OrganoRelease - A framework for modeling the release of organic chemicals from the use and post-use of consumer products

A review of machine learning applications in life cycle assessment studies

Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.

Non-target screening of volatile organic compounds in spray-type consumer products and their potential health risks

Widespread use of spray-type consumer products can raise significant concerns regarding their effects on indoor air quality and human health. In this study, we conducted non-target screening using gas chromatography-mass spectrometry (GC-MS) to analyze VOCs in 48 different spray-type consumer products. Using this approach, we tentatively identified a total of 254 VOCs from the spray-type products. Notably, more VOCs were detected in propellant-type products which are mostly solvent-based than in trigger-type ones which are mostly water-based. The VOCs identified encompass various chemical classes including alkanes, cycloalkanes, monoterpenoids, carboxylic acid derivatives, and carbonyl compounds, some of which arouse concerns due to their potential health effects. Alkanes and cycloalkanes are frequently detected in propellant-type products, whereas perfumed monoterpenoids are ubiquitous across all product categories. Among the identified VOCs, 12 compounds were classified into high-risk groups according to detection frequency and signal-to-noise (S/N) ratio, and their concentrations were confirmed using reference standards. Among the identified VOCs, D-limonene was the most frequently detected compound (freq. 21/48), with the highest concentration of 1.80 mg/g. The risk assessment was performed to evaluate the potential health risks associated with exposure to these VOCs. The non-carcinogenic and carcinogenic risks associated with the assessed VOC compounds were relatively low. However, it is important not to overlook the risk faced by occupational exposure to these VOCs, and the risk from simultaneous exposure to various VOCs contained in the products. This study serves as a valuable resource for the identification of unknown compounds in the consumer products, facilitating the evaluation of potential health risks to consumers.

Risk assessment of chemicals and their mixtures are hindered by scarcity and inconsistencies between different environmental exposure limits

In chemical risk assessment, measured or modelled environmental concentrations are compared to environmental exposure limits (EELs), such as Predicted No Effect Concentrations (PNECs) or hazardous concentrations for 5% of species (HC05s) derived from species sensitivity distributions (SSDs). However, for many chemicals the EELs include large uncertainties or, in the worst case, the necessary data for their estimation are completely missing. This makes the assessment of chemical risks and any subsequent implementation of management strategies challenging. In this study we analyzed the uncertainty of EELs and its impact on chemical risk assessment. First, we compared three individual EEL datasets, two primarily based on experimental data and one based on computational predictions. The comparison demonstrates large disagreements between EEL data sources, with experimentally derived EELs differing by more than seven orders of magnitude. In a case-study, based on the predicted emissions of 2005 chemicals, we showed that these uncertainties lead to significantly different risk assessment outcomes, including large differences in the magnitude of the total risk, risk driver identification, and the ranking of use categories as risk contributors. We also show that the large data-gaps in EEL datasets cannot be covered by commonly used computational approaches (QSARs). We conclude that an expanded framework for interpreting risk characterization outcomes is needed. We also argue that the large data-gaps present in ecotoxicological data need to be addressed in order to achieve the European zero pollution vision as the growing emphasis on ambient exposures will further increase the demand for accurate and well-established EELs.

The chemical landscape of high-throughput new approach methodologies for exposure

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk

BACKGROUND

Large numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management.

OBJECTIVE

We developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals.

METHODS

We consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts.

RESULTS

Seventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks.

CONCLUSION

Requiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.

An equivalency iterative algorithm for cancer risk assessment of chemical mixtures with additive effects

To better estimate cumulative cancer risks and avoid the overestimated risk from the linear extrapolation, an equivalency iterative algorithm associated with a carcinogenesis hypothesis was introduced for a mixture of chemicals with the same mode of action (MOA). A lognormal dose-response function was applied for carcinogenic chemicals. Under some circumstances, the repetitive random iterative algorithm could be transformed into the nonrepetitive one. It was also demonstrated that the equivalent value for a lognormal-based equivalency iterative algorithm with the same shape parameter was independent of the operation order. Based on the theorems of the algorithm and Plackett and Hewlett's minimum effective dose assumption, the sum of toxicity-weighted dose for a mixture of chemicals was mathematically derived. Compared to the estimation of risk by the linear extrapolation method (e.g., cancer slope factors), the equivalency iterative algorithm for lognormal functions can avoid overestimated risk significantly, which can help better estimate the cumulative cancer risk for a mixture of chemicals with the same MOA.

