RESL Special Contribution Series SCS-0043

A Radiological Survey of Washington State

Eric A. Foss Kentwood High School Covington, WA, USA

Radiation Earth Science Laboratory, Nagoya, Japan 2008

A Radiological Survey of Washington State

Eric A. Foss Kentwood High School

Covington, WA

Abstract Nuclear Background Radiation occurs everywhere in the environment at low levels. This natural radiation comes from two main sources: radioactive elements in the earth’s crust, and cosmic rays. Using a homemade portable radiation survey system, I mapped the levels of background radiation across the State of Washington with 1022 measurements made while driving 3700 miles of highway. I used surface mapping and geographic information software to display the results and find the local geology for each measurement. In general, I found the lowest levels of radiation in the lowlands of Western Washington, and the highest levels in the northeast corner of the state. Using multivariate linear regression, I calculated the background radiation sensitivity to elevation and rock type, and proved the significance of local rock type with ANOVA tests (p<.0001). Using a variance reduction technique, I removed the influence of elevation and geology from my data set. Applying t-tests to the remainder, I found that the Eastern Washington scablands carved during the last Ice Age by the Great Missoula Floods were higher in background radiation level than the rest of the state by a statistically significant amount (t=5.17, P<.0001). I found that the loop of highway around the Hanford Site was not statistically different from the rest of the Columbia Basin. I also found some locations that were slightly greater than the government standards applied to the Hanford cleanup effort. The highest was within the city of Republic, Washington - probably related to a century of mining activity there. Introduction I have had an interest in radiation since the 6th grade. Last year for the Washington State Science and Engineering Fair (WSSEF) I built a portable radiation survey system as an engineering project [1]. I decided for WSSEF 2008, I wanted to put it to use on a research project. The question I asked myself while doing my research was “How is nuclear background radiation distributed across the State of Washington, does it have a relationship to elevation and geology, and are there any areas higher than normal?” I planned a survey of levels by driving enough state highways with my system to cover every part of the state within 50 miles. Once I gathered my data, I planned to display it with Google maps and surface mapping software, and analyze it with MATLAB software.

From my research on nuclear background radiation, I hypothesized that the radiation levels would depend on elevation and surrounding rock or soil type. I also hypothesized that radiation surrounding the Hanford Reservation would be higher than normal, because of the 60 years of nuclear activity there. I contacted Mr. Mike Brennan of the Washington State Department of Health, Division of Radiation Protection and he suggested I also look at the relationship between radiation levels and the scabland areas in Eastern Washington that were affected 15,000 years ago during the ice age Lake Missoula Floods. I watched a movie on the Great Floods [2] (one of the largest in earth history) and hypothesized that the flood washed granite type rocks and gravel which are higher in trace amounts of uranium from Montana and Idaho into the areas carved out by the rushing flood waters. I also wanted to search my data for local “hot spots”. To do this, I needed to learn how authorities decide what “normal” is. The independent variables for my research are the geographical coordinates of each measurement. From each pair of coordinates, I also found an elevation, a geologic rock type, and the nearness of Hanford and flood areas. The rock type, Hanford, and flood variables are categorical variables which I made to be dichotomous (either one or zero). The dependent variable is radiation level. My controlled variable was the counting period, which I chose as 5 minutes. Also controlled was the distance between the ground and my sensor, which was on the window of our car, 54 inches above the ground. Background Research Nuclear Background Radiation (NBR) is naturally occurring radiation that is everywhere in the environment. NBR comes from two main sources; either it comes up from the ground as radioactive elements that decay in rock or soil, or it comes down from the sky as a result of cosmic ray interactions with the earth’s atmosphere. A third but less important source could be man made, such as atomic testing or the Chernobyl accident. There have been nationwide surveys of background radiation that are similar to what I want to accomplish with my project on a more local scale. One such survey was made by the USGS [3][4] from data gathered over a number of years. Figure 1 shows the NBR from terrestrial sources based on aerial surveys with radiation detectors on airplanes. Figure 2 shows the levels of background cosmic radiation predicted from elevation data. I couldn’t find any references to surveys done across the entire State of Washington from ground level. I believe my survey is the first one of its kind.

