Environmental Sampling for Trace Analysis Ray E. Clement Ontario Ministry of the Environment Laboratory Services Branch 125 Resources Rd. P.O. Box 213 Rexdale, Ontario, Canada M9W 5L1
Hundreds of millions of dollars are spent worldwide each year on environmental issues—based directly on the results of chemical analyses. One would think that students in chemistry and engineering programs, who will be employed in the environmental field, should receive basic training in evaluating the reliability of such chemical analyses. However, it is unfortunately true that far too few of these graduates will have such training. Many new environmental programs being introduced in our colleges and universities emphasize the study of environmental issues r a t h e r t h a n t h e s c i e n t i f i c tools needed to investigate these issues. Few undergraduate programs emphasize the type of laboratory instruction in analysis that is needed to understand how reliable chemical data are generated. Although most analytical textbooks and courses discuss the importance of accurate sampling, students are seldom offered practical e x a m p l e s of t h e consequences of poor sampling. They may think that an analytical result generated by a million-dollar state-of-theart instrument is a number "carved in stone." And because such analyses are expensive, they believe there is no need for replication. Can students learn the basic principles of environmental sampling and analysis from a simple classroom experiment? To find out, a sampling experiment was conducted at Mohawk College (Canada) in a class of students who were not chemistry majors. Although the experiment was originally designed to illustrate sampling difficulties and analytical er-
A Classroom Experiment You Can Sink Your Teeth Into! rors that can occur in trace environm e n t a l d e t e r m i n a t i o n s , it became apparent that it could also be used to describe many analytical concepts— including the importance of sample treatment and the need for selective detection. Most important, it clearly demonstrates the reality of errors in analytical m e a s u r e m e n t s . Because the experiment is simple, interactive,
REPORT and directly analogous to the analytical principles described above, it is highly effective and even enjoyable. Design of the experiment Nestlé manufactures a candy product called Smarties (available in Canada), which are button-shaped chocolate candies t h a t have multicolored hard outer coatings. Individual Smarties are relatively uniform in shape and weight, and in appearance differ only in the color of the outer coating. (Any brand of multicolored candy can be substituted.) In the sampling experiment, different colors were used to represent different at-
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oms and molecules. Green was used to signify PCB molecules, blue represented Pb, brown designated Fe, and red symbolized chlorinated dioxin molecules. In addition, purple and pink r e p r e s e n t e d u n k n o w n molecules. A soil matrix was depicted by combining yellow and orange candies. The manufacture of a sample where the matrix predominated was difficult, because the Smarties, as marketed, are fairly evenly distributed among the various colors. To avoid having to purchase kilogram quantities of Smarties, a second type of candy similar in appearance to Smarties was purchased. This candy, called Reese's Pieces (manufactured by Hershey), comes in orange, yellow, and brown. The brown Reese's Pieces also represented Fe. After combining the Smarties and Reese's Pieces, the number of candies of each color were counted. Because the individual molecules and components of the soil matrix are almost uniform in size and shape, we assumed for the purpose of this experiment that the molecular weights of all the individual candy pieces were the same. Therefore, by counting the number of candies of the same color, the concentration of the molecule or atom r e p r e s e n t e d by t h a t colored candy could be determined in units of parts per hundred (pph) (Table I). Two volunteers from the class were selected to run the experiment. Student A was instructed to sample the contents of the plastic container in which the candies were brought to class by grabbing a small handful. After t h e first g r a b s a m p l e w a s taken, student A was told to make a second small grab and to keep this sample separate from the first. Student Β was given the same instruc tions, except he was told to t a k e large handfuls. The class was then told to determine the concentrations of all of our target analytes in parts per hundred—based on the four grab 0003-2700/92/0364-1076A/$03.00/0 © 1992 American Chemical Society
j 1
REPORT samples. An overhead projector was used to record the results.
Effect of sample size and replication on precision and accuracy The actual experimental data from this classroom experiment are shown in Table II. Quite a large difference can be seen in the replicate results from student A, who collected total sample sizes of seven candies for each replicate. Unfortunately, t h e analyte with the highest concentra tion, Fe, was not detected in either replicate sample. PCBs, Pb, and the two unknown substances were de tected in the first sample, each at 14 pph; only one of the unknowns was found in the second sample, at 28 pph. Clearly, the differences between the replicates are significant. The re sult of selecting a small sample size was to increase (make worse) the de tection limit of the determination. The comparison of individual results to the actual values seems pretty poor. However, when the averages of the values of the two determinations are taken, it can be seen that the es timates are much closer to the actual values; generally the estimated con centrations are within a factor of about 2—not too bad for trace analy sis. Only the results for Fe and the unidentified pink analyte are way off. In the case of dioxin, the method is not sensitive enough to detect such an ultratrace analyte. Real-life trace e n v i r o n m e n t a l analysis is conceptually not so differ ent from this example; when method ology is used that can barely detect analytes, it is not uncommon for the analytes to be observed in some sam ples but not in others, even though all samples were thought to be the same. For example, in a six-labora tory round-robin study of chlorinated dibenzo-/>-dioxins and chlorinated dibenzofurans introduced into blank water samples, a significant varia tion in laboratory results was noted (i). The reported concentrations of one compound, introduced at a con centration of 50 parts per quadril lion, varied from 37 to 62 parts per quadrillion, and two laboratories of the six could not detect the com pound at all. One way to improve detection lim its in environmental analysis is to obtain a larger sample. Student Β se lected replicate samples of 75 and 70 candies, respectively. The data in Ta ble II show the dramatic improve ment in the estimation of analyte concentrations by using a 10-fold
Table 1. "Contaminant" concentrations Color
Green Blue Brown Purple Pink Red Yellow/orange
"Contaminant"
Actual no. of "molecules" "
PCBs Pb Fe Unknown Unknown Dioxin Soil matrix
46 28 106 30 40 10 640
Actual concentration (pph) 5.11 3.11 11.78 3.33 4.44 1.11
—
" Total no.of units is 900.
