Groundwater Contamination of Heavy Metals through Soil Attenuation

GEOG 578 ­ GIS Applications May 12th, 2016

Cody Calkins Naomi Crump Tobin McGilligan Dan Schumacher

Table of Contents Page Number

Table of Contents ...... 1 Objectives ...... 2 Introduction and Background ...... 2 Methodology ...... 3 Results and Discussion ...... 6 Conclusions and Future Research ...... 9 Figures and Tables ...... 11 References ...... 17 Appendix A: Metadata...... 18 Appendix B: Tables 1­4/Additional Output . 19

1

Objectives

In Wisconsin, two­thirds of the population relies on groundwater for their drinking water supply (DNR 2016), and this resource is threatened by heavy metal contamination from industrial waste, landfills, and other sources. Our goal is to assess the vulnerability of Wisconsin groundwater to heavy metal contamination around four Environmental Protection Agency (EPA) ‘Superfund’ sites by improving a previous soil attenuation model with knowledge from contemporary research.

Introduction and Background

Sources of groundwater contamination have myriad origins in Wisconsin. Agriculture and animal products are an extremely important sector of the state’s economy, as evidenced by the nearly 300 Concentrated Animal Feeding Operations (CAFOs) permitted by the Wisconsin Department of Natural Resources (Seely, R. 2016). The manure and other waste from these CAFOs, when disposed of improperly, can contaminate groundwater and private wells in rural areas with pathogens including coliform, E. Coli, and nitrates. In more urban areas of the state, decades­old infrastructure corrodes, leeching chemicals and solvents into municipal water supplies. For this project, our specific focus was on groundwater contamination of heavy metals including lead, copper, zinc, cadmium, and chromium. These metals can be the byproducts of manufacturing processes, the precipitates of decomposing detritus, and even occur at high levels naturally in some aquifers (McCoy, M. K. 2016). In order to further define the scope of our project, we selected four EPA ‘Superfund’ sites with a history of contributing to heavy metal contamination in their areas. ‘Superfund’ sites are areas of contaminated land designated by the U.S. Environmental Protection Agency as posing an extreme risk to public health and the environment (EPA 2016). The EPA assigns

2 remediation liability to companies, governments, and/or individuals to clean up these sites, and then manages the cleanup process. Out of 55 ‘Superfund’ sites in Wisconsin, we chose 4 of the most hazardous that captured a diversity of heavy metal contaminants across different geographies. The selected sites and their contaminants are:

● Hechimovich Sanitary Landfill (Pb, Cr) in Dodge County ​ ​ ​ ● Janesville Old Landfill (Pb, Cr) in Rock County ​ ​ ​ ● Sauk County Landfill (Pb, Cr) in Sauk County ​ ​ ​ ● Tomah Municipal Landfill (Cu, Zn) in Monroe County ​ ​ ​

Importantly, the sites we selected have been under remediation for at least several years. In most cases, a majority of the cleanup process has been completed, and the sites are now under monitoring schedules that seek to contain and prevent any further contamination. Despite this reduced risk, understanding the specifics of groundwater contamination with regard to heavy metals is important in the siting of manufacturing zones, proper disposal of wastes, and in mitigating the effects of future incidents in Wisconsin and elsewhere.

Methodology

After selecting our areas of interest, the next step was to create our site profile in ArcMap. We entered the coordinates of each Superfund site as a point feature and created a 5­mile buffer around the point. This distance was decided as a large enough buffer to properly show soil attenuation in relation to our sites. We then downloaded the soil site files for each county and clipped them to fit our buffer zone for each site. With the soil sites created, we then used an extension of ArcMap called "Soil Data Viewer" and extracted information about each of the soil types surrounding our selected sites. This information included: ● Cation Exchange Capacity (for some soils)

