Senior Thesis

Chronological Nutrient Uptake of Quercus alba: the Stranglehold of pH Levels

by

Christopher Miller

B.S., Robert Morris University

This thesis is submitted in partial fulfillment of the degree requirements

for the B.S in Biology for the fall semester of 2014.

Thesis Advisor

Paul Badger, PhD

Associate Professor of Chemistry

Robert Morris University

Robert Morris University

December 2014

Quercus alba, nutrients, and pH 2

Abstract:

Acid rain continues to pour upon the earth each year effecting plant and animal

life along with it. Quercus alba is a common white oak seen throughout western

Pennsylvania. The goal of this research was to look at effects acid rain has on these

trees. Nutrients can often be readily absorbed under the right conditions, but if acid rain

levels are high then nutrient uptake by the tree becomes difficult. Acid rain refers to the

buildup of nitrogen oxides and sulfuric dioxides dissolved with oxygen and water vapor

to form what we call acid rain. A majority of this comes from the burning of fossil fuels

for electricity. 2/3 of all SO2 and 1/4 of all NOx come from electric power generation of

these fossil fuels (EPA, 2014). The focus was to look at Quercus alba in five year

increments to analyze nutrient uptake over time from fluctuating pH levels in the soil. A

tree cross section, or tree cookie as it’s sometimes referred, was used to count the

years of the trees existence. Five year chunks were chiseled out, grinded, chemically

digested, filtered, and put into an ICP spectrometer. This machine helped to analyze the

nutrients from each five year increment with the total range from 1951 to 2011 when it

was cut down. Correlations were then made from the data used. Some of the data

provided useful analysis while other elements such as sodium become difficult. Sodium

remains almost everywhere, so accurately measuring sodium levels over time can be

difficult. However, other elemental correlations could be found from promising numbers

collected from the tree. Contamination of test tubes or inaccurate readings of the ICP

may have skewed some elemental readings. This machine used three different wave

lengths to look at the composition of the samples from each time period. The amount

detected was then averaged to give us a concentration in parts per million (ppm). This


machine requires fine tuning and while some values seemed to be higher than normal,

other values obtained were values of importance that we can use to further our

understanding of nutrient uptake of Quercus alba from different pH levels.

Topic:

As acid rain contributes to lower pH levels of soil, toxic uptake of aluminum increases

while diminishing essential nutrients of iron, magnesium, and calcium uptake in Quercus

alba.

Background:

Quercus alba, or commonly known as the white oak, thrives from the Midwest,

across the eastern United states, and down to the most southern portions of Florida and

Texas. This deciduous tree can grow to massive heights of up

to 100 ft. They feature an acorn that can be eaten once

tannins are removed (NCstate, 2014). This fairly common tree

can house many animals in a variety of habitats. Form a bird nesting near the top to a

caterpillar eating away at leaves, these trees provide important homes for animals. This

tree has a moderate growth rate as well. Many factors can go into a trees health such

as the climate, natural disasters, pH levels, animal life, and human interactions. All of

these factors can play a huge role in any one tree. While these all can play a huge part,

the focus of this study is looking at how the pH level affects nutrient uptake over time.

The pH levels of soil, rain, rivers, and oceans have been observed for many

years now from such agencies as the Environmental Protection Agency (EPA) and the

National Atmospheric deposition program (NADP). The NADP monitors precipitation

chemistry specifically. Acid rain is a real issue that needs to be addressed in order for


our society to move forward. The term acid rain was first coined by a Scottish chemist

named Robert Angus Smith in 1972 (Jacobson, 2002). He sought to measure the

composition of acids in rain water, and he was the first inspector of alkali factories which

controlled the locations of such factories. He was the first man to really discover the

harm being done to rain water. Over the years, fossil fuels have made a dramatic

impact on acid rain accumulation. Burning fossil fuels decreases the pH of the rain in

turn making it more acidic. These two agencies have analyzed pH levels of rain over

time all over the United States. Below are two heat maps of pH levels in 1985 and from

2012:


A clear pattern can be spotted from this decrease of acid rain over the years. Trees

absorb nutrients based on the environment around them. Varying pH levels will vary the

amount of nutrients the tree will uptake. The pH has gone down over time, but in1985

extremely high amounts of acid rain were found. The map clearly indicates high levels

of acid rain in the northeast primarily. These maps help shows how pH levels have

declined since 1985. The darker red represented pH levels lower than 4.1. Constant

levels of this pH can harm the tree and effect how the tree uptakes nutrients. If humans

were to start drinking more acidic water everyday then physiological manifestations

would most likely make their way through our bodies hurting us in some way. While this

wasn’t the focus of the study, one can relate.


