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

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Towards a comprehensive sustainability methodology to assess anthropogenic impacts on ecosystems: Review of the integration of Life Cycle Assessment, Environmental Risk Assessment and Ecosystem Services Assessment

Nowadays, a variety of methodologies are available to assess local, regional and global impacts of human activities on ecosystems, which include Life Cycle Assessment (LCA), Environmental Risk Assessment (ERA) and Ecosystem Services Assessment (ESA). However, none can individually assess both the positive and negative impacts of human activities at different geographical scales in a comprehensive manner. In order to overcome the shortcomings of each methodology and develop more holistic assessments, the integration of these methodologies is essential. Several studies have attempted to integrate these methodologies either conceptually or through applied case studies. To understand why, how and to what extent these methodologies have been integrated, a total of 110 relevant publications were reviewed. The analysis of the case studies showed that the integration can occur at different positions along the cause-effect chain and from this, a classification scheme was proposed to characterize the different integration approaches. Three categories of integration are distinguished: post-analysis, integration through the combination of results, and integration through the complementation of a driving method. The literature review highlights that the most recurrent type of integration is the latter. While the integration through the complementation of a driving method is more realistic and accurate compared to the other two categories, its development is more complex and a higher data requirement could be needed. In addition to this, there is always the risk of double-counting for all the approaches. None of the integration approaches can be categorized as a full integration, but this is not necessarily needed to have a comprehensive assessment. The most essential aspect is to select the appropriate components from each methodology that can cover both the environmental and socioeconomic costs and benefits of human activities on the ecosystems.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models.

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.

Comparative Toxicological Evaluation of UV-Degraded versus Parent-Insensitive Munition Compound 1-Methyl-3-Nitroguanidine in Fathead Minnow

The US Army is replacing traditional munitions with insensitive munitions resistant to accidental detonation. Although the parent insensitive munition compound nitroguanidine (NQ) is generally not acutely toxic at concentrations >1000 mg/L in aquatic exposures, products formed by intensive ultraviolet (UV) degradation resulted in multiple-order of magnitude increases in toxicity. A methylated congener of NQ, 1-methyl-3-nitroguanidine (MeNQ), is also being assessed for potential use in insensitive munition explosive formulations; therefore, the present study investigated the hazard of parent versus UV-degraded MeNQ using fathead minnows (Pimephales promelas). Although up to 716 mg/L parent MeNQ caused no significant mortality or effects on growth in larval P. promelas fish in 7-d exposures, a similar concentration of MeNQ subjected to UV treatment resulted in 85% mortality. The UV treatment degraded only 3.3% of the MeNQ (5800 mg/L stock, UV-treated for 6 h), indicating that MeNQ degradation products have potentially high toxicity. The parent MeNQ exposure caused significantly decreased transcriptional expression of genes within the significantly enriched insulin metabolic pathway, suggesting antagonism of bioenergetics pathways, which complements observed, although nonsignificant, decreases in body weight. Significant differential transcriptional expression in the UV-degraded MeNQ treatments resulted in significant enrichment of pathways and functions related to the cell cycle, as well as erythrocyte function involved in O₂ /CO₂ exchange. These functions represent potential mechanistic sources of increased toxicity observed in the UV-degraded MeNQ exposures, which are distinct from previously observed mechanisms underlying increased toxicity of UV-degraded NQ in fish. Environ Toxicol Chem 2020;39:612-622. © 2019 SETAC.

A framework for sustainable nanomaterial selection and design based on performance, hazard, and economic considerations

Engineered nanomaterials (ENMs) and ENM-enabled products have emerged as potentially high-performance replacements to conventional materials and chemicals. As such, there is an urgent need to incorporate environmental and human health objectives into ENM selection and design processes. Here, an adapted framework based on the Ashby material selection strategy is presented as an enhanced selection and design process, which includes functional performance as well as environmental and human health considerations. The utility of this framework is demonstrated through two case studies, the design and selection of antimicrobial substances and conductive polymers, including ENMs, ENM-enabled products and their alternatives. Further, these case studies consider both the comparative efficacy and impacts at two scales: (i) a broad scale, where chemical/material classes are readily compared for primary decision-making, and (ii) within a chemical/material class, where physicochemical properties are manipulated to tailor the desired performance and environmental impact profile. Development and implementation of this framework can inform decision-making for the implementation of ENMs to facilitate promising applications and prevent unintended consequences.

The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology

The adverse outcome pathway (AOP) framework serves as a knowledge assembly, interpretation, and communication tool designed to support the translation of pathway-specific mechanistic data into responses relevant to assessing and managing risks of chemicals to human health and the environment. As such, AOPs facilitate the use of data streams often not employed by risk assessors, including information from in silico models, in vitro assays and short-term in vivo tests with molecular/biochemical endpoints. This translational capability can increase the capacity and efficiency of safety assessments both for single chemicals and chemical mixtures. Our mini-review describes the conceptual basis of the AOP framework and aspects of its current status relative to use by toxicologists and risk assessors, including four illustrative applications of the framework to diverse assessment scenarios.

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

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. Environ Toxicol Chem 2017;36:845-859. © 2017 SETAC.