Procedure LIAM (LDAR, IR Camera, Analyzing & Modeling) to determine the contribution of ambient emissions and combustion of hydrocarbon

  • M. Esmaeili
  • K. SaebEmail author
  • R. Amirnezhad
  • F. G. Fahimi
Original Paper


In special petroleum areas where the petrochemical, petroleum and gas industries are located, ambient emissions are the first and most environmental pollutants that are undetectable. The present study aims to assess the environmental pollutants resulting from leaks and fixed resources of Olefin unit in the Arya Sasol Petrochemical Complex located in Asaluyeh, south of Iran. In this study, the LIAM method (LDAR, IR Camera, Analyzing & Modeling) was for sampling process during four seasons from 2016 to 2017. Leak points of the unit were detected by IR Camera and LDAR program. AERMODE software was also used to model the dispersion of SO2, NOX, CO2 and particulate matters released from the fixed resources. In the next, IDW method in ArcGIS 10.2 was conducted to interpolate the environmental pollutants. The interpolation of the annual average of pollutants showed that the concentration of benzene, butadiene, ethylbenzene, heptane and SO2 in some sections is higher than the environmental standards. The results of AERMODE modeling showed that the maximum 24-h concentration and annual average of SO2 only in autumn have exceeded the clean air standard. The combination of proposed methods in this study can be used as a smart way to evaluate the industrial pollutants.


LDAR LIAM IR Camera AERMODE modeling Petroleum areas 



The authors wish to thank all who assisted in conducting this work.


