into soils. Lagrangian dispersion models using real-time meteorological and topographic data provide a strong refinement. Advance simulation tools are being developed in this sense. The tool integrates a numerical model output records from European Centre for Medium range Weather Forecast (ECMWF) with a lagrangian atmospheric dispersion model (FLEXPART). The composite model ECMWF/FLEXTRA results can be coupled with tritium dose secondary phase pathway assessment tools. Nominal tritium discharge operational reference and selected incidental ITER-like plant systems tritium form source terms have been assumed. The real-time daily data and mesh-refined records together with lagrangian dispersion model approach provide accurate results for doses to population by inhalation or ingestion in the secondary phase.

Abstract. Backtrajectory differences and clustering sensitivity to the meteorological input data are studied. Trajectories arriving in Southeast Spain (Elche), at 3000, 1500 and 500 m for the 7-year period 2000–2006 have been computed employing two widely used meteorological data sets: the NCEP/NCAR Reanalysis and the FNL data sets. Differences between trajectories grow linearly at least up to 48 h, showing faster growing after 72 h. A k-means cluster analysis performed on each set of trajectories shows differences in the identified clusters (main flows), partially because the number of clusters of each clustering solution differs for the trajectories arriving at 3000 and 1500 m. Trajectory membership to the identified flows is in general more sensitive to the input meteorological data than to the initial selection of cluster centroids.

Potassium (K) is a critical nutrient for crops, as it is a major constituent in fertilizer formulations. With increasing concentrations of K in agricultural soil, it is necessary to understand its movement and retention in the soil. Sub-surface modeling is an alternative method to overcome the exhausting and uneconomical methods to study and determine the actual concentration of K in soil. HYDRUS-1D is considered an effective finite-element model which is suitable for sub-surface modeling. This model requires the input of ground-station meteorological (GM) data taken at a daily timestep for the simulation period. It can be a limiting factor in the absence of ground stations. The study compares K predictions in surface and sub-surface soil layers under Boro rice cultivation obtained with the usage of different meteorological datasets. Thus, the main hypothesis of the study was to validate that, in the absence of GM data, satellite-based meteorological data could be utilized for simulating the K concentration in soil. The two meteorological datasets that are considered in the study included the GM and satellite-derived NASA-Power (NP) meteorological datasets. The usage of a satellite meteorological product at a field scale may help in applying the method to other regions where GM data is not available. The numerical model results were validated with field experiments from four experimental fields which included varied K doses. The concentration in soil was assessed at the regular depths (0–5, 5–10, 10–15, 15–30, 30–45 and 45–60 cm), and at various stages of crop growth, from bare soil and sowing, to the tillering stages. The concentration of K was measured in the laboratory and also simulated through the optimized model. The modeled values were compared with measured values statistically using relative root mean square error (RMSER) and Nash–Sutcliffe modeling efficiency (E) for simulating K concentration in the soil for the Boro rice cropping pattern with both GM data and NP data. The model was found most suitable for the 0–30 cm depth on all days and for all treatment variations.

Until the definitive paper of Thomson (1987), arguably the most pressing problem in Lagrangian particle modelling was which form of the Langevin equation should be used for inhomogeneous, non-stationary, non Gaussian turbulence to ensure well-mixedness, i. e. to ensure that particle accumulations did not occur in regions of low turbulence. Since that time, the major topics of research in Lagrangian dispersion modelling have been associated with convectively unstable conditions. This review paper focuses on two recent topics: (1) the closure problem associated with specification of the probability density function (PDF) for vertical turbulent fluctuations, and (2) the most appropriate boundary conditions to apply at the ground and at the top of the convectively mixed layer, particularly when simulating the fumigation1 process. The reviewed studies emphasise the importance of turbulence and concentration data from a laboratory saline water tank (Hibberd and Sawford, 1994) for testing the closure schemes and the various methods for incorporating entrainment processes into stochastic models.

Eulerian air quality models that require gridded meteorological inputs have to adapt to recent advances in meteorological models for fully compressible atmosphere. When the input meteorological data are recast with a robust fully compressible governing set of equations, chemistry-transport models can follow the dynamic and thermodynamic descriptions of the meteorological data closely. For evaluating mass consistency in meteorological data, one may take advantage of the characteristics of the governing set of equations applicable for a specific vertical coordinate system. This paper discusses how the data from meteorological models should be used in air quality simulations. It proposes a general methodology to conserve mass of trace species in air quality models by maintaining consistency in the wind and air density fields. Limitations of several simplifying assumptions on atmospheric dynamics are also discussed. In summary, it attempts to bridge the information gap between dynamic meteorologists ...

Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become numerically rather costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the MPI/OpenMP/OpenACC hybrid parallelization of MPTRAC were conducted on the JUWELS Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop/s. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 108 particles driven by the European Centre for Medium-Range Weather Forecasts' ERA5 reanalysis, the performance evaluation showed a maximum speedup of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file-I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model.

Abstract. Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become rather numerically costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the Message Passing Interface (MPI) – Open Multi-Processing (OpenMP) – OpenACC hybrid parallelization of MPTRAC were conducted on the Jülich Wizard for European Leadership Science (JUWELS) Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop s−1. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 108 particles driven by the European Centre for Medium-Range Weather Forecasts' fifth-generation reanalysis (ERA5), the performance evaluation showed a maximum speed-up of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model.

ETEX: A European tracer experiment; observations, dispersion modelling and emergency response

Development and application of a high resolution hybrid modelling system for the evaluation of urban air quality

<p>Atmospheric transport and dispersion models are used by Volcanic Ash Advisory Centers (VAACs) to provide timely information on volcanic ash clouds to mitigate the risk of aircraft encounters. Inaccuracies in dispersion model forecasts can occur due to the uncertainties associated with source terms, meteorological data and model parametrizations. Real-time validation of model forecasts against observations is therefore essential to ensure their reliability. Forecasts can also benefit from comparison to model output from other groups; through understanding how different modelling approaches, variations in model setups, model physics, and driving meteorological data, impact the predicted extent and concentration of ash. The Met Office, the National Centre for Atmospheric Science (NCAS) and the British Geological Survey (BGS) are working together to consider how we might compare data (both qualitatively and quantitatively) from the atmospheric dispersion models NAME, FALL3D and HYSPLIT, using meteorological data from the Met Office Unified Model and the NOAA Global Forecast System (providing an effective multi-model ensemble). Results from the model inter-comparison will be used to provide advice to the London VAAC to aid forecasting decisions in near real time during a volcanic ash cloud event. In order to facilitate this comparison, we developed a Python package (ash-model-plotting) to read outputs from the different models into a consistent structure. Here we present our framework for generating comparable plots across the different partners, with a focus on total column mass loading products. These are directly comparable to satellite data retrievals and therefore important for model validation. We also present outcomes from a recent modelling exercise and discuss next steps for further improving our forecast validation.</p>

Abstract. A numerical scheme to perform data assimilation of concentration measurements in Lagrangian models is presented, along with its first implementation called Ocean Plastic Assimilator, which aims to improve predictions of the distributions of plastics over the oceans. This scheme uses an ensemble method over a set of particle dispersion simulations. At each step, concentration observations are assimilated across the ensemble members by switching back and forth between Eulerian and Lagrangian representations. We design two experiments to assess the scheme efficacy and efficiency when assimilating simulated data in a simple double-gyre model. Analysis convergence is observed with higher accuracy when lowering observation variance or using a circulation model closer to the real circulation. Results show that the distribution of the mass of plastics in an area can effectively be improved with this simple assimilation scheme. Direct application to a real ocean dispersion model of the Great Pacific Garbage Patch is presented with simulated observations, which gives similarly encouraging results. Thus, this method is considered a suitable candidate for creating a tool to assimilate plastic concentration observations in real-world applications to estimate and forecast plastic distributions in the oceans. Finally, several improvements that could further enhance the method efficiency are identified.

Influence of hydro-meteorological data spatial aggregation on streamflow modelling

: In October 1994, the tracer gas perfluoro-methyl-cyclohexane (PMCH) was released over a 12-hour period from a location in northwestern France and tracked at 168 sampling locations in 17 countries across Europe (100s of kilometers). This release, known as the European Tracer Experiment (ETEX), resulted in the collection of a wealth of data. IDA has obtained the predictions of 46 transport and dispersion models from 17 countries from the Joint Research Centre, European Commission as well as the PMCH sampling data. This paper applies a previously described user-oriented measure of effectiveness (MOE) methodology to evaluate the predictions of the 46 models against the long-range ETEX observations. The paper extends previous work by computing MOE values that are based on true areas (e.g., in square kilometers) and on actual European population distributions. The overall objective of this paper is to document the procedures used to estimate the area-based and population-based MOE values.

