IndexIntroductionMethodsDiscussionConclusionIntroductionWater analysis has many facets and parts, depending on its classification according to the Florida Department of Environmental Protection. Some of the criteria analyzed from surface water resources include industrial and agricultural discharges, concentrations of silver, lindane and lead, carcinogens and mutagens, to name a few. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Something that is not normally included in these reports is the hardness of the water. Water hardness is normally defined as the measurement of the concentrations of calcium and magnesium ions in water. It is known to have no adverse health effects due to the body using intestinal absorption to prevent excessive calcium intake, however it is known to cause calcium carbonate build-ups in the pipes, water system and even on plates in the form of “water stains”. As a result, there are many methods for removing ions from hard water to reduce its hardness. Examples include ion exchange, using exchange resins to react to remove Ca2+ ions from solution, to more natural methods such as Moringa oleifera (drumstick tree) seeds, which are ground into a powder and added to water. In this experiment we tested a water sample from a lake on the University of South Florida campus, also testing 3 random water samples and using 2 different filtration systems, in order to determine which method produced a water "sweeter". These two systems will be a cross-linked cation resin and a mixed deionization resin. To determine water hardness, a titration with EDTA (ethylenediaminetetraacetic acid), calagite indicator and a pH buffer is required. EDTA reacts with calcium according to the equation Ca+2 + EDTA-4 ---> CaEDTA-2, which is an extremely stable molecule in water6. An indicator calagite is used for its ability to bind to calcium to form a red color. However, because EDTA is a stronger ligand than calmagite, the Ca2+ ions are absorbed by the EDTA, resulting in a blue hue indicating that all the calcium has reacted. Finally, the ammonium buffer serves to neutralize the pH of the acid and maintain a pH of 10, the optimal pH for monoprotic "blue" calmagite, which allows the indicator to function. After filtering, another titration will be performed to help determine the Ca2+ concentration, which should be lowered. We will also use a conductivity meter to measure conductivity before and after filtration. Since Ca2+ contributes to the overall conductivity of water, the post-filter water should have less conductivity than the post-filter water. As for which filter system I think will work best, I think cation resin should work best for specific effect on inline cations and not all ions like deionization resin, which also has an effect on negative ions. Methods Solution Standardization: The first step in the entire process was to create a standardized EDTA solution that can be used in the rest of the experiments and determine the reaction rate the solution has with calcium solutions. The goal was to create a 0.1 M solution of EDTA that can be subsequently diluted into a 0.01 M solution to subsequently titrate the water samples and, as previously mentioned, find the reaction rate and satisfies the formula nM1V1= M2V2 and find “n”. The first step was to use the molar mass of EDTA, which is 372.238grams per mole, to determine the amount of EDTA needed to create 250 ml of 0.1 M EDTA solution. In addition to this, a 0.1 M solution was needed to be created using calcium nitrate to titrate using the EDTA, al order to complete the other side of the equation, however since it was only used once, only 50 mL was prepared. Once again the molar mass of calcium nitrate (Ca(NO3)2) was found, which was 164,088 grams per mol. From here the calcium solution was buffered to pH 10 using ammonium buffer and a pH meter and 5-7 drops of calmagite indicator were added to the calcium solution. Once the calcium is prepared, you can fill the burette with an EDTA solution and run it for a while to either remove the excess or clear the tip. From here the calcium solution is placed on the magnetic stirrer and the titration can begin. During the titration, it was important to monitor the calcium pH and add ammonium buffer as needed to maintain the pH in range. Once the solution has changed from a reddish-pink color to a blue-violet color, the titration has reached the end point and is complete. If the ammonium buffer is not added or is added continuously, the indicator will not change and will remain the same color shown by our first trace. With this data, the "n" value could be determined and used for calculations for the water hardness of the random samples and the pooled sample. Water hardness for a series of samples, before and after filtration: Now that EDTA has been standardized, it was possible to accurately determine the water hardness of the samples. The first step in this process is to run the 3 random water samples. The purpose of these is to help determine the accuracy of the EDTA solution when titrating solutions. As in the previous part, the pH meter and ammonium were used to buffer 50 mL of each sample to pH 10. From here 5-7 drops of indicator