Florida Hard Water Analysis and Filtration Report

Introduction

Water analysis has many facets and parts to it, depending on its classification according to the Florida Department of Environmental Protection. Some of the criteria analyzed by surface water supplies include industrial and agricultural discharge, silver, lindane and lead concentrations, carcinogens, and mutagenes to name a few

1. Something not normally included in these reports is water hardness. Water hardness is normally defined as the measurement of the concentrations of calcium and magnesium ions in water.
2. It is known to have no known adverse health effects due to the body’s use of the intestinal absorption to prevent excess calcium intake, however it is known to cause build ups of calcium carbonate in pipes, water system and even on dishes in the form of “water spots”.
3. As a result, there are many methods of removing ions from hardwater in order to reduce its hardness. Examples include ion exchange, using exchange resins to react in order to remove the Ca2+ ions from the solution, to more natural methods such as Moringa oleifera (Drumstick Tree) seeds, which are ground to powder and added to water.

In this experiment we tested a water sample from a lake on the University of South Florida campus while also testing 3 random samples of water and used 2 different filtration systems, in order to determine which method resulted in “softer” water. These two systems will be a cross-linked cation resin and a mixed deionization resin. To determine water hardness, a titration using, EDTA (Ethylenediaminetetraacetic acid), calmagite indicator, and a pH buffer. EDTA reacts with calcium according to the equation Ca+2 + EDTA-4 ---> CaEDTA-2, which is an extremely stable molecule in water6. A calmagite indicator is used to its ability to bind with calcium to form a red color. However, since EDTA is a stronger ligand than calmagite, the Ca2+ ions are absorbed by the EDTA, resulting in a blue hue indicating all the calcium has reacted. Finally, the ammonium buffer is to neutralize the pH of the acid, and to maintain a pH of 10, the optimum pH for the monoprotic “blue” calmagite, which allows for the indicator to work. After filtering, another titration will be performed to help determine the Ca2+ concentration, which should be lowered. As well as this, we will be using a conductivity meter to measure the conductivity before and after the filtration. Since Ca2+ contributes to the overall conductivity of the water, the water post filter should have less conductivity than after the filter.

As for which filtering system I believe will work better, I think the cation resin should work better to it specifically effecting cations online and not all ions like the deionization resin, which also has an effect on negative ions.

Methods

Standardization of Solution: The first step of the entire process was to create a standardized solution of EDTA that can be used in the rest of the experiments and to determine the reaction rate that the solution has with the calcium solutions.

The objective of this was to create a 0.1 M solution of EDTA which can be later be diluted to 0.01 M solution in order titrate the water samples later, and as mentioned earlier find the reaction rate and fulfill the formula nM1V1=M2V2 and find “n”. The first step in this was to use the molar mass of EDTA, which is 372.238 grams per mol, to determine the amount of EDTA needed to create 250 ml of 0.1 M EDTA solution.

As well as this, a 0.1 M solution was needed to be created using of calcium nitrate to titrate using the EDTA, in order to fill out the other side of the equation, however since it only had a onetime use, only 50 mL was prepared. One 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 10 pH using the ammonium buffer and a pH meter, and 5-7 drops of calmagite indicator was added to the calcium solution. Once the calcium was prepared the Burette could be filled with EDTA solution and ran through for a bit to both get rid of excess and to clear out the tip. From here the calcium solution was put on the magnetic stirrer and the titration can commence. During the titration, it was important to watch the pH of the calcium and to add ammonium buffer as needed to keep the pH in range. Once the solution changed from a reddish-pink color to a violet-blue color, the titration had reached the endpoint and was complete. If the ammonium buffer is not added or continually added, then the indicator will not change and will remain the same color as shown by our first trail.With this data, the value “n” could be determined and used for the calculations for the water hardness of the random samples and the collected sample.

Water hardness for a set of samples, before and after filtration: Now that the EDTA was standardized, it was possible accurately determine the water hardness of the samples.

The first step of this process is to do the 3 random water samples. The purpose of these to help determine the precision of the EDTA solution when titrating solutions. Like the previous part, the pH meter and the ammonium were used to buffer 50 mL of each sample to a pH of 10. From here the 5-7 drops of indicator were added to each solution and mixed well. Then the samples were split into 15 mL portions, creating 9 total samples, 3 trials for each. Now that the calcium samples were prepared, the EDTA had to be diluted in order to create a more precise titration. It was best to dilute a portion of the 250 mL of 0.1 M into 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 had been prepared, the 9 samples were titrated until the indicator changes, adding buffer during the titration as to maintain pH.

