QSPR study on the polyacrylate–water partition coefficients of hydrophobic organic compounds

  • Tengyi ZhuEmail author
  • Heting Yan
  • Rajendra Prasad Singh
  • Yajun Wang
  • Haomiao ChengEmail author
Resource Recovery from Wastewater, Solid Waste and Waste Gas: Engineering and Management Aspects


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.


HOCs SPME QSPR Polyacrylate–water partition coefficient Hydrophobicity Applicability domain 


Funding information

The current work was funded by the National Natural Science Foundation of China (Grant Nos. 21607123 and 51809226) and the Jiangsu Provincial Laboratory for Water Environmental Protection Engineering (Grant No. W1802).

Supplementary material

11356_2019_6389_MOESM1_ESM.docx (126 kb)
ESM 1 (DOCX 126 kb)


  1. Alexander DL, Tropsha A, Winkler DA (2015) Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55:1316–1322CrossRefGoogle Scholar
  2. Allan IJ, Kees B, Albrecht P, Branislav V, Mills GA, Richard G (2009) Field performance of seven passive sampling devices for monitoring of hydrophobic substances. Environ Sci Technol 43:5383–5390CrossRefGoogle Scholar
  3. Altman DG, Bland JM (2005) Standard deviations and standard errors. Bmj 331:903CrossRefGoogle Scholar
  4. Arthur CL, Killam LM, Buchholz KD, Pawliszyn J, Berg JR (1992) Automation and optimization of solid-phase microextraction. Anal Chem 64:1960–1966CrossRefGoogle Scholar
  5. Assoumani A, Lissalde S, Margoum C, Mazzella N, Coquery M (2013) In situ application of stir bar sorptive extraction as a passive sampling technique for the monitoring of agricultural pesticides in surface waters. Sci Total Environ 463:829–835CrossRefGoogle Scholar
  6. Basant N, Gupta S (2017) QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes. Environ Sci Pollut Res 24:14430–14444CrossRefGoogle Scholar
  7. Baynes RE, Xia XR, Barlow BM, Riviere JE (2007) Partitioning behavior of aromatic components in jet fuel into diverse membrane-coated fibers. J Toxicol Environ Health A 70:1879–1887CrossRefGoogle Scholar
  8. Caballero J, Tundidor-Camba A, Fernandez M (2007) Modeling of the inhibition constant (Ki) of some cruzain ketone-based inhibitors using 2D spatial autocorrelation vectors and data-diverse ensembles of Bayesian-regularized genetic neural networks. QSAR Comb Sci 26:27–40CrossRefGoogle Scholar
  9. Cao DS, Deng ZK, Zhu MF, Yao ZJ, Dong J, Zhao RG (2017) Ensemble partial least squares regression for descriptor selection, outlier detection, applicability domain assessment, and ensemble modeling in QSAR/QSPR modeling. J Chemom 31:e2922CrossRefGoogle Scholar
  10. Chalk AJ, Beck B, Clark T (2001) A temperature-dependent quantum mechanical/neural net model for vapor pressure. J Chem Inf Comput Sci 41:1053–1059CrossRefGoogle Scholar
  11. Channar PA, Saeed A, Larik FA, Rashid S, Iqbal Q, Rozi M, Younis S, Mahar J (2017) Design and synthesis of 2, 6-di (substituted phenyl) thiazolo [3, 2-b]-1, 2, 4-triazoles as α-glucosidase and α-amylase inhibitors, co-relative pharmacokinetics and 3D QSAR and risk analysis. Biomed Pharmacother 94:499–513CrossRefGoogle Scholar
  12. Chao KP, Wang VS, Liu CW, Lu YT (2018) QSAR studies on partition coefficients of organic compounds for polydimethylsiloxane of solid-phase microextraction devices. Int J Environ Sci Technol 15:2141–2150CrossRefGoogle Scholar
  13. Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52:2044–2058CrossRefGoogle Scholar
  14. Dean JR, Tomlinson WR, Makovskaya V, Cumming R, Hetheridge M, Comber M (1996) Solid-phase microextraction as a method for estimating the octanol-water partition coefficient. Anal Chem 68:130–133CrossRefGoogle Scholar
  15. Dong J, Cao D-S, Miao H-Y, Liu S, Deng B-C, Yun Y-H, Wang N-N, Lu A-P, Zeng W-B, Chen AF (2015) ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform 7:1–10CrossRefGoogle Scholar
