| Abstrakt: |
Purpose: Milk is an important agricultural product due to its ubiquity in the global economy. Brazil is considered one of the largest milk producers in the world. However, milk production systems generate significant emissions of particulate matter (PM), and life cycle assessment (LCA) can be used to manage their environmental impacts. Thus, the aim of this research was to analyze the human health impacts of PM from milk production systems in Brazil and the sensitivity of the LCA results with the application of different characterization models. Methods: Four milk production systems were analyzed from cradle-to-farm gate, considering 1 kg of fat and protein-corrected milk as a functional unit. The life cycle impact assessment considered five characterization models: Oberschelp et al. (2020), Fantke et al. (2017, 2019), Van Zelm et al. (2016), Tang et al. (2018), and Frischknecht and Jolliet (2016), exploring their levels of regionalization (global, national, and regional factors). Spearman's correlation analysis was applied to verify the consistency of the milk systems' ranking according to the characterization model selection. Results and discussion: Impact results showed high variance due to differences in the inventories, variations in the geographical scopes, list of elementary flows covered by the models, and differences in the characterization factors. Correlation analysis showed lower Spearman's coefficients for Oberschelp et al. (2020) at the state level and Van Zelm et al. (2016) at the national level, indicating that the regionalized characterization factors present higher variation in the milk systems' ranking. Conclusion: Given that the recommendation for this category is the use of regionalized factors, this research concluded that Oberschelp et al. (2020) model was the most suitable for use in the Brazilian milk production systems. For future research, it is recommended similar studies for other impact categories that require regionalized factors and the development of characterization factors by archetypes for primary and secondary substances. [ABSTRACT FROM AUTHOR] |
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