The Health of Twin Lakes in Jones State Forest

THE HEALTH OF TWIN LAKES IN JONES STATE FOREST

Justin Huang, James Gerhard, Jesse Miller, Mason Pai, Ayaka Saito

The Academy of Science & Technology

9th Grade

3701 College Park Dr.

The Woodlands, TX 77384

936 709-3250

Headmaster: Dr. Susan Caffery

Teacher: Dr. Sara Fox

Assessing the Health of the Twin Lakes at Jones State Forest by Measuring Levels of pH, Alkalinity, Carbon Dioxide, Salinity, and Water Hardness

Table of Contents

Abstract	3
Introduction	4
Method	9
Results	16
Discussion	28
References	34
Appendix A. The Correlation between pH and Alkalinity	36
Appendix B. Results From pH Tests	37
Appendix C. Results From Alkalinity Tests	49
Appendix D. Results From Water Hardness Tests	41
Appendix E. Results From Carbon Dioxide Tests	43

Abstract

The W. G. Jones State Forest is an urban forest in the piney woods region, containing various aquatic species in its waters that distinguish the forest from its urban surroundings. This project aimed to assess two lakes in the forest for their ability to sustain life by measuring the pH, alkalinity, dissolved carbon dioxide, water hardness and salinity levels using various chemical tests. In addition, a gadget was created to gather samples from the lake bottoms to compare the biological availability of nutrients at the bottom of a lake in comparison to the surface. Meanwhile, samples between "Clear" lake and "Murky" lake were also compared to determine whether there were any significant differences in their chemical properties. It was hypothesized that the pH and alkalinity of the lake water would be at acceptable levels as determined by government regulations, although carbon dioxide, hardness, and salinity were predicted to be slightly higher than their indicated averages due to human activity near the area, such as construction and pollution. Following testing, it was determined that the chemical properties of the lakes were within sustainable ranges. With a significance threshold of , the researchers concluded that the pH and carbon dioxide levels differed significantly between the surface and the bottom of the lake, while all other variables did not have statistically significant differences between the surface and lake bottom samples. The "Clear" lake and "Murky" lake samples were also not statistically different.

Assessing the Health of the Twin Lakes at Jones State Forest by Measuring Levels of pH, Alkalinity, Carbon Dioxide, Salinity, and Water Hardness

Freshwater biomes contain many dynamic and highly productive ecosystems. Despite making up only three percent of the Earth’s waters, freshwater bodies are said to house over 10,000 species (National Geographic, n.d.). The health of these ecosystems is influenced by natural elements and adequate limnology, which is the study of the physical, chemical, and biological components of an aquatic ecosystem (Simon et al., 2013). These factors have become increasingly important in recent years, as the effects of human activity and urbanization, such as runoffs from chemical plants, deforestation, and industrial farming, have disrupted various freshwater ecosystems, leading to concerns about the sustainability of aquatic life (National Geographic Society, 2012). In this study, the student researchers assessed the levels of pH, alkalinity, dissolved carbon dioxide, salinity, and water hardness of the twin lakes in the Jones State Forest, a 1,700-acre urban forest in the piney woods region surrounded by apartments and roads, to determine the overall health of the lakes. Has human activity impacted the health of the forest’s aquatic ecosystems, and if so, which variables has it affected the most?

One variable that is commonly considered when studying water quality is the pH level. The pH value of a liquid is placed on a scale from 0 to 14, which identifies the acidity or basicity of a certain fluid (Water Science School, 2019). A pH value of 7 indicates a neutral solution; anything above 7 is classified as basic, and anything below 7 is classified as acidic. Lakes are generally considered to be healthy when the pH value is between 6 and 8, though this value may fluctuate due to environmental or human factors such as acid rain or runoff from agricultural waste (Simon et al., 2013). Lakes with a pH value of 5.0 or less will begin to display negative effects of acidic environments, including an imbalance of nutrients and impeded fish reproduction (Water Science School, 2019). Once the pH value falls below 4.0, many aquatic organisms begin to die due to an increase of toxic metals in the water and a lack of biologically available nutrients. Inversely, many species of fish may also die at lakes with pH levels of more than 9.0.

Unlike pH, alkalinity is the measurement of a water's ability to neutralize acids (Knight, 2020). Alkalinity is important for lakes, as it mitigates the effects of acid rain and maintains an ideal pH for the health and development of aquatic organisms. For freshwater lakes, an alkalinity level of 20 ppm or above is considered healthy (United States Environmental Protection Agency [EPA], 2022). Alkalinity allows lakes to have a degree of resistance against acidification, helping lakes to maintain their own pH (Knight, 2020). Therefore, alkalinity is a crucial variable to study to determine the future sustainability of a lake.

In addition to pH and alkalinity, other factors, including the presence of various nutrients and elements, are also important in maintaining a healthy aquatic ecosystem. The first element studied was dissolved carbon dioxide, which can be an important factor in maintaining a balanced energy pyramid within the ecosystem as many aquatic plants and algae require carbon dioxide during photosynthesis (Institute of Food and Agricultural Sciences [IFAS], 2020). Additionally, the concentration of dissolved carbon dioxide can signify whether there is a healthy balance between dissolved carbon dioxide and dissolved oxygen, as plants release oxygen as a product of photosynthesis. The carbon dioxide levels can also affect species outside of water bodies, as lakes around the world have over three times more carbon buried in their sediments than all of the oceans combined (Rose, 2019). This makes dissolved carbon dioxide in lakes both a crucial factor for aquatic organisms and for the cycling of carbon in the atmosphere.

