Final 1: Water Quality In and Around Miami University and Oxford

This topic submitted by Joshua Basinger, Mandy Sulik, Chad Ziepfel, and Melissa Hamann at 10:37 am on 12/6/01. Additions were last made on Friday, April 19, 2002. Section: Dorsey

**Please note Excel Graphs did not cut and paste into this document.

This project is being conducted to investigate the watershed in which we live. Ultimately, we are discovering how we, as humans, effect the watershed. One creek, which runs through Pfeffer Park, was the site of our study. We are studied the seasonal trends and human impact on the environment. Due to the unseasonable temperature changes, any direct correlation was hard to back with data that substantiated general trends. As a result of our lab, we hope to provide a greater understanding of water quality to our peers and ourselves.

I. Introduction

The watershed in which we live effects each and every one of us. It is where we get the most vital substance in our life, water. In 1995 alone, an estimated 745 million gallons per day was drawn from surface water and ground water aquifers of the Great and Little Miami River Basins. Three hundred and fifty eight million gallons, roughly 48% of the total water withdrawn, was from streams alone ( However, the total number of gallons withdrawn has increased due to the rise in population in and around the Great and Little Miami River Basins.

The purpose of our student-generated lab was to determine the environmental effects of Miami University and the community of Oxford on the local, Lower Great Miami, watershed. We wanted to determine how we effect the watershed, how our university effects it, and how others in the area effect the Lower Great Miami. While doing this, we took into consideration the effects of the seasons upon the chemical composition of the water.

To obtain a well-developed sense of the water quality, we decided that it would be advantageous for our group to narrow down the sample size of the watershed to two sample locations. However, we completed multiple surveys at repeated intervals at these sample sites. Collins Creek, the steam that runs through Pfeffer Park, is the stream in which we conducted our survey. This stream is one that has changes in urban environments, rural environments, agricultural runoff, vegetation variance, soil variance, and provided us with many factors to examine as we tried understand the entire Lower Great Miami watershed.

In general, we hypothesized that there would be general correlations between the changing seasons and the chemical status of the water in Collins Creek. In particular, we predicted that the temperature would to decrease while Dissolved Oxygen and pH would increase. As a whole, the water quality in and around Oxford would increase as the season’s change. We believed that this would be caused by the lack of rainfall and agricultural runoff as well as the decrease in temperature which, usually, causes DO levels to increase.

Using the data we collected, we wanted to inform our classmates and ourselves of the pros and cons of our water, which aspects meet national standards, and which fall short, we will propose ways in which to improve the water quality surrounding Oxford. As a result of our student-generated lab, we wanted to draw attention and awareness to the watershed in which we all reside.

II. Relevance of your research question

We are living in an era in which water quality is gaining awareness. In the seventies, point source distribution was recognized as the major source of pollution to our streams. Hundreds of thousands of tests and a few decades later, scientists have concluded that the non-point source distribution is the primary influencing factor to watershed health. Non-point source distribution includes agricultural runoff, sediment deposition caused by urbanization and vegetation degradation, and precipitation that becomes polluted with chemicals when it has to travel across impermeable surfaces before reaching an estuary.
According to the United States Geological Survey (USGS), the Great and Little Miami River Basins drain approximately 7,350 square miles of Ohio and Indiana. Eighty percent of the area is in southwestern Ohio. In 1995, 2.8 million people lived within the basins. Over 5,800 square miles, around 79% of the total land is used for agricultural activities. The other 21% of use is divided into three categories: 13% of the area is for residential, commercial, and industrial, 7% forests, and 1% wetlands (

Since the watershed affects a large population and affects vast tracts of land, the Lower Great Miami, as part of the Great and Little Miami River Basins, is monitored frequently compared to other watersheds within the state of Ohio. When the National Water Quality Assessment (NAWQA) Program was implemented in 1991, the Miami River Basins were selected as part of the study. The overall goals of the NAWQA Program are to describe current water-quality conditions for a large part of the nation's freshwater streams and aquifers (water-bearing sediments and rocks), describe how water quality is changing over time, and improve our understanding of the principal natural and human factors affecting water quality (

The United States Environmental Protection Agency (USEPA), identified the Lower Great Miami as one of 529 watersheds that has a “high level of potential impact caused by agricultural runoff.” A series of pesticide, nitrogen, and sediment tests were conducted between 1990 and 1995 ( The Miami region received a high composite score indicating that there is a greater risk of water quality impairment in the area.

In October 1991, the Lower Great Miami was given a score of 4 out of a 6 point IWI, Index of Watershed Indicators, score for overall water resource quality. A four means that there are less serious problems, but the watershed has a high vulnerability none the less ( .

III. Materials and Methods

Methodology of our lab included sampling water from test sites upstream from Pfeffer Park and inside the park’s boundaries. To obtain a well-rounded water assessment, we tested for pH, Dissolved Oxygen (DO), and temperature. All of these variables will were compared to the season and the location of the sampling.
To conduct these experiments, we obtained test instruments from Western’s Peer Science Tutoring Center. All of our tests were conducted using probes that attach to a Texas Instruments Graphing calculator.

