Background Concentrations of Trace Metals in Florida Surface Soils: Taxonomic and Geographic Distributions of Total-total and

Background Concentrations of Trace Metals in Florida Surface Soils: Taxonomic and Geographic Distributions of Total-total and Total-recoverable Concentrations of Selected Trace Metals A report by Drs. Ming Chen, Lena Q. Ma, Willie G. Harris, and Arthur G. Hornesby of the SOIL AND WATER SCIENCE DEPARTMENT UNIVERSITY OF FLORIDA for the State University System of Florida FLORIDA CENTER FOR SOLID AND HAZARDOUS WASTE MANAGEMENT December, 1999 Report #99-7

ACKNOWLEDGEMENTS The authors would like to thank the Florida Center for Solid and Hazardous Waste Management for its financial support (Contract No. 96011017). The authors are indebted to those who participated in the Florida Cooperative Soil Survey. Their collection and characterization of a large number of Florida soil samples made this study possible. The authors are grateful for the assistance from Ms. Angela Choate, Ms. Elizabeth Kennelley, Dr. Cornelis Hoogeweg, Mr. Timthy Fitzpatrick, and Dr. Jolio Arrecis. The helpful suggestions by Ms. Zoe Kulakowski and Ms. Ligia Mora-Applegate of the Florida Department of Environmental Protection and Dr. Stephen Roberts of the University of Florida Center for Environmental and Human Toxicology are gratefully acknowledged. The authors also wish to thank the executive director of the Center, Mr. John Schert, for his advice and support throughout the project. Ming Chen, Ph.D Research Scientist Lena Q. Ma, Ph.D Associate Professor Additional copies of this report can be obtained by contacting: Florida Center for Solid and Hazardous Waste Management 2207-D NW 13th Street, Gainesville, FL 32609 Phone: (352) 392-6264 Fax: (352) 864-0183 E-mail: center@fcshwm.org WWW: http://www.fcshwm.org ii

TABLE OF CONTENTS TITLE PAGE Acknowledgment...... ii Table of Contents........ iii List of Figures...... vii List of Tables...... viii List of Abbreviations, Acronyms & Units of Measurement...... xi Abstract... xvi Executive Summary..... xix I. Total-recoverable Concentrations of Selected Trace Metals in 210 Representative Florida Soils............ 1-1 1.1 Introduction.... 1-2 1.2 Material and Methods...... 1-2 1.2.1 Sample Collection/Selection....... 1-2 1.2.2 Sample Digestion...........1-9 1.2.3 Metal Analysis............1-9 1.2.4 Data Analysis........... 1-9 1.3 Results and Discussion...... 1-11 1.3.1 Statistical Summary of Chemical and Physical Properties of 210 Soil Samples... 1-11 1.3.2 Statistical Summary of Total Concentrations of 8 Trace Elements in 210 Soil Samples... 1-16 1.3.3 Precision and Accuracy for Determination of Total-recoverable Trace Elements Using Certified Reference Materials... 1-19 1.3.4 Statistical Summary of Total-recoverable Concentrations of 8 Trace Elements in 210 Soil Samples... 1-22 iii

1.3.5 Relations between Metal Concentrations and Other Chemical and Physical Soil Characteristics... 1-38 1.4 Literature Cited........ 1-41 II. Spatial Analysis of Background Concentrations of Eight Heavy Metals in Florida Surface Soils............ 2-1 2.1 Introduction.... 2-1 2.2 Material and Methods......... 2-2 2.2.1 State Soil Geographic Database...... 2-2 2.2.2 Georeferencing Sample Locations....... 2-3 2.2.3 Map Development........... 2-4 2.3 Results and Discussion........ 2-10 2.3.1 Sample Locations........ 2-10 2.3.2 Heavy Metal Distributions...... 2-10 2.3.2.1 Arsenic...... 2-10 2.3.2.2 Cadimium...... 2-15 2.3.2.3 Chromium...... 2-18 2.3.2.4 Copper...... 2-21 2.3.2.5 Nickel.... 2-24 2.3.2.6 Lead.......2-27 2.3.2.7 Selenium....... 2-30 2.3.2.8 Zinc...... 2-33 2.4 Conclusions.......... 2-36 2.5 Literature Cited........2-37 III. Arsenic Concentrations in Florida Surface Soils: 1. Distribution in Different Soil Types........... 3-1 3.1 Introduction.....3-1 3.2 Material and Methods...... 3-2 3.2.1 Materials......... 3-2 3.2.2 Sample Analysis........ 3-2 3.2.3 Data Analysis........... 3-3 iv

3.3 Results and Discussion...... 3-3 3.3.1 Arsenic Concentrations in Different Soil Orders....3-3 3.3.2 Factors Affecting Arsenic Concentrations Soils...... 3-4 3.3.3 High Arsenic in Wetland Soils Orders... 3-5 3.4 Acknowledgement.... 3-7 3.5 Literature Cited.......... 3-7 IV. Arsenic Concentrations in Florida Surface Soils: 2. Geographical Distribution.. 4-1 4.1 Introduction.....4-1 4.2 Experimental Section........4-1 4.2.1 Materials..........4-1 4.2.2 Georeferencing of Sample Locations.......4-2 4.2.3 Map Development...........4-2 4.3 Results and Discussion...... 4-2 4.3.1 Geographical Distribution of Seven Soil Orders and Sampling Sites.. 4-2 4.3.2 Geographical Occurrence of Arsenic Background Concentrations.. 4-3 4.3.3 Spatial Distribution of Arsenic Based on Soil Suborders.....4-4 4.4 Acknowledgement... 4-4 4.5 Literature Cited.......... 4-4 V. Arsenic Concentrations in Florida Surface Soils: 3. Total-recoverable vs. Total Concentrations............ 5-1 5.1 Introduction.....5-1 5.2 Material and Methods.......5-1 5.2.1 Materials.......5-1 5.2.2 Sample Analysis.........5-1 5.2.3 Data Analysis......... 5-2 5.3 Results and Discussion.......5-3 5.3.1 Summary Statistics and Frequency Distribution for Total and Total-recoverable Concentrations of Arsenic..... 5-3 5.3.2 Temporal and Spatial Distributions of Total and Total-recoverable v

Concentrations of Arsenic....5-3 5.3.3 Regional Distribution of Total and Total-recoverable Concentrations of Arsenic in 448 Florida Surface Soils.... 5-4 5.3.4 Taxonomic Distributions of Total and Total-recoverable Concentrations of Arsenic in 448 Florida Surface Soils.......5-16 5.3.5 Factors Affecting Concentrations of Total and Total-recoverable Arsenic in Florida Surface Soils..... 5-22 5.4 Literature Cited or Related to This Project..... 5-25 vi

LIST OF FIGURES Figure 1-1. Definition of West, North, Central, and South Florida Regions.. 1-14 Figure 2-1. Geographical Distribution of 7 Soil Orders in Florida. 2-6 Figure 2-2. Geographical Distribution of Soil Suborders in Florida.. 2-7 Figure 2-3. Spatial Distribution of Site Locations. 2-11 Figure 2-4. Spatial Distribution of Arsenic Concentration at Individual Sample Site.. 2-12 Figure 2-5. Spatial Distribution of Arsenic Concentrations Based on Suborders. 2-13 Figure 2-6. Spatial Distribution of Arsenic Concentrations Based on Counties.. 2-14 Figure 2-7. Spatial Distribution of Cadmium Concentrations Based on Suborders. 2-16 Figure 2-8. Spatial Distribution of Cadmium Concentrations Based on Counties 2-17 Figure 2-9. Spatial Distribution of Chromium Concentrations Based on Suborders 2-19 Figure 2-10. Spatial Distribution of Chromium Concentrations Based on Counties....2-20 Figure 2-11. Spatial Distribution of Copper Concentrations Based on Suborders 2-22 Figure 2-12. Spatial Distribution of Copper Concentrations Based on Counties. 2-23 Figure 2-13. Spatial Distribution of Nickel Concentrations Based on Suborders 2-25 Figure 2-14. Spatial Distribution of Nickel Concentrations Based on Counties...2-26 Figure 2-15. Spatial Distribution of Lead Concentrations Based on Suborders 2-28 Figure 2-16. Spatial Distribution of Lead Concentrations Based on Counties.... 2-29 Figure 2-17. Spatial Distribution of Selenium Concentrations Based on Suborders 2-31 Figure 2-18. Spatial Distribution of Selenium Concentrations Based on Counties.. 2-32 Figure 2-19. Spatial Distribution of Zinc Concentrations Based on Suborders 2-34 Figure 2-20. Spatial Distribution of Zinc Concentrations Based on Counties...2-35 Figure 3-1. Cumulative Probability Curve for Arsenic Concentrations in Florida Surface soils.... 3-11 Figure 4-1. Georeference of 422 Soil Samples 4-8 Figure 4-2. Map of Arsenic Concentrations in Florida Surface Soils.. 4-9 Figure 4-3. Potential Arsenic Contamination Sites Screened by Upper Baseline Limit 4-10 Figure 5-1. Distribution Frequency of Total-recoverable Arsenic in Florida vii

Surface soils.... 5-8 Figure 5-2. Log-Normal Probability Plot for Concentrations of Total and Total-Recoverable Arsenic in 448 Florida Soils 5-9 Figure 5-3. Temporal Variation in Mean Concentrations of Total and Total-recoverable As in Florida Surface Soils during Sampling Period 1967-1989.. 5-10 Figure 5-4. Geometric Mean Concentrations of Total and Total-recoverable As in Disturbed Florida Soils during the Sampling Period 1971~1989.. 5-11 Figure 5-5. Spatial Variation in GM Concentrations of Total and Total-recoverable As in 448 Florida Soils along with Sampling Depth.. 5-12 Figure 5-6. Baseline Range and GM Concentration of Total and Total-recoverable Arsenic in Florida Soils Based on Soil Suborders. 5-17 Figure 5-7. Regression between Total and Total-Recoverable Concentrations of Arsenic in 448 Florida Soils... 5-23 Figure 5-8. Regression between Total or Total-recoverable Concentrations of Arsenic and Total Al and Fe in 448 Florida Soils 5-24 LIST OF TABLES Table 1-1. Geographic and Taxonomic Information of 210 Florida Soil Samples... 1-3 Table 1-2. Taxonomic Representative of Selected Florida Soil Samples..... 1-7 Table 1-3. Geographical Representative of Selected Florida Soils from 51 Counties.... 1-8 Table 1-4. Locations and site description of soil samples collected from south Florida...1-10 Table 1-5. Distribution Range and Average of Soil Properties of 210 Florida Soils.1-12 Table 1-6. Chemical and Physical Properties of 210 Florida Soils based on Soil Orders..1-13 Table 1-7. Chemical and Physical Properties of 210 Florida Soils based on Regions...1-15 Table 1-8. Total concentrations (mg/kg) of 8 trace elements of 210 Florida soils.1-16 Table 1-9. Total Concentrations of 8 Trace Metals in 210 Florida Soils Based on Soil Orders 1-17 Table 1-10. Total Concentrations of 8 Trace Elements in 210 Florida Soils Based on Regions.1-18 viii

Table 1-11. QA/QC Results for ICP and GF-AAS Determinations.. 1-20 Table 1-12. Precision and Accuracy of 16 Elements in NIST SRMs 2709, 2710, 2711, and 2781 Digested by EPA Methods 3051a.1-21 Table 1-13. Properties and Total-recoverable Concentrations of 8 Heavy Metals in 210 Florida Surface Soil Samples..1-23 Table 1-14. Ranges in Total-recoverable Concentration (mg/kg) and Summary Statistics of 8 Metals in 210 Florida Surface Soils.1-34 Table 1-15. Mean Concentrations (mg/kg) of 8 Trace Metals in Florida Soils Based on Soil Orders.1-35 Table 1-16. Mean Concentrations (mg/kg) of 8 Trace Metals in Florida Soils Based on Regions...1-36 Table 1-17. Total-recoverable Concentrations of 8 Trace Metals in Soils Collected from South Florida..1-37 Table 1-18. Collection Coefficients and Regression Statistics between Total-recoverable Concentrations and Total-total Concentrations of 8 Trace Metals in Soils of Florida.1-39 Table 1-19. Correlation coefficients between total-recoverable concentrations of 8 trace metals and soil chemical and physical properties in Florida soils 1-40 Table 2-1. Dominant Soil Orders and Suborders in Florida 2-5 Table 2-2. Map Units with Missing Data...2-9 Table 2-3. Spatial Layers...2-38 Table 2-4. Databases. 2-39 Table 2-5. Soil Samples With Unknown or Corrected Georeferenced Locations.2-40 Table 3-1. Geometric Mean Arsenic Concentrations (As, mg/kg) of 7 Soil Orders and 12 Soil Suborders..3-12 Table 3-2. Concentrations of Total Aluminum & Iron and Characteristics of Selected Florida Surface Soils.3-13 Table 3-3. High Arsenic Concentration Sites Screened by Upper Baseline Limits of 7.02 mg/kg and Related Soil Properties..3-14 Table 4-1. Potential Arsenic Contamination Sites Screened by Upper Baseline ix

