Evaluación y aplicación de la incertidumbre de medición en la determinación de las emisiones de fuentes fijas: una revisión
Palabras clave:
Incertidumbre de medición, Guía para la Expresión de la Incertidumbre de Medida GUM, Método Monte Carlo, métodos estocásticos
Resumen
Este artículo presenta una revisión de metodologías comúnmente citadas en la literatura para la estimación de la incertidumbre, como es la metodología no estocástica de la Guía para la Expresión de la Incertidumbre de Medida (GUM), la cual provee una estructura de estimación con limitaciones en su implementación, como son: cálculo de derivadas parciales, suposición de linealidad de los modelos e identificación de las fuentes de incertidumbre y sus distribuciones de probabilidad. Por otro lado, se discuten otros métodos para estimar la incertidumbre, como son: Monte Carlo, Conjuntos Difusos, Intervalo Generalizado, Inferencia Bayesiana, Caos Polinomial y Bootstrap, que a diferencia de la GUM, presentan limitaciones de costo computacional y requieren de conocimientos más especializados para su implementación. El objetivo de este artículo es reportar el grado de aplicación y difusión de los métodos de estimación de la incertidumbre en las emisiones de fuentes fijas, encontrándose que la mayoría se enfoca en estudios usados para la elaboración de inventarios de gases de efecto invernadero (GHG), y son escasos los orientados a la medición de las emisiones de fuentes fijas usando monitoreos de lectura directa, como también los métodos definidos por la Agencia de Protección Ambiental de los Estados Unidos (US EPA). Se discute finalmente las fortalezas y debilidades que dan lugar al fomento de nuevas investigaciones en esta área del conocimiento.
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[51] D. Theodorou, L. Meligotsidou, S. Karavoltsos, A. Burnetas, M. Dassenakis, and M. Scoullos, “Comparison of ISO-GUM and Monte Carlo methods for the evaluation of measurement uncertainty: Application to direct cadmium measurement in water by GFAAS,” Talanta, vol. 83, no. 5, pp. 1568–1574, Feb. 2011.
[52] D. Theodorou, Y. Zannikou, G. Anastopoulos, and F. Zannikos, “Coverage interval estimation of the measurement of Gross Heat of Combustion of fuel by bomb calorimetry: Comparison of ISO GUM and adaptive Monte Carlo method,” Thermochim. Acta, vol. 526, no. 1–2, pp. 122–129, Nov. 2011.
[53] S. Sediva and M. Havlikova, “Comparison of GUM and Monte Carlo method for evaluation measurement uncertainty of indirect measurements,” in Proceedings of the 14th International Carpathian Control Conference (ICCC), 2013, pp. 325–329.
[54] A. E. Torres-Abello, “Metodología para la Estimación de Incertidumbres Asociadas a Concentraciones de Sólidos Suspendidos Totales Mediante Métodos de Generación Aleatoria,” TecnoLógicas, no. 26, pp. 181–200, 2011.
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[57] R. Bun, K. Hamal, M. Gusti, and A. Bun, “Spatial GHG inventory at the regional level: accounting for uncertainty,” Clim. Change, vol. 103, no. 1–2, pp. 227–244, Nov. 2010.
[58] M. Lesiv, A. Bun, and M. Jonas, “Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15,” Clim. Change, vol. 124, no. 3, pp. 505–518, Jun. 2014.
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[2] BIPM et al., International vocabulary of metrology-Basic and general concepts and associated terms (VIM), 3rd ed. JCGM, 2012.
[3] D. Romano, A. Bernetti, and R. De Lauretis, “Different methodologies to quantify uncertainties of air emissions,” Environ. Int., vol. 30, no. 8, pp. 1099–1107, Oct. 2004.
[4] H.-J. von Martens, “Evaluation of uncertainty in measurements—problems and tools,” Opt. Lasers Eng., vol. 38, no. 3–4, pp. 185–206, Sep. 2002.
[5] BIPM et al., Evaluation of measurement data - Guide to the expression of uncertainty in measurement. JCGM, 2008.