Over time, there has been a reduction in the pH levels most likely due to increase

awareness of the detrimental effects that pH can have on wildlife. Technology plays a

role in this reduction of pH level over time. SO2 emissions have decreased since 1970

even though coal use has gone up 225% (Hayward, 2011). The burning of coal releases

sulfur oxides into the air, but cost effective “scrubbers” help to remove these toxic

emissions. Using low sulfur coal as well from the western United States has helped to

reduce these emissions (Hayward, 2011). These new regulations are largely due to the

amendment passed in 1990 that sought to decrease toxic emissions by 50% by 2010

(Marquardt, 2014). This amendment was successful as the reported emissions level in

2010 was 8.9 million tons produced annually compared to that of 19.9 million in 1980

(Marquardt, 2014). The New York Times even made an article responding to such high

levels of acid rain in 1989 for the state of Pennsylvania. It was recorded that

Pennsylvania had the highest accumulation of acid rain compared to 131 sites in 46

states (NYT, 1989). This report was made by the Natural Resources Defense Council.

Today, they report that most coal-fired plants are located in the Ohio River Valley and

the eastern and southern Appalachian mountain states (NRDC, 2011). This is largely

due to the plants being built before 1970. They do not have to have the pollution control

equipment that will reduce costly emissions into the air such as sulfur dioxide.

Pennsylvania will continue to lead the way in emissions unless this equipment can be

incorporated into the outdated plants.

Nutrient uptake can have other factors that play a role in how well the tree takes

in nutrients. CO2 enrichment in the air also plays a major role in how well the tree takes

in nutrients for growth in the long run. One-year-old white oaks were grown in deficient


soil but with increased CO2 levels for one experimental group and ambient levels of CO2

for the other experimental group in the study. This study showed an 85% increase in

growth for the CO2 enriched trees compared to that of normal conditions (Norby et al.

1986). There were more nutrients needed in order for growth to happen from the trees

in this study. The soil concentrations of essential nutrients were found lower in the

enriched CO2 air compared to the normal air. The trees began depleting all resources

as more CO2 was being pumped into the air. Stomata of the plant have more access for

the gas to pass into the plant for simple sugars to be made during photosynthesis

(Evert, 2013). Nitrogen, Sulfur, and Boron were also analyzed in the study but not be

affected by the uptake of enriched CO2 air though.

Now, we must focus on how acid rain actually affects the soil. Leaching is the

common term used in association with acid rain. This process involves the addition of

hydrogen ions that displace important nutrients in the soil such as calcium to lower

subsoil’s that plants cannot access (Ophardt, 2003). The soil remains more negatively

charged and the nitrogen oxides or sulfur dioxides donate protons allowing for essential

nutrients to be displaced. This creates an acidic atmosphere in the topsoil that plants

use regularly. Calcium is used to stabilize wood structure and cell membranes while

also being important in a variety of cellular processes (Templer et al 2006). Depletion of

these elements occurs only if the rate of displacement occurs faster than the rate being

absorbed by the plant though (Likens, 2011). Plants are still able to survive acid rain fall

as long as the plant can still readily absorb calcium through roots to be transported

throughout the plant.


Aluminum acts a little differently than vital nutrients such as calcium though. This

element harms the tree with increasing amounts of concentrations. This element exists

naturally in an insoluble, nontoxic form as aluminum hydroxide lying about the soil.

When acid rain is introduced, a neutralization reaction takes place to form an aluminum

sulfate that can then be dissolved into water. This of course can then be absorbed by

the roots of the plant (Ophardt, 2003). Accumulations of the sulfur and nitrogen also

have a negative impact on wildlife. These two can leach into nearby streams, lakes, or

rivers increasing the acidity of the water. This creates many problems for animal life in

the water. Fish can only handle so much acid in the water before they die.

Procedure:

A tree cookie was first obtained at Robert Morris University from a tree cut down

in 2011. The rings were counted using a microscope for precise reading of the rings.