Limits (UBLs) of Individual Soil Suborder and Soil Site Description.. 4-7 Table 5-1. Total-total Concentrations of As in 448 Florida Surface Soils (mg kg -1 ).. 5-5 Table 5-2. Total-recoverable Concentrations of As in 448 Florida Surface Soils (mg kg -1 )...5-6 Table 5-3. Comparison and Distribution Test for Total-total and Total-recoverable As Concentrations in 448 Florida Surface Soils.5-7 Table 5-4. Mean concentrations of Total-total and Total-recoverable Arsenic in Florida Soils Based on Counties..5-13 Table 5-5. Mean Concentrations and Upper Confidence Limits (UCLs) for the Mean of Total-total and Total-recoverable Arsenic in Florida Soils Based on Regions (South, Central, North, and West).. 5-15 Table 5-6. Mean Concentrations and Upper Confidence Limits (UCLs) for the Mean of Total-total and Total-recoverable Arsenic in Florida Soils based on Soil Orders... 5-18 Table 5-7. Summary Statistics in Concentration Ranges and Upper Confidence Limits (UCLs) for the Mean of Total-total and Total-recoverable Arsenic in Florida Soils Based on Wetness of These Soils.5-19 Table 5-8. Comparison of Concentration Ranges and Upper Confidence Limits (UCLs) for the Mean of Total-Recoverable Arsenic in Florida Soils Based on Several Soils Associations...5-20 Table 5-9. Comparison of Concentration Ranges and Upper Confidence Limits (UCLs) for the Mean of Total-Recoverable Arsenic in Florida Soils based on Several Soils Associations 5-21 x

LIST OF ABBREVIATIONS, ACRONYMS & UNITS OF MEASUREMENT ABBREVIATIONS/ACRONYMS AM: Arithmetric Meam ASD: Arithmetric Standard Deviation CEC: Cation Exchange Capacity CRMs: Certified Reference Materials CSHWM: Center for Solid and Hazardous Waste Management CV: Coefficient of Variances EPA: Environmental Protection Agency ESRI: Environmental Systems Research Institute FCSSP: Florida Cooperative Soil Survey Program FDEP: Florida Department of Environmental Protection FDACS: Florida Department of Agriculture and Consumer Services GM: Geometric Mean GIS: Geographic Information System GSD: Geometric Standard Deviation HDPE: High-Density Polyethylene ICP-OES: Inductively Couple Plasma Optical Emission Spectrometer LANDSAT: Land Remote Sensing Satellite LBL: Lower Baseline Limit LCL: Lower Confidence Limit for the Mean MDLs: Method Detection Limits MUID: Map Unit Identifier NATSGO: National Soil Geographic Data Base NIST: National Institute of Standard and Technology NRCS: Natural Soil Conservation Service OC: Organic Carbon PLSS: Public Land Survey System QA/QC: Quality Assurance/ Quality Control xi

RQAP: Research Quality Assurance Plan RSD: Relative Standard Deviation SCS: Soil Conservation Service SSURGO: Soil Survey Geographic Data Base SRMs: Standard Reference Materials SSLs: Soil Screening Levels STATSGO: State Soil Geographic Data Base STD: Standard Deviation SWSD: Soil and Water Science Department UBL: Upper Baseline Limit UCL: Upper Confidence Limit for the Mean UFSCL: University of Florida Soil Characterization Laboratory USDA: United States Department of Agriculture USEPA: United States Environmental Protection Agency USGS: United States Geographical Service xii

DEFINITIONS Precision: will be assessed by calculating coefficients of variation for replicated sets of data. The coefficient of variation is defined as the standard deviation divided by the mean. The precision of duplicate pairs will be calculated using percent relative standard deviation (%RSD) in this study. CV = % RSD = 100 (s / m) where: m = Mean (arithmetic average) of the data points xi, and s = standard deviation; and the relative percent difference (RPD) will be used when only two samples available. RPD = 100 {(x 1 - x 2 ) / [x 1 + x 2 ]} Accuracy: will be judged by comparing the observed value, x, with the true value, t, for reference samples. Accuracy will be expressed as the percent recovery (%R) on a relative basis by the following equation: Accuracy = %R = 100 (x - t) / t Matrix effects on accuracy will also be assessed by examining spike recoveries and calculating the percent recovery as follows: Spike recovery = 100 (xs - x) / s where xs is the measured concentration of the spiked sample, x is the unspiked sample concentration, and s is the spike value. Method Detection Limit (MDL): is proposed to be method detection level, and is defined to be 3 times the standard deviation derived from the study. It is the minimum concentration of a substance that can be measured and reported with 99% confidence of its presence in the sample matrix. Skewness and Kurtosis: Moment coefficients of skewness and kurtosis express how the shapes of sample frequency distribution curves differ from ideal Gaussian (normal). Skewness was calculated as third moment of the population mean. In asymmetrical distributions, skewness can be positive or negative. A positively skewed distribution has a longer tail to the right and a negatively skewed distribution has a longer tail to the left. Kurtosis describes the heaviness of the tails for a distribution. It was calculated as fourth moment of the population mean minus three. They are both close to zero for normally distributed data. xiii

Background Concentration: Background concentration referred to the concentration of an element found in soils without human influence. This measurement depicts an idealized situation and single values are hard to establish because concentrations vary depending on how physical, chemical, and biological processes, and anthropogenic contributions have affected parent geological material at a site. However, the background concentration concept used in this study includes both the naturally occurring and local/regional anthropogenic contributions and the range of background concentrations will be discussed. Baseline Concentration: Baseline concentration measures an expected range of elemental concentrations around a mean in a normal sample medium and is defined as 95% of the expected range of background concentrations. Based on normal distribution theory, the expected range can be expressed as the average ± 2 standard deviation (AM ± 2ASD). Since the data are positively skewed, the GM and GSD were used to calculate the expected range of 95% of the population from GM/GSD 2 to GM X GSD 2, where, GM X GSD 2 is the upper baseline limit (UBL), and GM/GSD 2 is the lower baseline limit (LBL). Confidence Intervals for the Mean: A confidence interval for the mean describes an interval in which the true mean will fall with a specified level of certainty, 100(1-α). For a one-sided test, α is assigned to only one tail of the distribution. This is usually the upper tail for environmental applications as people most commonly wish not to exceed a regulatory action level. Historically, confusion has existed concerning the calculation of confidence limits for the mean of a lognormally distributed variable. The primary difficulty with confidence limit calculations is that the mean and the variance of the lognormally distributions are not independent. As a result, there have been several recommendations for calculation of confidence intervals. In this study, the following methods was used to calculate an approximate the upper one-sided 100(1-α)% confidence limit (UCL) and the lower one-sided 100α% confidence limit (LCL) for the true mean. UCL LCL 1 α α = exp = exp µ H 2 1 a ( + 0.5 + δ ) µ y δ H n 1 2 a ( + 0.5 + δ ) y δ n 1 where, µ y is the arithmetic mean of the log-transformed sample data, δ is the standard deviation of the log-transformed data, n is the number of samples, H 1-α and H α are the H-statistic tabulated constant from tables provided by Land (1975) for the upper and lower confidence limits. The values of H 1-α and H α depend on µ y, n and the chosen confidence level α (Gilbert, 1987). xiv

MEASUREMENT UNITS %: Unit for contents of sand, silt, and clay in Florida soils; concentrations of Fe and Al in NIST SRMs or Florida surface soils. g kg -1 : Unit for organic carbon content in soils. mmol kg -1 : Unit for CEC of Florida soils. mg/kg or ppm: Unit for concentrations of As, Cd, Cr, Cu, Ni, Pb, Se, and Zn in NIST SRMs or Florida surface soils. mg/l or ppm: Unit for concentrations of As, Cd, Cr, Cu, Ni, Pb, Se, and Zn in spiked digestates. mile & feet: Unit for distance. acre: Unit for area. xv

ANNUAL REPORT March 15, 1998 - March 14, 1999 PROJECT TITLE: Background Concentrations of Trace Metals in Florida Surface Soils PRINCIPAL INVESTIGATORS: Lena Q. Ma, W. G. Harris and A. G. Hornsby AFFILIATION: Soil and Water Science Department, University of Florida COMPLETION DATE: 2/28/1999 PHONE NUMBER: (352) 392-1951 KEY WORDS: background concentration, surface soil, Florida, arsenic, trace metals, geographic information system (GIS), EPA digestion methods, total-total concentration, total-recoverable concentration, taxonomic distribution, geographical distribution. ABSTRACT: Background concentrations of trace metals are important for establishing metal concentration criteria for cleaning up contaminated soils and land application of nonhazardous materials. The objective of the third year s study of this three years project was to establish total-recoverable background concentrations for 8 trace metals (As, Cd, Cr, Cu, Ni, Pb, Se, and Zn) in Florida surface soils and to compare the total-recoverable to the total-total metal concentrations in soils. Another purpose of this study was to digitize and georeference the total-total concentration data of the 8 metals in 448 Florida soils using geographic information system (GIS) so that their relationship with soil properties and land use can be determined. We have achieved the following this year: (1) A revised research quality assurance plan (RQAP) was submitted to the FDEP QA/QC section before the project started. (2) 210 geographic and taxonomic representative soil samples were selected from a previous 448-samples pool using SAS program. (3) Total-recoverable concentrations of 8 trace metals As, Se, Cd, Cr, Cu, Ni, Pb, and Zn in 210 soils were determined using EPA Method 3051a (microwave, HNO 3 -HCl) and GF-AAS. (4) Total-recoverable concentrations of arsenic in additional 238 soil samples were determined using EPA Method 3051a (microwave, HNO 3 -HCl) and GF-AAS, and (5) Spatial and taxonomic distribution of arsenic concentrations in Florida soils were determined and presented using GIS. Total-recoverable concentrations in 210 Florida surface soil samples for 5 (As, Cr, Cu, Se, and Zn) out of the 8 trace metals tested correlated with the total-total concentrations in the soils at a 95% percent confidence level and a coefficient of determination > 50%. Taxonomic distributions of the total-recoverable concentrations for most of the metals (As, Cd, Cu, Ni, Se, and Zn) are generally comparable with the results for total-total xvi

concentrations of these metals in 448 Florida surface soils in our previous study. Soils from south Florida tend to have higher total-recoverable concentrations of As, Cd, and Se than soils from other regions. Geographical distributions of total-total concentrations of the 8 trace metals in 448 Florida surface soils are highly variable. STATSGO soil maps were applied to generate a series of maps displaying the area weighted average background concentration trace metals at both the soil Suborder level and the county based maps. The highest background concentrations for all the studied metals were computed for Hemists and Saprists at the soil Suborders level. Application of the generated background concentration maps in a policymaking framework allows the policymaker to establish reference concentrations for trace metal cleanup. Soil related differences in ranges of trace metal concentrations should be accounted for in setting of limits. Probability distribution shows total-total arsenic concentrations depend on soil types, and certain soil Suborders in South Florida are naturally high in arsenic. The overall arsenic concentrations followed a log-normal distribution. Geometric mean (GM) of arsenic concentrations generally follows the soil taxonomic order of Histosols > Inceptisols, Mollisols Ultisols Alfisols, Entisols > Spodosols. The greatest arsenic concentrations were associated with wet soil Suborders Hemists, Saprists, Aquents, Aquolls, and Aquepts. Lowest arsenic concentrations were found for Aquods, Orthods, and Psamments, Suborders with low clay and metal oxides contents. Soil compositions (clay, organic matter, and total Fe and Al) are important controlling factors for arsenic concentrations in Florida surface soils. Soil reactions (ph and Eh), bioconcentration, and runoff accumulation may also be important biogeochemical factors for high arsenic in wet soils. Evaluation of arsenic within the soil media by application of geostatistics and geographic information systems provides valuable additional information. Findings of this study show that high arsenic concentrations are generally located in South Florida and low in North and Central Florida. Individual high arsenic site scattered all over Florida and reflected the comprehensive influences from soil type, mineral deposition, land use, and biogeochemstry of arsenic in soils. It was also found that most useful taxonomic category for grouping Florida soils with respect to metals is the Suborder. Geographic distribution of soils with elevated arsenic concentrations, screened by upper baseline limit (UBL) of individual soil Suborder, is in the Leon-Lee belt along the Ocala Uplift District to Southwestern Flatwoods District. Soils with low to moderate arsenic concentrations are more easily identified as to whether or not they were elevated via anthropogenic arsenic sources than soils with high arsenic concentrations. Extrapolation of the data using single criteria regardless the spatial differences in arsenic concentrations may underestimate possible arsenic contamination in upland soils. In addition, total-recoverable arsenic concentrations in the rest sample set of the 448 Florida surface soil samples pool (238) were determined using EPA Method 3051a (HCl- HNO 3 digestion) and compared to the total-total arsenic concentrations. Results are as follows: (1) Both the total-total and the total-recoverable arsenic concentrations in Florida surface soils followed log-normal distribution and declined since 1980 s. (2) Geometric mean (GM) concentration of the total-recoverable arsenic was less, while the coefficient of xvii

variance is larger, than that of the total-total arsenic. (3) Organic soils, marl soils, and soil samples from wet soil Suborders (Hemists, Saprists, Aquents, Aquolls, and Aquepts) have significantly higher concentrations for both total-total and total-recoverable arsenic than other soils or soil Suborders in Florida. The sets of data should therefore be properly grouped and to define background concentrations individually. (4) 95% upper confidential limit (UBL) of the mean for total-total and total-recoverable arsenic in Florida soils reduced to 0.80 mg/kg and 0.60 mg/kg, respectively, when the wet soils are excluded from data set. xviii