[6] BIPM et al., Evaluation of measurement data - An introduction to the “Guide to the expression of uncertainty in measurement” and related documents. JCGM, 2009.
[7] BIPM et al., Evaluation of measurement data - Supplement 2 to the “Guide to the expression of uncertainty in measurement” - Extension to any number of output quantities. JCGM, 2011.
[8] K. Rypdal and K. Flugsrud, “Sensitivity analysis as a tool for systematic reductions in greenhouse gas inventory uncertainties,” Environ. Sci. Policy, vol. 4, no. 2–3, pp. 117–135, Apr. 2001.
[9] O. Velychko and T. Gordiyenko, “The use of guide to the expression of uncertainty in measurement for uncertainty management in National Greenhouse Gas Inventories,” Int. J. Greenh. Gas Control, vol. 3, no. 4, pp. 514–517, Jul. 2009.
[10] O. N. Velichko and T. B. Gordienko, “Methods of calculating emissions of pollutants into the atmosphere and estimating their uncertainty,” Meas. Tech., vol. 52, no. 2, pp. 193–199, Feb. 2009.
[11] L.-I. Tong, C.-W. Chang, S.-E. Jin, and R. Saminathan, “Quantifying uncertainty of emission estimates in National Greenhouse Gas Inventories using bootstrap confidence intervals,” Atmos. Environ., vol. 56, pp. 80–87, Sep. 2012.
[12] A. Ferrero and S. Salicone, “The random-fuzzy variables: A new approach to the expression of uncertainty in measurement,” IEEE Trans. Instrum. Meas., vol. 53, no. 5, pp. 1370–1377, 2004.
[13] H. Cheng and A. Sandu, “Uncertainty quantification and apportionment in air quality models using the polynomial chaos method,” Environ. Model. Softw., vol. 24, no. 8, pp. 917–925, Aug. 2009.
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[17] G. Buonanno, G. Ficco, C. Liguori, and A. Pietrosanto, “The influence of the uncertainty on monitoring stack emissions in a waste-to-energy plant,” in 2008 IEEE Instrumentation and Measurement Technology Conference, 2008, pp. 1771–1776.
[18] Legal Information Institute, “Environmental Protection Agency,” in CFR, Legal Information Institute.
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[20] J. F. P. Gomes, V. M. a Cruz, and M. L. C. Ribeiro, “Estimation of uncertainty in the determination of nitrogen oxides emissions,” Accredit. Qual. Assur., vol. 11, no. 3, pp. 138–145, May 2006.
[21] BIPM et al., Evaluation of measurement data - Supplement 1 to the “Guide to the expression of uncertainty in measurement” - Propagation of distributions using a Monte Carlo method. JCGM, 2008.
[22] M. A. Azpurua, C. Tremola, and E. Paez Barrios, “Comparison of the Gum and Monte Carlo Methods for the Uncertainty Estimation in Electromagnetic Compatibility Testing,” Prog. Electromagn. Res. B, vol. 34, pp. 125–144, 2011.
[23] H. Ramebäck et al., “Implementing combined uncertainty according to GUM into a commercial gamma spectrometric software,” J. Radioanal. Nucl. Chem., vol. 282, no. 3, pp. 979–983, Dec. 2009.
[24] EURACHEM and CITAC, Quantifying Uncertainty in Analytical Measurements, 3rd ed. Germany, 2012.
[25] M. A. L. Traple, A. M. Saviano, F. L. Francisco, and F. R. Lourenço, “Measurement uncertainty in pharmaceutical analysis and its application,” J. Pharm. Anal., vol. 4, no. 1, pp. 1–5, Feb. 2014.
[26] J. Choi, E. Hwang, H. Y. So, and B. Kim, “An uncertainty evaluation for multiple measurements by GUM,” Accredit. Qual. Assur., vol. 8, no. 1, pp. 13–15, 2003.
[27] J. Choi, D. Kim, E. Hwang, and H.-Y. So, “An uncertainty evaluation for multiple measurements by GUM, II,” Accredit. Qual. Assur., vol. 8, no. 5, pp. 205–2017, 2003.
