Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

Micro-environmental factors impact breathing zone exposures: A simulated petrochemical manufacturing facility task

This study investigates the impact of micro-environmental factors on worker breathing zone exposure levels in petrochemical facilities. A laboratory simulation study evaluated near-field exposure to methane for a typical maintenance task. Individual and combinations of micro-environmental factors significantly affected methane exposure. Airflow direction and speed were significant determinants of exposure concentration reduction. A side airflow direction at medium to high speed produced the lowest gas concentration in the breathing zone. Worker body orientation relative to the methane emission point was also a critical factor affecting gas concentration in the worker's breathing zone. The study provides insights into how variations in airflow and small changes in position impact near-field exposures for petrochemical tasks, guiding industrial hygiene professionals' training on qualitative exposure estimation and providing input for near-field exposure modeling to guide quantitative exposure and risk assessment.

Review of generic scenario environmental release and occupational exposure models used in chemical risk assessment

Under the Toxic Substances Control Act (TSCA), the United States Environmental Protection Agency (USEPA) is required to determine whether a new chemical substance poses an unreasonable risk to human health or the environment before the chemical is manufactured in or imported into the United States. This manuscript provides a review of the process used to evaluate the risk associated with a chemical based on the scenarios and models used in the evaluation. Specifically, the Generic Scenarios and Emission Scenario Documents developed by the USEPA were reviewed, along with background documentation prepared by USEPA to identify the core elements of the environmental release and occupational exposure scenarios used to assess the risk of the chemical being evaluated. Additionally, this contribution provides an overview of methods used to model occupational exposures and environmental releases as part of the chemical evaluation process used in other jurisdictions, along with work being performed to improve these models. Finally, the alternative methods to evaluate occupational exposures and environmental releases that may be used as part of the decision-making process regarding a chemical are identified. The contribution provides a path forward for reducing the time required and improving the chemical evaluation of the unreasonable risk determination regarding the manufacture or import of a chemical.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

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.

The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk

Exposure to chemicals is influenced by associations between the individual's location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual's community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.

Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis

With the increasing availability of high-resolution mass spectrometers, suspect screening and non-targeted analysis are becoming popular compound identification tools for environmental researchers. Samples of interest often contain a large (unknown) number of chemicals spanning the detectable mass range of the instrument. In an effort to separate these chemicals prior to injection into the mass spectrometer, a chromatography method is often utilized. There are numerous types of gas and liquid chromatographs that can be coupled to commercially available mass spectrometers. Depending on the type of instrument used for analysis, the researcher is likely to observe a different subset of compounds based on the amenability of those chemicals to the selected experimental techniques and equipment. It would be advantageous if this subset of chemicals could be predicted prior to conducting the experiment, in order to minimize potential false-positive and false-negative identifications. In this work, we utilize experimental datasets to predict the amenability of chemical compounds to detection with liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). The assembled dataset totals 5517 unique chemicals either explicitly detected or not detected with LC-ESI-MS. The resulting detected/not-detected matrix has been modeled using specific molecular descriptors to predict which chemicals are amenable to LC-ESI-MS, and to which form(s) of ionization. Random forest models, including a measure of the applicability domain of the model for both positive and negative modes of the electrospray ionization source, were successfully developed. The outcome of this work will help to inform future suspect screening and non-targeted analyses of chemicals by better defining the potential LC-ESI-MS detectable chemical landscape of interest.

Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data

This study proposes methods to improve data mining workflows for modeling chemical manufacturing life cycle inventory. Secondary data sources can provide valuable information about environmental releases during chemical manufacturing. However, the often facility-level nature of the data challenges their utility for modeling specific processes and can impact the quality of the resulting inventory. First, a thorough data source analysis is performed to establish data quality scoring and create filtering rules to resolve data selection issues when source and species overlaps arise. A method is then introduced to develop context-based filter rules that leverage process metadata within data sources to improve how facility air releases are attributed to specific processes and increase the technological correlation and completeness of the inventory. Finally, a sanitization method is demonstrated to improve data quality by minimizing the exclusion of confidential business information (CBI). The viability of the methods is explored using case studies of cumene and sodium hydroxide production in the United States. The attribution of air releases using process context enables more sophisticated filtering to remove unnecessary flows from the inventory. The ability to sanitize and incorporate CBI is promising because it increases the sample size, and therefore representativeness, when constructing geographically averaged inventories. Future work will focus on expanding the application of context-based data filtering to other types and sources of environmental data.