The rings dated back to 1951 with the outer rings being the oldest due to the tree

growing outwards from the vascular cambium (Evert, 2013). The tree cookie was lightly

marked with a graphite pencil every five years. Next, a chisel and hammer was used to

accurately break up the tree cookie into five year increments. Roughly 2-3 grams were

collected into sample baggies labeled with the corresponding year and mass. These

larger chunks then had to be grinded down using a saw mill into tiny wood chippings

that can be digested. This was also collected into a sample baggy with the mass of the

wood chippings located on the bag.

The next step of the procedure involves a digestion. 40 large test tube were

obtained and placed into a metal rack. Increments of .5g were weighed out for each five


year range collected in the baggies. Three test tubes were used for each range of

years, so a digest could be run three times for each year range. The masses of the test

tubes contents were as followed:

Year range Test tube #1 (g) Test tube #2(g) Test tube #3(g)

2006-2011 .54 .56 .57

2001-2006 .54 .53 .55

1996-2001 .52 .59 .51

1991-1996 .54 .51 .58

1986-1991 .51 .55 .59

1981-1986 .53 .53 .56

1976-1981 .51 .51 .59

1971-7976 .54 .56 .56

1966-1971 .51 .52 .52

1961-1966 .50 .54 .51

1956-1961 .51 .52 .50

1951-1956 .52 .51 .53

Table 1. Contents of test tubes for digestion are recorded above between .50 and .59.

The digestion was now ready to be done. The following procedure was used to carry out

the digestion for organic material:

10 mL of 1:1 HNO was added to each tube

The Block digester was set up for 95⁰C. The samples were then placed onto the

heating element for 15 minutes.

5 mL of concentrated HNO was then added to each tube and refluxed for 30 minutes.

2 mL of water and 3 mL of 30% H2O2 were then added to each test tube after

cooling.

The rack was placed back on the block digester and 8 mL were then added in 1

mL aliquots to each tube while heating continued at 95⁰C.

10 mL of concentrated HCl was then added to each tube and heated for 15

minutes

Finally, each tube was filtered using Whatman no. 4 filter papers.

The liquid was collected in 50 mL centrifuge tubes.

Distilled water was then added to the 50 mL line on each centrifuge tube.

Four test tubes were also used without any contents except the water and acid added to

each tube. This helped us to assure no contamination occurred between samples.


The final process of the experiment was to use the ICP spectrometer to gather

information about all of the samples. The contents of the centrifuge tubes were simply

poured into smaller test tubes about 1 inch from the top. A test tube rack five columns

wide and 12 rows deep was used to periodically set up all of the test tubes starting with

the earliest year range. Each column had 12 slots for the test tubes. The smallest year

range was first. At the end of each column, in the 12th row, there was a blank filled with

water to observe if a carry-over from previous samples was happening. However, no

carry over took place. There were 3 columns completely filled up with the blanks at the

end. The final column had three test tubes with our final year range of 06-11 , the four

blank test tubes from the digestion, and the blank tube with nothing but distilled water.

There were 44 total tubes set up for the machine to run. The machine was run for a

major cation mix and common element mix. These mixes provided known

concentrations used to help calibrate the machine as accurately as possible. A quality

control was used to help calibrate the machine as well. After the machine was done

calibrating, the machine took roughly two hours to run its probe through all of the

samples. The machine was first used for the MCM and then used for the CEM. The data

was collected, converted to excel, and analyzed.

Results:

Major Cation Mix

The following four tables show the concentrations of Ca, Al, Fe, and Mg found using the

major cation mix for the ICP spectrometer. The standard error was calculated for all

concentrations found using: SE=SD/√𝑛 where “n” represents, 3, the number of each

mass used for each year range. Values in red indicate questionable results.


Table 2. Calcium shown with concentration in ppm +/- the standard error for the major cation mix.

Table 3. Aluminum shown with concentration in ppm +/- the standard error for the major

cation mix.

Tree ring year range METHOD ELEM

AVG (ppm) +/- SE MAX MIN SD

TREERING-51-56 Major_Cation_Mix_6(v31) Ca 530+/-26.344 624 465 45.6









TREERING-96-01 Major_Cation_Mix_6(v31) Ca 642+/-72.768 798 450 126



Tree ring year range METHOD ELEM

AVG (ppm)+/-SE MAX MIN SD

TREERING-51-56 Major_Cation_Mix_6(v31) Al 19.3+/- 1.399 22.8 14.1 2.42

TREERING-56-61 Major_Cation_Mix_6(v31) Al 13.5+/- 1.399 22.1 7.41 4.41

TREERING-61-66 Major_Cation_Mix_6(v31) Al 12.3+/-2.004 16.5 4.78 3.47





TREERING-86-91 Major_Cation_Mix_6(v31) Al 11+/-2.233 15.5 3.71 3.87






Table 4. Magnesium shown with concentration in ppm +/- the standard error for the major cation mix.