The information will be gathered in a group with a minimum of two people twice a week. This ensured that there was always someone to double check data gained. As soon as the data was gathered it was written in an Excel chart.
To expand knowledge of water quality, we asked for class involvement in our study. We wanted them to learn how to conduct the experiments and how to compare the results to what is “normal.”
Our in-class presentation included a PowerPoint presentation that was used to describe water quality to our peers. We included multiple definitions that describe water quality, helpful Web sites, and interesting statistical data. Since our presentation was first, we had not collected a large amount of data. The data that we had collected, however, was presented to the class.
Our experimental design was statistically sound; it was straightforward, yet allowed insight to deeper water quality problems. Our statistical knowledge was gained from this course, Calculus, and previous Statistic classes that we have taken. The only advice that we asked for pertaining to our lab was obtaining and using the experimental instruments. In the end, however, we ended up teaching ourselves how to calibrate and use the instruments.
The results were unbiased because they were numbers obtained from the electronic instruments. After obtaining the data, we input the data into Excel to produce graphs. From these graphs, we observed the general correlation between test sites and temperature.

III. Results

Our observations were all recorded as we took the data. The findings are best conveyed using tables and color coded graphs. The types of statistics that are most beneficial to our study are general correlation trends to see how the properties of water quality interrelate.
Our data collections on Wednesdays and Fridays, except for holidays, are organized in the chart below.

Date Time Temp. Up. pH Up. DO Up. Temp. In. pH In. DO In.
12-Oct 11:20 16.527 7.032 broken 17.045 7.191 broken
17-Oct 14:30 17.834 6.297 6.542 14.435 6.56 5.369
24-Oct 14:30 17.039 5.532 7.74 12.729 4.822 6.479
26-Oct 11:20 17.678 5.464 8.173 13.694 5.291 8.783
31-Oct 14:30 17.74 5.672 8.659 14.972 5.534 9.394
2-Nov 11:20 17.982 5.374 8.8 14.364 5.624 10.287
7-Nov 14:30 12.835 5.472 9.374 11.072 5.495 11.038
9-Nov 11:20 7.485 5.766 9.81 7.619 5.276 11.58
14-Nov 14:30 8.395 5.781 10.198 9.389 5.295 12.962
16-Nov 11:20 9.754 5.823 10.23 10.234 5.562 13.237
21-Nov 14:30 10.285 5.892 10.196 10.592 6.129 14.962
28-Nov 14:30 10.298 6.729 11.111 12.345 6.437 16.216
30-Nov 11:20 10.974 5.935 2.288 11.442 6.179 0.966

All the abiotic data gathered was organized into the chart below. This chart allows us to look at the overall patterns of the water quality as time progresses and the seasons change.

This conclusive graph allows for easy comparison between the “upstream” and “in the park” sites. All three variables did not show any significant trends among test sites. The DO was the most surprising of all the data. We expected that the DO levels would be lower downstream, or “in the park,” since higher DO levels indicate healthier streams and the water would have traveled a greater distance through the city of Oxford. To best explain this unexpected finding, one must consider the drainage tiles and fallen sediment fences that surround the sampling site “upstream.” These two factors have caused the water quality to decrease in that area due to human impact on the stream. Temperature and pH both seemed to keep the same general trends in both sites.
Temperature upstream did not gradually go down like we expected. The unseasonably warm weather caused the gradual trend in during the end of our study. We had hypothesized that the DO levels would increase as the seasons progressed. We were correct in this hypothesis. The drastically low final DO level was, more than likely, due to the increase water volume from the three days of rain prior to the last sampling. Not having any direct correlation, the pH did not fluctuate that sufficiently enough to provide meaningful results.

Data from in the park, or downstream, is plotted in the above graph. The pH and the temperature were closely related for the “in the park” sampling site. Besides the levels with the x-variable as 8, all points rise and fall simultaneously. DO for this site climbed steadily throughout our sampling period, until the drastic plummet on the last sampling day. This plummet happened at both test sites.

IV. Discussions and Conclusions

We expected that the seasons would drastically change over the course of three months. However, this is Ohio and the weather did not work with us. According to our previous knowledge of water treatment, we thought that the dissolved oxygen would increase as the season’s progressed; it did this until the very end when it sharply decreased at points of the stream. We also thought the pH level was supposed to increase; due to the weather’s inability to change seasons and the abundance of rain, the pH level leveled out with no real noticeable changes.
Our experiment was not directly related to any other group from our section of the course. However, we have seen other students from upper level Natural Systems classes collecting data down at the pond below Peabody. There was also one group that tested the fish in the same steam that we did our study. Therefore, our study has very relevant information to contribute to Western’s knowledge of health of our watershed.

Our data is important because we have successfully shown that our stream water at Miami University has potential contamination due to the drastic fluctuation in water levels. Repairing sediment fences, eliminating contaminants dumped into the stream, and placing protruding sewer pipes in the middle of the creek would allow Collins Creek to improve its current water quality standing. Until we can find better ways to keep our natural waters clean around Miami, the health of fish, aquatic insects, faculty, staff, students, and Oxford residents is highly susceptible to the environmental changes of our water caused by both humans and Mother Nature.
For further investigation it would be helpful to take data from more locations and over the course of a year or more. The longer a period of time for water testing would allow us to view water’s health over the course of multiple seasons. It would also add a more interesting element if we had access to reported dumped contaminants in the stream. Comparing the contaminants to the water quality would allow us to see if it really is the weather that causes dramatic shifts or the seasons.

V. Bibliography

“Agricultural Runoff Potential- 1990-1995.” 8 Sept. 2001: n-pag. On-line. Internet. 17 March 1999. Available WWW:

“National Water-Quality Assessment Program Great and Little Miami River Basins Fact Sheet FS-117-97.” 8 Sept. 2001:n-pag. On-line. Internet. August 1997. Available WWW:

“Watershed Profile: Lower Great Miami.” 8 Sept. 2001: n-pag. On-line. Internet. 7 Sept. 2001. Available WWW:

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