Labeling a Sample

Labels must include reference to the field position from which the sample was taken and to the page in the project notebook on which sampling is described. Numbering of the designated sampling areas, as indicated in Figure 5.9, is done sequentially. The sample areas below the surface are indicated by Greek letters. In this instance, one would know that sample 15β is the second sampling area below surface area 15. This gives an unequivocal designation and location to this sample. The question might be asked as to why Greek letters are used when the Roman alphabet will do. What most researchers find is that they have use for the alphabet in designating other aspects of the sample. We therefore save our alphabet for these uses and use the Greek alphabet for designating subsurface sampling areas. A sample number must be unique, but allow for easy lookup when needed.

The project notebook page on which the sampling is described is on the label, which must also show the date and time sampling was performed as well as who took the sample. Additionally, where it is to be stored and shipped must be shown, as well as the shipping route.

There are other considerations in deciding on how to label a sample. First, during handling, will the samples be subject to wetness, even if only from workers’ hands? The answer is always yes. This means that any numbering or other designation that can be removed with water will be removed, or at least smeared until it is no longer recognizable. The first step is thus to use only “tested” permanent markers (e.g., a test sample number is applied to the sample container when it is dry, and then it is wiped with water, soap and water, etc.).

Another strategy is to put the sample label in several places on the sample container.

Thus a copy of the label can be put on the bottom of the sample container, since people will not be rubbing against the bottom as much as the sides. The drawback is of course is

that the bottom may be more moist that the top, and this may lead to smearing. The bottom may also rub by sliding during transport. If the sample container is one that is folded over and sealed by holding the folded area in place with ears, then the top that is folded over is a good spot for a copy of the label. Other good labeling places will exist on other types of sample containers.

This is not to indicate that the label should not be placed in some prominent location. It should. It must occur prominently on the side on the container and must contain all the information indicated above and information as to the organization requiring the sampling or paying for the analysis. Other pertinent data, such as phone numbers and e-mail addresses, should be included as necessary. This label is for convenience and efficiency in processing samples. Sample numbers in other places are for safety and need only be the sample number. Serious consideration should be given to including bar coding on the label. (See Chapter 3.) Bar codes can be placed on all samples and a duplicate placed in the project notebook and the chain of custody document. (See Chapter 8.) Not only does this simplify keeping track of samples, it will also decrease the chance of error in reading and reporting sample labels [26, 27].

5.18. CONCLUSIONS

Sampling starts when all the preliminary data have been gathered and the equipment has been assembled. A map of the area to be sampled is prepared, giving its position on the Earth’s surface. Once this is done, the next step, assuming ground-penetrating radar (GPR) and remote sensing are not to be used or have already been used, is to do a transect sampling, including contaminated and uncontaminated areas. With these data a detailed sampling plan involving gridding and labeling the areas to be sampled is completed. The sampling plan may be random or may include a combination of random and nonrandom components. As the samples are taken they are placed in suitable containers for transport and analysis and given appropriate labels relating them to the field notebook and position on the Earth’s surface. Additional tools such as monitoring wells and GIS may also be used to obtain a more complete picture of the sampled field.

QUESTIONS

1. Describe the differences between a grab sample, transect sampling, and detailed sampling.

2. What are two coordinate systems used in GPS?

3. Describe all the information that a label should contain and where it should be on the sample container. In answering this question do not limit yourself to this chapter alone.

4. What is the accuracy of GPS and remote sensing?

5. What is unusual about samples that are taken deep in water or soil that is saturated with water? What precautions need to be taken with these samples?

6. There are three common types of samplers. Name the sampler types and describe the situations to which they are best suited.

7. Explain how and why Greek letters might be used in labeling a sample.

8. The spectral range for satellite imagery is over what range of the electromagnetic spectrum?

9. Explain which sample container would be used for a soil sample containing a heavy metal contaminant and one containing a volatile organic carbon (VOC) contaminant.

Explain the bases of your choices.

10. Explain grid sampling.

REFERENCES

1. Wagner G, Desaules A, Muntau H, Theocharopoulos S, Quevauviller P.

Harmonization and quality assurance in pre-analytical steps of soil contamination studies—Conclusions and recommendations of the CEEM soil project. Sci Total Environ. 2001; 264:103–117.

2. Créptin J, Johnson RL. Soil sampling for environmental assessment. In: Carter MR, ed.

Soil Sampling and Methods of Analysis. Ann Arbor; MI: Lewis, 1993:5–18.

3. Patterson GT, Site description. In: Carter MR, ed. Soil Sampling and Methods of Analysis. Ann Arbor; MI: Lewis, 1993:1–3.

4. Petersen RG, Calvin LD. Sampling. In: Klute A, ed. Methods of Soil Analysis. Part 1—Physical and Mineralogical Methods. 2nd ed. Madison, WI: American Society of Agronomy and Soil Science Society of America, 1994:33–51.

5. Letham L. GPS Made Easy. Seattle: Mountaineers, 2001.

6. Conyers, LB, Goodman D. Ground-Penetrating Radar: An Introduction for Archaeologists. New York: Rowman & Littlefield, 1997.

7. Lunetta RS, Elvidge CD. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Chelsea, MI: Ann Arbor Press, 1998.

8. GSFC Earth Sciences (GES) Distributed Active Archive Center, http://xtreme.gsfc.nasa.gov/.

9. Earth Observing System. http://edcimswww.cr.usgs.gov/pub/imswelcome/index.html.

10. Korte GB. The GIS Book. 5th ed. Albany, NY: OnWord Press, 2001.

11. Description and Sampling of Contaminated Soils: A Field Pocket Guide. EPA 625–

12–91–002. Cincinnati, OH: National Center for Environmental Publications, 1991.