Another variable studied in this evaluation was salinity, which describes the concentration of dissolved salts in a body of water (Water Science School, 2018b). According to a study by Cañedo-Argüelles et al. (2018), Salt deposition can occur naturally within aquatic biomes often through salts carried from oceans through precipitation, and rock weathering. However, it is noted that human processes can speed up the accumulation of salts, such as through saline irrigation return flows from agricultural practices and land clearings that bring saline groundwater to the surface. Salinity is usually measured in ppt, or parts per thousand, which refers to the weight of the dissolved salt in comparison to the rest of the water (Water Science School, 2018b). The standard for freshwater lakes is less than 1 part per thousand. Although aquatic organisms are able to maintain healthy saline levels within their bodies, this is highly dependent on the salinity of the water they are in (Cañedo-Argüelles et al., 2018). For example, an unexpected increase in salinity changes the osmotic pressure within the cells of the organisms in the water, causing the cells to wither. If not stabilized, there may be a delay in the growth and reproduction of these organisms, and in some situations may cause certain species to die, leading to decreased biodiversity within the ecosystem.

The last variable studied was water hardness, which measures the amount of dissolved calcium and magnesium. Water hardness is measured by the milligrams of calcium carbonate found in one liter of water, with 0 to 60 ppm being classified as soft and over 180 ppm as very hard (Water Science School, 2018a). Although hard water is not considered to be directly harmful to aquatic organisms, it can cause mineral build-up in lakes, making the water appear cloudy (Manning, 2022). In addition, a lack of hardness may indicate a low concentration of dissolved minerals that are important for the development of certain aquatic species.

Given the research on the factors that affect the various factors in lakes, the student researchers hypothesized that the pH and alkalinity values of each lake would be at acceptable levels for aquatic life as determined by the United States Geological Survey. Although most bodies of water often vary from a neutral level of 7 by only one value on the pH scale, due to the piney woods ecosystem of the Jones State Forest, pH levels were predicted to be slightly acidic at around 4.5-6.5 (Texas A&M Forest Service, 2022). Additionally, the student researchers hypothesized that the concentration of dissolved carbon dioxide, salinity, and water hardness may be higher than the indicated averages for lakes due to the construction of new buildings near the forest and the continuous use of vehicles that emit gas waste as they drive past.

To evaluate the five variables of the twin lakes, chemical tests were conducted on samples from different locations and two depths of the lakes. The researchers collected samples from five locations in each lake, for a total of ten locations, as displayed in Figures 1 and 2. Samples were taken at surface level and at the lake bottom, as the amount of sunlight that reaches the water may be restricted by the depth, thus affecting the temperature and rate of biochemical reactions, especially in deeper parts of the lakes. In addition, samples were collected from different locations in order to increase the sample size from each lake and minimize any errors in assessing the average values of each studied variable.

The independent variables in this study were the depth and locations of the samples collected, and the dependent variables were the analyzed chemical levels of pH, alkalinity, carbon dioxide, salinity, and water hardness. In this study, the healthy levels of each variable as designated by the United States Geological Survey were used as the control variable.

Figure 1

Sample Locations in "Clear" Lake

Note. The points represent each water sample collection location. The map was created by the student researchers using Google Earth.

Figure 2

Sample Locations in "Murky" Lake

Note. The points represent each water sample collection location. The map was created by the student researchers using Google Earth.

Method

The samples for this experiment were gathered at Jones State Forest, and chemical analysis was done at the school lab, which was a BSL1 lab. Both locations were supervised by Dr. Sara Fox. Additionally, when creating the gadget, a mechanical saw was used. To prevent any potential dangers, the researchers used gloves and goggles during the building process, and operated under the supervision of an adult. In addition, the alkalinity, pH, carbon dioxide, and hardness test kits contained several chemicals that may have posed a risk upon ingestion or contact with open wounds and sensitive areas such as the eyes. To negate this risk, gloves and goggles were worn during testing to avoid infection, and water samples and chemicals were disposed of into drains through proper disposal procedures to ensure that chemicals did not contaminate the ground or waterways. Additionally, the researchers used a gadget for the collection of the water samples to prevent excessive contact of the skin with the water to avoid potentially harmful microorganisms and bacteria from entering the body of the researchers. For further preventative measures, the researchers wore gloves and goggles and avoided allowing open wounds to be in contact with the water. Closed-toed shoes and clothes with no exposed skin were worn as an extra precaution. Accessories were removed prior to water collection and testing.

Materials

(1) Boat
(1) Kayak
(1) LaMotte Wide Range pH testing kit

(1) Wide Range pH Indicator
(2) 12 mL Plastic Test Tubes with cap
(1) Wide Range pH Octa-Slide 2 Bar, 5.0-10.0 pH range
(1) Octa-Slide 2 Viewer

(1) LaMotte Alkalinity Test Kit

(50) BCG-MR Indicator Tablets
(60mL) Alkalinity Titration Reagent B
(1) 20 mL Plastic Test Tube with Cap, with 5/10/15mL markings
(1) Direct Reading Titrator (0-200 Range)
(1) Alkalinity Endpoint Color Chart

(1) LaMotte Total Water Hardness Kit

(15mL) Hardness Reagent #5
(15mL) Hardness Reagent #6 Solution
(60mL) Hardness Reagent #7
(1) 40mL Glass Test Tube with Cap
(1) Direct Reading Titrator, (0-200 Range)
(1) Pipet, 0.5mL

(1) LaMotte Carbon Dioxide Test Kit

15mL Phenolphthalein Indicator, 1%
60mL Carbon Dioxide Reagent B
(1) Direct Reading Titrator, (0-50 Range)
(1) 40mL Glass Test Tube with Cap

