SPATIAL ANALYSIS OF DISSOLVED OXYGEN LEVELS USING ORDINARY

KRIGING METHODS NEAR THE CONFLUENCE OF BOZEMAN CREEK AND THE EAST

GALLATIN RIVER IN BOZEMAN, MT.

by

Jacqueline Lorene Frank

A professional paper submitted in partial fulfillment

of the requirements for the degree

of

Master of Science

in

Environmental Engineering

MONTANA STATE UNIVERSITY

Bozeman, Montana

April, 2015

©COPYRIGHT

by

Jacqueline Lorene Frank

2015

All Rights Reserved

iii

TABLE OF CONTENTS

ABSTRACT ...................................................................................................................... VI

INTRODUCTION .............................................................................................................. 1

PROJECT BACKGROUND .............................................................................................. 2

DISSOLVED OXYGEN .................................................................................................... 4

DATA COLLECTION METHODS ................................................................................... 5

KRIGING METHODS ....................................................................................................... 8

RESULTS ......................................................................................................................... 11

CONCLUSIONS AND RECOMMENDATIONS ........................................................... 21

REFERENCES CITED ..................................................................................................... 24

APPENDICES .................................................................................................................. 28

APPENDIX A: SCHEDULE OF WELL OWNERSHIP ................................................. 29

APPENDIX B: ORIGINAL MATLAB CODE FROM DR. KATHRYN PLYMESSER 31

APPENDIX C: MATLAB CODE .................................................................................... 35

APPENDIX D: EXCEL MATLAB DATA ...................................................................... 46

iv

LIST OF TABLES

Table Page

1. A table showing measured Dissolved Oxygen data for the three dates analyzed. .. 6

2. A subset of predicted DO concentrations and the associated variance, calculated

using data measured on 8.27.2014……………………………................………14

v

LIST OF FIGURES

Figure Page

1. A map of the site showing 10 of the 12 water sampling wells. .............................. 7

2. A plot of the semivariogram for DO data collected on 08.27.2014. ..................... 12

3. A plot of the semivariogram for DO data collected on 09.02.2014. ..................... 13

4. A plot of the semivariogram for DO data collected on 09.11.2014. ..................... 13

5. A 3-D concentration map showing predicted DO concentrations over the site area

calculated using data measured on 08.27.2014…………………………………..16

6. A 3-D concentration map showing predicted DO concentrations over the site area

calculated using data measured on 09.02.2014…………………………………..17

7. A 3-D concentration map showing predicted DO concentrations over the site area

calculated using data measured on 09.11.2014…………………………………..18

8. A map showing the plan view of the site with predicted, scaled DO concentrations

calculated using data measured on 08.27.2014…………………………………..19

9. A map showing the plan view of the site with predicted, scaled DO concentrations

calculated using data measured on 09.02..2014……………………………….....20

10. A map showing the plan view of the site with predicted, scaled DO concentrations

calculated using data measured on 09.11.2014…………………………………..21

vi

ABSTRACT

Spatial prediction methods, including ordinary kriging methods, are used to estimate the

value of a parameter at a location where it is not measured, based on data collected at nearby

locations. For this project dissolved oxygen data are analyzed from 3 different dates, collected at

12 well locations near the confluence of Bozeman Creek and the East Gallatin River in

Bozeman, MT with the goal of gaining a broader understanding of the wetland environment in

that area. Ordinary kriging methods are used to interpolate intermediate dissolved oxygen

concentrations, as well as the variance (error) of the predictions on a fine scale over the site for

each date on which data were collected. The process uses semivariograms to establish the

relationship between distance and spatial weight, but in this case semivariograms presented no

trend. The lack of trend indicates that either there is not enough measured data to accurately

predict unknown concentrations, or that there is indeed no spatial relationship. However, the

measured data gives evidence that the site exhibits characteristics typically described of wetlands

and has the potential to remove nitrogen and organic pollutants from the water, thereby

improving water quality.

1

INTRODUCTION

The confluence of Bozeman Creek and the East Gallatin River in Bozeman Montana

shows signs of a history as a typically classified wetland area. Over time, the land in this area has

been cultivated and filled in by various land owners, and business ventures (Deford, 2014). The

City of Bozeman and the Trust for Public Lands have partnered together to create a public park

at this site, while at the same time aiming to reconstruct and restore the wetland function of the

area, and improve water quality in both the East Gallatin River and Bozeman Creek (Deford,

2014).

Montana State University was brought on as a partner to this project to help understand,

and further study the hydraulics, hydrology, and effects of the project on water quality (Deford,

Stein, Cahoon, Hartshorn, & Ewing, 2014). Project leader Lilly Deford describes the project as

looking specifically at “how, or even if, the restoration efforts will affect surface water quality in

the East Gallatin River” (Deford, 2014). Deford’s project focuses on nitrogen and phosphorous

as water chemistry parameters, but includes other water chemistry parameters including pH and

dissolved oxygen. This paper analyzes a small subset of dissolved oxygen data, providing

additional insight to the overall project. Dissolved oxygen concentrations from three separate

dates over a 15 day period were observed at 12 well locations. The concentrations were

analyzed using ordinary kriging methods with the goal of predicting an interpolated dissolved

oxygen concentration map of the wetland site. Creating and analyzing a predicted dissolved

oxygen surface map is beneficial because dissolved oxygen concentrations can indicate the type

2

of nutrient processes that are expected in the area, specifically denitrification, as well as help

determine if the site is behaving overall as a typical wetland after initial construction efforts.

Ordinary kriging was used as a method of interpolation of unknown points because it has

the advantage of also giving error estimates, or variances, in addition to the predicted values

(Bohling, 2005). Ordinary kriging methods have also been proven to effectively predict

dissolved oxygen concentrations in other settings (Murphy, Curriero, & Ball, 2010).

Semivariograms were first developed, and used to predict dissolved oxygen concentrations and

associated variance values. The predicted concentrations were then used as the basis of

dissolved oxygen surface maps for each date over the area being analyzed.

PROJECT BACKGROUND

The area surrounding the confluence of Bozeman Creek and the East Gallatin

River was historically a wetland setting, but has been significantly altered by different

landowners, mostly as a result of agricultural practices; water has been routed through the area

without interacting with the land long enough to create saturated conditions (Deford, 2014). The

city of Bozeman and the Trust for Public Land are working to create a city park near the

confluence, while restoring the wetland environment at the same time, a project titled The Story

Mill Ecological Restoration Project (City of Bozeman; Trust for Public Land, 2014). “The

restoration will close off the drainage ditches in an attempt to back up the groundwater and

create a more saturated, reducing, wetland like landscape. They will also be removing fill from

some of the floodplain. This is in the hopes of increasing surface water interaction with the

wetland during high flow events, when there will be significant amounts of urban and

3

agricultural runoff” (Deford, Project Goals, 2014). In addition to restoring the historic wetland,

the project will increase public green space and offer “opportunities to connect with nature, our

neighbors, and community”, while also providing educational opportunities for all ages to learn

about this history and science of the site (City of Bozeman; Trust for Public Land, 2014).

Montana State University is assisting with technical and scientific expertise, tasked with

monitoring conditions at the site and helping understand the effects of the restoration project on

surface water quality (Deford, Progress Report, 2014). The multiple objectives of the MSU

component of the study are to:

1. Develop metrics for defining success of wetland restoration efforts,

2. Monitor short- and long-term wetland function,

3. Document progress toward achieving the project restoration objectives,

4. Contribute to public awareness of the importance of wetlands,

5. And help better understand how wetlands influence surface water quality and quantity

(Deford, Progress Report, 2014).

Deford is looking at “how, or even if, the restoration efforts will affect surface water

quality in the East Gallatin River… [and is] tracking surface and groundwater levels and

chemical properties, focusing on the nutrients Nitrogen and Phosphorous” (Deford, Project

Goals, 2014). Due to expected increased interaction of surface water with the wetland, “it is

anticipated that the restoration efforts will improve the watershed’s ability to treat nitrogen and

phosphorous” and therefore decrease nutrient levels in East Gallatin River (Deford, Progress

Report, 2014). Deford developed testing methods including a wetland monitoring matrix

tracking water levels, total nitrogen, total phosphorus, nitrate, nitrite, phosphate, sulfate,

chloride, pH, electrical conductivity and dissolved oxygen (Deford, Progress Report, 2014).

4

Deford installed monitoring equipment and is continuing to collect data, aiming to better

understand and quantify wetland function and nutrient cycling (Deford, Progress Report, 2014).

This paper focuses on analyzing a subset of dissolved oxygen (DO) data collected at the

site, and provides additional insight to the overall project with the goal of gaining a broader

understanding of the wetland function. Dissolved oxygen is a parameter of interest because

“Dissolved oxygen (DO) in water is essential for the biochemical processes which determine the

fate of nitrogen and organic pollutants…in constructed wetlands” (Sewwandi, Weragoda, &

Tanaka, 2010). In addition, dissolved oxygen levels are significantly different in wetlands versus

surface water, therefore DO data can help quantify the extent to which the site is functioning as a

wetland.

DISSOLVED OXYGEN

Dissolved Oxygen (DO) levels differ in wetlands compared to surface waters, because

“DO concentrations in …fresh water will range from 7.56 mg/L at 30 degrees Celsius to 14.62

mg/L at zero degrees Celsius” (Minnesota Pollution Control Agency, 2009) and wetland areas

that can be described as bogs, for example, have DO concentrations ranging from 0-6 ppm or 0-

6mg/l (Mullin, 2011). Therefore dissolved oxygen levels can help provide insight into whether or

not the restoration efforts are promoting a wetland environment.

Dissolved oxygen (DO) levels can be used “to assess the stability of various trace

metals…and organic contaminants in ground water” (Rose & Long, 1988) and can also help

predict the expected microbial processes occurring in a wetland area. Denitrification, for

example, is a process that occurs in anaerobic or anoxic conditions (Faulwetter, et al., 2009), and

5

anoxic groundwater conditions are defined as having “no dissolved oxygen or a very low

concentration of dissolved oxygen (that is, less than 0.5 milligrams per liter)” (USGS, n.d.).

“Respiration and fermentation are the major mechanisms by which microorganisms break down

organically-derived pollutants into assumed harmless substances such as carbon dioxide (CO2),

nitrogen gas (N2) and water (H2O)” (Faulwetter, et al., 2009) and dissolved oxygen

concentrations can help determine which processes are expected to be occurring in the area

(Faulwetter, et al., 2009). It’s also been shown that “lower redox potentials are linked to reduced

conditions” (Faulwetter, et al., 2009), and one of the larger project goals as earlier discussed is

creating a more reducing landscape (Deford, Project Goals, 2014). Dissolved oxygen has a high

redox potential, and is in fact the most biologically reactive oxidant out of naturally occurring

constituents in water (Stumm & Morgan, 1981), and because we expect reducing conditions and

low redox potential (Deford, Project Goals, 2014) we therefore expect DO concentrations to be

low. This can be beneficial because when microbes are starved for oxygen, they select the next

most energetic compound, which is typically nitrate (Ponnamperuma, 1972); therefore when DO

concentrations are low in the anoxic range, it gives evidence that nitrates can be removed from

the water through denitrification (Woltermade, 2000) and contribute to the larger project goal of

improving “the watershed’s ability to treat nitrogen and phosphorous” (Deford, Progress Report,

2014).

DATA COLLECTION METHODS

The dissolved oxygen data shown in Table 1 was analyzed from three different dates,

taken at 12 different groundwater well locations across the site. The location of wells is shown

6

in Figure 1. Figure 1 shows only 10 of the 12 locations, with two well locations not pictured

because they are slightly out of view from the aerial photograph, but are just off the image

on the South side. Data was analyzed from samples collected on August 27th, September 2nd,

and September 11th, 2014. Dissolved oxygen concentrations from these dates were selected for

analyses as the observations were from the time of year which is expected to be representative of

baseline conditions.

Table 1- A table showing measured Dissolved Oxygen data for the three dates analyzed.

Well ID Sample ID DO (mg/l) DO% DO (mg/l) DO% DO (mg/l) DO%

TPL3 1 -- -- 0.81 9.10 1.02 10.80

TPL6 2 1.00 11.40 1.96 23.00 1.38 14.70

TPL8 3 1.83 21.10 2.95 34.00 -- --

TPL10 4 4.58 52.50 0.03 0.30 4.18 45.60

TPL14 5 0.36 3.90 0.84 9.40 4.17 44.80

MSU1 6 0.15 1.70 0.25 2.90 2.98 31.10

MSU2 7 0.09 1.00 0.23 2.60 0.25 2.70

MSU3 8 0.05 0.05 0.27 2.90 0.31 3.20

MSU4 9 0.17 0.17 0.28 3.20 0.56 5.80

MSU6 10 0.31 0.31 1.68 18.30 5.29 55.60

MSU7 11 0.12 1.12 0.30 3.40 0.61 6.30

F5 12 0.42 4.60 0.62 6.90 0.64 6.70

Dissolved Oxygen Concentration Data8/27/2014 9/2/2014 9/11/2014

7

Figure 1- A map of the site showing 10 of the 12 water sampling wells.

The wells used to collect data were installed by three different entities at different times,

including the Trust for Public Land (listed as TPL wells in Table 1), Hyalite Engineers (well F5

in Table 1), and Montana State University (listed as MSU wells in Table 1). A schedule of well

owners is provided in Appendix A. Montana State University installed “2-inch diameter PVC

wells to a maximum depth of 7 ft. with a 3-inch diameter hand auger. [MSU] wells are slotted for

the deepest two feet, and solid for the remaining length up to, and above the ground surface.

Silica sand was used as a well casing around the slotted portion, and clay removed from the well

hole was used around the solid portion. This allows for groundwater to freely flow into the well,

8

while keeping the well from clogging and surface water from entering” (Deford, Stein, Cahoon,

Hartshorn, & Ewing, 2014). The geo-position of well locations are referenced using the North

American Datum of 1983, and were surveyed using Trimble GPS equipment (Deford, Stein,

Cahoon, Hartshorn, & Ewing, 2014).

Water chemistry data, including the dissolved oxygen observations in Table 1, were

collected by Lilly Deford and research assistants working under her direction, using a Hach LDO

Probe (IntelliCALTM LDO101) (Deford, Stein, Cahoon, Hartshorn, & Ewing, 2014).

Measurements were taken after first pumping wells dry, then reading DO concentrations

immediately after recharge, holding the probe in the center of the water column (Deford, Stein,

Cahoon, Hartshorn, & Ewing, 2014). This method is considered to be “representative of the

surrounding groundwater because the water has had little time to equilibrate with the

atmosphere” (Deford, Stein, Cahoon, Hartshorn, & Ewing, 2014). DO measurements were

recorded after consecutive readings stabilized in time to within 0.2mg/L of each other. (Deford,

Stein, Cahoon, Hartshorn, & Ewing, 2014).

KRIGING METHODS

Ordinary kriging is a method of spatial prediction used to estimate the value of a

parameter in question at a location where it is not measured, based on data collected at nearby

locations (Murphy, Curriero, & Ball, 2010). Data analysis using spatial prediction methods are

used because dissolved oxygen concentration in shallow groundwater have the potential to be

spatially correlated, i.e. the concentration of dissolved oxygen at one location is related to the

concentration at nearby locations.

9

Ordinary kriging has advantages over other spatial prediction methods (Bohling, 2005)

and is a proven method of analyzing and predicting groundwater surface elevations (Varouchakis

& Hristopulos, 2013) (Nikroo, Kompani-Zare, Sepaskhah, & Fallah Shamsi, 2010), as well as

water chemistry properties including dissolved oxygen (Murphy, Curriero, & Ball, 2010). All

interpolation algorithms assume a decreasing weight function with increased separation distance,

but kriging is advantageous because the methods incorporate a data driven weighting function,

rather than an arbitrary function (Bohling, 2005). Kriging has further advantages over other

spatial prediction methods including compensating for data clusters, and giving an error estimate,

i.e. variance, in addition to predicting values at unknown locations (Bohling, 2005).

Ordinary kriging involves two steps; first the degree of similarity between measured data

points is plotted as a function against their separation distance to determine if and how the data is

spatially correlated, and second that relationship is used to interpolate among measured points to

estimate values at unknown locations (Reams, Huso, Vong, & McCollum, 1997). The first step

utilizes the semi-variance statistic (h) defined as “half the expected squared difference between

values a given distance, h, apart:

(ℎ) =1

2𝐸[𝑧(𝑥𝑖) − 𝑧(𝑥𝑖 + ℎ)]2

=1

2𝑁(ℎ)∑ [𝑧(𝑥𝑖) − 𝑧(𝑥𝑖 + ℎ)]2

𝑁(ℎ)

𝑖−1

where

z(𝑥𝑖) = measured sample value at point 𝑥𝑖 (𝑥𝑖 can be multidimensional),

z(𝑥𝑖 + ℎ) = sample value at a point a distance of h from 𝑥𝑖, and

N(h) = total number of pairs of points within an h of each other” (Reams, Huso, Vong, &

McCollum, 1997). The semi-variance statistic (h) is plotted against the separation distance h;

this plot is called the semivariogram (Reams, Huso, Vong, & McCollum, 1997). If a trend can be

10

determined from the semivariogram and a representative curve can be fit to the data, the

relationship between semi-variance and separation distance is then used to determine weighting

factors utilized in the second step (Reams, Huso, Vong, & McCollum, 1997). Once that

relationship is known, weighting factors are calculated based on the separation distance between

points, giving higher weights to points closer to the point being estimated, and used to “estimate

the value of Z at some unmeasured point 𝑥𝑜, [as] a linear combination or weighted average of all

the observed variables:

�̂�(𝑥0) = 1

𝑧(𝑥1) + 2

𝑧(𝑥2) + ⋯ + 𝑛

𝑧(𝑥𝑛),

where

1

= coefficients or weights associated with each of the observed values” (Reams, Huso,

Vong, & McCollum, 1997).

Dissolved oxygen concentrations were analyzed using ordinary kriging methods using a

MATLAB (MATLAB, 2014) program written by Kathryn Plymesser, (Plymesser, 2014). The

code is presented in Appendix B, and uses a linear semi-variance model. This base code was

augmented slightly to use data found in different spreadsheets, and the new code is archived in

Appendix C. The spreadsheet based presentation of the data referenced by the code can be found

in Appendix D.

