Bayesian estimation of airborne fugitive emissions using a Gaussian plume model

Dispersion of hazardous gas releases represents a major threat to health and to the environment; therefore, the prediction of the dispersion features raises great interest. In recent years, with the increase in computational capacity, the interest in Computational Fluid Dynamics (CFD) tools to evaluate dispersion analysis has increased. With the growing use of CFD tools to perform dispersion analysis in different scenarios, it is imperative to amplify the availability of experimental data in order to allow validations studies to contribute to understanding the real capacity of CFD tools to properly represent real cases. The second stage of the field tests conducted by a joint venture between the University of Sao Paulo and the Universitat Politecnica de Catalunya was established to investigate the performance of CFD tools when analyzing cloud dispersion of hazardous substances by means of ad-hoc experimentation. The experiments consisted of CO2 clouds formation and dispersion tracking of releases of up to 0.85 kg s−1 of about 40 s of duration in a 600-m2discharge area. We provide a description of the tests and the main results of the simulation using the FLACS software; the peak concentrations for 51 sensors placed at the dispersion cloud path are provided here and compared with CFD simulations. In general terms, the CFD simulator presented good performance; all the statistical performance measures were well within the acceptable range. However, the simulator performance presented sensitivity to the wind profile, especially when cross wind occurs.

Experimental data and CFD performance for CO2 cloud dispersion analysis

. Artificial rain is explored as a remedy to climate change caused farmland drought and bushfires. Increasing the ion density in the open air is an efficient way to generate charged nuclei from atmospheric aerosols and induce precipitation or eliminate fog. Here we report on the development of the large commercial installation scale atmospheric ion generator based on corona plasma discharges, experimental monitoring and numerical modeling of the parameters and range of the atmospheric ions, and application of the generated ions to produce charged aerosols and induce precipitation at a scale of a large cloud chamber. The coverage area of the ions generated by the large corona discharge installation with the 7.2 km long wire electrode and applied voltage of −90 kV is studied under prevailing weather conditions including wind direction and speed. By synergizing over 300 000 localized corona discharge points, we demonstrate a substantial decrease of the decay of ions compared to a single corona discharge point in the open air, leading to a large-scale (30 m × 23 m × 90 m) ion coverage. Once aerosols combine with the generated ions, charged nuclei are produced. The higher wind speed has led to the larger areas covered by the plasma generated ions. The cloud chamber experiments (relative humidity 130 ± 10 %) suggest that the charged aerosols generated by ions with the density of ~ 104/cm3 can accelerate the settlement of moisture by 38 %. These results are promising for the development of large-scale installations for the effective localized control of atmospheric phenomena.

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Large-scale ion generation for precipitation of atmospheric aerosols

Source characterization of airborne pollutant emissions by hybrid metaheuristic/gradient-based optimization techniques

Direct effect of atmospheric turbulence on plume rise in a neutral atmosphere

On the modeling of atmospheric pollutant dispersion during a diurnal cycle: A finite element study

We propose a parsimonious, yet effective, susceptible–exposed–infected–removed-type model that incorporates the time change in the transmission and death rates. The model is calibrated by Tikhonov-type regularization from official reports from New York City (NYC), Chicago, the State of São Paulo, in Brazil and British Columbia, in Canada. To forecast, we propose different ways to extend the transmission parameter, considering its estimated values. The forecast accuracy is then evaluated using real data from the above referred places. All the techniques accurately provided forecast scenarios for periods 15 days long. One of the models effectively predicted the magnitude of the four waves of infections in NYC, including the one caused by the Omicron variant for periods of 45 days using out-of-sample data.

Nowcasting and forecasting COVID-19 waves: the recursive and stochastic nature of transmission

Bayesian Inference and Wind Field Statistical Modeling Applied to Multiple Source Estimation.

. Source identification methodologies use inverse problems techniques combined with a dispersion model and observational data to estimate relevant source parameters. This work proposes a time-dependent model to estimate source parameters of multiple point releases. The forward problem or dispersion model accounts for the time variation of the wind field using a Fourier series that best fits the wind field time series of the experimental data. The source parameters are estimated by an adaptive Monte Carlo Markov Chain algorithm.

Roseane A.S. Albani

Papers

On the modeling of atmospheric pollutant dispersion during a diurnal cycle: A finite element study

Nowcasting and forecasting COVID-19 waves: the recursive and stochastic nature of transmission

Bayesian Inference and Wind Field Statistical Modeling Applied to Multiple Source Estimation.

A Bayesian Inference Model for the Estimation of Time-Dependent Pollutant Emissions

Atmospheric boundary layer flow simulations with OpenFOAM using a modified k-epsilon model consistent with prescribed inlet conditions