(1) AZ-8371 Salinity Pen Salinity meter
(1) Mechanical Saw
(1) Glass stirring Rod
(20) 500-mL Plastic Water Bottles
Gadget

(1) ½” Diameter 10-foot PVC pipe
(2) 1-liter Disposable LifeWtr Bottles
(2) Compression Fittings
(1) Compression Fitting Holder
(1) ½” Diameter Three-way PVC Connector
(1) ¾” Diameter PVC Check Valve
(1) 8 fl-oz OATEY Purple PVC Primer
(1) 8 fl-oz OATEY Regular Clear PVC Cement
(1) ½” Diameter PVC Cap

Procedures

Gadget Construction

First, the researchers used the mechanical saw to cut the 10 foot PVC pipe into a 5 foot PVC pipe segment. Then, the three-way PVC Connector was attached to the bottom of the pipe, and the Compression Fitting Holder was attached to the horizontal side of the 3-way PVC Connector. Following this step, the caps of the Compression Fitting Holder were unscrewed, and the two Compression Fittings were attached on both sides of the Compression Fitting Holder. Finally, the Compression Fitting Holder caps were attached over the Compression Fittings.

Next, the PVC primer was applied to the inside of each Compression Fitting, and the researchers waited two minutes for the primer to dry. Once dried, the Clear PVC Cement was applied to the inside of the Compression Fittings. The caps of the LifeWtr bottles were then removed, which allowed the student researchers to apply primer and cement to the bottles, and the top of the LifeWtr bottles were stuck into the Compression Fittings. Lastly, the combined primer and cement was left to set overnight.

To prepare the gadget for sample collection, the PVC Check Valve was connected to the bottom of the 3-way PVC Connector, and the PVC cap sealed the PVC pipe from the opposite end of the Compression Fitting Holder. See Figure 3 for a diagram visualizing this process.

Figure 3

Technical Diagram of Water-Collecting Gadget

Note. This 3D model was made by the student researchers using TINKERCAD software. The researchers used the gadget by sealing the top while lowering the gadget into the water. After reaching the designated depth, the cap was opened, and the water was able to flow into the LifeWtr bottles.

General Sample Collection

The boat was rowed to the locations indicated in Figures 1 and 2. Then, two water bottles were filled at each location; one at the surface, and another from the bottom.

Sample Collection From the Bottom of the Lake. To begin sample collection, the gadget was placed in the water and lowered until the bottom of the gadget reached the lake floor, with a cap covering the top part of the gadget. The gadget was then lifted eight centimeters for the bottom to allow water to come into the gadget without the risk of the sediments clogging the gadget. Next, the cap on the top of the gadget was unscrewed, allowing the water to come in. After ten seconds, the gadget was lifted back out of the water, and the water collected in the LifeWtr bottles was transferred to a water bottle, with the location, lake, and depth marked.

Sample Collection From the Surface. Before sample collection, the cap of the water bottle was removed. The water bottle was then submerged eight centimeters under the water until the water sample filled the bottle to the top. Then, the bottle was retracted from the water and released. Finally, the bottle was labeled with the location and the lake.

General Testing Procedures

Before testing, any prior chemicals remaining in the test tubes were poured into the drain, and the tubes were sanitized using distilled, or reverse-osmosis (RO) water.

Testing for pH. In addition, the Wide Range pH Octa-Slide 2 Bar was slid into the Octa-Slide 2 Viewer and placed to the side. Then, the sample water was poured into a test tube to the 10 mL mark, and eight drops of Wide Range pH Indicator were added to the test tube. Next, the test tube was capped and mixed for about five seconds, before being inserted into the Octa-Slide 2 Viewer. Finally, the viewer was held to a light source, and the pH was taken by matching the sample color to the color standard as noted on the Octa-Slide 2 Viewer.

Testing for Alkalinity. To begin the testing process, a 20 mL test tube was filled with 10 mL of sample water. A BCG-MR Indicator tablet was then added to the water, and the sample was mixed using a stirring rod until the tablet dissolved. If the solution did not turn blue-green, the alkalinity was zero, and the testing would conclude. If the solution became a blue-green, the adapter tip of the Direct Reading Titrator was inserted into the center hole of the titration tube cap of the Alkalinity Titration Reagent B bottle. The bottle was then flipped, and the plunger of the titrator was slowly pulled out until the large ring on the plunger was opposite the zero line on the scale. Following the preparation of the titrator, the bottle was turned right side up, and the titrator was removed from the cap. The titrator was then inserted into the center hole of the cap of the test tube. The titrator was pressed, and Titration Reagent B was slowly dropped into the test tube while the test tube was gently swirled between drops until the solution turned from blue-green to purple. Finally, the scale was read where the ring on the titrator met the titrator barrel as Total Alkalinity measured in ppm CaCO3.

Testing for Dissolved Carbon Dioxide. First, the sample water was poured into a test tube to the 20 mL mark, and two drops of 1% Phenolphthalein Indicator were added to the test tube. Following this step, the student researchers only proceeded if the test tube remained colorless. If the tube turned red, it was determined that there was no free carbon dioxide in the water sample, and the testing concluded. Next, the adapter tip of the Direct Reading Titrator was inserted into the center hole of the titration tube cap of the Carbon Dioxide Reagent B bottle. The bottle was then flipped, and the plunger of the titrator was slowly pulled out until the large ring on the plunger was opposite the zero line on the scale. Following the preparation of the titrator, the bottle was turned right side up, and the titrator was removed from the cap. Finally, while gently swirling the tube, the Carbon Dioxide Reagent B was added to the test tube one drop at a time until a faint pink color appeared and persisted for at least 30 seconds. The test results were then read directly from the scale where the large ring on the titrator met the titrator bottle as ppm carbon dioxide.