Figure 1 Terrestrial sources of NBR [2]

Figure 2 Cosmic sources of NBR [3] All rock contains trace amounts of radioactive elements, primarily Uranium-238, Potassium-40 and thorium-232 (and its decay chains) . [5] For example, Uranium can vary from 0.5 to 5 parts per million, depending on the rock type. Figure 3 shows the chain of decay products for Uranium 238 [6]. A particular human health hazard is Radon, a gas that is formed as a result of decaying radium which is a decayed product of uranium. Radon has a half-life about 4 days and decays into other elements such as polonium and bismuth. After radon is formed underground it makes its way through the cracks in the Earth’s crust and eventually reaches the surface. Houses that are built into the ground sometimes have Radon accumulate inside them. This accumulation proves to be a health hazard and causes cancer. As part of my engineering project last year, I operated my system near abandon coal mines in Renton, WA to see if I could detect higher levels of radon. I saw no higher levels.

Figure 3 Decay chain of Uranium [6]

Igneous rocks usually contain the highest levels of radionuclides. Among igneous rocks, granite forms from cooling magma inside the earth, and basalt comes out of volcanoes and is formed on the earth’s surface from lava. According to NCRP Report 94, granite can have up to 3 parts per million of Uranium. Salic (light colored) basalt can have up to 4.7 parts per million. But most basalt in Washington is the mafic (dark colored) type and contains less than 1 part per million of uranium. [5] Because granite forms and cools slowly deep within the earth, it gives the different elements time to concentrate within the molten magma before solidification.

Figure 4. Diagram of cosmic ray reaction with the earth’s atmosphere [7]

Most cosmic rays originate from extra-solar sources within our own galaxy such as rotating neutron stars, supernovae, and black holes and some even come from sources in other galaxies. When cosmic rays hit a particle in our atmosphere, that atmospheric particle splits into two or more other radioactive particles, which in turn, hits more atmospheric particles and so on, see Figure 4. [7] The result of this chain of events is a shower of radioactive particles. At higher elevations the atmosphere is thinner, which would cause the radiation level to be higher. GIS stands for Geographic Information System and describes software which lets you capture, store, analyze and manage data and associated attributes which are spatially referenced to the Earth. This includes Google Earth. Since my measurements include global positioning satellite (GPS) locations, I will use GIS

techniques to sort my measurements by rock type and separate the flood data from non-flood. Equipment, Software, and Procedure My survey system contains a Geiger counter probe (Pasco Scientific model SN-792AA with mica window) which works by having special gasses inside its vacuum tube. When a radioactive particle enters the tube it ionizes the gases. The positively charged ions in the gas go to the negatively charged probe inside the tube and the electrons go to the positively charged probe. This creates an electrical pulse which gets amplified and detected by a counting circuit. The counts are measured by a USB data acquisition device which is read every 5 minutes by a LABVIEW™ program in my laptop computer. To gather the data, I used my homemade portable radiation survey system and my laptop computer [1]. I attached my system to the outside of our car window and ran my LABVIEW™ data collection program while driving many of the highways and freeways of Washington State with my family. I set the counting period to be 5 minutes. At 60 miles an hour this is one measurement every 5 miles or so. I made my program so that it would display the number of counts for a designated period of time as well as latitude and longitude from a GPS receiver and save both of these, along with a few other variables in an Excel file. Once I had my data collected, I used several software packages and tools to assist me in analyzing and presenting it in different ways. With software called 3DField, I expanded (spatially interpolated) my data to a grid that I plotted on a color map, similar to the USGS maps in figures 1 and 2. I used GIS techniques to match my readings to rock type, flood and Hanford areas. GIS software included ESRI ArcGIS Explorer, Microsoft MapPoint and Google Earth. MATLAB™ is a mathematical computing environment and programming language. After buying a student license, my Dad showed me how to work MATLAB and how to program with it. I wrote a program that read my data in from an Excel file and computed the slope of the radiation sensitivity to elevation with linear regression. Then I subtracted from each reading the amount that was due to the influence of elevation. I let MATLAB sort this data into rock types and did an ANOVA/Tukey test to prove the significance of the local rock. Then I ran multivariate linear regression to find the influence of rock types and subtracted them from the data. I ran t-tests on this statistically adjusted data to see if the Missoula Flood and Hanford areas were high. I used a Vassar College professor’s website to compute the ANOVA and t-tests. Lastly, after taking out the elevation and rock influence, I used a Washington State Department of Health standard for checking for locations in the state that were higher for other reasons.

