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Wang SS, Lin P, Wang CC, Lin YC, Tung CW. Machine learning for predicting chemical migration from food packaging materials to foods. Food Chem Toxicol 2023:113942. [PMID: 37451598 DOI: 10.1016/j.fct.2023.113942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/26/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
Food contact chemicals (FCCs) can migrate from packaging materials to food posing an issue of exposure to FCCs of toxicity concern. Compared to costly experiments, computational methods can be utilized to assess the migration potentials for various migration scenarios for further experimental investigation that can potentially accelerate the migration assessment. This study developed a nonlinear machine learning method utilizing chemical properties, material type, food type and temperature to predict chemical migration from package to food. Nine nonlinear algorithms were evaluated for their prediction performance. The ensemble model leveraging multiple algorithms provides state-of-the-art performance that is much better than previous linear regression models. The developed prediction models were subsequently applied to profile the migration potential of FCCs of high toxicity concern. The models are expected to be useful for accelerating the assessment of migration of FCCs from package to foods.
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Affiliation(s)
- Shan-Shan Wang
- Ph.D. Program in Environmental and Occupational Medicine, College of Medicine, Kaohsiung Medical University and National Health Research Institutes, Kaohsiung, 80756, Taiwan; Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, 10675, Taiwan
| | - Pinpin Lin
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, 10617, Taiwan
| | - Ying-Chi Lin
- College of Pharmacy, Kaohsiung Medical University, Kaohsiung, 80756, Taiwan; School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, 80756, Taiwan
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, 10675, Taiwan.
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2
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El-Masri H, Paul Friedman K, Isaacs K, Wetmore BA. Advances in computational methods along the exposure to toxicological response paradigm. Toxicol Appl Pharmacol 2022; 450:116141. [PMID: 35777528 PMCID: PMC9619339 DOI: 10.1016/j.taap.2022.116141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
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.
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Affiliation(s)
- Hisham El-Masri
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
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3
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Ciffroy P, Mertens B, Van Hoeck E, Van Overmeire I, Johansson E, Alfonso B, Baderna D, Selvestrel G, Benfenati E. Modeling the migration of chemicals from food contact materials to food: The MERLIN-expo/VERMEER toolbox. Food Chem Toxicol 2022; 166:113118. [PMID: 35605713 DOI: 10.1016/j.fct.2022.113118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 03/21/2022] [Accepted: 05/04/2022] [Indexed: 11/30/2022]
Abstract
Evaluating the migration of chemicals from food contact materials (FCM) into food is a key step in the safety assessment of such materials. In this paper, a simple mechanistic model describing the migration of chemicals from FCM to food was combined with quantitative property-property relationships (QPPRs) for the prediction of diffusion coefficients and FCM-Food partition coefficients. The aim of the present study was to evaluate the performance of these operational models in the prediction of a chemical's concentration in food in contact with a plastic monolayer FCM. A comparison to experimental migration values reported in literature was conducted. Deterministic simulations showed a good match between predicted and experimental values. The tested models can be used to provide insights in the amount and the type of toxicological data that are needed for the safety evaluation of the FCM substance. Uncertainty in QPPRs used for describing the processes of both diffusion in FCM and partition at the FCM-Food interface was included in the analysis. Combining uncertainty in QPPR predictions, it was shown that the third quartile (75th percentile) derived from probabilistic calculations can be used as a conservative value in the prediction of chemical concentration in food, with reasonable safety factors.
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Affiliation(s)
- P Ciffroy
- EDF, Division Recherche et Développement, Laboratoire National d'Hydraulique et Environnement, 6 quai Watier, 78401, Chatou, France.