3 ● Surface Texture ● Organic Matter ● Saturated Hydraulic Conductivity ● pH ● Drainage Class ● Slope ● Permeability Using this information we created a general attenuation profile for each site. We

accomplished this by using Good & Madison's Attenuation Profile (​ Good & Madison 1987). ​ Their profile assigned a subjective 1 – 10 score based on that soil’s properties to hold a contaminant in the profile, preventing it from moving into the groundwater. Then, they summed the scores of each variable and assigned the following attenuation categories based on the soil’s placement within the summed range: least, marginal, good, or best. Our first map in this project followed their work closely and allowed us to create a soil attenuation map for general contaminants around each of our sites. Since the focus of our project was heavy metal contamination, a general attenuation map was not enough. Our project expanded upon Good & Madison's scoring system to better suit heavy metal attenuation. Originally, we considered simply weighting each of the original variables relating to their relevance to heavy metal attenuation in order to accomplish this. For instance, pH and organic matter content are very important factors in heavy metal attenuation. Therefore, we would assign a higher overall multiplicative weight to these variables whereas for other properties like drainage class or texture, which do not have much of an impact on heavy metals, would be assigned a lower multiplicative weight. pH is important because if the heavy metal is outside its range of solubility, it will precipitate out and may become bound in the soil, and higher amounts of organic matter mean there is a greater chance for organic­metal complexes to form. It should be noted that the most common metal ion form of our heavy metals was used in this analysis. Rather than weighting the more important variables, we decided it would be better to reclassify each of Good & Madison's variables as they relate to heavy metal attenuation.

4 The first scoring system we adapted was the depth to bedrock. Because most metals typically stay in the first few inches or foot of the soil, we coalesced the deeper classes into one. So rather than having 20­30 in, 30­40 in, and 40+ in, our updated scoring system has only 20+ inches deep. Initially, we had hoped to create separate maps for each heavy metal based on pH. However, we found that the range in pH from metal to metal did not vary enough to justify creating five maps for each site. Instead, we settled on three new pH classes suitable for heavy metals in general. Good & Madison based their organic matter content scoring off the soil taxonomic class. We felt this was too broad and instead classified organic matter content based on the actual percentage of organic matter content. There are two variables not considered in Good & Madison’s work that we aimed address; slope and cation exchange capacity. Take slope for example, steeper slopes may have shallower soils and thus a weakened ability to retain contaminants. These steeper slopes are also more prone to erosion and their contents may be lost to lower spots in the landscape where more contaminants could accumulate. Slope was scored based on its slope classification defined by the NRCS. Soils are assigned a letter from A to F and each letter is assigned a slope percentage. A­slopes being the shallowest slopes to F­slopes being the steepest. This made it easy to assign each class a score since shallower slopes are more prone to contaminants remaining in the soil, those soils were assigned a lower score. Cation exchange capacity is a soil’s ability to retain and exchange cations. Unfortunately, the data provided for cation exchange capacity was inconsistent. And in the end we decided to leave it out of our final analysis, fearing the soil types that had no reported CEC would be misrepresented. Using this adapted attenuation profile, we were able to calculate new overall scores. From these scores we created classes similar to the way it was done in the general attenuation profile. From these classes we created our second map for each site. Once we created these two maps we thought it would be interesting to compare the the results of each map together on a single map. To accomplish this we compared the

5 categorical values from each of the maps. Then based on how that soil category changed from the original map to our custom map, we gave it a new classification of either: Promoted, Demoted, or No Change. If a soil type went from a lower category to higher category, it was classified as promoted. For example, if a soil type went from "Good" on the original map to "Best" on our heavy metal map, it was assigned "Promoted". Similarly, if a soil type was assigned a lower class on our map than it had originally, it was classified as "Demoted". If the classification was the same on both maps it was assigned "No Change".

Results and Discussion

*See figures 1 ­ 13 and Tables 1, 2, and 3 in Figures and Tables ​ In the end, we had created twelve total maps (excluding the well context map), three for each of the counties. Two of those maps showed the models used to determine attenuation ability of all of the soils in those counties and the third is a comparison between the two to show where there were changes. Two tables were also created to show the overall rank of each soil based on their final score and category. The scores ranged from 25 points at the low end to 69 at the high end. The top and bottom twenty soils were looked at to see differences between the highest and lowest scoring soils for both attenuation models. Custom Model: See Table 2 for the top and bottom ranking soils for this model. The ​ typically high ranking soils had surface texture of loam or silt loam, a subsurface texture of clay, clay loam, or silty clay loam. All of the top ranking soils also were well drained, shallowly sloping, and had a moderate amount of organic matter ranging from 1.5 to 4%. Because of the textures of these soils, they also had a relatively low to moderate saturated hydraulic conductivity meaning that water can move through the soil but isn’t necessarily impeded. Depth played a significant role in how well a soil ranked but it wasn’t the only factor. On the lower end, some a couple of the soils scored very low for depth at around 18 inches but on average the depth was well over 40 inches. The pH of the soil didn’t seem to play a significant enough roll between the two groups to show any interesting differences.

