In the process of refining and developing our project, our group has referenced many invaluable resources. The following is a summary of each source used. The article titled "Water Quality Conservation for the Citarum River in West Java" details the social and economic uses of the Citarum River in West Java Province and Jakarta City, and explores the valuable role it plays in the development of the city and well being of its citizens. This relationship between the people and the river is not always beneficial, however, and the text reveals the pollution problems that human interaction has caused on the surface water. The article contains a comprehensive review of a study conducted from 1989 to 1993, the objective of which was to “define the recommendation for the optimization of water quality conservation” (Bukit, 1995). As a result of the study, new plans for reducing the domestic and industrial pollution load have been undertaken. This text has provided us with a frame work to develop on a small scale in Oxford, Ohio. "Elemental content in baseflow water samples as an indicator for surface contamination with toxic and other elements" reviewed surface water samples collected from the aseflow at the mouth of six creeks near Town Lake in Austin, Texas. The study evaluated water contamination in comparison with the nearby water treatment plant through measuring “The concentration of 24 elements by instrumental neutron activation analysis on the total solid separated, and by below-boiling point evaporation” (Iskander, Lyday, 1995). This study further broadened our individual design in its connecting the environment with society.
Taken from our class reader, "How Safe is Your Water?" details the modern dangers that exist within tap water and explores their potential to harm the population. “The drinking water supply in this country is definitely getting worse, and we’re only beginning to uncover the problem areas” (Olson, qtd. by Kotz, 1995). It identifies the common ailments found in processed water, such as lead, pesticides, chlorine, and in rare cases, industrial chemicals (benzene, TCE, PCBs), and cryptosporidium (microbial). This article was responsible for initially sparking our interest in investigating the contamination in the water supply, and many of the revealed contaminations have been incorporated into our study. Another article, "Relationship between coliform culturability and organic matter in low nutritive waters," involved the study of coliform growth and culturability, or "count on nutritive medium" (Boualam, M., 2002), of ten bacteria species in relation to the amount of organic matter in the water. Water collected after heavy rain was used, as well as that considered to be an algal bloom. "Bacterial growth was measured in the two types of water, regardless of the initial concentration of DOC (dissolved organic carbon)" (Boualam, M., 2002). The overall findings indicate that coliform bacteria lose their culturability in both water types at a rate varying by the DOC level in the water. The lower the DOC concentration the more rapidly the culturability declined. This study may be very similar to our findings with regards to coliform bacteria growth as our testing sites vary from flowing to stagant.
"Cost effective policies for alternative distributions of stochastic water pollution," extends our study objectives by outlying costal water management policies for the "alternative distributions of stochastic water pollution" (Ing-Marie, 2002) in Sweden. This study uses probability distributions in the form of normal and lognormal formulas to make a final determination on the best form of low-cost water management. This article looks towards future methods of prevention, and as a result can be applied to our study in both identifying the problem, performing statistical analysis, and offering management solutions to the City of Oxford for the future.
In the past, a multitude of pollution studies have been conducted on the natural systems, the water table being no exception. We plan on pulling from these past efforts in expanding the general knowledge for the future; thus making local impact for the sake of global contributions. Our study is specific to the Oxford community, and will benefit the locale by means of raising awareness as to the status of the creek and surrounding areas; however, it will also encourage national interest through nationwide sources such as the United States Geological Survey, through their water source monitoring units. When completed, our report could be sent to the USGS and added to their local monitoring efforts database for the Oxford/Hamilton area.
Materials and Methods
In order to conduct the experiments our group surveyed approximately ł mile of Four-Mile Creek in Oxford, Ohio, a segment of creek with university horse stables directly to the south. From this section of the creek, we identified five points of interest both upstream, centered, and within potential pollution runoff areas. This will create maximum variation between the sights sampled (from pristine to contaminated) thus allowing us to eventually compare and test our null hypothesis. Over the course of a 28 day period, on a weekly basis, we will travel to our creek site and collect individual samples in multiple categories at the five designated points. We will test for: nitrates (found in pesticides and herbicides), chlorine, pH levels, and bacteria (fecal culiform). In order to accomplish this sampling we will require university provided testing kits for each method, sampling vials, topographic land and elevation maps, a digital camera, and city of Oxford construction records (regarding the mid-creek dam and concrete bank deposits). Sampling will take place on weekly intervals from October 31st to November 28th, 2002.
The class will be divided into five groups each testing a water samples collected from individual sites for all of the specified water quality tests. We will ensure unbiased results by aiding each group and adhering to strict standards on a weekly basis to ensure that every test is consistent with the next. Furthermore, we will compare the class collected data to that of our own from previous weeks making sure that it is similar in value.
