Naturally-Occurring Metals Concentrations in Michigan Soils: A 2015 Survey

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1 Western Michigan University ScholarWorks at WMU Master's Theses Graduate College Naturally-Occurring Metals Concentrations in Michigan Soils: A 2015 Survey Zachary Lowell Spotts Western Michigan University, spottsza@gmail.com Follow this and additional works at: Part of the Geology Commons, Sedimentology Commons, and the Soil Science Commons Recommended Citation Spotts, Zachary Lowell, "Naturally-Occurring Metals Concentrations in Michigan Soils: A 2015 Survey" (2015). Master's Theses This Masters Thesis-Open Access is brought to you for free and open access by the Graduate College at ScholarWorks at WMU. It has been accepted for inclusion in Master's Theses by an authorized administrator of ScholarWorks at WMU. For more information, please contact maira.bundza@wmich.edu.

2 ii NATURALLY-OCCURRING METALS CONCENTRATIONS IN MICHIGAN SOILS: A 2015 SURVEY by Zachary Lowell Spotts A thesis submitted to the Graduate College in partial fulfillment of the requirements for the degree of Master of Science Geosciences Western Michigan University May 2015 Thesis Committee: Duane R. Hampton, Ph.D., Chair Alan Kehew, Ph.D. Daniel Cassidy, Ph.D.

3 NATURALLY-OCCURRING METALS CONCENTRATIONS IN MICHIGAN SOILS: A 2015 SURVEY Zachary Lowell Spotts, M.S. Western Michigan University, 2015 Closure of hazardous waste sites can be aided through the use of a soil background survey for metals. Data for these studies come from either field sampling or publicly available environmental files. Previous studies were conducted in Michigan, known as the Michigan Background Soil Survey (MBSS). The current update is a refinement and improvement of the 2005 update. New data was collected from publicly available environmental files and other sources. Both the current update and the 2005 update differentiated data by soil type and glacial lobe. GIS analysis was used to observe spatial distribution of the data and to determine whether metals in soils were influenced by glacial drift thickness and/or bedrock. Impact on environmental health from naturallyoccurring soils was addressed by comparing soil data to environmental protection criteria. Further refinement and improvement are needed beyond the current update. Field sampling can address spatial distribution concerns. Similarly, bedrock analysis for metals can enhance the understanding of how bedrock influences metals in soils. Speciation of metals should be analyzed for those metals in soils above environmental criteria. Better understanding of the naturally-occurring environment can lead to better decision making that is needed to protect humans and the biosphere.

4 Copyright by Zachary Lowell Spotts 2015

5 ii ACKNOWLEDGEMENTS I would like to begin by acknowledging my advisor, Dr. Duane Hampton. His support, knowledge, and guidance throughout my graduate education and thesis work inspired me to pursue the study of naturally-occurring metals in soils. Secondly, I would like to thank the other members of my thesis committee, Dr. Alan Kehew and Dr. Daniel Cassidy. Their insight and knowledge about glacial geology and environmental geochemistry, respectively, helped review and enhance my thesis work. Next, I would like to thank the Michigan Department of Environmental Quality Remediation and Redevelopment Division for the opportunity to support a new soil background survey for the state of Michigan. This opportunity gave me a chance to learn more about the environment, environmental regulations and expand my knowledge about glacial geology and environmental geochemistry. Lastly, I would like to thank my parents, Lowell and Karen, for their support and love throughout my life s education. Much love. Zachary Lowell Spotts ii

6 TABLE OF CONTENTS ACKNOWLEDGMENTS... LIST OF TABLES... LIST OF FIGURES... ii iv v INTRODUCTION... 1 BACKGROUND... 5 LIMITATIONS METHODS AND MATERIALS RESULTS AND DISCUSSION FUTURE WORK CONCLUSIONS APPENDICES A. Shacklette (USGS) 1984 Summary Statistics B. Ohio EPA 1996 Summary Statistics C. Washington State 1994 Summary Statistics D. State of Oregon 2013 Summary Statistics E. MDNR 1991 Summary Statistics F. MBSS 2005 Summary Statistics G. MBSS 2005 Sand Summary Statistics H. MBSS 2005 Clay Summary Statistics REFERENCES iii

7 LIST OF TABLES 1. Summary Statistics Sand Summary Statistics Clay Summary Statistics Soil Criteria Five Survey Comparison Hypothetical Site Data iv

8 LIST OF FIGURES 1. Soil Background Study Area Sand Locations Map Clay Locations Map Glacial Drift Thickness Map Michigan Bedrock Map Sand Aluminum Map Clay Aluminum Map Sand Arsenic Map Clay Arsenic Map Sand Barium Map Clay Barium Map Sand Chromium Map Clay Chromium Map Sand Cobalt Map Clay Cobalt Map Sand Copper Map Clay Copper Map Sand Iron Map Clay Iron Map Sand Lead Map v

9 List of Figures Continued 21. Clay Lead Map Sand Lithium Map Clay Lithium Map Sand Magnesium Map Clay Magnesium Map Sand Manganese Map Clay Manganese Map Sand Nickel Map Clay Nickel Map Sand Sodium Map Clay Sodium Map Sand Strontium Map Clay Strontium Map Sand Titanium Map Clay Titanium Map Sand Vanadium Map Clay Vanadium Map Sand Zinc Map Clay Zinc Map vi