12. Bates TE. Soil handling and preparation. In: Carter MR, ed. Soil Sampling and Methods of Analysis. Ann Arbor; MI: Lewis, 1993:19–24.

13. 5th ed. Sample Handling and Transmittal Guide, http://www.rdc.uscg.gov/msl/downloads1.html.

14. Dent D, Young A. Soil Survey and Land Evaluation. Boston: Allen & Unwin, 1981.

15. Koryta J, Stulik K. Ion Selective Electrodes. 2nd ed. Cambridge: Cambridge University Press, 1984.

16. Hollands KR. Comparing Topography Soil Sampling with Other Known Precision Ag Methods. http://www.sbreb.org/96/soilmgmt/96p98.htm.

17. Battaglin WA, Hay LE. Effects of sampling strategies on estimates of annual mean herbicide concentrations in midwestern rivers. Environ Sci Tech. 1996; 30:889–896.

18. Koziel JA, Noah J, Pawliszyn J. Field sampling and determination of formaldehyde in indoor air with solid-phase microextraction and on-fiber derivatization. Environ Sci Tech 2001; 35:1481–1486.

19. Brady NC, Weil R. The Nature and Properties of Soils. 12th ed. Upper Saddle River, NJ: Prentice-Hall, 1999:656–658.

20. Fisher MM, Brenner M, Reddy KR. A simple, inexpensive piston corer for collecting undisturbed sediment/water interface profiles. J Paleolim 1992; 7:157–161.

21. Hess-Kosa K, Hess K. Indoor Air Quality: Sampling Methodologies. New York:

Lewis, 2001.

22. Compilation of EPA’s Sampling and Analysis Methods. 2nd ed. Keith LH, ed. New York: Lewis, 1996.

23. National Field Manual of the Collection of Quality Data. Techniques of Water-Resource Investigations. Book 9. Handbooks for Water-Water-Resource Investigations.

http://water.usgs.gov/owq/fieldmanual/ (search for Book 9).

24. Monitor Well Design, Installation and Documentation at Hazardous and/or Toxic Waste Sites (Technical Engineering and Design Guides as adapted from the U.S. Army Corps of Engineers, no. 17). American Society of Civil Engineers. Reston, VA: ASCE Press, 1996.

25. Klesta EJ Jr, Bartz JK. Quality assurance and quality control. In: Klute A, ed.

Methods of Soil Analysis. Part 3—Chemical Methods. Madison, WI: American Society of Agronomy and Soil Science Society of America, 1996:19–48.

26. Holkham T, Holkman T. Label Writing and Planning. New York: Aspen, 1996.

27. Bushnell R, Dooley T. Bar Code Compliance Labeling for the Supply Chain: How to Do It. Surf City, NJ: Quad II, 2000.

6 Statistics

Statistics provide very powerful tools for analyzing data, including tools for analyzing sampling activities. Some of these common tools are determining the number of samples needed, standard deviation, regression analysis, and extraneous values, and predicting the component values between sampled points. In all cases calculators or computers equipped with standard software, including spreadsheets, can be used to do statistical calculations. In order to understand what information the statistic is providing, however, and to be able to explain this to others, it is important to know how the statistic is calculated. This is also important when trying to understand what others are trying to explain using statistics.

It is common to hear people talk about median, mean, average, one standard deviation, and so on. When such conversations occur, are the speakers using the terms correctly?

How does what they are saying affect your sampling plan? On the other hand, can the statistics being used in the sampling plan be explained to others?

There are a great many symbols used in statistics. It is important to be able to keep these in mind while studying and interpreting statistical results. Table 6.1 gives some of the most commonly used symbols and the terms they represent. These will be particularly important, whether calculation of statistics is done by hand or using a computer, because once the computer has done the calculation the experimenter must still interpret the statistical results.

Statistics can be calculated by hand; however, in most cases calculations will be carried out using computers and more or less complicated statistics programs. Although for simple calculations a handheld calculator can be used, in all cases a computer will be preferred. In the case of geostatistics the calculations are never done by hand or handheld calculators because the equations are too complex. Keep in mind that some statistics programs are difficult to learn and use, while others are not. In addition, some statistics programs leave the experimenter in charge; some take charge.

For example, some statistics programs require setting up the statistics or the sampling plan before obtaining or entering the data. Other programs allow the input of the data and subsequent application of various statistical calculations. Because it is not always clear what information will be needed or which statistic might need to be applied, the latter approach is usually preferable. In Figure 6.1, after the data are entered it is found that two data points are missing (X in A2 and Y in A6). If all the statistics had been set up beforehand [e.g., the number of samples and the degrees of freedom (n and n−1)], the whole setup would have to be changed because of the missing data points.

If the data are entered and the statistics subsequently calculated, two possible adjustments could be made. First the data could be calculated

FIGURE 6.1 Total petroleum hydrocarbons (TPH) levels in various areas of a field.

without the missing data points using a smaller n and n−1. On the other hand, it might be decided to calculate the missing data points using kriging (described below). In either case, being able to manipulate the data after they are entered would facilitate these changes.

TABLE 6.1 Important Statistical Symbols and Their Meaning

Symbol What it is How is it obtained

µ Population mean Add all data points and divide by the number of data points in the population.

Sample mean Add all sample data points and divide by the number of sample data points taken.

s² Sample variance Obtained by taking the difference between the sample mean and each sample and squaring and dividing by n—1.

s Sample standard

deviation

Take the square root of s², the sample standard variance.

H Hypothesis The experimenter determines the null and alternate hypothesis.

t t statistic Obtained from table of t-values.

F F statistic Obtained from table of F-values.