A Novel Approach Using Hierarchical Clustering To Select Industrial Chemicals for Environmental Impact Assessment

Chemical hazard assessment toward safer electrolytes for lithium-ion batteries

Commercialization of rechargeable lithium-ion (Li-ion) batteries has revolutionized the design of portable electronic devices and is facilitating the current transition to electric vehicles. The technological specifications of Li-ion batteries continue to evolve through the introduction of various high-risk liquid electrolyte chemicals, yet critical evaluation of the physical, environmental, and human health hazards of these substances is lacking. Using the GreenScreen for Safer Chemicals approach, we conducted a chemical hazard assessment (CHA) of 103 electrolyte chemicals categorized into seven chemical groups: salts, carbonates, esters, ethers, sulfoxides-sulfites-sulfones, overcharge protection additives, and flame-retardant additives. To minimize data gaps, we focused on six toxicity and hazard data sources, including three empirical and three nonempirical predictive data sources. Furthermore, we investigated the structural similarities among selected electrolyte chemicals using the ChemMine tool and the simplified molecular input line entry system inputs from PubChem to evaluate whether chemicals with similar structures exhibit similar toxicity. The results demonstrate that salts, overcharge protection additives, and flame-retardant additives contain the most toxic components in the electrolyte solutions. Furthermore, carbonates, esters, and ethers account for most flammability hazards in Li-ion batteries. This study supports the complementary use of quantitative structure-activity relationship models to minimize data gaps and inconsistencies in CHA. Integr Environ Assess Manag 2024;00:1-14. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds

An efficient search for optimal solutions in Bayesian optimization (BO) entails providing appropriate initial samples when building a Gaussian process regression model. For general experimental designs without compounds or molecular descriptors in explanatory variable x, selecting initial samples with a larger D-optimality allows little correlation between x in the selected samples, which leads to effective regression model building. However, in the case of experimental designs with compounds, a high correlation always exists between molecular descriptors calculated from chemical structures, and compounds with similar structures form clusters in the chemical space. Therefore, selecting the initial samples uniformly from each cluster is desirable for obtaining initial samples with maximum information on experimental conditions. As D-optimality does not work well with highly correlated molecular descriptors and does not consider information on clusters in sample selection, we propose an initial sample selection method based on clustering and apply it to the optimization of coupling reaction conditions with BO. We confirm that the proposed method reaches the optimal solution with up to 5% fewer experiments than random sampling or sampling based on D-optimality. This study makes a contribution to the initial sample selection method for BO, and we are convinced that the proposed method improves the search performance of BO in various fields of science and technology if initial samples can be determined using cluster information appropriately formed by utilizing domain knowledge.

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

Studies of Emission Processes of Polymer Additives into Water Using Quartz Crystal Microbalance-A Case Study on Organophosphate Esters

Plastic materials contain various additives, which can be released during the entire lifespan of plastics and pose a threat to the environment and human health. Despite our knowledge on leakage of additives from products, accurate and rapid approaches to study emission kinetics are largely lacking, in particular, methodologies that can provide in-depth understanding of polymer/additive interactions. Here, we report on a novel approach using quartz crystal microbalance (QCM) to measure emissions of additives to water from polymer films spin-coated on quartz crystals. The methodology, being accurate and reproducible with a standard error of ±2.4%, was applied to a range of organophosphate esters (OPEs) and polymers with varying physicochemical properties. The release of most OPEs reached an apparent steady-state within 10 h. The release curves for the studied OPEs could be fitted using a Weibull model, which shows that the release is a two-phase process with an initial fast phase driven by partitioning of OPEs readily available at or close to the polymer film surface, and a slower phase dominated by diffusion in the polymer. The kinetics of the first emission phase was mainly correlated with the hydrophobicity of the OPEs, whereas the diffusion phase was weakly correlated with molecular size. The developed QCM-based method for assessing and studying release of organic chemicals from a polymeric matrix is well suited for rapid screening of additives in efforts to identify more sustainable replacement polymer additives with lower emission potential.

