Two Apes, a Time Traveller, and a Wraith


Our two original variables are Dissolved Oxygen Demand WQI and Turbidity WQI; our third variable is Biological Oxygen Demand WQI. We study these variables to test how clean the water is with the turbidity variable. We also want to know how much oxygen there is in water with the dissolved oxygen demand variable. The tools we used to study these methods are the lamotte water treatment kit, tubes, tablets, refractometer and the Flip camcorder to record a video of how the place looks. For our methods, we did actual field work, going out to test moving and still riparian zones. Our hypothesis is that we will se varying degrees of correlation between our variables: when BOD WQI variable rises, then the dissolved oxygen WQI will decrease; when the dissolved oxygen WQI rises, then the turbidity will decrease; when the BOD WQI rises, so will the turbidity variable. Based on our analysis, our data supports our hypothesis, but is limited by it's r-squared value's amount, which is not really a correlation to begin with. See analysis for clarification.

In initiating this project, we expect to see a clear correlation between our variables. In specific, when we have a high BOD WQI, the DOC WQI will be low as a result. In addition, when the DOC WQI is high, then the turbidity WQI will be low. Thus, the higher the BOD WQI is, the higher the Turbidity will be.

The tools we used are as follows: a LaMotte Water Testing Kit, a refractometer, the Internet, pencils and paper, and most importantly, our brains. We collected our data by recording readings from the LaMotte Water Testing Kit's tools, such as dropping in special tablets that show us in color how much of a concentration of a variable there is. From that, we recorded them onto paper, and later, onto the Internet. Using the Internet, we changed our data into variables and points on a graph to find if there is a correlation. We chose our specific water quality variables because we thought they would be related based on what each individual variable can do to the other variable.

•Turbidity represents the transparency of a liquid, in this case water. Turbidity usually is used to measure the amount of opaque substances are floating in the water. This is important in interpreting how efficiently plants can grow in liquids, taking only light into account. This variable was chosen as it determines the ability of plants in the water to make oxygen and thus support life.
•Biological Oxygen Demand represents how much oxygen is required to sustain aerobic life in the environment. In the research of others, this is often used to represent an environment's overall health. This is because oxygen is essential in any living ecosystem, thus a living aquatic ecosystem needs sufficient oxygen. This variable is universal in the project due to the essential nature of this variable.
•Dissolved Oxygen Content represents how much oxygen is regularly found in water quantities, such as rivers and oceans. DOC is used in other studies as a means to understand how much oxygen a liquid has, as oxygen portrays signs of life, that breathe oxygen. Such studies occur in soil and water quality testing, discovering the amount of oygen found in either prospect. This variable is independent, and most other variables, such as the Biological Oxygen Demand variable, are dependent on its capabilities to know how much oxygen has been dissolved.

Analysis and Data
Negative Linear Funtion
Negative Linear Funtion

In our first graph concerning our first variable, the Turbidity WQI, and our BOD WQI variable, the correlation between the two is not strong. Though it is a negative linear function, its r-squared value is not close enough to either 1 or -1, which would show that the relationship is very powerful.
Negative Linear Relationship
Negative Linear Relationship

In our second graph concerning our two variables that are not the BOD WQI variable, Turbidity WQI and DOC WQI, the relationship shown is not very strong. Though it is a positive linear function, its r2-value is not close enough to either 1 or -1, much like the previous correlation.
Positive Linear Correlation
Positive Linear Correlation

In our third and final graph between our second variable, the DOC WQI, and the BOD WQI variable, much like the first correlation, is also not strong. Similar to the first correlation, it is a negative linear function, but its r2-value is also not close enough to either -1 or 1.


Due to the non-existent correlation between any of our variables, we can prove that their is not any correlation between out first variable and our BOD WQI variable. Although the correlation between any of our variables is not remotely strong, their slopes prove otherwise; 2/3 functions reflected what we said in our hypothesis. The odd one out is the Turbidity WQI Vs. DOC WQI chart, which goes against what we said in our hypothesis.


Though our data is a bit faulty here and there due to missing readings and human error and the like, it did support our data. The first two graphs supported our hypothesis, but it seemed to be that the third graph is the true anomaly in this experiment. For us, it does't make sense for oxygen to rise as the water gets harder to see, as that would go against what photosynthesis does in sunlight. One limiting factor in our research is the missing data; to be human is to err, and how many times did we err. Broken tubes, courtesy of Mr. Simons, forgotten data, courtesy of Keanu Erguiza, etc. are just a few of the things that ruined our data more than it should've. Another limiting factor is the amount of time we had to cover on this project. Due to our puny existence and limited time in our class, we only had 3 weeks to perform these experiments, when data would be collected for months and years, giving scientists a more accurate depiction of the quality of the water they were testing. Finally, one last limiting factor is Mother Nature herself, as she would continually screw up our readings. It was coincidental, but each time we went to go test the water is our designated zones, it just so happened to rain, therefore making the water we tested heavily influenced by rainwater than existing water in the rivers and lakes. One possible variable to either refute or support our hypothesis is the percent of trash found in our sites; this variable is capable of affecting both all 3 variables, as trash has pollutants affecting the BOD WQI and blocks vision when in the way, affecting both the Turbidity WQI and DOC WQI.

Works Cited
  1. "Water Quality Monitoring". Five Creeks. 3/22/10 <http://www.fivecreeks.org/monitor.html>.
  2. Simons, Ariel L.. "WaterQualityProject". AP Environmental Science: Environmental Charter High School. 3/22/10 <http://spreadsheets.google.com/pub?key=tbKgEgaXqP_dbQm19KfH4pQ&output=html>.