Abstract
A soil sampling strategy for spatially correlated variables using the tools of geostatistical analysis is developed. With a minimum of equations, the logic of geostatistical analysis is traced from the modeling of a semi-variogram to the output isomaps of pollution estimates and their standard deviations. These algorithms provide a method to balance precision, accuracy, and costs. Their axiomatic assumptions dictate a two-stage sampling strategy. The first stage is a sampling survey, using a radial gird, to collect enough data to define, by a semi-variogram, the ranges of influence and the orientation of the correlation structure of the pollutant plume. The second stage is a census of the suspected area with grid shape, sizes and orientation dictated by the semi-variogram. The subsequent kriging analysis of this data gives isopleth maps of the pollution field and the standard error isomap of this contouring. These outputs make the monitoring data understandable for the decision maker.
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References
BarnsM. G.: 1980, ‘The Use of Kriging for Estimating the Spatial Distribution of Radionuclides and Other Spatial Phenomena’, TRANS STAT (Statistics for Environmental Studies), November 1980, No. 13, pub. by Battelle Memorial Istitute, Richland, Washington. pp. 1–22.
BrownK. W. and BlackS. C.: 1983, ‘Quality Assurance and Quality Control Data Validation Procedures Used for the Love Canal and Dallas Lead Soil Monitoring Program’, Environ. Monit. and Assess. 3, 113–122.
BrownK. W., BeckertW. F., BlackS. C., FlatmanG. T., MullinsJ. W., RichittE. P., and SimonS. J.: 1983, ‘The Dallas Lead Monitoring Study: EMSL-LV Contribution’, EPA 600/X-83-007 Environmental Monitoring Systems Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Las Vegas, Nevada 89114. 2 Vol., pp. 1–240, 1–265.
ClarkI.: 1979, Practical Geostatistics, Applied Science Publishers. London. 1979. pp. 130.
DavidM.: 1977, Geostatistical Ore Reserve Estimation, Elsevier Scientific Publishing Company. Amsterdam, Oxford, New York. 1977. pp. 1–364.
DavisJ. C.: 1973, Statistics and Data Analysis in Geology, John Wiley and Sons Inc., New York. pp. 1–545.
Doctor, P. G. and Gilbert, R. O.: 1978, ‘Two Studies in Variability for Soil Concentration: With Aliquot Size and With Distance’, Selected Environmental Plutonium Research Report of the Naeg, NVO-192 (2). pp. 405–451.
Efron, B. and Morris, C.: 1977, ‘Stein's Paradox in Statistics’, Sci. Am. May 1977. pp. 119–127.
Finkelstein, P. L. and Seilkop, S. K.: 1981, ‘Interpolation Error and the Spatial Variability of Acid Precipitation’, VII Conference on Probability and Statistics in Atmospheric Sciences. American Meteorological Society Proceedings, Nov. 1981. pp. 206–212.
HughesJ. P. and LettenmaierD. P.: 1981, ‘Data Requirements for Kriging: Estimation and Network Design’, Water Resour. Res. 17(6), 1641–1650.
JournelA. G. and HuybregtsCh. J.: 1978, Mining Geostatistics, Academia Press. London and New York. pp. 1–597.
McBratneyA. B., WebsterR., and BurgessT. M.: 1981, ‘The Design of Optimal Sampling Scenes for Local Estimation and Mapping of Regionalized Variables-I’, Computers and Geosciences 7(4), 331–365.
Royal, A. G.: 1981, ‘A Practical Introduction to Geostatistics’, Unpublished Course Notes, Leeds University. p. 103.
Royal, A. G., Newton, M. J., and Sarin, V. K.: 1972, ‘Geostatistical Factors in Design of Mine Sampling Programmes’, Institution of Mining and Metallargy. April 1972. pp. A82Ā88.
SampsonR. J.: 1975, Surface II Graphics System, Research Associate, Computer Surfaces Section, Kansas Geological Survey. Lawrence, Kansas. pp. 1–240.
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Flatman, G.T., Yfantis, A.A. Geostatistical strategy for soil sampling: the survey and the census. Environ Monit Assess 4, 335–349 (1984). https://doi.org/10.1007/BF00394172
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DOI: https://doi.org/10.1007/BF00394172