  16. Doong R, Chang S (2000) Determination of distribution coefficients of priority polycyclic aromatic hydrocarbons using solid-phase microextraction. Anal Chem 72:3647–3652CrossRefGoogle Scholar
  17. Droge S (2008) A closer look at the sorption behavior of nonionic surfactants in marine sediment. Utrecht UniversityGoogle Scholar
  18. Du H, Hu Z, Bazzoli A, Zhang Y (2011) Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method. PLoS One 6:e22367CrossRefGoogle Scholar
  19. Endo S, Droge STJ, Goss KU (2011) Polyparameter linear free energy models for polyacrylate fiber-water partition coefficients to evaluate the efficiency of solid-phase microextraction. Anal Chem 83:1394–1400CrossRefGoogle Scholar
  20. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environ Health Perspect 111:1361–1375CrossRefGoogle Scholar
  21. Fernandez M, Tudidor-Camba A, Caballero J (2005) Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimidine derivatives using artificial neural network ensembles. J Chem Inf Model 45:1884–1895CrossRefGoogle Scholar
  22. Frazey PA, Barkley RM, Sievers RE (1998) Solid-phase microextraction with temperature-programmed desorption for the analysis of iodination disinfection byproducts. Anal Chem 70:638–644CrossRefGoogle Scholar
  23. Fu Z, Chen J, Li X, Wang Y, Yu H (2016) Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds. ChemospherE 148:118–125CrossRefGoogle Scholar
  24. Gissi A, Lombardo A, Roncaglioni A, Gadaleta D, Mangiatordi GF, Nicolotti O, Benfenati E (2015) Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: the bioconcentration factor (BCF). Environ Res 137:398–409CrossRefGoogle Scholar
  25. Golbraikh A, Tropsha A (2000) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol Divers 5:231–243CrossRefGoogle Scholar
  26. Górecki T, Khaled A, Pawliszyn J (1998) The effect of sample volume on quantitative analysis by solid phase microextraction Part 2.† Experimental verification. Analyst 123:2819–2824CrossRefGoogle Scholar
  27. Górecki T, Yu X, Pawliszyn J (1999) Theory of analyte extraction by selected porous polymer SPME fibres. Analyst 124:643–649CrossRefGoogle Scholar
  28. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRefGoogle Scholar
  29. Habibi YA, Danandeh JM (2009) Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis. Monatsh Chem 140:1279–1288CrossRefGoogle Scholar
  30. Haftka JJH, Scherpenisse P, Jonker MTO, Hermens JLM (2013) Using polyacrylate-coated SPME fibers to quantify sorption of polar and ionic organic contaminants to dissolved organic carbon. Environ Sci Technol 47:4455–4462CrossRefGoogle Scholar
  31. Heringa MB, Hermens JLM (2003) Measurement of free concentrations using negligible depletion-solid phase microextraction (nd-SPME). Trac Trends Anal Chem 22:575–587CrossRefGoogle Scholar
  32. Hong Q, Chen JW, Wang Y, Wang B, Li XH, Li F, Wang YN (2009) Development and assessment of quantitative structure-activity relationship models form bioconcentration factors of organic pollutants. Chin Sci Bull 54:628–634CrossRefGoogle Scholar
  33. Huang L, Jolliet O (2019) A combined quantitative property-property relationship (QPPR) for estimating packaging-food and solid material-water partition coefficients of organic compounds. Sci Total Environ 658:493–500CrossRefGoogle Scholar
  34. Ibezim E, Duchowicz PR, Ortiz EV, Castro EA (2012) QSAR on aryl-piperazine derivatives with activity on malaria. Chemom Intell Lab Syst 110:81–88CrossRefGoogle Scholar
  35. Kotowska U, Garbowska K, Isidorov VA (2006) Distribution coefficients of phthalates between absorption fiber and water and its using in quantitative analysis. Anal Chim Acta 560:110–117CrossRefGoogle Scholar
  36. Leslie HA, Oosthoek AJP, Busser FJM, Kraak MHS, Hermens JM (2002) Biomimetic solid-phase microextraction to predict body residues and toxicity of chemicals that act by narcosis. Environ Toxicol Chem 21:229–234CrossRefGoogle Scholar