EXECUTIVE SUMMARY Annual Report March 15, 1998 - March 14, 1999 PROJECT TITLE: Background Concentrations of Trace Metals in Florida Surface Soils PRINCIPAL INVESTIGATOR: Lena Q. Ma AFFILIATION: Soil Chemistry /Soil and Water Sciences /University of Florida CO-PRINCIPAL INVESTIGATOR: Willie G. Harris AFFILIATION: Soil Mineralogy /Soil and Water Sciences /University of Florida CO-PRINCIPAL INVESTIGATOR: Arthur G. Hornsby AFFILIATION: Soil Physics /Soil and Water Sciences /University of Florida QUALITY ASSURANCE OFFICER: Ming Chen AFFILIATION: Soil Chemistry /Soil and Water Sciences /University of Florida COMPLETION DATE: 3/14/1999 OBJECTIVES Determine total-recoverable concentrations of eight trace metals As, Cd, Cr, Cu, Ni, Pb, Se, and Zn in 210 representative Florida surface soil samples; Compare the total-recoverable to the total-total metal concentrations in soils and determine the effects of soil properties on metal recovery by two digestion methods; Digitize and georeference these data on geographic information system (GIS) so that their relationship with soil properties and land use can be determined. METHODOLOGY Soil samples has been selected and digested in a microwave digestion oven using EPA Method 3051a and analyzed using a GFAAS. Elemental concentrations, grouped by soil types and geographic locations, has been analyzed and compared to the total-total metal concentrations using a SAS statistical program. GIS softwares ArcView has been used to digitize trace metal concentrations in Florida surface soils, together with their physical and chemical properties. RATIONALE Information on site-specific background concentrations of trace metals in Florida surface soils is critical for evaluating land application of non-hazardous waste materials and xix

monitoring the mobility of toxic metals from contaminated sites to adjacent areas. In this year s project, total-recoverable concentrations of 7 trace metals (As, Cd, Cr, Cu, Ni, Pb, Se, and Zn) in 210 and As in 448 representative Florida soil surface samples were determined. These elements are all of environmental concern and are regulated by federal laws as toxic elements. The soil samples represent all four major soil types (Spodosol, Entisol, Ultisol and Histosol) and three minor soil types (Inceptisol, Alfisols and Mollisols) in Florida. Their physical, chemical, and mineralogical properties (soil texture, organic carbon content, cation exchange capacity, ph, mineral composition etc.) as well as sample site locations are all well documented and readily available. Total-total concentrations of 15 trace metals in 448 Florida surface soils have been determined in previous study. Comparison of total-total and total-recoverable digestion methods for metals determination using certified and 40 Florida surface soils was also done. Results showed that concentrations of most trace metals in Florida generally followed the order of Histosols Mollisols = Inceptisols Ultisols = Entisols Alfisols Spodosols, and EPA Method 3051a provided comparable values for EPA Method 3050. RESULTS Research quality assurance plan (RQAP) implementation. It was composed of 12 sections, based on FDEP RQAP manual and review checklists, and contained all elements required by FDEP. Selection of 210 soil samples was made from a previous 448-soil-samples pool, according to their taxonomic acreage and geographic distribution in Florida. Those samples are from 51 Florida counties and include 59 Spodosols, 46 Entisols, 39 Ultisols, 29 Alfisols, 22 Histosols, 9 Mollisols and 6 Inceptisols. Additional 8 soil samples were collected from South Florida since there are more contamination cases reported in that area. These additional soil samples are used to provide more detailed information about soil metal concentrations in marl soils. Total-recoverable concentrations of other 8 trace metals As, Se, Cd, Cr, Pb, Ni, Cu, and Zn in 218 soil samples were determined using EPA Method 3051a based on the FDEP approved research quality assurance plan. Digitized and georeferenced total-total concentrations of these 8 trace metals in 448 Florida soil samples on GIS. Twelve monthly progress reports were submitted to the Florida Center for Solid and Hazardous Waste Management (FCSHWM). A manuscript entitled Comparison of Four EPA Digestion Methods Using Certified and Florida Soils, by M. Chen and L.Q. Ma was published by Journal of Environmental Quality (27:1294-1300, 1998). xx

A manuscript entitled Baseline concentrations 15 Trace Metals in Florida Surface Soils, by M. Chen, L.Q. Ma and W.G. Harris was published by Journal of Environmental Quality (28:1173-1181, 1999). A manuscript entitled Background Concentrations of Phosphorus in Florida Surface Soils was prepared and presented at the Soil and Crop Science Society of Florida 58 th Annual Meeting (Sept., 1998 at Daytona Beach, FL) and will be published. Another manuscript entitled Concentrations of P, K, and Selected Trace Elements (Al, Fe, Mn, Cu, Zn, and As) in Soils from South Everglades was prepared and presented at the Soil and Crop Science Society of Florida 59 th Annual Meeting (Sept., 1999 at Sarasota, FL) Two presentations were given at the 90th Annual Meetings of the Soil Science Society of American (Oct.18-22, 1998 at Baltimore, MD). Two presentations were given at the 91ST Annual Meetings of the Soil Science Society of American (Oct.31-Nov.4, 1999 at Salt Lake City, UT). Two manuscripts have been prepared and submitted to, and additional two manuscripts have been prepared for Journal of Environmental Quality for publication. Total-recoverable concentrations of As in the rest sample set of the 448 soil samples pool (238) was digested and determined using EPA Method 3051a, for establishing background concentrations of total-recoverable arsenic based on the 448 representative Florida surface soil samples. CONCLUSIONS Taxonomic distributions of total-recoverable concentrations for most of the 8 trace metals (As, Cd, Cr, Cu, Ni, Pb, Se, and Zn) in 210 Florida surface soil samples are generally comparable with the results for total-total concentrations of these metals in 448 Florida surface soils. Geometric mean (GM) concentrations and follow the order of Histosols > Inceptisols, Mollisols Ultisols Alfisols, Entisols > Spodosols. Geographical distributions of total-total concentrations of trace metals are highly variable. The highest background concentrations for all the 8 trace metals were computed for Hemists and Saprists at the soil Suborders level. Soil related differences in ranges of trace metal concentrations should be accounted for in establishing reference concentrations for trace metals cleanup. Total-total concentrations of Cd, and Se in Florida surface soils are significantly higher in South Florida than in Central and North Florida. Total-recoverable concentrations of As in Florida surface soils are related to total-total arsenic concentrations of the soils. Both the total-total and the total-recoverable arsenic concentrations in Florida surface soils followed log-normal distribution. xxi

Organic soils, marl soils, and soils from wet soil Suborders (Hemists, Saprists, Aquents, Aquolls, and Aquepts) have significantly greater concentrations for both total-total and total-recoverable arsenic than other soils or soil Suborders. Ninety five percent upper confidence limit (UCL) for the mean of the total-total and total-recoverable arsenic in Florida soils reduced to 0.80 mg/kg and 0.60 mg/kg, respectively, when the wet soils are excluded from data set. xxii

I. TOTAL-RECOVERABLE CONCENTRATIONS OF EIGHT TRACE METALS IN 210 REPRESENTATIVE FLORIDA SURFACE SOILS 1.1 INTRODUCTION MING CHEN AND LENA Q. MA, Land application is rapidly becoming an economically feasible and environmentally sound alternative for disposing non-hazardous materials (Basta, 1995). However, the presence of potentially toxic metals in these materials has caused great concern to both public and government regulators. Currently, several federal and state rules regulate land disposal of these materials. Standards in allowable metal concentrations often vary (EPA, 1994; FDEP, 1994a,b). The key question is what should be the maximum allowable metal concentrations in these materials for land application. Natural background levels of trace metals in soils where these materials will be applied are obviously logical criteria. While published data for other regions may be helpful in establishing background metal levels (Bini et al., 1988; Chen et al., 1991; Davies and Wixson, 1985; Dudka, 1993; Edelman and Bruin, 1985; Frank et al., 1976; Holmgren et al., 1993; Logan and Miller, 1983; McGrath, 1987; Pierce et al., 1982; Shacklette and Boerngen, 1984), it is important to have information that is specific for Florida soils. Florida soils form primarily in sandy and loamy marine sediments (Myers and Ewel, 1990). They are especially prone to leaching due to sandy textures, high hydraulic conductivity and low reactivity. Soil elemental compositions vary considerably, reflecting differences in parent materials and soil formation processes (Thornton, 1982). We have determined total concentrations of Fe, Al, Cu, Ni, Zn, Mn, As, Cd, Cr, Hg, and Pb in 40 Florida soil profiles (Ma et al., 1997). Take As for example, its total concentrations in surface horizons of three different soils Ultisols, Entisols, and Spodosols are significantly different (3.33, 1.36 and 0.20 mg kg -1 ). However, Florida Department of Environmental Protection (FDEP) sets up 0.8 and 3.7 mg kg -1 as the soil clean up goals for As in residential and industrial soils, based on direct exposure (FDEP, 1995, 1999). Apparently, such clean up standards are not applicable to all soil types and there is a need to establish background levels of trace metals in different soils in Florida. Currently, four EPA digestion methods are available to determine metal concentrations in soils: three are for "total-recoverable" concentration - EPA Methods 3050 (hot-plate, HNO 3 -HCl), 3051 (microwave, HNO 3 ), and 3051a (microwave, HNO 3 -HCl) and one is for "total-total" concentration-epa Method 3052 (microwave, HNO 3 -HCl-HF). More recently, we have determined background concentrations of 15 trace metals (Ag, As, Ba, Be, Cd, Cr, Cu, Hg, Mo, Mn, Ni, Pb, Sb, Se, and Zn) in 450 representative Florida surface soils using the EPA Method 3052 for total-total concentrations (Chen et al., 1998). Unfortunately, current EPA regulation uses the EPA Method 3050 instead of 3052 for both land application and soil cleanup. It is recognized that these total-recoverable digestion procedures (3050, 3051, and 3051a) do not recover all metals in soil samples (Sawhney and Stilwell, 1994). We have 1-1

found that the total-total digestion method provided greater recoveries for nine trace metals Ag, Be, Cr, Mo, Ni, Pb, Sb, Se, and Zn than the total-recoverable digestion methods (3050, 3051, and 3051a) in 40 Florida surface soils (Chen and Ma, 1998). It provided comparable recoveries for six trace metals As, Ba, Cd, Cu, Hg, and Mn. To make our data more useful to FDEP, we have determined "total-recoverable" metal concentrations in Florida soils. Based on our study (Chen and Ma, 1998), EPA Methods 3051a and 3050 are comparable for determining concentrations of 15 trace metals in 40 Florida soils. In addition, the EPA Method 3051a is more precise and easier to use than the EPA Method 3050 (Chen and Ma, 1998). As such, we will use the EPA Methods 3051a for the current study. 1.2 MATERIALS AND METHODS 1.2.1. Sample Collection/Selection. Soil samples has been selected to assure both taxonomic and geographic representation. The soil orders (USDA soil taxonomy; Soil Survey Staff, 1994) which have been identified in Florida, in order of descending abundance are: Spodosols, Entisols, Ultisols, Histosols, Alfisols, Mollisols, and Inceptisols. Our plan was to weigh the number of samples for each order by the estimated extent of areal occurrence in Florida. An initial pool of prospective samples were randomly selected from each county in which a given soil order occurs. From this pool a final random selection for that order is made. This sampling approach is intended to avoid geographical clustering of samples in cases where some areas may have been more heavily sampled than others. The number of soil samples used in this study was based on the variability in elemental concentrations in Florida soils (Ma et al., 1997). At least 191 soil samples are required to establish a statistically valid database for Florida soils (95% confidence level and 20% accepted error). A total of 210 soil samples were randomly selected from a 448 surface soils pool which was previously selected for the determination of background concentration of trace metals in Florida surface soils using EPA Method 3052 (Chen et al., 1998). To accomplish the selection both taxonomic & geographic representatively, a SAS program of random numbers was written to guarantee (i) proportions to the areal coverage percents of all the 7 soil orders in Florida; (ii) pick-up at least 2 samples for each of the 51 counties; and (iii) as many samples as we could from Dade County. However, based on the avaliability of the archived soils, only two soil samples were selected from Dade County. Detailed information of the 210 soil samples were listed in Table 1-1. The taxonomic and geographical distributions of the samples were presented in Table 1-2 and Table 1-3, respectively. In addition to the archive soil samples, additional eight samples were collected from Dade County since there are more contamination cases reported in that area. However, these additional eight soil samples are not included in calculating metal concentrations in Florida soils to preserve the geographic and pedogenic representation of the data. These additional soil samples are used to provide more detailed information about soil metal concentrations in Dade County. 1-2