10 1 INTRODUCTON During the late 1980's, the Michigan Department of Natural Resources (MDNR) first started compiling soil sampling data of naturally-occurring background concentrations for metals. This database would eventually be known as the Michigan Background Soil Survey (MBSS). An update of this database occurred during 1991; the 1991 MBSS was implemented to expand the database of the previous survey. Each survey looked at background data in two distinct ways: soil type and glacial lobe. In 2005 the MBSS database was updated with additional background sampling data collected from After three significant updates to the MBSS database, data gaps still persist. The current soil background survey has addressed data gaps left by previous surveys. A more complete soil background for metals database will allow for better regulatory decisions. Site closure plans are regulatory decisions pertaining to contamination. These plans are submitted to the Michigan Department of Environmental Quality (MDEQ) for review and approval. Cleanups and corrective action work at hazardous waste facilities are included in these plans (MDNR 1991). Analyses of soils at a facility must meet certain criteria to protect human health and the environment. In order for a closure plan to be accepted, soils must be remediated until they are at or below local background concentrations. Having gaps in a background soil database could lead to facilities being closed that are not remediated to true background concentrations. Also, this might lead properties that have soils with background or below background concentrations being designated as contaminated facilities requiring cleanup. This lack of data would result in wasting money and other resources to clean

11 2 up a property that was not contaminated by human activities. The primary goal of this soil background update is to fill in data gaps that the previous surveys left in the database. This soil background update will address southern Michigan. The counties included in this survey are Allegan, Barry, Berrien, Branch, Calhoun, Cass, Eaton, Hillsdale, Ingham, Jackson, Kalamazoo, Lenawee, Livingston, Macomb, Monroe, Oakland, St. Joseph, Van Buren, Washtenaw, and Wayne. These counties are the location of much historical industrial activity and contamination. Expanding data in southern Michigan will provide more representative, naturally-occurring metals in soil information. The most recent update (2005) to the MBSS had many counties in southern Michigan with no data. Spatial distribution of metals in soils in the 2005 MBSS was lacking and needs to be updated to improve regulatory decisions. In order to expand the database, data was mined from existing MDEQ files. New United States Geological Survey (USGS) data was also used in this study. Regulatory files pertaining to Part 201 of the Michigan Natural Resources and Environmental Protection Act (1994) supplied most of the new data. A small portion of the data came from Part 213 files. These regulatory files are concerned with sites of environmental contamination. Ideal soil background data comes from a study unequivocally designed to collect naturallyoccurring metals data. A study explicitly designed for soil background would address concerns about human influence on data being added to the database. Although this study did not collect specifically designed soil background data, the data was collected with a concern for human influence. Data obtained from MDEQ files were scrutinized to ensure appropriate soil background data were added to the MBSS database.

12 3 A soil background data quality objectives document was produced to guide data evaluation. Samples used in this study were not taken from areas of environmental contamination, including but not limited to: fill areas, railroad tracks or railway areas, orchards known to have had arsenic applications, farmed lands known to have had waste sludge applications, areas affected by past or present waste management practices, parking lots, roads or roadsides, areas of concentrated air pollutant depositions, or areas affected by contaminated runoff. Grab and incremental samples were used; composite samples were excluded. Incremental sampling used in this study was designed specifically to address soil background and involves organized sampling points that estimate mean concentrations. This type of sampling involves a small volume of media that is taken from various locations and combined into one larger sample. Better representative data than other sampling methods is provided by this sampling methodology (ITRC 2015). Also, data of suspect quality including data that was estimated (flagged J), has blank contamination (flagged B) or rejected (flagged R) was excluded from the database. Twenty six metals were included in this study. These are the metals analyzed in this survey: aluminum (Al), antimony (Sb), arsenic (As), barium (Ba), beryllium (Be), boron (B), cadmium (Cd), total chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), lithium (Li), magnesium (Mg), manganese (Mn), mercury (Hg), molybdenum (Mo), nickel (Ni), selenium (Se), silver (Ag), sodium (Na), strontium (Sr), thallium (Tl), titanium (Ti), vanadium (V), and zinc (Zn). Metals data were gathered from uncontaminated soils. All soil types were included: sand, clay, silty clay, sandy silt, etc. The concentration data for each metal in the database were examined

13 4 with GIS to see the spatial distribution of each metal, and using basic statistical analyses including mean, median, standard deviation, and the range of concentrations.

14 5 BACKGROUND Inorganic metal concentrations in soils are determined by inorganic metal concentrations in source bedrock and other source materials. Climatic and biological factors also play an important role generating inorganic metal concentrations in soils (Shacklette 1984). Literature on soil background is extensive and applies to many different environmental problems including: forestry, agriculture, and soil remediation. Soil background surveys in most states are associated with remediation of hazardous waste sites. Some state agencies have collected samples specifically for soil background surveys, while other state agencies have compiled soil background data from publicly available environmental files. One of the very first soil background surveys was published by Shacklette This survey evaluated 46 elements and reported arithmetic means for the data (See Appendix A). According to Shacklette, presenting the data this way allows it to be easily compared to other data in literature. Phase one of this study collected 963 samples all over the United States; 355 samples were collected for phase two of this study. Phase two obtained analyses of 11 more elements than phase one. These elements include: antimony, bromine, carbon, germanium, iodine, rubidium, silicon, sulfur, thorium, tin and uranium. Another study that addresses the issue of soil background is the evaluation of background metal concentrations in Ohio soils. This data set evaluated 20 metals from 64 sites within Ohio. These sites are found within 36 of Ohio's 88 counties. Data was compiled from public Ohio Environmental Protection Agency files for Resource Conservation and Recovery Act (RCRA) closure, RCRA corrective action and