Testing for Salinity. To maintain proper sanitation, the electrode at the tip of the salinity meter was cleaned using distilled or RO water. Then, the water sample was poured into the small beaker to the 20 mL mark. Next, the salinity meter was turned on, and the settings were changed so the results were measured in ppt. The electrode at the tip of the salinity meter was then placed in the water sample, making sure that the electrode was fully submerged. Finally, the results were collected when the value of the salinity stabilized. The results were as shown on the screen of the salinity meter in ppt of dissolved salts.

Testing for Hardness. To begin testing, the sample water was poured into a test tube to the 12.9 mL mark, and five drops of Hardness Reagent #5 were added to the test tube. The tube was then capped and swirled for approximately five seconds. Next, five drops of Hardness Reagent #6 Solution was added, and the tube was again capped and swirled. If the solution turned blue by this step, it was determined that there was no measurable amount of hardness in the sample, and the testing was concluded there. If the solution turned red, the researchers proceeded to the following steps. Next, the adapter tip of the Direct Reading Titrator was inserted into the center hole of the titration tube cap of the Hardness Reagent #7 bottle. The bottle was then flipped, and the plunger of the titrator was slowly pulled out until the large ring on the plunger was opposite the zero line on the scale. Following the preparation of the titrator, the bottle was turned right side up, and the titrator was removed from the cap. Finally, while gently swirling the tube, the Hardness Reagent #7 was added to the test tube one drop at a time until the red color changed to a clear blue. The test results were then read directly from the scale where the large ring on the titrator met the titrator bottle as ppm total hardness in CaCO3.

Results

In this study, a total of twenty water samples were collected from two lakes in the Jones State Forest for an assessment of the water quality in the forest. Of the samples collected, ten samples were collected from "Clear" lake, with five samples at surface level and five samples from the lake bottom at different locations as displayed in Figure 1. Likewise, ten samples were collected from "Murky" lake, with five samples at surface level and five samples from the lake bottom at different locations as shown in Figure 2. Tests for the levels of pH, alkalinity, dissolved carbon dioxide, salinity, and water hardness were run on each sample.

The first variable analyzed was pH level. Figures 4 and 5 show the average pH values of the samples in "Clear" and "Murky" lake respectively, with the surface level samples being indicated by a blue bar and samples taken at the lake bottom being indicated by a green bar. "Clear" lake showed more variation amongst its locations than "Murky" lake. No sample was indicated to have a pH level below 5.6 or higher than 6.6. The lowest pH values were detected in lake bottom samples C, E, and J with pH measurements of 5.73, 5.77, and 5.77 respectively. See Appendix B for a table listing all pH values.

Figure 4

pH Values in "Clear" Lake

Note. This chart shows the pH of "Clear" lake, with the error bars representing the standard error. pH values were taken as the average of three independent trials.

Figure 5

pH Values in "Murky" Lake

Note. This chart shows the pH of "Murky" lake, with the error bars showing the standard error. pH values were taken as the average of three independent trials.

To determine whether there was a significant difference between the pH levels of the surface samples and the lake bottom samples as presented in Figures 4 and 5, a t-test was performed using the equation:

(1)

where and denoted the means of the variables being studied, and denoted the standard deviations of each group, and and denoted the number of samples in each group.

A t-test determines the level of significance of the variation between two sample groups by comparing the statistical means of the two data sets. In the case of this study, the significance threshold for all tests was determined to be 0.05. The null hypothesis stated that the pH values of the surface samples and the lake bottom samples did not have a significant difference, and was to be rejected only if the resulting p-value was less than 0.05. The resulting p-value was 0.004, which successfully rejected the null hypothesis.

In addition to testing for significance between the two depths, another t-test was performed on the data displayed in Figures 4 and 5 using (1) to determine whether the difference between pH levels for samples collected in "Murky" lake and "Clear" lake was statistically significant. The null hypothesis stated that the two lakes did not have significant differences in their pH values. The p-value found was 0.343, which failed to reject the null hypothesis.

Next, the averages of the alkalinity tests are displayed below in Figures 6 and 7, measured in ppm calcium carbonate. The full data can be viewed in Appendix C. The green bar represents the alkalinity levels of surface samples, and the blue bar represents the alkalinity levels of lake bottom samples. Lake bottom sample E and I indicated the lowest alkalinity values of approximately 18.67 and 19.33 ppm, while lake bottom sample J displayed an unexpectedly high alkalinity level of approximately 27.33 ppm.

Figure 6

Alkalinity Values in "Clear" Lake

Note. This chart shows the total alkalinity of different locations at "Clear" lake measured in ppm CaCO3. Alkalinity values were taken as the average of three independent trials.

Figure 7

Alkalinity Values in "Murky" Lake

Note. This chart shows the total alkalinity of different locations at "Murky" lake measured in ppm CaCO3. Alkalinity values were taken as the average of three independent trials.

To view the variation of the alkalinity levels across the two lakes visually, data was displayed on a heatmap, as shown in Figure 8. The dark blue represents areas with lower alkalinity, while the bright yellow areas indicate higher levels of alkalinity. Samples A, B, C, D and E of “Clear” Lake are located in the bottom half of the map, and samples F, G, H, I and J of “Murky” lake are located in the top half of the map.