With the semivariograms established, dissolved oxygen concentrations and the

associated variance were estimated using a range of 1000 meters, and a slope of 1 to ensure over

half of all measured points were used to predict unknown concentrations. DO concentrations

were estimated across the site on a grid pattern with a spacing of 50 meters, starting at

(1579782.07 Easting, 532197.33 Northing) and ending at (1578368.61 Easting, 530821.50

Northing) which is an area roughly 1,413 meters East to West, and 1,375 meters North to South,

11

referenced using the North American Datum of 1983. The code for ordinary Kriging can be

found in Appendix C, and the data referenced in the code can be found in Appendix D. Finally,

predicted DO concentrations were then plotted on a 3-D graph (see figures 5-7 in the results

section) using code found in Appendix C, and data found in Appendix D. The function trisurf

was used (MATLAB, 2014) to plot DO concentrations over the area of interest, and therefore the

automatic color scale for each day analyzed was based on that day’s minimum and maximum

predicted DO value, resulting in color scales that could not be compared across different dates.

This was compensated for by manipulating the kriged DO data output before using that data to

plot the concentration maps, by changing the value at the first point (1578450, 530800) to be

6mg/L DO; this forced the automatic color scale in MATLAB to be a 0-6mg/L DO scale. After

3D DO concentration maps were generated for each date using MATLAB, they were changed to

2D graphs with color representing DO concentration, and overlaid on the map of water wells to

give a plan-view picture of DO concentrations across the site for each date.

RESULTS

The semivariograms for each date analyzed can be seen in Figures 2-4. The

semivariograms do not display any detectable trend in the data, showing there is not enough

measured data, or that the spatial interval is too coarse, or that there is simply no spatial trend.

The outcome is that a spatial trend could not be deduced from the semivariograms using ordinary

kriging methods. Without a spatial trend, a correlation cannot be made between separation

distance and weighting factors, to accurately predict the dissolved oxygen concentration in

12

unknown locations. Therefore, if ordinary kriging methods are used, the variance is expected to

be high, showing that predicted values cannot be trusted as accurate calculations.

Figure 2 – A plot of the semivariogram for DO data collected on 08.27.2014.

13

Figure 3 – A plot of the semivariogram for DO data collected on 09.02.2014.

Figure 4 – A plot of the semivariogram for DO data collected on 09.11.2014.

14

In light of the inconclusiveness of the semivariograms, the measured data were used with

ordinary kriging to predict DO values at unknown locations for each data analyzed; a small

sample of the predicted DO values and the associated variance at each point for one date on

08.27.2014 are shown in Table 2, and full results are provided in Appendix D. The variance

values are very high as expected, and are often 2 orders of magnitude higher than the predicted

DO concentration, showing that the error in the predicted value is far greater than the value itself

and that predictions are tenuous at best.

Table 2- A subset of predicted DO concentrations and the associated variance, calculated using

data measured on 08.27.2014.

15

Although predicted DO values at unknown locations have high variances, 3-dimensional

DO concentration maps were generated for each date, which were then compared to see if

general trends could be determined, such as areas of persistent low or high DO concentrations.

The unscaled DO concentration maps for each date are shown in Figures 5-7. As before, the

automatic color scale for each day analyzed was based on that day’s minimum and maximum

predicted DO value, resulting in color scales that could not be compared over different

dates. To compensate for this, the kriged DO data output by MATLAB was manipulated before

using that data to plot the 2-D concentration maps, by changing the value at the first point

(1578450, 530800) to be 6mg/L DO; this forced the automatic color scale to be a 0-6mg/L DO

scale. The concentration maps for each day were then displayed as 2-D contour interval graphs

with color representing DO concentration, and overlaid on the water well map to give a plan-

view picture of DO concentrations across the site for each date. These plan view, scaled DO

concentration maps are shown in Figures 8-10, and are all shown on a scale of 0-6mg/L DO, and

can be compared across dates for general trends. As seen in Figures 8-10, there are no major

trends that carry across all dates, and therefore the predicted DO values combined with their

associated variance values are not accurate enough to contribute additional knowledge about DO

concentrations beyond the measured data. However, the measured data do allow us to draw some

general conclusions, which are discussed in the next section.

16

Figure 5- A 3-D concentration map showing predicted DO concentrations over the site area

calculated using data measured on 08.27.2014.

17

Figure 6 – A 3-D concentration map showing predicted DO concentrations over the site area

calculated using data measured on 09.02.2014

18

Figure 7 – A 3-D concentration map showing predicted DO concentrations over the site area

calculated using data measured on 09.02.2014

19

Figure 8 – A map showing the plan view of the site with predicted, scaled DO concentrations

calculated using data measured on 08.27.2014.

20

Figure 9 – A map showing the plan view of the site with predicted, scaled DO concentrations

calculated using data measured on 09.02.2014.

21

Figure 10 – A map showing the plan view of the site with predicted, scaled DO concentrations

calculated using data measured on 09.11.2014.

CONCLUSIONS AND RECOMMENDATIONS

The semivariograms showed no correlation between measured DO concentrations and the

separation distance between points, which in this case likely indicates that the well locations

analyzed were too far apart to correlate DO concentrations with their spatial location. Therefore

no spatial trend could be deduced from the semivariograms using ordinary kriging methods.

Without a spatial trend, accurate weighting factors needed to calculate unknown DO

22

concentrations cannot be predicted based on relative distances, and therefore the variance or

error calculated is often orders of magnitude higher than the predicted DO concentration value.

This shows there is not enough data to accurately predict DO concentrations at unknown

locations across the site using ordinary kriging methods.

However, while accurate concentrations of DO were not able to be predicted using

ordinary kriging methods, other project goals were met. Overall, the maximum measured DO

levels of 5.29 mg/L show that the area is acting as a wetland environment which falls within the

DO concentration range of bogs from 0-6mg/L (Mullin, 2011). In addition, over 50% of

measured DO data is within the anoxic range of 0-0.5mg/L (USGS, n.d.), showing anoxic

microbial processes can be expected. Anoxic environments have potential to reduce nitrates,

thereby removing nutrients from the water through denitrification (Woltermade, 2000). Removal

of nutrients leads to improved surface water quality downstream from the wetland area, which is

one of the overall project goals; therefore there is evidence that the restoration efforts have

potential to increase water quality.

When considering the measured data, shown in Table 1, questions were raised as to why

some DO values on a given day are considerably higher than the other wells measured in the

same day; no definitive conclusions can be made, but potential explanations that have been

speculated include hypothesis that outlier measurements were the result of human error,

subsurface hydrology factors, or other unknown ecological factors. Questions were also raised as

to why average DO concentrations were noticeably higher on September 11, 2014 compared to

the other two dates; again no definitive conclusions can be made as many different factors

influence DO concentrations simultaneously, however it is hypothesized that these

measurements were higher because they were taken later in the day compared to the other days,

23

which would result in higher DO concentrations as plants have access to more sunlight and are

photosynthesizing, giving off dissolved oxygen from respiration at higher rates during the middle

of the day, as demonstrated in the Jackson Bottom Wetlands Preserve, in Hillsboro Oregon

(Hillsboro Parks and Recreation, 2015).

Overall, it was concluded that further analyses of dissolved oxygen data is not warranted

at this time; installing more wells would be required to accurately predict DO concentrations,

and given time, labor, and cost constraints it would not be a reasonable investment of resources

to do so. Additional data analyses of other water chemistry parameters by creating

semivariograms would help determine if well placement and density is adequate to predict other

factors relating to water quality, and is recommended as part of the overall Story Mill Ecological

Restoration Project.

24

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25

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Rose, S., & Long, A. (1988). Monitoring Dissolved Oxygen in Ground Water: Some Basic

Considerations. Groundwater Monitoring & Remediation, Winter.

Sewwandi, B., Weragoda, S., & Tanaka, N. (2010). Effect of Submerged and Floating Plants on

Dissolved Oxygen Dynamics and Nitrogen Removal in Constructed Wetlands. Tropical

Agricultural Research, 21(4), 353-360.

Stumm, W., & Morgan, J. J. (1981). Aquatic Chemisty. 2nd. New York: Wiley-Interscience.

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http://toxics.usgs.gov/definitions/anoxic.html

Varouchakis, Ε., & Hristopulos, D. (2013). Comparison of Stochastic and Deterministic Methods

for Mapping Groundwater Level Spatial Variability in Sparsely Monitored Basins.

Environmental Monitoring and Assessment, 185(1), 1-19.

27

Woltermade, C. (2000). Ability of restored wetlands to Reduce Nitrogen and Phosphorus

Concentrations in Agricultural Drainage Water. Journal of Soil and Water Conservation,

55, 303–309.

28

APPENDICES

29

APPENDIX A

SCHEDUE OF WELL OWENERSHIP

30

31

APPENDIX B

ORIGINAL MATLAB CODE FROM DR. KATHRYN PLYMESSER

32

Appendix B - Original MATLAB Code from Dr. Kathryn Plymesser – Ordinary Kriging

%ordkrig.m %uses ordinary kriging and a linear semivariance model to predict the %elevation z0 and its associated variance at a specified location x0,

y0.

data=xlsread('WellsMatlab.xls','MatlabData','B2:D16'); X=data(:,1:3); %select x, y, and ground-surface elevation data [dtX,b]=detrendsurf(X); %detrend surface

svrange=input('enter the range for the linear semivariance model \n fit

to the control data: '); alpha=input('enter the slope of the linear semivariance model \n fit to

the control data: ');

for x0=540300:50:540750 for y0=4851650:50:4852100

mz0=b(1)+b(2)*x0+b(3)*y0;

x=[dtX(:,1);x0]; y=[dtX(:,2);y0]; z=dtX(:,3); n=length(x); xx=repmat(x,1,n); yy=repmat(y,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %calculating the distances between points h0=h(end,:); dx0=dx(end,:); dy0=dy(end,:); %distances from the

estimation point hn=find(h0<svrange); %select points within range to use in

estimate dxn=dx0(hn); dyn=dy0(hn); hh=sqrt(dxn.^2+dyn.^2); %distances of selected points from the

estimation point xi=x(hn); yi=y(hn); m=length(xi); xxi=repmat(xi,1,m); yyi=repmat(yi,1,m); dxxi=xxi-xxi'; dyyi=yyi-yyi'; hhi=sqrt(dxxi.^2+dyyi.^2); gamh=alpha*hhi; %calculate semivariance S_top=[gamh(1:m-1,1:m-1) ones(m-1,1)]; S_last=[ones(1,m-1) 0]; S=[S_top;S_last]; %semivariance matrix S B=[gamh(1:m-1,end);1]; %semivariance vector B lamv=S\B; %lamv are the weights zi=lamv(1:m-1)'*(z(hn(1:m-1))); %calculate estimated z z0=zi+mz0; var0=lamv'*B; %calculate the variance in estimate of z k=[x0,y0,z0,var0]; dlmwrite('elevation.txt',k,'-append','precision','%.6f') end end

33

Appendix B - Original MATLAB Code from Dr. Kathryn Plymesser – Semi-variogram

%semivar.m data=xlsread('WellsMatlab.xls','MatlabData','B143:D153'); %select x, y, and ground-surface elevation data dtX=detrendsurf(data); %detrend surface x=dtX(:,1); y=dtX(:,2); z=dtX(:,3); %read x,y,z values n=length(x); nn=n*n; %calculate distance between each pair of points xx=repmat(x,1,n); yy=repmat(y,1,n); zz=repmat(z,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %eliminate duplicate values in the lower triangular part of the matrix % and sort values by distance hv=reshape(triu(h),nn,1); [nr,nc,nhv]=find(hv); %nhv are the nonzero values of hv [sh,ih]=sort(nhv); %calculate the squared differences in z values for each pair of points zvar=(zz-zz').^2; vz=reshape(zvar,nn,1); nvz=vz(nr); sv=nvz(ih); %only compare distances that are half or less of the largest distance hmax=max(sh)./2; nm=length(find(sh<=hmax)); nbin=200; hinc=hmax/nbin; %set the number of distance bins to 20 % adjust bin width to be round number % The values for the magnitude of hinc (10, 100, 1000) may need to be % adjusted depending on domain size and distance units if hinc<=10, hinc=floor(hinc); elseif hinc<=100, hinc=floor(hinc/10)*10; elseif hinc<=1000, hinc=floor(hinc/100)*100; end; hvec=0:hinc:nbin*hinc; %vector of bin endpoints for i=1:nbin-1; ib=[]; ib=find(sh>hvec(i)&sh<=hvec(i+1)); %find distances falling in

bins if ~isempty(ib), ni(i)=length(ib); gamh(i)=sum(sv(ib))./(2*ni(i)); %calculate semivariance for each

distance bin else gamh(i)=0; ni(i)=0; %set semivariance to 0 if no data fall in a

bin end; end; hvmn=(hvec(1:nbin-1)+hvec(2:nbin))./2; %find the mid-point of each bin plot(hvmn,gamh,'bo') %plot the results xlabel('Distance (ft)') ylabel('Semivariance') xlswrite('WellsMatlab.xls',gamh,'MatlabSemi2','A1') xlswrite('WellsMatlab.xls',hvmn,'MatlabSemi2','A2')

34

Appendix B - Original MATLAB Code from Dr. Kathryn Plymesser – detrendsurf function

function [dtX,b] = detrendsurf(X) % remove the 1st order trend surface from a data matrix X % with N rows and 3 columns: values of x, y, and z [nr,nc] = size(X); % determine no of data (rows) xew=X(:,1); xns=X(:,2); z=X(:,3); A=[nr sum(xew) sum(xns);... sum(xew) sum(xew.^2) sum(xew.*xns);... sum(xns) sum(xew.*xns) sum(xns.^2)]; rhs=[sum(z);sum(xew.*z);sum(xns.*z)]; b=A\rhs; % solution of matrix equation zhat=b(1)+b(2).*xew+b(3).*xns; zres=z-zhat; %residuals dtX=[xew xns zres];

35

APPENDIX C

MATLAB CODE

36

Appendix C - MATLAB Code – Ordinary Kriging 08.27.2014

%ordkrig.m %uses ordinary kriging and a linear semivariance model to predict the %elevation z0 and its associated variance at a specified location x0,

y0.

data=xlsread('DO_MatLab_Data.xlsx','MatLabData08.27.2014','B3:D12'); X=data(:,1:3); %select x, y, and ground-surface elevation data [dtX,b]=detrendsurf(X); %detrend surface

svrange=input('enter the range for the linear semivariance model \n fit

to the control data: '); alpha=input('enter the slope of the linear semivariance model \n fit to

the control data: ');

for x0=530800:50:532200 for y0=1578450:50:1579800

mz0=b(1)+b(2)*x0+b(3)*y0;

x=[dtX(:,1);x0]; y=[dtX(:,2);y0]; z=dtX(:,3); n=length(x); xx=repmat(x,1,n); yy=repmat(y,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %calculating the distances between points h0=h(end,:); dx0=dx(end,:); dy0=dy(end,:); %distances from the

estimation point hn=find(h0<svrange); %select points within range to use in

estimate dxn=dx0(hn); dyn=dy0(hn); hh=sqrt(dxn.^2+dyn.^2); %distances of selected points from the

estimation point xi=x(hn); yi=y(hn); m=length(xi); xxi=repmat(xi,1,m); yyi=repmat(yi,1,m); dxxi=xxi-xxi'; dyyi=yyi-yyi'; hhi=sqrt(dxxi.^2+dyyi.^2); gamh=alpha*hhi; %calculate semivariance S_top=[gamh(1:m-1,1:m-1) ones(m-1,1)]; S_last=[ones(1,m-1) 0]; S=[S_top;S_last]; %semivariance matrix S B=[gamh(1:m-1,end);1]; %semivariance vector B lamv=S\B; %lamv are the weights zi=lamv(1:m-1)'*(z(hn(1:m-1))); %calculate estimated z z0=zi+mz0; var0=lamv'*B; %calculate the variance in estimate of z k=[x0,y0,z0,var0]; dlmwrite('elevation_08_27_2014.txt',k,'-

append','precision','%.6f') end end

37

Appendix C - MATLAB Code – Ordinary Kriging 09.02.2014

%ordkrig.m %uses ordinary kriging and a linear semivariance model to predict the %elevation z0 and its associated variance at a specified location x0,

y0.

data=xlsread('DO_MatLab_Data.xlsx','MatLabData09.02.2014','B3:D13'); X=data(:,1:3); %select x, y, and ground-surface elevation data [dtX,b]=detrendsurf(X); %detrend surface

svrange=input('enter the range for the linear semivariance model \n fit

to the control data: '); alpha=input('enter the slope of the linear semivariance model \n fit to

the control data: ');

for x0=530800:50:532200 for y0=1578450:50:1579800

mz0=b(1)+b(2)*x0+b(3)*y0;

x=[dtX(:,1);x0]; y=[dtX(:,2);y0]; z=dtX(:,3); n=length(x); xx=repmat(x,1,n); yy=repmat(y,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %calculating the distances between points h0=h(end,:); dx0=dx(end,:); dy0=dy(end,:); %distances from the

estimation point hn=find(h0<svrange); %select points within range to use in

estimate dxn=dx0(hn); dyn=dy0(hn); hh=sqrt(dxn.^2+dyn.^2); %distances of selected points from the

estimation point xi=x(hn); yi=y(hn); m=length(xi); xxi=repmat(xi,1,m); yyi=repmat(yi,1,m); dxxi=xxi-xxi'; dyyi=yyi-yyi'; hhi=sqrt(dxxi.^2+dyyi.^2); gamh=alpha*hhi; %calculate semivariance S_top=[gamh(1:m-1,1:m-1) ones(m-1,1)]; S_last=[ones(1,m-1) 0]; S=[S_top;S_last]; %semivariance matrix S B=[gamh(1:m-1,end);1]; %semivariance vector B lamv=S\B; %lamv are the weights zi=lamv(1:m-1)'*(z(hn(1:m-1))); %calculate estimated z z0=zi+mz0; var0=lamv'*B; %calculate the variance in estimate of z k=[x0,y0,z0,var0]; dlmwrite('elevation_09_02_2014.txt',k,'-

append','precision','%.6f') end end

38

Appendix C - MATLAB Code – Ordinary Kriging 09.11.2014

%ordkrig.m %uses ordinary kriging and a linear semivariance model to predict the %elevation z0 and its associated variance at a specified location x0,

y0.

