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Carbon Capture and Storage (CCS) is of interest to the scientific community as a way of achieving significant global reduction of atmospheric CO2 emission in the medium term. CO2 would be transported from large emission points (e.g. coal fired power plants) to storage sites by surface/shallow high pressure pipelines. Modelling of CO2 atmospheric dispersion after leakages from transportation facilities will be required before starting large scale CCS projects. This paper deals with the evaluation of the atmospheric dispersion CFD tool Fluidyn-PANACHE against Prairie Grass and Kit Fox field experiments. A description of the models for turbulence generation and dissipation used (k–ɛ and k–l) and a comparison with the Gaussian model ALOHA for both field experiments are also outlined. The main outcome of this work puts PANACHE among the “fit-for-purpose” models, respecting all the prerequisites stated by Hanna et al. [Hanna, S.R., Chang, J.C. and Strimaitis, D.G., 1993. Hazardous gas model evaluation with field observations. Atmospheric Environment, 27, 2265–2285] for the evaluation of atmospheric dispersion model performance. The average under-prediction has been ascribed to the usage of mean wind speed and direction, which is characteristic of all CFD models. The authors suggest a modification of performance ranges for model acceptability measures, within the field of high pressure CO2 transportation risk assessment, with the aim of accounting for the overall simplification induced by the usage of constant wind speed and direction within CFD atmospheric dispersion models. [Copyright &y& Elsevier] more...

Abstract: In order to suggest a new methodology for selecting an appropriate dispersion model, various statistical measures having respective characteristics and recommended value ranges were integrated to produce a new single index by using fuzzy inference where eight statistical measures for various model results, including fractional bias (FB), normalized mean square error (NMSE), geometric bias mean (MG), geometric bias variance (VG), within a factor of two (FAC2), index of agreement (IOA), unpaired accuracy of the peak concentration (UAPC), and mean relative error (MRE), were taken as premise part variables. The new methodology using a single index was applied to the prediction of ground-level SO2 concentration of 1-h average in coastal areas, where eight modeling combinations were organized with fumigation models, σy schemes for pre-fumigation, and modification schemes for σy during fumigation. As a result, the fumigation model of Lyons and Cole was found to have better predictability than the modified Gaussian model assuming that whole plume is immerged into the Thermal Internal Boundary Layer (TIBL). Again, a better scheme of σy (fumigation) was discerned. This approach, which employed the new integrated index, appears to be applicable to model evaluation or selection in various areas including complex coastal areas. [Copyright &y& Elsevier] more...

Abstract: A comprehensive model evaluation has been conducted for MM5-CMAQ for the period of 1–10 July 1999 of the Southern Oxidants Study episode with a coarse-grid horizontal spacing of 32-km and a nested fine-grid spacing of 8-km. Meteorological and chemical predictions from simulations with both grids are compared with observations from both routine monitoring networks (e.g., CASTNet, IMPROVE, and AIRS-AQS) and special studies (e.g., SOS99/SOS99NASH, SEARCH, and ARIES). In this Part I paper, five simulated meteorological variables (i.e., temperature, relative humidity (RH), wind speed, wind direction, and planetary boundary layer (PBL) height) are evaluated. While MM5 reproduces well the diurnal variations for temperature and RH, and the minimum temperatures at all sites, it tends to overpredict maximum temperatures and underpredict both maximum and minimum RHs on most days at most sites. MM5 predictions agree reasonably well for wind speeds but poorly for wind direction and the maximum mixing depths. The significant overpredictions in the PBL heights can be attributed to the positive biases of the maximum 2-m temperatures and to the Medium Range Forecast (MRF) model PBL scheme and the Oregon State University (OSU)-Land Surface Model used in the MM5 simulations. For wind speed/direction and the U- and V-component of the wind speed, the normalized mean bias (NMB) and the normalized mean bias factor (NMBF) are the most robust statistical measures because of the dominance of the extremely small observed values in the normalization for those variables. [Copyright &y& Elsevier] more...