Figure 8

Alkalinity in the Twin Lakes at Two Depths

Note. These maps display the levels of alkalinity of the water in the twin lakes. The map on the left shows the alkalinity from the lake floor, and the map on the right shows the alkalinity from the surface of the lakes.

To determine the significance of the differences between the two lakes and between the two depths, two t-tests using (1) were run on the data. The null hypothesis between surface samples and lake bottom samples stated that the alkalinity levels do not vary significantly between depths. The resulting p-value was 0.489, which failed to reject the null hypothesis. For the correlation between the two lakes, the null hypothesis stated that the alkalinity levels for the two lakes did not vary by a significant amount. The null hypothesis failed to be rejected with a resulting p-value of 0.333.

The following are the averages of the hardness tests, which are displayed below in Figures 9 and 10, measured in ppm calcium carbonate. Samples taken at surface level are depicted with a green bar, and samples taken at the lake bottom are depicted with a blue bar. All trials resulted in values of between 11 and 38 ppm CaCO3, and averages for all samples were between 16 and 32 ppm CaCO3. However, the lake bottom samples F and G indicated a hardness of over 400 ppm for one of the trials, and lake bottom sample J resulted in such for two trials. Hence, to avoid outliers that fall out of the general range of the hardness levels by over 100 ppm CaCO3, the displayed data consists of the trials in which the results did not exceed the general averages. See Appendix D for the full data.

Figure 9

Water Hardness in "Clear" Lake

Note. This chart shows the hardness of the water measured in ppm CaCO3 at "Clear" Lake. Water Hardness values were taken as the average of three independent trials.

Figure 10

Water Hardness in "Murky" Lake

Note. This chart shows the hardness of the water measured in ppm CaCO3 at "Murky" Lake. Water Hardness values were taken as the average of three independent trials, with the exception of trials F, G, and J.

To view the variation of the hardness levels across the lakes visually, data was displayed on a heatmap in Figure 11. The dark blue represents areas with lower water hardness levels, while the bright yellow areas indicate higher levels of hardness. Samples A, B, C, D and E of “Clear” Lake are located in the bottom half of the map and samples F, G, H, I and J of “Murky” Lake are located in the top half of the map.

Figure 11

Water Hardness in the Twin Lakes at Two Depths

Note. These maps display the levels of water hardness of the water in the twin lakes. The map on the left shows the water hardness levels from the lake floor, and the map on the right shows the water hardness levels from the surface of the lakes. Red represents higher water hardness levels and blue represents lower water hardness levels.

To determine whether there is a significant correlation between the two lakes, or the two depths at which the samples were collected, a t-test using (1) was performed on the data. The null hypothesis for the difference between the two lakes stated that the hardness levels for "Murky" lake and "Clear" lake are the same. The resulting p-value was 0.5, which failed to reject the null hypothesis, indicating that the hardness levels in the two lakes did not have a significant difference and any variation in hardness occurred by natural chance. The null hypothesis for the difference between the two depths stated that the depth of the sample did not affect the hardness of the sample and the variation between the depths were not statistically significant. The resulting p-value of 0.033 managed to reject the null hypothesis.

Next, Figures 12 and 13 display the results of the dissolved carbon dioxide tests on samples from "Clear" lake and "Murky" lake respectively. See Appendix E for the full data. The green bars represent the dissolved carbon dioxide levels of the surface samples and the blue bars represent the dissolved carbon dioxide levels of the lake bottom samples measured in ppm carbon dioxide. Although the general range of dissolved carbon dioxide was between 10 ppm and 70 ppm, there were some locations, predominantly in “Murky” lake, that had a carbon dioxide level outside of these ranges.

Figure 12

Dissolved Carbon Dioxide in "Clear" Lake

Note. This chart shows the level of dissolved carbon dioxide in the water at "Clear" Lake, measured in ppm of CO₂.

Figure 13

Dissolved Carbon Dioxide in "Murky" Lake

Note. This chart shows the level of dissolved carbon dioxide in the water at "Murky" Lake, measured in ppm of CO₂.

To view the variation of the carbon dioxide levels across the lakes visually, this lake bottom data is displayed on a heatmap in Figure 14. The dark blue represents areas with lower carbon dioxide concentrations, while the bright yellow areas indicate high levels of carbon dioxide. Samples A, B, C, D and E are located in the bottom half of the map, and samples F, G, H, I and J are located in the top half of the map.

Figure 14

Dissolved Carbon Dioxide in the Twin Lakes at Two Depths

Note. These maps display the levels of dissolved carbon dioxide of the water in the twin lakes. The map on the left shows the dissolved carbon dioxide amounts from the lake floor, and the map on the right shows the carbon dioxide amounts from the surface of the lakes. Red represents higher carbon dioxide levels and blue represents lower carbon dioxide levels.

To test for the significance of the variation between surface samples and lake bottom samples, a t-test using (1) was performed. The null hypothesis stated that the difference between the two depths were not significant and any variation occurred purely by chance. The resulting p-value was 0.002. In addition, a second t-test was performed using (1) to test for the significance of the variation between samples from "Murky" lake and "Clear" lake. The null hypothesis stated that the variation in dissolved carbon dioxide levels between the two lakes was not significant and occurred by natural chance. The resulting p-value was 0.054, which failed to reject the null hypothesis.

Finally, samples were tested for salinity. Upon completion of the three trials for each sample, an average salinity level of 0.02 parts per thousand was determined for all samples except surface sample F and surface sample J, both of which indicated a salinity level of 0.01 for one of the trials, which lowered their averages to approximately 0.017.