  37. Lick W (2006) The sediment-water flux of HOCs due to “diffusion” or is there a well-mixed layer? If there is, does it matter? Environ Sci Technol 40:5610–5617CrossRefGoogle Scholar
  38. Lin S, Yang X, Liu H (2019) Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals. Ecotoxicol Environ Saf 179:40–49CrossRefGoogle Scholar
  39. Ling Y, Klemes MJ, Steinschneider S, Dichtel WR, Helbling DE (2019) QSARs to predict adsorption affinity of organic micropollutants for activated carbon and β-cyclodextrin polymer adsorbents. Water Res 154:217–226CrossRefGoogle Scholar
  40. Liu H, Yang X, Lu R (2016) Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin. Chemosphere 156:1–7CrossRefGoogle Scholar
  41. Liu H, Wei M, Yang X, Yin C, He X (2017) Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water. Sci Total Environ 574:1371–1378CrossRefGoogle Scholar
  42. Magdic S, Boyd-Boland A, Jinno K, Pawliszyn JB (1996) Analysis of organophosphorus insecticides from environmental samples using solid-phase microextraction. J Chromatogr A 736:219–228CrossRefGoogle Scholar
  43. Mardones C, Baer DV, Silva J, Retamal MJ (2008) Determination of halophenolic wood preservant traces in milk using headspace solid-phase microextraction and gas chromatography–mass spectrometry. J Chromatogr A 1215:1–7CrossRefGoogle Scholar
  44. Mayer P, Vaes WHJ, Hermens JLM (2000) Absorption of hydrophobic compounds into the poly(dimethylsiloxane) coating of solid-phase microextraction fibers: high partition coefficients and fluorescence microscopy images. Anal Chem 72:459–464CrossRefGoogle Scholar
  45. MOPAC2016, James JPS (2016) Stewart computational chemistry. Colorado Springs, CO, USA.
  46. Moriguchi I, Kanada Y (1977) Use of van der Waals volume in structure-activity studies. Chem Pharm Bull 25:926–935CrossRefGoogle Scholar
  47. Nabi D, Arey JS (2017) Predicting partitioning and diffusion properties of nonpolar chemicals in biotic media and passive sampler phases by GC × GC. Environ Sci Technol 51:3001–3011CrossRefGoogle Scholar
  48. OECD (2008) Health at a glance 2007: OECD Indicators Complete Edition - ISBN 9264027327. Sourceoecd Social Issues/migration/health volume 2007: i-198(198)Google Scholar
  49. Oemisch L, Goss KU, Endo S (2013) Determination of oil-water partition coefficients of polar compounds: silicone membrane equilibrator vs. SPME passive sampler. Anal Bioanal Chem 405:2567–2574CrossRefGoogle Scholar
  50. Ohlenbusch G, Kumke MU, Frimmel FH (2000) Sorption of phenols to dissolved organic matter investigated by solid phase microextraction. Sci Total Environ 253:63–74CrossRefGoogle Scholar
  51. Olejnik S, Mills J, Keselman H (2000) Using Wherry’s adjusted R 2 and Mallow’s Cp for model selection from all possible regressions. J Exp Educ 68:365–380CrossRefGoogle Scholar
  52. Oluwaseye A, Uzairu A, Shallangwa G, Abechi S (2018) QSAR studies on derivatives of quinazoline-4 (3H)-ones with anticonvulsant activities. J Eng Exact Sci 4:0255–0264CrossRefGoogle Scholar
  53. Ou W, Liu H, He J, Yang X (2018) Development of chicken and fish muscle protein–water partition coefficients predictive models for ionogenic and neutral organic chemicals. Ecotoxicol Environ Saf 157:128–133CrossRefGoogle Scholar
  54. Paschke A, Popp P (2003) Solid-phase microextraction fibre–water distribution constants of more hydrophobic organic compounds and their correlations with octanol–water partition coefficients. J Chromatogr A 999:35–42CrossRefGoogle Scholar
  55. Patil RB, Barbosa EG, Sangshetti JN, Sawant SD, Zambre VP (2018) LQTA-R: A new 3D-QSAR methodology applied to a set of DGAT1 inhibitors. Comput Biol Chem 74:123–131CrossRefGoogle Scholar
  56. Pawliszyn J (1995) Solid phase microextraction: theory and practice. Wiley-VCH, CanadaGoogle Scholar