15 6 Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) projects (Cox 1996). Summary statistics for this study can be observed in Appendix B. Statistics run for this survey include: probability plots, outliers, data distribution, median, mean, range, variance, standard deviation, skewness, and kurtosis. Washington State developed a soil background survey and published the results in Approximately 490 samples were collected from 166 sites within Washington. Aluminum, arsenic, beryllium, cadmium, chromium, iron, mercury, magnesium, manganese, nickel, lead and zinc were evaluated in this study. Many sampling locations had more than one sample collected. All measured values from each location were averaged to help determine background concentrations. Non-detect values posed a different problem for this study; one-half the detection limit was used to replace nondetect values. The 90 th percentile value for each element was used to calculate natural background values (Washington 1994). See Appendix C for summary statistics pertaining to this study. Oregon compiled a database from many different data sources and collected additional samples to produce a soil background study. Sixteen metals were evaluated in this study: Sb, As, Ba, Be, Cd, Cr, Cu, Pb, Mn, Hg, Ni, Se, Ag, Tl, V and Zn. The database contained 5127 samples throughout the state. Areal distribution and density of sample locations were assessed to determine spatial bias in the data. Spatial location was also compared to the physiographic regions in Oregon. Statistics (See Appendix D) in this study include: minimum, maximum, mean, median, outlier removal, 25 th, 75 th, 90 th and 95 th percentile concentrations. Box plots were used to further evaluate the data (State of Oregon 2013).

16 7 Michigan has looked at the soil background issue on separate occasions. In 1991, a survey of Michigan's soil and metal concentrations was conducted. This survey compiled samples from Michigan s environmental regulatory agency files containing facility data and waste management division sampling. Each sample was divided by soil type: topsoil, clay, silt, sand and peat. Data was compared by glacial lobe (Lake Michigan, Saginaw, Huron-Erie, and Superior). Three hundred forty-eight samples were included in this study. Mean, standard deviation, minimum and maximum were calculated and one-half the detection limit was used for data below the detection limit. Sixteen different metals were evaluated for this update to the MBSS. These include: Ag, Al, As, Ba, Cd, Co, Cr, Ca, Fe, Hg, Li, Mn, Ni, Pb, Se, and Zn (MDNR 1991) Summary statistics for six of these metals can be seen in Appendix E. A major update to the MBSS occurred in More samples were added from USGS and Army Corp of Engineers data. General soil types were reviewed again for this new version of the MBSS. Spatial location of the samples was broken down by glacial lobe distinction. This study included additional metals: Be, Cu, Mg, Mo, Na, Sr, Tl, Ti and V. Percentage of non-detect values was calculated for each metal. Basic statistics were performed to determine the mean, median, standard deviation and range of the data. The distribution of data for each metal was also estimated for this study (See Appendix F, G and H) (MDEQ 2005). Both previous Michigan surveys looked at soil samples concerning glacial lobes. Glacial geology is very complex and must be taken into account when looking at Michigan soils. During the end of the Pleistocene epoch, many different instances of advancing and retreating ice sheets shaped the Great Lakes region. Throughout this time

17 8 period, the geologic record of glacial drift was overridden and obliterated by later and more energetic re-advances of ice (Leverett 1915). Much of the previous glacial history, before the last ice retreat, is either incomplete or not present anymore. The current surface environment shows a record of what happened during the last ice retreat. Glacial lakes, topography, and fluvial drainage patterns had a major impact on the current surface environment of Michigan. In Michigan as elsewhere, the ice was fragmented into different glacial lobes or sections of ice. These glacial lobes moved separately from one another and produced a unique record of glacial deposits. Sediments from different glacial lobes can overlap one another depending on topography, drainage patterns and glacial lake locations. It is nearly impossible to distinguish a border between glacial lobes due to the complexity of glacial deposits. Glacial lobe boundaries used for the 2005 and the 2015 Michigan studies are more administrative boundaries than distinctive physiographic boundaries. A better way to differentiate between glacial lobes is to note each glacial lobe and their interlobate areas. Interlobate areas are areas in between each glacial lobe. These areas have a great deal of overlap regarding glacial sediment deposition. Deposits from glaciers come from many different environments. Sand and clay glacial deposits are produced in higher or lower energy environments, respectively. Clay is deposited in glacial lakes, and other low energy environments. Shale bedrock erosion and weathering as well as erosion of previous glacial sediments also have an effect on clay deposition. Sand is deposited in outwash plains, eskers (a landform produced by glacial rivers), dunes, shorelines of lakes, and other higher energy environments. Erosion and weathering of sandstone bedrock and

