The present work defines a methodology that allows the estimation of the measurement uncertainty associated to the pollutant emissions from stationary sources sampled according to the EPA Sampling Methods 1 to 8, which are defined in the Code of Federal Regulations Title 40 Part 60 (Appendices A1 to A4) from the United States Environmental Protection Agency. Therefore, a non-stochastic method was used as the one proposed in the Guide to the Expression of Uncertainty in Measurement (GUM), and another stochastic method such as Monte Carlo Method (MCM). The development of the estimation using the non-stochastic method proposed in the GUM included the identification of sources of uncertainty, establishment of the mathematical model, association of sources and input variables to a statistical distribution, estimation of standard and combined uncertainty, quantification of the effective degrees of freedom and finally quantification of the expanded uncertainty, for each of the input variables and measurands. The previous, it required the implementation of different routines in ®Matlab, which feedback from the information collected during the emissions studies (data tabulated in an electronic sheet of ®Excel). In addition, the influence of the inclusion of higher order terms of the Taylor series on the quantification of uncertainty was explored, assuming no correlation between the input variables.