data=xlsread('DO_MatLab_Data.xlsx','MatLabData09.11.2014','B3:D12'); X=data(:,1:3); %select x, y, and ground-surface elevation data [dtX,b]=detrendsurf(X); %detrend surface

svrange=input('enter the range for the linear semivariance model \n fit

to the control data: '); alpha=input('enter the slope of the linear semivariance model \n fit to

the control data: ');

for x0=530800:50:532200 for y0=1578450:50:1579800

mz0=b(1)+b(2)*x0+b(3)*y0;

x=[dtX(:,1);x0]; y=[dtX(:,2);y0]; z=dtX(:,3); n=length(x); xx=repmat(x,1,n); yy=repmat(y,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %calculating the distances between points h0=h(end,:); dx0=dx(end,:); dy0=dy(end,:); %distances from the

estimation point hn=find(h0<svrange); %select points within range to use in

estimate dxn=dx0(hn); dyn=dy0(hn); hh=sqrt(dxn.^2+dyn.^2); %distances of selected points from the

estimation point xi=x(hn); yi=y(hn); m=length(xi); xxi=repmat(xi,1,m); yyi=repmat(yi,1,m); dxxi=xxi-xxi'; dyyi=yyi-yyi'; hhi=sqrt(dxxi.^2+dyyi.^2); gamh=alpha*hhi; %calculate semivariance S_top=[gamh(1:m-1,1:m-1) ones(m-1,1)]; S_last=[ones(1,m-1) 0]; S=[S_top;S_last]; %semivariance matrix S B=[gamh(1:m-1,end);1]; %semivariance vector B lamv=S\B; %lamv are the weights zi=lamv(1:m-1)'*(z(hn(1:m-1))); %calculate estimated z z0=zi+mz0; var0=lamv'*B; %calculate the variance in estimate of z k=[x0,y0,z0,var0]; dlmwrite('elevation_09_11_2014.txt',k,'-

append','precision','%.6f') end end

39

Appendix C - MATLAB Code – Semi-variogram 08.27.2014

%semivar.m data=xlsread('DO_MatLab_Data.xlsx','MatLabData08.27.2014','B3:D13'); %select x, y, and ground-surface elevation data dtX=detrendsurf(data); %detrend surface x=dtX(:,1); y=dtX(:,2); z=dtX(:,3); %read x,y,z values n=length(x); nn=n*n; %calculate distance between each pair of points xx=repmat(x,1,n); yy=repmat(y,1,n); zz=repmat(z,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %eliminate duplicate values in the lower triangular part of the matrix % and sort values by distance hv=reshape(triu(h),nn,1); [nr,nc,nhv]=find(hv); %nhv are the nonzero values of hv [sh,ih]=sort(nhv); %calculate the squared differences in z values for each pair of points zvar=(zz-zz').^2; vz=reshape(zvar,nn,1); nvz=vz(nr); sv=nvz(ih); %only compare distances that are half or less of the largest distance hmax=max(sh)./2; nm=length(find(sh<=hmax)); nbin=200; hinc=hmax/nbin; %set the number of distance bins to 20 % adjust bin width to be round number % The values for the magnitude of hinc (10, 100, 1000) may need to be % adjusted depending on domain size and distance units if hinc<=10, hinc=floor(hinc); elseif hinc<=100, hinc=floor(hinc/10)*10; elseif hinc<=1000, hinc=floor(hinc/100)*100; end; hvec=0:hinc:nbin*hinc; %vector of bin endpoints for i=1:nbin-1; ib=[]; ib=find(sh>hvec(i)&sh<=hvec(i+1)); %find distances falling in

bins if ~isempty(ib), ni(i)=length(ib); gamh(i)=sum(sv(ib))./(2*ni(i)); %calculate semivariance for each

distance bin else gamh(i)=0; ni(i)=0; %set semivariance to 0 if no data fall in a

bin end; end; hvmn=(hvec(1:nbin-1)+hvec(2:nbin))./2; %find the mid-point of each bin plot(hvmn,gamh,'bo') %plot the results xlabel('Distance (ft)') ylabel('Semivariance') xlswrite('WellsMatlab.xls',gamh,'MatlabSemi2','A1') xlswrite('WellsMatlab.xls',hvmn,'MatlabSemi2','A2')

40

Appendix C - MATLAB Code – Semi-variogram 09.02.2014

%semivar.m data=xlsread('DO_MatLab_Data.xlsx','MatLabData09.02.2014','B3:D13'); %select x, y, and ground-surface elevation data dtX=detrendsurf(data); %detrend surface x=dtX(:,1); y=dtX(:,2); z=dtX(:,3); %read x,y,z values n=length(x); nn=n*n; %calculate distance between each pair of points xx=repmat(x,1,n); yy=repmat(y,1,n); zz=repmat(z,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %eliminate duplicate values in the lower triangular part of the matrix % and sort values by distance hv=reshape(triu(h),nn,1); [nr,nc,nhv]=find(hv); %nhv are the nonzero values of hv [sh,ih]=sort(nhv); %calculate the squared differences in z values for each pair of points zvar=(zz-zz').^2; vz=reshape(zvar,nn,1); nvz=vz(nr); sv=nvz(ih); %only compare distances that are half or less of the largest distance hmax=max(sh)./2; nm=length(find(sh<=hmax)); nbin=200; hinc=hmax/nbin; %set the number of distance bins to 20 % adjust bin width to be round number % The values for the magnitude of hinc (10, 100, 1000) may need to be % adjusted depending on domain size and distance units if hinc<=10, hinc=floor(hinc); elseif hinc<=100, hinc=floor(hinc/10)*10; elseif hinc<=1000, hinc=floor(hinc/100)*100; end; hvec=0:hinc:nbin*hinc; %vector of bin endpoints for i=1:nbin-1; ib=[]; ib=find(sh>hvec(i)&sh<=hvec(i+1)); %find distances falling in

bins if ~isempty(ib), ni(i)=length(ib); gamh(i)=sum(sv(ib))./(2*ni(i)); %calculate semivariance for each

distance bin else gamh(i)=0; ni(i)=0; %set semivariance to 0 if no data fall in a

bin end; end; hvmn=(hvec(1:nbin-1)+hvec(2:nbin))./2; %find the mid-point of each bin plot(hvmn,gamh,'bo') %plot the results xlabel('Distance (ft)') ylabel('Semivariance') xlswrite('WellsMatlab.xls',gamh,'MatlabSemi2','A1') xlswrite('WellsMatlab.xls',hvmn,'MatlabSemi2','A2')

41

Appendix C - MATLAB Code – Semi-variogram 09.11.2014

%semivar.m data=xlsread('DO_MatLab_Data.xlsx','MatLabData09.11.2014','B3:D13'); %select x, y, and ground-surface elevation data dtX=detrendsurf(data); %detrend surface x=dtX(:,1); y=dtX(:,2); z=dtX(:,3); %read x,y,z values n=length(x); nn=n*n; %calculate distance between each pair of points xx=repmat(x,1,n); yy=repmat(y,1,n); zz=repmat(z,1,n); dx=xx-xx'; dy=yy-yy'; h=sqrt(dx.^2+dy.^2); %eliminate duplicate values in the lower triangular part of the matrix % and sort values by distance hv=reshape(triu(h),nn,1); [nr,nc,nhv]=find(hv); %nhv are the nonzero values of hv [sh,ih]=sort(nhv); %calculate the squared differences in z values for each pair of points zvar=(zz-zz').^2; vz=reshape(zvar,nn,1); nvz=vz(nr); sv=nvz(ih); %only compare distances that are half or less of the largest distance hmax=max(sh)./2; nm=length(find(sh<=hmax)); nbin=200; hinc=hmax/nbin; %set the number of distance bins to 20 % adjust bin width to be round number % The values for the magnitude of hinc (10, 100, 1000) may need to be % adjusted depending on domain size and distance units if hinc<=10, hinc=floor(hinc); elseif hinc<=100, hinc=floor(hinc/10)*10; elseif hinc<=1000, hinc=floor(hinc/100)*100; end; hvec=0:hinc:nbin*hinc; %vector of bin endpoints for i=1:nbin-1; ib=[]; ib=find(sh>hvec(i)&sh<=hvec(i+1)); %find distances falling in

bins if ~isempty(ib), ni(i)=length(ib); gamh(i)=sum(sv(ib))./(2*ni(i)); %calculate semivariance for each

distance bin else gamh(i)=0; ni(i)=0; %set semivariance to 0 if no data fall in a

bin end; end; hvmn=(hvec(1:nbin-1)+hvec(2:nbin))./2; %find the mid-point of each bin plot(hvmn,gamh,'bo') %plot the results xlabel('Distance (ft)') ylabel('Semivariance') xlswrite('WellsMatlab.xls',gamh,'MatlabSemi2','A1') xlswrite('WellsMatlab.xls',hvmn,'MatlabSemi2','A2')

42

Appendix C - MATLAB Code – function: detrendsurf

function [dtX,b] = detrendsurf(X) % remove the 1st order trend surface from a data matrix X % with N rows and 3 columns: values of x, y, and z [nr,nc] = size(X); % determine no of data (rows) xew=X(:,1); xns=X(:,2); z=X(:,3); A=[nr sum(xew) sum(xns);... sum(xew) sum(xew.^2) sum(xew.*xns);... sum(xns) sum(xew.*xns) sum(xns.^2)]; rhs=[sum(z);sum(xew.*z);sum(xns.*z)]; b=A\rhs; % solution of matrix equation zhat=b(1)+b(2).*xew+b(3).*xns; zres=z-zhat; %residuals dtX=[xew xns zres];

43

Appendix C - MATLAB Code – Plot DO Concentration (Scaled to 6) 08.27.2014

num=xlsread(' DO_Concentration_Data_Scaledto6_08_27_2014.xlsx') x=num(:,1); y=num(:,2); z=num(:,3); tri = delaunay(x,y); trisurf(tri,x,y,z);

44

Appendix C - MATLAB Code – Plot DO Concentration (Scaled to 6) 09.02.2014

num=xlsread(' DO_Concentration_Data_Scaledto6_09_02_2014.xlsx') x=num(:,1); y=num(:,2); z=num(:,3); tri = delaunay(x,y); trisurf(tri,x,y,z);

45

Appendix C - MATLAB Code – Plot DO Concentration (Scaled to 6) 09.11.2014

num=xlsread(' DO_Concentration_Data_Scaledto6_09_11_2014.xlsx') x=num(:,1); y=num(:,2); z=num(:,3); tri = delaunay(x,y); trisurf(tri,x,y,z);

46

APPENDIX D

EXCEL MATLAB DATA

47

Appendix D – Excel MATLAB Data

MatLabData08.27.2014

48

Appendix D – Excel MATLAB Data

MatLabData09.02.2014

49

Appendix D – Excel MATLAB Data

MatLabData09.11.2014

50

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

530800 1578450 6 700.2728

530800 1578500 0.584658 625.6978

530800 1578550 0.65666 556.8845

530800 1578600 0.735512 495.2222

530800 1578650 0.824191 441.7928

530800 1578700 0.925451 396.6221

530800 1578750 1.040866 357.8864

530800 1578800 1.111849 321.5221

530800 1578850 1.260555 283.0701

530800 1578900 1.423131 237.7072

530800 1578950 1.598201 183.2233

530800 1579000 1.809449 124.9177

530800 1579050 1.944886 98.8082

530800 1579100 2.00765 142.4134

530800 1579150 2.000049 207.3776

530800 1579200 2.06316 270.3473

530800 1579250 2.16345 328.7527

530800 1579300 2.298448 383.5274

530800 1579350 2.460249 435.6183

530800 1579400 2.671907 486.6344

530800 1579450 2.858883 534.957

530800 1579500 3.041788 581.8272

530800 1579550 3.212965 627.7528

530800 1579600 3.366924 673.3654

530800 1579650 3.500382 719.3412

530800 1579700 3.824689 782.9832

530800 1579750 3.919803 832.0083

530800 1579800 3.995268 882.9367

530850 1578450 0.41857 647.4001

530850 1578500 0.479384 568.4699

530850 1578550 0.54334 495.3719

530850 1578600 0.613594 430.3082

530850 1578650 0.694406 375.6638

530850 1578700 0.790258 332.8232

530850 1578750 0.833401 299.9917

530850 1578800 0.963507 272.6443

530850 1578850 1.133019 242.8796

530850 1578900 1.304054 204.0779

530850 1578950 1.46854 150.7533

530850 1579000 1.649883 79.33117

530850 1579050 1.823819 12.21833

51

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

530850 1579100 1.785594 99.76596

530850 1579150 1.793072 171.3904

530850 1579200 1.851942 232.4918

530850 1579250 1.963822 287.6945

530850 1579300 2.122054 339.8512

530850 1579350 2.313729 390.0514

530850 1579400 2.523637 438.4085

530850 1579450 2.737409 484.8737

530850 1579500 2.943067 529.6672

530850 1579550 3.131508 573.3545

530850 1579600 3.338392 618.418

530850 1579650 3.479016 662.5385

530850 1579700 3.558251 708.6741

530850 1579750 3.884415 771.3712

530850 1579800 3.952945 821.5878

530900 1578450 0.323683 600.4436

530900 1578500 0.376735 516.4056

530900 1578550 0.430753 437.5686

530900 1578600 0.48918 366.9023

530900 1578650 0.557851 308.741

530900 1578700 0.644358 267.4949

530900 1578750 0.702465 243.0511

530900 1578800 0.828703 229.1548

530900 1578850 0.977736 214.5451

530900 1578900 1.115489 190.5434

530900 1578950 1.255775 153.3379

530900 1579000 1.39208 107.5157

530900 1579050 1.491272 83.88791

530900 1579100 1.504219 114.4333

530900 1579150 1.520689 161.0182

530900 1579200 1.591004 207.1459

530900 1579250 1.727899 253.3655

530900 1579300 1.923268 300.729

530900 1579350 2.157636 348.379

530900 1579400 2.40961 394.8493

530900 1579450 2.649119 439.16

530900 1579500 2.884983 481.4982

530900 1579550 3.096224 522.4971

530900 1579600 3.287626 563.3296

530900 1579650 3.432117 604.9383

530900 1579700 3.58533 650.1986

52

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

530900 1579750 3.63131 697.0296

530900 1579800 3.916025 760.4306

530950 1578450 0.23535 561.3569

530950 1578500 0.280784 471.9564

530950 1578550 0.323922 386.1611

530950 1578600 0.367504 306.981

530950 1578650 0.416767 240.5691

530950 1578700 0.443301 197.2243

530950 1578750 0.547669 183.7019

530950 1578800 0.680256 188.7748

530950 1578850 0.804133 193.9201

530950 1578900 0.922511 188.6019

530950 1578950 1.02782 171.3108

530950 1579000 1.124045 148.6792

530950 1579050 1.166487 135.0848

530950 1579100 1.175493 139.7084

530950 1579150 1.196795 156.9504

530950 1579200 1.280016 183.0076

530950 1579250 1.453141 218.9043

530950 1579300 1.700648 262.5665

530950 1579350 1.990568 308.9936

530950 1579400 2.293586 354.1678

530950 1579450 2.588183 396.3533

530950 1579500 2.858932 435.6319

530950 1579550 3.094851 473.1631

530950 1579600 3.288989 510.5517

530950 1579650 3.438648 549.3562

530950 1579700 3.545403 590.7718

530950 1579750 3.594881 637.4689

530950 1579800 3.809992 697.5659

531000 1578450 0.156921 532.4279

531000 1578500 0.196612 438.5571

531000 1578550 0.230336 346.1184

531000 1578600 0.258973 256.6679

531000 1578650 0.284933 175.291

531000 1578700 0.298652 119.7413

531000 1578750 0.406801 119.9956

531000 1578800 0.531081 152.0993

531000 1578850 0.650143 178.8013

531000 1578900 0.749309 190.3372

531000 1578950 0.83957 187.6793

53

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531000 1579000 0.877402 175.9617

531000 1579050 0.876653 161.5953

531000 1579100 0.849889 148.73

531000 1579150 0.844554 140.6169

531000 1579200 0.93285 147.2362

531000 1579250 1.158997 177.5589

531000 1579300 1.483078 223.385

531000 1579350 1.845372 271.966

531000 1579400 2.208983 316.8598

531000 1579450 2.552396 356.4808

531000 1579500 2.860567 391.7319

531000 1579550 3.121956 424.7049

531000 1579600 3.328824 457.848

531000 1579650 3.478588 493.3155

531000 1579700 3.574547 532.5503

531000 1579750 3.592163 576.8363

531000 1579800 3.608911 624.9292

531050 1578450 0.09122 515.612

531050 1578500 0.12912 419.6512

531050 1578550 0.161901 323.6194

531050 1578600 0.1799 227.8887

531050 1578650 0.185376 131.9764

531050 1578700 0.173317 39.81386

531050 1578750 0.280721 65.504

531050 1578800 0.422537 129.6372

531050 1578850 0.537081 171.2994

531050 1578900 0.618361 192.9397

531050 1578950 0.68421 198.2141

531050 1579000 0.684617 190.5494

531050 1579050 0.644782 172.1087

531050 1579100 0.570395 143.3381

531050 1579150 0.497054 107.3157

531050 1579200 0.550555 90.49734

531050 1579250 0.865777 128.7318

531050 1579300 1.294716 186.8407

531050 1579350 1.735709 240.33

531050 1579400 2.159565 284.5798

531050 1579450 2.551164 320.1909

531050 1579500 2.896922 349.6679

531050 1579550 3.183582 376.4617

531050 1579600 3.400802 404.2029

54

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531050 1579650 3.545036 435.8365

531050 1579700 3.621786 473.0009

531050 1579750 3.613369 516.5907

531050 1579800 3.598476 565.0165

531100 1578450 0.039447 511.6231

531100 1578500 0.087341 415.8729

531100 1578550 0.119568 322.3683

531100 1578600 0.142972 230.407

531100 1578650 0.103303 142.4263

531100 1578700 0.16044 74.75578

531100 1578750 0.256041 85.84978

531100 1578800 0.380519 135.112

531100 1578850 0.481085 173.2008

531100 1578900 0.56632 195.633

531100 1578950 0.588441 203.6179

531100 1579000 0.569323 197.9

531100 1579050 0.50541 177.5743

531100 1579100 0.399571 139.828

531100 1579150 0.257185 80.29945

531100 1579200 0.14143 9.310357

531100 1579250 0.672661 95.41307

531100 1579300 1.191165 165.7828

531100 1579350 1.687314 219.8522

531100 1579400 2.156599 259.7326

531100 1579450 2.589636 288.2386

531100 1579500 2.971629 309.2135

531100 1579550 3.283869 327.5373

531100 1579600 3.509113 348.4022

531100 1579650 3.640173 375.8366

531100 1579700 3.685653 411.4912

531100 1579750 3.639017 455.3089

531100 1579800 3.581319 504.9516

531150 1578450 0.01122 517.0774

531150 1578500 0.058912 426.4942

531150 1578550 0.102448 338.9637

531150 1578600 0.142856 257.1002

531150 1578650 0.137889 186.7332

531150 1578700 0.205752 142.3455

531150 1578750 0.297711 136.5025

531150 1578800 0.40012 155.8025

531150 1578850 0.484696 178.4175

55

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531150 1578900 0.552235 195.3964