| | - B Mertens
- Chemical and Physical Health Risks, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium; Department of Biomedical Sciences, University of Antwerp, Wilrijk, Belgium
| | - E Van Hoeck
- Chemical and Physical Health Risks, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium
| | - I Van Overmeire
- Chemical and Physical Health Risks, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium
| | - E Johansson
- AFRY, Facilia Sweden Section, Frösundaleden 2, SE16970, Stockholm, Sweden
| | - B Alfonso
- AFRY, Facilia Sweden Section, Frösundaleden 2, SE16970, Stockholm, Sweden
| | - D Baderna
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - G Selvestrel
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - E Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
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Zhu T, Tao C. Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127037. [PMID: 34530267 DOI: 10.1016/j.jhazmat.2021.127037] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Polydimethylsiloxane-air partition coefficient (KPDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict KPDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the Radj2, QBOOT2 and Qext2 are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental KPDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
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5
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Aurisano N, Fantke P, Huang L, Jolliet O. Estimating mouthing exposure to chemicals in children's products. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:94-102. [PMID: 34188178 PMCID: PMC8770116 DOI: 10.1038/s41370-021-00354-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 05/16/2023]
Abstract
BACKGROUND Existing models for estimating children's exposure to chemicals through mouthing currently depends on the availability of chemical- and material-specific experimental migration rates, only covering a few dozen chemicals. OBJECTIVE This study objective is hence to develop a mouthing exposure model to predict migration into saliva, mouthing exposure, and related health risk from a wide range of chemical-material combinations in children's products. METHODS We collected experimental data on chemical migration from different products into saliva for multiple substance groups and materials, identifying chemical concentration and diffusion coefficient as main properties of influence. To predict migration rates into saliva, we adapted a previously developed migration model for chemicals in food packaging materials. We also developed a regression model based on identified chemical and material properties. RESULTS Our migration predictions correlate well with experimental data (R2 = 0.85) and vary widely from 8 × 10-7 to 32.7 µg/10 cm2/min, with plasticizers in PVC showing the highest values. Related mouthing exposure doses vary across chemicals and materials from a median of 0.005 to 253 µg/kgBW/d. Finally, we combined exposure estimates with toxicity information to yield hazard quotients and identify chemicals of concern for average and upper bound mouthing behavior scenarios. SIGNIFICANCE The proposed model can be applied for predicting migration rates for hundreds of chemical-material combinations to support high-throughput screening.
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Affiliation(s)
- Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800, Kgs. Lyngby, Denmark
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800, Kgs. Lyngby, Denmark.
| | - Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
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6
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Wu Y, Song Z, Little JC, Zhong M, Li H, Xu Y. An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes. ENVIRONMENT INTERNATIONAL 2021; 156:106748. [PMID: 34256300 DOI: 10.1016/j.envint.2021.106748] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/09/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.
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Affiliation(s)
- Yaoxing Wu
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Zidong Song
- Department of Building Science and Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - John C Little
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Min Zhong
- Bureau of Air Quality, Pennsylvania Department of Environmental Protection, Harrisburg, PA 17101, USA
| | - Hongwan Li
- Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, TX 78712, USA
| | - Ying Xu
- Department of Building Science and Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China; Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, TX 78712, USA.
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7
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Jolliet O, Huang L, Hou P, Fantke P. High Throughput Risk and Impact Screening of Chemicals in Consumer Products. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2021; 41:627-644. [PMID: 33073419 PMCID: PMC8246852 DOI: 10.1111/risa.13604] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 06/13/2020] [Accepted: 08/25/2020] [Indexed: 05/20/2023]
Abstract
The ubiquitous presence of more than 80,000 chemicals in thousands of consumer products used on a daily basis stresses the need for screening a broader set of chemicals than the traditional well-studied suspect chemicals. This high-throughput screening combines stochastic chemical-product usage with mass balance-based exposure models and toxicity data to prioritize risks associated with household products. We first characterize product usage using the stochastic SHEDS-HT model and chemical content in common household products from the CPDat database, the chemical amounts applied daily varying over more than six orders of magnitude, from mg to kg. We then estimate multi-pathways near- and far-field exposures for 5,500 chemical-product combinations, applying an extended USEtox model to calculate product intake fractions ranging from 0.001 to ∼1, and exposure doses varying over more than nine orders of magnitude. Combining exposure doses with chemical-specific dose-responses and reference doses shows that risks can be substantial for multiple home maintenance products, such as paints or paint strippers, for some home-applied pesticides, leave-on personal care products, and cleaning products. Sixty percent of the chemical-product combinations have hazard quotients exceeding 1, and 9% of the combinations have lifetime cancer risks exceeding 10-4 . Population-level impacts of household products ingredients can be substantial, representing 5 to 100 minutes of healthy life lost per day, with users' exposures up to 103 minutes per day. To address this issue, present mass balance-based models are already able to provide exposure estimates for both users and populations. This screening study shows large variations of up to 10 orders of magnitude in impact across both chemicals and product combinations, demonstrating that prioritization based on hazard only is not acceptable, since it would neglect orders of magnitude variations in both product usage and exposure that need to be quantified. To address this, the USEtox suite of mass balance-based models is already able to provide exposure estimates for thousands of product-chemical combinations for both users and populations. The present study calls for more scrutiny of most impacting chemical-product combinations, fully ensuring from a regulatory perspective consumer product safety for high-end users and using protective measures for users.
