Henry Aceves, Ana Del Angel Aguilar, Victoria Ou, Grace Yuan, & SandulManage

The Academy of Science and Technology

9th Grade

3701 College Park Dr. The Woodlands, TX 77384

936 709-3250

Headmaster: Dr. Susan Caffery

Supervising Teacher: Dr. Sara Fox

May 13, 2022

Analyzing the Effects of Humans Upon the NaturalEnvironment of W.G. Jones State Forest by Assessing Trash, Air, Soil, and Water Pollution

Analyzing the Human Impacts Upon the NaturalEnvironment of W.G. Jones State Forest by Assessing Air, Soil, Trash, and Water Pollution

Table of Contents

Abstract

W.G. Jones State Forest is an urban forest that is home to an abundance ofwildlife and emphasizes learning experiences for the surrounding community. In order to assess the health ofthis vital ecosystem, researchers investigated the negative impacts of humanson the forest, primarily focusing on water and air pollution. Three study siteswere chosen to represent areas that varied in their proximity to human development. The levels of waterconductance and water pollution were measured, as was the level of sulfur dioxide in the air and pH andnitrogen-phosphorus-potassium values of the soil. The results of the analyses showed that the conductance,phosphorus and potassium levels, as well as various water pollutant indicators were statistically differentfrom the acceptable levels, indicating that proximity to human development is detrimental to the health ofthis ecosystem. The results of this study can beapplied to other urban forests under similar living conditions so that new regulations can be placed tominimize human impacts on the natural environment.

Analyzing the Human Impacts Upon the NaturalEnvironment of W.G. Jones State Forest by Assessing Air, Soil, Trash, and Water Pollution

W.G. Jones State Forest (JSF) isan urban forest located in southeastern Texas that was established primarily for educational use for students and communitymembers (“State Forests andArboretums,” n.d.). It attracts 80,000visitors annually and is one of the largest urban forests in the nation, yet it is at great risk of beingnegatively impacted by human activities, including the development of infrastructure, use of chemicals, andhabitat fragmentation. This is why the following research question was chosen to be studied according to avariety of criteria discussed below: “Have humans negatively impacted the forest?”

In thisproject, human impacts were measured and compared between three areas, theTwin Ponds region, the FM (Farm to Market) 1488 region including Sweetwater Lake, and the Marsh region; thelast of which served as a control group. Theindependent variable for this study was the location underobservation to observe how proximity to FM 1488 affected pollution levels,while the dependent variables were water conductance, water health indicators, andsurface sulfur dioxide levels measured throughout experimentation. The other dependent variables were soilpH and N-P-K values, which were contrasted solely between the Marsh and the FM 1488 study areas, with theformer area once again serving as a control group.

The first dependent variable underobservation was water conductance. Conductance is directly proportional to the number of ions or salts in asolution, a value which increases after the introduction of toxic substances from centers of human activitysuch as farms, cities, factories, and roadways (“Conductivity, Salinity &Total Dissolved Solids,” 2014; Denchak, 2018). Thus, water conductance measures the presence of pollutants within abody of water. These pollutants are detrimental to aquatic ecosystems, resulting in reduced oxygen levels,an increase in algal blooms, and inadequate living conditions for both plants and animals. It washypothesized that the higher exposure to pollutants released by cars androad-building materials would result in greater conductance in the waterclosest to areas near roadways.

Tofurther investigate the water quality within JSF, several water health indicators were measured across thethree study areas. Over time, development surrounding the forest has increased, resulting in higherpollution levels due to runoff into various bodies of water. The accumulation of these toxins in theenvironment results in reduced cellular function by inhibiting essential biochemical reactions and damagingcells (Shanbehzadeh et al., 2014; Briffa et al., 2020). Considering the harmful effects of these pollutants,nine different water health indicators were chosen to be analyzed: water hardness, cyanuric acid, totalchlorine, free chlorine, nitrate, nitrite, lead, copper, and fluoride. waterhardness is the amount of dissolved calcium and magnesium within the water, and cyanuric acid is a chemicalcompound that allows chlorine compounds such as free and total chlorine to last longer (Water Science School, 2018; “What is CyanuricAcid” (CYA), 2022).Free chlorine is chlorine that has not yet bound to potential substances, while totalchlorine is the sum of both bounded and unbounded chlorine; both of which cannegatively hamper plants and animals with natural bodies of freshwater (Wolfe,2022).