Combining in Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: A Case Study of Decabromodiphenyl Ether Alternatives

Alternatives assessment is applied for minimizing the risk of unintentionally replacing a hazardous chemical with another hazardous chemical. Central challenges are the diversity of properties to consider and the lack of high-quality experimental data. To address this, a novel alternatives assessment procedure was developed based on in silico data and multicriteria decision analysis (MCDA) methods. As a case study, 16 alternatives to the flame retardant decabromodiphenyl ether were considered. The hazard properties included persistence (P), bioaccumulation potential (B), toxicities (T), and mobility in water (M). Databases were consulted and 2866 experimental data points were collected for the target chemicals; however, these were mostly replicate data points for some hazard criteria for a subset of alternatives. Therefore, in silico data and three MCDA strategies were tested including heat mapping, multiattribute utility theory (MAUT), and Elimination Et Choix Traduisant la REalité (ELECTRE III). The heat map clearly showed that none of the target chemicals are hazard-free, whereas MAUT and ELECTRE III agreed on ranking the "least worst" choices. This study identified several challenges and the complexity in the alternatives assessment processes motivating more case studies combining in silico and MCDA approaches.

Indoor phthalates from household dust in Qatar: implications for non-dietary human exposure

Phthalates are ubiquitous semi-volatile organic compounds in the indoor environment present in various consumer products such as cosmetics, polyvinylchloride (PVC) flooring, food packing, and many others. Indoor phthalate concentrations were investigated in 15 buildings including 11 homes, 3 laboratories, and 1 from a hospital in Qatar. Dust samples were collected from vacuum cleaning bags usually used for cleaning homes, labs, and hospitals. The main objectives of this study was to determine the occurrence and concentration of phthalates in dust in Qatar and consequently to estimate the non-dietary human exposure. Eleven phthalates was analyzed. The major identified phthalate compounds at homes in Qatar were bis(2-ethylhexyl) phthalate unlabeled (DEHP) and diisononyl phthalate (DINP) at a geometric mean of 288 μg/g (median 395 μg/g) and 106 μg/g (median 101 μg/g) accounting for 57% and 23% of the total measured phthalates, respectively. The major phthalate compounds found in the first lab building were DEHP and DINP with a median of 4861 μg/g and 943 μg/g, respectively, accounting for 82% and 16% of the total phthalates. For the second lab building, the major phthalates were DEHP with a median of 466 μg/g, accounting for 20% of the total phthalates measured, and DINP median of 1725 μg/g, accounting for 71% of the total measured phthalates. The dust sample tested from hospital building had DEHP as the major phthalate compound with a median of 793 μg/g, accounting for 4.0% of the total measured phthalates, and DINP with a median of 19,626 μg/g, accounting for 94%. The estimated human non-dietary exposure for children, adults, and toddlers was based on phthalate concentrations (median) and found to be 225 ng/kg bw/day for children, 2328 ng/kg bw/day for adults, and 2099 ng/kg bw/day for toddlers.

Identification of potential aryl hydrocarbon receptor ligands by virtual screening of industrial chemicals

We have developed a virtual screening procedure to identify potential ligands to the aryl hydrocarbon receptor (AhR) among a set of industrial chemicals. AhR is a key target for dioxin-like compounds, which is related to these compounds' potential to induce cancer and a wide range of endocrine and immune system-related effects. The virtual screening procedure included an initial filtration aiming at identifying chemicals with structural similarities to 66 known AhR binders, followed by 3 enrichment methods run in parallel. These include two ligand-based methods (structural fingerprints and nearest neighbor analysis) and one structure-based method using an AhR homology model. A set of 6445 commonly used industrial chemicals was processed, and each step identified unique potential ligands. Seven compounds were identified by all three enrichment methods, and these compounds included known activators and suppressors of AhR. Only approximately 0.7% (41 compounds) of the studied industrial compounds was identified as potential AhR ligands and among these, 28 compounds have to our knowledge not been tested for AhR-mediated effects or have been screened with low purity. We suggest assessment of AhR-related activities of these compounds and in particular 2-chlorotrityl chloride, 3-p-hydroxyanilino-carbazole, and 3-(2-chloro-4-nitrophenyl)-5-(1,1-dimethylethyl)-1,3,4-oxadiazol-2(3H)-one.

