Fang Li, Anxiang Lu, Jihua Wang, and Tianyan You


  1. [1] Z. Li, Z. Ma, T.J. van der Kuijp, Z. Yuan Z, and L. Huang,A review of soil heavy metal pollution from mines in China:Pollution and health risk assessment, Science of the TotalEnvironment, 468–469, 2014, 843–853.
  2. [2] S. Khalid, M. Shahid, N.K. Niazi, B. Murtaza, I. Bibi, and C.Dumat, A comparison of technologies for remediation of heavymetal contaminated soils, Journal of Geochemical Exploration,182, 2017, 247–268.
  3. [3] H. Chen, Y. Teng, S. Lu, Y. Wang, and J. Wang, Contaminationfeatures and health risk of soil heavy metals in China, Scienceof the Total Environment, 512–513, 2015, 143–153.
  4. [4] Q. Duan, J. Lee, Y. Liu, H. Chen, and H. Hu, Distributionof heavy metal pollution in surface soil samples in China: Agraphical review, Bulletin of environmental contamination andtoxicology, 97(3), 2016, 303–309.
  5. [5] K.S. Balkhair and M.A. Ashraf, Field accumulation risks ofheavy metals in soil and vegetable crop irrigated with sewagewater in western region of Saudi Arabia, Saudi Journal ofBiological Sciences, 23(1), 2016, S32–S44.
  6. [6] T. Radu and D. Diamond, Comparison of soil pollution concentrations determined using AAS and portable XRF techniques,Journal of Hazardous Materials, 171(1–3), 2009, 1168–1171.
  7. [7] B. Song, G. Zeng, J. Gong, J. Liang, P. Xu, Z. Liu, et al., Evaluation methods for assessing effectiveness of in situ remediationof soil and sediment contaminated with organic pollutants andheavy metals, Environment International, 105, 2017, 43–55.
  8. [8] H. Rowe, N. Hughes, and K. Robinson, The quantification andapplication of handheld energy-dispersive x-ray fluorescence(ED-XRF) in mudrock chemostratigraphy and geochemistry,Chemical Geology, 324–325, 2012, 122–131.
  9. [9] A. Turner, H. Poon, A. Taylor, and M.T. Brown, In situ determination of trace elements in Fucus spp. by field-portable-XRF, Science of the Total Environment, 593–594, 2017,227–235.
  10. [10] E.L. Shuttleworth, M.G. Evans, S.M. Hutchinson, and J.J.Rothwell, Assessment of lead contamination in peatlands usingfield portable XRF, Water, Air, & Soil Pollution, 225(2), 2014,1844–1856.
  11. [11] N.G. Paltridge, L.J. Palmer, P.J. Milham, G.E. Guild,and J.C.R. Stangoulis, Energy-dispersive X-ray fluorescenceanalysis of zinc and iron concentration in rice and pearl milletgrain, Plant and Soil, 361(1–2), 2012, 251–260.
  12. [12] W. Hu, B. Huang, D.C. Weindorf, and Y. Chen, Metalsanalysis of agricultural soils via portable X-ray fluorescencespectrometry, Bulletin of Environmental Contamination andToxicology, 92(4), 2014, 420–426.
  13. [13] A.G. Caporale, P. Adamo, F. Capozzi, G. Langella, F. Terribile,and S. Vingiani, Monitoring metal pollution in soils usingportable-XRF and conventional laboratory-based techniques:Evaluation of the performance and limitations according tometal properties and sources, Science of the Total Environment,643, 2018, 516–526.
  14. [14] A. Chandrasekaran, R. Ravisankar, N. Harikrishnan, K.K.Satapathy, M.V. Prasad, and K.V. Kanagasabapathy, Multi-variate statistical analysis of heavy metal concentration in soilsof Yelagiri Hills, Tamilnadu, India – spectroscopical approach,Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 137, 2015, 589–600.
  15. [15] A. Turner and A. Taylor, On site determination of trace metalsin estuarine sediments by field-portable-XRF, Talanta, 190,2018, 498–506.
  16. [16] S. Chakraborty, T. Man, L. Paulette, S. Deb, B. Li, D.C.Weindorf, et al., Rapid assessment of smelter/mining soilcontamination via portable X-ray fluorescence spectrometryand indicator kriging, Geoderma, 306, 2017, 108–119.
  17. [17] S. Zhou, Z. Yuan, Q. Cheng, Z. Zhang, and J. Yang, Rapidin situ determination of heavy metal concentrations in polluted water via portable XRF: Using Cu and Pb as example,Environmental Pollution, 243(Pt B), 2018, 1325–1333.
  18. [18] B. Lemi`ere, A review of pXRF (field portable X-ray fluorescence) applications for applied geochemistry, Journal ofGeochemical Exploration, 188, 2018, 350–363.
  19. [19] M. Qu, Y. Wang, B. Huang, and Y. Zhao, Spatial uncertaintyassessment of the environmental risk of soil copper usingauxiliary portable X-ray fluorescence spectrometry data andsoil pH, Environmental Pollution, 240, 2018, 184–190.
  20. [20] E.O. Kazimoto, C. Messo, F. Magidanga, and E. Bundala,The use of portable X-ray spectrometer in monitoring anthropogenic toxic metals pollution in soils and sediments ofurban environment of Dar es Salaam Tanzania, Journal ofGeochemical Exploration, 186, 2018, 100–113.