18 previous glacial sediments also affects where sand is deposited. 9

19 10 LIMITATIONS Even though the 2015 soil background effort has done a good job, data gaps still exist. Many counties have limited data, and counties with an adequate amount of data still lack good spatial distribution. Sand data have good spatial distribution, with only Barry and Lenawee counties lacking good spatial distribution and number of samples. These two counties do not have the ideal 12 samples per county agreed upon as a goal of this study. Clay data have poor spatial distribution. They are generally lacking except in the Huron-Erie glacial lobe. Limited clay data are found in the Lake Michigan glacial lobe, and clay data are limited as well in the southern portion of the Saginaw lobe. Much of the data collected are limited to the Michigan ten metals: arsenic, barium, cadmium, chromium, copper, lead, silver, selenium, silver, and zinc. Another limitation with this study was that all metals were analyzed for total metals rather than looking at separate species of each metal. This is important because different species or valence states of metals behave as if they were different entities, have different chemistries and toxicities, and thus should be considered separately. Aerial deposition of pollution from industrial and other human activities affect various naturally-occurring metals in soils. This is another limitation of this and previous studies. Studying aerial deposition patterns of pollution could produce areas that are not affected by industrial and other human activities. If this is possible, areas of naturally-occurring metals in soil can be found. A study specifically designed to look at background metals in soils as well as specific species of each metal would address concerns with the current dataset. Field sampling for background metals could fix data gaps and spatial distribution issues. Better

20 11 information about background conditions in soils will lead to better decision-making for the science community, the MDEQ, and the regulated community.

21 12 METHODS AND MATERIALS The first step in developing a database for the MDEQ of naturally-occurring metals in soils was to mine publicly available environmental files. Past soil background studies and USGS data (Smith 2013) were also used to expand data coverage. Files stemming from one part of the Michigan Natural Resources and Environmental Protection Act (1994) were examined for soil background data. Part 201 of this Act was the main source of files for data mining. Brownfield redevelopment files were also used to find data. These files address concerns with environmental contamination. A data quality objectives document was used to guide data collection. This data quality objectives document helped ensure only naturally-occurring metals data were entered into the database. Different categories of data, combined to form metadata, also helped certify metals data. Categories of data involved with data validity include: soil boring logs, site history, known contamination, satellite images, laboratory data on organic and inorganic analytes, maps, and summary data tables. These categories assisted data inspection by helping identify samples from obvious fill material, parking lots, analytical data free of analytical flags, drains or ditches, roads or roadsides, railroad tracks and railway areas, et cetera. Regarding soil classification, many of the samples were classified using the United Soil Classification System (USCS). Most of the samples were distinguished in boring logs as sand or clay and nothing more. Future studies should use a consistent soil classification system in order to easily compare data across different studies. Only sand and clay samples were used for the 2015 study; silt samples were not included in this study. Also, samples with organics were thrown out and not evaluated.

22 13 Analysis of data included statistical analysis and geographic information systems (GIS) analysis. R studio version was used to statistically analyze the data. ArcGIS was used for spatial analysis of the data. Statistics run using R studio included: mean, median, standard deviation, range, percent non-detect and distribution. Kriging was used in ArcGIS to produce an interpolated contour map of concentrations for each metal.

23 14 RESULTS AND DISCUSSION Analysis of soil background data starts with scrutiny of outliers. Due to a relatively small dataset, outliers are not excluded from analysis. Percentage of nondetect values helps determine which method will be used to produce statistics. Data distribution for each metal is examined using histograms and goodness-of-fit tests. Normal, lognormal, or non-parametric distributions are used in this study. Summary statistics are calculated for each metal based on the chosen distribution for that metal. Mean, median, standard deviation, and the range of concentrations are summary statistics utilized for each metal. A total of 709 samples are used for this update to the MBSS. Sand data has 381 samples and clay data has 328 samples. New sand data contains 265 samples and new clay data involves 95 samples. The MBSS 2005 contributed 95 sand samples and 231 clay samples. USGS data (Smith 2013) contributed 21 sand samples and 2 clay samples. Table 1 shows summary statistics for the entire study area. Tables 2 and 3, respectively, show sand summary statistics and clay summary statistics for each glacial lobe and the entire study area. See Figure 1 for a map of the study area. Spatial distribution of the data, separated by soil type, is shown in Figures 2 and 3. A glacial drift thickness map is shown in Figure 4 and a Michigan bedrock map is shown in Figure 5. Comparing sand data versus clay data, clay samples have higher mean metal concentrations than sand samples. This can be attributed to the electrostatic forces on solid surfaces. Clays have much great surface area per unit mass than sands. These forces help bind positive ions to the surfaces of clay minerals. Binding of ions to the

24 Table 1: Summary Statistics Inorganic Metal Number of Samples Percent Nondetect Assumed Distribution of Data Mean (mg/kg) Standard Deviation Median (mg/kg) Range (mg/kg) Aluminum (Al) % Lognormal Antimony (Sb) % Nonparametric 0.39 < Arsenic (As) % Lognormal Barium (Ba) % Lognormal Beryllium (Be) % Nonparametric <0.5 < Boron (B) 8 75 % Nonparametric <2 < Cadmium (Cd) % Nonparametric < Chromium (Cr) % Lognormal Cobalt (Co) % Lognormal Copper (Cu) % Lognormal Iron (Fe) % Lognormal Lead (Pb) % Lognormal Lithium (Li) 70 9 % Lognormal Magnesium (Mg) 99 0 % Lognormal Manganese (Mn) % Lognormal Mercury (Hg) % Nonparametric <0.05 < Molybdenum (Mo) % Nonparametric <1 Nickel (Ni) % Lognormal Selenium (Se) % Nonparametric 0.37 < Silver (Ag) % Nonparametric <0.2 < Sodium (Na) % Censored-Nor Strontium (Sr) 68 0 % Normal Thallium (Tl) % Nonparametric <0.5 < Titanium (Ti) 39 0 % Lognormal Vanadium (V) % Lognormal Zinc (Zn) % Normal

