Environmental, health, and safety assessment of chemical alternatives during early process design: The role of predictive modeling and streamlined techniques

Industrial chemicals are important for many aspects of modern life, though they can be harmful to the environment and human health. Environmental or safety concerns identified during the early design and selection of chemicals could motivate choices as to safer alternatives and process setups. There is a growing interest in developing more rapid, and streamlined assessment methods to obtain a first indication of the potential impacts linked to the nature and use of industrial chemicals. This work applies predictive modeling and streamlined techniques to estimate the potential environmental, health, and safety hazards associated with specific chemical structures. The assessment is performed during the design and selection of promising candidates for a particular process as part of the computer-aided molecular design (CAMD) and process setup. The case of phase-change solvents used for post-combustion carbon capture is examined. Furthermore, the refinement of predictive models through the incorporation of knowledge already existing in the field (prior knowledge) is investigated. A procedure for knowledge extraction from scientific articles that applies text mining is proposed. The results show that incorporating impact assessment criteria into the CAMD facilitates the molecular design by enriching the Pareto front of candidates. The use of predictive models that estimate molecular properties, such as acute aquatic toxicity, bioconcentration, and persistency are found to support the identification of the optimal solvents for CO2 capture. Given the role of sustainability-related properties in tasks such as CAMD, the improved performance and the interpretability of the aquatic toxicity predictive models developed here and using prior knowledge are important. The process level assessment of the phase-change solvent systems indicated that phase-change solvent alternatives could provide benefits, not only in terms of reduced energy consumption but also lower impacts on human health and the environment. \ua0However, the degradation behaviors of these compounds should be properly assessed and controlled to ensure beneficial performances compared to conventional carbon capture solvents. Overall, predictive modeling and streamlined life-cycle assessments (LCAs), as well as environmental, health, and safety evaluation methods were revealed to be valuable for defining the critical aspects that influence the potential impacts of chemicals and in supporting decisions concerning the molecular and process designs

A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Exposure to chemicals generally occurs in the form of mixtures. However, the great majority of the toxicity data, upon which chemical safety decisions are based, relate only to single compounds. It is currently unfeasible to test a fully representative proportion of mixtures for potential harmful effects and, as such, in silico modelling provides a practical solution to inform safety assessment. Traditional methodologies for deriving estimations of mixture effects, exemplified by principles such as concentration addition (CA) and independent action (IA), are limited as regards the scope of chemical combinations to which they can reliably be applied. Development of appropriate quantitative structure-activity relationships (QSARs) has been put forward as a solution to the shortcomings present within these techniques – allowing for the potential formulation of versatile predictive tools capable of capturing the activities of a full contingent of possible mixtures. This review addresses the current state-of-the-art as regards application of QSAR towards mixture toxicity, discussing the challenges inherent in the task, whilst considering the strengths and limitations of existing approaches. Forty studies are examined within – through reference to several characteristic elements including the nature of the chemicals and endpoints modelled, the form of descriptors adopted, and the principles behind the statistical techniques employed. Recommendations are in turn provided for practices which may assist in further advancing the field, most notably with regards to ensuring confidence in the acquired predictions.publishedVersio

Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005

There are many national, regional and international programmes – either regulatory or voluntary – to assess the hazards or risks of chemical substances to humans and the environment. The first step in making a hazard assessment of a chemical is to ensure that there is adequate information on each of the endpoints. If adequate information is not available then additional data is needed to complete the dataset for this substance. For reasons of resources and animal welfare, it is important to limit the number of tests that have to be conducted, where this is scientifically justifiable. One approach is to consider closely related chemicals as a group, or chemical category, rather than as individual chemicals. In a category approach, data for chemicals and endpoints that have been already tested are used to estimate the hazard for untested chemicals and endpoints. Categories of chemicals are selected on the basis of similarities in biological activity which is associated with a common underlying mechanism of action. A homologous series of chemicals exhibiting a coherent trend in biological activity can be rationalised on the basis of a constant change in structure. This type of grouping is relatively straightforward. The challenge lies in identifying the relevant chemical structural and physicochemical characteristics that enable more sophisticated groupings to be made on the basis of similarity in biological activity and hence purported mechanism of action. Linking two chemicals together and rationalising their similarity with reference to one or more endpoints has been very much carried out on an ad hoc basis. Even with larger groups, the process and approach is ad hoc and based on expert judgement. There still appears to be very little guidance about the tools and approaches for grouping chemicals systematically. In November 2005, the ECB Workshop on Chemical Similarity and Thresholds of Toxicological Concern (TTC) Approaches was convened to identify the available approaches that currently exist to encode similarity and how these can be used to facilitate the grouping of chemicals. This report aims to capture the main themes that were discussed. In particular, it outlines a number of different approaches that can facilitate the formation of chemical groupings in terms of the context under consideration and the likely information that would be required. Grouping methods were divided into one of four classes – knowledge-based, analogue-based, unsupervised, and supervised. A flowchart was constructed to attempt to capture a possible work flow to highlight where and how these approaches might be best applied.JRC.I.3-Toxicology and chemical substance

Topological Analysis of Void Spaces in Tungstate Frameworks: Assessing Storage Properties for the Environmentally Important Guest Molecules and Ions: CO<inf>2</inf>, UO<inf>2</inf>, PuO<inf>2</inf>, U, Pu, Sr²⁺, Cs⁺, CH<inf>4</inf>, and H<inf>2</inf>

The identification of inorganic materials, which are able to encapsulate environmentally important small molecules or ions via host-guest interactions, is crucial for the design and development of next-generation energy sources and for storing environmental waste. Especially sought after are molecular sponges with the ability to incorporate CO2, gas pollutants, or nuclear waste materials such as UO2 and PuO2 oxides or U, Pu, Sr2+ or Cs+ ions. Porous framework structures promise very attractive prospects for applications in environmental technologies, if they are able to incorporate CH4 for biogas energy applications, or to store H2, which is important for fuel cells e.g. in the automotive industry. All of these applications should benefit from the host being resistant to extreme conditions such as heat, nuclear radiation, rapid gas expansion, or wear and tear from heavy gas cycling. As inorganic tungstates are well known for their thermal stability, and their rigid open-framework networks, the potential of Na2O-Al2O3-WO3 and Na2O-WO3 phases for such applications was evaluated. To this end, all known experimentally-determined crystal structures with the stoichiometric formula MaM’bWcOd (M = any element) are surveyed together with all corresponding theoretically calculated NaaAlbWcOd and NaxWyOz structures that are statistically likely to form. Network descriptors that categorize these host structures are used to reveal topological patterns in the hosts, including the nature of porous cages which are able to accommodate a certain type of guest; this leads to the classification of preferential structure types for a given environmental storage application. Crystal structures of two new tungstates NaAlW2O8 (1) and NaAlW3O11 (2) and one updated structure determination of Na2W2O7 (3) are also presented from in-house X-ray diffraction studies, and their potential merits for environmental applications are assessed against those of this larger data-sourced survey. Overall, results show that tungstate structures with three-nodal topologies are most frequently able to accommodate CH4 or H2, while CO2 appears to be captured by a wide range of nodal structure types. The computationally generated host structures appear systematically smaller than the experimentally determined structures. For the structures of 1 and 2, potential applications in nuclear waste storage seem feasible.J. M. C. is indebted to the Fulbright Commission for a UK-US Fulbright Scholar Award hosted by Argonne National Laboratory where work done was supported by DOE Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acssuschemeng.5b0036