[28] S. G. Rabinovich, “Accuracy of single measurements,” Accredit. Qual. Assur., vol. 12, no. 8, pp. 419–424, Aug. 2007.
[29] R. Kessel, M. Berglund, and R. Wellum, “Application of consistency checking to evaluation of uncertainty in multiple replicate measurements,” Accredit. Qual. Assur., vol. 13, no. 6, pp. 293–298, Jun. 2008.
[30] M. Priel, “From GUM to alternative methods for measurement uncertainty evaluation,” Accredit. Qual. Assur., vol. 14, no. 5, pp. 235–241, 2009.
[31] G. Nam, C.-S. Kang, H.-Y. So, and J. Choi, “An uncertainty evaluation for multiple measurements by GUM, III: using a correlation coefficient,” Accredit. Qual. Assur., vol. 14, no. 1, pp. 43–47, Jan. 2009.
[32] F. Attivissimo, A. Cataldo, L. Fabbiano, and N. Giaquinto, “Systematic errors and measurement uncertainty: An experimental approach,” Measurement, vol. 44, no. 9, pp. 1781–1789, Nov. 2011.
[33] M. A. F. Martins, R. Requião, and R. A. Kalid, “Generalized expressions of second and third order for the evaluation of standard measurement uncertainty,” Measurement, vol. 44, no. 9, pp. 1526–1530, Nov. 2011.
[34] A. Williams, “EURACHEM/CITAC workshop on recent developments in measurement uncertainty,” Accredit. Qual. Assur., vol. 17, no. 2, pp. 111–113, Apr. 2012.
[35] P. Wei, Q. P. Yang, M. R. Salleh, and B. Jones, “Symbolic Computation for Evaluation of Measurement Uncertainty,” in 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007, 2007, pp. 1–4.
[36] J. M. Jurado and A. Alcázar, “A software package comparison for uncertainty measurement estimation according to GUM,” Accredit. Qual. Assur., vol. 10, no. 7, pp. 373–381, Jul. 2005.
[37] T. a. Zang, “On the expression of uncertainty intervals in engineering,” Theor. Comput. Fluid Dyn., vol. 26, no. 5, pp. 403–414, Oct. 2012.
[38] F. Attivissimo, N. Giaquinto, and M. Savino, “A Bayesian paradox and its impact on the GUM approach to uncertainty,” Measurement, vol. 45, no. 9, pp. 2194–2202, Nov. 2012.
[39] H. Imai, “Expanding needs for metrological traceability and measurement uncertainty,” Measurement, vol. 46, no. 8, pp. 2942–2945, Oct. 2013.
[40] M. Thompson and S. L. R. Ellison, “Dark uncertainty,” Accredit. Qual. Assur., vol. 16, no. 10, pp. 483–487, Oct. 2011.
[41] G. Chew and T. Walczyk, “A Monte Carlo approach for estimating measurement uncertainty using standard spreadsheet software,” Anal. Bioanal. Chem., vol. 402, no. 7, pp. 2463–2469, Mar. 2012.
[42] M. Cox, P. Harris, and B. R.-L. Siebert, “Evaluation of Measurement Uncertainty Based on the Propagation of Distributions Using Monte Carlo Simulation,” Meas. Tech., vol. 46, no. 9, pp. 824–833, Sep. 2003.
[43] M. Ángeles Herrador and A. G. González, “Evaluation of measurement uncertainty in analytical assays by means of Monte-Carlo simulation,” Talanta, vol. 64, no. 2, pp. 415–422, Oct. 2004.
[44] M. G. Cox and B. R. L. Siebert, “The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty,” Metrologia, vol. 43, no. 4, pp. S178–S188, Aug. 2006.
[45] A. B. Forbes, “An MCMC algorithm based on GUM Supplement 1 for uncertainty evaluation,” Measurement, vol. 45, no. 5, pp. 1188–1199, Jun. 2012.