the assessment of individual exposure to toxicants in industrially contaminated areas is difficult when multiple productions are actively close to residential areas. Two thermoelectric power plants (one recently converted to coal) and a large harbour have been operating since the Sixties in the area of Civitavecchia (North of Rome, Lazio Region, Central Italy). Detailed exposure assessment of pollutants from industrial sources, heating (biomass combustion), and urban traffic were not available. to assess the individual exposure of residents by using both dispersion models and human biomonitoring. cross-sectional study. residents in the area of Civitavecchia since 2001 aged 35-69 years were enrolled and their addresses were geocoded. Exposure assessment to power plants emissions (PM10), traffic (NOx), harbour (PM10), and biomass burning (PM10) was performed at individual residential address using Lagrangian dispersion models. The "Ambiente e Biomarcatori a Civitavecchia" study (Environment and biomarkers in Civitavecchia - ABC study) made available urinary concentrations of metals (i.e., Cd, Pt, W, Pd, Hg, Tl, Cr, Pd, Ni, As) in a sample of residents, while information on personal characteristics, lifestyles, work history, clinical history, and use of drugs was collected by interview. for each metal, a linear regression analysis was implemented to study the association between its log-transformed values (adjusted for urinary creatinine) and the linear terms of the estimated pollutants concentrations, adjusted for age, gender, period, and other risk factors (i.e., educational level, smoking habit, alcohol, BMI). Geometric Mean Ratios (GMR) and the corresponding confidence intervals (95%CI) were computed. in the ABC sample of 1,141 residents (42% male, mean age 53.5 years, SD 9.7), there was a positive association between PM10 from power plants and urinary cadmium (Cd) (GMR: 1.12; 95%CI 1.00-1.25), traffic pollution (NOx) and platinum (Pt) (GMR: 1.17; 95%CI 1.00-1.38), PM10 from biomass burning and Cd (GMR: 1.14; 95%CI 1.05-1.24), and tungsten (W) (GMR: 1.19; 95%CI 1.03-1.37) and palladium (Pd) (GMR: 1.11; 95%CI 1.00-1.23). Other associations observed did not reach statistical significance. although it may not be said that dispersion models can replace biomonitoring studies, they certainly represent an excellent tool for exposure assessment and, therefore, they may be used for the estimation of the individual exposure of populations living in industrially contaminated areas.

<p>Most of worldwide population lives in urban areas, demanding for air quality information with a high spatio-temporal resolution. The most promising approaches for estimating urban air quality within the complex urban topography are small sensor networks and simulation models.</p><p>The TRAFAIR project focuses on understanding the role of traffic emissions on urban air quality by the combination of dispersion modelling, space- and time-resolved gas monitoring by lower cost sensors and realistic traffic flow rates by dynamic traffic model based on real time traffic data. Test cities of TRAFAIR are Modena, Florence, Pisa, Livorno, Zaragoza and Santiago de Compostela.</p><p>Depending on the size of the urban area, from 6 to 13 sensors units are deployed across each city since August 2019, providing estimates of NO, NO<sub>2</sub>, CO and O<sub>3</sub>, along with RH and temperature. Metal oxide sensors are deployed in Tuscany (Florence, Pisa, Livorno) and electrochemical cells are used elsewhere. The units are calibrated on a regular basis by co-location at the air quality regulatory stations and subsequently deployed across the town to monitor several representative locations (e.g. Low Emission Zones, hospital surroundings). For each sensor the raw readings (e.g. mV for electrochemical cells) are collected and a regression model (e.g. Random Forest) is applied to derive a calibration function, exploiting the data from the regulatory stations during co-location periods; for instance in Modena, the first short-term calibration provided a model with a Mean Absolute Error between 5 – 6 ppb and 2 – 4 ppb for NO and NO<sub>2</sub> respectively.</p><p>The sensors are used for both real-time urban air quality mapping and to test and validate the 24hr forecast service of NOx by the microscale lagrangian dispersion model GRAL. The simulation domains, covering the urban area of each TRAFAIR city, have a horizontal resolution of 4 m and allow to account for the presence of buildings. The dispersion model mainly focuses on NOx by traffic emissions, although domestic heating will be also included in the analysis. Vehicular emissions are based either upon historical traffic data (e.g. induction loops), or upon previously available traffic flow simulation, or upon traffic pattern reconstruction using a traffic flow model followed by a cluster analysis to group streets with similar pattern.</p><p>The final goal of the project is the development of a tool to support local policymakers and to inform citizenship about the quality of air and the impact of urban emission sources, particularly traffic. A secondary goal of the project is the development of a valuable QA/QC protocol for small sensor units and the optimization of the modelling chain for the forecast of traffic and domestic heating impact on local air quality at the urban scale.</p>