greater sample size. In these larger handfuls each analyte in the "soil" was detected in both replicates ex cept for "dioxin," which was observed in only one sample. The concentra tions of all analytes are more accu rate and more precise for the largesample-size experiment with the single exception of PCBs, where the mean concentration as determined from the first sampling experiment (small sample size) was closer to the actual value. As in the first sampling experiment, the mean estimates from two determinations were closer to the correct values than the estimates from an individual sampling event (with the single exception of PCBs, where both replicates from student Β produced low estimates). The close ness of the mean analyte values from student Β to the actual concentra tions is quite reassuring.
contaminant plume that is emitted from a plant. Some s a m p l e " c l e a n u p " w a s needed before analytes could be accu rately detected. In this experiment, cleanup consisted of sorting the can dies into groups according to color, after which they could be assayed by counting. For the large sample, an accurate quantitative determination could not be made without this sort ing step, although without cleanup it is still possible to do a qualitative de termination of the analytes present by recording the different colors ob served. The physical separation of the can dies into different colors is analogous to various chromatographic separa tion methods t h a t are the basis of most real cleanup schemes. Note that the cleanup is more difficult to per form with the large sample. In real life, it is not difficult to overload Sample preparation and analyte cleanup methods by choosing sample detection sizes greater t h a n those for which the cleanup is designed. In such A number of important concepts re cases, choosing a large sample size lating to sample preparation and an will increase rather than reduce the alyte detection can be illustrated by detection limits achievable. In this this simulated soil-sampling experi experiment the detection limits were ment. Before sampling, the class was not good for the small-sized samples asked if any special sample prepara taken initially, but sample cleanup tion were needed. Almost everyone and analysis were much easier. Thus suggested t h a t t h e sampling con the students learn to choose analysis tainer should be well mixed before methods that are sufficient for the samples were withdrawn. It was also specific application, and not to reach important (for this specific experi automatically for the most sensitive ment) that the sampler take a "blind" state - of- fjhe- art method available. In sample; otherwise, the sample se-j lected could be biased toward t h e ! addition, the analysis of the larger sampler's favorite color. Although it % sample w a s more costly, because sample preparation and analyte de is not recommended ttaat samplers in tection took more time to complete. the real world do th$ir work with their eyes closed, it is certainly possi The detector in this experiment ble t h a t t h e samples %l}ey collect was the human eye. By observing the could be biased and not representa various colors in the sample, the dif tive of the population of interest. For ferent analytes were identified (qual example, water stream samples may itative analysis). By counting the be collected at the most convenient n u m b e r of candies in each color location, which may be outside of a group, quantitative estimates of
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Table II. "Contaminant" data Student A: #1 Analyte
No.
PCBs (green) Pb (blue) Fe (brown) Unknown (purple) Unknown (pink) Dioxin (red) Soil matrix (yellow/ orange) Total
Student A: #2
Student B: #1
Student B: #2
Mean cone, (pph)
Actual cone.
No.
Cone, (pph)
A
B
2.7
1
1.4
7
2.1
5.11
2
2.7
5
7.1
7
4.9
3.11
ND
9
12
8
11.4
ND
11.7
11.78
14
ND
3
4.0
1
1.4
7
2.7
3.33
14
28
2
2.7
4
5.7
21
4.2
4.44
ND
ND
0
ND
2
2.8
ND
1.4
1.11
Cone, (pph)
Cone. (PPh)
No.
Cone, (pph)
14
ND
2
14
ND
ND
7
No.
