Identificação de origem das fontes de Partículas Sedimentadas por meio do acoplamento de Modelos de Dispersão e Receptor

• Ano de defesa: 2023
• Autor(a) principal: Schmidt, Isamara Maria
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Engenharia Ambiental; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Ambiental
• Palavras-chave em Português: Partículas Sedimentadas; Modelos de Dispersão e Receptor; Engenharia Sanitária
• Link de acesso: http://repositorio.ufes.br/handle/10/12413

The identification of origin and quantitative distribution of PM and its sources is an important step in providing information that guides the development of air quality management strategies. This Master's dissertation aimed to investigate the coupled use of the CALPUFF atmospheric dispersion model with the CMB receptor model in order to minimize or eliminate the effects of source collinearity on the results provided by the receptor model. The study by Santos et al. (2017) was utilized, in which the authors identified contributing sources to the results of sedimented particle deposition at eight receptor points in the RGV during the period from April 2009 to March 2011, with the need to group some sources to meet the CMB model parameters. This resulted in two source groups: coal and steel. To overcome this limitation, the CALPUFF dispersion model was used to simulate the dry deposition of PM in the RGV. Then, using the CMB output data along with the CALPUFF simulations, a methodology for integration between the two models was developed, allowing for the specific contribution of each inventory source to be identified and enabling the separation of sources previously grouped by the CMB. As a result, it was found that the sources of ore, Vale furnaces, and Vale pellets had a significant impact on dry deposition for the steel group, while for the coal group, emissions related to coal transport and storage, mainly from the Vale company, had a greater impact. It is worth noting that these findings have significant implications for air quality management in the RGV, providing a solid foundation for decision-making aimed at protecting public health and the environment. Finally, it should be emphasized that this work paves the way for further investigations to further refine the coupling methodology between these models.