Discussions

The pH, alkalinity, water hardness, carbon dioxide, and salinity levels of "Clear" lake and "Murky" lake were measured for an assessment of their abilities to sustain a healthy ecosystem. To measure any possible differences in the variables between different depths in the lakes, samples were taken at surface level and at the lake bottom using the gadget designed for this project. Additionally, the resulting values for each variable were cross-compared across the two lakes to determine any significant correlations between the two lakes.

To begin, it was hypothesized that pH would remain largely unaffected by human activities and would therefore be in normal ranges for the freshwater ecosystems in the forest. As shown in Figures 4 and 5, the pH for both lakes fell between 5.6 and 6.6. Although a pH of below 6.5 is considered slightly acidic for freshwater lakes, due to the naturally acidic environment of a piney woods ecosystem, the pH levels were estimated to be lower at approximately 4.5-6.5 (Texas A&M Forest Service, 2022). This meant that neither lake displayed an unnaturally high amount of acid in its waters, which allows for a stable amount of nutrients to sustain its ecosystems. The two t-tests that were run on the data showed that while depth significantly affected the pH levels in both lakes at a p-value of 0.004, the pH values did not differ significantly between the two lakes at a p-value of 0.343. This may suggest that the two lakes have similar sources of acid and therefore generally maintain the same acidity levels. Additionally, in both lakes, lake bottoms seemed to have lower pH levels, indicating a more acidic environment. The only exceptions were location D, where the surface sample had a lower pH value, and location G, where both depths had the same average pH level. This may have been due to changes in carbon dioxide concentrations that increased the acidity of the lake bottom, which is discussed in more detail later.

Secondly, the alkalinity levels of the lakes were also consistent with the hypothesis, as most samples displayed an alkalinity level of above 20 ppm which indicates a healthy level of alkalinity in balance with the pH. As displayed in Figures 6 and 7, there were some differences between surface samples and lake bottom samples, which were especially notable at locations A, E, I, and J, also seen in Figure 8. However, when two t-tests that determined the level of significance for the alkalinity levels between the two depths and between the two lakes were conducted, the resulting p-values of 0.489 and 0.333 respectively failed to reject both null hypotheses, indicating that the alkalinity values did not vary significantly between the two depths or between the two lakes. This may suggest that the variability in alkalinity was not related to the fluctuation in pH values, as pH had a significant difference between surface samples and lake bottom samples yet the alkalinity levels did not match those changes in pH (see Appendix A). This may be explained by the fact that the effects of alkalinity often begin affecting pH values significantly once the pH falls below the average level of the area. For this study, no samples indicated a highly acidic solution, which may explain why alkalinity did not affect acidity levels as it was predicted to.

Next, the results for hardness, salinity, and carbon dioxide are discussed, which were hypothesized to be slightly higher than the average for freshwater ecosystems due to the construction around the forest that may have contaminated the water. However, as seen in Figures 9 and 10, all samples resulted in a hardness level of between 16 and 32 ppm CaCO3, indicating that the water in the lakes was generally soft and did not contain a large amount of minerals. "Clear" lake generally displayed higher hardness levels for its surface samples, while "Murky" lake had higher hardness levels for its lake bottom samples. In addition, the two t-tests ran on the data suggested that while there was no significant difference in the hardness levels between the two lakes at a p-value of 0.5, there was significant variation between surface samples and lake bottom samples. The resulting p-value of 0.033, which rejected the null hypothesis, indicated that the hardness levels between surface samples and lake bottom samples had a statistically significant difference. This can be seen clearly in Figure 11, which illustrates that lake bottom samples, on average, had lower hardness levels (see left map) in comparison to the surface samples (see right map). Although this is not enough data to explain the cause for why hardness seems to be higher towards the surface, it may be an indicator of an external source of minerals that have accumulated towards the surface of the lakes.

In addition to hardness, all samples had salinity levels that fell below one part per thousand, which was determined to be the average salinity for freshwater lakes. This indicated that neither lake had an abundance of dissolved salts (Water Science School, 2018b). Although it was initially hypothesized that salinity levels would be higher due to human activity near the area, our data suggested otherwise. It seems unlikely that the salinity in the lakes would rise to alarming levels in the near future due to little variation that occurred between the samples which indicates a stable salinity in both lakes.

Lastly, the dissolved carbon dioxide levels displayed different patterns between the two lakes in its results as seen in Figures 12 and 13. Most notably, "Clear" lake generally had lower levels of carbon dioxide in comparison to "Murky" lake, and had less variation between surface and lake bottom samples. However, most lake bottom samples had a higher concentration of dissolved carbon dioxide in both lakes, which is especially noticeable in locations F, G and J as seen in Figure 14. Two t-tests were run, one that tested for the level of significance for the carbon dioxide levels between the two lakes, and another that tested for the level of significance for the carbon dioxide concentrations between the surface samples and the lake bottom samples. The resulting p-values were 0.054 and 0.002 respectively, indicating that while the two lakes did not have a significant difference, the carbon dioxide levels differed significantly between the two depths. This is likely due to the collection of dead organisms near the bottom of the lakes that cause decomposers to quickly exhaust the oxygen near the bottom and cause a concentration of carbon dioxide to occur there. Consequently, this may have caused the variation between surface samples and lake bottom samples for pH as seen in Figures 4 and 5, as high levels of carbon dioxide are often associated with high acidity.

