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.