Table 1-1. Geographic and taxonomic information of 210 Florida soil samples Lab # UFSCL # Date County Region Harizon Order Soil Series 1 21 11/18/71 Broward S Oa1 Histosols Lauderhill 2 23 10/02/78 Pasco C Oa1 Histosols Samsula 3 56 01/28/71 Palm Beach S Oap Histosols Okeelanta 4 59 01/28/71 Palm Beach S Oap Histosols Terra Ceia 5 308 08/01/72 Broward S Oa1 Histosols Lauderhill 6 371 07/24/72 Santa Rosa W Oa1 Histosols Dorovan 7 539 03/01/73 Palm Beach S A Entisols Basinger 8 651 07/10/73 Osceola C A Entisols Satellite 9 656 07/09/73 Osceola C A Spodosols Myakka 10 718 07/19/73 Marion N Ap Ultisols Wacahoota 11 723 07/19/73 Marion N Ap Alfisols Boardman 12 799 04/06/67 Brevard C Oa1 Histosols Terra Ceia 13 810 04/24/67 Brevard C Oi1 Histosols Montvorde 14 1129 10/01/73 Palm Beach S Ap Alfisols Boca 15 1229 01/30/74 Volusia N A1 Spodosols Immokalee 16 1293 03/06/74 Palm Beach S A Alfisols Holopaw 17 1299 03/06/74 Palm Beach S A Spodosols Oldsmar 18 1320 04/16/74 Osceola C A Alfisols Riviera 19 1341 04/17/74 Osceola C A Entisols Adamsville 20 1346 04/18/74 Osceola C A Spodosols Smyrna 21 1359 04/19/74 Osceola C Oa Histosols Brighton 22 1375 12/07/73 Santa Rosa W A Ultisols Dothan 23 1380 07/16/74 Hernando C A Entisols Candler 24 1391 07/15/74 Hernando C A1 Entisols Tavares 25 1480 08/01/74 Jackson W Ap Ultisols Troup 26 1485 08/19/74 Volusia N A Spodosols Smyrna 27 1518 08/22/74 Volusia N A Spodosols Myakka 28 1549 10/17/74 Duval N A Entisols Kershaw 29 1561 12/16/74 Santa Rosa W Ap Ultisols Lucy 30 1623 01/06/75 Hernando C A Spodosols Myakka 31 1630 01/07/75 Hernando C A1 Spodosols Eaugallie 32 1685 01/20/75 Volusia N A1 Alfisols Tuscawilla 33 1752 02/11/75 Osceola C A1 Mollisols Delray 34 1758 02/12/75 Osceola C A1 Inceptisols Placid 35 1872 04/07/75 Jackson W A Ultisols Fuquay 36 2072 06/26/75 Santa Rosa W A1 Inceptisols Rutlege 37 2155 07/24/75 Hernando C A Ultisols Sparr 38 2162 05/29/80 Alachua N A Spodosols Pomona 39 2240 08/04/75 St. Lucie C A1 Alfisols Pineda 40 2256 08/06/75 Leon W A1 Entisols Kershaw 41 2320 09/18/75 Duval N A Ultisols Pelham 42 2388 09/29/75 Jackson W A Inceptisols Apalachee 43 2435 09/29/75 St. Lucie C A Alfisols Winder 44 2537 01/27/76 Martin S A1 Spodosols Nettles 45 2551 01/28/76 Martin S A1 Spodosols Waveland 46 2671 03/31/76 Volusia N A Ultisols Scoggin 47 2753 06/04/80 Alachua N A1 Spodosols Wauchula 48 2779 04/13/76 Martin S A Entisols Palm Beach 49 2791 05/12/76 Martin S Ap Entisols Hallandale 1-3

50 2806 06/23/76 Martin S Oap Histosols Gator 51 2810 06/24/76 Jackson W A Alfisols Yonges 52 2885 08/05/76 Santa Rosa W A Entisols Bohicket 53 2993 10/20/76 Martin S A Alfisols Riviera 54 3019 10/20/76 Polk C A1 Alfisols Non-designated series 55 3077 02/22/77 Pasco C A Spodosols Pomona 56 3087 02/23/77 Pasco C A1 Inceptisols Sellers 57 3190 06/10/80 Alachua N A11 Alfisols Ledwith 58 3304 05/19/77 Leon W Ap Alfisols Lutterloh 59 3354 09/08/77 Pasco C Ap Ultisols Millhopper 60 3402 10/18/77 Leon W A Spodosols Leon 61 3417 10/19/77 Leon W AP Ultisols Albany 62 3473 10/27/77 Polk C Ap Spodosols Zolfo 63 3596 04/10/78 St. Johns N A Ultisols Sparr 64 3631 04/12/78 St. Johns N A1 Spodosols St.Johns 65 3656 05/01/78 Lee S A Spodosols Immokalee 66 3744 05/09/78 Lee S A Spodosols Oldsmar 67 3758 05/11/78 Columbia N A Ultisols Bonneau 68 3776 05/23/78 Columbia N Ap Alfisols Ichetucknee 69 3794 06/22/78 Pasco C A1 Entisols Candler 70 3820 07/24/78 St. Johns N A Spodosols Immokalee 71 3831 07/25/78 St. Johns N A1 Spodosols Myakka 72 3838 07/25/77 St. Johns N Ap Alfisols Riviera 73 3846 07/25/78 St. Johns N Ap Spodosols Smyrna 74 3888 06/10/80 Alachua N Ap1 Ultisols Sparr 75 3895 06/11/80 Alachua N Ap Entisols Tavares 76 3908 10/03/78 Charlotte S A Spodosols Punta 77 4016 11/07/78 Hardee C A Spodosols Smyrna 78 4050 11/08/78 Walton W A Entisols Lakeland 79 4128 02/06/79 Bay W A Spodosols Leon 80 4225 03/06/79. St. Johns N Ap Inceptisols Placid 81 4335 06/12/79 Columbia N A Entisols O'leno 82 4363 07/12/79 Hardee C A Spodosols Myakka 83 4386 11/20/79 Hardee C Oa Histosols Kaliga 84 4395 12/06/79 Columbia N Ap Spodosols Mascotte 85 4411 12/06/79 Columbia N Ap Spodosols Hurricane 86 4417 12/17/79 Columbia N Ap Spodosols Leon 87 4444 01/23/80 Hendry S Ap Alfisols Pineda 88 4532 02/20/80 Charlotte S A Alfisols Winder 89 4542 02/19/80 Charlotte S Oa1 Histosols Gator 90 4570 03/24/80 Charlotte S Oa1 Histosols Terra Ceia 91 4602 03/25/80 Bay W Ap Spodosols Pottsburg 92 4614 03/27/80 Bay W Ap Ultisols Plummer 93 4619 04/09/80 Bay W A Ultisols Albany 94 4647 05/05/80 Broward S Ap Entisols Dade 95 4696 11/18/80 Citrus N Ap Ultisols Arredondo 96 4732 11/12/80 Walton W A Entisols Newhan 97 4750 12/01/80 Walton W Ap Ultisols Dothan 98 4811 02/02/81 Jefferson W A Entisols Alpin 99 4949 06/08/81 Citrus N A1 Spodosols Eaugallie 100 4985 06/10/81 Citrus N A Entisols Candler 101 4998 07/21/81 Jefferson W Ap Ultisols Surrency 102 5082 11/23/81 Citrus N Oa1 Histosols Durbin 103 5093 12/14/81 Polk C Ap Spodosols Immokalee 1-4

104 5201 03/03/82 Hendry S A11 Mollisols Chobee 105 5241 03/31/82 Putnam N A Spodosols Centenary 106 5247 03/31/82 Putnam N A Spodosols Pomona 107 5257 03/31/82 Putnam N A Alfisols Holopaw 108 5263 04/01/82 Putnam N A Entisols Tavares 109 5275 05/11/82 Indian River C Ap Spodosols Immokalee 110 5294 05/12/82 Indian River C A Entisols Archbold 111 5299 05/12/82 Indian River C A Spodosols Electra 112 5339 05/25/82 Sumter C Ap Alfisols Sumterville 113 5350 05/26/82 Sumter C Ap Ultisols Millhopper 114 5364 05/27/82 Sumter C Ap Ultisols Apopka 115 5369 06/08/82 Citrus N Ap Ultisols Kendrick 116 5469 11/02/82 Indian River C Oap Histosols Gator 117 5487 11/03/82 Indian River C A Entisols Astatula 118 5519 11/17/82 Jefferson W Ap Alfisols Nutall 119 5524 11/17/82 Jefferson W A1 Alfisols Tooles 120 5566 01/10/83 Hendry S A Spodosols Wabasso 121 5604 01/13/83 Hendry S Oap Inceptisols Plantation 122 5680 02/22/83 Sumter C A Spodosols Myakka 123 5695 02/23/83 Sumter C Ap Entisols Adamsville 124 5829 10/19/83 Clay N A Ultisols Blanton 125 5834 10/19/83 Clay N A Spodosols Hurricane 126 5845 10/20/83 Clay N A Entisols Ortega 127 5855 10/20/83 Clay N A Spodosols Mandarin 128 5873 12/06/83 Polk C A1 Entisols Astatula 129 5877 12/07/83 Polk C Ap Entisols Candler 130 5911 12/14/83 Madison W Ap Alfisols Cantey 131 5919 12/14/85 Madison W Ap Ultisols Blanton 132 5926 02/13/84 Sumter C Ap Ultisols Arredondo 133 6034 05/09/84 Highlands C Ap Spodosols Smyrna 134 6048 05/08/84 Madison W A Entisols Alpin 135 6060 05/09/84 Madison W Ap Ultisols Albany 136 6093 05/15/84 Desoto C Ap Entisols Basinger 137 6102 05/16/84 Desoto C Ap Entisols Valkaria 138 6177 10/02/84 Nassau N A Entisols Ortega 139 6227 10/30/84 Madison W Oa1 Histosols Pamlico 140 6242 10/31/84 Madison W Ap Ultisols Plummer 141 6257 11/07/84 Clay N A Spodosols Leon 142 6299 01/28/85 Okaloosa W A Spodosols Leon 143 6339 01/30/85 Okaloosa W A Ultisols Troup 144 6357 02/12/85 Highlands C A Spodosols Eaugallie 145 6364 02/12/85 Highlands C A1 Mollisols Chobee 146 6408 02/27/85 Desoto C 0a Mollisols Chobee 147 6470 04/30/85 Nassau N Ap Spodosols Boulogne 148 6532 05/14/85 Dade S Ap Entisols Biscayne 149 6538 05/15/85 Dade S Ap Entisols Perrine 150 6542 05/15/85 Dade S Ap Entisols Pennsuco 151 6546 05/15/85 Dade S Ap Mollisols Krome 152 6579 05/29/85 Wakulla W Ap Entisols Ortega 153 6603 06/24/85 Bradford N Ap Ultisols Ocilla 154 6637 06/26/85 Union N Ap Ultisols Albany 155 6643 06/26/85 Union N Ap Ultisols Blanton 156 6662 06/27/85 Union N Ap Spodosols Sapelo 157 6679 08/05/85 Levy N Ap Alfisols Otela 1-5

