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Nonparametric estimation of petroleum accident risk to improve environmental protection

Abstract

The researchers collected and examined 10 years of petroleum-related accidents in the state capital of New York (NY) to develop a preliminary model (N = 1,005). The goal of the research was to propose an evidence-driven methodology to inform urban environmental policy making and emergency preparedness planning. Albany, NY, USA, was a preferentially selected sample site since it was a large city in an environmentally sensitive region with controversial oil–gas fracking policies being debated within government. The objective of the study was to develop a predictive model from petroleum accident data using nonparametric inferential statistical techniques to avoid the constraints inherent of normal distribution assumptions. A statistically significant model was formulated and tested, which indicated that the probability of petroleum accidents in the gas–oil industry was almost six times higher than their occurrence by people in other groups, such as electricity generation, transportation, hospitals, universities, warehouses, government, businesses, and residences.

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References

  1. Amjady N, Vahidinasab V (2013) Security-constrained self-scheduling of generation companies in day-ahead electricity markets considering financial risk. Energy Conver Manag 65(1):164–172

  2. Bertrand JWM, Fransoo JC (2002) Operations management research methodologies using quantitative modeling. Int J Op Prod Manag 22(2):241–264

  3. Branigin RD (2011) Toward a better model for managing scheduling delays and soft cost claims under builders risk insurance policies. Brief 40(4):1–13

  4. Catalao JPS, Pousinho HMI, Contreras J (2012) Optimal hydro scheduling and offering strategies considering price uncertainty and risk management. Energy 37(1):237–244

  5. Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Lawrence Erlbaum Associates, Mahwah, NJ

  6. Diamantoulaki I, Angelides DC (2013) Risk-based maintenance scheduling using monitoring data for moored floating breakwaters. Struct Saf 41(2):107

  7. Ellram LM (1996) The use of the case study method in logistics research. J Bus Logist 17(2):93–138

  8. Fleiss JL, Nee JCM, Landis JR (1979) Large sample variance of kappa in the case of different sets of raters. Psychol Bull 86(1):974–977

  9. Ghadikolaei HM, Ahmadi A, Aghaei J, Najafi M (2012) Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty. Renew Sustain Energy Rev 16(7):4734–4743

  10. Goodwin Y, Strang KD (2012) Socio-cultural and multi-disciplinary perceptions of risk. Int J Risk Conting Manag 1(1):1–11

  11. Hari Nugroho DS (2010). Risk-based inspection scheduling planning for intelligent agent in the autonomous fault management. AIP Conf Proc

  12. Jun DH, Rayes KE (2011) Fast and accurate risk evaluation for scheduling large-scale construction projects. J Comput Civil Eng 25(5):407–417

  13. Kazempour SJ, Moghaddam MP (2011) Risk-constrained self-scheduling of a fuel and emission constrained power producer using rolling window procedure. Int J Electr Power Energy Syst 33(2):359–368

  14. Krajewski LJ, Ritzman LP, Malhotra MK (2010) Operations management, 9th edn. Pearson Education, Upper Saddle River, NJ

  15. Li J, Liu L, Ren J, Duan H, Zheng L (2012) Behavior of urban residents toward the discarding of waste electrical and electronic equipment: a case study in Baoding, China. Waste Manag Res 30(11):1187–1197

  16. Mangan J, Lalwani C, Gardner B (2004) Combining quantitative and qualitative methodologies in logistics research. Int J Phys Distrib Logist Manag 34(7):565–578

  17. Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91

  18. Matlow AG, Wray R, Richardson SE (2012) Attitudes and beliefs, not just knowledge, influence the effectiveness of environmental cleaning by environmental service workers. Am J Infect Control 40(3):260

  19. Mendelssohn IA, Andersen AL, Baltz DM, Caffey RH, Carman KR, Fleeger JW, Joye SB, Lin Q, Maltby E, Overton EB, Rozas LR (2010) Oil impacts on coastal wetlands: implications for the Mississippi river delta ecosystem after the deepwater horizon oil spill. Bioscience 62(6):562–574

  20. Nersesian R (2012) Energy risk management. Palisades, New York, NY

  21. New York State Navigation (NYS) Law, New York State Law (2009)

  22. Nysdec.(2013). Chemical and pollution control [petroleum and petroleum spill data from 1978 to 2013]. Albany, NY: New York State Department of Environmental Conservation (NYSDEC), Division of Mineral Resources

  23. Pousinho HMI, Mendes VMF, Catalao JPS (2012) Scheduling of a hydro producer considering head-dependency, price scenarios and risk-aversion. Energy Convers Manag 56(1):96–103

  24. Simchi-Levi D, Chen X, Bramel J (2005) The logic of logistics: theory, algorithms, and applications for logistics and supply chain management, 2nd edn. Springer, New York

  25. Simonovi SP (2010) Systems approach to management of disasters: methods and applications. Wiley, NY

  26. Somasundaram P, Babu MR (2012) Risk invoked self-scheduling of a Genco in the day-ahead energy market. J Eng Appl Sci 7(2):136–146

  27. Sparks, R. (2013, July 15). Investigators find bodies of more victims. Make progress identifying remains. Lac-Mégantic: what we know, what we don’t. Associated Press pp. A6–A7

  28. Stemler SE (2004) A comparison of consensus, consistency, and measurement approaches to estimating interrater reliability. Pract Assess Res Eval 9(4):66–78

  29. Strang KD (2011) Applying multidisciplinary logistic techniques to improve operations productivity at a mine. Logist Res J 3(4):207–219

  30. Strang KD (2012a) Applied financial nonlinear programming models for decision making. Int J Appl Decis Sci 5(4):370–395

  31. Strang KD (2012b) Importance of verifying queue model assumptions before planning with simulation software. Eur J Oper Res 218(2):493–504

  32. Strang KD (2012c) Logistic planning with nonlinear goal programming models in spreadsheets. Int J Appl Logist 2(4):1–14

  33. Strang KD (2013a) Planning for hurricane Isaac using probability theory in a linear programming model. Int J Risk Conting Manag 2(1):51–65

  34. Strang KD (2013b) Risk management research design ideologies, strategies, methods and techniques. Int Risk Conting Manag 2(2):1–26

  35. Taleb NN (2007) The black swan. The impact of the highly improbable. Penguin, London

  36. Thompson H (2012) Fracking boom spurs environmental audit: as hydraulic fracturing unlocks new gas reserves, researchers struggle to understand its health implications. Nature 485(5):557–560

  37. Vanasselt MBA, Renn O (2011) Risk governance. Risk Res J 14(4):431–449

  38. Vogt PW, Gardner DC, Haeffele LM (2012) When to use what research design. Guilford, New York

  39. Witter RZ, Mckenzie L, Stinson KE, Scott K, Newman LS, Adgate J (2013) The use of health impact assessment for a community undergoing natural gas development. Am J Public Health 103(6):1002–1010

  40. Worthington D (2009) Reflections on queue modelling from the last 50 years. J Oper Res Soc 60(1):S83–S92

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Correspondence to Kenneth David Strang.

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Strang, K.D., Nersesian, R.L. Nonparametric estimation of petroleum accident risk to improve environmental protection. Environ Syst Decis 34, 150–159 (2014). https://doi.org/10.1007/s10669-013-9476-z

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Keywords

  • Uncertainty quantification
  • Oil and gas industry
  • Petroleum accidents
  • Risk mitigation
  • Urban planning
  • Nonparametric inferential statistics