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Affiliation(s)
- Olivier Jolliet
- Environmental Health Sciences, School of Public HealthUniversity of MichiganAnn ArborMIUSA
| | - Lei Huang
- Environmental Health Sciences, School of Public HealthUniversity of MichiganAnn ArborMIUSA
| | - Ping Hou
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMIUSA
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and EconomicsTechnical University of Denmark2800 KgsLyngbyDenmark
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8
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Eichler CMA, Hubal EAC, Xu Y, Cao J, Bi C, Weschler CJ, Salthammer T, Morrison GC, Koivisto AJ, Zhang Y, Mandin C, Wei W, Blondeau P, Poppendieck D, Liu X, Delmaar CJE, Fantke P, Jolliet O, Shin HM, Diamond ML, Shiraiwa M, Zuend A, Hopke PK, von Goetz N, Kulmala M, Little JC. Assessing Human Exposure to SVOCs in Materials, Products, and Articles: A Modular Mechanistic Framework. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:25-43. [PMID: 33319994 PMCID: PMC7877794 DOI: 10.1021/acs.est.0c02329] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
A critical review of the current state of knowledge of chemical emissions from indoor sources, partitioning among indoor compartments, and the ensuing indoor exposure leads to a proposal for a modular mechanistic framework for predicting human exposure to semivolatile organic compounds (SVOCs). Mechanistically consistent source emission categories include solid, soft, frequent contact, applied, sprayed, and high temperature sources. Environmental compartments are the gas phase, airborne particles, settled dust, indoor surfaces, and clothing. Identified research needs are the development of dynamic emission models for several of the source emission categories and of estimation strategies for critical model parameters. The modular structure of the framework facilitates subsequent inclusion of new knowledge, other chemical classes of indoor pollutants, and additional mechanistic processes relevant to human exposure indoors. The framework may serve as the foundation for developing an open-source community model to better support collaborative research and improve access for application by stakeholders. Combining exposure estimates derived using this framework with toxicity data for different end points and toxicokinetic mechanisms will accelerate chemical risk prioritization, advance effective chemical management decisions, and protect public health.
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Affiliation(s)
- Clara M A Eichler
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24060, United States
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Elaine A Cohen Hubal
- Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina 27711, United States
| | - Ying Xu
- Department of Building Science, Tsinghua University, Beijing 100084, China
| | - Jianping Cao
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China
| | - Chenyang Bi
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24060, United States
| | - Charles J Weschler
- Environmental and Occupational Health Sciences Institute, Rutgers University, Piscataway, New Jersey 08854, United States
- International Centre for Indoor Environment and Energy, Department of Civil Engineering, Technical University of Denmark, Lyngby 2800, Denmark
| | - Tunga Salthammer
- Fraunhofer WKI, Department of Material Analysis and Indoor Chemistry, Braunschweig 38108, Germany
| | - Glenn C Morrison
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Antti Joonas Koivisto
- Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki 00014, Finland
| | - Yinping Zhang
- Department of Building Science, Tsinghua University, Beijing 100084, China
| | - Corinne Mandin
- University of Paris-Est, Scientific and Technical Center for Building (CSTB), French Indoor Air Quality Observatory (OQAI), Champs sur Marne 77447, France
| | - Wenjuan Wei
- University of Paris-Est, Scientific and Technical Center for Building (CSTB), French Indoor Air Quality Observatory (OQAI), Champs sur Marne 77447, France
| | - Patrice Blondeau
- Laboratoire des Sciences de l'Ingénieur pour l'Environnement - LaSIE, Université de La Rochelle, La Rochelle 77447, France
| | - Dustin Poppendieck
- Engineering Lab, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Xiaoyu Liu
- Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina 27711, United States
| | - Christiaan J E Delmaar
- National Institute for Public Health and the Environment, Center for Safety of Substances and Products, Bilthoven 3720, The Netherlands
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hyeong-Moo Shin
- Department of Earth and Environmental Sciences, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Miriam L Diamond
- Department of Earth Sciences, University of Toronto, Toronto, Ontario M5S 3B1, Canada
| | - Manabu Shiraiwa
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Andreas Zuend
- Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec H3A0B9, Canada
| | - Philip K Hopke
- Center for Air Resources Engineering and Science, Clarkson University, Potsdam, New York 13699-5708, United States
