Limitations of toxicity characterization in life cycle assessment: Can adverse outcome pathways provide a new foundation?

Abstract

Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. Integr Environ Assess Manag 2016;12:580-590. Published 2015. This article is a US Government work and is in the public domain in the USA.

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

“…As can be inferred from Table 2, only one document 17 meets the four search criteria, one document 13 covers three search criteria. Most of the documents [61][62][63][64]68,70,73] cover two search criteria, the most common being the conjunction of criteria I and II. Only two documents [71,73] meet a single search criterion.…”

“…Finally, some applications consist of using artificial intelligence techniques to address specific aspects of the LCA such as sensitivity to certain factors [70], to asses biological effects using a tool quantitative outcome pathway (qAOP) [63], toxicity characterization of chemical emissions [61] or assess normalization factors in LCA methodology [62].…”

“…In the study of the state of the art carried out as part of the process of construction of the proposed conceptual framework, various work approaches have been shown. For example, several of these works [63,66,68,69,71] use the LCA life cycle analysis technique to improve the design. There are also works that focus on the exploitation phase [67,70,72,73], while others focus on the study of the methodology followed in the LCA [61,62,65].…”

Sensitivity-based research prioritization through stochastic characterization modeling

“…However, further development and adoption of current human toxicity and ecotoxicity LCIA models faces challenges related to the large number and diverse properties of relevant emitted substances, limited availability of high quality data, and sparse treatment of parameter uncertainty or variability (Alfonsín et al 2014;Gust et al 2015;Rosenbaum 2015). For example, there is a large discrepancy between the ≈ 10,000 substances included in the latest Ecoinvent inventory library (Weidema 2013) and the ≈ 1,200 human toxicity and 2,500 ecotoxicity CFs available from the recent USEtox 2.0 update (http://usetox.org).…”

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals

“…Clearly, relating results from laboratory toxicity tests to ecosystem-level consequences highlights the magnitude of the challenge for the ecological risk assessor. Although significant scientific advances are occurring in predictive ecotoxicology, such as the development of mechanistic effects models [4] and quantitative adverse outcome pathways (AOPs) [5], the principal challenge still lies in linking organismal (or suborganismal) data to ecosystem properties and processes that people care about.Part of the problem is that ecological systems are complex and difficult to study. Ecosystems and their components (populations, communities) display nonlinear dynamics that can change in time and space.…”