A theorem on a product of lognormal variables and hybrid models for children's exposure to soil contaminants

This study developed hybrid Bayesian models to investigate the modeling process for children's exposure to soil contaminants, which involves the intrinsic uncertainty of the exposure model, people's judgments regarding random variables, and limited data resources. A hybrid Bayesian p-box was constructed, which was facilitated by a multiple integral dimensionality reduction (MIDR) theorem. The results indicated that exposure frequency (EF) dominated the exposure dose. The hybrid Bayesian p-box for the Frequentist-Bayesian (F-B) model at the 95th percentile of the simulated average daily dose (ADD) values corresponded to a 4.40 order-of-magnitude difference between the upper and lower bounds of the p-box. This considerable uncertainty was magnified by the combination of the highest posterior density (HPD) regions for three groups of the distribution parameters. For the Interior-Bayesian (I-B) hybrid model, the uncertainty of the outcomes, namely, [1.75 × 10^-8, 2.18 × 10^-8] mg kg^-1d^-1, was limited by the HPD regions for only one parameter unless the hyperparameters for the variables' distributions were further evaluated. It was concluded that the hybrid models could provide a novel understanding of the complexity of the exposure modeling process compared to the traditional modeling method.

PBCLM: A top-down causal modeling framework for soil standards and global sustainable agriculture

To help countries worldwide regulate agricultural soil standards for organic contaminants, this study developed the pastoral-based chemical lifecycle management (PBCLM) modeling framework, which comprehensively models the bottom-up causation of the chemicals' lifecycle at each level of the cattle industry and delivers top-down regulatory strategies. The lifecycle models for a total of 308 hydrophobic organic contaminants were constructed. The results indicated that the octanol-water partitioning coefficient (log K_OW) values had the greater impact on the unit-legal-limit-based concentrations for contaminants at the producer level (i.e., grass) or higher. In addition, the analysis of the weather variables indicated that pastoral farming in warmer and drier places might lead to the bioaccumulation of more contaminants. By comparing the reference legal limits that were derived by the PBCLM, current soil standards might not be effective in protecting human health or harmonizing downstream food regulations. The PBCLM can help regulatory agencies better promulgate soil regulations to ensure sustainable agriculture.

Occurrence and Concentration of Chemical Additives in Consumer Products in Korea

As the variety of chemicals used in consumer products (CPs) has increased, concerns about human health risk have grown accordingly. Even though restrictive guidelines and regulations have taken place to minimize the risks, human exposure to these chemicals and their eco-compatibility has remained a matter of greater scientific concern over the years. A major challenge in understanding the reality of the exposure is the lack of available information on the increasing number of ingredients and additives in the products. Even when ingredients of CPs formulations are identified on the product containers, the concentrations of the chemicals are rarely known to consumers. In the present study, an integrated target/suspect/non-target screening procedure using liquid chromatography-high resolution mass spectrometry (LC-HRMS) with stepwise identification workflow was used for the identification of known, suspect, and unknown chemicals in CPs including cosmetics, personal care products, and washing agents. The target screening was applied to identify and quantify isothiazolinones and phthalates. Among analyzed CPs, isothiazolinones and phthalates were found in 47% and in 24% of the samples, respectively. The highest concentrations were 518 mg/kg for benzisothiazolone, 7.1 mg/kg for methylisothiazolinone, 2.0 mg/kg for diethyl phthalate, and 21 mg/kg for dimethyl phthalate. Suspect and non-target analyses yielded six tentatively identified chemicals across the products including benzophenone, ricinine, iodocarb (IPBC), galaxolidone, triethanolamine, and 2-(2H-Benzotriazol-2-yl)-4, 6-bis (1-methyl-1-phenylethyl) phenol. Our results revealed that selected CPs consistently contain chemicals from multiple classes. Excessive use of these chemicals in daily life can increase the risk for human health and the environment.

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.

Advancements in Life Cycle Human Exposure and Toxicity Characterization

BACKGROUND

The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA).

OBJECTIVES

The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose–response modeling.

DISCUSSION

The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose–response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose–response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential.

CONCLUSIONS

Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks. https://doi.org/10.1289/EHP3871.