Regionalização exploratória do fator de efeito para a categoria de impacto formação de material particulado na AICV

Abstract

The goal of Life Cycle Assessment (LCA) is to analyze a process or product life cycle and to quantify the impacts caused in each cycle step. This instrument depends on four steps, being the Life Cycle Impact Assessment (LCIA) the one considered in this work. LCIA relates the Life Cycle Inventory step with impact categories by characterization models. These models aim to calculate a Characterization Factor (CF), which will measure the impact level to the considered impact categories in an LCA study. Most of these models for particulate matter formation impact category had oriented development studies to countries with different social and geographic characteristics from Brazil and, rarely take into account brazilian context information in its modeling. This work’s significance includes the scientific advance and the uncertainty decrease in LCIA results for Brazilian reality. The CF to the category approached is calculated by multiplying the Intake Factor (IF) and the Effect Factor (EF). In this regard, this work goal is to perform an exploratory regionalization, considering São Paulo state, to the effect factor of Fantke et al. (2019) model. To do so, a detailed analysis of the calculus evolved in Fantke et al. (2019) model, including a few variables sensibility analysis, with some cities and regions in Brazil, and an exploratory regionalization, exclusivily for citis in São Paulo State, with real Brazilian organizations values, as a sequence of Giusti (2021) exploratory regionalization. In the end, the results of this work were compared to the original model (Fantke et al., 2019) and Giusti (2021), in order to analyze the variables’ impact on the final results. When comparing Giusti (2021) and Fantke et al. (2019), it was obtained -80,00% as the biggest percentage variance, in São José dos Campos-SP city, while the comparison with the present work and Giusti (2021), the most percentage variance was -23,10%, in São Jose do Rio Preto-SP, and with the presente work and Fantke et al. (2019), it was -77,20% the biggest percentage variance, in Campinas-SP. This work results indicate the importance of conducting studies with a database closer to reality to make the LCIA researches more applicable and more reliable in Brazil since the differences in the results after the variables change are significant.