Parameterization and evaluation of the CALMET/CALPUFF model system in near-field and complex terrain - Terrain data, grid resolution and terrain adjustment method

Rzeszutek, Mateusz

doi:10.1016/j.scitotenv.2019.06.379

Cited by 42 publications

(15 citation statements)

References 40 publications

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“…The CALifornia PUFF Model (CALPUFF) is an example of a diagnostic model [42]. CALPUFF has been utilized to analyze emission fluxes from industrial and urban sites, as well as the associated air quality and health implications [43][44][45][46][47][48]. When applied to complex topography, CALPUFF has been shown to be less accurate when modeling atmospheric dispersion under the thermally stable condition [49].…”

Section: Modeling Techniques To Quantify Emission Fluxesmentioning

confidence: 99%

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

et al. 2022

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Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R2 (-) values higher than 0.8 (-). Considering the bias (Tonnes h−1), the ML predicted the emission fluxes within 6.3%, 3.3%, and 0.3% of WRF predictions for the old mine, new mine, and the pond, respectively.

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Section: Modeling Techniques To Quantify Emission Fluxesmentioning

confidence: 99%

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

et al. 2022

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“…Finally, having found no publicly available, lightweight Gaussian puff implementation, we make our implementations in both Python and R available. These implementations do not account for certain complex transport dynamics (e.g., plume rise, wind shear, and complex terrain) that are included in more comprehensive industrial-scale puff implementations (e.g., CALPUFF 19 ). However, accounting for these effects is likely not necessary on the production-style facilities that we focus on in this work, and would require data about emission temperatures, velocities, and boundary layer dynamics that are not always available in practice.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Gaussian plume and puff atmospheric dispersion models on oil and gas facilities

Jia

Daniels

Hammerling

2023

Preprint

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Characterizing methane emissions on oil and gas facilities often relies on a forward model to describe the atmospheric transport of methane. Here we compare two forward models: the Gaussian plume, a commonly used steady-state dispersion model, and the Gaussian puff, a time varying dispersion model that approximates a continuous release as a sum over many small "puffs". We compare model predictions to observations from a network of point-in-space continuous emissions monitoring systems (CEMS) collected during a series of controlled releases. Specifically, we use the Pearson correlation coefficient and mean absolute error (MAE) as metrics to assess the fit of the model predictions to the observed concentrations, with the former assessing the fit of pattern and the latter the fit of amplitude. The Gaussian puff outperforms the Gaussian plume using both metrics with average correlation coefficients of 0.38 and 0.31 and average MAEs of 0.70 and 0.74, respectively. We also investigate how the frequency at which puffs are generated affects the accuracy and computational cost of the Gaussian puff and propose guidelines for choosing an appropriate value. Finally, we provide open-source implementations of the Gaussian puff model in Python and R that are tailored for use on oil and gas facilities.

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“…The research and applications of the gas dispersion model in the emergency field mainly concentrate on the nuclear accident (Liu et al, 2017;Li et al, 2018;Ulimoen et al, 2022), chemical industrial park (Huang et al, 2019;Cheng et al, 2021), gas pipeline (Mishra et al, 2015 CFD;Yan et al, 2016), and road transportation (Fallah-Shorshani et al, 2015;Kota et al, 2013). Compared with the environmental assessment cases, the emergency simulation often requires a near-field, finer spatial-temporal computational grid and further analysis and adjustment of the model parameters (Rzeszutek, 2019).…”

Section: Introductionmentioning

confidence: 99%

Comparative study on numerical simulation based on CALPUFF and wind tunnel simulation of hazardous chemical leakage accidents

Zhang

2022

Front. Environ. Sci.

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CALPUFF, as a Lagrangian puff modeling system, is mostly used in the field of atmospheric environment research and risk assessment. CALPUFF performs well for short-term and short-range release scenarios over complex terrain, as well as long-term and long-range transportation. Therefore, this article uses the CALPUFF model to simulate a toxic gas leakage accident in a hazardous chemical plant in an urban area, focusing on the influence of local buildings. Wind tunnel experiments are performed in accordance with the CALPUFF experiments to assess the model’s accuracy in cases of chemical leakage accidents. The results of the wind tunnel experiment are superimposed on the map of CALPUFF calculation, and the quantitative analysis is also performed. The comparative results show that the simulation results of the CALPUFF are mainly affected by factors such as wind direction, wind speed, and the complexity of the surface environment. With less influence of buildings, such as the south and north wind, the CALPUFF simulation is consistent with the wind tunnel experiment, having a correlation coefficient of over 0.7 in most cases, while under the east wind, the consistency is significantly lower due to the influence of buildings. In addition, it is found that the wind tunnel experiment is more accurate in the near field of the pollution source, while CALPUFF is more suitable for simulating the overall trend of gas dispersion. The comparison and evaluation of the capabilities of different methods on gas dispersion simulation are helpful in guiding the emergency response during hazardous chemical leakage accidents.

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Parameterization and evaluation of the CALMET/CALPUFF model system in near-field and complex terrain - Terrain data, grid resolution and terrain adjustment method

Cited by 42 publications

References 40 publications

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

Comparison of the Gaussian plume and puff atmospheric dispersion models on oil and gas facilities

Comparative study on numerical simulation based on CALPUFF and wind tunnel simulation of hazardous chemical leakage accidents

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