6 On the other hand, those soils which ranked low were sandy or mucky in composition and very poorly or excessively drained allowing for rapid and easy movement of contaminants into and through the soil. While some of the soils had very high organic matter contents, they were also the muck soils that aren’t capable of really capturing contaminants easily. Because of their sandy or mucky composition, these soils also had very high saturated hydraulic conductivities meaning water can move through the soil rapidly. Slope was slightly at play but there wasn’t too significant of a difference between these two groups. Original Model: See Table 1 for the top and bottom ranking soils for this model. Like ​ the custom model, many of the same values for variables remained the same. High ranking soils were typically high in silt or clay, were well drained, low to moderate permeability, and had a high organic matter content. For organic matter content, however; unlike the custom model, was operationalized based on the taxonomic class of that soil. All of the soils that ranked high were either a Mollisol or an Alfisol. Similarly, like the custom model, the low scoring soils were sandy, course, or mucky textured, poorly or excessively drained, and had high permeability. However; unlike the top scoring soils, majority of these soils were classified as Entisols, which are low in organic matter content, or Histosols which are essentially only organic matter and have very little ability to retain contaminants. And like the custom model, pH didn’t seem to be significantly different between the two groups. The range for pH was around 6 for the higher scoring soils and slightly lower for the lower scoring soils. So why did pH not make a significant difference like we thought? This may be due to the way soil pH is sampled and measured in the lab or the soil formation factors for all of the soils assessed were relatively similar hence the similarity in the values. Difference Maps (Figures 9­12): Overall, when looking at all 399 soils, 56 were ​ promoted, 77 were demoted, and 277 did not change from the original map to the custom scoring system. However; these numbers could change depending how someone decided to break up their final summed scores into the four different classes. If this wasn’t consistent amongst the four maps due to different ranges in the summed scores, then each map would

7 have to be looked at independently to see any trends in types of soils that were demoted, promoted, or remained the same between the two models. Rock County Differences: There did not appear to be any significant differences ​ between soils that were promoted or demoted from the original model (Figure 1) to the custom model (Figure 2) when comparing the changes to the originally scored soils. Values for all the variables had similar ranges. For instance, there were coarsely textured soils that were promoted and demoted. There were also soils with drainage classes that were well or poorly drained in both groups. When comparing the change in classes to the customly scored soils, slope seems to play a role in whether the soil was promoted or demoted. Soils that typically had a low slope were demoted while those with a high slope were promoted. However; when comparing the actual average values for slope, the average slope of those that were demoted was around 5.7% and those that were promoted had an average slope of 8.6%. The slope also ranged from 1 to 37.5% for the demoted soils but only had a range of 1 to 27.5% in the promoted soils. These types of values were similar for the permeability, depth, pH and organic matter contents in that the ranges and average values for those variables were not too far off. Sauk County Differences: The differences between the original (Figure 3) and ​ custom (Figure 4) Sauk County site maps appear to give the site a more moderate attenuation profile; the best attenuation sites were demoted and many of the worst attenuation soils were promoted. Most of the soils that were promoted were sandy loam or fine sandy loam texture, while the soils that were demoted were of loam, silt loam, and silty clay loam texture. Additionally, soils that were demoted had an average slope of 9.7% while those promoted had an average slope of 4.25%. Although there were soils with high slopes that were promoted, they tended to be accompanied with excessively drainage and a low permeability score. The demoted soil with a low slope was paired with a poor drainage class as well as a high organic matter content. pH values did not seem to vary significantly between the changed soils. The permeability score in demoted soils tended to be less than 15, while in promoted soils it was primarily significantly above 15. To describe the

8 apparent pattern in the category differences, surface and subsoil texture, slope, drainage class, and permeability appeared to be the main influences in determining the change.

