water quality lesson plan

Lesson Plan Using Water Quality Data-1

Analyzing Water Quality Data Using X-Y Plots

Students can analyze water quality data by making plots using a variety of parameters (e.g., turbidity, secchi depth, pH, conductivity, dissolved oxygen) for the x and y axes. (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 By making such plots students will not only become more familiar with the data, but they will become more proficient at plotting numbers (by hand or with a computer), learning the usefulness of such plots, and getting a feel for how scientists analyze large data sets. Importantly, such an exercise will help students appreciate the vital role mathematics plays in science.

If the students have collected enough of their own data to make plots, 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 a cruise, go to AWRI’s web page (http://www.gvsu.edu/wri/education/).

Before making the assignment discuss positive and negative correlations and what they indicate. Equally important, discuss what a lack of correlation (“shotgun pattern”) indicates. Also discuss “suspect data”, data that is inaccurate due to human or machine error. Discuss how x-y plots may help spot suspect data (e.g., all data may fall on a “line” except for one or two data points). For their plots the students might use a different color for each water body (e.g., Lake Michigan, Spring Lake, Grand River) and different symbols (circles, triangles) for the various water depths (top, bottom). For ease of grading, you might ask all students to use the same color and symbol scheme for each plot made.

Also discuss with the students in some detail the data that is to be plotted. Explain that the water in the epilimnion and hypolimnion should be thought of as two separate water masses (see "Seasonal Lake Stratification"). Therefore, lumping data from top and bottom water together (e.g., to obtain a mean value) is normally not a valid exercise. If data is available for a 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 lake water. A class discussion can determine if, for plotting purposes, that data will be treated as lake data, river data, or, as a special category, “plume” data (see "The River Plume").

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 2½ 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 plots

The students can make several plots and determine if they see relationships between the various parameters. You might chose different plots for them to make, both those that do and those that do not show relationships. For example, secchi depth and turbidity should show a relationship (although not a linear one) as might conductivity and turbidity, but conductivity and % saturation probably will not show a relationship. After the students have made plots that you suggest, you might ask them to make a plot using two parameters that they are interested in examining.

Example exercise

1. If you were to make a plot of conductivity versus turbidity, do you predict you would see a relationship? If so, do you predict the relationship would be positive or negative? Explain your reasoning for your prediction.

2. Now make a plot of conductivity (y-axis, numbers should increase up) versus turbidity (x- axis, numbers should increase to the right). Use a different color for each body of water (Lake Michigan, Spring Lake, Grand River), different symbols for water depths (top, bottom), and include a key. Be sure to label the axes and indicate the units.

3. Do you observe a relationship? If so, is it positive or negative? Is it linear? If it is, derive an equation for the “best fit” line through the points. How can such an equation be used as a predictive tool? State in words what the distribution of the data tells you about conductivity and turbidity for these stations (i.e., explain why the data plot as they do).

4. Do data for the different water bodies plot in different areas of the graph? If so, explain why that might be the case (i.e., explain why conductivity and turbidity might be relatively high or low in the various water bodies).

5. Based on your plot do you believe any of the data is “suspect” (inaccurate due to human or machine error)? Explain.

6. If your task was to interpret the data for conductivity and turbidity, which would be easier to interpret, the data in table form, or the data plotted on a x-y plot such as the one you made? Explain your reasoning.

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