Results Figure 5 shows my survey system, mounted in a plastic box, clipped to the window of my dad’s car. Inside my box I have a GPS receiver, my Geiger-Muller counter, and some other devices such as an air particulate sensor, and a temperature sensor.

hown on car window at Dry Falls and Mt.

the dates they took place, the number of measurements taken, and some of the cities we

total of 3688 miles and made a total of he driving around got very tiresome, especially when

of Eastern Washington. I sort of ng at restaurants during the trip.

Figure 5 Radiation survey system sRainier Table 1 shows each of my trips including of miles driven, the number went through on each trip. I traveled a 1022 measurements. All of tnavigating around the more desolate parts enjoyed staying in the hotels and eati

Date Miles Driven Measurements Routes Taken 6/28/2007 108 37 Covington-Carnation-Monroe-Seattle-

Covington 7/5/2007 229 49 Covington-Mt Rainier(Sunrise)-

Yakima-Ellensburg-Covington 7/29/2007 178 22 Covington-Bremerton-Brinnon-

Tacoma-Covington 8/4/2007 221 63 Covington-Bremerton-Brinnon-

Shelton-Tacoma-Covington 8/5/2007 64 17 Covington-Black Diamond-Enumclaw-

Camp Shepard-Enumclaw-Black Diamond

8/22/2007 1191 331 Cov.-Vancouver-Hanford-Clarkston-Spokane-Republic-Omak-Covington

9/2/2007 473 131 Covington-V en-Coulee antage-ReardCity-Wa e-Cov. terville-Wenatche

9/9/2007 200 83 Covington-Mt. Rainier(Paradise)-Mossyroc ovington k-Chehalis-C

1 0/12/2007 545 163 C s-ov.-Shelton-Sequim-Port AngeleFork ov.s-Westport-Astoria-Longview-C

11/3/2007 479 126 Co r-vington-Mt.St. Helens-VancouveGolde ton ndale-Yakama-Coving

Total 3688 1 022

Table 1 Measurement summary

I w o convert f the counts/minu readings towas important to do this because I wanted to compare min d units. I rated my system running it al451P ionization survey meter I borrowed. The Inovision ac ted microR tgens over time easured fosystem and got a sensitivity of about 1.4 counts/minute p

Figure 6 is an image from Microsoft Ma nt, which isWindows. I created this by exporting my ta into Mapfeature, which passes the latitudes and longitudes whermeasurements were made at and marks them with pushpins.

anted t all o te micro Roentgens/hour. It y data with that of others

standar calib by ongside an Inovision meter had a mode that

cumula oen . I m r about 4 hours on each er uR/hr.

pPoi an atlas program for Point through an Excel dae each one of my 1022

Figure 6 Measurement points exported to Microsoft MapPoint

GPS Visualizer [8] is a great website that will take data that has latitude and longitude coordinates and plot them as dots on a map. It will also let you give it one other variable and color the dots depending on the levels of that variable. This is a good way to display geographic data because you have a choice of three different types of maps and the option of making a KML file that you can open up in Google Earth to view your data. Figure 7 is an example of that. GPS Visualizer can also give you elevation for any set of latitude longitude pairs and that is how I found elevation for all my 1022 data points.

Figure 7 GPS Visualizer file imported into Google Maps showing my data with color coded radiation level (units are in counts/minute)

After gathering all of my data I wanted to use an efficient way to look up a geologic rock type for each of my 1022 measurement locations. I found out that the University of Washington library has GIS licenses for student use. GIS stands for geographic information system and is basically a system for capturing, storing, analyzing and managing data and associated attributes which are spatially referenced to the Earth. My dad arranged for me to meet with a GIS

ts you view and extract data from GIS files. After the librarian showed me how to use it, we worked hard

get the geologic data I needed. After I went home with the file, I found that the rock types didn’t agree with other state maps I had, and came to the conclusion

igure 8

librarian to learn how to use Arc Explorer, the leading GIS software program produced by ESRI. Arc Explorer is a program that that le

to

that the GIS geologic data file that I was using was too high a resolution. Fis an example of the Arc Explorer viewer which shows some of my measurement locations as red pushpins.

Figure 8 Measurement locations viewed in ESRI Arc Explorer

I ended up going back to Google maps and importing an image overlay of a tate Department

of Natural Resources, shown in Figure 9. The colors on the map represent either medium resolution geologic map published by the Washington S

metamorphic, sedimentary, glacial, igneous volcanic (basaltic) or igneous intrusive (granitic) rocks. [9]

Figure 9 Geology of Washington State

Figure 10 shows how I overlaid the geologic map on Google maps with pushpins at my measurement locations. I made new columns in my Excel data file for each

Testing igneous rock types was interested in finding out if igneous type rocks had about the same level of

difference between them. I studied this by taking a

ck

e emitted by some stone building les that people might have in their homes. Figure 12 shows results from tiles

of the rock types and designated each point as either a one or a zero depending on if the point was within that rock type.