  57. Pawliszyn J, Paschke A, Popp P (1999) Estimation of hydrophobicity of organic compounds. In: Solid-phase microextraction. UK, Cambridge, pp 140–156Google Scholar
  58. Poerschmann J (2000) Sorption of hydrophobic organic compounds on nonpolar SPME fibers and dissolved humic organic matter? Part III: application of the solubility parameter concept to interpret sorption on solid phase microextraction (SPME) fiber coatings. J Microcolumn Sep 12:603–612CrossRefGoogle Scholar
  59. Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2009) Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. Eur J Med Chem 44:5023–5028CrossRefGoogle Scholar
  60. Rebhun M, Smedit FD, Wetabula JR (1996) Dissolved humic substances for remediation of sites contaminated by organic pollutants, binding-desorption model predictions. Water Res 30:2027–2038CrossRefGoogle Scholar
  61. Riahi S, Pourbasheer E, Ganjali MR, Norouzi P (2009) Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. J Hazard Mater 166:853–859CrossRefGoogle Scholar
  62. Ribeiro FAL, Ferreira MMC (2005) QSAR model of the phototoxicity of polycyclic aromatic hydrocarbons. J Mol Struct THEOCHEM 719:191–200CrossRefGoogle Scholar
  63. Roy K, Ambure P (2016) The “double cross-validation” software tool for MLR QSAR model development. Chemom Intell Lab Syst 159:108–126CrossRefGoogle Scholar
  64. Roy K, Kabir H (2012) QSPR with extended topochemical atom (ETA) indices: modeling of critical micelle concentration of non-ionic surfactants. Chem Eng Sci 73:86–98CrossRefGoogle Scholar
  65. Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18–33CrossRefGoogle Scholar
  66. Roy K, Ambure P, Aher RB (2017) How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? Chemom Intell Lab Syst 162:44–54CrossRefGoogle Scholar
  67. Rusina TP, Smedes F, Klanova J (2010) Diffusion coefficients of polychlorinated biphenyls and polycyclic aromatic hydrocarbons in polydimethylsiloxane and low-density polyethylene polymers. J Appl Polym Sci 116:1803–1810Google Scholar
  68. Sabatino M, Rotili D, Patsilinakos A, Forgione M, Tomaselli D, Alby F, Arimondo PB, Mai A, Ragno R (2018) Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches. J Comput Aided Mol Des 32:435–458CrossRefGoogle Scholar
  69. Schneider AR, Porter ET, Baker JE (2007) Polychlorinated biphenyl release from resuspended Hudson River sediment. Environ Sci Technol 41:1097–1103CrossRefGoogle Scholar
  70. Selassie CD (2003) History of quantitative structure-activity relationships. In: Abraham DJ (ed) Burger’s medicinal chemistry and drug discovery, 6th edn. Wiley, New York, pp 1–96Google Scholar
  71. Shirey R, Mindrup R (1999) A systematic approach for selecting the appropriate SPME fiber. Sigma-Aldrich, BellefonteGoogle Scholar
  72. Singh KP, Gupta S, Mohan D (2014) Evaluating influences of seasonal variations and anthropogenic activities on alluvial groundwater hydrochemistry using ensemble learning approaches. J Hydrol 511:254–266CrossRefGoogle Scholar
  73. Smith S, Furay VJ, Layiwola PJ, Menezes-Filho JA (1994) Evaluation of the toxicity and quantitative structure-activity relationships (QSAR) of chlorophenols to the copepodid stage of a marine copepod (Tisbe battagliai) and two species of benthic flatfish, the flounder (Platichthys flesus) and sole (Solea solea). Chemosphere 28:825–836CrossRefGoogle Scholar
  74. Stanton DT, Jurs PC (1990) Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Anal Chem 62:2323–2329CrossRefGoogle Scholar
  75. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  76. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77CrossRefGoogle Scholar
  77. USEPA (2012) Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11.United States Environmental Protection Agency, Washington, DCGoogle Scholar
  78. Vaes WHJ, Ramos EU, Verhaar HJM, Seinen W, Hermens JLM (1996) Measurement of the free concentration using solid-phase microextraction: binding to protein. Anal Chem 68:4463–4467CrossRefGoogle Scholar
