Health effects of particulate matter formation in Life Cycle Impact Assessment: critical review and recommendation of models for Brazil

Purpose The impact of particulate matter (PM) formation on human health in Life Cycle Impact Assessment (LCIA) can be characterized through combining intake fractions with effect factors. However, although there are several characterization models used in different LCIA methods, most of them were not developed for the Brazilian context. This study aims to evaluate the existing models used in LCIA for characterizing PM impacts on human health with respect to their applicability for Brazil. Methods We review existing PM characterization models followed by a critical analysis regarding each model's scope, scientific robustness, and applicability for Brazil, by applying the simple multi-attribute rating technique (SMART). Results and discussion The results show that the most recent models, with global coverage and availability of characterization factors (CFs) for Brazil, are the most appropriate. In addition, the highest-scoring models are able to connect with a wide range of goals and scopes that can be defined in Life Cycle Assessment case studies. Although the models of van Zelm et al. (2016), Fantke et al. (2017, 2019), and Oberschelp et al. (2020) are recommended for Brazil, important limitations have been observed. The van Zelm et al. (2016) model provides only country-level CFs and is not further regionalized. The Fantke et al. (2017, 2019) model does not currently provide CFs for PM precursors and is for urban factors limited to cities with more than 100,000 inhabitants. The Oberschelp et al. (2020) model does not currently provide data for indoor environments and uses background concentration data mainly applicable to the Northern Hemisphere. Conclusion Several models for evaluating PM impacts in LCIA are already available. Considering the identified limitations within these models, future work should aim to address these limitations and derive improved and more representative CFs for Brazil.