Figure 10 Google Earth with Geologic Map as an imported image overlay

I radiation, or if there was a big collection of twelve different igneous rocks I purchased online from Ward’s Natural Science and used a Geiger counter, Technical Associates Model TBM-35 and an HP5335A universal counter to measure the counts. I placed each roone after another on the GM counter which was hooked to my universal counter to add up the counts. I did this for 2 minutes on each rock. I then converted counts per minute to uR/hr using the published conversion factor (3.35) for the GM tube. The results are shown in figure 11. I was also interested in the radiation that may btipurchased at home improvement stores.

Obsidian

Pumace

ScoriaBasalt

Rhyolite

Rhyolite porphyry

Biotite granitePegmatite

SyeniteDorite

GabbroPeridotite

0

5

10

15

20

25

ur/hr

Obsidian Pumace Scoria Basalt Rhyolite Rhyoliteporphyry

Biotitegranite

PegmatiteSyenite Dorite Gabbro Peridotite

Rock types

Natural Radiation Level of Igneous Rocks

Figure 11 Natural Radiation measured from igneous rocks

granite,ubatuba

white marble tan marble

granite, white

travertine

granite, verde butterfly

green slate

0

5

10

15

20

25

30

35

ur/hr

granite,ubatuba white marble tan marble granite, white travertine granite, verdebutterfly

green slate

Rock type

Natural Radiation from Stone Building Materials

Figure 12 Natural radiation measured from common building stone

I found that obsidian may be the most radioactive igneous rock. For the building tiles granite in general was the highest. If a person were to spend most of their days against a granite slab, the dosage of radiation might exceed the Hanford cleanup limit discussed later in this report. Verde Butterfly, which is the building tile that has the highest level of radiation in figure 12, happens to be the granite which we had installed as our kitchen countertops. Display The original method I was going to use for display of the data was the mapping toolbox and interpolation routines in MATLAB ®. I made a program that would interpolate my data in color over a map of Washington. There were some problems with the interpolated MATLAB map shown in Figure 11.

124oW 122oW 120oW 118oW

46oN

47oN

48oN

49oN

Background Radiation - Washington State

uRoe

ntge

n/hr

4

6

8

10

12

14

16

18

Figure 11 Background Radiation Map of Washington Using MATLAB

The biggest problem was that the interpolation only worked within the boundaries of my data. It also made the interpolation with big blocky triangles. What I wanted was the interpolation to be smoother between the different levels and extend to the very edges of the state.

I decided to look for another program that would interpolate my data better. An internet search introduced me to 3DField. [10] 3DField is a data contouring and surface plotting program that makes good color maps and 3D plots. With 3DField I have many choices in how I interpolate and display my data: simple contours,

circle values, kriging, delauney triangles, and color polygons are a few choices. One of the first things I noticed was the price. Seeing that I was not made of money and that I had already spent $99 dollars on a MATLAB student license, I emailed the developer of 3DField, Vladimir Galushko in Moscow, and asked if there was a student discount, which he kindly gave me. Figure 12 shows my original data gridded and plotted with 3DField. The interpolation method I went with was the Radial Basis Function. This was better than the Matlab interpolation because it extended the estimates out to the boundaries of the state, which I defined in a boundary point’s file.

Figure 12 Background Radiation Map of Washington Using 3DField

Hanford The US government acquired the land for the Hanford Site in 1943 to build large industrial facilities to produce plutonium, which served a vital role in the nation's defense. Hanford's mission expanded during the Cold War era to include research and development activities associated with the peaceful uses of atomic energy. Now the Columbia Generating Station provides energy to half the state.[11] Figure13 shows the location of the Hanford Site in Washington [12]. The color picture in the middle is where the government is most worried about radiation levels, along the north side of Hanford by the Columbia River. We drove just on the other side of the river on state highway 24. The photo on the right shows me near the entrance to Uranium Street, inside the reservation.

Figure 13 Location of the Hanford Nuclear Site in Eastern Washington [12]

It was quite interesting driving through Hanford; my first thought was “oh boy, high counts, maybe even enough to glow in the dark” but as it turns out the levels of radiation I saw from the highways were about the same as elsewhere. This may be due to many years of clean-up activity.