Combined Chemoinformatics Approach to Solvent Library Design Using clusterSim and Multidimensional Scaling

Reported here is a rational approach for the selection of solvents intended for use in physical form screening based on a novel chemoinformatics analysis of solvent properties. A comprehensive assessment of eight clustering methods was carried out on a series of 94 solvents described by calculated molecular descriptors using the clusterSim package in R. The effectiveness of clustering methods was evaluated using a range of statistical measures as well as increasing efficiency of solid form discovery using a cluster-based solvent selection approach. Multidimensional scaling was used to illustrate cluster analysis on a two-dimensional solvent map. The map presented here is a valuable tool to aid efficient solvent selection in physical form screens. This tool is equally applicable to any scientific area which requires a solubility dependent decision on solvent choice.

Non-target screening and prioritization of potentially persistent, bioaccumulating and toxic domestic wastewater contaminants and their removal in on-site and large-scale sewage treatment plants

On-site sewage treatment facilities (OSSFs), which are used to reduce nutrient emissions in rural areas, were screened for anthropogenic compounds with two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). The detected compounds were prioritized based on their persistence, bioaccumulation, ecotoxicity, removal efficiency, and concentrations. This comprehensive prioritization strategy, which was used for the first time on OSSF samples, ranked galaxolide, α-tocopheryl acetate, octocrylene, 2,4,7,9-tetramethyl-5-decyn-4,7-diol, several chlorinated organophosphorus flame retardants and linear alkyl benzenes as the most relevant compounds being emitted from OSSFs. Twenty-six target analytes were then selected for further removal efficiency analysis, including compounds from the priority list along with substances from the same chemical classes, and a few reference compounds. We found significantly better removal of two polar contaminants 2,4,7,9-tetramethyl-5-decyn-4,7-diol (p=0.0003) and tris(2-butoxyethyl) phosphate (p=0.005) in soil beds, a common type of OSSF in Sweden, compared with conventional sewage treatment plants. We also report median removal efficiencies in OSSFs for compounds not studied in this context before, viz. α-tocopheryl acetate (96%), benzophenone (83%), 2-(methylthio)benzothiazole (64%), 2,4,7,9-tetramethyl-5-decyn-4,7-diol (33%), and a range of organophosphorus flame retardants (19% to 98%). The environmental load of the top prioritized compounds in soil bed effluents were in the thousands of nanogram per liter range, viz. 2,4,7,9-tetramethyl-5-decyn-4,7-diol (3000ngL^-1), galaxolide (1400ngL^-1), octocrylene (1200ngL^-1), and α-tocopheryl acetate (660ngL^-1).

TyPol - a new methodology for organic compounds clustering based on their molecular characteristics and environmental behavior

Following legislation, the assessment of the environmental risks of 30000-100000 chemical substances is required for their registration dossiers. However, their behavior in the environment and their transfer to environmental components such as water or atmosphere are studied for only a very small proportion of the chemical in laboratory tests or monitoring studies because it is time-consuming and/or cost prohibitive. Therefore, the objective of this work was to develop a new methodology, TyPol, to classify organic compounds, and their degradation products, according to both their behavior in the environment and their molecular properties. The strategy relies on partial least squares analysis and hierarchical clustering. The calculation of molecular descriptors is based on an in silico approach, and the environmental endpoints (i.e. environmental parameters) are extracted from several available databases and literature. The classification of 215 organic compounds inputted in TyPol for this proof-of-concept study showed that the combination of some specific molecular descriptors could be related to a particular behavior in the environment. TyPol also provided an analysis of similarities (or dissimilarities) between organic compounds and their degradation products. Among the 24 degradation products that were inputted, 58% were found in the same cluster as their parents. The robustness of the method was tested and shown to be good. TyPol could help to predict the environmental behavior of a "new" compound (parent compound or degradation product) from its affiliation to one cluster, but also to select representative substances from a large data set in order to answer some specific questions regarding their behavior in the environment.

