Environmental and Ecological Statistics

, Volume 8, Issue 2, pp 91–107 | Cite as

Cost analysis of composite sampling for classification

  • Glen D. Johnson
  • Ganapati P. Patil


When an environmental sampling objective is to classify all the sample units as contaminated or not, composite sampling with selective retesting can substantially reduce costs by reducing the number of units that require direct analysis. The tradeoff, however, is increased complexity that has its own hidden costs. For this reason, we propose a model for assessing the relative cost, expressed as the ratio of total expected cost with compositing to total expected cost without compositing (initial exhaustive testing). Expressions are derived for the following retesting protocols: (i) exhaustive, (ii) sequential and (iii) binary split. The effects of both false positive and false negative rates are also derived and incorporated. The derived expressions of relative cost are illustrated for a range of values for various cost components that reflect typical costs incurred with hazardous waste site monitoring. Results allow those who are designing sampling plans to evaluate if any of these compositing/retesting protocols will be cost effective for particular applications.

cost-effectiveness environmental sampling human health sampling observational economy 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Boswell, M.T. and Patil, G.P. (1990) Composite Sample Designs for Characterizing Continuous Sample Measures Relative to a Criterion. Technical Report No. 90-1001; Center for Statistical Ecology and Environmental Statistics, Penn State University, University Park, PA.Google Scholar
  2. Dorfman, R. (1943) The detection of defective members of large populations. Annals of Mathematical Statistics, 14, 436-40.Google Scholar
  3. Edland, S.D. and van Belle, G. (1994) Decreased sampling costs and improved accuracy with composite sampling. In Environmental Statistics, Assessment and Forcasting, C.R. Cothern, and N.P. Ross, (eds) Lewis Publishers, Boca Raton, pp. 29-55.Google Scholar
  4. Fabrizio, M.C., Frank, A.M., and Savino, J.F. (1995) Procedures for formation of composite samples from segmented populations. Environmental Science and Technology, 29, 1137-43.Google Scholar
  5. Gilbert, R.O. (1987) Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold Co., New York.Google Scholar
  6. Gill, A. and Gottlieb, D. (1974) The identification of a set by successive intersections. In Information and Control. Ellis Horwood, Chichester, pp. 20-35.Google Scholar
  7. Lovison, G., Gore, S.D., and Patil, G.P. (1994) Design and analysis of composite sampling procedures: A review. In Handbook of Statistics, Vol. 12. Environmental Statistics, G.P. Patil, and C.R. Rao, (eds), Elsevier, New York, pp. 103-66.Google Scholar
  8. Sterrett, A. (1957) On the detection of defective members of large populations. Annals of Mathematical Statistics, 28, 1033-36.Google Scholar
  9. U.S.EPA (1985a) Verification of PCB Spill Cleanup by Sampling and Analysis. EPA-560/5-85-026.Google Scholar
  10. U.S.EPA (1985b) Removal Program Representative Sampling Guidance, Vol.1: Soil. PB92-963408.Google Scholar
  11. U.S.EPA (1989) Methods for Evaluating the Attainment of Cleanup Standards, Vol.I: Soils and Solid Media. EPA/230/02-89/042.Google Scholar
  12. U.S.EPA (1994) Guidance for Planning for Data Collection in Support of Environmental Decision Making Using the Data Quality Objectives Process, Final. EPA QA/G-4.Google Scholar
  13. U.S.EPA (1995) EPA Observational Economy Series, Volume 1: Composite Sampling. EPA-230-R-95-005.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Glen D. Johnson
    • 1
  • Ganapati P. Patil
    • 1
  1. 1.Center for Statistical Ecology and Environmental Statistics, Department of StatisticsPennsylvania State UniversityUniversity Park

Personalised recommendations