Lesson Plan Using Water Quality Data - 2


Analyzing Water Quality Data Using Computation Sheets

Students can evaluate water quality in a very limited sense by investigating analyses of a single parameter (e.g., secchi depth, turbidity, color, pH, conductivity, or dissolved oxygen) or in more detail by considering such parameters collectively.  This lesson plan introduces a simple method to help students accomplish the latter.  (To learn how these and other analyses are made and what each indicates about water quality, go to http://www.gvsu.edu/wri/education/manual.htm

If the students have collected enough of their own data to evaluate water quality, using that data will likely be most interesting to the students.  Otherwise, water quality data is available at the Grand Valley State University (GVSU) Annis Water Resources Institute (AWRI) web page (http://www.gvsu.edu/wri/education/waterdata.htm).  Students collected the data archived there while on cruises aboard GVSU’s D.J. ANGUS (Lake Michigan off Grand Haven, Michigan, and the nearby Grand River and Spring Lake; Figure 1) or the W.G. JACKSON (Lake Michigan off Muskegon, Michigan, and the nearby Muskegon Lake and Muskegon River; Figure 2).  To learn how to arrange to take your students on an educational cruise, go to http://www.gvsu.edu/wri/education/

You will need to hold a class discussion concerning the data that is to be analyzed.  Explain that the water in the epilimnion and hypolimnion should be thought of as two separate water masses and that these water masses likely have different values for many of the parameters measured (see “Seasonal Lake Stratification”).  Therefore, on the “Water Quality Computation Sheet” below, top and bottom water are listed separately.  If data is available for a river plume (see “The River Plume”), discuss how that data might be treated.  For example, if AWRI data is used, you might see data listed as “Lake Michigan” for the “Body of Water”, but designated as “Grand Haven Plume” under “Area”.  That means that although the station was physically located in Lake Michigan, if the sample was taken from the “top”, it likely is Grand River water.  However, if the data is from the “bottom”, it may be representative of  lake water.  A class discussion can determine if, when filling out the Computation Sheets, that data will be treated as lake data or river data, or if that data should be omitted for this particular analysis.  Alternatively, after discussing the unique situation presented by the plume data, you could let each student decide how to handle that data.  (See question 2 below under “Example Exercise”.)  You should also examine the data set you plan to use to see if you think it contains “suspect data”, data that is inaccurate due to human or machine error.  Again, you can hold a class discussion and talk about when a scientist is justified in omitting a piece of data during data analysis.

The data

If you elect to use AWRI’s data set you should familiarize yourself with the station locations by finding them on the attached maps (Grand River and Spring Lake, Figure 1; Muskegon River and Muskegon Lake; Figure 2).  The various locations (e.g., Prospect Point, Yacht Club Hole, Power Plant) are noted on the maps.  The drainage basin for the Grand River (http://www.gvsu.edu/wri/education/education_images/manual_images/fig_4.jpg), the longest river in Michigan, includes both agricultural and urban areas in its approximately 5,500 square mile drainage area (http://www.gvsu.edu/wri/isc/lowgrand/study.htm).  Spring Lake, a spring-fed, drowned river valley, enters the Grand River approximately 2˝ miles from the river’s mouth at Lake Michigan.  The Lake Michigan shoreline in this area is lined with sand dunes, some over 100 feet high.  The Grand Haven/Spring Lake/Ferrysburg area is largely residential and becomes a tourist mecca in the summer months.  Spring Lake itself is heavily developed, largely with private homes.  In contrast to the Grand River, the area encompassed by the Muskegon River’s drainage basin (http://www.gvsu.edu/wri/education/education_images/manual_images/fig_2.jpg) is much less (2,723 square miles; http://www.gvsu.edu/wri/isc/muskegon/studyarea.htm) and much less developed.  It empties into Muskegon Lake, a deep-water harbor largely surrounded by development.  The Lake Michigan shoreline in the Muskegon area is also lined with sand dunes, again some over 100 feet high.

You also need to be familiar with the abbreviations used in the AWRI database.  “Top” indicates the sample was probably taken 2-3 feet from the surface and normally represents the epilimnion in a lake.  Data listed under “Bottom” usually means that the sample was taken 2-3 feet from the lake or river bottom.  Normally that depth would represent the hypolimnion in a lake.

