Abstract
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.
Highlights
Life cycle impact assessment (LCIA) frequently encounters the problem of quantitative characterization of health impact from human exposure to toxic substances
We model the daily oral doses of two lipophilic persistent organic chemicals by ∼330,000 virtual Americans varying in age, sex, and race/ethnicity (NonHispanic White, Non-Hispanic Black, Non-Hispanic Asian, and Hispanic)
The agreement between modeled and observed variations indicates that variabilities in age, sex, and race/ethnicity account to a large extent for inter-individual variabilities in human exposure to the two compounds, the inter-individual variability within each demographic group is not considered in this work
Summary
Life cycle impact assessment (LCIA) frequently encounters the problem of quantitative characterization of health impact from human exposure to toxic substances. The human health characterization approach in the USEtox framework (Rosenbaum et al, 2008) is the most widely utilized Under this approach, several regional factors influencing characterization of the Linear Assumptions May Overestimate Impact human health impact in LCIA such as environmental parameters, population size, and population exposure patterns are considered. The characterization of human exposure to chemicals often builds on anthropometric and exposure factors representative of medians or averages of a population in a region of interest Underlying this practice is an assumption that all individuals across a population share homogeneous exposure and identical heath response to a marginal increase in exposure. The use of linear representation of dose-response relationship and the assumption of homogenous population are largely shared by other LCIA methodologies (European Commision, 2010; Wegener Sleeswijk and Heijungs, 2010; Bare, 2012; Fazio et al, 2018; Bulle et al, 2019) and prevail in the LCIA practice nowadays
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