When applying the findings of this study to the actual freshwater lakes in the forest, there were a number of possible errors during testing that may have affected the results of this experiment which are crucial to keep in mind. First, many of the water test kits used subjective methods for the collection of data, which relied on the student researchers to match the color of the sample to the color chart provided by the test kit, such as for the alkalinity and pH kits. Similarly, some kits required careful monitoring of the sample until it turned to a color that was only verbally dictated by the manual. Sometimes the description given was vague and had multiple possible interpretations. The student researchers sought to minimize any errors by having multiple researchers confirm a result before the recording of the data, and each test was run a total of three times independently by multiple student researchers. Although this may have resulted in results being slightly disproportionate to their actual value, these possible errors had minimal effect on the main goal of this study, as the levels still fell under the indicated levels of the variables with the subjectiveness in mind.

Another possible error resulted from the containers the water samples were stored in. Due to the air-tight environment of the bottles, the levels of dissolved carbon dioxide in the samples may have been affected over time, as there was an estimated one week break between the collection of the sample and the testing period. The containers also prevented the cycling of carbon dioxide and oxygen that regularly occurs in lakes in a natural environment, which may have affected the concentration of dissolved carbon dioxide as well. Consequently, the results of the dissolved carbon dioxide tests may only be applicable to water that is kept in closed compartments rather than to the actual dynamic environments of lakes and other freshwater bodies. For the purpose of this project, only one trial for the testing of the dissolved carbon dioxide was kept for the final analysis of all variables.

In addition to the closed environment of the water-sample containers, there were some samples in which algae and other organisms were collected with the water. As mentioned previously, the one-week gap between sample collection and testing could change the chemical composition of the water since the organisms may continue to grow in the bottle. There were some instances where the biological mass would directly interfere with the results of the testing, such as in lake bottom samples F, G, and J, where hardness testing was discontinued due to the hardness results exceeding 400 ppm. Due to the extremity of these results, they were eliminated in the final graph as outliers. The effect of these issues on the final results were mitigated by completing three trials for each test and taking the average of those three trials.

Further investigations of the aquatic ecosystems in the Jones State Forest may be done through additional samples collected from the marsh region of the forest. Testing the same variables and possibly extending to other minerals may increase the information that is available. Another consideration for future studies would be to study the effect of time on each variable. This was especially evident with carbon dioxide, as its concentration often changed depending on the number of photosynthetic organisms in the sample bottle. In contrast with the conditions of the bottled samples used in this study, carbon dioxide levels may be tested directly near the lake as well, which would eliminate the issue of time passing between sample collection and testing.

Through the results observed from this study, it can be concluded that the twin lakes in Jones State Forest are healthy, as all of the variables tested fell within a healthy range for an aquatic ecosystem set by government regulators. Overall, the appropriate water quality is still able to be maintained by nature. Although this project only researched two lakes, this information could apply to future studies on urban forests and the contribution of lakes to their environment. Studying the health of lakes in urban forests may be crucial as human activity continues to expand into nature.

References

Cañedo-Argüelles, M., Kefford, B., & Schäfer, R. (2018). Salt in freshwaters: causes, effects and prospects - introduction to the theme issue. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 374(1764), 20180002. Retrieved April 3, 2022 from https://doi.org/10.1098/rstb.2018.0002

Institute of Food and Agricultural Sciences. (2020). Plant Management in Florida Waters. University of Florida. Retrieved April 25, 2022 from https://plants.ifas.ufl.edu/manage/overview-of-florida-waters/water-quality/photosynthesis/

Knight, E. (2020). What is Alkalinity? Orenda Technologies. Retrieved April 3, 2022 from https://blog.orendatech.com/what-is-alkalinity

Manning, E. (2022). What is the Ideal Water Hardness Level? USA Water Quality. Retrieved May 13, 2022 from https://www.usawaterquality.org/ideal-water-hardness/

National Geographic. (n.d.). Freshwater Fish. Retrieved April 3, 2022 from https://www.nationalgeographic.com/animals/fish/facts/freshwater-fish

National Geographic Society. (2012). Lake. In National Geographic Resource Library. Retrieved March 19, 2022, from https://www.nationalgeographic.org/encyclopedia/lake/

Rose, K. (2019, February 14). Lakes & Climate Change. Lake Scientist. Retrieved April 2, 2022, from https://www.lakescientist.com/lake-facts/lakes-climate-change/

Simon, T. P., Morehouse, R., & Worden, S. L. (2013). Physical and chemical limnology of three lakes within Hoosier National Forest. Proceedings of the Indiana Academy of Science, 121(2), 97+. Retrieved February 24, 2022 from https://link.gale.com/apps/doc/A352038046/GRNR?u=j170902014&sid=bookmark-GRNR&xid=c5dd6ebc

Trees of Texas. (2022). Pineywoods. Texas A&M Forest Service. Retrieved May 4, 2022 from https://texastreeid.tamu.edu/content/texasEcoRegions/Pineywoods/

United States Environmental Protection Agency. (2022). National Recommended Water Quality Criteria - Aquatic Life Criteria Table. Retrieved May 9, 2022 from https://www.epa.gov/wqc/national-recommended-water-quality-criteria-aquatic-life-criteria-table

Water Science School. (2018a). Hardness of Water. United States Geological Survey. Retrieved April 3, 2022 from https://www.usgs.gov/special-topics/water-science-school/science/hardness-water

Water Science School. (2018b). Saline Water and Salinity. United States Geological Survey. Retrieved April 3, 2022 from https://www.usgs.gov/special-topics/water-science-school/science/saline-water-and-salinity

Water Science School. (2019). pH and Water. United States Geological Survey. Retrieved March 22, 2022 from https://www.usgs.gov/special-topics/water-science-school/science/ph-and-water

Appendix A

The Correlation Between pH and Alkalinity

Although pH and alkalinity were similar, they differ slightly in their definitions. The pH value is strictly how acidic or basic a liquid is, while alkalinity is defined as the amount of acid needed to lower the pH to a certain level; more formally, alkalinity is a liquid’s ability to neutralize acids.