Enhancing life cycle chemical exposure assessment through ontology modeling

In its 2014 report, A Framework Guide for the Selection of Chemical Alternatives, the National Academy of Sciences placed increased emphasis on comparative exposure assessment throughout the life cycle (i.e., from manufacturing to end-of-life) of a chemical. The inclusion of the full life cycle greatly increases the data demands for exposure assessments, including both the quantity and type of data. High throughput tools for exposure estimation add to this challenge by requiring rapid accessibility to data. In this work, ontology modeling was used to bridge the domains of exposure modeling and life cycle inventory modeling to facilitate data sharing and integration. The exposure ontology, ExO, is extended to describe human exposure to consumer products, while an inventory modeling ontology, LciO, is formulated to support automated data mining. The core ontology pieces are connected using a bridging ontology and discussed through a theoretical example to demonstrate how data from LCA can be leveraged to support rapid exposure modeling within a life cycle context.

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

Use of Bio-Based Plastics in the Fruit Supply Chain: An Integrated Approach to Assess Environmental, Economic, and Social Sustainability

The challenge of developing a sustainable production system includes the reduction of emissions, the efficient use of resources, and the transition to renewable energy. The bioeconomy proposes a development model aimed at reducing impacts and risks associated with the use of non-renewable resources considering the life cycle of products. The European Union is promoting products from renewable sources focused on biochemicals and bio-based plastics, which are high added value products when compared to biofuels. The aim of this paper is to consider sustainability in terms of the environmental, economic, and social aspects of use of bio-based plastics in the fruit chain, considering the case study of raspberry supply chains in northwestern Italy. Different analyses (life-cycle assessment (LCA), life-cycle costing (LCC), and externality assessment (ExA)) were used to assess the impacts along the whole chain by means of an integrated approach. The results show that the bio-based plastic scenario has lower environmental and social impacts than the conventional one, whereas the latter is the best choice according to a classic economic approach. The introduction of bio-based plastics as a replacement for traditional plastics in agri-food chains is the first step toward the use of renewable resources with a low impact on society.

Linking Molecular Structure via Functional Group to Chemical Literature for Establishing a Reaction Lineage for Application to Alternatives Assessment

The evaluation of potential alternatives for chemicals of concern (CoC) requires an understanding of their potential human health and environmental impacts during the manufacture, use, recycle and disposal life stages. During the manufacturing phase, the processes used to produce a desired chemical are defined based on the sequence of chemical reactions and unit operations required to produce the molecule and separate it from other materials used or produced during its manufacture. This paper introduces and demonstrates a tool that links a chemical's structure to information about its synthesis route and the manufacturing process for that chemical. The structure of the chemical is entered using either a SMILES string or the molecule MOL file, and the molecule is searched to identify functional groups present. Based on those functional groups present, the respective named reactions that can be used in its synthesis routes are identified. This information can be used to identify input and output materials for each named reaction, along with reaction conditions, solvents, and catalysts that participate in the reaction. Additionally, the reaction database contains links to internet references and appropriate reaction-specific keywords, further increasing its comprehensiveness. The tool is designed to facilitate the cataloging and use of the chemical literature in a way that allows user to identify and evaluate information about the reactions, such as alternative solvents, catalysts, reaction conditions and other reaction products which enable the comparison of various reaction pathways for the manufacture of the subject chemical. The chemical manufacturing processing steps can be linked to a chemical process ontology to estimate releases and exposures occurring during the manufacturing phase of a chemical.

Health risks of chemicals in consumer products: A review

Increasingly diverse chemicals are used in consumer products, while our understanding of their exposure pathways and associated human health risks still lags behind. This paper aims to identify the dominant patterns of exposure pathways and associated health risks of chemicals used in consumer products reported in the peer-reviewed literature. We analyzed 342 articles covering 202 unique chemicals, and distilled the information on the functional uses, product applications, exposure routes, exposure pathways, toxicity endpoints and their combinations. We found that the volume of the literature addressing human health risks of chemicals in consumer products is increasing. Among others, phthalates, bisphenol-A, and polybrominated diphenyl ethers were the most frequently discussed chemical groups in the literature reviewed. Emerged from our review were a number of frequently reported functional use/product application combinations, including plasticizers, polymers/monomers, and flame retardants used in food contact products, personal care products, cosmetics, furniture, flooring, and electronics. We also observed a strong tendency that the number of publications on a chemical surges following major regulatory changes or exposure incidents associated with the chemical. We highlight the need to develop the capacity and the mechanism through which human health risks of chemicals in consumer products can be identified prior to their releases.

