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:
MajorCation 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 yearrange 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 yearrange 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
Tree ring year
range
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

Tree ring year
range
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
Tree ring year
range
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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