Estimation of the variance of sampling of process analytical and environmental emissions measurements

Abstract

Chemical and process industries are nowadays required by regulations to estimate and control the level of their environmental discharges and many pollutants have legal limits that should not be exceeded. It is essential, therefore, to know the uncertainty of the measurements. For the laboratory measurements most laboratories have a quality control system — and for accredited laboratories it is a must. The uncertainty arising from sampling may not be under control, however, and the methods to evaluate it are still less well known. The analytical process is an error-generating process generally involving several sampling steps. Depending on the material or process the sampling may constitute by far the largest part of the global estimation error. Several case studies in this paper illustrate process analytical measurements, which are applied to estimate, e.g., total emissions from industrial sources. Methods which can be used to evaluate the uncertainty arising from sampling are discussed and illustrated with case studies. The approach discussed in this paper and utilized to estimate the sampling errors in process analysis, has been developed by Pierre Gy. Gy's theory is based on a careful analysis of the different error components derivable both from the material properties and from the process variability, not forgetting the design of the sampling equipment. In sampling continuous process the results are usually auto-correlated. Gy's method is used to estimate the sampling variances, which provides variance estimates for estimating the mean of auto-correlated series. The statistics of stratified sampling is also discussed.