Regionalização no contexto brasileiro de modelos de avaliação de impacto do ciclo de vida para a categoria de formação de material particulado

• Ano de defesa: 2021
• Autor(a) principal: Giusti, Gabriela
• Orientador(a): Silva, Diogo Aparecido Lopes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus Sorocaba
• Programa de Pós-Graduação: Programa de Pós-Graduação em Planejamento e Uso de Recursos Renováveis - PPGPUR-So
• Palavras-chave em Português: Poluição atmosférica; Modelo de caracterização; Fator de inalação; Fator de Efeito; Regionalização na ACV; Atmospheric pollution; Characterization model; Intake factor; Effect factor; Regionalization
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA SANITARIA::SANEAMENTO AMBIENTAL::QUALIDADE DO AR, DAS AGUAS E DO SOLO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/14044

Particulate matter (PM) is an air pollutant emitted by anthropic activities directly associated with human health's impacts. One way to estimate the environmental impacts of PM is through Life Cycle Assessment (LCA) studies, which through characterization models associate an elementary flow to an impact category using characterization factors (FCs). However, the main current models for estimating CFs in LCA do not fully address Brazil's heterogeneity. Thus, the main objective of this work was to develop a model recommendation and a partial regionalization of CFs for the PM formation category in Brazil's context. For this, the state of the art on the subject was studied with a systematic bibliographic review (SBR) of the literature, and the available characterization models were identified. The models were critically evaluated and the most suitable ones for application in Brazil were recommended based on the methodology of the Life Cycle Impact Assessment Network (RAICV) and multicriteria decision analysis. Subsequently, models that could be regionalized in the national context were identified, and the models by Fantke et al. (2017, 2019) were selected for the development of a partial regionalization by changing its calculation variables: urban, rural, and total population; urban and rural land area; precipitation, and; mortality due to health effects. As a result, the SBR collected 210 documents. The exploratory search identified 16 characterization models and the recommendation analysis indicated the models by Fantke et al. (2017), Fantke et al. (2019), UNEP and SETAC (2016), and Van Zelm et al. (2016) as the most suitable for application in Brazil. However, it was found that these would still need to be regionalized for better use in the national context. The partial regionalization of Fantke et al. (2017, 2019)’ models resulted in a significant percentage variation of the CFs values concerning the original data. In general, significant reductions in the new calculated and regionalized CFs were observed concerning the originals, leading to the conclusion that the original data of the model possibly generate an overestimation of the impacts associated with the emission of PM in Brazil. A case study evidenced this observation by showing a 50% reduction in the impact of damage to human health by forming PM in a Brazilian production system for wood panels production. It is concluded with the partial regionalization that the data currently available for Brazil, despite having an important territorial variability, still present uncertainties for the country and are available in an uneven way between the regions (only 126 municipalities of the 5,570 existing ones), being necessary continuity of the regionalization procedure for this category. Also, CFs for secondary PM with the same spatial quality and easy access at the level of cities and archetypes are still a demand in the area, less explored in the literature. It is therefore recommended that future studies aim to continue this regionalization of CFs for the formation of PM in the country.