The Great Missoula Flood The Great Missoula Flood refers to the cataclysmic floods that swept periodically across eastern Washington and down the Columbia River Gorge at the end of the last ice age during the Pleistocene period. These glacial lake outburst floods were the result of sudden collapses of the ice dam on the Clark Fork River that created Glacial Lake Missoula. After each ice dam break, the waters of the lake would rush down the Clark Fork, Pend Oreille, and Columbia Rivers, flooding much of Eastern Washington and the Willamette Valley in western Oregon. After the break, the ice would reform, recreating Glacial Lake Missoula once again. Figure 14 shows how I got my measurement points identified in the Lake Missoula Flood area. I overlayed a PBS NOVA [13] map onto the Google Earth image of Washington State using the Google “image overlay” function. This is a cool function that lets you adjust curvature, size, and transparency of the inserted image. Then I used it to compare each point in my data with the map and filled in columns in my data file for whether each point was in a flood or non flood area.

Figure 14 Method of identifying Missoula Flood areas using Google Maps overlay

Figure 15 Location of uranium mine claims in Washington

Uranium Mining I was curious to see if there was any relationship between levels of radiation and Uranium mines in the state. Figure 15 is a map I made with red dots showing where all of the Uranium mines or claims are. [14] One place where I measured some high levels was north of Spokane. These could have been from the Midnite Mine. The Midnite Mine is an inactive former uranium mine, and is now a Superfund site, in the Selkirk Mountains of Eastern Washington. Located within the reservation of the Spokane Tribe of Indians, the mine was operated from 1955 until 1981.

Two open pits, backfilled pits, and a number of waste rock piles and ore stockpiles remain on site. In addition to elevated levels of radioactivity, heavy metals mobilized in acid mine drainage pose a potential threat to human health and the environment. The site drains to Blue Creek, which enters the Spokane Arm of Franklin D. Roosevelt Lake. [15]

It is possible that some ore may have fallen out of a truck during operation or cleanup. The highway North of Spokane where I measured several high levels is about 30 miles east from the mine. Data Analysis Measuring radiation is not like measuring temperature, voltage, or motion. These quantities can be measured with a great deal of accuracy because the uncertainty is a small percentage of the measured value. Radiation is a stochastic process and occurs at random intervals with a Poisson distribution centered on a mean value. The standard deviation of a set of radiation data is a parameter that depends on the counting period and indicates the spread of the measured values around the mean. The longer the counting period, the better the estimate of the average will be, and the smaller the standard deviation compared to the mean. Also, the longer the counting period, the closer the Poisson distribution comes to the Gaussian distribution. I used a five minute counting period with a mean of about 60 counts, which lets me use normal distribution functions to identify “hot spots”. I used multivariate linear regression in MATLAB to build a model of radiation level. My model equation for finding radiation from elevation and rock type is

DaSaMaVaIaEaaR 6543210 ++++++= Where: R is the predicted background radiation level E= elevation, feet I= igneous rock? V= volcanic rock? M= metamorphic rock?

S= sedimentary rock? D= dirt? (I,V,M,S,D are either 1 or 0, depending on geology)

60 aa − are the regression coefficients In matrix form, if X is my matrix of independent variables, Y is my array of measurements, and a is my array of regression coefficients:

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Then the problem can be expressed as Y=Xa This is a set of 1022 equations in 7 unknowns. The regression coefficients can be found from the equation

YXa \= Where “\” is the MATLAB left division operator which finds the least squares solution for overdetermined data. Next I performed a variance reduction in my data by adjusting statistically for elevation and geology. If: Y’= Radiation data array with elevation and rock type influences removed X’= Matrix of dependent variables without the constant a’= Array of regression coefficients without baseline Where:

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I made the adjustment by subtracting elevation and rock influence from my entire measured data set. I performed this in MATLAB as follows: ''' aXYY −= Matlab Programming These are the steps in my MATLAB program to analyze the data:

• Read CSV data file and place into matrix and convert counts to uR/hr

• Pull some columns out of my data matrix and put them into arrays

• Calculate mean and standard deviation for my original data and write to an Excel file

• Find a simple least squares fit for elevation and make a statistical adjustment for elevation only, calculate correlation and COD, sort the adjusted data into rock types for VassarStats ANOVA tests

• Find the multivariate linear regression for rock type and make a statistical adjustment on

the data for rock type as well, calculate correlation and COD, sort the data for history for VassarStats t-tests.