25 SAND LAKE MICHIGAN SAGINAW HURON-ERIE Study Area n x SD n x SD n x SD n x SD Range Al L Dist. Sb non 18 < <0.3 < As L Ba L Be non 26 < < B non 0 6 <2 2 < <2 < Cd non 69 < < <0.2 < Cr L Co L Cu L Fe N Pb L Li L Mg L Mn L Hg non 63 < < < <0.05 < Mo non 25 <1 29 <1 14 <1 68 <1 <0.2 5 Ni L Se non < <0.275 < Ag non 61 < < < <0.1 < Na C-L < Sr N Tl non 24 < < < <0.5 < Ti L V L Zn L Table 2: Sand Summary Statistics

26 17 Table 3: Clay Summary Statistics CLAY LAKE MICHIGAN SAGINAW HURON-ERIE Study Area n x SD n x SD n x SD n x SD Range Dist. Al L Sb non <0.5 < As L Ba L Be C-L B non Cd C-N < Cr L Co L Cu N Fe N Pb L Li L Mg L Mn L Hg non 6 < < < <0.05 < Mo C-L Ni N Se non 11 < < < <0.5 < Ag non 11 < < < <0.455 < Na C-N Sr N Tl non < < <0.5 < Ti N V L Zn N

27 Figure 1: Soil Background Study Area 18

28 19 Figure 2: Sand Locations Map Figure 3: Clay Locations Map

29 Figure 4: Glacial Drift Thickness Map (Esch 2012) 20

30 Figure 5: Michigan Bedrock Map (MDNR 1987) 21

31 22 surfaces of clay minerals increases the metal concentrations within shale, which is essentially lithified clay. Clayey glacial sediments are formed from weathering and erosion of shale bedrock in addition to weathering of other minerals. Higher concentrations of certain metals in clay are most likely due to high concentrations of that metal in source shale bedrock (Shacklette 1984). Sand mean values for each metal are on average two to three times lower than clay mean values for each metal. Also, concentration ranges for each metal in clay samples are larger than ranges for sand samples. Aluminum sand data for each glacial lobe are very similar. An increase in the number of samples would allow for a better understanding of naturally-occurring metals in sand. Clay data pertaining to aluminum are also sparse. From the current data, the Huron-Erie glacial lobe has a higher mean concentration than the Saginaw glacial lobe; the Lake Michigan glacial lobe only has one data point. The entire study area is over soil criteria pertaining to residential drinking water of 1 mg/kg (MDEQ 2013) (See Figures 6 and 7). Arsenic (As) has comparable concentrations within sand in the Saginaw and Huron-Erie lobes. There is a lower occurrence of arsenic in sand within the Lake Michigan lobe than the other two glacial lobes (See Figure 8). Clay data shows the highest mean concentration in the Huron-Erie lobe (See Figure 9). More clay data for arsenic is needed from the Lake Michigan lobe to further analyze this data. This tendency for soils to have higher concentrations of arsenic in the Huron-Erie lobe can possibly be attributed to arsenopyrites and arsenic-rich iron oxyhydroxides in the Marshall Formation and the Coldwater Shale (Kolker 2003). Additional studies are

32 23 Figure 6: Sand - Aluminum Map Figure 7: Clay - Aluminum Map

33 24 Figure 8: Sand - Arsenic Map Figure 9: Clay - Arsenic Map

34 25 necessary to further confirm this statement, but these results do look promising. Regarding health criteria for arsenic, most of the sand data from the Saginaw and Huron-Erie glacial lobes are over criteria. All of the Huron-Erie lobe clay data is over criteria. Some parts of the Saginaw lobe are also over criteria. Criteria for arsenic start at 4.6 mg/kg for residential drinking water protection and groundwater surface water interface protection (GSI) (See Table 4). Barium (Ba) data show a higher occurrence in the Huron-Erie glacial lobe versus the other two lobes for both soil types. Mean concentration in the Huron-Erie lobe is more than one and a half times higher in sand than the other lobes. Huron-Erie clay data is a little less than one and a half times higher than the other glacial lobes (See Figures 10 and 11). Chromium (Cr) data within sand are similar between the Saginaw and Huron- Erie lobes. The Lake Michigan glacial lobe has a lower occurrence of chromium in sand. A difference in the data does occur within clay. Huron-Erie lobe data shows a higher incident of chromium in clay than the other lobes. Clay data for the Huron-Erie lobe is around one and a half times higher than the other two glacial lobes (See Figures 12 and 13). Cobalt (Co) sand and clay data have higher concentrations in the Huron-Erie glacial lobe. Within the Saginaw lobe, sand and clay data for cobalt is greater than the Lake Michigan lobe (See Figures 14 and 15). Regarding environmental health criteria within clay-textured soils, the entire study area is over 0.8 mg/kg for residential drinking water protection. Almost all of the study area is above criteria within sand soils.