  79. Vaes WHJ, Ramos EU, Verhaar HJM, Cramer CJ, Hermens JLM (1998) Understanding and estimating membrane/water partition coefficients: approaches to derive quantitative structure property relationships. Chem Res Toxicol 11:847–854CrossRefGoogle Scholar
  80. Valor I, Pérez M, Cortada C, Apraiz D, Moltó JC, Font G (2001) SPME of 52 pesticides and polychlorinated biphenyls: extraction efficiencies of the SPME coatings poly (dimethylsiloxane), polyacrylate, poly (dimethylsiloxane)-divinylbenzene, Carboxen-poly (dimethylsiloxane), and Carbowax-divinylbenzene. J Sep Sci 24:39–48CrossRefGoogle Scholar
  81. Verbruggen EMJ, Vaes WHJ, Parkerton TF, Hermens JLM (2000) Polyacrylate-coated SPME fibers as a tool to simulate body residues and target concentrations of complex organic mixtures for estimation of baseline toxicity. Environ Sci Technol 34:324–331CrossRefGoogle Scholar
  82. Wang Y, Chen J, Yang X, Lyakurwa F, Li X, Qiao X (2015) In silico model for predicting soil organic carbon normalized sorption coefficient (K OC) of organic chemicals. Chemosphere 119:438–444CrossRefGoogle Scholar
  83. Wehrens R, Putter H, Buydens LM (2000) The bootstrap: a tutorial. Chemom Intell Lab Syst 54:35–52CrossRefGoogle Scholar
  84. Wildman SA, Crippen GM (1999) Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci 39:868–873CrossRefGoogle Scholar
  85. Wohland T, Rigler R, Vogel H (2001) The standard deviation in fluorescence correlation spectroscopy. Biophys J 80:2987–2999CrossRefGoogle Scholar
  86. Wood DJ, Carlsson L, Eklund M, Norinder U, Stålring J (2013) QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality. J Comput Aided Mol Des 27:203–219CrossRefGoogle Scholar
  87. Xia XR, Baynes RE, Monteiro-Riviere NA, Riviere JE (2005) Membrane uptake kinetics of jet fuel aromatic hydrocarbons from aqueous solutions studied by a membrane-coated fiber technique. Toxicol Mech Methods 15:307–316CrossRefGoogle Scholar
  88. Xia XR, Baynes RE, Monteiro-Riviere NA, Riviere JE (2007) An experimentally based approach for predicting skin permeability of chemicals and drugs using a membrane-coated fiber array. Toxicol Appl Pharmacol 221:320–328CrossRefGoogle Scholar
  89. Yang X, Liu H, Yang Q, Liu J, Chen J, Shi L (2016) Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships. Chemosphere 163:373–381CrossRefGoogle Scholar
  90. Yangali-Quintanilla V, Sadmani A, Mcconville M, Kennedy M, Amy G (2010) A QSAR model for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors) by nanofiltration membranes. Water Res 44:373–384CrossRefGoogle Scholar
  91. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474CrossRefGoogle Scholar
  92. Yu HX, Lin ZF, Feng JF, Xu TL, Wang LS (2001) Development of quantitative structure activity relationships in toxicity prediction of complex mixtures. Acta Pharmacol Sin 22:45–49Google Scholar
  93. Yuan M, Liu B, Liu E, Sheng W, Zhang Y, Crossan A, Kennedy I, Wang S (2011) Immunoassay for phenylurea herbicides: application of molecular modeling and quantitative structure–activity relationship analysis on an antigen–antibody interaction study. Anal Chem 83:4767–4774CrossRefGoogle Scholar
  94. Zhang L, Zhou PJ, Yang F, Wang ZD (2007) Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives. Chemosphere 67:396–401CrossRefGoogle Scholar
  95. Zhu T, Jafvert CT, Fu D, Hu Y (2015) A novel method for measuring polymer-water partition coefficients. Chemosphere 138:973–979CrossRefGoogle Scholar
  96. Zhu T, Wu J, He C, Fu D, Wu J (2018) Development and evaluation of MTLSER and QSAR models for predicting polyethylene-water partition coefficients. J Environ Manag 223:600–606CrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and EngineeringYangzhou UniversityYangzhouChina
  2. 2.School of Civil EngineeringNanjingChina

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