• Find mean and standard deviation of the adjusted data, write to Excel file

• Make scatter plots and histograms of measured and adjusted data

• Write out measured and adjusted data to text files for 3DField to read

• Search the adjusted data for counts that exceeded 15 mrem/year over the 90th percentile

and list them out in the Excel file After running my MATLAB program, Table 2 was calculated. It shows the distribution of my 1022 points of original measured data in microRoengens/hr.

distribution of raw data (y) mean stddev mn+3sdvs9.458205 1.73336 14.65829

Table 2 Original data statistics

“mn+3sdvs” is the mean of my data plus three times the standard deviation. This number is the 99.9th percentile and is where values that are greater are not very likely to happen by chance.

A scatter plot is a way to show your data plotted as points on a graph. MATLAB can do this easily. It can also identify the slope of the data showing the relationship between two variables; see Figure 16.

-2000 0 2000 4000 6000 80005

10

15

20scatter plot of radiation vs elevation

elev, ft

u|r/h

r

y = 0.0009*x + 8.5

data 1 linear

Figure 16 Scatter plot of measured data plotted against elevation

The greater the slope with elevation, the more the measured radiation levels spread out. This increases the standard deviation. I wanted to reduce the scatter by subtracting out effects of elevation and geology. Linear regression is a way of taking a set of data that has random scatter in it and finding an equation for a best fit line though that data. I used linear regression in MATLAB to get the sensitivities to elevation and rock type shown in Table 3.

multivariate linear regression, elevation, rock type baseline elev iginter volcan meta sedm aluv 7.97837 0.000806 1.246393 0.556479 1.498015 0.449235 0.569413 Table 3 Linear regression coefficients of elevation and rock type

In table 3, the baseline is the background radiation at sea level without rock effects. To find the estimated level for a particular location, multiply each coefficient by its variable and add them together. Since the rock types are either one or zero, only one rock type will add to the total, and it will be the amount of the coefficient and will be the number shown in the table. I had MATLAB subtract these effects for each measurement point in my data and refigure the distribution, Table 4.

distribution of elevation and rock corrected data y3

mean stddev mn+3sdvs7.97837 1.382364 12.12546

Table 4 Data statistics with elevation and rock type removed

The standard deviation has gone down from 1.73 to 1.38. This is good because with a smaller standard deviation the unusual readings stand out better. In statistics, this is known as a Variance Reduction Technique (VRT) called “control variates”. Figure 17 is the same scatter plot as above with the elevation and rock type removed. This would be like if the elevation of the whole state is zero and the rock type has no effect.

-2000 0 2000 4000 6000 80000

5

10

15scatter plot of radiation with no elev and rock effects

elev, ft.

u|r/h

r

y = 2.9e-018*x + 8

data 1 linear

Figure 17 Scatter plot of measured data after subtracting effect of elev &

geology

4 6 8 10 12 14 16 180

50

100

150histogram of measured radiation level

u|r/hr

num

ber o

f occ

uren

ces

2 4 6 8 10 12 140

50

100

150

200histogram of measured radiation without elevation or rocktype

u|r/hr

num

ber o

f occ

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Figure 18 Before correction Figure 19 After correction

Figure 18 and 19 are histograms of my data before and after removing elevation and rock type. By making the correction, the bell shape of my data is improved. Figures 20 and 21 show the 3DField interpolated data maps before and after subtracting rock type and elevation. In Figure 21, it looks like there are a number of hot spots that remain. By taking out elevation and rock types it is easier to point out spots that might be significant.

Figure 20 Original interpolated data Figure 21 after elev. and geol. Removal Correlation coefficients show the strength and direction of a linear relationship between two random variables. Coefficient of determination (COD) is the square of the correlation and it gives the exact relationship in percent. Table 5 is a Coefficient of determination matrix for my data, calculated from MATLAB. The first row in the matrix shows the percentage of the influence of each variable that impacts the background radiation. According to this data the highest influence on background radiation is elevation which explains 34% of the variation in my original data. (highlighted in red) coefficient of determination (COD) Rad Elev igintr volcan meta sedim alluv Rad 1 0.340619 0.121495 0.01055 0.030091 0.002966 0.08753 Elev 0.340619 1 0.216114 0.057729 0.016332 0.001508 0.249147 Igintr 0.121495 0.216114 1 0.024851 0.003553 0.010911 0.105999 volcan 0.01055 0.057729 0.024851 1 0.01101 0.028435 0.326512 meta 0.030091 0.016332 0.003553 0.01101 1 0.004834 0.028938 sedim 0.002966 0.001508 0.010911 0.028435 0.004834 1 0.13627 alluv 0.08753 0.249147 0.105999 0.326512 0.028938 0.13627 1