531150 1578950 0.564331 203.9391

531150 1579000 0.535474 201.6933

531150 1579050 0.471434 185.8485

531150 1579100 0.384068 154.2079

531150 1579150 0.305306 109.6602

531150 1579200 0.360814 82.91995

531150 1579250 0.721415 118.7625

531150 1579300 1.202009 171.122

531150 1579350 1.699585 213.8984

531150 1579400 2.188781 243.3585

531150 1579450 2.662758 260.861

531150 1579500 3.08102 269.9326

531150 1579550 3.423134 276.7678

531150 1579600 3.656585 288.8734

531150 1579650 3.764387 312.1121

531150 1579700 3.760724 347.6856

531150 1579750 3.658603 393.3716

531150 1579800 3.544865 445.3859

531200 1578450 0 532.9752

531200 1578500 0.043335 446.9542

531200 1578550 0.101676 365.7506

531200 1578600 0.179954 291.7669

531200 1578650 0.168254 231.4337

531200 1578700 0.258895 190.5427

531200 1578750 0.360101 171.6356

531200 1578800 0.460188 169.3524

531200 1578850 0.552541 175.834

531200 1578900 0.597357 186.4085

531200 1578950 0.591624 196.8182

531200 1579000 0.560321 201.7411

531200 1579050 0.510898 196.5024

531200 1579100 0.466529 180.2113

531200 1579150 0.470188 159.2282

531200 1579200 0.594467 150.3055

531200 1579250 0.885017 165.7362

531200 1579300 1.295116 193.9691

531200 1579350 1.762272 219.289

531200 1579400 2.251453 234.3729

531200 1579450 2.739829 237.6682

531200 1579500 3.200098 231.5778

56

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531200 1579550 3.587538 223.0349

531200 1579600 3.838656 223.6615

531200 1579650 3.914788 243.5928

531200 1579700 3.833381 282.1944

531200 1579750 3.652115 332.24

531200 1579800 3.46904 387.6997

531250 1578450 0 555.2606

531250 1578500 0.034146 473.1539

531250 1578550 0.107367 396.3742

531250 1578600 0.206643 326.3245

531250 1578650 0.200872 267.5027

531250 1578700 0.312392 221.5041

531250 1578750 0.4308 188.1073

531250 1578800 0.54511 165.9753

531250 1578850 0.642072 156.2879

531250 1578900 0.679453 162.3255

531250 1578950 0.671487 179.5933

531250 1579000 0.637686 196.5803

531250 1579050 0.601775 205.3867

531250 1579100 0.589898 204.9008

531250 1579150 0.635917 199.9814

531250 1579200 0.777742 198.9394

531250 1579250 1.03488 206.6473

531250 1579300 1.392911 219.3816

531250 1579350 1.822122 229.3186

531250 1579400 2.297689 230.2903

531250 1579450 2.800708 219.1695

531250 1579500 3.307967 196.1092

531250 1579550 3.769468 167.127

531250 1579600 4.068423 150.7783

531250 1579650 4.076297 170.3254

531250 1579700 3.853722 218.3576

531250 1579750 3.583269 275.2596

531250 1579800 3.324363 334.0382

531300 1578450 0 581.8843

531300 1578500 0.025314 502.4959

531300 1578550 0.11143 427.9321

531300 1578600 0.228243 358.25

531300 1578650 0.225623 296.4348

531300 1578700 0.35632 241.6058

531300 1578750 0.511499 191.4627

57

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531300 1578800 0.645788 146.4918

531300 1578850 0.762264 114.6117

531300 1578900 0.798843 119.0542

531300 1578950 0.771446 152.9491

531300 1579000 0.727985 187.2376

531300 1579050 0.697896 210.7644

531300 1579100 0.702667 223.2717

531300 1579150 0.764765 228.7417

531300 1579200 0.905192 232.1601

531300 1579250 1.134818 236.3022

531300 1579300 1.449735 239.9255

531300 1579350 1.837151 239.0469

531300 1579400 2.284212 229.2875

531300 1579450 2.78157 207.0454

531300 1579500 3.321029 169.7766

531300 1579550 3.882504 117.5137

531300 1579600 4.333631 68.7624

531300 1579650 4.201403 99.08588

531300 1579700 3.782264 165.4853

531300 1579750 3.392398 227.8071

531300 1579800 3.073376 286.5082

531350 1578450 0 611.6771

531350 1578500 0.012088 533.9792

531350 1578550 0.136777 458.4813

531350 1578600 0.238 389.2043

531350 1578650 0.233822 323.2368

531350 1578700 0.407448 258.3839

531350 1578750 0.557313 195.0827

531350 1578800 0.720465 126.8437

531350 1578850 0.890114 54.95453

531350 1578900 0.922417 63.08532

531350 1578950 0.854803 129.5498

531350 1579000 0.799807 180.8076

531350 1579050 0.771879 215.2382

531350 1579100 0.78299 236.0634

531350 1579150 0.846522 247.4157

531350 1579200 0.974563 253.0519

531350 1579250 1.173922 255.0699

531350 1579300 1.444398 253.2831

531350 1579350 1.781051 245.7509

531350 1579400 2.178164 229.8199

58

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531350 1579450 2.631717 202.7303

531350 1579500 3.139357 161.6085

531350 1579550 3.697165 103.6055

531350 1579600 4.246587 33.98494

531350 1579650 3.97237 73.67229

531350 1579700 3.456788 139.2753

531350 1579750 3.021838 193.6824

531350 1579800 2.688102 244.9218

531400 1578450 0 644.1629

531400 1578500 0 567.5891

531400 1578550 0.124524 492.1041

531400 1578600 0.231882 421.8228

531400 1578650 0.266297 348.978

531400 1578700 0.40613 282.6957

531400 1578750 0.558159 214.0911

531400 1578800 0.739642 141.033

531400 1578850 0.889472 70.31613

531400 1578900 0.927787 74.49794

531400 1578950 0.874594 135.6041

531400 1579000 0.82878 186.7062

531400 1579050 0.807966 222.5874

531400 1579100 0.821847 245.3943

531400 1579150 0.879414 258.2683

531400 1579200 0.988153 263.963

531400 1579250 1.1524 264.0678

531400 1579300 1.372719 258.796

531400 1579350 1.64685 247.4254

531400 1579400 1.971055 229.0261

531400 1579450 2.339517 202.9252

531400 1579500 2.738301 168.9831

531400 1579550 3.123853 129.7781

531400 1579600 3.354463 100.574

531400 1579650 3.217155 108.0033

531400 1579700 2.840596 136.8103

531400 1579750 2.455744 167.6635

531400 1579800 2.161058 204.4312

531450 1578450 0 679.2183

531450 1578500 0 603.6192

531450 1578550 0.099541 528.3273

531450 1578600 0.263627 450.4009

531450 1578650 0.238799 382.6008

59

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531450 1578700 0.373966 317.0184

531450 1578750 0.514163 252.1233

531450 1578800 0.682619 191.0179

531450 1578850 0.789456 146.9421

531450 1578900 0.836893 142.4755

531450 1578950 0.830833 171.1533

531450 1579000 0.811466 205.6667

531450 1579050 0.80354 233.4082

531450 1579100 0.819688 252.0152

531450 1579150 0.867896 262.4547

531450 1579200 0.953374 266.2044

531450 1579250 1.078608 264.0955

531450 1579300 1.243429 256.13

531450 1579350 1.445594 242.0883

531450 1579400 1.681088 222.5025

531450 1579450 1.94183 199.2413

531450 1579500 2.206893 175.0856

531450 1579550 2.425288 153.2912

531450 1579600 2.506192 138.6715

531450 1579650 2.375309 134.1723

531450 1579700 2.078934 134.1526

531450 1579750 1.748601 136.6782

531450 1579800 1.517582 156.5045

531500 1578450 0 716.7924

531500 1578500 0 642.2362

531500 1578550 0.134272 554.4726

531500 1578600 0.237807 486.5273

531500 1578650 0.19257 421.0999

531500 1578700 0.318451 358.8938

531500 1578750 0.442828 300.5174

531500 1578800 0.601509 250.097

531500 1578850 0.688613 215.8026

531500 1578900 0.739998 204.668

531500 1578950 0.75857 213.0996

531500 1579000 0.761641 229.3333

531500 1579050 0.766245 244.35

531500 1579100 0.783812 254.5862

531500 1579150 0.821239 259.6661

531500 1579200 0.882088 260.0454

531500 1579250 0.967081 255.5877

531500 1579300 1.074484 245.3365

60

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531500 1579350 1.200843 228.508

531500 1579400 1.341974 206.2647

531500 1579450 1.492644 183.2085

531500 1579500 1.639526 165.9022

531500 1579550 1.745341 156.8026

531500 1579600 1.749055 151.4468

531500 1579650 1.604369 142.3192

531500 1579700 1.321976 121.944

531500 1579750 0.982528 91.77877

531500 1579800 0.801789 93.57294

531550 1578450 0 756.77

531550 1578500 0.001714 659.9271

531550 1578550 0.097001 592.0916

531550 1578600 0.197783 525.607

531550 1578650 0.132929 462.459

531550 1578700 0.248856 403.8869

531550 1578750 0.359631 350.6394

531550 1578800 0.51488 305.7053

531550 1578850 0.592305 273.2987

531550 1578900 0.645733 255.4077

531550 1578950 0.676829 249.5578

531550 1579000 0.693998 249.8595

531550 1579050 0.707329 251.0358

531550 1579100 0.724834 250.6215

531550 1579150 0.751402 248.4733

531550 1579200 0.788831 244.9108

531550 1579250 0.836016 238.8602

531550 1579300 0.889222 227.4788

531550 1579350 0.94283 207.7855

531550 1579400 0.991692 179.6298

531550 1579450 1.037142 149.9676

531550 1579500 1.090039 134.6393

531550 1579550 1.136012 139.5853

531550 1579600 1.113272 147.8317

531550 1579650 0.975181 142.77

531550 1579700 0.709166 114.6513

531550 1579750 0.331923 56.25242

531550 1579800 0.14811 35.68667

531600 1578450 0 776.3342

531600 1578500 0 697.2193

531600 1578550 0.051763 630.8052

61

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531600 1578600 0.149166 565.7136

531600 1578650 0.064875 504.5342

531600 1578700 0.172185 448.5789

531600 1578750 0.272934 398.0791

531600 1578800 0.428469 354.6863

531600 1578850 0.500622 320.4408

531600 1578900 0.555193 295.4007

531600 1578950 0.593047 277.6028

531600 1579000 0.618463 263.6364

531600 1579050 0.637042 250.6222

531600 1579100 0.653541 237.8415

531600 1579150 0.670632 226.8745

531600 1579200 0.688402 219.5064

531600 1579250 0.704184 214.1728

531600 1579300 0.712474 204.9521

531600 1579350 0.705065 184.7709

531600 1579400 0.672493 149.1933

531600 1579450 0.613364 101.7138

531600 1579500 0.583678 78.09995

531600 1579550 0.629987 109.3566

531600 1579600 0.628003 139.4143

531600 1579650 0.527797 147.2619

531600 1579700 0.33468 130.88

531600 1579750 0.110916 99.41452

531600 1579800 0.03348 99.54841

531650 1578450 0 814.3003

531650 1578500 0 735.1254

531650 1578550 0.00468 667.9995

531650 1578600 0.094582 605.7844

531650 1578650 0.182091 546.9221

531650 1578700 0.092746 491.174

531650 1578750 0.1866 441.3775

531650 1578800 0.344139 397.0685

531650 1578850 0.413341 359.1382

531650 1578900 0.468765 326.7494

531650 1578950 0.510698 298.1009

531650 1579000 0.541224 270.755

531650 1579050 0.563193 242.9689

531650 1579100 0.57897 215.4474

531650 1579150 0.58943 192.8335

531650 1579200 0.593384 181.717

62

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531650 1579250 0.587376 181.5307

531650 1579300 0.565513 180.7665

531650 1579350 0.519119 166.7729

531650 1579400 0.436627 131.7401

531650 1579450 0.304298 71.47305

531650 1579500 0.179616 19.79311

531650 1579550 0.284808 93.70925

531650 1579600 0.308408 139.2995

531650 1579650 0.258288 159.9894

531650 1579700 0.147221 161.8886

531650 1579750 0.038161 158.0992

531650 1579800 0.013814 171.6923

531700 1578450 0 854.0211

531700 1578500 0 774.3454

531700 1578550 0 706.6318

531700 1578600 0.035996 645.1472

531700 1578650 0.119732 586.8483

531700 1578700 0.012928 531.1044

531700 1578750 0.102271 480.5962

531700 1578800 0.262503 434.0222

531700 1578850 0.330248 391.6695

531700 1578900 0.386675 352.2202

531700 1578950 0.431668 313.8389

531700 1579000 0.466062 274.2415

531700 1579050 0.491073 231.5823

531700 1579100 0.507549 186.2973

531700 1579150 0.515237 145.5724

531700 1579200 0.512228 128.5053

531700 1579250 0.495172 141.5393

531700 1579300 0.45977 157.7868

531700 1579350 0.400605 157.4397

531700 1579400 0.313322 136.0676

531700 1579450 0.205532 101.1947

531700 1579500 0.133416 85.86382

531700 1579550 0.141136 115.0704

531700 1579600 0.147981 151.5556

531700 1579650 0.114125 177.7311

531700 1579700 0.055031 193.8807

531700 1579750 0.006421 207.8907

531700 1579800 0.007654 231.5667

531750 1578450 0 892.8112

63

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531750 1578500 0 812.4678

531750 1578550 0 745.9776

531750 1578600 0.050459 684.6874

531750 1578650 0.055744 625.0966

531750 1578700 0.131866 569.7184

531750 1578750 0.020523 516.4815

531750 1578800 0.099742 467.3802

531750 1578850 0.251071 420.3375

531750 1578900 0.308941 374.7306

531750 1578950 0.356894 328.4251

531750 1579000 0.395054 279.0873

531750 1579050 0.423708 224.1894

531750 1579100 0.442879 161.661

531750 1579150 0.451897 93.03087

531750 1579200 0.44858 56.68393

531750 1579250 0.429653 102.5198

531750 1579300 0.394201 141.091

531750 1579350 0.34098 154.3294

531750 1579400 0.272033 146.0639

531750 1579450 0.196209 128.4292

531750 1579500 0.143829 120.9678

531750 1579550 0.115649 135.4362

531750 1579600 0.092851 163.6938

531750 1579650 0.060077 193.7904

531750 1579700 0.023141 221.1542

531750 1579750 0.000774 248.2155

531750 1579800 0.041 281.1629

531800 1578450 0 970.2186

531800 1578500 0 864.7455

531800 1578550 0 783.6919

531800 1578600 0 722.1347

531800 1578650 0.066024 663.0214

531800 1578700 0.138252 606.3795

531800 1578750 0 549.9957

531800 1578800 0.020287 498.0366

531800 1578850 0.175398 447.1053

531800 1578900 0.235291 396.7

531800 1578950 0.286569 344.9373

531800 1579000 0.328972 289.8969

531800 1579050 0.362275 229.4427

531800 1579100 0.38605 161.5155

64

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531800 1579150 0.399463 85.78098

531800 1579200 0.400617 40.169

531800 1579250 0.386799 96.78578

531800 1579300 0.359566 139.068

531800 1579350 0.320444 154.8328

531800 1579400 0.27336 148.4733

531800 1579450 0.226245 130.2602

531800 1579500 0.183452 118.3609

531800 1579550 0.141792 130.5562

531800 1579600 0.09928 163.7195

531800 1579650 0.058851 203.5411

531800 1579700 0.026752 242.7317

531800 1579750 0.012653 281.2716

531800 1579800 0.058906 322.8506

531850 1578450 0 1010.177

531850 1578500 0 940.6139

531850 1578550 0 821.01

531850 1578600 0 758.8478

531850 1578650 0.000841 698.7439

531850 1578700 0.072156 640.6254

531850 1578750 0.137465 584.2925

531850 1578800 0 528.1293

531850 1578850 0.145011 473.8759

531850 1578900 0.203036 419.9474

531850 1578950 0.253019 364.9843

531850 1579000 0.294539 308.0511

531850 1579050 0.327157 248.756

531850 1579100 0.350342 188.6248

531850 1579150 0.363631 135.6636

531850 1579200 0.367203 112.7752

531850 1579250 0.36123 129.081

531850 1579300 0.345221 151.3299

531850 1579350 0.319798 158.0086

531850 1579400 0.29285 143.7614

531850 1579450 0.269203 110.77

531850 1579500 0.244663 78.27623

531850 1579550 0.193787 98.55394

531850 1579600 0.132502 153.4353

531850 1579650 0.081891 209.6561

531850 1579700 0.047494 261.6053

531850 1579750 0.064976 310.9483

65

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531850 1579800 0.080944 360.0997