The final threat imposed by humans on the natural environment of JSF thatwas assessed within the main research project is a reduction in soil quality. This is of major concernbecause soil quality determines the capability of soil to support living organisms as well as sustainadequate air and water quality (“3 Types of Soil Health Indicators,” 2020). Soil itself is indispensable because of its role as a water filter, growingmedium, antibiotic supplier, and habitat for plants and animals (“Why soilmatters,” 2020). However, various human activities such as runoff fromfertilizers, can negatively affect soil quality, compromising its ability to support ecosystems (Eugenio etal., 2018). This makes it imperative for the human impacts on soil quality to be assessed within W.G. JonesState Forest. The soil quality indicators that were measured within this research project were pH, whichmeasures the acidity or alkalinity of soil, and extractable N-P-K values, which measure the amount ofnitrogen (N), phosphorus (P), and potassium (K) within the soil, all of which are crucial elements foreffective plant growth (Ma et al., 2020). The expected pH ofsoil within the Texas Piney Woods region was between 6.5 and 4.5 (“Texas Ecoregions - Pineywoods,” n.d.). It was hypothesized that due to greater exposure to the hydrocarbonsreleased by cars on FM 1488 and surrounding commercial buildings, the soil pH of the FM 1488 study areawould be greater than the normal parameters for soil pH in the Texas Piney Woods region and the soil pH of the Marsh area, as the introduction of these hydrocarbons can increase soil pHlevels. It was also hypothesized that because the areasurrounding the FM 1488 study region is likely exposed to the application of fertilizers by homeowners, thesoil in the FM 1488 study area would have greater N-P-Klevels than the Marsh area soil.

In addition to altering the balance of soil and water, thepresence of humans frequently results in excessive amounts of trash pollution. Much of this pollution isplastic and many plastic products undergo chemical leaching, a process where chemical additives infiltrate soil and water, which, like heavy metals, can negatively affect basic cell functions and endocrine activity (Zimmerman et al., 2021). Theamount of trash pollution was determined along Middle Lake Trail, the Twin Ponds, the Marsh area, and a controlarea, a space that is isolated from human activity. The independent variable of the experiment conducted wasthe location being evaluated, while the dependent variable was the number of trash items found. It was hypothesized that areas of high human activity would result in increased amountsof trash. To analyze trash pollution, ArcGIS, a mapping software, was used to plot coordinate points on the interactive Human Impacts W.G. Jones State Forestmap. By spatially analyzing trash pollutionwithin the forest, recommendations may be offered to the JSF foresters forpossible trash and recycling bins to be placed near large waste clusters.

Tofurther involve and inform JSF visitors, four informational signs were constructed and placed near notableareas within the forest. These signs included information regarding specific trails, bodies of water,animals, and plants. Additionally, scannable QR codes were posted on the signs to allow visitors to easilyaccess the JSF Linktree and Geocache system to learn more about the forest and advance the connectionbetween community members and the natural environment at W.G. Jones State Forest.

Method

The primary safety concerns forthis project were potential injury from sharp tools and materials including an auger, a power drill, a tablesaw, wires, multimeter probes, and lumber. In addition, possible pathogens could be contracted from thelakewater within the forest’s freshwater bodies and trash items retrieved in the forest.

During water sample collection,protective gloves were worn to prevent contamination from the water. Boots and waders were also used to keepthe researchers dry as they collected water samples within the freshwater bodies of JSF. While picking uptrash items, latex gloves were also worn to avoid contamination from any of the samples collected. Allcollected trash items were placed in a trash bag then disposed into a trashcan after obtaining theitem’s GPS coordinates.

Throughout gadget construction, work gloves and safety glasses were worn,sleeves were rolled up, and hair was tied back to mitigate potential injury while working. All powered toolswere turned off when not in use so as to not harm the researchers.

Materials

Measuring the Water Conductivity of the FM 1488, Twin Ponds, and Marsh Study Areas

• Water Quality: Conductance Circuit

Measuring Water Health Indicators in the FM 1488,Twin Ponds, and Marsh Study Areas

• Sixty 16-in-1 water testing strips witha color-coded key

Assessment of Soil Within the FM 1488 Study Area and Marsh Study Area

• Two Luster Leaf 1601 Rapitest Test Kitsfor Soil pH, Nitrogen, Phosphorus, and Potash that each include:

• One soil sampler probe
• Twenty 200 mL beakers
• One 50 mL beaker
• 11 L of distilled water

Measurement of Trash Pollution Near Middle Lake Trail, Marsh, Twin Ponds, and Control Group Study Areas