Table 5. Iron shown with concentration in ppm +/- the standard error for the major cation mix.

Tree ring year range METHOD ELEM AVG (ppm)+/-SE MAX MIN SD

TREERING-51-56 Major_Cation_Mix_6(v31) Mg 81.3+/-5.819 97.2 65.3 10.1




TREERING-71-76 Major_Cation_Mix_6(v31) Mg 48+/-2.952 53.8 42.3 5.11





TREERING-96-01 Major_Cation_Mix_6(v31) Mg 32.2+/-3.657 40.2 22 6.33

TREERING-01-06 Major_Cation_Mix_6(v31) Mg 179+/-11.150 206 152 19.3

TREERING-06-11 Major_Cation_Mix_6(v31) Mg 339+/-20.674 386 288 35.8

Tree ring year range METHOD ELEM AVG (ppm)+/- SE MAX MIN SD

TREERING-51-56 Major_Cation_Mix_6(v31) Fe 25.6+/-4.555 43.7 19.3 7.89



TREERING-66-71 Major_Cation_Mix_6(v31) Fe 83.7+/-13.412 119 63.3 23.2



TREERING-81-86 Major_Cation_Mix_6(v31) Fe 39.3+/-.813 41.1 36.4 1.41





TREERING-06-11 Major_Cation_Mix_6(v31) Fe 61+/-6.062 76.8 51.7 10.5


Common Element Mix

The next four tables are used in comparison as they stress the same elements used in

the major cation mix .Questionable results are again highlighted in red. An error in

preperation of these samples may have occurred. This error does not take away from

the other values obtained as they show a positve correlation.

Table 6. Aluminum shown with concentration in ppm +/- the standard error for the

common element mix.

Table 7. Magnesium shown with concentration in ppm +/- the standard error.