35 26 Table 4: Soil Criteria (MDEQ 2013) Hazardous Substance Chemical Abstract Service Number Statewide Default Background Level Groundwater Protection Residential Drinking Water Protection Criteria Groundwater Surface Water Interface Protection Criteria Aluminum (B) E+06 1,000 NA Contact Direct Contact Criteria 5.0E+7 (DD) Antimony NA 4,300 94,000 (X) 1.80E+05 Arsenic ,800 4,600 4,600 7,600 Barium (B) , E+06 (G) 3.70E+07 Beryllium NA 51,000 (G) 4.10E+05 Boron (B) NA 10, E+5 (X) 4.8E+7 (DD) Cadmium (B) ,200 6,000 (G,X) 5.50E+05 Chromium (III) (B,H) ,000 (total) 1.0E+9 (D) (G,X) 7.90E+08 Cobalt , , E+06 Copper (B) , E+06 (G) 2.00E+07 Iron (B) E+07 6,000 NA 1.60E+08 Lead (B) , E+05 (G,X) 4.00E+05 Lithium (B) ,800 3,400 8, E+6 (DD) Magnesium (B) NA 8.00E+06 NA 1.0E+9 (D) Manganese (B) E+05 1,000 (G,X) 2.50E+07 Mercury (Total) (B,Z) Varies 130 1, (M); E+05 Molybdenum (B) NA 1,500 64,000 (X) 2.60E+06 Nickel (B) , E+05 (G) 4.00E+07 Selenium (B) , E+06 Silver (B) ,000 4, (M); E+06 Sodium NA 4.60E+06 NA 1.0E+9 (D) Strontium (B) NA 92, E E+08 Thallium (B) NA 2,300 4,200 (X) 35,000 Vanadium NA 72, E E+5 (DD) Zinc (B) , E+06 (G) 1.70E+08 All criteria are expressed in units of parts per billion (ppb). One ppb is equivalent to 1 microgram per kilogram (ug/kg). Criteria with 6 or more digits are expressed in scientific notation.

36 27 Figure 10: Sand - Barium Map Figure 11: Clay - Barium Map

37 28 Figure 12: Sand - Chromium Map Figure 13: Clay Chromium Map

38 29 Figure 14: Sand - Cobalt Map Figure 15: Clay Cobalt Map

39 30 Copper (Cu) data have similar spatial distributions to arsenic, barium, and cobalt. Sand mean concentration for copper is higher in the Huron-Erie lobe than the Lake Michigan and Saginaw lobes. Clay mean data follows the same trend as the sand mean data (See Figures 16 and 17). Iron (Fe) has limited data in both sand and clay. From the sand data, iron is in higher concentration in the Saginaw glacial lobe. Clay data show the highest iron concentration in the Huron-Erie lobe. More data are needed (See Figures 18 and 19). Also, the whole study area is above 6 mg/kg residential drinking water protection criteria for both sand and clay. Lead (Pb) data within sand are very similar between the Saginaw and Huron-Erie lobes. The Lake Michigan lobe lead data for sand is lower than the other two lobes. There is a major difference shown in the clay data. Higher concentrations of lead occur in the Saginaw glacial lobe (See Figures 20 and 21). Lithium (Li) data are very limited and it would be difficult to defend conclusions made from observation of this data. Data in sand are about the same for the Huron-Erie and Saginaw glacial lobes. Clay data show a higher occurrence of lithium in the Huron- Erie lobe, but more data is needed to fully support this conclusion (See Figures 22 and 23). Residential drinking water criteria of 3.4 mg/kg are exceeded over the entire study area regarding clay soils. Almost all of the study area sand data is above criteria. Magnesium (Mg) sand data display higher concentrations in the Saginaw lobe. Clay data show higher magnesium concentrations in the Huron-Erie lobe. Both sand and clay have very limited number of samples. More data needs to be collected to show how magnesium naturally occurs in Michigan soils (See Figures 24 and 25).

40 31 Figure 16: Sand - Copper Map Figure 17: Clay - Copper Map

41 32 Figure 18: Sand - Iron Map Figure 19: Clay - Iron Map

42 33 Figure 20: Sand - Lead Map Figure 21: Clay - Lead Map

43 34 Figure 22: Sand - Lithium Map Figure 23: Clay - Lithium Map

44 35 Figure 24: Sand - Magnesium Map Figure 25: Clay - Magnesium Map

45 36 Manganese (Mn) also lacks data in sand and clay. The current data show a similar trend to magnesium (See Figures 26 and 27). As with magnesium, additional manganese data are needed to show the true trend of the data in southern Michigan. Sand and clay data for manganese are above residential drinking water protection criteria of 1 mg/kg for the whole study area. Nickel (Ni) displays trends similar to arsenic, cobalt, barium, and copper. Sand and clay concentrations are close between the Saginaw and Huron-Erie lobes, but higher concentrations occur in the Huron-Erie lobe. The Lake Michigan lobe data is lower than the other two lobes for sand and clay (See Figures 28 and 29). Inadequate sodium (Na), strontium (Sr) and titanium (Ti) data does not allow for sound conclusions (See Figures 30, 31, 32, 33, 34 and 35). Having less than 30 total samples hinders analysis of strontium. Expanded data collection would vastly increase knowledge about these three metals in Michigan soils. With the data available, strontium data in the Huron-Erie lobe clays is above 92 mg/kg for residential drinking water criteria. Most of the Saginaw lobe near the Huron-Erie lobe is above criteria for strontium in clay. Vanadium (V) sand data yield higher concentrations in the Huron-Erie glacial lobe (See Figures 36 and 37). Clay data does not permit an accurate analysis due to lack of data. An area of the Huron-Erie glacial lobe is above criteria for clay samples. Residential drinking water protection criteria for vanadium is 72 mg/kg (See Figures 36 and 37). Zinc (Zn) data have tendencies comparable to As, Ba, Co, Cu, and Ni. Sand has higher zinc concentrations in the Huron-Erie glacial lobe. This finding also occurs for