In early October (10-8) while first surveying Four-Mile creek our group noticed extremely low water levels, and a variety of dry points among the streambed and shoreline. This was slightly discouraging, and plans for an alternate site were discussed; however, rainfall picked up in mid-October, thus filling the creekbed and resulting in a healthy stream flow throughout the duration of the sampling process. After collecting and running tests on our water samples, we used Statview 5.0 to run statistical analyses on all of the data (see Fig. Set 1). We agreed that running individual ANOVA tests using Site, Date, and Site and Date together would be most effective in analyzing the data with minimum possibility for misleading statistics. ANOVA tests tell us if there are significant differences within a specific test group (Nitrates, Chlorine, pH, or Bacteria) when comparing Site or Date. If the P-Value of the ANOVA test is < .05, that means there are significant differences. A Post-Hoc test tells us where these significant differences are, which is known by locating the P-Values that are < .05.
The mean for each variable is as follows:
Nitrates—.171 Chlorine—.005 pH—7.684 Bacteria—1000.000
According to the means, the water samples yielded only trace amounts of Nitrates considering the range of the scale for the Nitrate test. The Chlorine levels in the water samples were usually zero, yielding a low means of .005. The pH tests yielded a slightly basic mean of 7.684, however close to being neutral. The water samples yielded the highest possible results for Bacteria on every test: >1000. Thus the mean for Bacteria is of course 1000.000.
The standard deviation for each variable is as follows:
Nitrates—.124 Chlorine—.023 pH—.849 Bacteria—0.000
The standard of error for each variable is as follows:
Nitrates—.033 Chlorine—.005 pH—.195 Bacteria—0.000
The ANOVA Table for Nitrates (see Fig. Set 2) says there are significant results. F (4, 9) = 32.029, P< .0001. The Post-Hoc test for Nitrates (Fisher’s PLSD for Nitrates) shows significant differences between Site 1 (S1) and S3 (P< .0001), S2 and S3 (P< .0001), S3 and S4 (P< .0001), S3 and S5 (P< .0001), and S4 and S5 (P< .0180). There is an “S” beside each significant difference on this table. So looking at the Interaction Bar Plot for Nitrates, we can observe that S3 has a much greater mean than the other four sites. Likewise, S4 has a greater mean than S5. Because these site-relationships are significant according to their P-values, we can assume that there is a reason why such data is yielded, especially for the extreme data found at S3. The possibilities for these reasons will be discussed in the Discussion section of the lab report.
The ANOVA Table for Chlorine says there are no significant results. F (4, 14) = .921, P = .4791. Because S3 on the first test date (10-31-02) was the only water sample to contain any amount of Chlorine (.01 mg/L), the Interaction Bar Plot for Chlorine has results only for S3, as well as the Post-Hoc test for Chlorine. And because the ANOVA Table for Chlorine had no significant results, the Post-Hoc test has no significant results as well.
The ANOVA Table for pH says there are no significant results. F (4, 14) = .921, P = .4793. The closest value to being significant on the Post-Hoc test is the relationship between S4 and S5 (P = .1390). The Interaction Bar Plot for pH shows that S4 has the lowest mean, and the relationship between S4 and S5 is close to being significant, however not.
The ANOVA Table for Bacteria yields no results, because the values have zero variance. As well, the Post-Hoc test and Interaction Bar Plot for Bacteria yield no results.
The ANOVA Table for Nitrates yields no results, because there is missing data for the second test date (11-7-02). As well, the Post-Hoc test and Interaction Bar Plot for Nitrates yield no results.
The ANOVA Table for Chlorine says there are no significant results. F (3, 15) = 1.316, P = .3061. The Interaction Bar Plot for Chlorine only has values for the first test date (10-31-02) because this was the only date that any amount of Chlorine was found in the water samples. Likewise, the Post-Hoc test shows the Date-relationships as being those involving Oct. 31. For these three relationships, the P-Value (P = .1163) was close to being significant.
The ANOVA Table for pH says there are no significant results. F (3, 15) = 3.208, P = .0534. However, the P-Value is so near significant that it might be considered so for our purposes. Interestingly enough, the Post-Hoc test for pH yields two significant differences between Nov. 7 and Nov. 14 (P = .0276), and Nov. 7 and Nov. 19 (P = .0115). The Interaction Bar Plot for pH shows that Nov. 7 has a lesser mean than Nov. 14 and Nov. 19, whose mean differences with Nov. 7 are significant according to the Post-Hoc test. We can assume that there is a reason why such relationships are significant, which will be discussed in the Discussion section.
The ANOVA Table for Bacteria yields no results, because the values have zero variance. As well, the Post-Hoc test and Interaction Bar Plot for Bacteria yield no results.
An ANOVA Test was attempted using both Site and Data together, but for Nitrates, Chlorine, and pH there is too much missing data to yield results. Again, for Bacteria the values have zero variance so there are no results. An ANOVA Test using both Site and Data would allow for the examination of the effects of Site and Data at the same time to find and understand trends in the interaction between these two variables.
Discussion & Conclusions
At first glance, the results given above do not seem very rich in implications for interesting discussion. While there seems to be a lack of data that obviously supports our hypothesis, there actually is. To support our hypothesis, we will use our observations of Four Mile Creek and surrounding areas, water sample tests, statistical data, and city records. We will derive relationships between human interactions with the test site and the results of the tested water samples.
