1
|
Keshavarz MH, Shirazi Z, Jafari M, Jannesari F. The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients. CHEMOSPHERE 2024; 349:140855. [PMID: 38048827 DOI: 10.1016/j.chemosphere.2023.140855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/24/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible measured data of PUF-air partition coefficients for 170 chemicals are used to develop and test the novel model. In comparison to available quantitative structure-property relationship (QSPR) methods for the prediction of PUF-air partition coefficients that need complex descriptors, the here used descriptors are simpler. The assessed various statistical factors of the simple method containing 147 (training) and 23 (test) organic compounds can verify the external and internal cross-validations. Various statistical parameters confirm the high reliability of the novel model as compared with the outputs of complex multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods. The values of R-squared (R2), and root mean square error (RMSE) of the new model are for training/test sets are 0.924/0.894 and 0.374/0.318, respectively. Meanwhile, R2 and RMSE values for three comparative models training/test sets are (i) MLR: 0.848/0.670 (R2) and 0.531/0.573 (RMSE); (ii) ANN: 0.902/0.664 (R2) and 0.425/0.560 (RMSE); (iii) SVM: 0.935/0.794 (R2) and 0.351/0.419 (RMSE). Thus, the new model the simplest approach with higher reliability in comparison to the best available methods.
Collapse
Affiliation(s)
| | - Zeinab Shirazi
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
| | - Mohammad Jafari
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
| | | |
Collapse
|
2
|
Bajagain R, Kim PG, Kwon JH, Hong Y. Determination of partition coefficients of phthalic acid esters between polydimethylsiloxane and water and its field application to surface waters. JOURNAL OF HAZARDOUS MATERIALS 2023; 448:130933. [PMID: 36860070 DOI: 10.1016/j.jhazmat.2023.130933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/18/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Phthalic acid esters (PAEs) or phthalates are endocrine-disrupting chemicals and among the most frequently detected hydrophobic organic pollutants, which can be gradually released from consumer products into the environment (e.g., water). This study measured the equilibrium partition coefficients for 10 selected PAEs, with a wide range of logarithms of the octanol-water partition coefficient (log Kow) from 1.60 to 9.37, between poly(dimethylsiloxane) (PDMS) and water (KPDMSw) using the kinetic permeation method. The desorption rate constant (kd) and KPDMSw for each PAEs were calculated from kinetic data. The experimental log KPDMSw for the PAEs ranges from 0.8 to 5.9, which is linearly correlated with log Kow values up to 8 from the literature (R2 > 0.94); however, it slightly deviated for the PAEs with log Kow values greater than 8. In addition, KPDMSw decreased with the temperature and enthalpy for PAEs partitioning in PDMS-water in an exothermic manner. Furthermore, the effects of dissolved organic matter and ionic strength on the partitioning of PAEs in PDMS were investigated. PDMS was used as a passive sampler to determine the aqueous concentration of plasticizers in river surface water. The results of this study can be used to evaluate the bioavailability and risk of phthalates in real environmental samples.
Collapse
Affiliation(s)
- Rishikesh Bajagain
- Department of Environmental Engineering, Korea University Sejong Campus, 2511 Sejong-ro, Sejong City, 30019, South Korea
| | - Pil-Gon Kim
- Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea
| | - Jung-Hwan Kwon
- Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea
| | - Yongseok Hong
- Department of Environmental Engineering, Korea University Sejong Campus, 2511 Sejong-ro, Sejong City, 30019, South Korea.
| |
Collapse
|
3
|
Zhu T, Zhang Y, Tao C, Chen W, Cheng H. Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159348. [PMID: 36228787 DOI: 10.1016/j.scitotenv.2022.159348] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/21/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R2adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Yu Zhang
- 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
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| |
Collapse
|
4
|
Tao C, Chen Y, Tao T, Cao Z, Chen W, Zhu T. Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 311:119857. [PMID: 35944777 DOI: 10.1016/j.envpol.2022.119857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.