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, United States
| | | | - Markku Kulmala
- Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki 00014, Finland
| | - John C Little
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24060, United States
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9
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Aurisano N, Huang L, Milà I Canals L, Jolliet O, Fantke P. Chemicals of concern in plastic toys. ENVIRONMENT INTERNATIONAL 2021; 146:106194. [PMID: 33115697 DOI: 10.1016/j.envint.2020.106194] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/04/2020] [Accepted: 10/05/2020] [Indexed: 05/24/2023]
Abstract
We present a list of Chemicals of Concern (CoCs) in plastic toys. We started from available studies reporting chemical composition of toys to group plastic materials, as well as to gather mass fractions and function of chemicals in these materials. Chemical emissions from plastic toys and subsequent human exposures were then estimated using a series of models and a coupled near-field and far-field exposure assessment framework. Comparing human doses with reference doses shows high Hazard Quotients of up to 387 and cancer risk calculated using cancer slope factors of up to 0.0005. Plasticizers in soft plastic materials show the highest risk, with 31 out of the 126 chemicals identified as CoCs, with sum of Hazard Quotients >1 or child cancer risk >10-6. Our results indicate that a relevant amount of chemicals used in plastic toy materials may pose a non-negligible health risk to children, calling for more refined investigations and more human- and eco-friendly alternatives. The 126 chemicals identified as CoCs were compared with other existing regulatory prioritization lists. While some of our chemicals appear in other lists, we also identified additional priority chemicals that are not yet covered elsewhere and thus require further attention. We finally derive for all considered chemicals the maximum Acceptable Chemical Content (ACC) in the grouped toy plastic materials as powerful green chemistry tool to check whether chemical alternatives could create substantial risks.
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Affiliation(s)
- Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark
| | - Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Llorenç Milà I Canals
- Economy Division, United Nations Environment Programme, 1 Rue de Miollis, 75015 Paris, France
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark.
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10
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Zhu T, Chen W, Singh RP, Cui Y. Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods. JOURNAL OF HAZARDOUS MATERIALS 2020; 399:123012. [PMID: 32544766 DOI: 10.1016/j.jhazmat.2020.123012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 05/15/2020] [Accepted: 05/20/2020] [Indexed: 06/11/2023]
Abstract
Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (KPDMS-w), PDMS-air (KPDMS-a) and PDMS-seawater partition coefficients (KPDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R2adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Wenxuan Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | | | - Yanran Cui
- Institute for Integrated Catalysis, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99354, United States
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11
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Zhu T, Yan H, Singh RP, Wang Y, Cheng H. QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:17550-17560. [PMID: 31493082 DOI: 10.1007/s11356-019-06389-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (KPA-w). The major variables influencing KPA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R2) and cross-validation coefficient (Q2) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log KPA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.
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Affiliation(s)
- Tengyi Zhu
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Heting Yan
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | | | - Yajun Wang
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Haomiao Cheng
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
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Zhu T, Jiang Y, Cheng H, Singh RP, Yan B. Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 190:110179. [PMID: 31927194 DOI: 10.1016/j.ecoenv.2020.110179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 12/31/2019] [Accepted: 01/05/2020] [Indexed: 06/10/2023]
Abstract
Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R2) of 0.949 and cross-validation coefficient (QLoo2) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.
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Affiliation(s)
- Tengyi Zhu
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Yue Jiang
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Haomiao Cheng
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | | | - Bipeng Yan
- Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
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