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GY SAMPLING THEORY IN ENVIRONMENTAL STUDIES 2: SUBSAMPLING ERROR MEASUREMENTS
Citation:
Gerlach, R. W., J M. Nocerino, C. A. Ramsey, AND B. C. Venner. GY SAMPLING THEORY IN ENVIRONMENTAL STUDIES 2: SUBSAMPLING ERROR MEASUREMENTS. ANALYTICA CHIMICA ACTA 490(1-2):159-168, (2003).
Impact/Purpose:
The overall objective of the chemometrics and environmetrics program and this task is to examine and evaluate the statistical procedures and methods used in the measurement or experimentation process and to improve those procedures and methods (if deemed inadequate) by investigating, developing, and evaluating statistical methods, algorithms, and software to reduce data uncertainty. The measurement or experimentation process encompasses: decision objectives and design, sampling design, sampling, experimental design, quality control, data collection, signal processing and data manipulation, data analysis, validation, and decision analysis. Other general objectives of the program are to: evaluate certain existing, developed, or potential performance measurements for information content, relevancy, and cost-effectiveness. The objectives of the sampling research area are to provide the Agency with improved state-of-the-science guidance, strategies, and techniques to more accurately and effectively collect solid particulate field and laboratory subsamples that best represent the extent and degree of contamination at a given site.
Description:
Sampling can be a significant source of error in the measurement process. The characterization and cleanup of hazardous waste sites require data that meet site-specific levels of acceptable quality if scientifically supportable decisions are to be made. In support of this effort, the U.S. Environmental Protection Agency is investigating methods that relate sample characteristics to analytical performance. Predicted uncertainty levels allow appropriate study design decisions to be made, facilitating more timely and less expensive evaluations. Gy sampling theory can predict a significant fraction of sampling error when certain conditions are met. We report on several controlled studies of subsampling procedures to evaluate the utility of Gy sampling theory applied to laboratory subsampling practices. Several sample types were studied and both analyte and non-analyte containing particles were shown to play important roles affecting the measured uncertainty.
Gy sampling theory was useful in predicting minimum uncertainty levels provided the theoretical assumptions were met. Predicted fundamental errors ranged from 46% to 68% of the total measurement variability. The study results also showed sectorial splitting outperformed incremental sampling for simple model systems and suggested that sectorial splitters divide each size fraction independently. Under the limited conditions tested in this study, incremental sampling with a spatula produced biased results when sampling particulate matrices with grain sizes about 1 mm.