[46] H. Janssen, “Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence,” Reliab. Eng. Syst. Saf., vol. 109, pp. 123–132, Jan. 2013.
[47] D. a. Sheen and H. Wang, “The method of uncertainty quantification and minimization using polynomial chaos expansions,” Combust. Flame, vol. 158, no. 12, pp. 2358–2374, Dec. 2011.
[48] E. D. Attanasi and T. C. Coburn, “A Bootstrap Approach to Computing Uncertainty in Inferred Oil and Gas Reserve Estimates,” Nat. Resour. Res., vol. 13, no. 1, pp. 45–52, Mar. 2004.
[49] T. Saffaj and B. Ihssane, “A Bayesian approach for application to method validation and measurement uncertainty,” Talanta, vol. 92, pp. 15–25, Apr. 2012.
[50] I. Park and R. V Grandhi, “A Bayesian statistical method for quantifying model form uncertainty and two model combination methods,” Reliab. Eng. Syst. Saf., vol. 129, pp. 46–56, Sep. 2014.
[51] D. Theodorou, L. Meligotsidou, S. Karavoltsos, A. Burnetas, M. Dassenakis, and M. Scoullos, “Comparison of ISO-GUM and Monte Carlo methods for the evaluation of measurement uncertainty: Application to direct cadmium measurement in water by GFAAS,” Talanta, vol. 83, no. 5, pp. 1568–1574, Feb. 2011.
[52] D. Theodorou, Y. Zannikou, G. Anastopoulos, and F. Zannikos, “Coverage interval estimation of the measurement of Gross Heat of Combustion of fuel by bomb calorimetry: Comparison of ISO GUM and adaptive Monte Carlo method,” Thermochim. Acta, vol. 526, no. 1–2, pp. 122–129, Nov. 2011.
[53] S. Sediva and M. Havlikova, “Comparison of GUM and Monte Carlo method for evaluation measurement uncertainty of indirect measurements,” in Proceedings of the 14th International Carpathian Control Conference (ICCC), 2013, pp. 325–329.
[54] A. E. Torres-Abello, “Metodología para la Estimación de Incertidumbres Asociadas a Concentraciones de Sólidos Suspendidos Totales Mediante Métodos de Generación Aleatoria,” TecnoLógicas, no. 26, pp. 181–200, 2011.
[55] M. G. Cox, “Propagation of distributions by a Monte Carlo method, with an application to ratio models,” Eur. Phys. J. Spec. Top., vol. 172, no. 1, pp. 153–162, Jun. 2009.
[56] T. E. Lovett, F. Ponci, and A. Monti, “A Polynomial Chaos Approach to Measurement Uncertainty,” IEEE Trans. Instrum. Meas., vol. 55, no. 3, pp. 729–736, Jun. 2006.
[57] R. Bun, K. Hamal, M. Gusti, and A. Bun, “Spatial GHG inventory at the regional level: accounting for uncertainty,” Clim. Change, vol. 103, no. 1–2, pp. 227–244, Nov. 2010.
[58] M. Lesiv, A. Bun, and M. Jonas, “Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15,” Clim. Change, vol. 124, no. 3, pp. 505–518, Jun. 2014.
[59] K. Rypdal and W. Winiwarter, “Uncertainties in greenhouse gas emission inventories — evaluation, comparability and implications,” Environ. Sci. Policy, vol. 4, no. 2–3, pp. 107–116, Apr. 2001.
[60] H. C. Frey and J. Zheng, “Quantification of Variability and Uncertainty in Emission Inventories: A Prototype Software Tool with Application to Utility NOx Emissions,” Proceedings, Annu. Meet. Air Waste Manag. Assoc., p. 19, Sep. 2001.
Cómo citar
[1]
J. J. Cárdenas-Monsalve, A. F. Ramírez-Barrera, y E. Delgado-Trejos, «Evaluación y aplicación de la incertidumbre de medición en la determinación de las emisiones de fuentes fijas: una revisión», TecnoL., vol. 21, n.º 42, pp. 231–244, may 2018.
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2018-05-14
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Artículos de revisión