• Apple Maps phone application

Gadget: Construction of the Informational Signs

Procedures

Measuring the Water Conductivity of the FM 1488, Twin Ponds, and Marsh Study Areas

Making the Conductance Sensor and Measuring Circuit. First, a straw was cut with scissors into a 3 inch piece while acopper wire was cut into two 17 cm pieces. Each of the copper wire pieces were wrapped around both ends ofthe straw separately. This apparatus was referred to as the “conductance sensor.” To constructthe circuit, the positive (red) testing probe was plugged into port “mA” while the negative(black) probe was plugged into "COM” of the multimeter. Additionally, the electrical wire was cutinto a 7 cm piece and 1 cm was stripped off one of the ends afterward. The stripped end was wrapped aroundthe negative testing probe, while the unstripped end was taped onto the 3 V coin battery included in thecircuit using electrical tape. In addition, another set of alligator clips, “Clips A” was alsoconnected to the battery and one of the copper wire ends. The opposite end of the negative testing probe wasclamped using the alligator clips, “Clips B”. The other end of Clips B was clamped onto theremaining copper wire end. The final circuit can be referred to in Figure A1 of Appendix A.

Testing Samples. Using one of the 50 mL centrifuge tubes,a 50 mL sample of the marsh water was obtained by going into the marsh and collecting water from thelittoral zone, or the nearshore. From the centrifuge tube,the sample was transferred into a 100 mL beaker. Next, the conductance sensor was dipped into the beaker andthe multimeter was turned on. The displayed voltage was recorded in the research notebook. Finally, theconductance sensor was removed and washed for further trials. This testing process was repeated for nineteenadditional samples within the marsh area. The 50 mL centrifuge tubes used were rinsed thoroughly withdistilled water for each of the future tests. Afterward, the process described to collect the Marsh areasamples was repeated to collect samples from Sweetwater Lake.

Measuring Water Health Indicators in the FM 1488,Twin Ponds, and Marsh Study Areas

Utilizing one of the 50 mL centrifuge tubes, a 50 mL sample of the marsh waterwas obtained by going into the marsh and collecting water from the littoral zone into a centrifuge tube.After the sample was obtained, the centrifuge tube was capped and stored within a backpack for later use.This process was repeated with the other centrifuge tubes nineteen more times atvarious testing areas along the edge of the Marsh,spaced 10 meters apart. Then, the water collection procedures were repeated with surface water fromSweetwater Lake, Clear Lake, and Murky Lake, with ten samples acquired from each by directly scooping up thesamples with a centrifuge tube. The samples were taken around the edge of the lake, with each samplelocation spaced approximately five meters apart.

A 16-in-1 water testing strip was placed within each centrifuge tube for 2seconds and left to rest for thirty seconds. Afterward, the colors of the water hardness, cyanuric acid,total chlorine, free chlorine, nitrate, nitrite, lead, copper, and fluoride sections of the testing stripwere compared with the testing strip key. This process was repeated for each water sample collected.

Assessment of Soil Within the FM 1488 Study Area and Marsh Study Area

To start, ten soil samples of atleast 11 cm3 were collected roughly ten meters apart from one another in the Marsh study area using asoil sampler probe and stored in a soil sampling bag. A soil sample was then poured into the pH colorcomparator until the soil fill line was reached, and distilled water was added to the pH color comparatoruntil the water fill line was reached. The pH testing powder provided by the testing kit was poured into thecolor comparator, then shaken for 1 minute. The color comparator was left to rest for another minute to waitfor results to appear. In natural lighting, the color of the solution was compared to the color chartprovided on the color comparator and results were recorded by best matching the color of the solution tothose displayed on the chart. The pH color comparator was then washed and rinsed with distilled water anddried thoroughly with a paper towel for the next sample to be tested. This procedure was repeated ninemore times until all ten soil samples had been tested.

Next, 10 cm3 of aremaining soil sample was measured out using a 50 mL beaker, while 50 mL ofdistilled water was poured into the 200 mL beaker. The soil within the 50 mLbeaker was then poured into the 200 mL beaker and mixed with the distilled water for 1 minute. This solutionwas left to rest for 24 hours. Afterward, the liquid portion of the settled solution was transferred intothe color comparator included in the nitrogen testing kit utilizing a pipette until the solution reached thefill line. The solution was also poured into the reference chamber of the nitrogen testing kit. Once thepowder provided by the testing kit was also added into the color comparator, the color comparator was sealedand then shaken for 1 minute. After the colors developed for 10 minutes, the results were measured bymatching the color of the solution to the nitrogen color chart. The nitrogen color comparator and the 50 mLbeaker were washed, cleaned with distilled water, and dried thoroughly with a paper towel. This process wasrepeated nine more times, one for each soil sample collected. Then, the procedures used to test nitrogenlevels were followed in order to determine thephosphorus and potassium content of the soil samples, utilizing the corresponding phosphorus and potassiumcolor comparators and indicator powders, respectively. Following this, all previous soil collection and soiltesting procedures were repeated with samples collected from “Sweetwater Lake,” located near FM1488.

