Issue 11, p. 193 (2022)


Fake data? The need for Theory of Sampling concepts in environmental research and investigations

  • R. Brewer  
  • C. Ramsey
  • M. Heskett
  • J. Song
President, EnviroStat, Inc., P.O. Box 339, Vail, Arizona 85641, United States of America
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Senior Scientist, Element Environmental, 98-030 Hekaha St Unit 9, Aiea, Hawai′i 96701, United States of America
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Professor, Institute of Soil Science, Chinese Academy of Sciences, No. 71 East Beijing Road, Nanjing, Peoples Republic of China, Post Code: 210008
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 Corresponding Author
Senior Geologist/Environmental Scientist, Hawaii Department of Health, 2785 Waimano Home Road, Suite #100, Pearl City, Hawai’i 96782, United States of America.
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The sampling intensive mining and environmental industries share a common need for representative data but differ in the motivations to accomplish this objective. The desire to obtain representative sample data for “commodities” in the mining industry is driven by anticipated economic gain and the exploitation of natural resources. The desire to obtain representative sample data for “contaminants” in the environmental industry is driven by anticipated social gain and the protection of natural resources. In terms of obtaining reliably representative data, motivation driven by economic gain has thus far been the clear winner. Theory of Sampling concepts are well established and tested in the mining industry. The environmental industry, in contrast, has traditionally been plagued by scientifically unsound sampling practices and data that are not reliably representative of conditions in the field. This has significant implications for topics ranging from the efficient identification and remediation of contaminated industrial lands to the accurate assessment of risk to human health and the environment.

This paper explores the nature and cause of this dichotomy and presents a methodical approach for application of Theory of Sampling concepts to environmental testing of soil, water and air. Much of the problem is tied to a general recognition of compositional and distributional heterogeneity in contaminated media but unawareness of a method to control it or an understanding of the magnitude of potential error. As a result, published regulatory guidance focused on classical sampling and statistical methods appropriate for testing of “finite element” media. A lone exception is testing of indoor air, where concepts of “Decision Units” and sampling methods more appropriate for testing of “infinite element” media have long been employed to control and represent heterogeneity.

The solutions are, in hindsight, relatively simple. Pushback from affected parties and even scientists and environmental agencies can be significant, however. This is primarily due to a lack of training of environmental professionals in the Theory of Sampling and the common absence of clear evidence of erroneous or misleading sample data in the field. Reluctance to change is also tied in some cases to implications regarding liability for past and ongoing projects. The need for more reliable, efficient and science-based methods to assess and address risk posed by environmental contamination is clear, however. Progress will be made by countries like China that are beginning to tackle legacies of early development and are able to learn from the successes as well as the mistakes of countries that have been addressing environmental contamination for several decades. Training of environmental workers as well as pressure from liability-savvy responsible parties, attorneys and financial institutions will continue to force the industry to evolve, to the benefit of the environment as well as stakeholders on all sides.




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