Table 5 Matrix of Coefficients of Determination Statistical Tests Analysis of variance (ANOVA) is a statistical method in which the observed variance is separated into components according to different explanatory variables. In this case it was sorted by rock type. I used a Vassar College professor’s website [16] to calculate ANOVA because I also wanted to apply the Tukey test which MATLAB didn’t have. The reason I wanted to use the Tukey test was because I wanted to see how the rock type influences compared between groups as well as find which rock had the highest influence. Table 6 below is the ANOVA data summary. The probability, P, shows the likelihood that the difference in the means has happened by chance. The low probability, <.0001, supports my research hypotheses about significance of rock type. The Tukey “Honest Significant Difference (HSD)” Table 7 shows the significance between individual rock types.

Data Summary

Samples Intrus volc meta sedim alluv Total

N 84 222 39 111 565 1021

∑ X 753.7838 1865.4714 366.0731 928.9121 4813.4231 8727.6636

-Mean 8.9736 8.403 9.3865 8.3686 8.5193 8.5482

∑ 2X 6999.7977 16245.6229 3517.8081 7972.87 41901.7 76637.8

Variance 2.8389 2.5793 2.1491 1.8108 1.5861 1.9926 Std.Dev. 1.6849 1.606 1.466 1.3457 1.2594 1.4116 Std.Err. 0.1838 0.1078 0.2347 0.1277 0.053 0.0442

ANOVA Summary Source SS df MS F P Treatment [between groups]

51.3397 4 12.8349 6.5823 <.0001

Table 6 ANOVA Results for area geology Tukey HSD Test HSD[.05]=0.56; HSD[.01]=0.66 intrus vs volc P<.05 intrus vs meta non-significant intrus vs sedim P<.05 intrus vs alluv non-significant volc vs meta P<.01 volc vs sedim non-significant volc vs alluv non-significant meta vs sedim P<.01 meta vs alluv P<.01 sedim vs alluv non-significant

Table 7 Tukey HSD tests for area geology I also wanted to test my research hypothesis about the Missoula Flood area. The mean for the flood area is higher than the non-flooded part of the state, shown in Table 8. Since this was a test of only two groups, I used a t-test from the same website as above [16]. The probability, P, is less than one out of ten thousand that this difference happened by chance. This supports my hypothesis that the Missoula Flood increased the background radiation in flooded areas.

Summary Data Flood No flood Total

n 209 813 1022

∑ X1768.34 6385.55 8153.89

∑ 2X 15312.6 51693.3 67005.8

SS 350.674 1539.19 1951.06 mean 8.461 7.8543 7.9784

MeanA—MeanB t df

0.6067 5.75 1020

one-tailed <.0001

P two-tailed <.0001 Table 8 t-Test for significance of Missoula Flood area radiation levels Finally, I tested the highway around Hanford against the rest of the eastern Washington scabland area. The results are shown in Table 9. Summary Data

Hanford Loop Scablands Total

n 36 209 245

∑ X 311.66 1768.34 2080.00

∑ 2X 2731.95 15312.6 18044.5

SS 33.7966 350.674 385.654 mean 8.6573 8.461 8.4898

MeanA—MeanB t df

0.1963 0.86 243 one-tailed 0.19532

P two-tailed 0.39064 Table 9 t-Test for significance of Hanford loop radiation levels There is a 20% probability that this could have happened by chance. This supports the null hypotheses: The levels of radiation around the perimeter of the Hanford site are about the same as the surrounding Columbia Basin. “Hot Spot” Identification The Washington State Department of Health document “Hanford Guidance for Radiological Cleanup” [17] says “The dose limit for release of a site is 15 mrem/y……90th percentile background radionuclide concentrations shall be used when subtracting the background contribution from measurements made at a site.” I had my MATLAB program find the measurements that exceeded the statewide 90th percentile after adjusting for elevation and rock type and adding

the recommended 15 mrem/yr (mean=7.98, 90th pct=9.75, +15mrem/yr=11.46). Table 10 shows the measurement number, location, and levels that exceeded this and might need to be checked by government officials.