Measurement of Trash Pollution Near Middle Lake Trail, Marsh, Twin Ponds, and Control Group Study Areas

A total of 625 m2 plots of land were evaluated in an area isolated from the park trails by counting thenumber of trash items and noting their coordinates using the Apple Maps mobile application; this area servedas the control group for experimentation. If an item was clearly broken, itspieces were considered a single item within the context of data collection. The same procedure was repeatedin the variable tests, with one plot of land assigned to each of the following areas: Middle Lake Trail,Clear Lake, Murky Lake, and the Marsh area.

Gadget: Construction of the Informational Signs

Researchers began by staining every piece of wood on all sides and allowing them to dryovernight in a well-ventilated area. To begin construction, researchers attached one of the cedar beams ontothe far right side of the pine face, with a 4 inch allowance from the top of the cedar beam, by adding threescrews from the front of the face evenly spaced and going through the face and beam. Then, three screws wereadded to attach another one of the support beams onto the rear side of the pine face, 9 inches from thebottom of the face and with the smallest edge touching the previously installed cedar beam. The final cedarbeam was then connected to the far left edge of the face with the same 4 inch allowance as the other beam,utilizing the same method previously followed. To finalize construction, the plastic informational signswere screwed onto the front of the pine face with a screw through each corner, and the copper toppers wereadded to the top of the signs using polyurethane adhesive, applied with the caulk gun. The finalized productis shown below (Figure 1).

To install the signs, the adult supervisor for the project utilized the auger todrill two holes 2¼ feet deep to 1½ to 2½ deep holes, with the depth varying between installation locations, into the ground 46½inches apart from each other. Finally, the legs of the sign were lowered into the holes and the dirt removedby the auger back was compacted back into the holes, around the support beams of the informationalsign.

Results

Throughoutthe course of the project, experimentation was conducted to evaluate the influenceof humans upon the natural environment of W.G. Jones State Forest. The hypothesisfor this portion of the project was that as proximity to the FM 1488 roadway increased, the concentrationsof the pollutants measured would also increase as a direct result of the contaminants released by the carsand infrastructure. Within the three primary study areas for this project—theMarsh, Twin Ponds, and FM 1488—conductance was measured as a mean of twenty water samples from eacharea, and multiple water health indicators were observed including waterhardness, cyanuric acid, total chlorine, free chlorine, nitrate, nitrite, lead, copper, and fluoride. Soil pH and N-P-K values were alsomeasured utilizing Rapitest Soil Testing Kits, with ten samples being collected from both the FM 1488 andMarsh study areas. In addition, three surface-level sulfurdioxide readings were taken by the 5th Period Air and Soil groups within the same study areas as thoseobserved in water analysis utilizing Gastec sulfur dioxide detector tubes. Multiplestatistical tests were performed to evaluate the data collected throughoutexperimentation. The statistical tests utilized during this project were a one-way analysis of variance(ANOVA) test, a chi-squared test, a z-test, and a t-test displayed with sample calculations in (1), (2), and (3) respectively.

t-value (1)

chi-squared value 7.825 (2)

z-value0.808 (3)

Water conductance was evaluatedacross the three study areas, with twenty trials conducted in each area. Initial measurements were taken inmilliamps (mA) for electrical current, and later converted into conductances in siemens (S) as seen in (4).However, since this equation includes the resistance (R) of the circuit, (5) was used to find this unknown.Each data set was compared to one another utilizing a t-test of statistical significance, with a one-tailedhypothesis utilized in each statistical test. The one-tailed hypothesis was that the Marsh study area wouldhave a lower mean water conductance than the FM 1488 and Twin Ponds study areas and that the meanconductance of the Twin Ponds study area would be smaller than that of the FM 1488 study area. The p-valuesacquired from all three of these tests were less than 0.0005. The mean conductance of each study area witherror bars portraying the standard error of each data set is shown in Figure 2 below. The x-axis containsthe separate study areas, while the y-axis shows the mean conductance of each study area in siemens.