Monroe County Differences: In Monroe county (Figures 5 & 6), our custom scoring ​ model had an ‘equalizing’ effect on soil attenuation classification. That is to say, soils classified as ‘least’ or ‘marginal’ were generally promoted, while soils classified as ‘best’ or ‘good’ were demoted. Again, adding slope to Good & Madison’s scoring model affected the results significantly; most of the change in Monroe County was a result of taking the soil’s slope into account. The most common surface texture in Monroe County was silt loam, and the soils had a wide diversity of permeability and slope. Dodge County Differences: The differences between the original (Figure 7) and ​ custom (Figure 8) Dodge County map demotes nearly all of the "Best" category to "Good". All of the demoted soil sites were well drained. Most of the demoted soils had a depth to bedrock between 30 and 40 inches deep. The average slope of the demoted soils was 10.6% They also happened to have a clay loam or silty clay loam as a subsurface texture. Polygons that were promoted had an average slope of 6.7%. Most of the promoted soil types had a silt loam surface texture. There did not appear to be any other connection between promoted soils and individual variables. Finally, we decided to include a layer of well points to provide a contextual element to our analysis (Figure 13). This is meant to provide awareness about the vulnerability of the state’s water sources. Many of the wells fell within marginal or least soil attenuation zones, which leaves them more vulnerable to contaminants than those in good or best zones. Based on this data layer, it’s clear to see how the dumping of heavy metals and general contaminants can become a problem for not only those in the immediate area, but also those in zones where soil attenuation potential is marginal and very poor.

Conclusions and Future Research

9 We feel that our methods have created a good model for assessing the vulnerability of groundwater to heavy metal contaminants. With that being said, there are some limitations to our analysis that we would like to address. First, our map is meant to give a broad overview of heavy metal vulnerability. This means that our map should not be used as a replacement for site specific analysis. Site specific analysis would provide more in depth details about variables. Factors like cation exchange capacity, pH, and organic material could be more reliable with an actual sample at a given site. Through both the original scoring, custom scoring, and comparison maps we did a lot of data entering by hand. We needed to join in certain variables like subsurface texture and depth to bedrock from outside sources. Additionally, we entered the scores and categories for every soil type by hand. Although we were careful and do not see any mistakes, we want to acknowledge that there was potential for "human error" throughout our project. Our methodology allows for a couple of different ways to expand upon our scoring system. Firstly, it would be simple to add more variables into the scoring system. For example, if we were able to retrieve data for cation exchange capacity it would allow us to improve our scoring of heavy metals. Another way we could improve our scoring system is by weighting the more important variables like we had initially planned. This could give an even more accurate assessment of heavy metal attenuation. Additionally, our scoring system could be edited to focus on other contaminants. Our methodology currently sets the scores for variables like pH, organic material, slope, and depth to bedrock to focus on heavy metal contaminants. These variables could be changed so that they apply to any other kind of contaminant. We hope that our results can provide awareness to homeowners that use wells as their primary source of water. Homes that fall within some of the lower scoring areas on our custom map should be sure to get their wells tested regularly. Homeowners should also be aware of the dangers of spreading pesticides or dumping contaminants near their wells.

Figures 1­13

10

Figure 1: Soil attenuation for general contaminants in Janesville Old Landfill

Figure 2: Soil attenuation for heavy metals in Janesville Old Landfill

11

Figure 3: Soil attenuation for general contaminants in the Sauk County site

Figure 4: Soil attenuation for heavy metals in the Sauk County Site

12

Figure 5: Soil attenuation for general contaminants in Monroe County

Figure 6: Soil attenuation for heavy metals in Monroe County

13

Figure 7: Soil attenuation for general contaminants in Hechimovich Landfill site

Figure 8: Soil attenuation for heavy metals in Hechimovich Landfill site

14

Figure 9: Janesville Old Landfill, difference between original and custom scoring.

Figure 10: Sauk County Landfill, difference between original and custom scoring.

15

Figure 11: Monroe County, difference between original and custom scoring.

Figure 12: Hechimovich Landfill, difference between original and custom scoring.