Emissions of two phthalate esters and BDE 209 to indoor air and their impact on urban air quality

Estimated emissions of decabrominated diphenyl ether (BDE 209) and the two phthalate esters diethylhexyl phthalate (DEHP) and diisononyl phthalate (DINP) to indoor air in the Stockholm conurbation, Sweden were used to assess the contribution of chemical outflows from the indoor environment to urban outdoor air pollution for these substances, by applying the recently developed Stockholm MUltimedia URban fate (SMURF) model. Emission rates of DINP from PVC materials were measured and published emission rates of DEHP were adapted to Swedish conditions. These were used as input to the model, as well as recently reported estimates of BDE 209 emissions to indoor air in Stockholm. Model predicted concentrations were compared to empirical monitoring data obtained from the literature and from additional measurements of phthalates in ventilation outlets and urban air performed in the current study. The predicted concentrations of the phthalates DINP and DEHP in indoor air and dust were within a factor of 1.5-10 of the measured concentrations. For BDE 209, predicted indoor concentrations were within the measured ranges, but measured concentrations showed a much larger variability. An adjusted emission scenario to better fit observed concentrations indoors was employed for DEHP and final outcomes resulted in estimated indoor emissions of 250 (50-1250), 2.9 (0.58-15), and 0.068 (0.014-0.34) kg year(-1) for DEHP, DINP and BDE 209. These emissions could not explain the observed concentrations in urban air of the phthalates, suggesting an underestimation of background inflow or existence of additional sources in the outdoor environment. For BDE 209, the assessment indicates that the Stockholm indoor environment contributes about 25% to the air pollution load in inflowing background air, but additional monitoring data in urban air are needed to confirm this conclusion.

Robustness in experimental design: A study on the reliability of selection approaches

The quality criteria for experimental design approaches in chemoinformatics are numerous. Not only the error performance of a model resulting from the selected compounds is of importance, but also reliability, consistency, stability and robustness against small variations in the dataset or structurally diverse compounds. We developed a new stepwise, adaptive approach, DescRep, combining an iteratively refined descriptor selection with a sampling based on the putatively most representative compounds. A comparison of the proposed strategy was based on statistical performance of models derived from such a selection to those derived by other popular and frequently used approaches, such as the Kennard-Stone algorithm or the most descriptive compound selection. We used three datasets to carry out a statistical evaluation of the performance, reliability and robustness of the resulting models. Our results indicate that stepwise and adaptive approaches have a better adaptability to changes within a dataset and that this adaptability results in a better error performance and stability of the resulting models.

Are chemicals in articles an obstacle for reaching environmental goals? - Missing links in EU chemical management

It is widely acknowledged that the management of risks associated with chemicals in articles needs to be improved. The EU environmental policy states that environmental damage should be rectified at source. It is therefore motivated that the risk management of substances in articles also takes particular consideration to those substances identified as posing a risk in different environmental compartments. The primary aim of the present study was to empirically analyze to what extent the regulation of chemicals in articles under REACH is coherent with the rules concerning chemicals in the Sewage Sludge Directive (SSD) and the Water Framework Directive (WFD). We also analyzed the chemical variation of the organic substances regulated under these legislations in relation to the most heavily used chemicals. The results show that 16 of 24 substances used in or potentially present in articles and regulated by the SSD or the WFD are also identified under REACH either as a substance of very high concern (SVHC) or subject to some restrictions. However, for these substances we conclude that there is limited coherence between the legislations, since the identification as an SVHC does not in itself encompass any use restrictions, and the restrictions in REACH are in many cases limited to a particular use, and thus all other uses are allowed. Only a minor part of chemicals in commerce is regulated and these show a chemical variation that deviates from classical legacy pollutants. This warrants new tools to identify potentially hazardous chemicals in articles. We also noted that chemicals monitored in the environment under the WFD deviate in their chemistry from the ones regulated by REACH. In summary, we argue that to obtain improved resource efficiency and a sustainable development it is necessary to minimize the input of chemicals identified as hazardous to health or the environment into articles.