Conductance (S) = (4)

Resistance (Ω) = (5)

The pollutants and water healthindicators evaluated in the three primary study areas were hardness, cyanuric acid, free chlorine, totalchlorine, nitrate, nitrite, lead, copper, and fluoride. Water testing strips and a corresponding indicatorchart were utilized to formulate measurements, and the mean of these measurements are displayed in Figure 3below. In the graph, the x-axis identifies the pollutant measured, while the y-axis shows the concentrationof the pollutant. Each line is color-coded to represent a study area, and each point on the line displaysthe concentration of the pollutant in mg/L in comparison to the pollutant measured.

The data retrieved from each location was compared by conducting nine ANOVA testson each water health indicator measured. The ANOVA tests for hardness, total chlorine, free chlorine,nitrate, nitrite, lead, copper, and fluoride all produced a p-value of less than 0.05. Each of theindicators underwent three separate t-tests with two-tailed hypotheses: Test 1, which compared the Marsh region to the 1488 region, Test 2, which compared the TwinsPonds region to the 1488 region, and Test 3, which compared the Marsh region to the Twin Ponds region.The entirety of the results collected for these nine indicators as well as a simplifiedvisual displaying the p-values of the t-tests are displayed within the appendix (Appendix C-G). In addition, multiple z-tests of statistical significance with 19degrees of freedom were conducted comparing the mean water indicator values in concentration of pollutants(mg/L) of each study area to the national standard for each indicator as shown in the calibration chart ofthe 16-in-1 water testing strips used and as displayed in Figures F1-F4 in Appendix F. A p-value of lessthan 0.05 was observed in the z-tests for free chlorine, nitrate, nitrite, copper, and fluoride in the Marsharea samples; no indicators in the Twin Ponds area samples; and free chlorine, nitrate, and copper in the FM1488 area samples. All other water health indicator z-tests produced a p-value of greater than 0.05.

In addition to the assessment ofwater quality, standard measurements were taken to evaluate the soil between the FM 1488 and Marsh studyareas. The measurements taken were N-P-K measurements according to a categorical scale ranging fromdepleted, deficient, adequate, sufficient, and surplus; and pH measurements for each of the ten soil samplesacquired at each of the two testing locations. The frequency of each category measured in the nitrogen,phosphorus, and potassium data is shown in Figure 4 below. The y-axis shows the type of measurementconducted with the larger column groupings displaying the study area from which measurements were taken,while the x-axis displays the frequency at which each soil testing category was observed.

A chi-squared test of statisticalsignificance was performed to compare the distribution of the N-P-K data between the FM 1488 and Marsh studyareas. The resulting p-values for phosphorus and potassium were both less than 0.05, while that of thenitrogen data was greater than 0.05. A t-test of statistical significance was conducted comparing the soilpH values of the Marsh and FM 1488 study areas, yielding a p-value greater than 0.05.

In collaboration with 5th PeriodAir and Soil, the data shown in Figure 5 regarding sulfur dioxide concentrations was obtained (P.Maddipatla, personal communication, April 30, 2022). The measurements were taken at three locationsutilizing Gastec sulfur dioxide detector tubes. The x-axis shows the area under study, while the y-axisshows the concentration of sulfur dioxide in parts per million (ppm).

A z-test of statistical significancewas performed comparing the means of each sulfur dioxide data set to one another with a prescribed 2 degreesof freedom. The resulting p-values were 0.81, 0.92, and0.81 respectively. Theprecise location was recorded for each air and soil test, and these locations are shown in Figures 6 and 7below.

Lastly, the impact of trash pollution was assessed by plotting the location ofeach trash item found in the forest. After the GPS coordinates of each piece of trash were recorded, theywere then plotted onto a map in ArcGIS, which is displayed in Figure 8.

Using the "Find Clusters” analysis tool in ArcGIS, clusters of trash,which in this project were defined as five or more pieces of trash within close proximity of each other,were found and labeled with different colors. Each cluster set was given its own unique color, and if a point was not near any other data point, it waslabeled as an outlier and consequently colored gray. The map displaying trash pollution coordinates afterthe clustering tool was applied is shown in Figure 9.

Then the “Find Clusters” tool was used again to identify clusters,but this time with clusters being defined as ten or more pieces of trash within close proximity of eachother since the cluster would have a wider range. The locations were marked on the map shown in Figure10.