The most important relationship we have found is of that between the high levels of Bacteria found in every water sample and the Miami University horse stables along the south bank of Four Mile Creek. The stables run from S2 to S4. Because the stables house many horses, there is a large amount of horse feces deposited in the area. As it is likely that this feces would run off into Four Mile Creek from rain—there is a stream running into Four Mile Creek at the S2—every water sample tested off the charts for Bacteria content. We presume this Bacterium to be Fecal Coliform, which is basically feces. As is observed on Figure Set 1, all the Bacteria amounts are a steady 1000.000, however, the actual data was >1000. This quantity could not be communicated on Statview 5.0. You will notice too on Fig. Set 1 that we tested for Bacteria on only two test dates: the first (10-31-02) and the second (11-14-02). This was due to the high cost of the Bacteria tests. If we were to perform this test again, we would test for Bacteria in every water sample; however, we believe that the results would have been the same: no variance at a steady >1000. While testing for Bacteria levels in a creek is nothing new, we do believe that looking at the situation with the stables and surrounding wildlife in mind is a valuable addition to the Butler County community. Four Mile Creek is saturated with bacteria and it is visible in the water samples. They are a light brown-yellowish color. Our water should not be polluted in such a way. Further investigation might look into the effects of this high bacteria content on specific species in the area.
Another important relationship we have found is that between the levels of Nitrates and Chlorine found at S3. While only the S3 water sample on the first test date (10-31-02) showed any traces of Chlorine, S3 was the site of the highest levels of Nitrates for all three of the dates tested (10-31-02, 11-14-02, 11-19-02). This is interesting because at this site are several signs of high pollution. S3 is point at which the large broken concrete slabs begin, running down east along the south bank, and stopping at the dam. S4 is on the west side of the dam, and S5 is on the east side of the dam. Jutting out from the south bank at S3 was a small peninsula-like rock formation that created an area of stagnant water. As can be seen in the photographs on the visual aid, there was thick yellow foam on top of the water by the south bank at S3 on all of the test dates. We included this foam in our water samples. We assume there is a connection between the given circumstances at S3 and the results for Nitrates and Chlorine. While the amounts of both were relatively trace even at their highest, the S3 data was significantly greater than that for the other four test sites. We conclude that S3 is highly significantly eutrophic, compared with the other four test sites. If we were to perform this test again, we would make sure that all of our testing materials are available in the correct quantities. Our Nitrates test tests for the second test date (11-7-02) were not completed because we ran out of the necessary powder pillows. This was unfortunate, but regardless, an interesting pattern arose in the three Nitrates tests we ran.
As the test sites S1-S5 run from downstream (west) to upstream (east), and the results of the Nitrates test follow a consistent bell curve from S1 to S5. S3 is the peak of the bell curve at each test site, while S1 and S5 are consistently the low points. This is an interesting find that should be looked into more: we really have no idea why the samples yielded such data. There must be a logical explanation for this; there is little chance it could be merely coincidence.
Although there is significant data regarding the relationship between dates Nov. 7 and Nov. 14, and Nov. 7 and Nov. 19 for pH, there seems to be no relationship between this and the environment. The pH results were always slightly basic, and around 8.000. We could find no patterns between the observed human interaction and the data.
In all, Four Mile Creek is a disturbed environment, especially between S3 and S5. We have not explored the creek further downstream or upstream than S1 or S5. However, there seems to be plenty of interesting data within our confines. Between S4 and S5 is a low concrete dam. It did not seem to have any effect on the water samples; however inquiries into its origins and purpose proved futile. Calls were made to local Oxford and Butler County authorities, and the Four Mile Creek dam was not in their records. There seemed to be general confusion as to its existence. We were directed to state authorities in Columbus who as well had no record of the dam’s existence. This seems to be a problem. Who built the dam? Who owns the dam? Why is it there? Does it have any connection to the unsightly and dangerous broken concrete slabs piled along the south bank? How many heavy machines were used to put the dam in place? There are old heavy equipment tracks leading from the road to the dam. These are all provoking questions that should be answered. They are fuel for further investigation. People must be responsible for their public lands, and government agencies should be held accountable for their actions in the pursuit of environmental awareness and sustainability.
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Boualam, M. "Relationship between coliform culturability and organic matter in low nutritive waters." Elsevier Science. Copyright 2002.
Bukit, Nana Terangna. “Water quality conservation for the Citarum River in West Java.” Copyright 1995. Elsevier Science. ISSN: 0273-1223.
“Clarification of the Use of Biological Data and Information in the 2002 Integrated Water Quality Monitoring and Assessment Report Guidance.” Environmental Protection Agency.
Ing-Marie, Gren. "Cost effective policies for alternative distributions of stochastic water pollution." Journal of Environmental Management. Vol. 66 Issue 2, p145, 13p. Copyright 2002.
Iskander, Felib Y., Lyday, Mike. “Elemental content in baseflow water samples as an indicator for surface contamination with toxic and other elements.” Copyright 1995. Elsevier Science. ISSN: 0048-9697.
Kotz, Deborah. “How Safe is Your Water?” Good Housekeeping. November, 1995. Copyright 1995.
“Surface Water Data for Ohio: Butler County.” United States Geological Survey.
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