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J. Frank Professional Paper - Final Draft

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J. Frank Professional Paper - Final Draft

  1. 1. 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
  2. 2. ©COPYRIGHT by Jacqueline Lorene Frank 2015 All Rights Reserved
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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.
  7. 7. 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
  8. 8. 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
  9. 9. 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).
  10. 10. 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
  11. 11. 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
  12. 12. 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 Data 8/27/2014 9/2/2014 9/11/2014
  13. 13. 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,
  14. 14. 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.
  15. 15. 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 2 𝐸[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
  16. 16. 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 𝑥 𝑜, [as] a linear combination or weighted average of all the observed variables: 𝑧̂( 𝑥0) = 1 𝑧(𝑥1) + 2 𝑧(𝑥2) + ⋯ +  𝑛 𝑧(𝑥 𝑛), 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,
  17. 17. 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
  18. 18. 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.
  19. 19. 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.
  20. 20. 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.
  21. 21. 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.
  22. 22. 16 Figure 5- A 3-D concentration map showing predicted DO concentrations over the site area calculated using data measured on 08.27.2014.
  23. 23. 17 Figure 6 – A 3-D concentration map showing predicted DO concentrations over the site area calculated using data measured on 09.02.2014
  24. 24. 18 Figure 7 – A 3-D concentration map showing predicted DO concentrations over the site area calculated using data measured on 09.02.2014
  25. 25. 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.
  26. 26. 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.
  27. 27. 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
  28. 28. 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,
  29. 29. 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.
  30. 30. 24 REFERENCES CITED
  31. 31. 25 Bohling, G. (2005, October). Kriging. Kansas Geological Survey. City of Bozeman; Trust for Public Land. (2014, September 11). Story Mill Community Project Conceptual Park Plan. Bozeman, MT. City of Bozeman; Trust for Public Land. (2014, May). Story Mill Ecological Restoration. Bozeman, MT. Deford, L. (2014, Fall). GPHY 504 – Modeling Concepts Assignment. Deford, L. (2014). Progress Report for the Story Mill Wetland Monitoring Project. Deford, L., Stein, O., Cahoon, J., Hartshorn, A., & Ewing, S. (2014). Montana State University’s Involvement with the Story Mill Wetland Annual Report. Montana State University, Bozeman, MT. Faulwetter, J., Gagnon, V., Sundberg, C., Chazarenc, F., Burr, M., Brisson, J., . . . Stein, O. (2009). Microbial processes influencing performance of treatment wetlands: A review. Ecological Engineering, 35(6), 987-1004. Hatch. (n.d.). Hatch LDO Probe. IntelliCALTM LDO101. Hillsboro Parks and Recreation. (2015). Aquatic Chemistry Overview. Retrieved March 2, 2015, from Jackson Bottom Wetlands Preserve: http://www.jacksonbottom.org/monitoring- restoration/aquatic-chemistry-overview/ MATLAB. (2014). Vers. R2014a. South Natick, MA: MathWorks. Minnesota Pollution Control Agency. (2009, February). Low Dissolved Oxygen in Water: Causes, Impact on Aquatic Life - An Overview. Water Quality/Impaired Waters. Mullin, K. (2011, August 25). Types of wetlands . Retrieved March 2, 2015, from http://www.ngwa.org/fundamentals/teachers/pages/types-of-wetlands.aspx
  32. 32. 26 Murphy, R. R., Curriero, F. C., & Ball, W. P. (2010, February). Comparison of Spatial Interpolation Methods for Water Quality Evaluation in the Chesapeake Bay. JOURNAL OF ENVIRONMENTAL ENGINEERING © ASCE. Nikroo, L., Kompani-Zare, M., Sepaskhah, A., & Fallah Shamsi, S. (2010). Groundwater Depth and Elevation Interpolation by Kriging Methods in Mohr Basin of Fars Province in Iran. Environmental Monitoring and Assessment, 166(1), 387-407. Plymesser, K. (2014). Assistant Professor, Engineering, Montana State University-Billings. Ponnamperuma, F. (1972). The Chemistry of Submerged Soils. Advances in Agronomy, 24, 29– 96. doi:10.1016/S0065-2113(08)60633-1 Reams, G. A., Huso, M. M., Vong, R. J., & McCollum, J. M. (1997). Kriging Direct and Indirect Estimates of Sulfate Deposition: A Comparison. United States: Forrest Service. 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. USGS. (n.d.). Anoxic. Retrieved April 4, 2015, from Environmental Health - Toxic Substances: 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.
  33. 33. 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.
  34. 34. 28 APPENDICES
  35. 35. 29 APPENDIX A SCHEDUE OF WELL OWENERSHIP
  36. 36. 30
  37. 37. 31 APPENDIX B ORIGINAL MATLAB CODE FROM DR. KATHRYN PLYMESSER
  38. 38. 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=SB; %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
  39. 39. 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')
  40. 40. 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=Arhs; % solution of matrix equation zhat=b(1)+b(2).*xew+b(3).*xns; zres=z-zhat; %residuals dtX=[xew xns zres];
  41. 41. 35 APPENDIX C MATLAB CODE
  42. 42. 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=SB; %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
  43. 43. 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=SB; %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
  44. 44. 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=SB; %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
  45. 45. 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')
  46. 46. 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')
  47. 47. 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')
  48. 48. 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=Arhs; % solution of matrix equation zhat=b(1)+b(2).*xew+b(3).*xns; zres=z-zhat; %residuals dtX=[xew xns zres];
  49. 49. 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);
  50. 50. 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);
  51. 51. 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);
  52. 52. 46 APPENDIX D EXCEL MATLAB DATA
  53. 53. 47 Appendix D – Excel MATLAB Data MatLabData08.27.2014
  54. 54. 48 Appendix D – Excel MATLAB Data MatLabData09.02.2014
  55. 55. 49 Appendix D – Excel MATLAB Data MatLabData09.11.2014
  56. 56. 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
  57. 57. 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
  58. 58. 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
  59. 59. 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
  60. 60. 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
  61. 61. 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
  62. 62. 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
  63. 63. 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
  64. 64. 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
  65. 65. 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
  66. 66. 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
  67. 67. 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
  68. 68. 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
  69. 69. 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
  70. 70. 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
  71. 71. 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
  72. 72. 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
  73. 73. 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
  74. 74. 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
  75. 75. 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
  76. 76. 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
  77. 77. 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
  78. 78. 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
  79. 79. 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
  80. 80. 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
  81. 81. 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
  82. 82. 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
  83. 83. 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
  84. 84. 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
  85. 85. 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
  86. 86. 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
  87. 87. 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
  88. 88. 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
  89. 89. 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

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