Tree ring year range

METHOD ELEM AVG MAX MIN SD

TREERING-51-56 Common_Elements_Mix_2(v20) Al 20.6+/-1.740 24.7 13.5 3.01


TREERING-61-66 Common_Elements_Mix_2(v20) Al 14+/-1.911 17.6 8.52 3.31

TREERING-66-71 Common_Elements_Mix_2(v20) Al 7.63+/-5.178 21.2 -6.4 8.97




TREERING-86-91 Common_Elements_Mix_2(v20) Al 8.13+/-5.084 17.7 -9.8 8.81


TREERING-96-01 Common_Elements_Mix_2(v20) Al 13+/-2.682 18.6 3.39 4.65


TREERING-06-11 Common_Elements_Mix_2(v20) Al -2.4+/-1.460 0.07 -8 2.53



TREERING-51-56 Common_Elements_Mix_2(v20) Mg 68.2+/-3.170 75.5 59.3 5.49



TREERING-66-71 Common_Elements_Mix_2(v20) Mg 25.3+/-10.584

41.2 -0.6 18.3

TREERING-71-76 Common_Elements_Mix_2(v20) Mg 39.2+/-.888 41.5 37 1.54

TREERING-76-81 Common_Elements_Mix_2(v20) Mg 42.4+/-1.725 47.8 38 2.99

TREERING-81-86 Common_Elements_Mix_2(v20) Mg 46.9+/-1.508 52 43 2.61

TREERING-86-91 Common_Elements_Mix_2(v20) Mg 28.8+/-12.045

48.5 -0.5 20.9

TREERING-91-96 Common_Elements_Mix_2(v20) Mg 27.5+/-1.446 31 23.2 2.5

TREERING-96-01 Common_Elements_Mix_2(v20) Mg 26.4+/-2.941 32 18.6 5.09

TREERING-01-06 Common_Elements_Mix_2(v20) Mg 154+/-4.182 166 143 7.24

TREERING-06-11 Common_Elements_Mix_2(v20) Mg -0.5+/-.009 -0.5 -0.6 0.02




TREERING-51-56 Common_Elements_Mix_2(v20) Fe 21.1+/-2.260 27.5 16.8 3.91



TREERING-66-71 Common_Elements_Mix_2(v20) Fe 56.8+/-25.533 110 -0.5 44.2

TREERING-71-76 Common_Elements_Mix_2(v20) Fe 29.9+/-.920 31.8 27.4 1.59

TREERING-76-81 Common_Elements_Mix_2(v20) Fe 41.3+/-3.566 49.1 33 6.18

TREERING-81-86 Common_Elements_Mix_2(v20) Fe 35.3+/-.745 37 33 1.29

TREERING-86-91 Common_Elements_Mix_2(v20) Fe 29.2+/-11.993 46.2 -0.5 20.8

TREERING-91-96 Common_Elements_Mix_2(v20) Fe 36+/-1.578 38.9 31.5 2.73

TREERING-96-01 Common_Elements_Mix_2(v20) Fe 33.6+/-.401 35.4 32.2 0.7

TREERING-01-06 Common_Elements_Mix_2(v20) Fe 44.4+/-1.730 48.7 40.1 3

TREERING-06-11 Common_Elements_Mix_2(v20) Fe -0+/-.175 0.84 -0.6 0.3

Table 8. Iron shown with concentration in ppm +/- the standard error for the common

element mix.

Table 9. Calcium shown with concentration in ppm +/- the standard error for the

common element mix. The following table highlights 15 more elements analyzed from the common

element mix. While they weren’t the focus, they still provided concentration values that

could be looked at over time within the white oak. Some values were very minute as these nutrients are less vital and of less importance to this research such as silver.

Working with such small values can bring error very easily. Some values located within the 01-11 range showed distinct values that did not line up with the preceding years. As stated before, contamination may have occurred in the samples for these years.

Tree ring year range METHOD ELEM AVG MAX MIN SD

TREERING-51-56 Common_Elements_Mix_2(v20) Ca 506+/-15.903 553 461 27.5



TREERING-66-71 Common_Elements_Mix_2(v20) Ca 358+/-147.476 569 -3.6 255




TREERING-86-91 Common_Elements_Mix_2(v20) Ca 460+/-190.025 753 -3.4 329


TREERING-96-01 Common_Elements_Mix_2(v20) Ca 644+/-80.034 784 424 139


TREERING-06-11 Common_Elements_Mix_2(v20) Ca -3+/-.295 -1.4 -4 0.51


Table 10. This table shows concentrations of each element in ppm over time from 1951-2011