Applying Environmental Release Inventories and Indicators to the Evaluation of Chemical Manufacturing Processes in Early Stage Development

As manufacturing processes are developed through the early stages of technology readiness, various assessments can be used to evaluate their performance. Performance indicators describe processes by transforming attributes into scores that represent desirable objectives. One type of assessment is obtained by determining the life cycle inventories of inputs and outputs for processes. For a functional unit of product, the user finds the resources used and the releases to the environment, which can be compared to results for similar processes and/or combined with other processes in the life cycle. In this work, an expanded range of process inputs and releases is modeled, including forklift/loader, fugitive, storage, boiler, and cooling tower emissions. A generic scenario approach for the cooling tower releases provides a first approximation of emission and wastewater flows. These inventory values are used in performance indicators that can be placed on a scale between fixed best- and worst-case limits with the GREENSCOPE methodology, thus allowing comparisons across various technologies. The processes of interest are two conversion pathways for producing cellulosic ethanol from biomass via thermochemical and biochemical routes. The results can be used in risk assessments, decision making, evaluation of research, and in spurring future technology development.

A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources

Exposure- and risk-based assessments for chemicals used indoors or applied to humans (i.e., in near-field environments) necessitate an aggregate exposure pathway framework that aligns chemical exposure information from use sources to internal dose and eventually to their potential for health effects. Such a source-to-effect continuum is advocated to balance the complexity of human exposure and the insufficiency of relevant data for thousands of existing and emerging chemicals. Here, we introduce the Risk Assessment, IDentification And Ranking-Indoor and Consumer Exposure (RAIDAR-ICE) model, which establishes an integrated framework to evaluate human exposure due to indoor use and direct application of chemicals to humans. As a model evaluation, RAIDAR-ICE faithfully reproduces exposure estimates inferred from biomonitoring data for 37 chemicals with direct and indirect near-field sources. RAIDAR-ICE generates different rankings for 131 chemicals based on different exposure- and risk-based assessment metrics, demonstrating its versatility for diverse chemical screening goals. When coupled with a far-field RAIDAR model, the near-field RAIDAR-ICE model enables assessment of aggregate human exposure. Overall, RAIDAR-ICE is a powerful tool for high-throughput screening and prioritization of human exposure to neutral organic chemicals used indoors.

Revisiting the Contributions of Far- and Near-Field Routes to Aggregate Human Exposure to Polychlorinated Biphenyls (PCBs)

The general population is exposed to polychlorinated biphenyls (PCBs) by consuming food from far-field contaminated agricultural and aquatic environments, and inhalation and nondietary ingestion in near-field indoor or residential environments. Here, we seek to evaluate the relative importance of far- and near-field routes by simulating the time-variant aggregate exposure of Swedish females to PCB congeners from 1930 to 2030. We rely on a mechanistic model, which integrates a food-chain bioaccumulation module and a human toxicokinetic module with dynamic substance flow analysis and nested indoor-urban-rural environmental fate modeling. Confidence in the model is established by successfully reproducing the observed PCB concentrations in Swedish human milk between 1972 and 2016. In general, far-field routes contribute most to total PCB uptake. However, near-field exposure is notable for (i) children and teenagers, who have frequent hand-to-mouth contact, (ii) cohorts born in earlier years, e.g., in 1956, when indoor environments were severely contaminated, and (iii) lighter chlorinated congeners. The relative importance of far- and near-field exposure in a cross-section of individuals of different age sampled at the same time is shown to depend on the time of sampling. The transition from the dominance of near- to far-field exposure that has happened for PCBs may also occur for other chemicals used indoors.

Comparative, collaborative, and integrative risk governance for emerging technologies

Various emerging technologies challenge existing governance processes to identify, assess, and manage risk. Though the existing risk-based paradigm has been essential for assessment of many chemical, biological, radiological, and nuclear technologies, a complementary approach may be warranted for the early-stage assessment and management challenges of high uncertainty technologies ranging from nanotechnology to synthetic biology to artificial intelligence, among many others. This paper argues for a risk governance approach that integrates quantitative experimental information alongside qualitative expert insight to characterize and balance the risks, benefits, costs, and societal implications of emerging technologies. Various articles in scholarly literature have highlighted differing points of how to address technological uncertainty, and this article builds upon such knowledge to explain how an emerging technology risk governance process should be driven by a multi-stakeholder effort, incorporate various disparate sources of information, review various endpoints and outcomes, and comparatively assess emerging technology performance against existing conventional products in a given application area. At least in the early stages of development when quantitative data for risk assessment remain incomplete or limited, such an approach can be valuable for policymakers and decision makers to evaluate the impact that such technologies may have upon human and environmental health.