Next, the “AggregatePoints” analysis tool in ArcGIS was used to create a color-coded grid on the map, as shown in Figure11. Each square on the grid represented a 625 m2 plot of land, and the squares were assigned colors depending on the number of data pointsfound in that plot of land. The darker the color assigned, the greater amount of trash present in the plot.Every plot of land was given a unique grid coordinate name, with the rows being labeled using numbers andthe columns being named using letters. For example, the left-hand most plot of land in the top row had thegrid coordinate name of A-1.

One plot of land from the grid was chosen to represent each of the followingareas: Middle Lake Trail (F-4), Clear Lake (H-5), Murky Lake (D-3), the Marsh area (I-10), and an isolatedarea that experienced fewer human impacts (H-8). The plots of land for each testing area are labeled inFigure 12.

The number of data points within each of these study areas were then compared toeach other to determine which places were most affected by trash pollution. According to the plots chosen,the testing area found with the most trash was Middle Lake Trail and the site with the least trash was theisolated area, as shown in Figure 13. The Marsh was second in the highest concentration of trash, and bothlakes had around the same amount of trash. The x-axis shows the five different testing areas, and the y-axisshows the number of trash items found in each study region.

Discussion

In order to determine the human impacts that have affected W.G. Jones StateForest, soil pH, nitrogen, phosphorus, and potassium levels were measured in addition to water conductanceand nine water health indicators within the Twin Ponds region, the FM 1488 region including Sweetwater Lake,and the Marsh region, which was used as a control throughout experimentation.

Concerning water conductance,three one-tailed t-tests were conducted to determine whether or not the differences between the means of thestudy areas were statistically significant. The critical value for this project was a p-value of 0.05throughout all statistical analyses. All conductance t-tests offered p-values less than 0.0005, rejectingthe null hypothesis that the variation in the results obtained between the study areas was due to chance andstrongly indicating that the data was statistically significant. The results clearly suggest that humaninfrastructure and activities have released additional ions or pollutants into the bodies of water closestto FM 1488, which can be of concern when further developing the roadway and the surrounding commercialareas.

In order to verify whether there was significant variation within the study areasin the measurement of various water health indicators, an ANOVA test was conducted. The ANOVA test produced p-values of 2.38 x 10-2, undefined (since allreadings were equal to 0), 1.75 x 10-10, 1.02 x10-9, 7.89 x 10-12, 2.44 x 10-6,8.95 x 10-8, 2.86 x 10-4, 2.41 x 10-4 for hardness, cyanuric acid, totalchlorine, free chlorine, nitrate, nitrite, lead, copper, and fluoride respectively.All p-values except that of cyanuric acid were less than 0.05 and thus the nullhypothesis that variations in measurements between study areas were simply due to chance was rejected. This suggests that exterior sitefactors, such as the heavy metals released by cars on the FM 1488 roadway and runofffrom nearby residential areas, caused higher concentrations of specific pollutants for certain areas than inothers. To further analyze these results, twenty-seven two-tailed t-tests were performed to cross-analyzethe data assessed with the ANOVA test. Utilizing this method, the specific datasets in which statisticalsignificance occurred could be accurately identified. The results of these t-tests can be found in table G1of Appendix G, with eighteen t-tests obtaining a p-value of less than 0.05, thus rejecting the nullhypothesis, and nine t-tests failing to reject the null hypothesis. Z-tests were conducted comparing theobserved data to the government regulations for various water health indicators. Each test conducted had 19degrees of freedom and was one-tailed. A p-value of greater than 0.05 was observed in the z-tests for freechlorine, nitrate, nitrite, copper, and fluoride in the Marsh area samples; no indicators in the Twin Pondsarea samples; and free chlorine, nitrate, and copper in the FM 1488 area samples. For all of these data setsabove, the null hypothesis, that the mean of the observed data lies within the standard distribution of thepopulation data, was rejected. This hypothesis was notrejected however for the remaining z-tests not listed above. This indicates that for the indicators in whichthe null hypothesis was successfully rejected, the concentration of pollutants within these study areaswere significantly greater than those outlined by standard regulations, according to Tables F1-F4 as shown in Appendix F.