16 Figure 13: Monroe County example of well point overlay on soil attenuation map.

References

(1) Cerqueira, B, E F. Covelo, M L. Andrade, and F A. Vega. "Retention and Mobility of Copper and Lead in Soils as Influenced by Soil Horizon Properties." Pedosphere 21, ​ ​ no. 5 (July 20, 2011): 603­14. http://www.sciencedirect.com/science/article/pii/S1002016011601628. ​ (2) DNR. 2016. Groundwater. http://dnr.wi.gov/topic/groundwater/ (last accessed May ​ ​ ​ ​ 1 2016). (3) EPA. 2016. Superfund. https://www.epa.gov/superfund (last accessed 12 May 2016) ​ ​ ​ (4) Good, L W., and F W. Madison. "Soils of Portage County and Their Ability to

17 Attenuate Contaminants." UWEX. http://wgnhs.uwex.edu/pubs/000403/. ​ ​ (5) Hornsby, Arthur G. "Groundwater and Public Policy Leaflet Series." University of Florida Soil Science Department. http://dnr.wi.gov/topic/Groundwater/documents/pubs/howcntm.pdf. ​ (6) Ketterings, Quirine, Shaw Reid, and Renuka Rao. "Agronomy Fact Sheet Series: Cation Exchange Capacity." Cornell University Cooperative Extension. http://nmsp.cals.cornell.edu/guidelines/factsheets.html. ​ (7) McCoy, M. K. 2016. As wells go deeper, radium levels rise in state tap water. Failure at ​ ​ the Faucet. Wisconsin Center for Investigative Journalism. http://wisconsinwatch.org/2016/03/as­wells­go­deeper­radium­levels­rise­in­stat e­tap­water/ (last accessed May 12 2016). ​ (8) Reddy, K J., L Wang, and S P. Gloss. "Solubility and mobility of copper, zinc and lead in an acidic environment." Plant and Soil 171 (1995): 53­58. ​ ​ https://www.researchgate.net (9) Seely, R. 2016. Bacteria in state’s drinking water is a ‘public health crisis’. Failure at ​ ​ the Faucet. Wisconsin Center for Investigative Journalism. http://wisconsinwatch.org/2016/05/bacteria­in­states­drinking­water­is­public­h ealth­crisis/ (last accessed May 12 2016). ​

Appendix A: Metadata

The data sources for this analysis are as follows: ● NCRS Web Soil Survey ● Soil Data Viewer in ArcMap ● SSURGO ● Geospatial Data Gateway ● Dept. of Agriculture, Trade and Consumer Protection Well Data Viewer

Appendix B: Tables 1­4 and Additional Output

18 Table 1. The top and bottom twenty soils in the Original Model of Soil Attenuation for Dane County Soils. Each soil was scored according to various chemical and physical properties. This model was developed by Good & Madison in the 1980's for Portage County, Wisconsin. Depth to Saturated Rank Soil Subsurface Organic Matter Drainage pH Score Attenuation Surface Texture Bedrock Hydraulic (Top 10) Symbol Texture (Taxonomy) Class in Water Sum Category (in) Conductivity $

1 DuA Silt Loam Clay Loam Mollisol Well drained 5.8 77 9.00 56 Best 2 OgA Silt Loam Silty Clay Loam Mollisol Well drained 5.8 80 9.00 56 Best 3 WnA Silt Loam Clay Loam Mollisol Well drained 5.8 66 9.00 56 Best 4 WoA Silt Loam Silty Loam Mollisol Well drained 6.5 64 9.00 56 Best 5 103C2 Silt Loam Clay Alfisol Well drained 6.5 73 2.64 56 Best 6 TaA Silt Loam Silty Clay Loam Mollisol Well drained 6.2 60 3.45 56 Best 7 WwB Silt Loam Clay Alfisol Well drained 6.5 73 2.21 56 Best 8 PlA Silt Loam Silty Clay Loam Mollisol Well drained 6.7 53 15.95 54 Best 9 PmA Silt Loam Silty Clay Loam Mollisol Well drained 6.7 53 28.81 54 Best 10 FlB Silt Loam Silty Loam Alfisol Well drained 5.9 88 9.00 53 Best 11 PeC2 Silt Loam Clay Loam Alfisol Well drained 5.8 68 9.00 53 Best 12 PnA Loam Silty Clay Loam Mollisol Moderately well drained 6.1 53 9.00 53 Best 13 SaD Silt Loam Silty Clay Loam Alfisol Well drained 6.5 57 9.00 53 Best 14 WfB2 Loam Clay Loam Alfisol Well drained 5.8 50 9.00 53 Best 15 115B2 Silt Loam Silt Loam Alfisol Well drained 6.5 70 9.15 53 Best 16 133B2 Silt Loam Silty Clay Loam Alfisol Well drained 6.2 60 3.85 53 Best 17 1743F Loam Loam Alfisol Well drained 5.5 60 9.17 53 Best 18 PsA Silt Loam Silty Clay Loam Mollisol Well drained 6.7 60 15.95 53 Best 19 MsC2 Silt Loam Silty Clay Loam Mollisol Well drained 6.5 33 9.00 53 Best 20 BtB Silt Loam Silty Clay Alfisol Well drained 6.2 39 3.71 53 Best

