In order to reduce greenhouse gas (GHG) emissions from producers in the energy and industrial fields to mitigate climate change more effectively, accurate and reliable studies of methods capable of estimating GHG emissions with proper uncertainty levels should initially be carried out. Continuous emission monitoring systems (CEMS) directly measure GHG emissions by measuring the GHG concentrations and volumetric flow rates of flue gases in smokestacks. Most studies of CEMS have tended to focus on the accuracy and uncertainty levels associated with GHG concentrations rather than on volumetric flow measurements. Particularly, the uncertainty associated with volumetric gas flow measurements by CEMS at a smokestack considering the individual plant must be determined to reduce the overall uncertainty. In the present study, the uncertainties of stack gas flow measurements with an S-type Pitot tube to estimate GHG emissions by CEMS are evaluated by applying the GUM and Monte Carlo methods to an on-site smokestack of an energy power plant. Velocity measurements in the stack were conducted using an S-type Pitot tube mainly used in Korea. The mathematical model equations of the accumulated volumetric gas flowrate measurements for estimating GHG emissions by CEMS are expressed in the form of detailed uncertainty equations by GUM. In the uncertainty evaluations by GUM, the combined standard uncertainty equation is expressed with the average gas flow velocity, stack diameter, static pressure, temperature and water content. The relative expanded uncertainty for volumetric gas flowrate measurements with an S-type Pitot tube in a smokestack by GUM is estimated to be 3.81% for a coverage factor (k = 2) at a confidence level of approximately 95%. In addition, the Monte Carlo method was applied to evaluate the associated uncertainty of volumetric gas flowrate measurements by an S-type Pitot tube for comparison with the results by the GUM method. The results of the uncertainty evaluations by GUM and MCM show good agreement, with only a 0.05% difference in the uncertainty outcomes.

为了减少能源和工业领域生产者的温室气体排放，以更有效地缓解气候变化，首先应该对能够以适当的不确定性水平估算温室气体排放量的方法进行准确而可靠的研究。连续排放监测系统（CEMS）通过测量烟囱中的温室气体浓度和烟道气体积流量直接测量温室气体排放。对CEMS的大多数研究都倾向于将重点放在与温室气体浓度相关的准确性和不确定性水平上，而不是在体积流量测量上。特别地，必须确定与考虑单个工厂的烟囱中CEMS进行的体积气体流量测量相关的不确定性，以减少总体不确定性。在目前的研究中，通过将GUM和蒙特卡洛方法应用于能源发电厂的现场烟囱，评估了使用S型皮托管测量烟囱气体流量以估算CEMS产生的烟气的不确定性。堆栈中的速度测量是使用主要在韩国使用的S型皮托管进行的。通过CUM以详细的不确定性方程的形式来表示用于通过CEMS估算温室气体排放的累积体积气体流量测量的数学模型方程。在GUM的不确定度评估中，组合的标准不确定度方程式表示为平均气体流速，烟囱直径，静压，温度和水含量。用GUM在烟囱中用S型皮托管测量气体体积流量的相对不确定度估计为3。k = 2），置信度约为95％。另外，使用蒙特卡洛方法评估S型皮托管的体积气体流量测量的相关不确定性，以与GUM方法的结果进行比较。GUM和MCM的不确定性评估结果显示出很好的一致性，不确定性结果之间只有0.05％的差异。