Inter-Individual Variability and Non-linear Dose-Response Relationship in Assessing Human Health Impact From Chemicals in LCA: Addressing Uncertainties in Exposure and Toxicological Susceptibility

Current life cycle impact assessment (LCIA) practices use a characterization factor to linearly scale chemical emission to human health impact assuming a homogeneous exposure and toxicological susceptibility for the entire population. However, both exposure and toxicological susceptibility may vary within the population, making the same emission elicit disproportionate impacts. Here we explore how inter-individual variabilities in human exposure and toxicological susceptibility interact to affect the estimated overall health impacts on the population level. For exemplification, we use the PROTEX model to simulate the exposure of the general American population to dieldrin and heptachlor, two organochlorine pesticides that tend to accumulate in food items. Using a Monte-Carlo analysis, we characterize inter-individual variabilities in exposure by considering variations in anthropometrics and dietary patterns between ages, sexes, and racial groups. We assess the overall health impact on the population level in five scenarios with different combinations of assumptions in exposure (homogeneous/heterogeneous) and the dose-response relationship (linear/non-linear, homogeneous/heterogeneous susceptibility). Our results indicate human exposure can vary by a factor of six among the different demographic groups. Combined with a non-linear dose-response relationship with heterogeneous susceptibility, the estimated overall health impact is substantially higher than the results using homogeneous susceptibility. However, the current LCIA practice of using a linear dose-response relationship produces even higher results that may overestimate the health impacts.