Determination of radiological background fields designated for inverse modelling during atypical low wind speed meteorological episode

Pecha, Petr; Tichý, Ondřej; Pechova, Emilie

doi:10.1016/j.atmosenv.2020.118105

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Inverse Problems: In physical sciences [7,27,39,55], several recent advances have focused on solving inverse problems. Unlike standard inversion methods in mathematics, that rely on non-linear optimization for calculating inverse of a forward model, recent machine learning methods allow us to learn the inverse mapping from datasets.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Inverse Modeling: An Application in Hydrology

Sharma¹,

Ghosh²,

Renganathan³

et al. 2022

Preprint

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Rapid advancement in inverse modeling methods have brought into light their susceptibility to imperfect data. The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. For solving inverse problems in such applications, ensuring explainability is pivotal for tackling issues relating to data bias and large search space. We propose a probabilistic inverse model framework that can reconstruct robust hydrology basin characteristics from dynamic input weather driver and streamflow response data. We address two aspects of building more explainable inverse models, uncertainty estimation and robustness. This can help improve the trust of water managers, handling of noisy data and reduce costs. We propose uncertainty based learning method that offers 6% improvement in R 2 for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17% reduction in uncertainty (40% in presence of noise) and 4% higher coverage rate for basin characteristics.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Inverse Modeling: An Application in Hydrology

Sharma¹,

Ghosh²,

Renganathan³

et al. 2022

Preprint

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“…Therefore, a better understanding of these processes, systems and phenomena aids in the selection and definition of the region's atmospheric modeling system configuration. The presence of calm winds, especially in the tower-A region (at 60 m and 100 m) between the evening and morning, emphasizes the need of adopting suitable pollutant dispersion models in that location, as certain dispersion models are ineffective in calm wind circumstances [94][95][96][97]. Furthermore, the occurrence of statically stable stability classes suggests that the CNAAA area is less capable of turbulent diffusion.…”

Section: Wind and Stability Data Of The Local Atmosphere: Correlation Analysismentioning

confidence: 99%

Thermally and Dynamically Driven Atmospheric Circulations over Heterogeneous Atmospheric Boundary Layer: Support for Safety Protocols and Environment Management at Nuclear Central Areas

et al. 2021

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Ilha Grande Bay is located in Angra dos Reis, Rio de Janeiro State, Brazil. The area is characterized by different land cover, complex topography and proximity to the Atlantic Ocean. These aspects make it susceptible to thermally and dynamically induced atmospheric circulations such as those associated with valley/mountain and land/sea breeze systems, among others. The Almirante Álvaro Alberto Nuclear Complex (CNAAA) is located in this region, with a total of two nuclear power plants (NPPs) in operation in the Brazilian territory, Angra I and Angra II. Therefore, knowledge of local atmospheric circulation has become a matter of national and international security. Considering the importance of the meteorological security tool as a support for licensing, installation, routine operation and nuclear accident mitigation, the main aim of this study is the development of combined strategies of environmental statistical modeling in the analysis of thermally and dynamically driven atmospheric circulations over mountainous and coastal environments. We identified and hierarchized the influence of the thermally and mechanically driven forcing on the wind regime and stability conditions in the coastal atmospheric boundary layer over the complex topography region. A meteorological network of ground-based instruments was used along with physiographic information for the observational characterization of the atmospheric patterns in the spatial and time–frequency domain. The predominant wind directions and intensity are attributed to the combined action of multiscale weather systems, notably, the valley/mountain and continent/ocean breeze circulations, the forced channeling due to valley axis orientation, the influence of the synoptic scale systems and atmospheric thermal tide. The observational investigation of the combined influence of terrain effects and meteorological systems aimed to understand the local atmospheric circulation serves as support for safety protocols of the NPPs, contemplating operation and environmental management. The importance of the study for the adequacy and skill evaluation of computational modeling systems for atmospheric dispersion of pollutants such as radionuclide and conventional contaminants can be also highlighted, in order that such systems are used as tools for environmental planning and managing nuclear operations, particularly those located in regions over mountainous and coastal environments with a heterogeneous atmospheric boundary layer.

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“…AERMOD has been tested in several arrangements: industrial and agricultural facilities, with high-resolution land use modeling episodes, or multiple years spent conducting environmental impact assessments. It has been estimating the levels of exposure to gases and PM of the population living close to the installations (Cerqueira et al 2019, Macêdo and Ramos, 2020, Kelleghan et al, 2021, Pirhalla et al 2021, Tyovenda et al 2021, Pecha et al 2021Cimorelli et al, 2005; Motalebi and Guo, 2021). In the regulatory applications as environmental impact assessments (EIAs), AERMOD simulations cover the most recent 5-year period of atmospheric data.…”

Section: Gaussian Plume Model (Aermod)mentioning

confidence: 99%

Reactive hydrocarbons (C6-C11) in the Metropolitan Area of São Paulo

Coelho

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Os compostos orgânicos voláteis (COVs) são fundamentais nas reações atmosféricas, produzindo ozônio troposférico (O3) e aerossóis orgânicos secundários (SOA). Dentre os COVs, destacam-se os hidrocarbonetos (HCs), incluindo os compostos aromáticos (ex. (BTEX: benzeno, tolueno, etilbenzeno, m,p-e o-xilenos) sendo emitido diretamente na atmosfera, em áreas urbanas, por escapamentos de veículos, evaporação de combustível, uso de solventes, emissões de gás natural e processos industriais. São Paulo é o estado brasileiro com maior ocupação territorial e mais de 47 milhões de habitantes, somado ao maior desenvolvimento econômico e à maior frota automotiva, causando degradação da qualidade do ar. Na região metropolitana de São Paulo (RMSP), em particular, a frota de mais de 7 milhões de veículos tem sido reconhecida como a principal causa de eventos de poluição do ar, mas problemas de saúde e queixas da população do entorno de um importante complexo industrial e petroquímico do MASP também receberam atenção. Nesse contexto, o presente estudo avaliou os HCs próximos ao Complexo Petroquímico de Capuava (CPC) na RMSP, no período de 2016 a 2022. Na primeira etapa foram analisados dados de diferentes HCs com base em amostragens horárias em dois locais diferentes durante, 2016-2017. Na segunda parte do estudo foram analisadas as emissões descontroladas de material particulado (PM) e COVs, que ocorreu entre abril e maio de 2021 no CPC, com base nos dados da estação de qualidade do ar de Capuava (CETESB) e aplicando o modelo de pluma gaussiana -AERMOD (steady-state plume model), estimamos as concentrações de COV e PM10. Finalmente, uma análise dos dados horários de benzeno e tolueno foi fornecida, para seis estações de qualidade do ar (Capuava,

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Determination of radiological background fields designated for inverse modelling during atypical low wind speed meteorological episode

Cited by 6 publications

References 20 publications

Probabilistic Inverse Modeling: An Application in Hydrology

Probabilistic Inverse Modeling: An Application in Hydrology

Thermally and Dynamically Driven Atmospheric Circulations over Heterogeneous Atmospheric Boundary Layer: Support for Safety Protocols and Environment Management at Nuclear Central Areas

Reactive hydrocarbons (C6-C11) in the Metropolitan Area of São Paulo

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