Soil N-P-K and pH levels within the Marsh and FM 1488 study areas were alsomeasured in this project. To analyze if the independent variables, being the study areas, were independentof each other, a chi-squared statistical test was performed. A p-value of less than 0.05 was acquired forphosphorus and potassium between both regions, while a p-value of more than 0.05 was acquired for nitrogenand pH. From this data, it can be concluded that the tests for soil phosphorus and potassium weresignificant, as they had a p-value less than the critical value of 0.05, while the t-test for soil pH wasnot significant as it had a p-value of greater than 0.05, which was greater than the critical value. Theresults reveal that the null hypothesis was rejected for the phosphorus and potassium concentration levels,but the statistical tests failed to reject the null hypothesis with regard to pH and nitrogen levels.Overall, this data supports the claim that proximity to the FM 1488 roadway increasesphosphorus and potassium levels, while nitrogen and pH levels remained relatively constant.

Consideringthe air quality within Jones State Forest, the concentrations of sulfur dioxide throughout the various studyareas were analyzed via z-test and t-test. The z-tests were conducted to compare the data with the nationalstandard, 0.5 ppm, while the t-test was performed to contrast the different study areas (“Secondary National Ambient Air Quality Standards,” n.d.). After conducting the statistical tests, the null hypothesis was not rejected because thez-tests yielded p-values of 0.81, 0.92, and 0.81, all of which are greater than the critical p-value of0.05. The t-test also yielded p-values greater than 0.05, indicating that thevariation in sulfur dioxide concentrations (ppm) between the study areas was not statisticallysignificant and that sulfur dioxide concentrations are not sufficiently higherin areas closest to FM 1488 to conclude that areas closest to Fm 1488 experience higher SO2 concentrations. However, the acquired p-values are not optimal representations of whether the study areas variedin sulfur dioxide concentrations due to the small sample size of three utilized in testing. In further project extensions, it would be beneficial to conductadditional studies analyzing sulfur dioxide concentrations with larger sample sizes to obtain more accurateresults.

Finally, the impact of trashpollution on Jones State Forest was assessed by identifying clusters of trash and comparing different areasof the forest. The clusters of trash aided in finding potential locations to place trash cans, which areplaced in the approximate center of the clusters and labeled with a star, since the various clustersidentified places that experience high frequencies of trash pollutants (Figure 12). Additionally, fivesquare grids that were 625 m2 in area wereidentified within the range of trash data points collected, with each representing the Marsh area, MiddleLake Trail, Clear Lake, Murky Lake, and an isolated control area respectively. Statistical analysis was not performed between the study areas for trash pollutant data due tothe limited time between JSF visits, but further Human Impacts teams could begin to gradually accumulatetrash pollution data on a yearly basis to obtain a large enough sample size to conduct statistical analysison trash pollution data. Among the study areas that were compared, the most polluted area was found to beMiddle Lake Trail followed by the Marsh area. the hypothesis that the areas exposed the most to regularhuman activity would experience higher rates of trash pollution was supported, but as stated earlier,without the use of statistical analyses, no definite conclusion can bemade. That being said, it can still be reasonably suggested that the Middle LakeTrail and the Marsh area would benefit greatly from the installation of easilyaccessible trash cans because these areas displayed the highest frequencies oftrash pollution.

Although this experiment wasperformed to the best of its ability, there was still a possibility for errors within the lab procedure.For example, the samples were tested throughout different days,which could have affected the results because there could have been temperature variations or externalevents between the collection of samples that could have affected the results. Additionally, the watersamples were only retrieved from the surface rather than from various depths.Likewise, the samples were taken around the edges of the lake in various locations, while the central waterswere generally omitted due to accessibility. Further tests could include water fromthe center of the lake and from deeper waters to gain a more comprehensive understanding of the watercomposition of the water bodies in JSF. Because of thisproject’s limitations, its conclusions are restricted to only the water health during the spring timein periods of moderate to low precipitation. Future iterations of this project may take steps to expandconclusions with further testing.

The next steps of this project mainly revolve around extendingthe study area of the trash pollution analysis to additional trails such as Sweetleaf Nature Trail and NorthBoundary Trail Loop. By doing so, this would allow for the assessment of how trash pollution has spread todifferent areas of the park and, consequently, provide the data needed to counter this pollution by placingadditional trash bins in trash cluster areas. Additionally, further groups could analyze how the sewagesystem runoff from the nearby apartment complex and restaurants has affected the soil quality near theendpoints of the system. Sewage runoff poses great concerns as human and industrial wastes, oil, toxicmetals, pesticides, and litter can be pushed into the natural bodies of water, negatively impacting thewater quality and the living conditions for aquatic animals and plants (Water Science School, n.d.). In thisproject extension, the main focus would be whether the sewage system pollutes surrounding areas and whethergovernment action should be taken to limit possible pollution.