If you have a zip drive, you can download the AWRI file as a Microsoft Excel spreadsheet (http://www.gvsu.edu/wri/education/waterdata.htm).  If you do not already have “WinZip”, you will need to go to “WinZip Download”.  Students can then make plots and manipulate the files directly, or you can give them a subset of the data and ask them to make plots by hand, depending on the goals of the exercise. 

The computation sheets

This exercise uses a computation sheet modified from that developed by Gus Unseld, III as a simple means by which students can estimate water quality.  To obtain a valid estimate, the students should use data from numerous stations, probably at least 10 for each water body examined.  One sheet should be used for each water body (e.g., Lake Michigan, Spring Lake, or Muskegon Lake).  The student simply looks at each piece of data for the water body and makes a mark in the appropriate spot on the sheet.  In the end, a number of marks may be made for a particular range for a certain parameter (e.g., the 7.2-8.5 range for pH for top water may have a number of marks).  After working through all the data, the student separately adds up the number of marks for the “green” column, the “yellow” column, and the “red” column.  (The colors suggest whether or not the data for that parameter is estimated to represent good or poor water quality, or something in between.)  The student then does the calculations as indicated at the bottom of the sheet and determines if the lake has relatively good or relatively poor water quality based on the data used in the calculation.  You should stress to the students that the calculations they do are based on a relatively small data set.  Results for a large lake like Lake Michigan might vary considerably if more data were included.  A good class discussion might involve how the results would likely vary if more data were included for Lake Michigan from areas far offshore.  Remind the students that most of the data for Lake Michigan included in the calculations are from areas relatively close to shore, which probably greatly affects the analysis.

If you obtain data for this exercise from the AWRI database, it will not contain “sediment” data.  In that case, you will need to ask your students to ignore the category for “sediments”.  If you have your own sediment data, or data from a database that includes a description of the sediment, you can include it in the calculations.  In any case, the “sediments” category is the most difficult to evaluate as the data is not quantitative.  If the sediment is primarily clean (little or no organic debris) sand or clean sand and gravel, the mark should be in the “green” column.  Sediment in the shallow waters of Lake Michigan typically falls in this category.  If the sediment is coarse (sand and/or gravel), but contains significant amounts of organic material such as wood, bark, or leaves, the mark should be made in the “yellow” column.  Commonly, but not always, sediment from the Grand River falls in this category.  Finally, if the sediment is ooze it falls in the “red” column.  Ooze is defined herein as fine grained sediment (silt and/or clay) with abundant, very fine, organic material.  The organic debris usually can not be identified as such in the field, but it typically will result in the sediment being a black or dark greenish gray color.  When first collected it may have a rotten egg smell due to the decay of organic matter by anaerobic bacteria.  Most of the sediment in Spring Lake is ooze.

You will need to carefully explain to the students how to fill out the computation sheets.  Be sure to provide the students with one sheet for each of the water bodies to be evaluated (e.g., one sheet for Lake Michigan, one for Spring Lake, etc.).  To clarify the procedure for the students an overhead of an example computation sheet helps.

Example exercise

1. Before estimating the health of each of the water bodies, predict what you think the outcome of your calculations will be for each (e.g., will Lake Michigan be found to have good water quality or poor water quality?).  Explain your reasoning.

2. Fill out the computation sheets.  Please note whether or not you included the plume data and explain your reasoning.

3. Were you surprised by any of the results of your calculations?  Did you correctly predict the water quality for each of the water bodies?  Do you believe the results, or do you think the calculations themselves are questionable?

4. For each water body discuss which parameters (e.g., pH, conductivity, etc.) appear to be doing the most harm to the overall, calculated water quality.

5.  What do you predict for the future of each of the water bodies?  For example, for which do you expect water quality to improve?  Get worse?  Explain.

6.  Assume that you are in the state legislature and write a proposal that will improve the health of Lake Michigan and inland lakes.  In writing your proposal be sure to consider your answers to numbers 4 and 5 above and the results of your computation sheets.  Do you think your proposal could pass and become law?  Explain.
 

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