  1. Afzali A, Rashid M, Afzali M, Younesi V (2017) Prediction of air pollutants concentrations from multiple sources using AERMOD coupled with WRF prognostic model. J Clean Prod 166:1216–1225CrossRefGoogle Scholar
  2. Amuzadeh N, Kargar Sharifabadi H (2014) Two-dimensional numerical simulation of stack pollution from an industrial complex. Mech Eng Vib 5(4):33–38Google Scholar
  3. Anselin L, Gallo J) 2006 (Interpolation of air quality measures in hedonic house price models: spatial aspects. Spatial Econ Anal 1(1)Google Scholar
  4. Askariyeh MH, Kota SH, Vallamsundar S, Zietsman J, Ying Q (2017) AERMOD for near-road pollutant dispersion: evaluation of model performance with different emission source representations and low wind options. Transp Res Part D 57:392–402CrossRefGoogle Scholar
  5. Atabi F, Jafarigol F, Momeni M, Salimian M, Bahmannia G (2014) Dispersion modeling of CO with AERMOD in South Pars fourth gas refinery. J Env Health Eng 1(4):281–292CrossRefGoogle Scholar
  6. Babu BS (2016) Comparative study on the spatial interpolation technics in GIS. Int J Of Sci & Eng Res 7(2):550–554Google Scholar
  7. Bluett J, Gimson N, Fisher G, Heydenrych C, Freeman T, Godfrey J (2004) Good practice guide for atmospheric dispersion modeling. Ministry for the Environment, Wellington, New ZealandGoogle Scholar
  8. Chambers AK, Strosher M, Wootton T, Moncrieff J, McCready P) 2008(Direct measurement of fugitive emissions of hydrocarbons from a refinery. J Air Waste Manag Assoc 58(8):1047–1056Google Scholar
  9. Drago J (2012) Optical measuring technologies for detecting fugitive emission. World gas conference, 4–8 June, Kuala LumpurGoogle Scholar
  10. Environmental Protection Agency (1995) Protocol for Equipment leak emission estimates (emission standard division). Office of Air and Radiation Office of Air Quality Planning and Standards Research Triangle Park, North Carolina 27711Google Scholar
  11. Environmental Protection Agency (2009) Section 3.5: Natural gas system (IPCC source category 1B2b). Inventory of U.S greenhouse gas emissions and sinks: 1990–2007Google Scholar
  12. Environmental Protection Agency, EPA (2017) Method 21. Determination of volatile organic compound leaks, viewed 6 January 2017.
  13. Epperson D, Lev-On M, Taback H, Siegell J, Ritter K (2007) Equivalent leak definitions for smart LDAR (Leak Detection and Repair) when using optical imaging technology. J Air Waste Manag Assoc 57:1050–1060CrossRefGoogle Scholar
  14. Esmailnejad M, Eskandari Sani M, Barzaman S (2015) Evaluation and zoning of urban air pollution in Tabriz. Reg Plan 5(19):173–186Google Scholar
  15. Gibson MD, Kundu S, Satish M (2013) Dispersion model evaluation of PM2.5, NOX and SO2 from point and major line sources in Nova Scotia, Canada using AERMOD Gaussian plume air dispersion model. Atmos Pollut Res 4:157–167CrossRefGoogle Scholar
  16. Holst GC (2000) Common sense approach to thermal imaging SPIE. Optical Engineering Press, Washington, DCGoogle Scholar
  17. Homssi Ahmad M (2010) Smart leak detection and repair at Q-Chem. In: Proceedings of the 2nd annual gas processing symposium, pp 153–162Google Scholar
  18. Ikechukwu MN, Ebinne E, Idorenyin U, Raphael NI (2017) Accuracy assessment and comparative analysis of IDW, spline and kriging in spatial interpolation of landform (topography): an experimental study. J Geogr Inf Syst 9:354–371Google Scholar
  19. Kalhor M, Bajoghli M (2017) Comparison of AERMOD, ADMS and ISC3 for incomplete upper air meteorological data (case study: Steel plant). Atmos Pollut Res XXX:1–6Google Scholar
  20. Khabari Z, Nejad Corki F, Talebi Sh (2016) The development of the air pollution distribution model (AERMODE) in MATLAB software. Natural Environment, Natural Resources of Iran 69(2):377–399Google Scholar
  21. Kumar Jha D, Sabesan M, Das A, Vinithkumar NV, Kirubagaran R (2011) Evaluation of interpolation technique for air quality parameters in Port Blair, India. Univ J Environ Res Technol 1(3):301–310Google Scholar
  22. Maham A, Valizadeh Kamran H, Ghahremani M (2014) Evaluation of different land statistics methods to study regional variations of precipitation in the north-west of the country and suggest the best model using GIS. The first national conference on the application of advanced models Spatial Analysis (Remote Sensing and GIS) in Land Use. 5 to 6 March. Islamic Azad University, Yazd BranchGoogle Scholar
  23. Naranjo E, Baligaa S, Parka J, Bernascolleb P (2011) IR gas cloud imaging in oil and gas applications: immunity to false stimul. SPIE Proc 8013:1–10Google Scholar
  24. Ozkurt N, Sari D, Akalin N, Hilmioglu B (2013) Evaluation of the impact of SO2 and NO2 emissions on the ambient air-quality in the Çan-Bayramiç region of northwest Turkey during 2007–2008. Sci Total Environ 456–457:254–266CrossRefGoogle Scholar
  25. Sárközy F, Gáspár P (1992) Modelling of scalar fields represented by scattered 3D points. Period Polytech Civ Eng 36(2):187–200Google Scholar
  26. Sauger E, Fily S, Lejeune H, Thomas N (2013) Investigating the use of infrared cameras to detect VOCs. Seal Technol 4:8–12CrossRefGoogle Scholar
  27. Seangkiatiyuth K, Surapipith V, Tantrakarnapa K, Lothongkum AW (2011) Application of the AERMOD modeling system for environmental impact assessment of NO2 emissions from a cement complex. J Environ Sci 23(6):931–940CrossRefGoogle Scholar
  28. Shahrouie A (2013) Modeling and assessing the hazards of ambient emissions of CO, SO2 and NO2. Emissions of steel production process. Master’s Thesis. Islamic Azad University of Shahrood Branch, p 10Google Scholar
  29. Stephanie Saunier S, Haugland T, Pederstad A (2014) Quantifying cost-effectiveness of systematic leak detection and repair programs using infrared cameras. Clean Air Task Force (CATF) CL-13-27:36Google Scholar
  30. The United States Environmental Protection Agency, 2007. Leak Detection and Repair A Best Practices Guide. EPA-305-D-07-001.
  31. US Environmental Protection Agency (EPA) (2011) Emissions monitoring and analysis division research triangle park, User’s Guide for the AMS/EPA Regulatory. 454/B-03-001, Office of Air Quality Planning and Standards, North CarolinaGoogle Scholar
  32. Varck K, Warner S, Davis (1998) Air pollution, origin and control. Translated by Kazem Nadafi, Mohammad Sadegh Hasanvand, Mohsen Heidari, Ali Naghizadeh. 2013. Nas Scientific Cultural Institute Publications, Tehran, p 127Google Scholar
  33. Yousefi R, Zorufchi Bennis Kh, Derafshi S, Shaker Khatibi M (2016) Determination of the emission and simulation of the styrene and acrylonitrile dispersion from the ABS unit of the petrochemical industry. Civ Eng Modarres 16(5):243–252Google Scholar
  34. Zade S, Ingole N (2015) Air dispersion modelling to assess ambient air quality impact due to carbon industry. Int J Res Stud Sci Eng Technol 2(7):45–53Google Scholar
  35. Zhou L, Zeng Y (2007) Automatic alignment of infrared video frames for equipment leak detection. Anal Chim Acta 584:223–227CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.Department of Environment College of Natural ResourceIAU of TonekabonTehranIran

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