158 6717 12/10/85 Nassau N Ap Ultisols Ocilla 159 6740 12/10/85 Nassau N A Spodosols Leon 160 6779 01/22/86 Bradford N Ap Entisols Lakeland 161 6784 01/22/86 Bradford N A Entisols Penney 162 6796 01/27/86 Levy N A Inceptisols Waccasassa 163 6803 01/29/86 Levy N Ap Entisols Candler 164 6808 01/29/86 Levy N A Entisols Astatula 165 6812 01/29/86 Levy N Ap Ultisols Sparr 166 6836 03/04/86 Okaloosa W A Entisols Chipley 167 6849 03/05/86 Okaloosa W A Ultisols Saucier 168 6878 03/17/86 Wakulla W Ap Entisols Alpin 169 6895 03/18/86 Wakulla W Ap Entisols Kershaw 170 6901 03/19/86 Wakulla W Ap Entisols Ridgewood 171 6912 03/31/86 Levy N Oa Mollisols Demory 172 6994 05/13/86 Bradford N Ap Spodosols Pottsburg 173 7033 05/20/86 Flagler N A Alfisols Pineda 174 7057 05/21/86 Flagler N Oa1 Histosols Gator 175 7077 05/28/86 Levy N Ap1 Ultisols Millhopper 176 7096 06/03/86 Franklin W Ap Entisols Resoto 177 7101 06/03/86 Franklin W A Spodosols Mandarin 178 7115 06/04/86 Franklin W Ap Spodosols Leon 179 7120 06/04/86 Franklin W A Entisols Scranton 180 7149 07/14/86 Gilchrist N Ap Entisols Ridgewood 181 7219 12/15/86 Flagler N Ap Spodosols Smyrna 182 7271 03/24/87 Okaloosa W Ap Ultisols Bonifay 183 7307 04/07/87 Gilchrist N Ap Ultisols Albany 184 7347 04/28/87 Franklin W Ap Ultisols Blanton 185 7385 05/20/87 Flagler N A Mollisols Favoretta 186 7431 09/01/87 Collier S A Alfisols Boca 187 7435 09/01/87 Collier S A1 Mollisols Jupiter 188 7437 09/01/87 Collier S A Entisols Hallandale 189 7453 11/16/87 Franklin W Ap Spodosols Sapelo 190 7469 11/18/87 Franklin W Oa1 Histosols Dorovan 191 7472 11/18/87 Franklin W Ap Alfisols Meadowbrook 192 7490 03/07/88 Hamilton N A Ultisols Lowndes 193 7516 08/17/88 Calhoun W Ap Ultisols Alapaha 194 7550 05/19/88 Calhoun W A Entisols Lakeland 195 7560 06/01/88 Glades S A Alfisols Pineda 196 7579 06/02/88 Glades S Ap Alfisols Felda 197 7598 06/09/88 Hamilton N Ap Entisols Chipley 198 7625 06/14/88 Taylor W Ap Spodosols Leon 199 7637 06/14/88 Taylor W Ap Spodosols Chaires 200 7648 06/15/88 Taylor W Ap Entisols Ortega 201 7658 06/16/88 Taylor W Ap Spodosols Hurricane 202 7666 06/27/88 Monroe S Oa Histosols Matecumbe 203 7671 06/27/88 Monroe S A1 Entisols Lignumvitae 204 7715 12/06/88 Baker N Ap Ultisols Duplin 205 7771 12/13/88 Calhoun W Ap Entisols Foxworth 206 7787 12/14/88 Calhoun W Ap Spodosols Pottsburg 207 7867 04/11/89 Lafayette N Ap Alfisols Lutterloh 208 7907 04/13/89 Lafayette N Ap Entisols Ridgewood 209 7934 05/02/89 Baker N A Spodosols Sapelo 210 7951 05/03/89 Baker N A1 Entisols Ortega 1-6

Table 1-2. Taxonomic representation of selected Florida soil samples Areas # of Sample Soil order Acreage % Selected Available Spodosols 7,681,379 28 59 287 Entisols 6,214,334 22 46 238 Ultisols 5,395,084 19 39 266 Alfisols 3,917,985 14 29 182 Histosols 2,729,885 10 22 54 Mollisols 1,060,142 4 9 47 Inceptisols 827,055 3 6 29 Total 27,825,864 100 210 1103 1-7

Table 1-3. Geographic representation of selected Florida soils from 51 counties County Name # Of Samples Selected County Name # Of Samples Selected County Name # Of Samples Selected Alachua 6 Gilchrist 4 Monroe 3 Baker 4 Glades 2 Nassau 4 Bay 5 Hamilton 3 Okaloosa 4 Bradford 3 Hardee 4 Osceola 6 Brevard 2 Hendry 3 Palm Beach 5 Broward 4 Hernando 5 Pasco 6 Calhoun 3 Highlands 5 Polk 8 Charlotte 3 Indian River 5 Putnam 5 Citrus 5 Jackson 3 Santa Rosa 5 Clay 4 Jefferson 5 St. Johns 6 Collier 3 Lafayette 3 St. Lucie 4 Columbia 4 Lee 4 Sumter 5 Dade 10 Leon 4 Taylor 3 Desoto 4 Levy 5 Union 3 Duval 5 Madison 4 Volusia 5 Flagler 4 Marion 2 Wakulla 4 Franklin 5 Martin 5 Walton 7 1-8

Eight soil samples from 6 soil profiles were taken from Monroe County, Dade County and Collier County and were geo-referenced (Table 1-4 ). Another twenty-five soil samples from 5 soil profiles, collected from the Everglades Protection Area by Dr. Yuncong Li of the Tropical Research and Education Center in Homestead, were also used for assessing background concentrations of trace metals in soils in Miami-Dade County. 1.2.2. Sample Digestion. The EPA Method 3051a was used to digest soil samples. Sample blank, spike, and certified reference materials were used during digestion and analysis according to the FDEP QA/QC plan. Air-dried 1-mm sized soil samples were ground to pass 60-mesh nylon sieve and placed into precleaned plastic bottles. Approximately 1.00 g of soil sample was weighed into a 20 ml Teflon pressure digestion vessel. The digestion was performed in a CEM digestion microwave oven using 9 ml of concentrated HNO 3 and 3 ml of concentrated HCl of trace metal grade. The digested solution passed Watman 42 filter paper and the filtrate was collected in a 100 ml volumetric flask and brought to volume. The samples were then be stored in precleaned polyethylene bottles in a refrigerator until analysis. 1.2.3. Metal Analysis. Concentrations of trace metals As, Cd, Cr, Pb, and Se in the filtrates were determined using a Perkin-Elmer SIMMA 6000 graphite furnace atomic absorption spectrophotometer (GFAAS), following the FDEP-approved QA/QC protocol. Concentrations of Cu, Ni, and Zn were analyzed on a multichannel Jarrell-Ash ICP 61-E unit by USEPA SW 846 method 200.7. A total of 2,100 elemental analyses including standards and certified reference materials (210 samples x 1.25 QA samples x 8 elements) were performed. 1.2.4. Data Analysis. The statistical program SAS was used to analyze trace metal concentrations in Florida soils. Geometric means and standard deviations for trace metals were calculated based on soil types and locations. In addition, total-recoverable metal concentrations were compared to total-total metal concentrations. Soil properties affecting metal recovery were also determined. 1-9

Table 1-4. Locations and site description of soil samples collected from south Florida. Lab # Field ID Soil Material County Type Horizon Depth (inch) Rangeship Township Section 1 1 Marl Monroe Undisturbed C 0-9 E 29 S 66 26 2 2 Marl Monroe Undisturbed A and C 0-9 E 28 S 66 35 3 3-a Marl Monroe Undisturbed A 0-9 E 32 S 66 16 4 3-b Muck Monroe Undisturbed Oa 9-15 No 1/4 1/4 5 4-a Marl Dade Undisturbed A and C 0-6 E 38 S 54 24 NE SE 6 4-b Muck Dade Undisturbed Oa 6-9 7 5 Marl Dade Cropped field A and C 0-9 E 39 S 57 50 NE SW 8 6 M/M/M Intergrade Collier Undisturbed A 0-8 E 32 S 53 7 SE NE 1-10

1.3. RESULTS AND DISCUSSION 1.3.1. Statistical Summary of Chemical and Physical Properties of 210 Soil Samples Many factors affecting concentrations of trace metals in soils, including soil parent material, topography, climate, vegetation, management and time (Bradford et al., 1996). Tables 1-5, 1-6, and 1-7 list the range and mean concentrations of 7 soil chemical and physical properties in different soil orders and different regions. Four regions (south, central, north and west) are defined based on county boundries as showing in Figure 1-1. 1-11

Table 1-5. Selected soil properties of 210 Florida surface soils. Particle size distribution (%) O.C. ph-h 2 O CEC Bulk Density Sand Silt Clay % (1:1) Cmol/kg mg/m 3 Range 2.4-99.9 n.d.*-82.0 nd-73.4 0.09-53.6 2.7-8.0 0.28-308 0.12-1.72 GM ±GSD 88.5 ± 18.7 6.9 ± 11.9 4.6 ± 9.6 5.3 ± 11.4 5.0 ± 0.9 23.5 ± 43.5 1.27 ± 0.33 * n.d. = not detected. 1-12

Table 1-6. Chemical and Physical Properties of 210 Florida Soils based on Soil Orders Sand (%) Silt (%) Clay (%) O.C. (%) A 90.8 ± 9.22 ab 6.00 ± 6.54 bc 3.15 ± 3.40 bc 2.02 ± 2.06 c lfisols Entisols 81.6 ± 28.4 b 8.53 ± 19.7 b 5.38 ± 13.5 b 1.40 ± 1.95 c Inseptsols 71.2 ± 34.7 c 11.7 ± 9.91 ab 17.1 ± 25.2 a 7.07 ± 11.8 b Histosols n.d. n.d. n.d. 39.3 ± 11.9 a Spodosols 94.7 ± 3.74 a 3.67 ± 2.85 a 1.59 ± 1.23 c 2.26 ± 2.53 c Mollisols 63.2 ± 22.4 c 18.6 ± 12.4 a 18.2 ± 13.4 a 5.84 ± 4.04 b Ultisols 88.2 ± 11.0 ab 7.43 ± 7.57 bc 4.40 ± 4.50 bc 1.04 ± 0.55 c lfisols A ph (1:1) CEC (cmol/kg) Bulk density (mg/m 3 ) 5.38 ± 0.89 ab 11.5 ± 11.1 ab 1.31 ± 0.24 bc Entisols 5.28 ± 1.05 b 15.0 ± 38.9 b 1.38 ± 0.16 ab Inseptsols 5.27 ± 0.76 bc 24.6 ± 18.7 bc 1.14 ± 0.40 cd Histosols 4.96 ± 1.19 b 135 ± 56.2 b 0.33 ± 0.17 e Spodosols 4.49 ± 0.66 c 11.4 ± 9.08 c 1.30 ± 0.20 c Mollisols 5.99 ± 1.33 a 37.6 ± 25.6 a 0.96 ± 0.35 d Ultisols 5.04 ± 0.53 b 7.23 ± 2.75 b 1.45 ± 0.16 a Means within a column followed by the same letter are not significantly different (P<0.05) using Student t-test. n.d. = not detected. 1-13

Figure 1-1. Definitions of west, north, central, and south regions in Florida. West North Central South 1-14

Table 1-7. Chemical and Physical Properties of 210 Florida Soils based on Regions Sand (%) Silt (%) Clay (%) O.C (%) S 81.0 ± 32.6 b 13.8 ± 26.5 a 5.19 ± 7.55 a 10.8 ± 17.5 a outh Central 93.2 ± 7.60 a 4.04 ± 4.62 b 2.73 ± 3.94 a 7.67 ± 13.8 a North 89.7 ± 14.7 a 5.92 ± 6.35 b 4.28 ± 10.1 a 2.47 ± 4.00 b West 87.3 ± 18.8 ab 6.70 ± 7.35 b 5.96 ± 12.4 a 3.32 ± 9.37 b outh S ph (1:1) CEC (cmol/kg) Bulk density (mg/m 3 ) 6.08 ± 1.14 a 47.0 ± 71.8 a 1.11 ± 0.43 b Central 5.03 ± 0.83 b 28.6 ± 44.7 ab 1.19 ± 0.39 b North 4.84 ± 0.68 bc 14.6 ± 21.1 b 1.32 ± 0.22 a West 4.64 ± 0.68 c 15.9 ± 35.2 b 1.39 ± 0.25 a Means within a column followed by the same letter are not significantly different (P<0.05) using Student t-test. 1-15