Depth to Saturated Rank Soil Subsurface Organic Matter Drainage pH Score Attenuation Surface Texture Bedrock Hydraulic (Bottom 20) Symbol Texture* (Taxonomy) Class in Water Sum Category (in) Conductivity $

380 566A Sand Sand Entisol Moderately well drained 5.5 24 91.74 20 Least 381 568A Loamy Fine Sand Fine Sand Entisol Somewhat poorly drained 5.9 23 90.81 20 Least 382 596A Sand Sand Entisol Moderately well drained 5.5 24 91.74 20 Least 383 Pa Organic Organic Histosol Very poorly drained 7.0 35 13.52 20 Least 384 Ar Organic Organic Histosol Very poorly drained 7.0 34 13.07 20 Least 385 Pa Organic CTM Histosol Very poorly drained 7.0 35 13.52 20 Least 386 RrF Gravelly Sandy Loam CTM Mollisol Excessively drained 7.2 18 122.41 19 Least 387 498A Sandy Loam Sandy Loam Alfisol Somewhat poorly drained 5.5 24 17.50 19 Least 388 558A Loamy Sand Loamy Sand Alfisol Somewhat poorly drained 6.5 27 11.30 19 Least 389 RxC2 Gravelly Loam CTM Mollisol Excessively drained 7.2 18 120.18 19 Least 390 562D2 Loamy Sand Loamy Sand Entisol Somewhat excessively drained 5.9 23 88.49 18 Least 391 1224F Sand Sand Entisol Excessively drained 5.5 21 56.93 17 Least 392 1233F Sand Sand Entisol Excessively drained 5.5 21 56.93 17 Least 393 1548A Loamy Sand Fine Sand Entisol Somewhat poorly drained 5.9 23 90.81 17 Least 394 233C Sand Sand Entisol Excessively drained 5.9 21 56.93 17 Least 395 569A Muck Sand Entisol Poorly drained 4.8 22 88.25 17 Least 396 BoD Sand Sand Entisol Excessively drained 5.8 21 54.70 17 Least 397 PfD Loamy Sand Sand Entisol Excessively drained 6.2 28 92.00 17 Least 398 1599A Muck Sand Entisol Poorly drained 5 29 220.06 14 Least 399 CcB Loam CTM Alfisol Somewhat excessively drained 6.5 17 223.38 14 Least

* = soils that included were coursly textured. This includes objects with a size greater than sand like gravel. $ = Saturated hydraulic conductivity is measured in micrometers of water per second flow in the soil. Table 2. The top and bottom ten soils in the Custom Model of Soil Attenuation for Dane County Soils. Each soil was scored according to various chemical and physical properties.