46 37 Figure 26: Sand - Manganese Map Figure 27: Clay - Manganese Map

47 38 Figure 28: Sand - Nickel Map Figure 29: Clay - Nickel Map

48 39 Figure 30: Sand - Sodium Map Figure 31: Clay - Sodium Map

49 40 Figure 32: Sand - Strontium Map Figure 33: Clay - Strontium Map

50 41 Figure 34: Sand - Titanium Map Figure 35: Clay - Titanium Map

51 42 Figure 36: Sand - Vanadium Map Figure 37: Clay - Vanadium Map

52 43 the clay data and is similar to the distributions for copper and arsenic. Spatial distribution of sand and clay data for zinc can be seen in Figures 38 and 39. Effects of arsenopyrites and arsenic-rich iron oxyhydroxides on soils data can be seen in arsenic data for sand and clay. More investigation is needed to confirm these findings, but this shows promising confirmation that bedrock influences metal concentrations in soils. Areas of thin glacial drift seem to have higher occurrences of metals than areas of thick glacial drift. Clay soils have higher concentrations of metals than sand soils. The Huron-Erie glacial lobe has higher occurrences of some metals than the other two glacial lobes. Further investigation is needed to comprehend why this is occurring. It could be different bedrock compositions are responsible, or it could be the greater industrialization in this area has contributed to higher metal levels in soils thought to be largely unaffected by humans. Data are compared between USGS studies and Michigan studies to observe variances concerning four metals (See Table 5). Table 5: Five Survey Comparison USGS 1984 USGS 2013 MBSS 1991 MBSS As Cu Pb Zn

53 44 Figure 38: Sand - Zinc Map Figure 39: Clay - Zinc Map

54 45 Both USGS studies have very similar arsenic and zinc soil concentrations. The USGS 2013 study has lower concentrations of copper and lead than the 1984 study. For the Michigan studies, the 2005 and the 2015 survey have very similar soil concentrations for all four metals. Copper and zinc concentrations are higher in the 1991 study than the other two Michigan studies. Michigan has lower soil concentrations of arsenic, copper, lead, and zinc than the contiguous United States. Each metal in Michigan is at least one half or lower than the average metal concentration of that same metal in the United States. Instead of comparing only to the average concentration over the contiguous United States, it would be interesting to compare Michigan data with other states and regions of the entire United States. A hypothetical site in Calhoun County was investigated to explain how the soil background database will be used for sites with environmental contamination. This site contained a wood treating facility with a large area for lumber storage. Main constituents used for wood treatment at this site included: arsenic, chromium and copper. Recognized environmental concerns occurred in the lumber storage yard. Four soil borings were drilled to determine the extent of contamination on the property (See Table 6). Table 6: Hypothetical Site Data SB-01 (3') sand SB-01 (6') clay SB-02 (3') sand SB-02 (6') clay SB-03 (3') sand SB-03 (6') clay SB-04 (3') sand SB-04 (6') clay As (µg/kg) 3,800 5,200 30,000 42,000 2,800 4,800 25,000 45,000 Cr (µg/kg) 3,000 7,500 27,000 36,000 2,000 4,000 32,000 39,000 Cu (µg/kg) 4,200 5,600 36,000 55,000 1,500 3,000 30,000 38,000

55 46 Site geology consisted of sand on the surface to 4 feet and clay underlying the top layer of sand. Ideally, additional background samples would be taken at this site to compare with the soil background database as well as the other soil samples at the site. After comparing site data to background data and soil criteria the following was observed: two soil borings, SB-02 and SB-04, had sand and clay samples that were over criteria and background values. These soil borings come from an area on site where contamination has occurred. SB-01 had background concentrations for all metals in sand. Regarding clay, SB-01 had naturally-occurring arsenic concentrations above residential drinking water protection criteria and GSI protection criteria. Similarly, SB- 03 had background concentrations for all metals in sand and arsenic values in clay above environmental criteria.

56 47 FUTURE WORK Important work that would vastly increase the usefulness of the current and future soil background datasets include: in depth mineralogy and metals analysis of bedrock in Michigan and a soil background field study addressing metals of greatest environmental concern. Field sampling for soil background data would address spatial issues and produce a dataset with a comparable number of samples for each metal, and not waste time on metals with little or no environmental concern. Additional information on mineralogy and metals content of bedrock in Michigan would immensely improve analysis of the influence of glacial drift thickness and bedrock on metals in soils. Speciation of each metal should also be determined when conducting a soil background field study and bedrock analysis. The reason that is important is that different species or valence states of metals behave as if they were different entities, have different chemistries and toxicities, and thus should be considered separately. All of these analyses suggested for future work would help better understand current environmental conditions and how these conditions were affected by past geologic events.