1.3.2. Statistical Summary of Total-Total Concentrations of 8 Trace Elements in 210 Florida Soil Samples Univariate statistics and analysis of variance were used to test distribution normality and significant differences in concentrations between different soil orders and different regions of 8 trace elements (Tables 1-8, 1-9, and 1-10). Those data were determined using EPA method 3052 digestion and GFAAS(Chen et al., 1999). Table 1-8. Total-total concentrations (mg/kg) of 8 trace elements in 210 Florida soils. Element As Cd Cr Cu Ni Pb Se Zn Skewness 7.21 4.96 4.13 10.8 3.29 8.07 3.93 4.49 Kurtusis 67.8 29.2 23.9 135 14.8 75.8 19.0 23.7 Range 0.02-38.2 0.004-1.02 0.02-175 0.1-286 0.09-80.6 0.21-290 0.01-3.82 0.9-86.5 AM ± ASD 1.30±3.43 0.06±0.14 13.8±19.8 6.0±21.7 11.7±10.2 10.4±26.2 0.25±0.49 7.7±11.8 GM ± GSD 0.41±4.06 0.01±5.12 7.1±3.10 2.1±3.20 9.0±2.22 5.1±2.79 0.10±3.46 4.8±2.37 Arithmetic mean + arithmetic standard deviation Geometric mean + geometric standard deviation. 1-16

Table1-9. Total-total concentrations of 8 trace metals in 210 Florida soils based on soil orders lfisols A As Cd Cr Cu 3.01 ± 3.85 ab 74.8 ± 5.51 bc 6.52 ± 2.67 cd 2.02 ± 2.26 cd Entisols 2.72 ± 3.96 b 97.0 ± 5.28 b 7.80 ± 2.44 bc 1.77 ± 3.34 d Inseptsols 2.14 ± 4.78 abc 18.3 ± 5.27 c 15.1 ± 3.87 abc 5.16 ± 4.11 ab Histosols 1.66 ± 2.74 d 18.8 ± 4.42 c 16.2 ± 2.60 a 9.37 ± 2.98 a Spodosols 5.24 ± 3.45 a 175 ± 2.93 a 3.88 ± 3.59 e 1.16 ± 2.23 e Mollisols 1.00 ± 2.39 cd 44.6 ± 9.40 bc 18.4 ± 3.51 a 4.21 ± 6.36 abc Ultisols 1.86 ± 2.88 bc 91.1 ± 4.56 b 11.1 ± 2.05 b 2.35 ± 2.30 bcd lfisols A Ni Pb Se Zn 2.02 ± 2.26 b 7.19 ± 2.56 bcd 12.9 ± 3.55 a 3.81 ± 2.06 cd Entisols 1.77 ± 3.34 b 8.96 ± 1.70 cd 13.2 ± 3.15 a 4.98 ± 2.33 bc Inseptsols 5.16 ± 4.11 ab 8.43 ± 3.61 ab 5.13 ± 3.64 b 8.58 ± 3.18 ab Histosols 9.37 ± 2.98 a 14.7 ± 2.39 a 1.09 ± 2.25 c 9.91 ± 2.58 a Spodosols 1.16 ± 2.23 b 8.06 ± 2.23 d 14.4 ± 1.98 a 2.85 ± 1.87 d Mollisols 4.21 ± 6.36 ab 11.4 ± 1.56 a 4.21 ± 3.08 b 7.59 ± 3.40 ab Ultisols 2.35 ± 2.30 b 9.47 ± 2.30 bc 13.1 ± 2.78 a 6.44 ± 1.89 ab Geometric means within a column followed by the same letter are not significantly different (P<0.05) using Student t-test. 1-17

Table 1-10. Total-total concentrations of 8 trace elements in 210 Florida soils based on regions (South, Central, North and West Florida). Region As Cd Cr Cu 0.57 ± 5.76 a 0.017 ± 7.16 a 5.12 ± 4.84 c 2.93 ± 6.10 a outh Central 0.37 ± 4.58 a 0.012 ± 4.39 b 7.08 ± 2.51 abc 2.30 ± 3.69 ab North 0.36 ± 2.91 a 0.011 ± 4.87 b 9.09 ± 2.88 a 1.62 ± 2.12 b West 0.44 ± 4.16 a 0.010 ± 4.91 b 8.27 ± 2.72 ab 2.24 ± 2.45 ab outh Ni Pb Se Zn 8.12 ± 1.88 ab 4.05 ± 2.79 a 0.19 ± 4.54 a 4.63 ± 2.12 a Central 7.55 ± 2.69 b 5.62 ± 2.85 a 0.11 ± 3.57 b 4.63 ± 2.06 a North 9.35 ± 2.06 ab 5.36 ± 2.32 a 0.07 ± 2.77 b 4.44 ± 3.17 a West 10.5 ± 2.20 a 5.05 ± 3.34 a 0.08 ± 3.14 b 5.36 ± 3.52 a Geometric means within a column followed by the same letter are not significantly different (P<0.05) using Student t-test. 1-18

1.3.3. Precision and Accuracy for the Determination of Total-Recoverable Trace Elements Using Certified Reference Materials Concentrations of As, Cd, Cr, Pb, and Se in 210 Florida surface soil samples (210 + 25% QA samples) were analyzed using Perkin-Elmer SIMAA 6000 atomic absorption spectrophotometer at the SWSD Trace Metal Laboratory. Concentrations of Cu, Ni, Zn and other elements were analyzed on a multichannel Jarrell-Ash ICP 61-E unit by USEPA SW 846 method 200.7. Standard curves for the internal audits were excellent, with r 2 closing to 1.00 and recoveries within 90-110% (Table 1-11). Four NIST SRMs (2709, 2710, 2711, and 2781), representing sandy soil, highly and intermediately contaminated soils, and domestic sludge samples, were used in this study to evaluate the precision and accuracy of USEPA Method 3051a to determine total-recoverable concentrations of trace metals (Table 1-11 ). The ranges of precision for the total-recoverable method were between 0 to 20%, except for Cd, Cr, and Ni in a few cases. Accuracies for As, Cu, Ni, Se, and Zn were within 80-120% range, which is comparable to the total-total method. However, elemental recoveries for Cd (38.6~83.8%), Cr (58.9~87.4%), and Pb (61.6~81.3%) varied with metals and matrix. 1-19

Table 1-11. QA/QC results for ICP and GFAAS determinations Elements Concentration Recovery Precision µg L -1 % R %RSD As 2.50 105 3.7 Cd 1.25 98 1.7 Cr 0.50 93 2.6 Cu 1.25 95 0.8 Ni 2.00 94 12.5 Pb 1.25 100 1.1 Se 1.25 87 1.1 Zn 1.00 91 7.2 1-20

Table 1-12. Precision and accuracy of 16 elements in NIST SRMs 2709, 2710, 2711, and 2781 digested by EPA Methods 3051a. SRM 2709 (N=7) SRM 2710 (N=15) SRM 2711 (N=5) SRM 2781 (N=15) Element Concentration Accuracy Precision Concentration Accuracy Precision Concentration Accuracy Precision Concentration Accuracy Precision mg kg -1 %R %RSD mg kg -1 %R %RSD mg kg -1 %R %RSD mg kg -1 %R %RSD As 18.1 102.4 10.6 515.8 82.4 3.9 123.6 117.7 12.5 5.8 75.6 18.0 Ba 416.0 43.0 6.9 338.7 47.9 9.3 232.7 32.1 9.0 506.8 n.a. 9.4 Cd 0.15 38.6 30.6 12.1 55.3 15.0 20.7 49.7 3.0 8.2 63.8 12.8 Cr 113.6 87.4 19.7 23.0 58.9 8.5 37.4 79.6 27.3 153.5 76.0 11.3 Cu 37.6 108.7 8.4 2728 92.5 1.7 108.3 95.0 2.9 560.7 89.4 9.3 Mn 495.0 92.0 2.6 8148 80.7 2.2 544.7 85.4 2.8 730.1 n.a. 9.4 Ni 87.9 99.9 20.9 12.4 86.5 34.4 16.8 81.6 29.3 69.4 86.6 10.7 Pb 11.6 61.6 6.9 4348 78.6 3.4 944.4 81.3 1.4 131.7 65.2 11.0 Se 1.87 119.3 13.8 1.0 n.a. 11.2 1.68 110.8 10.1 12.7 79.2 11.1 Zn 97.6 92.1 6.7 6043 86.9 2.3 326.8 93.3 3.7 1066 83.8 9.2 Ca 15258 80.7 3.4 4734 37.9 4.6 22007 76.4 2.9 36628 93.9 9.5 Mg 13817 91.5 3.3 6280 73.6 3.1 8238 78.5 3.5 4715 79.9 9.4 K 5540 27.3 17.4 6828 32.4 9.5 7222 29.5 13.7 2557 52.2 10.4 P 577.2 93.1 6.9 1041 98.2 8.1 769.7 89.5 3.2 21292 88.0 9.3 Al 36165 48.2 17.5 27189 42.2 7.9 27857 42.7 10.3 10232 63.9 13.7 Fe 31160 89.0 2.4 29170 86.3 1.8 24781 85.7 2.2 24596 87.8 9.4 mean 79.7 11.1 69.4 7.9 76.8 8.6 77.5 10.9 n.a. = certificate value is not available. 1-21

1.3.4. Statistical Summary of Total-Recoverable Concentrations of 8 Trace Elements in 210 Florida Soil Samples Table 1-13 shows total-recoverable concentrations of 8 trace metals in 210 Florida surface soil samples. Table 1-14 lists the ranges of metal concentration and summary statistics for each metal. In general, elemental concentrations in these soils vary by a factor of up to 15000 (Cr, Cu), 5000 (Pb, As), 1800 (Zn) and 600 (Ni, Cd, Se). Coefficients of variation are the greatest for Cu and least for Ni (Table 1-14). Both arithmetic and geometric means were used to describe the central tendency and variation of the data. The arithmetic means (AM) and standard deviation (ASD) are best used as estimates of geochemical abundance of an element. The geometric mean (GM) and standard deviation (GSD), however, are better maximum likelihood estimators for most geochemical data (Gough et al., 1988). There is not much difference between totalrecoverable and total-total cocentrations of As, Se, Cd, Cu, and Zn in Florida soils (Chen et al., 1998) based on AMs. However, there are differences between these two data sets based on GMs. Moment coefficients of skewness and kurtosis describe how the shapes of sample frequency distribution curves differ from ideal Gaussian (normal) curves. Skewness (calculated as third moment of the population mean) refers to asymmetry of the upper and lower halves of the curve around the mean. Kurtosis (calculated as fourth moment of the population mean minus three) refers to deviations towards unsual flatness or pointedness of the curve peak (Bradford et al., 1996). Both of them should give a value of zero for normally distributed data (McGrath, 1987). Distributions of 8 trace metals in Florida soils in this study were strongly positive-skewed and heavy-tailed for original data. The log-transformation significantly reduced the skewness and kurtosis of the data. This is consistent with our previous results for total-total concentrations of trace metals in Florida surface soils (Chen et al., 1998). Taxonomic distributions of total-recoverable concentrations of 8 trace metals in 210 Florida surface soil samples (Table 1-15) are generally comparable with our previous results for total-total concentrations of these metals in 448 Florida surface soils (Chen et al., 1998), except for Cr and Pb. Table 1-16 lists geographical distributions of the total-recoverable metal concentrations in Florida. Samples from south Florida tends to have higher totalrecoverable concentrations of As, Cd, and Se than other regions. Total-recoverable concentrations of 8 trace metals in soil samples collected south Florida are shown in Table 1-17. These data supported our previous results, that is, in south Florida, soils have potentially higher background concentrations of certain trace metals than the other regions. 1-22

Table 1-13. Properties and total-recoverable concentrations of 8 tracemetals in 210 Florida surface soil samples UFSCL# ph O.C. Clay CEC Total Al Total Fe As Se Cd Pb Ni Cr Cu Zn 1:1 % cml/kg mg/kg mg/kg 21 5.9 38.2 N/D 63.3 6694 14226 12.2 4.03 0.242 7.22 13.6 9.52 5.20 24.2 23 3.8 32.7 N/D 141 34286 14851 6.03 0.92 0.095 10.4 9.62 67.6 9.21 12.7 56 6.6 38.0 N/D 103 5180 4452 2.75 1.18 0.131 4.92 6.92 7.70 46.6 29.5 59 5.7 47.8 N/D 92.8 2188 6306 2.75 1.16 0.063 2.53 2.53 3.47 39.8 21.9 308 7.5 39.0 N/D 150 3453 4453 3.99 2.45 0.108 2.63 7.81 4.53 4.03 7.19 371 4.4 46.2 N/D 88.9 4856 3698 1.62 0.40 0.216 249 2.88 4.28 12.8 46.8 539 5.4 0.67 0.7 2.5 385 218 0.11 0.06 0.044 2.52 0.14 0.51 0.18 2.20 651 4.9 0.49 0.3 2.0 110 54 0.04 0.01 0.011 0.78 0.07 0.01 0.04 0.10 656 4.5 4.04 3.2 17.3 251 109 0.10 0.01 0.036 4.91 0.77 0.22 0.02 1.79 718 5.5 1.78 7.4 13.4 802 255 0.10 0.01 0.003 0.03 2.05 0.99 0.62 0.20 723 5.7 1.65 8.7 12.2 8690 1891 0.09 0.04 0.060 5.02 1.92 21.2 1.07 5.95 799 5.8 48.0 N/D 165 2085 1067 8.42 1.06 0.106 1.34 0.07 2.61 0.49 49.5 810 5.1 42.7 N/D 114 2010 2457 0.96 0.30 0.251 9.80 18.2 9.30 2.91 15.1 1129 5.9 0.31 1.7 7.4 882 450 0.09 0.02 0.025 1.54 0.25 1.17 7.90 1.24 1229 4.5 1.35 1.2 5.6 88 51 0.04 0.01 0.022 0.78 0.07 0.01 0.02 0.10 1293 5.5 0.71 1.6 2.8 384 279 0.09 0.01 0.030 1.20 0.21 0.26 0.09 0.10 1299 5.0 0.23 2.1 2.7 379 151 0.04 0.03 0.011 1.71 0.25 0.32 0.08 0.10 1-23