Depth to Saturated Rank Soil Surface Subsurface Drainage Organic Matter (% pH in Average Slope Score Attenuation Bedrock Hydraulic (Top 20) Symbol Texture Texture Class of A Horizon) Water (%) Sum Category (in) Conductivity $ 1 105B2 Silt Loam Clay Well drained 2.5 6.5 73 2.50 4 69 Best 2 EdB2 Loam Silty Clay Well drained 4 6.7 18 7.79 4 69 Best 3 WwB Silt Loam Clay Well drained 2.5 6.5 73 2.21 4 69 Best 4 DuA Silt Loam Clay Loam Well drained 4 5.8 77 9.00 1 68 Best 5 JaA Loam Clay Loam Well drained 4 6.2 29 9.00 1 68 Best 6 WoA Silt Loam Silty Loam Well drained 3.5 6.5 64 9.00 1.5 68 Best 7 TaA Silt Loam Silty Clay Loam Well drained 3.5 6.2 60 3.45 1 68 Best 8 103C2 Silt Loam Clay Well drained 2.5 6.5 73 2.64 9 67 Best 9 EdC2 Loam Silty Clay Well drained 4 6.7 18 7.79 9 67 Best 10 HeA Loam Clay Loam Well drained 1.5 6.7 29 5.29 1.5 67 Best 11 SkA Loam Loam Well drained 2 6.7 30 9.00 1 67 Best 12 WwC2 Silt Loam Clay Well drained 2.5 6.5 73 2.21 8 67 Best 13 SuA Fine Sandy Loam Clay Loam Well drained 2 6.7 37 9.00 1 66 Best 14 133B2 Silt Loam Silty Clay (Loam) Well drained 2.5 6.2 60 3.85 4 66 Best 15 DuB2 Silt Loam Clay Loam Well drained 4 5.8 78 9.00 4 66 Best 16 JaB Loam Clay Loam Well drained 4 6.2 30 9.00 4 66 Best 17 OgA Silt Loam Silty Clay Loam Well drained 4 5.8 80 9.00 2.5 66 Best 18 PlA Silt Loam Silty Clay Loam Well drained 4 6.7 53 15.95 1 66 Best 19 PmA Silt Loam Silty Clay Loam Well drained 4 6.7 53 28.81 1 66 Best 20 DdA Silt Loam Silty Clay Loam Well drained 2 6.8 39 7.62 1 66 Best

Depth to Saturated Rank Soil Surface Subsurface Drainage Organic Matter (% pH in Average Slope Score Attenuation Bedrock Hydraulic (Top 20) Symbol Texture Texture* Class of A Horizon) Water (%) Sum Category (in) Conductivity $

380 2A Muck Organic Very poorly drained 87 7 35 16.85 1 30 Least 381 Ar Muck Organic Very poorly drained 63.5 7 34 13.07 1 30 Least 382 Hu Muck Organic Very poorly drained 45 7 80 22.00 1 30 Least 383 Hw Muck Organic Very poorly drained 70 7 80 22.00 1 30 Least 384 Pa Muck Organic Very poorly drained 55 7 35 13.52 1 30 Least 385 RrE ravelly Sandy Loa CTM Excessively drained 3 7.2 18 122.41 25 30 Least 386 RrF ravelly Sandy Loa CTM Excessively drained 3 7.2 18 122.41 37.5 30 Least 387 569A Muck Sand Poorly drained 45 4.8 22 88.25 1 30 Least 388 Ho Muck CTM Very poorly drained 45 0 80 22.00 1 30 Least 389 1599A Muck Sand Poorly drained 45 5 29 220.06 1 29 Least 390 CcC2 Loam CTM Somewhat excessively drained 2 6.5 17 223.38 9 29 Least 391 BoB Sand Sand Excessively drained 0.5 5.8 21 54.70 4 29 Least 392 PfB Loamy Sand Sand Excessively drained 1.25 6.2 28 92.00 4 29 Least 393 Pa Muck CTM Very poorly drained 55 0 35 13.52 1 28 Least 394 190F y Very Fine Sandy CTM Excessively drained 3.5 7.5 12 19.57 62.5 27 Least 395 CcD2 Loam CTM Somewhat excessively drained 2 6.5 17 230.20 16 27 Least 396 BoC Sand Sand Excessively drained 0.5 5.8 21 54.70 8 27 Least 397 PfC Loamy Sand Sand Excessively drained 1.25 6.2 28 92.00 8 27 Least 398 BoD Sand Sand Excessively drained 0.5 5.8 21 54.70 17 25 Least 399 PfD Loamy Sand Sand Excessively drained 1.25 6.2 28 92.00 14 25 Least