Collapse
Affiliation(s)
- Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ying Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Tianyun Tao
- College of Agriculture, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Zaizhi Cao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| |
Collapse
|
5
|
NAZİB ALİAS A, MOHAMED ZABİDİ Z. QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2022. [DOI: 10.18596/jotcsa.1083840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Zhu T, Chen W, Jafvert CT, Fu D, Cheng H, Chen M, Wang Y. Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118223. [PMID: 34583266 DOI: 10.1016/j.envpol.2021.118223] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (KPE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the KPE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (Kmic-w) and the LDPE-micelle partition coefficient (KPE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the KPE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental KPE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Yajun Wang
- School of Civil Engineering, Lanzhou University of Technology, 287 Langongping, Lanzhou, 730050, China
| |
Collapse
|
8
|
Alikhani N, Bousfield DW, Wang J, Li L, Tajvidi M. Numerical Simulation of the Water Vapor Separation of a Moisture-Selective Hollow-Fiber Membrane for the Application in Wood Drying Processes. MEMBRANES 2021; 11:membranes11080593. [PMID: 34436356 PMCID: PMC8397961 DOI: 10.3390/membranes11080593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/10/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022]
Abstract
In this study, a simplified two-dimensional axisymmetric finite element analysis (FEA) model was developed, using COMSOL Multiphysics® software, to simulate the water vapor separation in a moisture-selective hollow-fiber membrane for the application of air dehumidification in wood drying processes. The membrane material was dense polydimethylsiloxane (PDMS). A single hollow fiber membrane was modelled. The mass and momentum transfer equations were simultaneously solved to compute the water vapor concentration profile in the single hollow fiber membrane. A water vapor removal experiment was conducted by using a lab-scale PDMS hollow fiber membrane module operated at constant temperature of 35 °C. Three operation parameters of air flow rate, vacuum pressure, and initial relative humidity (RH) were set at different levels. The final RH of dehydrated air was collected and converted to water vapor concentration to validate simulated results. The simulated results were fairly consistent with the experimental data. Both experimental and simulated results revealed that the water vapor removal efficiency of the membrane system was affected by air velocity and vacuum pressure. A high water vapor removal performance was achieved at a slow air velocity and high vacuum pressure. Subsequently, the correlation of Sherwood (Sh)-Reynolds (Re)-Schmidt (Sc) numbers of the PDMS membrane was established using the validated model, which is applicable at a constant temperature of 35 °C and vacuum pressure of 77.9 kPa. This study delivers an insight into the mass transport in the moisture-selective dense PDMS hollow fiber membrane-based air dehumidification process, with the aims of providing a useful reference to the scale-up design, process optimization and module development using hollow fiber membrane materials.
Collapse
Affiliation(s)
- Nasim Alikhani
- School of Forest Resources, University of Maine, Orono, ME 04469-5755, USA; (N.A.); (M.T.)
| | - Douglas W. Bousfield
- Department of Chemical and Biological Engineering, University of Maine, Orono, ME 04469-5737, USA;
| | - Jinwu Wang
- USDA Forest Service, Forest Products Laboratory, Madison, WI 53726-2398, USA;
| | - Ling Li
- School of Forest Resources, University of Maine, Orono, ME 04469-5755, USA; (N.A.); (M.T.)
- Correspondence:
| | - Mehdi Tajvidi
- School of Forest Resources, University of Maine, Orono, ME 04469-5755, USA; (N.A.); (M.T.)
| |
Collapse
|
9
|
Zhu T, Cao Z, Singh RP, Cheng H, Chen M. In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 289:112437. [PMID: 33812149 DOI: 10.1016/j.jenvman.2021.112437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (KPE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (KPE-W), LDPE-air (KPE-A) and LDPE-seawater (KPE-SW) partition coefficients. These developed models have satisfying predictability (R2adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSEtra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q2ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSEext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown KPE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Zaizhi Cao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | | | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| |
Collapse
|
10
|
Zhu T, Gu L, Chen M, Sun F. Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches. CHEMOSPHERE 2021; 266:128962. [PMID: 33218721 DOI: 10.1016/j.chemosphere.2020.128962] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 06/11/2023]
Abstract
Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (KPUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the KPUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Liming Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Feng Sun
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| |
Collapse
|