meas# long Lat uR/hr Location 41 -121.7 45.6878 11.85 I-84, 7 mi w of Hood River 137 -117.335 46.433 11.50 hiway 12, 13 mi w of clarkston 156 -117.211 46.73374 11.48 Pullman 207 -118.738 48.64564 12.12 Republic 209 -118.741 48.64754 13.96 Republic 276 -120.184 48.47713 11.87 Winthrop 419 -120.533 46.62481 11.80 Yakima 888 -123.22 46.11558 12.55 clatskanie, OR

Table 10 “Hot spots” Conclusion I gathered a total of 1022 measurements over a large area and learned how to best interpolate my data to estimate the areas in-between my points. I found that the natural background radiation levels in Washington State are the lowest in the western lowlands and highest in the northeast part. The data supports my hypothesis that the level of radiation increases with elevation and is higher with certain underlying rock types. With some (fancy) math I removed the deterministic influences of elevation and rock type from my data. This lowered the standard deviation and tightened up the data so that the unusual areas in the state show up better. This also made the statistical tests more sensitive. Mr. Mike Brennan of the State Department of Health suggested the question of whether the Great Missoula Flood made the background radiation levels in the Columbia Basin higher. My data supports my hypothesis that the flood area levels are statistically higher than the rest of the state. My data supports the null hypothesis about Hanford by finding that levels along the perimeter highway around Hanford were about the same as the rest of the Columbia Basin. I found a few areas in the State that have unusual levels and may need to have a second look. Acknowledgements I wish to thank Mr. Mike Brennan of the Washington State Department of Health, Division of Radiation Protection for his advice and suggestions. Thanks to my science teachers at Kentwood High School, Mr. Alegado and Mr. Gregson for their inspiration. Thanks to Mr Vladimir Galouchko of Moscow, Russian Federation, for offering me an inexpensive student license for 3DField display software. Thanks to Ms. Yan Jiang, research librarian at the University of Washington Library for helping me with ESRI software to determine rock type for my measurement locations.

Thanks to my dad for his helpful suggestions, his support, and his help with all the driving References

1. “Nuclear Background Radiation Survey System”, E. Foss, Kentwood High School, Washington State Science & Eng. Fair, 2007

2. Video, “Mystery of the Megaflood”, NOVA Public Television, 2005 http://www.pbs.org/wgbh/nova/megaflood/

3. USGS map of calculated gamma-ray absorbed dose from terrestrial sources. http://pubs.usgs.gov/of/2005/1413/namrad.htm#TitlePage.htm

4. USGS cosmic-ray exposure calculated from the topography, http://pubs.usgs.gov/of/2005/1413/namrad.htm#TitlePage.htm5. Exposure of the Population of the United States and Canada from Natural

Background Radiation, NCRP Report no. 94, 1987 6. A short primer on radioactive decay – Uranium 238

http://www.atral.com/U2381.html 7. Thomas Jefferson National Accelerator Facility, Muon experiment.

http://www.jlab.org/~cecire/muonexp.html8. GPS Visualizer Webpage:

http://www.gpsvisualizer.com9. Page Size Geologic Map of Washington, Washington State Department of

Natural Resources http://www.dnr.wa.gov/geology/pagemap.htm10. 3DField website:

http://field.hypermart.net/11. History and archeology of Hanford

http://www.hanford.gov/doe/history/?history=archaeology12. Environmental Monitoring at the Hanford site http://hanford-site.pnl.gov/envreport/2001/summonitor.stm13. PBS show “Mystery of the Megaflood”, WGBH

http://www.pbs.org/wgbh/nova/megaflood/scablands.html14. Inventory of Washington Minerals, Bulletin 37, Section 20, Uranium map,

Washington State Department of Natural Resources http://dnr.wa.gov/geology/pubs/b37/15. Superfund sites in Washington (Hanford and Midnite mine)

http://www.scorecard.org/env-releases/land/rank-sites.tcl?fips_state_code=53

16. Vassar Stats, One-Way Analysis of Variance for Independent or Correlated Samples, Richard Lowry, Professor Emeritus

http://faculty.vassar.edu/lowry/VassarStats.html17. Hanford Guidance for Radiological Cleanup, State of Washington

Department of Health. Nov. 1977, pg 2 http://www.doh.wa.gov/ehp/rp/environmental/cleanup.pdf