* = soils that included were coursly textured. This includes objects with a size greater than sand like gravel. $ = Saturated hydraulic conductivity is measured in micrometers of water per second flow in the soil. Tabe 3: Values assigned to chemical and physical properties of soils by Good and Madison. The better that property was at attenuating contaminants, the higher the score it received. At the end, all of these scores were summed. The summation was then broken up into four categories of soil attenation; Best, Good, Marginal, and Least. A map was then created with these categories. Organic Matter Saturated Hydraulic Subsurface Texture Depth Of Soil for pH in Content Conductivity Drainage Surface Texture A & B Horizons Water (Taxonomy)* (Permeability) Class Score (A) Horizon Score (B) Horizon Score Score Score Score Score

well drained 10 loam, silt loam, sandy clay loam, silt 9 clay, silty clay, sandy clay, silt 10 Mollisol 8 0.00 - 0.01 (Very Low) 10 > 40 inches 10 > 6.6 6

clay, silty clay, clay loam, silty clay sandy clay loam, loam, silt loam, clay well to moderately well drained 7 8 7 Alfisol 5 0.01 - 10 (Moderate) 8 31 - 40 inches 8 < 6.6 4 loam, sandy clay loam, silty clay loam

loamy very fine sand, very fine sandy loamy very fine sand, very fine sandy Entisols, Inceptisols, moderately well drained 4 4 4 3 10 - 100 (High) 4 20 - 30 inches 3 loam, loamy fine sand, fine sandy loam loam, loamy fine sand, fine sandy loam Spodosols sand, loamy sand , sandy loam, organic sand, loamy sand, sandy loam,organic somewhat poorly drained, poorly 1 materials, and all textural classes with 1 materials, and all textural classes with 1 Histosols 1 100 + (Very High) 1 < 20 inches 1 drained, and very poorly drained coarse fragment class modifiers coarse fragment class modifiers

*Good and Madison originally assigned scores to a soil's organic matter content using the taxonomic class it fell under. This value can vary so the custom model uses real percentages of organic matter instead. Table 4: Additional variables or replacements for Good and Madison's model. Cation Exchange Organic Matter Slope*Score Score Score Capacity$ Content A (0 - 2%) 10 < 5.0 1 <0.5 1 B (3 - 6%) 8 5.0 - 11.0 2 0.5 - 2.0 2 C (6 - 12%) 6 11.0 - 17.0 4 2.0 - 3.3 4 D (12 - 20%) 4 17.0 - 24.0 6 3.3 - 4.7 6 E (20 - 40%) 2 24.0 - 28 8 4.7 - 6 8 (> 40%) 1 > 28.0 10 >6 10

*$ = Slope and CEC are new additions not considered by Good & Madison in their original model. Organic matter content replaces their taxonomic scoring system. Due to incomplete data, CEC was not scored. Conceptualization

Key Concept: Soil Attenuation

Operationalized Variables

Original Custom Model Model Variables Variables

Depth to Inches of Saturated Subsoil Bedrock A & B Horizons Hydraulic Conductivity Permeability

Drainage Soil Survey Soil Survey Percent of Organic Class Drainage Classes Taxonomy A Horizon Matter Content

Soil Survey Texture Per Unit Dry Wt Cation Exchange Texture Class Class of Cations:Soil Capacity (A AND B Horizons)

pH of Surface pH in Soil Survey Slope (A) Horizon water Slope Classes Class

Data Layers: NRCS SSURGO / Web Soil Survey

NRCS Soil Data Viewer for ArcGIS

EPA Superfund Site Profiles Implementation

EPA Superfund SSURGO Soil Data Viewer Data Layers Site Profiles

pH of Depth to Slope Drainage Surface Pull General Soil Bedrock Class Class Add Sites as Points Shapefiles for Monroe, (A) Horizon Operations Using Lat/Long Sauk, Rock, and Dodge Counties Organic Cation Subsoil Texture Matter Exchange Permeability Class Content Capacity

Buffer Join Tables (5 miles) Using FID or MUSYM

Site Outlines County Profiles

Clip

Soil Profiles for Each Site

Assign Scores to Variables Use New and Modified Varibles Following Good & Madison to Score Second / Custom Model

Sum Scores Sum Scores

Categorize Scores Categorize Scores (Based on Good & Madison) (Varied by County)

Good Best Map Categories Marginal Map Categories Least

Outputs Originally Scored Maps Customly Scored Maps of Soil Attenuation of Soil Attenuation

Score Categories Score Categories Best = 4 Best = 4 Good = 3 Good = 3 Marginal = 2 Marginal = 2 Least = 1 Least = 1

Subtract Custom From Original Scores

Table of Score Changes

Categorize Score Changes Score: >0 = Promoted 0 = No Change < 0 = Demoted

Map of Classification Changes