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# Developing a Water Quality Model (page 3)

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### Data Discussion

In looking at the correlation’s from the data I found some very surprising things. Only two of the six variables I tested had data that correlate throughout the year. The water temperature had a correlation of .83, which is close to one. The air temperature also was correlated between the two sites. It had a positive correlation of .93. All the other variables showed no correlation at all. T he pH was -.13; the dissolved oxygen had a correlation of -.25, the nitrates -.01, and the turbidity/clarity a correlation of .18. All of these numbers are so close to one (zero correlation) that I considered them basically 0. There is room for human error because a human does each test. I do not consider this much of a factor because anyone who helped me with doing the test would be certified in water quality monitoring.

### Analysis and Conclusion by Variable

#### Air Temperature

The air temperature data for Gravel Run and Centreville Landing had a positive correlation of .83. This means that as one reading was up, so was the other. The air temperature having a positive correlation makes sense because the sites are close together in terms of distance. Since they are near each other, they have the same climate. When it is extremely warm at Gravel Run, it would only make sense for it to be extremely warm at Centreville Landing.

#### Water Temperature

The water temperature at Gravel Run and Centreville Landing was also correlated. The data had a positive correlation of .93. This correlation also seems to make sense because if the air temperature is below freezing, then the water temperature should be below freezing. The biggest affect of the water temperature would be the air temperature. Since the air temperature was correlated, so should the water.

#### Dissolved Oxygen

The two sets of data were not at all correlated in terms of dissolved oxygen. There was zero correlation of -.25. I found this very strange because of several reasons. Since the water temperature was correlated, and the temperature directly affects the dissolved oxygen, I thought that they would be correlated. If the water is cooler, than the DO is higher. This is because cooler water holds more oxygen. When I found that the numbers did not correlate, I correlated two other sites on the Chester River in terms of dissolved oxygen. I found that these did have a positive correlation of about .84.

Some reasons that the data could not have correlated would be because of the movement of the water. There is faster moving water at Gravel Run and slower water at Centerville Landing. This would have an effect because faster moving water contains more oxygen than slower moving water. There is more shade at Gravel Run, causing it to be cooler and it does not let as much sun in for photosynthesis to take place. Centerville Landing is in full sun. There is less algae at Gravel Run, and it would produces more oxygen

#### Nitrates

The nitrate readings from both sites did not correlate either. The two sets of data had a correlation of -.01. The data could not have correlated for several reasons. Gravel Run receives more ground water than Centerville Landing containing more nitrates. The run off, ground water and rain in Centreville Landing is all very diluted by the salt water coming in. The salt water contains very few nitrates, thus diluting it. Another reason that Centreville Landings water and nitrate level are more diluted is because of other small stream that flow into it. There is also more agricultural land around Gravel Run and more run- off from the fields. At Centerville Landing, there are tides and at Gravel Run there aren’t any.

#### pH

The pH readings for both sites did not correlate either. They had a negative correlation of .13. This also was not what I expected. Some reasons for this could be are there is more ground water coming into Gravel Run, which might mean more acidic readings. At Centreville Landing there is more salinity, causing the water to be more basic. There might be more life at one site, causing more respiration, and when carbon dioxide is combined with water a type of acid is created. This would cause the water to have a lower and more acidic pH.

#### Turbidity/Clarity

The clarity and turbidity did not correlate either. I thought that there would be a negative correlation because the tests are testing opposite things. There was a positive correlation of .18. Reasons for this difference might be that there is more algae at Centreville Landing and the runoff that increases Gravel Run’s turbidity is more diluted.

### Conclusion

When looking at my results for finding the correlation of water quality for two sites on the Chester River, I can come to several conclusions. After I found out the correlation between the two sets of data, I started to find out why the two sets of data did or did not correlate. It is fairly hard to do this because of all the factors that have an effect on the water quality.

In looking at my data when it was all collected, I found many reasons that it could be wrong or not showing an accurate reading. There were some times during the year when it was not possible to monitor the sites, causing some data to be missing. Since sometimes the data was collected for one site but not the other, it is not possible to tell whether there was a correlation for that week, and it also would affect the entire years’ correlation.

When I found out that most of the data that I had thought would correlate did not, I immediately thought my project was bad and that I would never be able to figure out why it happened. When I sat down and actually thought and talked about it with Mr. Radcliffe, I realized I was wrong. I had thought that since the sites were close together and one flowed into the other they would naturally affect each other. I learned during this project that it does not really matter. Each site is so different because of the surroundings (agriculture, marshes, impervious surfaces, homes, forests), different land use around them, life in them, and because other streams flow into them. I plan on continuing this project in the future by building a computer model that might predict the water quality, just like you predict weather. This project is just a first step in doing so and a very important one. It showed to me that you need to be very site specific if you wish to predict water quality. Since each site is so different and there are so many factors that affect each one.