Search

Toxic substances, such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals, can accumulate in soil, posing a risk to human health and the environment. To reduce the risk of exposure, rapid identification and remediation of potentially hazardous soils is necessary. Adsorption of contaminants by activated carbons and clay materials is commonly utilized to decrease the bioavailability of chemicals in soil and environmental toxicity in vitro, and this study aims to determine their efficacy in real-life soil samples. Two ecotoxicological models (Lemna minor and Caenorhabditis elegans) were used to test residential soil samples, known to contain an average of 5.3, 262, and 9.6 ppm of PAHs, lead, and mercury, for potential toxicity. Toxicity testing of these soils indicated that 86% and 58% of soils caused ≤50% inhibition of growth and survival of L. minor and C. elegans, respectively. Importantly, 3 soil samples caused ≥90% inhibition of growth in both models, and the toxicity was positively correlated with levels of heavy metals. These toxic soil samples were prioritized for remediation using activated carbon and SM-Tyrosine sorbents, which have been shown to immobilize PAHs and heavy metals, respectively. The inclusion of low levels of SM-Tyrosine protected the growth and survival of L. minor and C. elegans by 83% and 78%, respectively from the polluted soil samples while activated carbon offered no significant protection. These results also indicated that heavy metals were the driver of toxicity in the samples. Results from this study demonstrate that adsorption technologies are effective strategies for remediating complex, real-life soil samples contaminated with hazardous pollutants and protecting natural soil and groundwater resources and habitats. The results highlight the applicability of these ecotoxicological models as rapid screening tools for monitoring soil quality and verifying the efficacy of remediation practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Remediation methods for soil contaminated with poly- and perfluoroalkyl substances (PFAS) are needed to prevent their leaching into drinking water sources and to protect living organisms in the surrounding environment. In this study, the efficacy of processed and amended clays and carbons as soil amendments to sequester PFAS and prevent leaching was assessed using PFAS-contaminated soil and validated using sensitive ecotoxicological bioassays. Four different soil matrices including quartz sand, clay loam soil, garden soil, and compost were spiked with 4 PFAS congeners (PFOA, PFOS, GenX, and PFBS) at 0.01-0.2 μg/mL and subjected to a 3-step extraction method to quantify the leachability of PFAS from each matrix. The multistep extraction method showed that PFAS leaching from soil was aligned with the total carbon content in soil, and the recovery was dependent on concentration of the PFAS. To prevent the leaching of PFAS, several sorbents including activated carbon (AC), calcium montmorillonite (CM), acid processed montmorillonite (APM), and organoclays modified with carnitine, choline, and chlorophyll were added to the four soil matrices at 0.5-4 % w /w, and PFAS was extracted using the LEAF method. Total PFAS bioavailability was reduced by 58-97 % by all sorbents in a dose-dependent manner, with AC being the most efficient sorbent with a reduction of 73-97 %. The water leachates and soil were tested for toxicity using an aquatic plant ( Lemna minor ) and a soil nematode ( Caenorhabditis elegans ), respectively, to validate the reduction in PFAS bioavailability. Growth parameters in both ecotoxicological models showed a dose-dependent reduction in toxicity with value-added growth promotion from the organoclays due to added nutrients. The kinetic studies at varying time intervals and varying pHs simulating acidic rain, fresh water, and brackish water suggested a stable sorption of PFAS on all sorbents that fit the pseudo-second-order for up to 21 days. Contaminated soil with higher than 0.1 μg/mL PFAS may require reapplication of soil amendments every 21 days. Overall, AC showed the highest sorption percentage of total PFAS from in vitro studies, while organoclays delivered higher protection in ecotoxicological models ( in vivo ). This study suggests that in situ immobilization with soil amendments can reduce PFAS leachates and their bioavailability to surrounding organisms. A combination of sorbents may facilitate the most effective remediation of complex soil matrices containing mixtures of PFAS and prevent leaching and uptake into plants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. In this paper, we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

An attractive approach to minimize human and animal exposures to toxic environmental contaminants is the use of safe and effective sorbent materials to sequester them. Montmorillonite clays have been shown to tightly bind diverse toxic chemicals. Due to their promise as sorbents to mitigate chemical exposures, it is important to understand their function and rapidly screen and predict optimal clay-chemical combinations for further testing. We derived adsorption free-energy values for a structurally and physicochemically diverse set of toxic chemicals using experimental adsorption isotherms performed in the current and previous studies. We studied the diverse set of chemicals using minimalistic MD simulations and showed that their interaction energies with calcium montmorillonite clays calculated using simulation snapshots in combination with their net charge and their corresponding solvent's dielectric constant can be used as inputs to a minimalistic model to predict adsorption free energies in agreement with experiments. Additionally, experiments and computations were used to reveal structural and physicochemical properties associated with chemicals that can be adsorbed to calcium montmorillonite clay. These properties include positively charged groups, phosphine groups, halide-rich moieties, hydrogen bond donor/acceptors, and large, rigid structures. The combined experimental and computational approaches used in this study highlight the importance and potential applicability of analogous methods to study and design novel advanced sorbent systems in the future, broadening their applicability for environmental contaminants., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals., Competing Interests: The authors have declared that no competing interests exist.

The Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework is presented for addressing the optimization of bi-level mixed-integer nonlinear programming problems. In this framework, bi-level optimization problems are approximated as single-level optimization problems by collecting samples of the upper-level objective and solving the lower-level problem to global optimality at those sampling points. This process is done through the integration of the DOMINO framework with a grey-box optimization solver to perform design of experiments on the upper-level objective, and to consecutively approximate and optimize bi-level mixed-integer nonlinear programming problems that are challenging to solve using exact methods. The performance of DOMINO is assessed through solving numerous bi-level benchmark problems, a land allocation problem in Food-Energy-Water Nexus, and through employing different data-driven optimization methodologies, including both local and global methods. Although this data-driven approach cannot provide a theoretical guarantee to global optimality, we present an algorithmic advancement that can guarantee feasibility to large-scale bi-level optimization problems when the lower-level problem is solved to global optimality at convergence.

A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes-Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity., Competing Interests: The authors have declared that no competing interests exist.

The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.

The objective of this work is to investigate the rate, extent, and structure of amphoteric proteins with charged solid surfaces over a range of applied potentials and surface charges. We use Electrochemical Quartz Crystal Microbalance with Dissipation Monitoring (E-QCM-D) to investigate the adsorption of amphoteric Bovine Serum Albumin (BSA) to a gold electrode while systematically varying the surface charge on the adsorbate and adsorbent by manipulating pH and applied potential, respectively. We also perform cyclic voltammetry-E-QCM-D on an adsorbed layer of BSA to elucidate conformational changes in response to varied applied potentials. We confirm previous results demonstrating that increasing magnitude of applied potential on the gold electrode is positively correlated with increasing mass adsorption when the protein and the surface are oppositely charged. On the other hand, we find that the rate of BSA adsorption is not governed by simple electrostatics, but instead depends on solution pH, an observation not well documented in the literature. Cyclic voltammetry with simultaneous E-QCM-D measurements suggest that BSA protein undergoes a conformational change as the surface potential varies., (Copyright © 2015 Elsevier Inc. All rights reserved.)