531900 1578450 0 1050.104

531900 1578500 0 980.3138

531900 1578550 0 873.4949

531900 1578600 0 795.1771

531900 1578650 0 733.8835

531900 1578700 0.005932 674.1678

531900 1578750 0.071646 615.811

531900 1578800 0 557.1299

531900 1578850 0.07378 500.2646

531900 1578900 0.134822 443.8834

531900 1578950 0.189121 387.2405

531900 1579000 0.236289 330.3995

531900 1579050 0.275891 274.4741

531900 1579100 0.307382 222.772

531900 1579150 0.330131 182.2391

531900 1579200 0.343527 161.5875

531900 1579250 0.347071 160.5998

531900 1579300 0.340725 165.8111

531900 1579350 0.327247 162.7138

531900 1579400 0.312707 141.7711

531900 1579450 0.304081 96.57789

531900 1579500 0.307805 21.84885

531900 1579550 0.239777 72.65619

531900 1579600 0.164596 152.8851

531900 1579650 0.10835 222.0846

531900 1579700 0.071709 283.5375

531900 1579750 0.090771 340.822

531900 1579800 0.103007 396.2239

531950 1578450 0 1090.151

531950 1578500 0 1019.97

531950 1578550 0 910.9317

531950 1578600 0 845.4466

531950 1578650 0 781.2787

531950 1578700 0 707.6221

531950 1578750 0.005871 647.3177

531950 1578800 0.067314 587.7882

531950 1578850 0 527.5313

531950 1578900 0.06833 468.716

531950 1578950 0.127362 410.6053

531950 1579000 0.180721 353.4699

66

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

531950 1579050 0.228073 298.921

531950 1579100 0.268871 250.0457

531950 1579150 0.302171 211.3174

531950 1579200 0.329498 186.661

531950 1579250 0.341237 175.5582

531950 1579300 0.342039 171.8649

531950 1579350 0.333952 166.2196

531950 1579400 0.321638 149.9176

531950 1579450 0.308935 119.606

531950 1579500 0.28919 92.31176

531950 1579550 0.239965 118.5053

531950 1579600 0.177909 182.1614

531950 1579650 0.126002 249.4554

531950 1579700 0.123121 313.775

531950 1579750 0 375.7898

531950 1579800 0 435.3112

532000 1578450 0 1130.529

532000 1578500 0 1059.854

532000 1578550 0 962.8779

532000 1578600 0 882.3881

532000 1578650 0 816.9605

532000 1578700 0 752.4548

532000 1578750 0 688.7157

532000 1578800 0.002878 617.8239

532000 1578850 0.061531 556.6091

532000 1578900 0.002243 494.3332

532000 1578950 0.06605 434.2042

532000 1579000 0.125666 375.2388

532000 1579050 0.180959 318.7712

532000 1579100 0.241153 267.3004

532000 1579150 0.282572 222.9118

532000 1579200 0.316401 189.3802

532000 1579250 0.338526 169.2949

532000 1579300 0.345129 162.5224

532000 1579350 0.337107 162.9882

532000 1579400 0.319882 161.7215

532000 1579450 0.297211 156.2088

532000 1579500 0.266616 157.4325

532000 1579550 0.22392 182.6557

532000 1579600 0.175281 231.3503

532000 1579650 0.050369 291.6224

67

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

532000 1579700 0.005515 353.5379

532000 1579750 0 415.1575

532000 1579800 0 476.3561

532050 1578450 0 1193.106

532050 1578500 0 1117.399

532050 1578550 0 1016.505

532050 1578600 0 920.2379

532050 1578650 0 853.6089

532050 1578700 0 787.6569

532050 1578750 0 722.2305

532050 1578800 0 657.177

532050 1578850 0 592.3769

532050 1578900 0.094097 526.1128

532050 1578950 0.028534 460.6268

532050 1579000 0.088432 397.5502

532050 1579050 0.146314 335.9587

532050 1579100 0.202117 276.7885

532050 1579150 0.255128 221.5067

532050 1579200 0.302766 173.1622

532050 1579250 0.3379 139.5567

532050 1579300 0.34915 132.5135

532050 1579350 0.335907 148.2615

532050 1579400 0.310008 168.9654

532050 1579450 0.278827 187.3605

532050 1579500 0.243442 208.3559

532050 1579550 0.168738 240.1907

532050 1579600 0.118993 285.05

532050 1579650 0.037288 340.2311

532050 1579700 0 399.0188

532050 1579750 0 459.602

532050 1579800 0 521.0268

532100 1578450 0 1233.039

532100 1578500 0 1157.254

532100 1578550 0 1055.824

532100 1578600 0 983.343

532100 1578650 0 900.7221

532100 1578700 0 831.0597

532100 1578750 0 762.2094

532100 1578800 0 693.9603

532100 1578850 0 626.1018

532100 1578900 0 558.4501

68

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

532100 1578950 0.052783 489.1809

532100 1579000 0 421.0681

532100 1579050 0 353.8795

532100 1579100 0.080428 286.2788

532100 1579150 0.219002 217.7319

532100 1579200 0.281298 149.0715

532100 1579250 0.336399 87.99208

532100 1579300 0.346028 79.84829

532100 1579350 0.314219 126.63

532100 1579400 0.270874 174.1929

532100 1579450 0.226267 214.1865

532100 1579500 0.181193 251.264

532100 1579550 0.13598 291.4328

532100 1579600 0.057045 338.9869

532100 1579650 0.015176 391.7137

532100 1579700 0 448.6379

532100 1579750 0 508.0845

532100 1579800 0 569.1291

532150 1578450 0 1274.39

532150 1578500 0 1198.531

532150 1578550 0 1096.894

532150 1578600 0 1023.987

532150 1578650 0 940.4796

532150 1578700 0 869.983

532150 1578750 0 800.0804

532150 1578800 0 730.5554

532150 1578850 0 661.1742

532150 1578900 0 591.6881

532150 1578950 0 521.822

532150 1579000 0.044727 450.0031

532150 1579050 0 377.8383

532150 1579100 0.018787 304.5212

532150 1579150 0.107244 227.8625

532150 1579200 0.198778 146.0872

532150 1579250 0.293078 57.65526

532150 1579300 0.324735 44.51689

532150 1579350 0.274172 125.0766

532150 1579400 0.225816 190.7665

532150 1579450 0.180123 245.0708

532150 1579500 0.107288 294.1663

532150 1579550 0.062009 341.7548

69

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_08_27_2014 Northing Easting DO Conc. Variance

532150 1579600 0.020553 391.5884

532150 1579650 0 444.7831

532150 1579700 0 501.0797

532150 1579750 0 559.7826

532150 1579800 0 620.3259

532200 1578450 0 1317.381

532200 1578500 0 1241.506

532200 1578550 0 1166.473

532200 1578600 0 1066.874

532200 1578650 0 994.436

532200 1578700 0 911.7409

532200 1578750 0 841.1702

532200 1578800 0 770.8539

532200 1578850 0 700.5664

532200 1578900 0 630.0606

532200 1578950 0 559.0593

532200 1579000 0 487.242

532200 1579050 0.035003 413.4964

532200 1579100 0.090477 339.2728

532200 1579150 0.027442 263.3029

532200 1579200 0.111384 188.8893

532200 1579250 0.182592 128.561

532200 1579300 0.210506 121.7876

532200 1579350 0.190987 170.5228

532200 1579400 0.155826 230.7915

532200 1579450 0.094161 288.3576

532200 1579500 0.051632 341.7734

532200 1579550 0.01214 393.542

532200 1579600 0 445.839

532200 1579650 0 499.9089

532200 1579700 0 556.1208

532200 1579750 0 614.365

532200 1579800 0 674.4114

70

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

530800 1578450 6 158.5898

530800 1578500 0.95398 107.8811

530800 1578550 0.991507 125.6566

530800 1578600 1.064331 173.3408

530800 1578650 1.167337 214.599

530800 1578700 1.303267 242.591

530800 1578750 1.4745 257.1312

530800 1578800 1.684571 258.2257

530800 1578850 1.919191 245.1042

530800 1578900 2.174802 216.5583

530800 1578950 2.439817 172.8043

530800 1579000 2.686455 120.7051

530800 1579050 2.7866 97.25586

530800 1579100 2.625717 141.4347

530800 1579150 2.380011 206.4319

530800 1579200 2.133688 269.312

530800 1579250 1.912198 327.5878

530800 1579300 1.720249 382.2247

530800 1579350 1.556618 434.1844

530800 1579400 1.40784 485.1356

530800 1579450 1.288588 533.3594

530800 1579500 1.18615 580.1427

530800 1579550 1.119046 627.7528

530800 1579600 1.041856 673.3654

530800 1579650 0.973827 719.3412

530800 1579700 0.93587 782.9832

530800 1579750 0.884998 832.0083

530800 1579800 0.839009 882.9367

530850 1578450 0.857517 115.7645

530850 1578500 0.824672 18.41355

530850 1578550 0.858032 75.05459

530850 1578600 0.921073 139.9951

530850 1578650 1.011945 183.7427

530850 1578700 1.140309 211.2168

530850 1578750 1.318413 226.1445

530850 1578800 1.53361 229.541

530850 1578850 1.787153 219.1651

530850 1578900 2.060972 192.4249

530850 1578950 2.35349 146.2131

530850 1579000 2.662542 78.40799

530850 1579050 2.904142 12.21822

71

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

530850 1579100 2.561925 99.74075

530850 1579150 2.251353 171.2886

530850 1579200 1.979409 232.2678

530850 1579250 1.749179 287.3224

530850 1579300 1.558822 339.3248

530850 1579350 1.402785 389.3775

530850 1579400 1.274247 437.599

530850 1579450 1.166986 483.9414

530850 1579500 1.076116 528.6237

530850 1579550 1.015317 573.3545

530850 1579600 0.935711 618.418

530850 1579650 0.876169 662.5385

530850 1579700 0.727958 708.6741

530850 1579750 0.791981 771.3712

530850 1579800 0.75318 821.5878

530900 1578450 0.786443 139.8029

530900 1578500 0.750218 83.27781

530900 1578550 0.750105 97.5643

530900 1578600 0.781743 136.0631

530900 1578650 0.845094 164.7046

530900 1578700 0.953878 183.4179

530900 1578750 1.129722 196.4966

530900 1578800 1.348529 204.4077

530900 1578850 1.600916 202.2985

530900 1578900 1.868094 185.3489

530900 1578950 2.132691 151.8205

530900 1579000 2.363121 107.3952

530900 1579050 2.440824 83.84643

530900 1579100 2.258035 114.3734

530900 1579150 1.988691 161.0112

530900 1579200 1.734143 207.1351

530900 1579250 1.52057 253.2913

530900 1579300 1.350219 300.5595

530900 1579350 1.215586 348.1037

530900 1579400 1.107364 394.4679

530900 1579450 0.99677 438.6753

530900 1579500 0.920441 480.9149

530900 1579550 0.869013 522.4971

530900 1579600 0.835148 563.3296

530900 1579650 0.786304 604.9383

530900 1579700 0.730266 650.1986

72

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

530900 1579750 0.595039 697.0296

530900 1579800 0.667501 760.4306

530950 1578450 0.738902 194.546

530950 1578500 0.692151 152.3145

530950 1578550 0.662315 141.302

530950 1578600 0.65293 145.1187

530950 1578650 0.675787 147.6292

530950 1578700 0.754122 150.0943

530950 1578750 0.91135 161.2699

530950 1578800 1.13588 178.3364

530950 1578850 1.387403 189.4195

530950 1578900 1.631899 187.0763

530950 1578950 1.841514 171.0566

530950 1579000 1.977269 148.6758

530950 1579050 1.986274 134.9451

530950 1579100 1.848491 139.5386

530950 1579150 1.630925 156.8722

530950 1579200 1.407508 182.9978

530950 1579250 1.226629 218.9

530950 1579300 1.090281 262.5266

530950 1579350 0.98696 308.899

530950 1579400 0.905045 354.0091

530950 1579450 0.837215 396.1249

530950 1579500 0.779519 435.3304

530950 1579550 0.739862 473.1631

530950 1579600 0.698205 510.5517

530950 1579650 0.662727 549.3562

530950 1579700 0.63279 590.7718

530950 1579750 0.468954 637.4689

530950 1579800 0.505076 697.5659

531000 1578450 0.71176 248.79

531000 1578500 0.652949 205.7681

531000 1578550 0.598239 177.0921

531000 1578600 0.546016 153.0426

531000 1578650 0.511335 125.0456

531000 1578700 0.530007 101.7023

531000 1578750 0.682012 114.0662

531000 1578800 0.931294 149.8752

531000 1578850 1.185692 178.0776

531000 1578900 1.403938 190.228

531000 1578950 1.559498 187.6703

73

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531000 1579000 1.62994 175.8048

531000 1579050 1.590944 161.3111

531000 1579100 1.442869 148.4822

531000 1579150 1.235738 140.4975

531000 1579200 1.040101 147.2082

531000 1579250 0.906182 177.5582

531000 1579300 0.823815 223.3795

531000 1579350 0.766031 271.9413

531000 1579400 0.718602 316.8054

531000 1579450 0.676463 356.3876

531000 1579500 0.644374 391.7319

531000 1579550 0.611289 424.7049

531000 1579600 0.58249 457.848

531000 1579650 0.558192 493.3155

531000 1579700 0.538311 532.5503

531000 1579750 0.43044 576.8363

531000 1579800 0.419726 624.9292

531050 1578450 0.708477 297.5962

531050 1578500 0.643238 249.9221

531050 1578550 0.565287 207.6772

531050 1578600 0.492804 163.7004

531050 1578650 0.407761 108.2033

531050 1578700 0.314694 37.38611

531050 1578750 0.495659 65.40636

531050 1578800 0.792718 129.6269

531050 1578850 1.043064 171.2831

531050 1578900 1.230664 192.8122

531050 1578950 1.339144 197.9121

531050 1579000 1.352179 190.1108

531050 1579050 1.270786 171.6685

531050 1579100 1.094014 143.0425

531050 1579150 0.854581 107.205

531050 1579200 0.649319 90.4805

531050 1579250 0.581145 128.7301

531050 1579300 0.569054 186.8407

531050 1579350 0.559571 240.3273

531050 1579400 0.544692 284.5681

531050 1579450 0.525475 320.1622

531050 1579500 0.508154 349.6679

531050 1579550 0.488559 376.4617

531050 1579600 0.471236 404.2029

74

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531050 1579650 0.457292 435.8365

531050 1579700 0.447083 473.0009

531050 1579750 0.353989 516.5907

531050 1579800 0.351134 565.0165

531100 1578450 0.731561 342.5832

531100 1578500 0.651598 289.6004

531100 1578550 0.58941 240.332

531100 1578600 0.520864 188.0826

531100 1578650 0.451295 128.8464

531100 1578700 0.404115 73.73885

531100 1578750 0.541801 85.78701

531100 1578800 0.784537 134.838

531100 1578850 0.995457 172.7644

531100 1578900 1.139342 195.0427

531100 1578950 1.196282 202.9186

531100 1579000 1.171244 197.1929

531100 1579050 1.054146 176.9963

531100 1579100 0.848513 139.4866

531100 1579150 0.564975 80.19892

531100 1579200 0.254929 9.310294

531100 1579250 0.328334 95.41045

531100 1579300 0.373277 165.7795

531100 1579350 0.394282 219.8509

531100 1579400 0.399296 259.7325

531100 1579450 0.394451 288.235

531100 1579500 0.386487 309.2135

531100 1579550 0.376339 327.5373

531100 1579600 0.367817 348.4022

531100 1579650 0.362646 375.8366

531100 1579700 0.361321 411.4912

531100 1579750 0.285234 455.3089

531100 1579800 0.289748 504.9516

531150 1578450 0.751369 383.1868

531150 1578500 0.705667 329.3798

531150 1578550 0.660703 277.3625

531150 1578600 0.619551 225.5285

531150 1578650 0.599614 175.7604

531150 1578700 0.619746 140.577

531150 1578750 0.72723 136.4972

531150 1578800 0.892494 155.5364

531150 1578850 1.038822 177.803

75

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531150 1578900 1.133071 194.5623

531150 1578950 1.151003 203.021

531150 1579000 1.087826 200.8302

531150 1579050 0.94674 185.1688

531150 1579100 0.73835 153.7897

531150 1579150 0.48885 109.4873

531150 1579200 0.293525 82.87452

531150 1579250 0.258059 118.7383

531150 1579300 0.277922 171.1025

531150 1579350 0.294254 213.8857

531150 1579400 0.300229 243.3534

531150 1579450 0.290001 260.8601

531150 1579500 0.283966 269.9326

531150 1579550 0.277913 276.7678

531150 1579600 0.274666 288.8734

531150 1579650 0.276306 312.1121

531150 1579700 0.283004 347.6856

531150 1579750 0.225784 393.3716

531150 1579800 0.237064 445.3859

531200 1578450 0.811487 424.3078

531200 1578500 0.783238 369.1793

531200 1578550 0.762119 316.4172

531200 1578600 0.757425 265.8469

531200 1578650 0.777885 221.0363

531200 1578700 0.832438 187.9218

531200 1578750 0.934862 171.4289

531200 1578800 1.061865 169.2841

531200 1578850 1.167686 175.3969

531200 1578900 1.215672 185.6579

531200 1578950 1.196248 195.9388

531200 1579000 1.098211 200.8843

531200 1579050 0.939794 195.7965

531200 1579100 0.740385 179.7236

531200 1579150 0.532626 158.948

531200 1579200 0.368943 150.1589

531200 1579250 0.284415 165.65

531200 1579300 0.253776 193.9107

531200 1579350 0.241835 219.2503

531200 1579400 0.23275 234.351

531200 1579450 0.220755 237.6682

531200 1579500 0.210068 231.5778

76

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531200 1579550 0.201168 223.0349

531200 1579600 0.198149 223.6615

531200 1579650 0.199689 243.5928

531200 1579700 0.213989 282.1944

531200 1579750 0.177156 332.24

531200 1579800 0.194536 387.6997

531250 1578450 0.882075 464.6133

531250 1578500 0.872398 408.3813

531250 1578550 0.87495 354.6674

531250 1578600 0.900016 303.3563

531250 1578650 0.95297 257.1038

531250 1578700 1.032135 218.0378

531250 1578750 1.149589 187.4727

531250 1578800 1.266172 165.9728

531250 1578850 1.35417 156.1043

531250 1578900 1.3697 161.8315

531250 1578950 1.29867 178.8956

531250 1579000 1.162019 195.8314

531250 1579050 0.98441 204.7134

531250 1579100 0.788004 204.3759

531250 1579150 0.597926 199.616

531250 1579200 0.441434 198.6996

531250 1579250 0.333696 206.4883

531250 1579300 0.267677 219.2733

531250 1579350 0.226451 229.2461

531250 1579400 0.197009 230.2459

531250 1579450 0.170351 219.1695

531250 1579500 0.149841 196.1092

531250 1579550 0.13346 167.127

531250 1579600 0.126887 150.7783

531250 1579650 0.136805 170.3254

531250 1579700 0.121587 218.3576

531250 1579750 0.140979 275.2596

531250 1579800 0.16365 334.0382

531300 1578450 0.95532 504.276

531300 1578500 0.962255 446.6978

531300 1578550 0.984529 391.1996

531300 1578600 1.032473 337.0299

531300 1578650 1.108879 285.8307

531300 1578700 1.207451 237.3328

531300 1578750 1.330549 190.3075

77

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531300 1578800 1.471602 146.3736