Once the 3 random samples are done, the collected sample 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 either the Cation Flux or the DI Resin to a filtration column and then adding the water to the column and filtering it through the bottom. Once the two samples were filtered with both types of filters, they once again had their conductivity measured. Next, all 3 samples where buffered to 10 pH using the ammonium buffer and then the indicator was added. Finally, all three samples were titrated to their endpoints and from here water hardness could be determined, before and after filtration. Sadly, due to time only one trial could be run for each of the 3 samples, as opposed to 2 or 3.

Discussion

Part 1: The point of this part of the process was to be able to determine the reaction rate of the EDTA solution, and to use it to standardize the solution. Even if the EDTA was not 0.1 M exactly, by determining the value of “n” in the equation of nM1V1= M2V2, the error is adjusted out and as a result as long as n is always on the same side as the value with EDTA, the Ca2+ concentration final calculated values should not be affected. As for why this had to be done, it was due to the imprecision involved in creating said solution. As for why we decided to make 0.1 M EDTA as opposed to 0.01 M EDTA was purely for ease of storage and the number we found, n, was completely independent of Molarity of EDTA.

Part 2: Unlike part 1, part 2 was not as straight forward and has a lot more data involved. Starting at the first data table, all of the EDTA values listed are the point in which the indicator changed color, from red to blue. As explained earlier, this is the point in which all the calcium has been absorbed by the EDTA and as a result, the titration is complete. From here, using the calculated “n” from week 1, the molarity concentrations of all the calcium samples could be determined much more accurately. Once the molarity was determined, the calcium ppm could be calculated according to the formulas above. Despite all these samples having a relatively low molarity of calcium, the water was all way above the limit for “Very Hard” water, showing how little Ca2+ ions are needed to create hard water. As for the collected sample, the reason the conductivity was taken before and after filtration was to determine how many ions did each filtration system take out. However, the ions removed and the amount of Ca2+ filtered out does not appear to be the same. Despite the flux not having much of an effect at all on conductivity, it had a much greater reduction in molarity and calcium ppm. The post filter 1 water was the only sample to fall into the “slightly hard” category, which is the lowest one. This incongruency is probably due to the DI resin not only targeting just cations like the flux, but also removing anions as well. If the lake water was majority anions like Cl- or F-, it’s very possible that the removal of these would have a much greater effect on the conductivity as opposed to just the cations.

Sources of error: The largest source of error in this entire experiment was the time constraints. There were only a few things in the entire experiment that didn’t get cut short by time and as a result many sets of data are left without multiple trials. The biggest of these is the collected sample, which we were only able to fit in one trial with. Another pretty large source of error was a small bit of EDTA that was dropped when we were making the initial solution. This is 100% the reason why our “n” is 1.36 instead of something much closer to 1. In order to weed out these errors the biggest thing would be to be more careful with adding the solids to the water to start the solution. Another huge thing is to allocate slightly more time to the experiment. Probably 15 more minutes and we could have gotten all of the trials done for the collected sample.

Conclusion

Going back to the initial objective of this, it was to determine the hardness of the local water around here and choose a filtration system that would best reduce its hardness. My hypothesis was that the ion flux would do a better job, due to it specifically targeting cations unlike the DI resin which targets any ion. While it may appear as the data supports this hypothesis and claim, I will assert that the hypothesis cannot be supported by the data. There is just simply not enough there. Due to the major error of only being able to run one trial, there just simply is not enough data. If more trials were to be run then maybe the hypothesis could hold true, but there is always a chance, however slim it may seem, that we completely screwed the titration of the last part horribly. As a result, the hypothesis is not supported by the data.

Research Connection

An article I found with a subject matter similar to this was “Moringa oleifera Seed Protein Adsorption to Silica: Effects of Water Hardness, Fractionation, and Fatty Acid Extraction” by Brittany A. Nordmark et al. published in 2018. In it, the discuss the ways in which the seeds of the Moringa oleifera tree are used as a filtration system, much like the ones we tested in our experiments. The way they went about this was by first crushing the seeds into a fine powder and then run through filtering paper to get just the protein responsible for the filtering. From there they added the proteins to a variety of types of water, modeled after freshwater data from the EPA and collected the differences in how the different proteins in the seeds did in a variety of situations. In the end, it was concluded that the seeds’ proteins could filter water to varying degrees depending on the situation and it overall took a very in depth look into this natural filtration style. The purpose behind the experiment was to find a way to maintain water purifying part of the seeds, all while removing the parts that promote the growth of microorganisms. In general, this was just a more advanced version of the experiment we just performed, only the filtration of different things.