were added to each solution and mixed well. Then the samples were divided into 15 ml portions, creating 9 total samples, 3 trials for each. Now that the calcium samples had been prepared, the EDTA needed to be diluted to create a more precise titration. It was best to dilute a portion of the 250 ml of 0.1 M in 250 ml of 0.01 M EDTA. This was done by adding 225 ml of water to 25 ml of EDTA solution. Now that both solutions have been prepared, the 9 samples have been titrated until the indicator changes, adding buffer during the titration to maintain the pH. Once the 3 random samples were performed, the collected sample was tested. For the collected sample, the first step was to measure its conductivity in mS. From here, the sample was split into 3 sets, one set was left alone and set aside, while the other 2 were filtered. This was done by adding cation flow or DI resin to a filtration column, then adding water to the column and filtering it through the bottom. Once the two samples were filtered with both types of filters, their conductivity was measured again. Next, all 3 samples were buffered to pH 10 using ammonium buffer and then the indicator was added. Finally, all three samples were titrated to their respective endpoints and from there it was possible to determine the hardness of the water, before and after filtration. Unfortunately, due to time it was only possible to perform one test for each of the 3 samples, rather than 2 or 3. DiscussionPart 1: the purpose of this part of the process was to be able to determine the reaction rate of the EDTA solution, and use it to standardize the solution. Even if EDTA were not exactly 0.1 M,by determining the value of “n” in the equation nM1V1= M2V2, the error is corrected and consequently as long as n is always on the same side of the value with EDTA, the final calculated values ​​of the Ca2+ concentration should not be affected. As for why this had to be done, it was because of the imprecision involved in creating such a solution. The reason we decided to make 0.1 M EDTA instead of 0.01 M EDTA was purely for ease of storage and the number we found, n, was completely independent of the molarity of the EDTA. Part 2: Unlike part 1, part 2 wasn't as simple and has a lot more data involved. Starting from the first data table, all EDTA values ​​listed represent the point at which the indicator changed color, from red to blue. As explained above, this is the point at which all the calcium has been absorbed by the EDTA and therefore the titration is complete. From here, using the calculated “n” from week 1, the molarity concentrations of all calcium samples could be determined much more accurately. Once the molarity has been determined, the ppm of calcium can be calculated according to the formulas above. Although all of these samples had relatively low calcium molarity, the water was well above the limit for “very hard” water, demonstrating how little Ca2+ ions are needed to create hard water. Regarding the collected sample, the reason why conductivity was measured before and after filtration was to determine how many ions were removed by each filtration system. However, the ions removed and the amount of Ca2+ filtered do not appear to be the same. Although flow had no significant effect on conductivity, it had a much greater reduction in molarity and calcium ppm. Post filter water 1 was the only sample to fall into the “slightly hard” category, which is the lowest. This inconsistency is likely due to the fact that DI resin not only targets only cations as the flux, but also removes anions. If the lake water were composed mostly of anions such as Cl- or F-, it is very likely that the removal of these would have a much greater effect on conductivity than cations alone. Sources of error: the largest source of error in all this The experiment was tied to time constraints. There were only a few things in the entire experiment that were not disrupted by time, and as a result, many data sets remain without further evidence. The largest of these is the collected sample, which we were only able to fit into one trial. Another rather significant source of error was a small amount of EDTA that was eliminated during the making of the initial solution. This is 100% why our “n” is 1.36 instead of something much closer to 1. To eliminate these errors the most important thing would be to be more careful when adding solids to the water to start the solution. Another important thing is to spend a little more time on the experiment. Probably another 15 minutes and we could have completed all the tests for the collected sample. Conclusion Going back to the initial goal, it was to determine the hardness of the local water around here and choose a filter system that would best reduce the hardness. hardness. My hypothesis was that ion flow would do a better job, as it specifically targeted cations unlike DI resin which targets any ion. While it may appear that the data supports this hypothesis and statement, I will state that the hypothesis cannot be supported by the data. There's simply not enough of it. Due to the serious mistake of only being able to perform one test, there is simply not enough data. If.