  21. [21] V.A. Sol´e, E. Papillon, M. Cotte, P. Walter, and J. Susini,A multiplatform code for the analysis of energy-dispersiveX-ray fluorescence spectra, Spectrochimica Acta Part B: AtomicSpectroscopy, 62(1), 2007, 63–68.
  22. [22] M.U.A. Bromba and H. Ziegler, Application hints for Savitzky–Golay digital smoothing filters, Analytical Chemistry, 53(11),1981, 1583–1586.
  23. [23] R.W. Schafer, What is a Savitzky–Golay filter?, IEEE SignalProcessing Magazine, 28(4), 2011, 111–117.
  24. [24] D. Acharya, A. Rani, S. Agarwal, and V. Singh, Applicationof adaptive Savitzky–Golay filter for EEG signal processing,Perspectives in Science, 8, 2016, 677–679.
  25. [25] Y. Liu, B. Dang, Y. Li, H. Lin, and H. Ma, Applications ofSavitzky–Golay filter for seismic random noise reduction, ActaGeophysica, 64(1), 2016, 101–124.
  26. [26] G. Viv´o-Truyols and P.J. Schoenmakers, Automatic selectionof optimal Savitzky–Golay smoothing, Analytical Chemistry,78(13), 2006, 4598–4608.
  27. [27] D. Gupta and S. Choubey, Discrete wavelet transform for imageprocessing, International Journal of Emerging Technology andAdvanced Engineering, 4(3), 2015, 598–602.
  28. [28] C. Ma, J. Li, and D. Wang, Optimal evaluation index systemand benefit evaluation model for agricultural informatizationin Beijing, International Journal of Robotics and Automation,33(1), 2018, 89–96.
  29. [29] A. Bhattacharyya, M. Sharma, R.B. Pachori, P. Sircar, andU.R. Acharya, A novel approach for automated detection offocal EEG signals using empirical wavelet transform, NeuralComputing and Applications, 29(8), 2016, 47–57.
  30. [30] D. De Yong, S. Bhowmik, and F. Magnago, An effective powerquality classifier using wavelet transform and support vectormachines, Expert Systems with Applications, 42(15–16), 2015,6075–6081.
  31. [31] Z. Lai, X. Qu, Y. Liu, D. Guo, J. Ye, Z. Zhan, et al., Imagereconstruction of compressed sensing MRI using graph-basedredundant wavelet transform, Medical Image Analysis, 27,2016, 93–104.
  32. [32] A. Bhattacharyya, R. Pachori, A. Upadhyay, and U. Acharya,Tunable-Q wavelet transform based multiscale entropy measurefor automated classification of epileptic EEG signals, AppliedSciences, 7(4), 2017, 385–402.
  33. [33] M. Hemmat Esfe, M.R. Hassani Ahangar, M. Rejvani, D.Toghraie, and M.H. Hajmohammad, Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid ofTiO2 using experimental data, International Communicationsin Heat and Mass Transfer, 75, 2016, 192–196.
  34. [34] S. Shi, D. Zhang, P. Feng, and L. Han, The enhancementarithmetic of BP neural network based on target optimizing,2018 International Conference on Computer Modeling, Simu-lation and Algorithm (CMSA 2018), (Beijing, China: AtlantisPress, 2018), 151, 2018, 137–141.
  35. [35] Q.G. Wen, K.F. Sun, and H. Yen, A method of temperatureprediction and velocity control based on BP artificial neuralnetwork, 2016 International Conference on Information Systemand Artificial Intelligence (ISAI) IEEE, Hong Kong, China,2016, 327–331.
  36. [36] G.-Z. Quan, Z.-H. Zhang, J. Pan, and Y.-F. Xia, Modelling thehot flow behaviors of AZ80 alloy by BP-ANN and the applications in accuracy improvement of computations, MaterialsResearch, 18(6), 2015, 1331–1345.
  37. [37] C. Yang, Z. Wang, L. Zheng, and D. Mao, Predicting equivalentstatic density of fuzzy ball drilling fluid by BP artificial neutralnetwork, Advances in Materials Science and Engineering, 2015,2015, 1–6.
  38. [38] S. Mammadli, Financial time series prediction using artificialneural network based on Levenberg–Marquardt algorithm,Procedia Computer Science, 120, 2017, 602–607.
  39. [39] M.G. Shirangi and A.A. Emerick, An improved TSVD-basedLevenberg–Marquardt algorithm for history matching and comparison with Gauss–Newton, Journal of Petroleum Science andEngineering, 143, 2016, 258–271.
  40. [40] A. Gholami, F. Honarvar, and H.A. Moghaddam, Modelingthe ultrasonic testing echoes by a combination of particleswarm optimization and Levenberg–Marquardt algorithms,Measurement Science and Technology, 28(6), 2017, 065001.
  41. [41] ¨O. C¸elik, A. Teke, and H.B. Yıldırım, The optimized artificialneural network model with Levenberg–Marquardt algorithmfor global solar radiation estimation in Eastern MediterraneanRegion of Turkey, Journal of Cleaner Production, 116, 2016,1–12.
  42. [42] J. Li, W.X. Zheng, J. Gu, and L. Hua, Parameter estimation algorithms for Hammerstein output error systems usingLevenberg–Marquardt optimization method with varying interval measurements, Journal of the Franklin Institute, 354(1),2017, 316–331.
  43. [43] M. Kayri, Predictive abilities of Bayesian regularization andLevenberg–Marquardt algorithms in artificial neural networks:A comparative empirical study on social data, Mathematicaland Computational Applications, 21(2), 2016, 20–30.

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