531300 1578850 1.577746 114.583

531300 1578900 1.56565 118.8074

531300 1578950 1.434914 152.4874

531300 1579000 1.25235 186.6647

531300 1579050 1.052043 210.1893

531300 1579100 0.851206 222.7701

531300 1579150 0.665003 228.3453

531300 1579200 0.507288 231.8654

531300 1579250 0.385315 236.0895

531300 1579300 0.296581 239.7748

531300 1579350 0.232546 238.9439

531300 1579400 0.184424 229.2221

531300 1579450 0.142977 207.0454

531300 1579500 0.111356 169.7766

531300 1579550 0.084128 117.5137

531300 1579600 0.066696 68.7624

531300 1579650 0.083261 99.08588

531300 1579700 0.093671 165.4853

531300 1579750 0.12127 227.8071

531300 1579800 0.146984 286.5082

531350 1578450 1.024711 543.6797

531350 1578500 1.044515 484.566

531350 1578550 1.087039 425.6623

531350 1578600 1.143768 369.0792

531350 1578650 1.234274 312.3439

531350 1578700 1.335687 253.5057

531350 1578750 1.476371 193.3302

531350 1578800 1.64232 126.4621

531350 1578850 1.812879 54.94723

531350 1578900 1.771741 63.00961

531350 1578950 1.552988 129.305

531350 1579000 1.329225 180.4289

531350 1579050 1.112207 214.8011

531350 1579100 0.907584 235.6363

531350 1579150 0.722239 247.0416

531350 1579200 0.562705 252.7478

531350 1579250 0.432292 254.8353

531350 1579300 0.329658 253.1099

531350 1579350 0.250084 245.6295

531350 1579400 0.183656 229.8199

78

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531350 1579450 0.13536 202.7303

531350 1579500 0.09621 161.6085

531350 1579550 0.063435 103.6055

531350 1579600 0.03757 33.98494

531350 1579650 0.060151 73.67229

531350 1579700 0.08608 139.2753

531350 1579750 0.115654 193.6824

531350 1579800 0.142976 244.9218

531400 1578450 1.085617 583.3966

531400 1578500 1.113823 522.8533

531400 1578550 1.164872 461.716

531400 1578600 1.22784 402.405

531400 1578650 1.309875 338.134

531400 1578700 1.418457 277.1565

531400 1578750 1.550621 211.7182

531400 1578800 1.701815 140.2799

531400 1578850 1.842184 70.20067

531400 1578900 1.793461 74.49628

531400 1578950 1.581423 135.5177

531400 1579000 1.358404 186.501

531400 1579050 1.143956 222.2972

531400 1579100 0.94393 245.0705

531400 1579150 0.763037 257.9546

531400 1579200 0.605054 263.6869

531400 1579250 0.47168 263.842

531400 1579300 0.362037 258.6228

531400 1579350 0.267955 247.4254

531400 1579400 0.198176 229.0261

531400 1579450 0.143682 202.9252

531400 1579500 0.102716 168.9831

531400 1579550 0.074697 129.7781

531400 1579600 0.063601 100.574

531400 1579650 0.077388 108.0033

531400 1579700 0.100531 136.8103

531400 1579750 0.127977 167.6635

531400 1579800 0.154313 204.4312

531450 1578450 1.135366 624.0045

531450 1578500 1.167578 562.4014

531450 1578550 1.221409 499.7205

531450 1578600 1.319239 432.0347

531450 1578650 1.357415 371.5044

79

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531450 1578700 1.451038 310.8706

531450 1578750 1.555596 249.1299

531450 1578800 1.657476 189.8182

531450 1578850 1.715144 146.6015

531450 1578900 1.668482 142.4383

531450 1578950 1.521987 171.1454

531450 1579000 1.335043 205.5879

531450 1579050 1.141595 233.2476

531450 1579100 0.955918 251.7996

531450 1579150 0.785287 262.2196

531450 1579200 0.633756 265.9798

531450 1579250 0.502887 263.901

531450 1579300 0.392144 255.9755

531450 1579350 0.294683 242.0883

531450 1579400 0.220485 222.5025

531450 1579450 0.163391 199.2413

531450 1579500 0.124261 175.0856

531450 1579550 0.103475 153.2912

531450 1579600 0.100608 138.6715

531450 1579650 0.111844 134.1723

531450 1579700 0.133912 134.1526

531450 1579750 0.157355 136.6782

531450 1579800 0.180719 156.5045

531500 1578450 1.173013 665.925

531500 1578500 1.205473 603.7266

531500 1578550 1.304764 528.1357

531500 1578600 1.29202 468.6926

531500 1578650 1.373585 409.7624

531500 1578700 1.44607 352.1913

531500 1578750 1.518455 296.9245

531500 1578800 1.57693 248.4159

531500 1578850 1.592696 215.1598

531500 1578900 1.543631 204.5012

531500 1578950 1.430956 213.0877

531500 1579000 1.279126 229.3216

531500 1579050 1.112369 244.2841

531500 1579100 0.946441 254.4648

531500 1579150 0.790715 259.5101

531500 1579200 0.650363 259.8808

531500 1579250 0.527289 255.4361

531500 1579300 0.415461 245.3365

80

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531500 1579350 0.324952 228.508

531500 1579400 0.249196 206.2647

531500 1579450 0.190888 183.2085

531500 1579500 0.155107 165.9022

531500 1579550 0.143223 156.8026

531500 1579600 0.149493 151.4468

531500 1579650 0.165013 142.3192

531500 1579700 0.185126 121.944

531500 1579750 0.203093 91.77877

531500 1579800 0.221645 93.57294

531550 1578450 1.198929 709.3657

531550 1578500 1.296667 625.6188

531550 1578550 1.330856 566.9057

531550 1578600 1.299624 508.0949

531550 1578650 1.367457 450.9009

531550 1578700 1.418487 396.686

531550 1578750 1.463066 346.4839

531550 1578800 1.491333 303.5382

531550 1578850 1.484019 272.3099

531550 1578900 1.432444 255.0443

531550 1578950 1.337266 249.4719

531550 1579000 1.210443 249.8562

531550 1579050 1.067479 251.0229

531550 1579100 0.921784 250.5691

531550 1579150 0.783197 248.3846

531550 1579200 0.65781 244.8029

531550 1579250 0.542689 238.8602

531550 1579300 0.446933 227.4788

531550 1579350 0.362199 207.7855

531550 1579400 0.286722 179.6298

531550 1579450 0.225346 149.9676

531550 1579500 0.192622 134.6393

531550 1579550 0.194766 139.5853

531550 1579600 0.213052 147.8317

531550 1579650 0.23817 142.77

531550 1579700 0.257164 114.6513

531550 1579750 0.268261 56.25242

531550 1579800 0.271895 35.68667

531600 1578450 1.268766 732.6749

531600 1578500 1.31629 664.7632

531600 1578550 1.34365 606.5576

81

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531600 1578600 1.293079 548.4621

531600 1578650 1.347467 492.7816

531600 1578700 1.379683 440.9362

531600 1578750 1.402692 393.4068

531600 1578800 1.410554 352.0479

531600 1578850 1.389216 319.0875

531600 1578900 1.3357 294.7979

531600 1578950 1.250566 277.3903

531600 1579000 1.140643 263.5913

531600 1579050 1.016237 250.6216

531600 1579100 0.888306 237.829

531600 1579150 0.766823 226.8342

531600 1579200 0.655308 219.5064

531600 1579250 0.563678 214.1728

531600 1579300 0.485473 204.9521

531600 1579350 0.414 184.7709

531600 1579400 0.34287 149.1933

531600 1579450 0.272524 101.7138

531600 1579500 0.236634 78.09995

531600 1579550 0.267026 109.3566

531600 1579600 0.307065 139.4143

531600 1579650 0.337142 147.2619

531600 1579700 0.353791 130.88

531600 1579750 0.368578 99.41452

531600 1579800 0.361537 99.54841

531650 1578450 1.28254 773.0415

531650 1578500 1.326672 704.2112

531650 1578550 1.253601 644.749

531650 1578600 1.277526 588.7532

531650 1578650 1.300763 534.9219

531650 1578700 1.336929 483.1443

531650 1578750 1.343869 436.2378

531650 1578800 1.33788 393.985

531650 1578850 1.307638 357.4193

531650 1578900 1.252962 325.884

531650 1578950 1.174335 297.726

531650 1579000 1.076064 270.6296

531650 1579050 0.965327 242.9461

531650 1579100 0.85125 215.4474

531650 1579150 0.742971 192.8335

531650 1579200 0.65356 181.717

82

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531650 1579250 0.586907 181.5307

531650 1579300 0.536266 180.7665

531650 1579350 0.4903 166.7729

531650 1579400 0.43802 131.7401

531650 1579450 0.369644 71.47305

531650 1579500 0.314758 19.79311

531650 1579550 0.391458 93.70925

531650 1579600 0.441165 139.2995

531650 1579650 0.480291 159.9894

531650 1579700 0.486481 161.8886

531650 1579750 0.478396 158.0992

531650 1579800 0.461574 171.6923

531700 1578450 1.650526 820.2921

531700 1578500 1.655119 749.1058

531700 1578550 1.242549 684.0642

531700 1578600 1.256972 628.3107

531700 1578650 1.26907 574.7049

531700 1578700 1.294534 522.7391

531700 1578750 1.289759 475.0387

531700 1578800 1.274415 430.5264

531700 1578850 1.238642 389.5958

531700 1578900 1.183787 351.0836

531700 1578950 1.110235 313.2799

531700 1579000 1.020775 274.0086

531700 1579050 0.924423 231.5823

531700 1579100 0.817349 186.2973

531700 1579150 0.718405 145.5724

531700 1579200 0.647023 128.5053

531700 1579250 0.613765 141.5393

531700 1579300 0.602343 157.7868

531700 1579350 0.59589 157.4397

531700 1579400 0.584459 136.0676

531700 1579450 0.565591 101.1947

531700 1579500 0.561053 85.86382

531700 1579550 0.594926 115.0704

531700 1579600 0.62905 151.5556

531700 1579650 0.635805 177.7311

531700 1579700 0.623665 193.8807

531700 1579750 0.599354 207.8907

531700 1579800 0.568183 231.5667

531750 1578450 1.653658 860.7503

83

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531750 1578500 1.654057 788.3017

531750 1578550 1.528744 727.6088

531750 1578600 1.514828 671.0129

531750 1578650 1.237413 612.8373

531750 1578700 1.234694 560.9915

531750 1578750 1.241824 510.5532

531750 1578800 1.217041 463.492

531750 1578850 1.181562 417.9273

531750 1578900 1.127715 373.3245

531750 1578950 1.059187 327.6715

531750 1579000 0.987112 279.0873

531750 1579050 0.892679 224.1894

531750 1579100 0.793439 161.661

531750 1579150 0.696106 93.03087

531750 1579200 0.633132 56.68393

531750 1579250 0.648617 102.5198

531750 1579300 0.684425 141.091

531750 1579350 0.724652 154.3294

531750 1579400 0.7645 146.0639

531750 1579450 0.793178 128.4292

531750 1579500 0.828443 120.9678

531750 1579550 0.844543 135.4362

531750 1579600 0.838411 163.6938

531750 1579650 0.811472 193.7904

531750 1579700 0.771506 221.1542

531750 1579750 0.72513 248.2155

531750 1579800 0.710109 281.1629

531800 1578450 1.513829 944.0417

531800 1578500 1.637746 840.2828

531800 1578550 1.513008 765.8229

531800 1578600 1.492994 708.6275

531800 1578650 1.468176 653.1028

531800 1578700 1.436774 599.3284

531800 1578750 1.200581 543.74

531800 1578800 1.171977 493.8048

531800 1578850 1.161056 447.1053

531800 1578900 1.103914 396.7

531800 1578950 1.036277 344.9373

531800 1579000 0.959811 289.8969

531800 1579050 0.877175 229.4427

531800 1579100 0.791895 161.5155

84

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531800 1579150 0.709176 85.78098

531800 1579200 0.661491 40.169

531800 1579250 0.710514 96.78578

531800 1579300 0.780723 139.068

531800 1579350 0.862291 154.8328

531800 1579400 0.950335 148.4733

531800 1579450 1.04032 130.2602

531800 1579500 1.102782 118.3609

531800 1579550 1.105532 130.5562

531800 1579600 1.057545 163.7195

531800 1579650 0.989126 203.5411

531800 1579700 0.916823 242.7317

531800 1579750 0.846443 281.2716

531800 1579800 0.81863 322.8506

531850 1578450 1.50883 985.2067

531850 1578500 1.494988 920.7394

531850 1578550 1.496818 803.5829

531850 1578600 1.471917 745.4934

531850 1578650 1.382871 698.7439

531850 1578700 1.35587 640.6254

531850 1578750 1.320818 584.2925

531850 1578800 1.283147 528.1293

531850 1578850 1.229615 473.8759

531850 1578900 1.16496 419.9474

531850 1578950 1.091361 364.9843

531850 1579000 1.010871 308.0511

531850 1579050 0.927019 248.756

531850 1579100 0.845915 188.6248

531850 1579150 0.780285 135.6636

531850 1579200 0.757737 112.7752

531850 1579250 0.797384 129.081

531850 1579300 0.879371 151.3299

531850 1579350 0.984436 158.0086

531850 1579400 1.117065 143.7614

531850 1579450 1.266075 110.77

531850 1579500 1.386017 78.27623

531850 1579550 1.363616 98.55394

531850 1579600 1.256316 153.4353

531850 1579650 1.144857 209.6561

531850 1579700 1.043483 261.6053

531850 1579750 0.989791 310.9483

85

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531850 1579800 0.914423 360.0997

531900 1578450 1.421179 1050.104

531900 1578500 1.412338 980.3138

531900 1578550 1.336529 873.4949

531900 1578600 1.384193 795.1771

531900 1578650 1.359818 733.8835

531900 1578700 1.329434 674.1678

531900 1578750 1.292091 615.811

531900 1578800 1.253995 557.1299

531900 1578850 1.201797 500.2646

531900 1578900 1.140931 443.8834

531900 1578950 1.074035 387.2405

531900 1579000 1.003742 330.3995

531900 1579050 0.934514 274.4741

531900 1579100 0.873767 222.772

531900 1579150 0.833524 182.2391

531900 1579200 0.829472 161.5875

531900 1579250 0.871254 160.5998

531900 1579300 0.954737 165.8111

531900 1579350 1.07389 162.7138

531900 1579400 1.227234 141.7711

531900 1579450 1.413179 96.57789

531900 1579500 1.63013 21.84885

531900 1579550 1.543697 72.65619

531900 1579600 1.387124 152.8851

531900 1579650 1.252868 222.0846

531900 1579700 1.136687 283.5375

531900 1579750 1.075849 340.822

531900 1579800 0.991793 396.2239

531950 1578450 1.41553 1090.151

531950 1578500 1.40337 1019.97

531950 1578550 1.322789 910.9317

531950 1578600 1.303469 845.4466

531950 1578650 1.279964 781.2787

531950 1578700 1.307551 707.6221

531950 1578750 1.269058 647.3177

531950 1578800 1.224334 587.7882

531950 1578850 1.180075 527.5313

531950 1578900 1.124473 468.716

531950 1578950 1.063972 410.6053

531950 1579000 1.002635 353.4699

86

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

531950 1579050 0.945017 298.921

531950 1579100 0.897982 250.0457

531950 1579150 0.870956 211.3174

531950 1579200 0.88413 186.661

531950 1579250 0.919947 175.5582

531950 1579300 0.997463 171.8649

531950 1579350 1.112979 166.2196

531950 1579400 1.258654 149.9176

531950 1579450 1.418819 119.606

531950 1579500 1.542103 92.31176

531950 1579550 1.525126 118.5053

531950 1579600 1.418373 182.1614

531950 1579650 1.300378 249.4554

531950 1579700 1.227645 313.775

531950 1579750 1.135663 375.7898

531950 1579800 1.050471 435.3112

532000 1578450 1.409454 1130.529

532000 1578500 1.394559 1059.854

532000 1578550 1.108537 962.8779

532000 1578600 1.288939 882.3881

532000 1578650 1.263356 816.9605

532000 1578700 1.233478 752.4548

532000 1578750 1.198933 688.7157

532000 1578800 1.207343 617.8239

532000 1578850 1.158815 556.6091

532000 1578900 1.113815 494.3332

532000 1578950 1.058766 434.2042

532000 1579000 1.004271 375.2388

532000 1579050 0.954283 318.7712

532000 1579100 0.945257 267.3004

532000 1579150 0.911586 222.9118

532000 1579200 0.905086 189.3802

532000 1579250 0.933598 169.2949

532000 1579300 1.001654 162.5224

532000 1579350 1.10524 162.9882

532000 1579400 1.230157 161.7215

532000 1579450 1.352318 156.2088

532000 1579500 1.433932 157.4325

532000 1579550 1.440688 182.6557

532000 1579600 1.38396 231.3503

532000 1579650 1.336512 291.6224

87

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

532000 1579700 1.250363 353.5379

532000 1579750 1.165626 415.1575

532000 1579800 1.085351 476.3561

532050 1578450 1.723189 1193.106

532050 1578500 1.670905 1117.399

532050 1578550 1.363167 1016.505

532050 1578600 1.277057 920.2379

532050 1578650 1.25014 853.6089

532050 1578700 1.21956 787.6569

532050 1578750 1.185134 722.2305

532050 1578800 1.146924 657.177

532050 1578850 1.105342 592.3769

532050 1578900 1.202078 526.1128

532050 1578950 1.137827 460.6268

532050 1579000 1.070547 397.5502

532050 1579050 1.009387 335.9587

532050 1579100 0.958076 276.7885

532050 1579150 0.9212 221.5067

532050 1579200 0.904778 173.1622

532050 1579250 0.918252 139.5567

532050 1579300 0.973252 132.5135

532050 1579350 1.065996 148.2615

532050 1579400 1.17385 168.9654

532050 1579450 1.272002 187.3605

532050 1579500 1.337065 208.3559

532050 1579550 1.344973 240.1907

532050 1579600 1.314524 285.05

532050 1579650 1.31096 340.2311

532050 1579700 1.24474 399.0188

532050 1579750 1.17345 459.602

532050 1579800 1.101886 521.0268

532100 1578450 1.702956 1233.039

532100 1578500 1.650957 1157.254

532100 1578550 1.342675 1055.824

532100 1578600 1.291971 983.343

532100 1578650 1.438256 900.7221

532100 1578700 1.378489 831.0597

532100 1578750 1.317357 762.2094

532100 1578800 1.255338 693.9603

532100 1578850 1.193135 626.1018

532100 1578900 1.131723 558.4501

88

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

532100 1578950 1.091321 489.1809

532100 1579000 1.050584 421.0681

532100 1579050 0.995911 353.8795

532100 1579100 0.94869 286.2788

532100 1579150 0.925295 217.7319

532100 1579200 0.894287 149.0715

532100 1579250 0.882197 87.99208

532100 1579300 0.920357 79.84829

532100 1579350 1.011965 126.63

532100 1579400 1.108792 174.1929

532100 1579450 1.190953 214.1865

532100 1579500 1.245496 251.264

532100 1579550 1.265125 291.4328

532100 1579600 1.305174 338.9869

532100 1579650 1.27235 391.7137

532100 1579700 1.223432 448.6379

532100 1579750 1.165752 508.0845

532100 1579800 1.10423 569.1291

532150 1578450 1.684605 1274.39

532150 1578500 1.633082 1198.531

532150 1578550 1.325814 1096.894

532150 1578600 1.276387 1023.987

532150 1578650 1.426513 940.4796

532150 1578700 1.368727 869.983

532150 1578750 1.310031 800.0804

532150 1578800 1.250902 730.5554

532150 1578850 1.191989 661.1742

532150 1578900 1.134139 591.6881

532150 1578950 1.078402 521.822

532150 1579000 1.042147 450.0031

532150 1579050 1.007166 377.8383

532150 1579100 0.959948 304.5212

532150 1579150 0.91898 227.8625

532150 1579200 0.884352 146.0872

532150 1579250 0.855467 57.65526

532150 1579300 0.881774 44.51689

532150 1579350 0.974313 125.0766

532150 1579400 1.059488 190.7665

532150 1579450 1.129103 245.0708

532150 1579500 1.220705 294.1663

532150 1579550 1.248086 341.7548

89

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_02_2014 Northing Easting DO Conc. Variance

