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 Manual 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 (Water Data). 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
If you elect to use AWRI’s data set you should familiarize yourself with the
station locations by finding them on the attached maps (
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.
You can download the AWRI data files as an Excel spreadsheet following the directions at: (Water Data). 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 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 dissolved oxygen 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.
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 (
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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BROUGHT TO YOU BY:
GRAND VALLEY STATE UNIVERSITY
DEPARTMENT OF GEOLOGY