1320 4.4 7.44 6.1 29.8 2116 986 0.58 0.38 0.068 2.22 11.6 2.05 1.50 0.10 1341 4.5 0.78 2.3 3.9 617 138 0.03 0.01 0.057 1.58 4.59 0.25 0.42 0.10 1346 3.3 11.2 5.1 49.4 579 361 0.15 0.01 0.053 1.87 7.76 0.18 0.82 0.10 1359 4.4 46.2 N/D 138 10324 4652 2.43 2.39 0.648 16.5 5.10 13.7 6.07 12.1 1375 4.3 1.43 10.4 13.2 10961 5354 0.08 0.91 0.036 5.07 1.17 11.3 2.72 5.34 1380 5.0 0.73 1.1 6.0 1886 807 0.15 0.36 0.034 2.38 1.77 1.95 0.02 2.27 1391 5.5 0.55 1.4 3.1 1011 558 0.01 0.01 0.057 1.40 0.07 0.75 0.02 1.42 1480 5.8 0.64 3.2 3.3 5053 2074 0.68 0.62 0.003 2.97 0.07 4.44 0.02 7.03 1485 4.1 4.23 4.1 24.1 377 318 0.40 0.34 0.065 3.44 0.07 1.06 0.02 1.30 1518 4.2 2.39 2.1 14.0 129 89 0.06 0.47 0.035 1.38 0.07 0.45 0.02 2.34 1549 5.3 1.02 2.2 4.5 1470 1147 0.29 0.20 0.020 4.33 3.43 2.83 1.12 3.02 1561 5.6 1.02 7.9 8.3 11590 5353 2.05 0.45 0.031 4.49 3.07 9.46 4.31 10.8 1623 4.1 1.98 2.2 10.3 163 92 0.01 0.07 0.016 2.23 0.07 0.06 0.02 1.16 1630 4.6 3.50 3.6 13.9 173 137 0.15 0.14 0.003 1.17 0.07 0.68 0.02 1.24 1685 6.0 3.08 1.0 16.8 1279 1075 0.05 0.10 0.039 4.30 1.20 2.26 0.02 3.88 1752 4.4 7.94 3.3 6.6 1585 773 0.27 0.59 0.104 13.6 0.07 2.73 0.24 1.48 1758 4.4 1.92 3.9 10.0 385 154 0.21 0.32 0.088 0.65 0.07 0.93 0.12 0.10 1872 5.7 0.58 4.0 3.5 7530 2755 1.00 0.25 0.060 4.15 0.42 4.95 1.09 4.77 2072 5.0 1.70 2.6 9.9 3656 1128 0.14 0.49 0.052 4.29 0.07 3.17 0.02 2.58 2155 5.1 1.60 4.0 9.0 1771 703 0.05 0.11 0.057 2.63 1.86 6.43 0.02 2.29 1-24

2162 4.6 1.28 0.8 7.1 265 101 0.05 0.08 0.092 1.65 7.44 0.55 0.02 1.15 2240 6.7 3.38 1.3 14.5 1485 2517 1.22 0.18 0.036 2.00 0.07 2.32 0.02 3.56 2256 5.5 0.89 3.2 3.6 3027 1884 0.31 0.11 0.033 2.34 0.07 2.37 0.02 3.33 2320 4.6 2.56 2.5 4.3 2162 1218 0.42 0.04 0.027 13.1 2.88 3.42 1.22 3.98 2388 5.2 1.65 66.2 28.5 63761 39599 3.63 1.48 0.162 28.5 22.7 47.7 33.2 89.7 2435 6.7 1.86 1.9 8.6 4056 2242 0.21 0.01 0.044 8.59 2.63 5.32 2.02 12.2 2537 4.9 1.19 0.7 5.4 167 138 0.07 0.01 0.145 0.51 43.6 0.56 0.04 0.10 2551 5.3 0.58 0.4 2.6 35 32 0.04 0.01 0.012 0.41 1.47 0.18 0.02 0.10 2671 4.1 2.39 3.9 10.9 1427 389 0.35 0.01 0.050 1.88 6.78 2.52 0.14 0.10 2753 4.2 1.78 1.3 12.2 227 88 0.11 0.07 0.034 1.08 4.47 0.67 0.14 2.27 2779 7.6 1.32 4.4 17.0 990 2658 3.73 0.54 0.050 2.04 1.65 9.37 0.88 2.48 2791 N/D 2.34 2.8 10.1 1007 424 0.21 0.04 0.035 1.73 4.58 1.58 6.79 2.34 2806 4.6 36.6 N/D 121 449 967 1.45 1.65 0.165 0.03 12.1 13.8 11.9 20.5 2810 6.2 1.80 16.6 18.8 7786 7630 1.17 0.28 0.165 13.9 4.68 38.3 2.57 18.8 2885 2.7 14.4 60.9 130 40345 31865 12.7 1.32 0.126 24.0 22.2 46.8 12.1 61.2 2993 7.0 0.46 0.6 1.5 270 144 0.15 0.01 0.012 0.83 0.07 1.37 0.07 1.17 3019 5.5 3.15 3.4 18.9 1752 765 0.23 0.06 0.033 3.48 0.07 2.82 5.22 4.43 3077 4.2 1.74 2.5 7.0 92 59 3.05 0.01 0.023 0.81 0.75 0.48 0.02 1.15 3087 4.3 3.66 6.5 19.7 958 207 2.10 0.15 0.025 4.41 0.07 1.17 1.34 1.26 3190 4.3 9.05 8.0 55.1 4029 2240 0.86 0.47 0.071 4.81 1.23 10.2 5.83 2.86 1-25

3304 5.1 0.57 1.9 3.3 1713 585 2.20 0.01 0.012 3.23 0.69 1.72 0.91 5.83 3354 6.0 0.95 0.5 5.0 2908 798 1.33 0.01 0.161 3.50 0.07 4.79 35.8 23.6 3402 4.2 2.26 1.0 11.5 296 201 6.28 0.09 0.047 5.05 0.07 0.44 1.30 1.18 3417 5.1 0.79 2.3 5.0 2300 740 0.20 0.20 0.040 2.90 1.00 1.78 1.60 3.20 3473 6.2 1.53 1.8 8.8 1260 243 3.17 0.01 0.003 6.09 0.07 1.06 1.18 1.12 3596 4.7 1.24 1.0 10.6 1087 402 0.01 0.01 0.003 2.25 2.44 1.04 1.69 4.83 3631 3.6 5.03 2.9 32.0 400 292 0.20 0.30 0.080 2.80 1.00 0.46 0.90 1.10 3656 5.1 1.02 0.6 15.0 131 93 0.01 0.01 0.012 0.55 1.36 0.14 1.01 0.10 3744 4.5 4.12 0.9 10.7 173 111 0.05 0.01 0.003 0.82 1.73 0.10 1.04 1.15 3758 5.5 1.00 3.4 5.0 4816 1295 0.12 0.01 0.003 1.64 2.62 7.93 1.62 4.90 3776 4.6 1.63 3.0 7.2 2376 831 0.23 0.12 0.003 2.88 2.21 5.00 1.26 1.13 3794 4.9 2.73 1.8 6.6 2574 692 0.11 0.04 0.025 6.19 2.88 1.67 1.97 2.48 3820 4.0 1.79 2.6 12.3 136 112 0.14 0.13 0.010 3.98 1.00 0.17 1.10 1.05 3831 3.6 15.6 2.7 37.5 271 170 0.18 0.15 0.041 1.97 1.87 0.18 1.52 0.10 3838 5.4 0.96 3.8 7.9 1599 583 0.21 0.01 0.043 2.85 0.86 2.93 0.34 2.45 3846 4.7 3.51 6.2 23.5 300 106 0.20 0.30 0.040 1.60 1.00 0.32 0.70 0.80 3888 4.6 0.28 2.2 5.3 2407 573 0.05 0.01 0.003 2.62 1.19 1.23 3.32 2.41 3895 4.2 1.41 1.5 4.9 1778 585 0.27 0.01 0.011 1.79 1.62 2.63 1.77 0.10 3908 4.8 0.87 0.6 5.4 96 53 0.03 0.01 0.003 0.47 1.33 0.01 0.94 0.10 4016 4.3 1.65 0.5 8.3 140 89 0.02 0.25 0.030 1.90 1.00 1.92 0.90 1.70 1-26

4050 5.0 0.63 1.9 3.5 2600 1640 0.50 0.20 0.030 4.80 1.00 1.98 1.90 5.90 4128 4.6 0.70 1.2 6.5 201 137 0.13 0.14 0.003 0.98 1.53 0.05 0.99 0.10 4225 6.2 1.42 1.2 7.8 1167 405 0.63 0.18 0.095 3.25 1.27 21.6 5.50 21.4 4335 4.7 1.39 73.4 47.3 84064 22705 3.95 1.77 0.053 26.0 11.1 193 2.28 34.5 4363 4.4 4.39 0.1 11.1 277 90 0.11 0.16 0.003 0.88 0.57 1.91 1.62 0.10 4386 3.8 30.1 N/D 125 317 114 1.01 0.05 0.012 1.00 1.58 0.69 1.27 0.10 4395 4.0 2.44 1.3 11.0 284 96 0.15 0.09 0.012 1.04 0.31 0.65 1.20 0.10 4411 5.3 0.41 1.7 10.9 1919 717 0.41 0.10 0.003 10.6 0.69 1.87 2.79 15.8 4417 4.7 3.39 3.9 17.3 1434 377 0.22 0.05 0.037 11.0 0.07 1.53 1.04 11.1 4444 7.0 0.70 1.1 3.5 505 992 2.74 0.04 0.027 5.55 0.07 4.75 0.33 2.66 4532 6.4 0.23 1.1 1.4 7557 2425 4.06 4.07 0.136 5.48 0.07 5.16 1.90 27.1 4542 4.3 49.3 N/D 120 1525 593 1.28 6.06 0.127 5.47 0.07 8.94 2.42 29.7 4570 4.7 53.0 N/D 99.5 1147 1127 0.30 0.06 0.023 1.89 0.07 0.95 0.02 15.1 4602 4.5 0.70 1.1 3.2 1803 1420 0.33 0.11 0.011 0.67 0.07 0.13 0.02 3.44 4614 4.5 1.29 3.8 7.3 2362 855 0.42 0.15 0.012 1.76 1.77 1.48 0.07 2.46 4619 4.7 0.45 2.7 4.7 10978 3620 0.16 0.01 0.054 6.58 8.91 8.48 0.02 10.9 4647 7.3 0.67 0.8 6.1 150 102 0.17 0.10 0.021 5.99 0.07 1.42 0.02 3.21 4696 5.0 0.83 4.8 8.5 7280 3036 1.05 0.01 0.012 2.74 0.07 9.80 0.33 3.46 4732 4.7 0.11 0.2 1.0 90 73 0.20 0.20 0.200 2.60 0.40 0.91 0.60 3.50 4750 4.7 1.02 6.9 7.5 7856 4201 1.65 0.32 0.028 6.63 0.07 7.04 2.79 12.4 1-27

