Uncertainty estimation in continuous ambient air quality monitoring is one of the most important concepts related to data quality assurance. Even though its significance has been continuously emphasized in the scientific literature, as well as in governmental directives and regulations, its scientific methodology has been only partly developed and published. Uncertainty estimation is in principle a statistical concept based on probability distributions of ambient air pollutant concentrations. The problem stems from very complex distributions due to highly inaccurate estimates and large temporal and spatial variability of ambient air quality. Distributions are always highly skewed, often polymodal and thus noncompliant with the assumptions of standard statistical methodology. This chapter offers an innovative approach based on approximation of distributions of ambient air pollutant concentrations by the truncated Weibull family of probability distributions or their mixtures in cases of polymodal distributions. Such distributions are then used in Monte Carlo simulations to estimate the required uncertainty, which is related to the ambient air quality parameters. These methods have been developed and applied using the data collected by the continuous air quality monitoring network of the Wood Buffalo Environmental Association (www.wbea.org/) in the Athabasca Oil Sands Region of north-eastern Alberta, Canada.