Another project extension includesthe application of project data to forest management. The results of this project can be applied to the Jones State Forest and other urban forestswith similar site and situational conditions. The information gathered can helpforest services control how human infrastructure should be constructed in relation to the forest, whileconsidering the effects of development on the well-being of the natural environment. Specifically, it canhelp JSF create new policies, such as zoning policies on the proximity of new restaurants, apartments, orother types of infrastructure that can be built around other parts of the forest. Additionally, newboundaries such as fences can be constructed around specific areas of the forest, limiting possible negativehuman interaction with the natural environment. Furthermore, new trash bins can be placed near areas ofconcentrated trash pollution in order to lessen the overall amount of chemical leaching that takes place asa result of these plastic products within the soil.

In addition to this group’s main research project, four informational signs were dispersed in various locations around the forest: near the main entrance, Twin Ponds, between Middle Lake Trail andDeep Gully Lake South, and along Gravel Pit Trail a few hundred meters outside of an inactiveRed-Cockaded Woodpecker colony. These signs wereconstructed using a UV protective coating and copper fence toppings so that the signs would not deteriorateduring the varying seasonal conditions and so that they would grab the attention of any passersby. The mainpurpose of these signs was to augment the interactivity within the forest, educating the community andengaging them with the natural wonders of the forest ecosystem. As stated before, since JSF is considered aresource education forest, many students and eager-to-learn community members visit the ecosystem to studydifferent aspects of the natural environment. These signs can assist these observersto navigate through and effectively learn about the forest with an aesthetic design and attachedGeocaches. The QR code connected to the JSF Geocaches is displayed on eachinformational sign, and it allows forest visitors to enter an interactive “treasure hunt” wherethey hunt to locate the Geocache boxes installed throughout JSF (see Figures B1-B4Appendix B). Furthermore, located in the center of the sign, there is a picture of aspecific natural attraction, which can range from landforms to animals and plants, alongside informationabout the attraction and its role within the forest. On the bottom left side of the sign, the official JonesState Forest Linktree has been posted to provide additional background information about the forest bysimply scanning a QR code. This is a great way to introduce and inform passersby, students, and communitymembers about the wonders of the Jones State Forest.

In closing,the human infrastructure and development around the W.G. Jones State Forest negatively affected variousaspects of its ecosystem, including water quality and soil composition. It was found that phosphorus andpotassium levels in soil, possible pollutants or salt concentrations from conductance data of water, andwater health indicator concentrations as shown in a number of water health indicators were statisticallygreater in study areas closest to the FM 1488 roadway. Additionally, there were high concentrations of trashpollution in close proximity to the Middle Lake Trail region, followed by the Marsh region. Future studies must focus on how the concentration of pollutants have changed over timewithin these study areas to gain a more comprehensive understanding of the concerns and risks upon thenatural environment of W.G. Jones State Forest as the roadway and surrounding infrastructure continue todevelop.

References

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STATE FORESTS AND ARBORETUMS: W. GOODRICH JONES STATE FOREST. (n.d.). Texas A&M Forest Service. Retrieved April 10, 2022, fromhttps://tfsweb.tamu.edu/jones-state-forest/

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Appendix A

Figure A1

Measuring Circuit Diagram

Photograph was taken by student researcher.

Appendix B

Figure B1

Informational Sheet Designs for W.G. Jones State Forest Signs — Jones State Forest

Figure B2

Informational Sheet Designs for W.G. Jones State Forest Signs — Loblolly Pine

Figure B3

Informational Sheet Designs for W.G. Jones State Forest Signs — Twin Ponds

Figure B4

Informational Sheet Designs for W.G. Jones State Forest Signs — Red-Cockaded Woodpecker

Appendix C

Table Displaying All Water Health Indicator Data Collected for the MarshStudy Area

Table C1

Table C2

Appendix D

Table Displaying All Water Health Indicator Data Collected for the Twin PondsStudy Area

Table D1

Table D2

Appendix E

Table Displaying All Water Health Indicator Data Collected for the FM 1488Study Area

Table E1

Table E2

Appendix F

Table Displaying All Water Health Indicator Government Regulations

Table F1

Table F2

Table F3

Table F4

Note. All data for the government regulations were takenfrom the 16-in-1 water testing strip calibration chart. They are listed above purelyfor reference for future extensions of this project.

Appendix G

Table G1

Table Displaying Resulting t-values and p-values of Statistical Analyses of WaterHealth Indicators