57 48 CONCLUSIONS To avoid needless cleanups of soils that have metals present due to natural processes, Michigan Department of Environmental Quality (MDEQ) tried to establish the natural background concentration of metals in uncontaminated soils. This study is an elaboration and refinement of MDEQ studies in 1991 and 2005, adding 265 new sand samples and 95 new clay samples plus other samples from a 2013 USGS study. One goal was to achieve a minimum of 12 samples per county. Another goal was to find all of the sample data in environmental files of existing analyses, since no resources were committed to field sampling. Statistical analyses involved in previous studies were refined. All of the soils data were compared with glacial lobes which are responsible for most of Michigan s land cover to see if there were deposits which shared high levels of certain metals. Soil samples were also examined by texture to see if there were significant differences between metals concentrations in sands and clays. Given the additional data collected for this study, sand data has good spatial distribution throughout the study area. A majority of clay data is concentrated in the Huron-Erie glacial lobe. Looking at the different soil textures, clay mean values for each metal are higher than sand mean values. Clay has higher concentrations of metals than sand. Several specific metals have similar spatial distributions within the study area: arsenic, barium, cobalt, copper, nickel, and zinc. Greater concentrations of these metals occur in soils overlying the Huron-Erie glacial lobe. Regarding soil criteria and environmental health protection, seven of the metals within this study sometimes exceed health criteria and could adversely affect human health and the environment: arsenic, cobalt, iron, lithium, manganese, strontium, and vanadium.

58 49 Areas with thin glacial drift have higher concentrations of metals in soils. Where glacial drift is thicker, soils have lower concentrations of metals. In areas of thin glacial drift, metal concentrations in soils are heavily influenced by metals within local bedrock. This influence from bedrock can be seen in clay data in areas with higher arsenic values near where the Coldwater Shale subcrops glacial drift. Sand data for arsenic show this influence near where the Marshall Formation subcrops glacial drift. High arsenic values can possibly be attributed to arsenopyrites and arsenic-rich iron oxyhydroxides within the Coldwater Shale and the Marshall Formation. Having metals in soils above soil criteria can negatively impact human health and the environment. When addressing human health, drinking water and agriculture are two aspects directly influenced by soils. Recreation is another way humans can be exposed to naturally high concentrations of metals in soils. Due to the limited scope of this study, additional work must be done to better understand the naturally-occurring environment. Future work to better understand naturally-occurring metals in soils includes: additional field sampling to get an improved spatial distribution of samples in each area, bedrock analysis of metals, and looking at speciation of metals for those metals that are above soil criteria. Better education for the public about these naturally-occurring metals concerns will also help protect human health. A better understanding of the naturallyoccurring environment and how past processes shaped the landscape today will provide the knowledge needed to help protect human health and the environment.

59 50 Appendix A Shacklette (USGS) 1984 Summary Statistics

60 51 Element Average Range Element Average Range (mg/kg) (mg/kg) (mg/kg) (mg/kg) Al 72, >10,000 As 7.2 < B 33 < Ba ,000 Be 0.92 <1-15 Br 0.85 < C, total 25, ,000 Ca 24, ,000 Ce 75 < Co 9.1 <3-70 Cr ,000 Cu 25 <1-700 F 430 <10 3,700 Fe 26, >100,000 Ga 17 <5-70 Ge 1.2 < Hg 0.09 < I 1.2 < K 15, ,000 La 37 < Li 24 <5-140 Mg 9, >100,000 Mn 550 <2 7,000 Mo 0.97 <3-15 Na 12,000 < ,000 Nb 11 < Nd 46 < Ni 19 <5-700 P 430 <20 6,800 Pb 19 < Rb 67 < S, total 1,600 <800 48,000 Sb 0.66 <1 8.8 Sc 8.9 <5-50 Se 0.39 < Si 310,000 16, ,000 Sn 1.3 < Sr 240 <5 3,000 Ti 2, ,000 Th U V 80 <7-500 Y 25 < Yb 3.1 <1-50 Zn 60 <5 2,900 Zr 230 <20 2,000

61 52 Appendix B Ohio EPA 1996 Summary Statistics

62 53 Metal Initial # of Obs. Final # of Obs. 25% (Q1) 50% (Median) 75% (Q3) 95% Mean (mg/kg) St.Dev. Mean +2St.D. Arsenic Barium Cadmium Chromium Lead Mercury Nickel Zinc

63 54 Appendix C Washington State 1994 Summary Statistics

64 55 units (mg/kg) Al As Be Cd Cr Cu Fe Pb Mn Hg Ni Zn State wide 37, , , Puget Sound 32, , , Clark County 52, , , Yakima Basin 33, , , Spokane 21, ,

65 56 Appendix D State of Oregon 2013 Summary Statistics

66 57 Metal Detection Frequency Non-Detect Range (Min Max) Detect Range (Min Max) Mean (mg/kg) Standard Calculation Method Deviation (Mean and SD) Antimony 145/234 62% Kaplan-Meier Arsenic 1,047/1,288 81% Kaplan-Meier Barium 1,330/1, % , Standard Beryllium 1,106/1,290 86% Kaplan-Meier Cadmium 224/1,243 18% Kaplan-Meier Chromium 1,331/1, % , Standard Copper 1,329/1, % Kaplan-Meier Lead 1,282/1,328 97% Kaplan-Meier Manganese 1,292/1, % , Standard Mercury 738/1,210 61% Kaplan-Meier Nickel 1,320/1, % 4 1 2, Kaplan-Meier Selenium 374/1,180 32% Kaplan-Meier Silver 83/1,221 7% Kaplan-Meier Thallium 114/208 55% Kaplan-Meier Vanadium 1,322/1, % Standard Zinc 1,325/1, % Standard

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