Element bolded with concentrations in ppm +/- standard error


Ag B Co Cr Cu

TREERING-51-56 -0.1+/-.385 9.13+/-.305 0.14+/-.028 1.82+/-.212 8.49+/-3.799

TREERING-56-61 0.13+/-.216 8.26+/-.220 0.32+/-.056 1.71+/-.311 3.83+/-.422

TREERING-61-66 0.09+/-.150 8.32+/-.183 0.3+/-.041 1.03+/-.153 3.29+/-.320

TREERING-66-71 0.11+/-.244 4.57+/-2.604 0.36+/-.167 1.54+/-.420 3.4+/-1.734

TREERING-71-76 0.03+/-.216 7.28+/-.165 0.43+/-.0175 1.19+/-.179 3.4+/-.343

TREERING-76-81 -0+/-.170 7.11+/-.080 0.45+/-.034 1.28+/-.189 2.65+/-.273

TREERING-81-86 0.03+/-.278 6.38+/-.186 0.43+/-.014 0.99+/-.184 3.31+/-.429

TREERING-86-91 -0+/-.209 3.72+/-2.270 0.51+/-.229 2.64+/-.685 1.63+/-.835

TREERING-91-96 0.05+/-.161 5.02+/-1.078 0.69+/-.031 1.27+/-.229 7.51+/-3.242

TREERING-96-01 0.12+/-.229 4.85+/-.776 0.65+/-.050 1.11+/-.172 7.45+/-3.724

TREERING-01-06 0.1+/-.264 3.26+/-.0676 1.25+/-.037 1.57+/-.170 4.38+/-.682

TREERING-06-11 -0.3+/-.270 -1.9+/-.0239 -0+/-.00774 0.43+/-.336 -0.3+/-.344

K Mn Na Ni P

TREERING-51-56 2335+/-146.346 59.7+/-1.462 257+/-2.979 5.06+/-1.261 17.7+/-.545

TREERING-56-61 2106+/-130.471 58.3+/-.631 237+/-14.337 108+/-84.782 16+/-.344

TREERING-61-66 1817+/-86.414 58.1+/-.691 209+/-3.921 46.8+/-31.232 15.9+/-.180

TREERING-66-71 1057+/-385.365 38+/-15.529 132+/-49.205 13+/-7.146 13.5+/-3.486

TREERING-71-76 1280+/-60.403 56.3+/-1.060 174+/-5.414 23.4+/-9.078 12.8+/-.155

TREERING-76-81 1220+/-51.144 54.5+/-1.365 183+/-12.389 5.32+/-.590 13.1+/-.194

TREERING-81-86 1127+/-46.279 58.8+/-.673 187+/-9.708 10.3+/-1.266 12.1+/-.242

TREERING-86-91 738+/-265.449 36.3+/-14.830 130+/-50.224 14.1+/-8.812 11.8+/-3.035

TREERING-91-96 1009+/-43.131 46.7+/-.855 162+/-9.009 5.55+/-1.237 13.8+/-.502

TREERING-96-01 885+/-118.285 38.8+/-5.382 159+/-5.785 5.47+/-1.708 10.9+/-1.010

TREERING-01-06 1777+/-58.895 69.7+/-1.120 165+/-2.809 12.4+/-1.856 97.5+/-3.173

TREERING-06-11 63.9+/-17.0652 0.01+/-.0163 7.51+/-1.249 -0.9+/-1.170 0.05+/-.0324

Pb Si Sn Ti Zn

TREERING-51-56 29.6+/-8.406 55.1+/-1.775 19.7+/-5.561 0.12+/-.0681 4.69+/-1.813

TREERING-56-61 80+/-45.624 50.4+/-.888 28.6+/-8.769 0.07+/-.0941 2.09+/-.615

TREERING-61-66 54.6+/-11.885 49.9+/-1.121 28.5+/-3.989 0.06+/-.0703 2.31+/-.606

TREERING-66-71 30.3+/-13.009 33.1+/-13.365 20.2+/-9.387 0.1+/-.0745 1.06+/-1.277

TREERING-71-76 80.3+/-15.340 48.4+/-1.859 47.5+/-8.111 0.11+/-.0722 2.07+/-.468

TREERING-76-81 84.1+/-4.685 49.3+/-1.238 53.5+/-4.228 0.15+/-.0776 1.64+/-.226

TREERING-81-86 85.4+/-3.998 45.4+/-.929 48.2+/-3.341 0.12+/-.0721 1.63+/-.224 TREERING-86-91 59.2+/-25.137 33.2+/-13.457 30.6+/-12.699 0.07+/-.0759 5.93+/-4.846

TREERING-91-96 24.7+/-1.538 38.7+/-5.966 24+/-7.043 0.13+/-.0689 1.82+/-.540

TREERING-96-01 39.1+/-1.739 41.8+/-4.749 21.32.278 0.18+/-.151 0.81+/-.373

TREERING-01-06 139+/-22.866 29.6+/-3.454 73.7+/-15.327 0.11+/-.0761 1.06+/-.114

TREERING-06-11 0.06+/-.0331 0.09+/-.272 -0.1+/-.025 0.09+/-.0653 -1.7+/-.0267


Discussion:

There were many positive findings from this lab after analyzing the data. As

stated before, calcium is less readily absorbed by plants as acid rain is higher. The

results of the calcium concentrations make sense with this assumption. Calcium levels

were higher in times of less acid rain. The calcium was able to be more readily

absorbed by the tree in more recent times due to less toxic emissions. The following

figure highlights nutrient availability for corresponding pH levels of soil:

Figure 3. The wider the band the more readily available the element. The pH scale lies on top. (Zuzek et all 2014)

This table can help us to further analyze our data. Calcium availability exists in

much more neutral soil like that of our findings. The tree was able to increase

concentration from 530 ppm in 1956 to 1,430 ppm in 2011 for the major cation mix. This

number is much larger as acid rain has diminished over the years .The common

element mix showed a same kind of correlation except for the outlier in the year range

2006-2011. The common element mix did not show as accurate data as the major

cation mix. This was likely due to the amount of elements tested for in the samples.