Because of their similarities, the researchers analyzed if there was a correlation between the pH levels in the lakes at Jones State Forest, and the corresponding alkalinity values. The results are shown in Figure A1.

Figure A1

pH vs Alkalinity values in the Lakes at Jones State Forest

Note. The x-axis on this graph shows the pH values of a location within the two lakes, and the y-axis shows the corresponding alkalinity value. The equation of the trendline is with This graph was created by the student researchers using Google Drive functionalities.

Appendix B

Results From pH Tests

Table B1

pH Levels of Surface Samples

Location	Trial 1 pH	Trial 2 pH	Trial 3 pH	Average pH
A	6.6	6.4	6.4	6.466667
B	6.3	6.2	6.2	6.233333
C	6.2	5.9	6.1	6.066667
D	6.1	5.8	5.9	5.933333
E	6.3	6	5.7	6
F	6.5	6	5.9	6.133333
G	6.4	5.9	6.1	6.133333
H	6.6	5.8	6	6.133333
I	6.1	6.1	6.1	6.1
J	6.3	5.9	6.2	6.133333

Note. This table shows the pH levels of the three trials conducted on the surface samples, along with their averages (mean).

Table B2

pH Levels of Lake Bottom Samples

Location	Trial 1 pH	Trial 2 pH	Trial 3 pH	Average pH
A	6.8	6.4	5.9	6.366666667
B	6.5	6.1	5.7	6.1
C	5.9	5.5	5.8	5.733333333
D	6.3	5.9	5.9	6.033333333
E	5.9	5.7	5.7	5.766666667
F	5.8	5.8	6.3	5.966666667
G	6.2	6	6.2	6.133333333
H	6.1	5.9	5.8	5.933333333
I	6.2	5.9	5.9	6
J	6	5.6	5.7	5.766666667

Note. This table shows the pH levels of the three trials conducted on the samples collected at the lake bottom, along with their statistical means.

Appendix C

Results From Alkalinity Tests

Table C1

Alkalinity Levels of Surface Samples

Location	Trial 1 Alkalinity	Trial 2 Alkalinity	Trial 3 Alkalinity	Average Alkalinity
A	20	25	24	23
B	20	24	22	22
C	18	25	24	22.33333333
D	16	29	23	22.66666667
E	14	29	34	25.66666667
F	16	21	32	23
G	12	25	31	22.66666667
H	16	28	20	21.33333333
I	16	29	24	23
J	14	23	22	19.66666667

Note. This table shows the alkalinity levels of the three trials conducted on the surface samples, along with their statistical means. Alkalinity was measured in ppm of CaCO3.

Table C2

Alkalinity Levels of Lake Bottom Samples

Location	Trial 1 Alkalinity	Trial 2 Alkalinity	Trial 3 Alkalinity	Average Alkalinity
A	20	32	25	25.66666667
B	14	25	28	22.33333333
C	13	33	25	23.66666667
D	19	24	24	22.33333333
E	14	27	15	18.66666667
F	14	21	29	21.33333333
G	22	29	20	23.66666667
H	13	25	26	21.33333333
I	12	24	22	19.33333333
J	16	40	26	27.33333333

Note. This table shows the pH levels of the three trials conducted on the samples collected at the lake bottom, along with their statistical means. Alkalinity was measured in ppm of CaCO3.

Appendix D

Results From Hardness Tests

Table D1

Hardness Levels of Surface Samples

Location	Trial 1 Hardness	Trial 2 Hardness	Trial 3 Hardness	Average Hardness
A	28	37	24	29.66666667
B	20	29	26	25
C	20	28	16	21.33333333
D	22	25	29	25.33333333
E	24	24	24	24
F	21	28	16	21.66666667
G	18	30	19	22.33333333
H	16	24	25	21.66666667
I	16	32	17	21.66666667
J	14	38	28	26.66666667

Note. This table shows the hardness levels of the three trials conducted on the surface samples, along with their statistical means. Hardness was measured in ppm of CaCO3.

Table D2

Hardness Levels of Lake Bottom Samples

Location	Trial 1 Hardness	Trial 2 Hardness	Trial 3 Hardness	Average Hardness
A	13	20	18	17
B	11	32	27	23.33333333
C	17	24	23	21.33333333
D	13	26		19.5
E	20	14	19	17.66666667
F	n/a	21	27	24
G	n/a	20	21	20.5
H	22	25	22	23
I	16	27	22	21.66666667
J	n/a	n/a	21	21

Note. This table shows the hardness levels of the three trials conducted on the samples collected at the lake bottom, along with their statistical means. Hardness was measured in ppm of CaCO3. Locations with “n/a” trial data indicate that there was interference from the water when trying to conduct the test, thus giving an inaccurate result.

Appendix E

Results From Carbon Dioxide Tests

Table E1

Carbon Dioxide Levels of Surface Samples

Location	Carbon Dioxide
A	8
B	11
C	23
D	13
E	26
F	32
G	33
H	28
I	31
J	30

Note. This table shows the levels of carbon dioxide in ppm for each of the locations, with samples being taken from the surface.

Table E1

Carbon Dioxide Levels of Lake Bottom Samples

Location	Carbon Dioxide
A	14
B	12
C	72
D	45
E	65
F	67
G	85
H	30
I	35
J	100

Note. This table shows the levels of carbon dioxide in ppm for each of the locations, with samples being taken from the bottom of the lake.