Purpose The aim of this paper is to provide science-based consensus and guidance for health effects modelling in comparative assessments based on human exposure and toxicity. This aim is achieved by (a) describing the USEtox™ exposure and toxicity models representing consensus and recommended modelling practice, (b) identifying key mechanisms influencing human exposure and toxicity effects of chemical emissions, (c) extending substance coverage. Methods The methods section of this paper contains a detailed documentation of both the human exposure and toxic effects models of USEtox™, to determine impacts on human health per kilogram substance emitted in different compartments. These are considered as scientific consensus and therefore recommended practice for comparative toxic impact assessment. The framework of the exposure model is described in details including the modelling of each exposure pathway considered (i.e. inhalation through air, ingestion through (a) drinking water, (b) agricultural produce, (c) meat and milk, and (d) fish). The calculation of human health effect factors for cancer and non-cancer effects via ingestion and inhalation exposure respectively is described. This section also includes discussions regarding parameterisation and estimation of input data needed, including route-to-route and acute-to-chronic extrapolations. Results and discussion For most chemicals in USEtox™, inhalation, above-ground agricultural produce, and fish are the important exposure pathways with key driving factors being Electronic supplementary material The online version of this article (
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Particulate matter (PM) is a significant contributor to death and disease globally. This paper summarizes the work of an international expert group on the integration of human exposure to PM into life cycle impact assessment (LCIA), within the UNEP/SETAC Life Cycle Initiative. We review literature-derived intake fraction values (the fraction of emissions that are inhaled), based on emission release height and "archetypal" environment (indoor versus outdoor; urban, rural, or remote locations). Recommended intake fraction values are provided for primary PM(10-2.5) (coarse particles), primary PM(2.5) (fine particles), and secondary PM(2.5) from SO(2), NO(x), and NH(3). Intake fraction values vary by orders of magnitude among conditions considered. For outdoor primary PM(2.5), representative intake fraction values (units: milligrams inhaled per kilogram emitted) for urban, rural, and remote areas, respectively, are 44, 3.8, and 0.1 for ground-level emissions, versus 26, 2.6, and 0.1 for an emission-weighted stack height. For outdoor secondary PM, source location and source characteristics typically have only a minor influence on the magnitude of the intake fraction (exception: intake fraction values can be an order of magnitude lower for remote-location emission than for other locations). Outdoor secondary PM(2.5) intake fractions averaged over respective locations and stack heights are 0.89 (from SO(2)), 0.18 (NO(x)), and 1.7 (NH(3)). Estimated average intake fractions are greater for primary PM(10-2.5) than for primary PM(2.5) (21 versus 15), owing in part to differences in average emission height (lower, and therefore closer to people, for PM(10-2.5) than PM(2.5)). For indoor emissions, typical intake fraction values are ∼1000-7000. This paper aims to provide as complete and consistent an archetype framework as possible, given current understanding of each pollutant. Values presented here facilitate incorporating regional impacts into LCIA for human health damage from PM.
This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
Background, aim, and scope Uncertainty information is essential for the proper use of life cycle assessment (LCA) and environmental assessments in decision making. So far, parameter uncertainty propagation has mainly been studied using Monte Carlo techniques that are relatively computationally heavy to conduct, especially for the comparison of multiple scenarios, often limiting its use to research or to inventory only. Furthermore, Monte Carlo simulations do not automatically assess the sensitivity and contribution to overall uncertainty of individual parameters. The present paper aims to develop and apply to both inventory and impact assessment an explicit and transparent analytical approach to uncertainty. This approach applies Taylor series expansions to the uncertainty propagation of lognormally distributed parameters. Materials and methods We first apply the Taylor series expansion method to analyze the uncertainty propagation of a single scenario, in which case the squared geometric standard deviation of the final output is determined as a function of the model sensitivity to each input parameter and the squared geometric standard deviation of each parameter. We then extend this approach to the comparison of two or more LCA scenarios. Since in LCA it is crucial to account for both common inventory processes and common impact assessment characterization factors among the different scenarios, we further develop the approach to address this dependency. We provide a method to easily determine a range and a best estimate of (a) the squared geometric standard deviation on the ratio of the two scenario scores, "A/B", and (b) the degree of confidence in the prediction that the impact of scenario A is lower than B (i.e., the probability that A/B<1). The approach is tested on an automobile case study and resulting probability distributions of climate change impacts are compared to classical Monte Carlo distributions. Results The probability distributions obtained with the Taylor series expansion lead to results similar to the classical Monte Carlo distributions, while being substantially simpler; the Taylor series method tends to underestimate the 2.5% confidence limit by 1-11% and the 97.5% limit by less than 5%. The analytical Taylor series expansion easily provides the explicit contributions of each parameter to the overall uncertainty. For the steel front end panel, the factor contributing most to the climate change score uncertainty is the gasoline consumption (>75%). For the aluminum panel, the electricity and aluminum primary production, as well as the light oil consumption, are the dominant contributors to the uncertainty. The developed Responsible Editor: Andreas Ciroth Electronic supplementary material The online version of this article (
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