Complications reading samples may have occurred during the use of the ICP

spectrometer. Magnesium is another element such as calcium that exists in larger

numbers when acid rain is low. The data collected for this was not as accurately

measured. We would expect the concentrations to increase over time but instead they

decreased over time as acid rain levels decreased. The validity of this is questionable.

The third major element looked at was iron. Both mixes showed an increase of iron over

time. While the figure above shows iron is available more readily at lower

concentrations, this does not mean the tree will absorb more. Tree health plays a role in

how much they absorb. If a tree does not exist in conditions suitable for it then less will

be absorbed by all nutrients. This iron concentration also showed minimal standard

error except one year range from 66-71 in the major cation mix. The common element

mix showed greater variation on the concentration levels. The final major element to be

looked at was silver. The major cation mix again showed a positive correlation for

aluminum uptake. We would expect to see a decrease over time due to a decrease in

acid rain over the years; this was observed more so in the major cation mix. The

common element mix again showed greater standard errors and more variations than

preferred. It did show a decreasing correlation as trees want to avoid absorbing

aluminum due to its toxic effects.

The other elements looked at did show positive correlations as well. Manganese

is another element vital to plant health. Manganese plays a role as an enzyme activator

during chlorophyll production and it is a structural component of the chloroplasts (Zuzek,

2014). Manganese did show a slight increase over time as we would expect. Another

element that showed a positive correlation was that of copper. We would expect the


concentrations to be higher in earlier years and lower in recent years. Copper is toxic to

plants and leaching again forms copper sulfates soluble in water to be absorbed by the

tree (Arduini et al 1995). There were elevated numbers for copper during the years

1991 to 2006. These values were actually higher but some sort of error must have

occurred with these concentrations levels obtained, or the tree was simply able to

absorb much more copper in those years.

Conclusion:

The data in the experiment looked at was on the smaller scale. Only one tree

cookie was looked at for the entire year range. It would be interesting to look at different

concentration levels as one would work up the tree, or we could simply use more tree

cookies for comparison. The value used for “n” in our standard error was small. A larger

sample size for each year range

would ultimately decrease the

standard error. Standard deviation

would have to remain at a

reasonable amount as well. It

would also be interesting to study

the affect mycorrhiza play on plant

nutrient uptake. This fungus occurs

everywhere and latches on to roots of plants where they from a symbiotic relationship.

They allow the plant to uptake more nutrients than they could alone by accessing a

greater amount of soil and creating a larger surface area for nutrient assimilation

(Ricklefs, 2010). Without the fungus the plant is less effective at nutrient uptake. This


white oak used in this research surely benefitted in some way from the role mycorrhiza

plays with plants. Looking at the role

mycorrhiza play on nutrient uptake under

acidic conditions could add useful

information. These factors would surely

provide more evidence of nutrient uptake of

Quercus alba.

Our main topic looking at the toxic uptake of aluminum and diminishing essential

nutrients of iron, magnesium, and calcium provided useful date that could be analyzed.

However, more research needs to be done as some data appeared to be skewed for

various reasons. Common trends of large standard errors can be viewed by looking at

the tables. The Year ranges 86-91, 66-71, and 06-11 for the four major elements looked

at all appeared to have much larger standard errors indicating something was wrong

with the data collected. Contamination of samples could have occurred. This tree

cookie did show correlations of nutrient uptake over time though. We were able to show

how acid rain levels greatly affect the nutrient concentrations over time. Acid rain was

much higher in 1950 than today. Many more regulations and much more awareness has

contributed to the vast improvement of lowering toxic emissions contributing to acid rain.

This is a real issue and should not be ignored. Our society continues to grow more each

day. We need to be aware of the affect we’re causing to the wild life around us. Acid

rain doesn’t just affect trees; it affects all of wildlife around us. Runoff of heavy metals

and aluminum kills fish such as trout or salmon while greatly effecting larval

development each year (Stewart, 2012). The low pH cannot be tolerated by the fish or


larvae ultimately leading to death. More needs to be done so we still aren’t pumping 8.9

million tons of toxic emissions into the air. More regulations to further lesson toxic

emissions should be explored in further studies. Plants provide food, medicine, and the

air we breathe. Learning to live with them is something we must do in order for our

society to continue to grow. Nutrient uptake of the white oak data collected in this

research helped to show the debilitating effects of acid rain on the tree’s ability to take in

nutrients over time.


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