532150 1579600 1.249696 391.5884

532150 1579650 1.229839 444.7831

532150 1579700 1.194279 501.0797

532150 1579750 1.148448 559.7826

532150 1579800 1.096612 620.3259

532200 1578450 1.667968 1317.381

532200 1578500 1.617054 1241.506

532200 1578550 1.564847 1166.473

532200 1578600 1.264251 1066.874

532200 1578650 1.215925 994.436

532200 1578700 1.362079 911.7409

532200 1578750 1.305827 841.1702

532200 1578800 1.24951 770.8539

532200 1578850 1.193725 700.5664

532200 1578900 1.13921 630.0606

532200 1578950 1.086839 559.0593

532200 1579000 1.03759 487.242

532200 1579050 1.005067 413.4964

532200 1579100 0.962414 339.2728

532200 1579150 0.936366 263.3029

532200 1579200 0.904606 188.8893

532200 1579250 0.888003 128.561

532200 1579300 0.908452 121.7876

532200 1579350 0.965548 170.5228

532200 1579400 1.029232 230.7915

532200 1579450 1.119986 288.3576

532200 1579500 1.166993 341.7734

532200 1579550 1.1936 393.542

532200 1579600 1.199901 445.839

532200 1579650 1.188273 499.9089

532200 1579700 1.162357 556.1208

532200 1579750 1.126012 614.365

532200 1579800 1.082638 674.4114

90

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

530800 1578450 6 159.2194

530800 1578500 0.911411 109.5001

530800 1578550 0.979245 130.5147

530800 1578600 1.08115 185.3496

530800 1578650 1.163037 238.5973

530800 1578700 1.204851 284.5267

530800 1578750 1.199344 324.2976

530800 1578800 1.190589 359.2494

530800 1578850 1.113926 389.8808

530800 1578900 1.014685 415.7078

530800 1578950 0.905834 436.3678

530800 1579000 1.088345 446.8366

530800 1579050 1.010159 458.491

530800 1579100 0.952757 468.6186

530800 1579150 0.820094 478.9855

530800 1579200 0.808805 493.2228

530800 1579250 0.82962 512.2641

530800 1579300 0.878824 536.4258

530800 1579350 0.949229 565.0292

530800 1579400 0.998182 600.1288

530800 1579450 1.082767 634.4813

530800 1579500 1.161981 670.4072

530800 1579550 1.46678 720.7028

530800 1579600 1.512069 758.8192

530800 1579650 1.538031 798.6972

530800 1579700 1.23811 857.5943

530800 1579750 1.239458 901.5558

530800 1579800 1.220061 948.273

530850 1578450 1.070484 115.7757

530850 1578500 1.014401 18.41681

530850 1578550 1.152551 76.30424

530850 1578600 1.30462 145.8907

530850 1578650 1.414018 198.5275

530850 1578700 1.467521 240.3728

530850 1578750 1.469781 276.823

530850 1578800 1.404346 310.4414

530850 1578850 1.463049 338.1078

530850 1578900 1.38505 362.6672

530850 1578950 1.259757 380.9971

530850 1579000 1.136861 392.9072

530850 1579050 1.029138 399.798

91

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

530850 1579100 0.875383 404.072

530850 1579150 0.81968 410.0198

530850 1579200 0.806416 420.8988

530850 1579250 0.836666 438.8113

530850 1579300 0.904686 463.7545

530850 1579350 0.999641 494.1243

530850 1579400 1.108713 527.8563

530850 1579450 1.219594 563.3208

530850 1579500 1.321768 599.6586

530850 1579550 1.611093 646.3653

530850 1579600 1.636097 687.5704

530850 1579650 1.669865 727.249

530850 1579700 1.364171 779.8984

530850 1579750 1.418324 827.1143

530850 1579800 1.389603 874.4116

530900 1578450 1.295026 140.2819

530900 1578500 1.314607 83.42991

530900 1578550 1.431396 97.72127

530900 1578600 1.586072 138.3217

530900 1578650 1.710105 172.38

530900 1578700 1.770007 201.1119

530900 1578750 1.847372 229.8113

530900 1578800 1.768138 261.7174

530900 1578850 1.662108 292.544

530900 1578900 1.502947 317.8555

530900 1578950 1.3346 334.8379

530900 1579000 1.172476 342.9723

530900 1579050 0.982486 343.702

530900 1579100 0.864322 340.97

530900 1579150 0.79191 340.0988

530900 1579200 0.776597 346.7507

530900 1579250 0.820938 364.1124

530900 1579300 0.915644 391.342

530900 1579350 1.043679 424.9747

530900 1579400 1.186719 461.4435

530900 1579450 1.221495 498.2793

530900 1579500 1.348222 534.9836

530900 1579550 1.624537 578.3757

530900 1579600 1.807958 615.7415

530900 1579650 1.849758 654.5051

530900 1579700 1.82428 698.8769

92

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

530900 1579750 1.53916 750.7748

530900 1579800 1.562846 802.0181

530950 1578450 1.549626 195.737

530950 1578500 1.618624 152.8951

530950 1578550 1.742532 141.3298

530950 1578600 1.898715 145.5638

530950 1578650 2.049142 150.4185

530950 1578700 2.155172 158.3925

530950 1578750 2.131134 180.1955

530950 1578800 2.014959 214.7137

530950 1578850 1.825036 250.3109

530950 1578900 1.611929 278.1285

530950 1578950 1.396777 294.3695

530950 1579000 1.165799 298.3525

530950 1579050 0.978869 291.8776

530950 1579100 0.827766 279.3711

530950 1579150 0.732317 268.7307

530950 1579200 0.658243 269.704

530950 1579250 0.715221 287.9112

530950 1579300 0.836393 320.1778

530950 1579350 0.996412 359.1886

530950 1579400 1.171997 399.2366

530950 1579450 1.344965 437.6386

530950 1579500 1.501166 473.9831

530950 1579550 1.768587 513.7389

530950 1579600 1.863441 549.3316

530950 1579650 1.919863 586.7143

530950 1579700 1.938693 627.021

530950 1579750 1.51912 678.3135

530950 1579800 1.464016 729.4157

531000 1578450 1.785004 250.8514

531000 1578500 1.881258 207.0852

531000 1578550 2.020458 177.5847

531000 1578600 2.188144 153.0525

531000 1578650 2.372482 125.4163

531000 1578700 2.498646 104.0169

531000 1578750 2.445037 122.0638

531000 1578800 2.228265 169.3728

531000 1578850 1.965702 214.154

531000 1578900 1.698571 245.6384

531000 1578950 1.428819 261.3715

93

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531000 1579000 1.18361 261.2483

531000 1579050 0.959897 246.612

531000 1579100 0.728749 221.5039

531000 1579150 0.60245 196.078

531000 1579200 0.573232 188.5896

531000 1579250 0.661372 211.1808

531000 1579300 0.834026 253.4905

531000 1579350 1.04651 300.3084

531000 1579400 1.269426 344.0644

531000 1579450 1.483348 382.7733

531000 1579500 1.780103 419.9362

531000 1579550 1.935995 452.3632

531000 1579600 2.045557 485.0647

531000 1579650 2.104856 520.2131

531000 1579700 2.116099 559.2589

531000 1579750 1.906158 606.3947

531000 1579800 1.844914 654.6321

531050 1578450 1.982168 300.6371

531050 1578500 2.089657 252.1928

531050 1578550 2.214178 208.9731

531050 1578600 2.40303 164.2572

531050 1578650 2.621299 108.3

531050 1578700 2.845062 37.41057

531050 1578750 2.704275 67.32287

531050 1578800 2.371588 138.2205

531050 1578850 2.051078 189.9908

531050 1578900 1.747201 222.7969

531050 1578950 1.459581 237.3389

531050 1579000 1.143605 233.7598

531050 1579050 0.895869 212.1851

531050 1579100 0.67511 173.8457

531050 1579150 0.499718 126.0988

531050 1579200 0.445041 101.7386

531050 1579250 0.592283 139.0913

531050 1579300 0.83814 198.5582

531050 1579350 1.110145 253.4844

531050 1579400 1.383232 298.8384

531050 1579450 1.64103 335.2022

531050 1579500 1.947064 366.6562

531050 1579550 2.131317 393.949

531050 1579600 2.255247 422.1084

94

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531050 1579650 2.313337 454.1512

531050 1579700 2.309708 491.7582

531050 1579750 2.110823 537.5927

531050 1579800 2.020241 586.7039

531100 1578450 2.134447 346.6163

531100 1578500 2.199214 292.6155

531100 1578550 2.339538 242.5604

531100 1578600 2.505321 189.5172

531100 1578650 2.625212 129.5349

531100 1578700 2.789122 73.89436

531100 1578750 2.672658 86.10267

531100 1578800 2.372445 138.0791

531100 1578850 2.055177 181.3073

531100 1578900 1.754631 209.5016

531100 1578950 1.419339 221.727

531100 1579000 1.143446 216.9446

531100 1579050 0.88782 193.4581

531100 1579100 0.65196 149.4512

531100 1579150 0.435188 83.3624

531100 1579200 0.27361 9.362014

531100 1579250 0.5679 96.56264

531100 1579300 0.880687 168.7223

531100 1579350 1.200201 224.593

531100 1579400 1.517437 266.0182

531100 1579450 1.818965 295.7477

531100 1579500 2.141311 318.3811

531100 1579550 2.357864 337.6088

531100 1579600 2.496562 359.2721

531100 1579650 2.547687 387.4777

531100 1579700 2.518353 423.9219

531100 1579750 2.31443 469.5368

531100 1579800 2.184653 520.1776

531150 1578450 2.183632 387.6417

531150 1578500 2.278961 333.1795

531150 1578550 2.389919 280.3759

531150 1578600 2.507434 227.6812

531150 1578650 2.530093 176.9213

531150 1578700 2.577467 140.9297

531150 1578750 2.48021 136.5156

531150 1578800 2.258727 156.5721

531150 1578850 1.962618 181.2127

95

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531150 1578900 1.679412 200.7124

531150 1578950 1.399892 210.9607

531150 1579000 1.138971 208.649

531150 1579050 0.900643 190.8384

531150 1579100 0.690387 156.3105

531150 1579150 0.525859 109.7822

531150 1579200 0.488369 82.9174

531150 1579250 0.681226 118.7399

531150 1579300 0.981507 171.4558

531150 1579350 1.316917 215.0666

531150 1579400 1.66539 245.523

531150 1579450 2.010178 264.0159

531150 1579500 2.358607 274.2012

531150 1579550 2.616177 281.8817

531150 1579600 2.773076 294.7944

531150 1579650 2.809154 318.8685

531150 1579700 2.737297 355.3385

531150 1579750 2.506758 402.4761

531150 1579800 2.325894 455.6045

531200 1578450 2.24164 429.3964

531200 1578500 2.312597 373.591

531200 1578550 2.38716 320.0068

531200 1578600 2.445926 268.4265

531200 1578650 2.389166 222.5268

531200 1578700 2.381863 188.5129

531200 1578750 2.284228 171.464

531200 1578800 2.09927 169.4977

531200 1578850 1.866989 176.5291

531200 1578900 1.610453 187.9157

531200 1578950 1.360043 198.8027

531200 1579000 1.129086 203.4816

531200 1579050 0.925662 197.3617

531200 1579100 0.762948 180.1682

531200 1579150 0.667301 158.9484

531200 1579200 0.684702 150.3078

531200 1579250 0.843279 165.77

531200 1579300 1.110013 193.9113

531200 1579350 1.440992 219.3801

531200 1579400 1.809323 234.8524

531200 1579450 2.203729 238.6688

531200 1579500 2.583074 233.1351

96

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531200 1579550 2.900547 225.1501

531200 1579600 3.08752 226.3737

531200 1579650 3.096448 247.0259

531200 1579700 2.953896 286.4597

531200 1579750 2.668441 337.6711

531200 1579800 2.424501 394.2079

531250 1578450 2.270746 470.1945

531250 1578500 2.315077 413.2485

531250 1578550 2.354249 358.6703

531250 1578600 2.368011 306.2787

531250 1578650 2.247681 258.8657

531250 1578700 2.207952 218.8853

531250 1578750 2.125588 187.6658

531250 1578800 1.94889 165.9772

531250 1578850 1.746433 156.3684

531250 1578900 1.522349 162.4933

531250 1578950 1.307798 179.7389

531250 1579000 1.113154 196.4998

531250 1579050 0.948929 204.9978

531250 1579100 0.830476 204.3926

531250 1579150 0.779998 199.6917

531250 1579200 0.822703 198.9781

531250 1579250 0.971436 206.7821

531250 1579300 1.216327 219.4063

531250 1579350 1.537004 229.2563

531250 1579400 1.915564 230.2745

531250 1579450 2.332765 219.3146

531250 1579500 2.779541 196.4633

531250 1579550 3.191273 167.7264

531250 1579600 3.443657 151.6885

531250 1579650 3.399695 171.7107

531250 1579700 3.097191 220.4625

531250 1579750 2.764013 278.2265

531250 1579800 2.451891 337.9383

531300 1578450 2.280581 510.2346

531300 1578500 2.299636 451.9063

531300 1578550 2.308166 395.5143

531300 1578600 2.286454 340.225

531300 1578650 2.118039 287.8355

531300 1578700 2.056969 238.4354

531300 1578750 1.979416 190.6893

97

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531300 1578800 1.811168 146.4149

531300 1578850 1.624111 114.6078

531300 1578900 1.427156 118.944

531300 1578950 1.248052 152.6519

531300 1579000 1.088882 186.7534

531300 1579050 0.959011 210.1956

531300 1579100 0.873331 222.8095

531300 1579150 0.849375 228.5574

531300 1579200 0.903391 232.2582

531300 1579250 1.044221 236.516

531300 1579300 1.270397 240.0821

531300 1579350 1.574304 239.0898

531300 1579400 1.948106 229.2577

531300 1579450 2.372819 207.0489

531300 1579500 2.874025 169.7874

531300 1579550 3.409903 117.5654

531300 1579600 3.839692 68.88282

531300 1579650 3.664516 99.43948

531300 1579700 3.178159 166.3143

531300 1579750 2.736228 229.2583

531300 1579800 2.372721 288.6855

531350 1578450 2.279311 549.9318

531350 1578500 2.276234 490.0423

531350 1578550 2.247015 430.0677

531350 1578600 2.21121 372.5149

531350 1578650 2.005214 314.5798

531350 1578700 1.991861 254.7751

531350 1578750 1.860427 193.9265

531350 1578800 1.69506 126.6253

531350 1578850 1.509679 54.95065

531350 1578900 1.33444 63.01863

531350 1578950 1.19061 129.309

531350 1579000 1.059749 180.4308

531350 1579050 0.954204 214.8445

531350 1579100 0.888503 235.7959

531350 1579150 0.876951 247.3689

531350 1579200 0.931429 253.2137

531350 1579250 1.058965 255.3346

531350 1579300 1.261082 253.5312

531350 1579350 1.535943 245.9177

531350 1579400 1.856007 230.0561

98

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531350 1579450 2.27766 202.8451

531350 1579500 2.767656 161.6565

531350 1579550 3.321136 103.624

531350 1579600 3.874053 33.9908

531350 1579650 3.567329 73.70522

531350 1579700 3.00122 139.5237

531350 1579750 2.525653 194.304

531350 1579800 2.159125 246.0425

531400 1578450 2.273184 589.8854

531400 1578500 2.251711 528.5538

531400 1578550 2.201888 466.3293

531400 1578600 2.147503 406.0713

531400 1578650 2.011807 340.4127

531400 1578700 1.905404 278.6556

531400 1578750 1.770158 212.5547

531400 1578800 1.633697 140.632

531400 1578850 1.445291 70.29323

531400 1578900 1.282129 74.52701

531400 1578950 1.14972 135.5577

531400 1579000 1.03189 186.5791

531400 1579050 0.937946 222.4515

531400 1579100 0.880051 245.3391

531400 1579150 0.869621 258.3495

531400 1579200 0.915486 264.1745

531400 1579250 1.022621 264.351

531400 1579300 1.192241 259.079

531400 1579350 1.390059 247.923

531400 1579400 1.687419 229.3762

531400 1579450 2.042033 203.1557

531400 1579500 2.441049 169.1308

531400 1579550 2.837578 129.87

531400 1579600 3.078423 100.6148

531400 1579650 2.934528 108.0037

531400 1579700 2.535107 136.8572

531400 1579750 2.123448 167.8847

531400 1579800 1.809132 204.9503

531450 1578450 2.26663 630.693

531450 1578500 2.23042 568.3012

531450 1578550 2.16489 504.5304

531450 1578600 2.376378 435.6199

531450 1578650 1.959121 373.9667

99

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531450 1578700 1.845155 312.6046