7
57
49
75
70
(pph)
ND indicates not detected
t h e i r c o n c e n t r a t i o n s were d e t e r mined. A serendipitous illustration of some of the difficulties in analyte detection could be made because the test sample was made up of a mixture of Smarties and Reese's Pieces. Although they appeared to be similar, Reese's Pieces are a bit smaller than Smarties. This size difference is not critical for the "soil matrix" itself, but it is important for the detection of the brown-colored candies used to represent Fe. Therefore the method of detection (color) by itself cannot distinguish between the two brown analytes. A determination of physical properties (size) is also needed to avoid an overestimation of Fe in the sample. This situation frequently occurs in i ultratrace environmental analysis. ^Another possible explanation for the TNilQ„brown catadies is that they both represent Fe, But in different forms (e.g., ferric and ferrous Fè|, It is often important to know which specific form of an analyte is present in the environment and, as illustrated in this experiment, this can be very difficult. In this case, a second method of detection (taste) can be used to distinguish between the S m a r t i e s and Reese's Pieces. (Note that taste is destructive detection; observation of color is nondestructive detection.) Other observations and limitations At this time some readers may be noting that the concentrations de-
tected are inaccurate because the students conducted sampling without replacement. In other words, the candies taken from the container by the first sampler were not replaced; consequently, the actual concentrations of the candies in subsequent samplings were affected by this omission. This is true, but by selecting a large original population size, this factor is not significant (at least for the purposes of this experiment). In real environmental sampling, the amount of analyte removed from the area sampled (e.g., field, lake, or ambient air) is trivial when compared with the total amount of analyte present. In fact, in real environmental sampling one samples such a small amount of the whole area to be tested t h a t a single sample quite likely is not representative of the area under investigation. Therefore when conducting actual environmental sampling it is important to analyze several samples thought to be {[representative of the area of interest. le average concentration from sevsamples will almost always give a more accurate estimate of the real analyte coricentration than a single sample. Alsiu the amount by which the concentrations of the analyte differ between t h e variousIreplicate samples will permit an estimate of the magnitude of the error in the determination. The s a m p l i n g e x p e r i m e n t with candies represents an idealized situation in which the entire population
is present in a plastic container, and therefore the concentrations of the various analytes could be determined exactly. In the environment, it is not possible to know the exact concentration of any analyte in the population. The best we can do is to estimate analyte concentrations by using a rigorous and careful sampling program, with judicious use of replication to determine the precision of our esti mates. The population used in this experiment is also static; the exact numbers of the various colored can dies could change only if some were removed by accident or design. The environment represents a dynamic situation in which analyte concentrations depend on weather conditions, time of day, type of industrial and natural processes occurring and many other factors (some of w|nch may be unknown). Because of the assumption that all of the colored candies represented individual analyte molecules or atoms, the calculation of analyte concentrations was greatly iimplified. In real life these analytes Biteeunique molecular and atomic weigms as well as physical a n d chemical properties. Therefore they respond differently to the detector used. (We can't count the true nftmbers of atoms or molecules in a sample.) To determine concentrations lflWHnTSfe, we must find the detector response to a n a l y t e s by evaluating standards prepared with known concentrations of these analytes. For this soil-sampling experi-
ANALYTICAL CHEMISTRY, VOL. 64, NO. 22, NOVEMBER 15, 1992 · 1079 A
REPORT ment, the concentrations of the two unknown analytes could not be determined until their identities were established. An interesting problem was presented to the students after the initial sampling experiment was completed. They were asked to sample the population to find a single poisoned candy that had been thrown in. In the absence of a specific detector that could selectively detect the poison (nobody volunteered to perform a taste test!), the students favored disposal of the entire population. This specific situation seldom occurs in an environmental analysis, but it is now obvious that the object i v e s of a s a m p l i n g e x p e r i m e n t should be clearly stated before any work is performed. The methods selected for any application depend on the specific results desired. Conclusions
iments capture the interest and attention of the students and encourage them to use their imaginations. Nobody is suggesting t h a t the students are now experts in environmental sampling and analysis, but after this experiment they are better prepared to consider the details of such methods used for environmental investigations. The most import a n t lesson is t h a t sampling and analysis methods are used to estimate the concentrations of analytes, and the answers obtained are subject to error. Proper methods are r e quired to give the most accurate and precise results. After the experiment and discussion, the r e m a i n i n g candies were passed around the class for individual sampling and detection by taste. The s t u d e n t s unanimously agreed t h a t the soil-sampling experiment was literally one they could sink their teeth into.
In the 30 minutes it took to conduct this experiment, students received a better understanding of the principles of environmental sampling and analysis than if they had read a textbook on the subject. Hands-on exper-
Reference (1) Tashiro, C; Clement, R. E.; Davies, S.; Oliver, B.; Munshaw, T.; Fenwick, J.; Chittim, B.; Foster, M. G. Chemosphere 1990, 20(10-12), 1313-17.
Suggested reading Principles of Environmental Sampling; Keith, L. H., Ed.; ACS Professional Reference Books; American Chemical Society: Washington, DC, 1988. Keith, L. H. Environmental Sampling and Analysis: A Practical Guide; Lewis Publishers: Chelsea, MI, 1992.
Ray E. Clement received a Ph.D. in analytical chemistry (1981) from the University of Waterloo under the supervision of F. W. Karasek. He then joined the Ontario Ministry of the Environment where he is a senior scientist in the R&D department. He has authored more than 100 publications, most of which concern trace determination of chlorinated dioxins and furans in environmental samples.
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