Soft Computing

, Volume 23, Issue 1, pp 59–75 | Cite as

Computational source term estimation of the Gaussian puff dispersion

  • Hui LiEmail author
  • Jianwen Zhang
  • Junkai Yi
Methodologies and Application


The hazardous or toxic chemical releases have a detrimental impact on public safety. Estimating source parameters is of particular importance in aiding emergency response and post-assessment. Source term estimation from sensor measurements with a given Gaussian puff dispersion model is a typical inverse problem, which can be transformed into an optimization problem. In this paper, we employed the particle swarm optimization, the Nelder–Mead method, and their hybrid method to solve the optimization problem. Furthermore, we proposed a three-dimensional neighborhood topology which considerably improves performance of the particle swarm optimization. We implemented all these algorithms in JAVA on an embedded system to make a preliminary estimation of the accidental puff release. Numerical experiments with synthetic datasets show that the particle swarm optimization maintains a balance between computation time, accuracy, robustness, and implementation complexity. In contrast, the hybrid algorithm has an advantage in computation time at the expense of more sophisticated implementation.


Gaussian puff Pasquill-Gifford model Nelder–Mead method Particle swarm optimization Neighborhood topology 



This work was supported by the National Natural Science Foundation of China under grants of the general technical foundation research joint fund (Project No. U1636208). And this project was also supported by the Ministry of Science and Technology of China under grants of the national key technology R&D program (Project No. 2015BAK39B02).

Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Human and animals rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Abo-Hammour Z (2014) Optimization solution of Troesch’s and Bratu’s problems of ordinary type using novel continuous genetic algorithm. Discrete Dyn Nat Soc 5:703–719MathSciNetGoogle Scholar
  2. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19–28Google Scholar
  3. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 1:1–23Google Scholar
  4. Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435CrossRefGoogle Scholar
  5. Abualigah LM, Khader AT, Hanandeh ES (2018a) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466CrossRefGoogle Scholar
  6. Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125CrossRefGoogle Scholar
  7. Abualigah LM, Khader AT, Hanandeh ES (2018c) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 5:1–25Google Scholar
  8. Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415MathSciNetCrossRefzbMATHGoogle Scholar
  9. Baghmisheh MTV, Peimani M, Sadeghi MH, Ettefagh MM, Tabrizi AF (2012) A hybrid particle swarm Nelder-Mead optimization method for crack detection in cantilever beams. Appl Soft Comput 12(8):2217–2226CrossRefGoogle Scholar
  10. Benarie M (1980) Critical considerations and improvements to the short-time Gaussian plume models. Urban Air Pollution Modelling. Palgrave Macmillan, UK, pp 65–87Google Scholar
  11. Berbekar E, Harms F, Leitl B (2015) Dosage-based parameters for characterization of puff dispersion results. J Hazard Mater 283(3):178–185CrossRefGoogle Scholar
  12. Bhattacharjee Y (2008) Combating terrorism, new efforts to detect explosives require advances on many fronts. Science 5882:1416–1417CrossRefGoogle Scholar
  13. Briggs GA (1973) Diffusion estimation for small emissions. Preliminary report. United States.
  14. Broyden CG (1967) Quasi-Newton methods and their application to function minimisation. Math Comput 21(99):368–381MathSciNetCrossRefzbMATHGoogle Scholar
  15. Cameron IT, Raman R (eds) (2005) Effect models for consequence analysis, chap 6. In: Process systems engineering, vol 6, pp 195–259.
  16. Cao X et al (2011) Dispersion coefficients for Gaussian Puff models. Bound Layer Meteorol 139:487–500CrossRefGoogle Scholar
  17. Cui H et al (2011) A tracer experiment study to evaluate the CALPUFF real time application in a near-field complex terrain setting. Atmos Environ 45(39):7525–7532CrossRefGoogle Scholar
  18. Elperin T, Fominykh A, Krasovitov B (2016) Effect of raindrop size distribution on scavenging of aerosol particles from gaussian air pollution plumes and puffs in turbulent atmosphere. Process Saf Environ Prot 102:303–315CrossRefGoogle Scholar
  19. Fourer R (1985) A simplex algorithm for piecewise-linear programming i: derivation and proof. Math Program 33:204–233MathSciNetCrossRefzbMATHGoogle Scholar
  20. Gifford FAJ (1961) Use of routine meteorological observations for estimating atmospheric dispersion. Nucl Saf 2:47–51Google Scholar
  21. Greenwood BW, Hudson JM, Bodner AI (1991) Analysis of community risk resulting from rupture of a sour gas pipeline. The analysis, communication, and perception of risk. Adv Risk Anal 9:63–76Google Scholar
  22. Griffiths RF (1994) Errors in the use of the briggs parameterization for atmospheric dispersion coefficients. Atmos Environ 28:2861–2865CrossRefGoogle Scholar
  23. Guha A (1997) A unified eulerian theory of turbulent deposition to smooth and rough surfaces. J Aerosol Sci 28:1517–1537CrossRefGoogle Scholar
  24. Holmes NS, Morawska L (2006) A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmos Environ 40:5902–5928CrossRefGoogle Scholar
  25. Hosseini B, Stockie JM (2016) Bayesian estimation of airborne fugitive emissions using a gaussian plume model. Atmos Environ 141:122–138CrossRefGoogle Scholar
  26. Johannesson G, Hanley B, Nitao J (2004) Dynamic Bayesian models via Monte Carlo–an introduction with examples. Int J Therm Sci 43:939–950CrossRefGoogle Scholar
  27. Kameyama K (2009) Particle swarm optimization–a survey. IEICE Trans Inf Syst E 92D(7):1724–1731Google Scholar
  28. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 congress on evolutionary computation, pp 1671–1676Google Scholar
  29. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the Nelder-Mead simplex method in low dimensions. Siam J Optim 9:112–147MathSciNetCrossRefzbMATHGoogle Scholar
  30. Li H, Zhang J (2017) Fast source term estimation using the PGA-NM hybrid method. Eng Appl Artif Intell 62:68–79CrossRefGoogle Scholar
  31. Li C et al (2017) Soft measurement of wood defects based on LDA feature fusion and compressed sensor images. J For Res 28(6):1274–1281CrossRefGoogle Scholar
  32. Lutman ER, Jones SR, Hill RA, Mcdonald P, Lambers B (2004) Comparison between the predictions of a gaussian plume model and a lagrangian particle dispersion model for annual average calculations of long-range dispersion of radionuclides. J Environ Radioact 75:339–355CrossRefGoogle Scholar
  33. Mckinnon KIM (1998) Convergence of the Nelder-Mead simplex method to a nonstationary point. SIAM J Optim 9:148–158MathSciNetCrossRefzbMATHGoogle Scholar
  34. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313MathSciNetCrossRefzbMATHGoogle Scholar
  35. Oettl D, Kukkonen J, Almbauer RA (2001) Evaluation of a gaussian and a lagrangian model against a roadside data set, with emphasis on low wind speed conditions. Atmos Environ 35:2123–2132CrossRefGoogle Scholar
  36. Pullen J et al (2005) A comparison of contaminant plume statistics from a Gaussian puff and urban CFD model for two large cities. Atmos Environ 39:1049–1068CrossRefGoogle Scholar
  37. Stern R, Yamartino RJ (2001) Development and first evaluation of micro-calgrid: a 3-d, urban-canopy-scale photochemical model. Atmos Environ 35:149–165CrossRefGoogle Scholar
  38. Sutton G (1955) Weather forecasting: the future outlook. Nature 4491:993–996CrossRefGoogle Scholar
  39. Venkatakrishnan V (1989) Newton solution of inviscid and viscous problems. AIAA J 27:885–891CrossRefGoogle Scholar
  40. Wang B (2017) Density peaks clustering based integrate framework for multi-document summarization. CAAI Trans Intell Technol 2(1):26–30CrossRefGoogle Scholar
  41. Yang H, Yu L, University NF (2017) Feature extraction of wood-hole defects using wavelet-based ultrasonic testing. J For Res 28:395–402CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina
  2. 2.Fluid Mechanics and Heat Transfer LaboratoryBeijing University of Chemical TechnologyBeijingChina
  3. 3.Beijing Information Science and Technology UniversityBeijingChina

Personalised recommendations