531450 1578750 1.708353 250.2348

531450 1578800 1.593374 190.435

531450 1578850 1.423479 146.9256

531450 1578900 1.265534 142.6373

531450 1578950 1.127523 171.3208

531450 1579000 1.008959 205.7899

531450 1579050 0.915083 233.5078

531450 1579100 0.854853 252.1366

531450 1579150 0.837107 262.6315

531450 1579200 0.868039 266.4398

531450 1579250 0.949764 264.3643

531450 1579300 1.08057 256.3964

531450 1579350 1.224846 242.5679

531450 1579400 1.449107 222.8656

531450 1579450 1.711641 199.5036

531450 1579500 1.994494 175.2709

531450 1579550 2.242976 153.4163

531450 1579600 2.353455 138.7394

531450 1579650 2.246731 134.1965

531450 1579700 1.939935 134.1535

531450 1579750 1.592611 136.7326

531450 1579800 1.353757 156.7074

531500 1578450 2.262591 672.7889

531500 1578500 2.21489 609.8106

531500 1578550 2.50456 532.6735

531500 1578600 2.214672 472.3848

531500 1578650 1.929015 412.398

531500 1578700 1.810181 354.1522

531500 1578750 1.672869 298.2979

531500 1578800 1.57833 249.3119

531500 1578850 1.416703 215.7471

531500 1578900 1.260694 204.9158

531500 1578950 1.117617 213.4269

531500 1579000 0.992217 229.6427

531500 1579050 0.890595 244.6183

531500 1579100 0.820788 254.8254

531500 1579150 0.790564 259.8951

531500 1579200 0.804379 260.2747

531500 1579250 0.860953 255.8147

531500 1579300 0.920092 245.8097

100

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531500 1579350 1.042757 228.8947

531500 1579400 1.186149 206.5593

531500 1579450 1.350844 183.423

531500 1579500 1.5342 166.0569

531500 1579550 1.69819 156.9133

531500 1579600 1.761908 151.5182

531500 1579650 1.667022 142.3634

531500 1579700 1.386928 121.9511

531500 1579750 1.027802 91.78203

531500 1579800 0.844486 93.62808

531550 1578450 2.262885 716.3877

531550 1578500 2.646375 630.9752

531550 1578550 2.546336 571.5459

531550 1578600 2.22605 511.9237

531550 1578650 1.921326 453.6896

531550 1578700 1.799595 398.848

531550 1578750 1.66228 348.0952

531550 1578800 1.584527 304.6797

531550 1578850 1.426071 273.1289

531550 1578900 1.269203 255.6507

531550 1578950 1.120093 249.9508

531550 1579000 0.984716 250.2635

531550 1579050 0.870343 251.3908

531550 1579100 0.78609 250.9133

531550 1579150 0.741188 248.71

531550 1579200 0.740669 245.107

531550 1579250 0.749783 239.2478

531550 1579300 0.816904 227.8112

531550 1579350 0.890074 208.0482

531550 1579400 0.953899 179.8192

531550 1579450 1.017377 150.0946

531550 1579500 1.124062 134.7268

531550 1579550 1.271726 139.6535

531550 1579600 1.369424 147.9065

531550 1579650 1.295843 142.8157

531550 1579700 1.046575 114.6713

531550 1579750 0.63891 56.2558

531550 1579800 0.445971 35.68667

531600 1578450 2.752723 739.3385

531600 1578500 2.708323 670.1746

531600 1578550 2.604287 611.2828

101

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531600 1578600 2.25689 552.4062

531600 1578650 1.935858 495.6955

531600 1578700 1.812968 443.262

531600 1578750 1.676411 395.2091

531600 1578800 1.612368 353.3822

531600 1578850 1.455093 320.0844

531600 1578900 1.296067 295.547

531600 1578950 1.140085 277.9646

531600 1579000 0.992796 264.0434

531600 1579050 0.862043 250.9857

531600 1579100 0.759225 238.127

531600 1579150 0.698622 227.081

531600 1579200 0.666917 219.7881

531600 1579250 0.707319 214.4169

531600 1579300 0.772469 205.1523

531600 1579350 0.824449 184.9195

531600 1579400 0.827334 149.2869

531600 1579450 0.773615 101.76

531600 1579500 0.798834 78.12377

531600 1579550 1.026382 109.3822

531600 1579600 1.20033 139.453

531600 1579650 1.198472 147.2953

531600 1579700 1.039703 130.9016

531600 1579750 0.802688 99.41452

531600 1579800 0.723509 99.54841

531650 1578450 2.827736 779.7672

531650 1578500 2.783512 709.6618

531650 1578550 2.420169 649.4516

531650 1578600 2.306952 592.7882

531650 1578650 2.179799 538.2975

531650 1578700 1.850205 485.5935

531650 1578750 1.715732 438.1818

531650 1578800 1.663686 395.4582

531650 1578850 1.50783 358.54

531650 1578900 1.347321 326.7261

531650 1578950 1.18533 298.353

531650 1579000 1.026083 271.0913

531650 1579050 0.876585 243.2798

531650 1579100 0.75001 215.6827

531650 1579150 0.655927 193.0303

531650 1579200 0.646557 181.8728

102

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531650 1579250 0.715884 181.6568

531650 1579300 0.822153 180.8656

531650 1579350 0.9068 166.8415

531650 1579400 0.915889 131.7757

531650 1579450 0.802374 71.48143

531650 1579500 0.692761 19.7932

531650 1579550 1.105535 93.71596

531650 1579600 1.330016 139.3154

531650 1579650 1.379399 159.9894

531650 1579700 1.302566 161.8886

531650 1579750 1.186107 158.0992

531650 1579800 1.14323 171.6923

531700 1578450 2.913746 820.2921

531700 1578500 2.871588 749.1058

531700 1578550 2.488614 688.8065

531700 1578600 2.3758 632.4123

531700 1578650 2.250647 578.1739

531700 1578700 1.911217 525.2746

531700 1578750 1.780734 477.0808

531700 1578800 1.740441 432.0928

531700 1578850 1.588139 390.7959

531700 1578900 1.429106 351.9798

531700 1578950 1.264861 313.9288

531700 1579000 1.097397 274.4585

531700 1579050 0.939786 231.8223

531700 1579100 0.774837 186.4585

531700 1579150 0.654309 145.6705

531700 1579200 0.647663 128.5663

531700 1579250 0.787709 141.5854

531700 1579300 0.989436 157.8237

531700 1579350 1.170289 157.4647

531700 1579400 1.284771 136.0792

531700 1579450 1.326125 101.197

531700 1579500 1.407938 85.86456

531700 1579550 1.613134 115.0725

531700 1579600 1.7643 151.5556

531700 1579650 1.79362 177.7311

531700 1579700 1.733832 193.8807

531700 1579750 1.647769 207.8907

531700 1579800 1.597508 231.5667

531750 1578450 3.010444 860.7503

103

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531750 1578500 2.971912 788.3017

531750 1578550 2.573131 727.6088

531750 1578600 2.462753 671.0129

531750 1578650 2.34163 616.372

531750 1578700 2.209704 563.9389

531750 1578750 1.871431 512.6594

531750 1578800 1.73842 465.1605

531750 1578850 1.698758 419.1757

531750 1578900 1.546326 374.25

531750 1578950 1.387004 328.3262

531750 1579000 1.250355 279.3661

531750 1579050 1.065105 224.3716

531750 1579100 0.877507 161.7583

531750 1579150 0.699531 93.06535

531750 1579200 0.661461 56.69216

531750 1579250 0.949331 102.5277

531750 1579300 1.287414 141.0996

531750 1579350 1.598274 154.336

531750 1579400 1.863244 146.0671

531750 1579450 2.085776 128.4336

531750 1579500 2.256067 120.9678

531750 1579550 2.37951 135.4362

531750 1579600 2.417155 163.6938

531750 1579650 2.36724 193.7904

531750 1579700 2.264837 221.1542

531750 1579750 2.15378 248.2155

531750 1579800 2.096681 281.1629

531800 1578450 2.915517 944.0417

531800 1578500 3.028093 840.2828

531800 1578550 2.672934 765.8229

531800 1578600 2.566827 708.6275

531800 1578650 2.45164 653.1028

531800 1578700 2.327453 599.3284

531800 1578750 1.987006 545.8865

531800 1578800 1.8646 495.5128

531800 1578850 1.932624 447.6715

531800 1578900 1.771676 397.1595

531800 1578950 1.608234 345.2874

531800 1579000 1.44263 290.1402

531800 1579050 1.275121 229.5884

531800 1579100 1.106596 161.581

104

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531800 1579150 0.943917 85.79478

531800 1579200 0.918184 40.16909

531800 1579250 1.295285 96.78613

531800 1579300 1.719451 139.069

531800 1579350 2.140082 154.8338

531800 1579400 2.558173 148.4733

531800 1579450 2.92816 130.2602

531800 1579500 3.193803 118.3609

531800 1579550 3.267348 130.5562

531800 1579600 3.176462 163.7195

531800 1579650 3.012722 203.5411

531800 1579700 2.833717 242.7317

531800 1579750 2.670588 281.2716

531800 1579800 2.572306 322.8506

531850 1578450 3.02936 985.2067

531850 1578500 2.951441 920.7394

531850 1578550 2.786938 803.5829

531850 1578600 2.686694 745.4934

531850 1578650 2.778174 698.7439

531850 1578700 2.634072 640.6254

531850 1578750 2.485567 584.2925

531850 1578800 2.138282 528.1293

531850 1578850 2.108741 473.8759

531850 1578900 1.967349 419.9474

531850 1578950 1.827623 364.9843

531850 1579000 1.692247 308.0511

531850 1579050 1.566305 248.756

531850 1579100 1.461888 188.6248

531850 1579150 1.413977 135.6636

531850 1579200 1.512672 112.7752

531850 1579250 1.81661 129.081

531850 1579300 2.240083 151.3299

531850 1579350 2.727317 158.0086

531850 1579400 3.245037 143.7614

531850 1579450 3.780295 110.77

531850 1579500 4.206966 78.27623

531850 1579550 4.197049 98.55394

531850 1579600 3.925505 153.4353

531850 1579650 3.636112 209.6561

531850 1579700 3.377957 261.6053

531850 1579750 3.190255 310.9483

105

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531850 1579800 3.02324 360.0997

531900 1578450 3.480813 1050.104

531900 1578500 3.373191 980.3138

531900 1578550 3.066008 873.4949

531900 1578600 3.049822 795.1771

531900 1578650 2.921571 733.8835

531900 1578700 2.789987 674.1678

531900 1578750 2.656174 615.811

531900 1578800 2.317074 557.1299

531900 1578850 2.308474 500.2646

531900 1578900 2.190763 443.8834

531900 1578950 2.08038 387.2405

531900 1579000 1.982728 330.3995

531900 1579050 1.907261 274.4741

531900 1579100 1.871716 222.772

531900 1579150 1.908521 182.2391

531900 1579200 2.061938 161.5875

531900 1579250 2.353277 160.5998

531900 1579300 2.76105 165.8111

531900 1579350 3.253481 162.7138

531900 1579400 3.813401 141.7711

531900 1579450 4.441586 96.57789

531900 1579500 5.141367 21.84885

531900 1579550 4.930541 72.65619

531900 1579600 4.505512 152.8851

531900 1579650 4.145308 222.0846

531900 1579700 3.842993 283.5375

531900 1579750 3.62535 340.822

531900 1579800 3.429222 396.2239

531950 1578450 3.604454 1090.151

531950 1578500 3.50395 1019.97

531950 1578550 3.201008 910.9317

531950 1578600 3.092078 845.4466

531950 1578650 2.981275 781.2787

531950 1578700 2.959784 707.6221

531950 1578750 2.84153 647.3177

531950 1578800 2.725145 587.7882

531950 1578850 2.417965 527.5313

531950 1578900 2.433045 468.716

531950 1578950 2.353223 410.6053

531950 1579000 2.293117 353.4699

106

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

531950 1579050 2.263563 298.921

531950 1579100 2.281125 250.0457

531950 1579150 2.368544 211.3174

531950 1579200 2.531935 186.661

531950 1579250 2.825912 175.5582

531950 1579300 3.21148 171.8649

531950 1579350 3.670699 166.2196

531950 1579400 4.183714 149.9176

531950 1579450 4.713397 119.606

531950 1579500 5.117444 92.31176

531950 1579550 5.097794 118.5053

531950 1579600 4.809891 182.1614

531950 1579650 4.491034 249.4554

531950 1579700 4.229627 313.775

531950 1579750 3.917635 375.7898

531950 1579800 3.706201 435.3112

532000 1578450 3.734998 1130.529

532000 1578500 3.641954 1059.854

532000 1578550 2.824388 962.8779

532000 1578600 3.246988 882.3881

532000 1578650 3.147773 816.9605

532000 1578700 3.049513 752.4548

532000 1578750 2.95383 688.7157

532000 1578800 2.93961 617.8239

532000 1578850 2.848521 556.6091

532000 1578900 2.685035 494.3332

532000 1578950 2.633441 434.2042

532000 1579000 2.605808 375.2388

532000 1579050 2.612124 318.7712

532000 1579100 2.611055 267.3004

532000 1579150 2.745008 222.9118

532000 1579200 2.949716 189.3802

532000 1579250 3.229475 169.2949

532000 1579300 3.577191 162.5224

532000 1579350 3.975908 162.9882

532000 1579400 4.397152 161.7215

532000 1579450 4.786918 156.2088

532000 1579500 5.045784 157.4325

532000 1579550 5.079985 182.6557

532000 1579600 4.928253 231.3503

532000 1579650 4.672261 291.6224

107

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

532000 1579700 4.424048 353.5379

532000 1579750 4.192959 415.1575

532000 1579800 3.989975 476.3561

532050 1578450 3.48343 1193.106

532050 1578500 3.440573 1117.399

532050 1578550 2.670599 1016.505

532050 1578600 3.410098 920.2379

532050 1578650 3.322446 853.6089

532050 1578700 3.237364 787.6569

532050 1578750 3.156652 722.2305

532050 1578800 3.082624 657.177

532050 1578850 3.018263 592.3769

532050 1578900 2.856659 526.1128

532050 1578950 2.769168 460.6268

532050 1579000 2.797954 397.5502

532050 1579050 2.856795 335.9587

532050 1579100 2.955309 276.7885

532050 1579150 3.102938 221.5067

532050 1579200 3.306032 173.1622

532050 1579250 3.564166 139.5567

532050 1579300 3.86783 132.5135

532050 1579350 4.199374 148.2615

532050 1579400 4.531002 168.9654

532050 1579450 4.818652 187.3605

532050 1579500 5.00598 208.3559

532050 1579550 5.033894 240.1907

532050 1579600 4.950091 285.05

532050 1579650 4.79142 340.2311

532050 1579700 4.598023 399.0188

532050 1579750 4.402067 459.602

532050 1579800 4.219174 521.0268

532100 1578450 3.640937 1233.039

532100 1578500 3.603033 1157.254

532100 1578550 2.835745 1055.824

532100 1578600 2.81174 983.343

532100 1578650 3.236245 900.7221

532100 1578700 3.202739 831.0597

532100 1578750 3.172657 762.2094

532100 1578800 3.147926 693.9603

532100 1578850 3.131139 626.1018

532100 1578900 3.125712 558.4501

108

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

532100 1578950 3.206464 489.1809

532100 1579000 3.015365 421.0681

532100 1579050 3.100011 353.8795

532100 1579100 3.220656 286.2788

532100 1579150 3.414736 217.7319

532100 1579200 3.608394 149.0715

532100 1579250 3.842013 87.99208

532100 1579300 4.098181 79.84829

532100 1579350 4.36497 126.63

532100 1579400 4.620026 174.1929

532100 1579450 4.833684 214.1865

532100 1579500 4.973133 251.264

532100 1579550 5.018456 291.4328

532100 1579600 4.977351 338.9869

532100 1579650 4.867988 391.7137

532100 1579700 4.721806 448.6379

532100 1579750 4.561963 508.0845

532100 1579800 4.403853 569.1291

532150 1578450 3.799415 1274.39

532150 1578500 3.766369 1198.531

532150 1578550 3.005513 1096.894

532150 1578600 2.988698 1023.987

532150 1578650 3.426379 940.4796

532150 1578700 3.402086 869.983

532150 1578750 3.382472 800.0804

532150 1578800 3.369453 730.5554

532150 1578850 3.365478 661.1742

532150 1578900 3.373602 591.6881

532150 1578950 3.397502 521.822

532150 1579000 3.501506 450.0031

532150 1579050 3.369124 377.8383

532150 1579100 3.493329 304.5212

532150 1579150 3.651039 227.8625

532150 1579200 3.842454 146.0872

532150 1579250 4.063739 57.65526

532150 1579300 4.289782 44.51689

532150 1579350 4.501529 125.0766

532150 1579400 4.700951 190.7665

532150 1579450 4.865923 245.0708

532150 1579500 4.978518 294.1663

532150 1579550 5.022108 341.7548

109

Appendix D – Excel MATLAB Data

DO_Concentration_Data_Scaledto6_09_11_2014 Northing Easting DO Conc. Variance

532150 1579600 5.000284 391.5884

532150 1579650 4.925751 444.7831

532150 1579700 4.81614 501.0797

532150 1579750 4.688163 559.7826

532150 1579800 4.554809 620.3259

532200 1578450 3.957983 1317.381

532200 1578500 3.929527 1241.506

532200 1578550 3.902205 1166.473

532200 1578600 3.168492 1066.874

532200 1578650 3.16317 994.436

532200 1578700 3.601617 911.7409

532200 1578750 3.59121 841.1702

532200 1578800 3.588147 770.8539

532200 1578850 3.594627 700.5664

532200 1578900 3.613255 630.0606

532200 1578950 3.646996 559.0593

532200 1579000 3.69903 487.242

532200 1579050 3.819276 413.4964

532200 1579100 3.90655 339.2728

532200 1579150 3.881828 263.3029

532200 1579200 4.053551 188.8893

532200 1579250 4.24225 128.561

532200 1579300 4.432358 121.7876

532200 1579350 4.611317 170.5228

532200 1579400 4.7727 230.7915

532200 1579450 4.90644 288.3576

532200 1579500 4.996389 341.7734

532200 1579550 5.036757 393.542

532200 1579600 5.027783 445.839

532200 1579650 4.976596 499.9089

532200 1579700 4.894255 556.1208

532200 1579750 4.792481 614.365

532200 1579800 4.681485 674.4114