4811 5.2 1.25 2.4 6.1 3340 1170 0.15 0.20 0.200 4.40 1.30 2.35 0.90 2.90 4949 4.9 4.64 0.8 19.9 171 204 0.14 0.24 0.037 1.49 12.8 39.4 0.02 1.22 4985 5.1 0.51 1.1 3.0 2130 870 0.03 0.20 0.200 2.30 0.90 1.37 0.60 2.20 4998 4.8 1.18 3.2 7.3 683 334 0.39 0.10 0.012 1.35 5.71 15.3 0.02 2.44 5082 4.7 21.5 N/D 138 17176 10341 6.00 1.49 0.235 23.6 7.45 78.6 5.37 19.6 5093 4.9 1.71 0.4 19.6 188 165 0.04 0.21 0.023 2.19 0.07 0.86 6.09 3.52 5201 7.7 2.60 16.1 35.2 14703 5670 0.55 0.55 0.048 4.63 0.31 26.0 0.33 9.50 5241 5.0 0.88 1.5 3.8 973 423 0.04 0.22 0.012 2.31 0.07 0.57 0.02 2.34 5247 4.2 3.44 2.8 15.6 0.07 0.01 0.002 0.10 5257 4.2 0.66 1.3 3.8 1048 573 0.44 0.21 0.011 2.41 0.07 0.80 0.02 2.25 5263 5.5 1.32 2.2 5.1 2107 851 0.21 0.16 0.003 2.50 0.07 1.30 0.02 3.72 5275 6.0 1.40 1.7 8.2 1207 125 0.12 0.19 0.003 1.42 0.07 1.04 0.02 2.57 5294 7.1 0.37 0.4 1.6 150 106 0.02 0.20 0.200 3.70 0.30 0.40 1.10 1.90 5299 4.6 0.57 1.1 3.8 115 98 0.17 0.01 0.012 3.49 0.07 0.02 0.18 0.10 5339 5.0 0.83 1.6 4.8 1360 531 0.03 0.01 0.036 1.11 0.07 1.91 4.49 0.10 5350 5.2 1.06 2.6 6.2 3840 1086 0.44 0.04 0.024 4.19 0.36 2.80 0.42 0.10 5364 4.8 0.84 2.0 4.7 4380 681 0.03 0.20 0.200 3.90 1.40 7.61 1.40 2.80 5369 4.9 1.00 7.7 8.6 12600 3940 0.72 0.20 0.700 6.90 4.80 12.1 2.70 4.80 5469 5.5 50.8 N/D 134 4437 6820 2.25 2.85 0.246 5.60 9.15 8.35 68.8 3.52 5487 5.4 0.45 0.6 0.3 315 492 0.14 0.01 0.020 1.62 1.12 0.35 0.52 0.10 1-28

5519 4.7 3.58 3.4 21.2 645 416 0.15 0.13 0.247 2.81 2.26 0.02 0.28 0.10 5524 4.7 3.42 3.1 17.9 412 317 0.06 0.23 0.028 1.42 47.5 0.28 0.02 0.10 5566 6.8 2.04 2.4 12.4 382 310 0.14 0.03 0.066 1.51 1.46 0.09 0.47 0.10 5604 5.8 33.5 N/D 59.2 1607 4839 2.16 1.61 0.139 3.41 6.29 2.66 29.0 0.10 5680 4.7 4.23 1.8 18.4 180 191 0.20 0.20 0.200 1.70 0.50 0.38 1.80 1.40 5695 5.9 0.99 1.5 3.8 724 312 0.18 0.02 0.006 1.39 2.03 0.43 1.15 1.81 5829 4.8 0.65 1.8 5.0 2070 725 0.62 0.20 0.200 2.45 0.55 0.35 1.95 1.40 5834 4.0 0.56 1.5 5.9 753 365 0.32 0.02 0.024 1.96 1.39 0.29 1.77 0.10 5845 4.2 0.91 0.9 6.4 586 438 0.12 0.01 0.023 2.77 1.56 0.20 0.95 0.10 5855 3.9 1.26 0.4 10.0 139 60 0.21 0.01 0.035 1.66 1.08 0.01 0.63 0.10 5873 4.9 0.52 1.3 4.3 833 375 0.13 0.02 0.011 1.25 1.76 0.63 0.66 9.01 5877 5.7 0.80 2.5 8.2 3311 1647 0.39 0.02 0.033 4.52 3.81 3.80 30.6 5.50 5911 4.7 1.37 5.5 12.4 5228 2214 0.18 0.16 0.035 3.47 3.16 10.0 3.12 7.00 5919 5.3 0.66 3.4 6.2 5019 1146 0.14 0.11 0.025 2.06 2.95 2.58 2.24 1.24 5926 6.5 0.93 4.3 7.7 7135 1985 0.26 0.01 0.082 3.85 4.21 12.0 2.34 5.85 6034 5.6 1.42 1.3 9.6 131 89 0.02 0.10 0.029 0.42 2.20 0.36 7.07 0.10 6048 4.8 0.76 0.1 4.2 2852 996 0.14 0.04 0.051 3.75 3.54 1.45 0.63 0.10 6060 4.5 1.03 2.4 9.5 5751 983 5.60 0.01 0.003 11.05 1.53 4.12 6.49 6.70 6093 4.3 2.26 1.9 8.7 408 295 0.05 0.09 0.003 0.80 1.12 0.46 0.84 5.51 6102 5.0 1.79 1.6 7.6 344 425 0.11 0.04 0.003 0.93 1.60 0.36 1.78 0.10 1-29

6177 5.2 1.54 2.8 6.7 3402 2018 0.49 0.03 0.003 5.88 1.75 6.68 0.61 7.67 6227 3.1 53.6 N/D 224 4142 1030 0.05 1.34 0.003 2.05 22.4 2.16 58.1 3.73 6242 5.0 1.68 4.2 10.5 4421 1251 0.59 0.05 0.003 3.35 2.83 3.32 5.00 5.91 6257 4.4 0.54 0.6 3.2 172 51 0.05 0.01 0.003 0.69 0.07 0.26 0.02 0.10 6299 4.7 0.48 0.3 1.4 129 71 0.02 0.01 0.003 0.76 0.07 0.10 0.02 1.08 6339 5.5 0.70 4.0 8.5 9988 4870 1.80 0.15 0.003 3.61 4.13 14.4 3.28 6.08 6357 4.2 1.02 0.8 3.9 93 115 0.02 0.07 0.003 0.53 1.73 0.05 0.17 1.30 6364 4.7 7.72 25.1 41.3 5890 2461 0.19 0.05 0.003 4.47 2.69 5.66 1.35 6.85 6408 5.7 13.8 N/D 94.4 23404 7172 0.68 0.70 0.003 14.26 13.2 55.4 7.70 25.1 6470 4.4 1.25 1.3 25.6 671 282 0.22 0.01 0.003 2.94 0.32 0.95 0.02 0.10 6532 6.5 2.11 12.3 26.5 1884 1499 9.05 1.15 0.230 19.40 9.62 43.9 49.9 130 6538 7.7 3.08 31.8 44.3 1238 919 3.34 0.98 0.003 12.40 9.09 8.69 60.3 20.3 6542 7.9 2.41 16.4 32.6 1164 1257 20.72 1.25 0.356 16.70 7.70 38.1 29.9 45.1 6546 7.7 3.03 18.2 36.4 28360 13931 3.20 0.88 0.456 33.02 13.6 107 244 121 6579 4.7 0.99 1.3 4.5 1446 685 0.20 0.01 0.003 2.85 5.04 2.18 1.14 9.30 6603 6.0 0.81 0.2 6.2 3597 1102 0.52 0.01 0.003 2.88 3.17 3.72 2.39 5.03 6637 5.3 0.69 1.9 6.5 3979 526 0.05 0.01 0.003 1.84 3.95 3.78 0.69 13.2 6643 5.1 1.26 3.1 9.9 4765 1023 0.68 0.01 0.013 4.43 2.45 5.00 1.37 2.61 6662 4.9 1.04 0.8 5.2 700 840 0.03 0.30 0.050 3.00 0.30 1.39 2.60 5.20 6679 5.1 0.50 2.1 4.7 2615 888 0.16 0.12 0.034 1.63 0.39 2.50 0.87 6.70 1-30

6717 4.8 0.79 1.6 4.9 1275 640 0.10 0.09 0.022 2.18 0.07 1.79 0.10 0.10 6740 3.5 0.99 0.8 5.7 95 52 0.09 0.07 0.047 1.17 0.58 0.18 0.56 0.10 6779 5.1 0.84 2.5 4.9 4308 1282 0.97 0.07 0.024 3.67 2.71 4.33 1.25 3.67 6784 5.0 0.77 1.5 4.8 2346 832 0.44 0.13 0.025 2.66 0.95 1.95 0.83 2.52 6796 6.0 5.62 22.0 37.1 20668 35289 37.6 0.76 0.159 13.8 19.1 67.8 3.80 11.1 6803 5.6 0.67 1.8 4.4 3123 861 0.31 0.08 0.013 1.98 2.09 2.74 2.35 5.25 6808 5.5 0.55 1.4 2.7 1648 695 0.25 0.06 0.010 2.21 0.81 1.65 0.25 1.04 6812 5.2 1.52 2.6 8.7 2338 1369 0.33 0.12 0.109 22.2 2.97 5.45 6.32 50.1 6836 4.3 1.59 2.5 7.8 2570 851 0.46 0.30 0.130 4.10 1.80 1.83 2.70 3.60 6849 4.9 0.30 25.8 9.8 5580 3860 1.20 0.40 0.070 8.60 1.90 4.93 3.00 5.60 6878 5.6 1.25 1.8 4.1 1430 464 0.14 0.18 0.040 3.83 1.79 4.32 1.03 0.10 6895 5.2 0.82 1.5 4.1 1890 745 0.03 0.50 0.050 3.30 1.00 1.15 1.60 2.80 6901 4.7 1.61 1.6 6.3 3479 1160 0.19 0.28 0.025 3.12 4.87 8.51 2.03 6.26 6912 7.1 6.77 N/D 28.8 1371 554 0.30 0.12 0.028 1.91 5.04 7.78 0.65 0.10 6994 4.7 1.05 0.6 8.7 3169 665 0.27 0.28 0.062 2.10 7.42 8.04 0.68 1.23 7033 4.5 0.57 0.2 3.3 326 92 0.02 0.05 0.041 0.71 1.91 4.60 0.15 0.10 7057 5.5 22.4 N/D 100 15720 5955 1.10 2.18 0.123 7.57 6.17 29.8 20.5 12.3 7077 4.4 1.17 0.1 6.9 4619 1275 0.14 0.10 0.034 3.17 7.47 11.9 1.19 10.1 7096 4.1 1.00 0.0 3.2 150 83 0.13 0.01 0.043 0.64 1.14 0.36 0.30 1.07 7101 4.3 0.88 1.5 3.6 164 75 0.13 0.01 0.044 1.33 0.44 0.34 0.31 2.19 1-31

7115 4.2 2.38 0.1 5.8 141 72 0.03 0.09 0.035 0.87 0.07 1.07 0.02 0.10 7120 4.0 1.30 1.3 7.5 541 87 0.14 0.19 0.003 0.19 0.07 0.01 0.11 1.18 7149 4.2 0.96 0.1 5.1 133 45 0.04 0.08 0.003 1.16 0.07 0.01 1.16 0.10 7219 3.7 1.82 1.1 6.1 148 50 0.15 0.15 0.003 0.07 0.07 0.01 0.42 0.10 7271 5.2 0.47 3.2 4.3 5346 2691 1.04 0.11 0.003 1.36 0.07 1.54 3.23 7.98 7307 4.3 1.00 1.9 6.6 1414 408 0.02 0.01 0.003 1.27 0.07 0.01 0.39 1.10 7347 4.5 0.43 1.2 2.4 1067 608 0.12 0.05 0.003 0.42 0.07 0.01 0.55 3.17 7385 5.4 1.86 40.2 38.4 24917 13246 1.64 0.66 0.003 10.8 9.27 42.4 3.02 10.0 7431 5.2 1.83 0.5 8.8 245 155 0.12 0.18 0.003 1.01 2.40 0.01 1.19 1.29 7435 5.2 2.97 6.3 20.0 6236 4360 2.19 0.45 0.003 2.60 1.43 11.8 1.50 4.21 7437 5.7 1.92 2.9 7.6 2949 2618 2.62 0.17 0.003 0.95 0.66 5.81 1.26 4.01 7453 3.5 1.32 1.5 8.6 791 325 0.13 0.05 0.003 1.21 0.07 0.01 1.25 1.13 7469 3.2 11.3 17.9 57.0 10382 1262 0.52 0.55 0.003 4.31 6.58 2.55 3.65 3.64 7472 3.5 2.67 1.9 10.9 352 237 0.40 0.09 0.003 0.23 0.07 0.01 1.18 0.10 7490 5.2 0.69 4.8 7.3 9090 2349 0.59 0.01 0.003 3.69 2.57 3.33 2.37 6.23 7516 4.2 1.44 4.1 9.0 3669 465 0.25 0.08 0.003 1.26 1.37 0.50 1.80 1.10 7550 4.6 0.60 2.0 4.1 3922 2170 0.79 0.01 0.003 2.33 2.37 0.68 2.67 8.21 7560 5.4 0.75 0.7 2.5 186 74 0.01 0.03 1.849 0.03 0.97 0.01 2.51 0.10 7579 5.5 2.39 3.6 14.4 1042 625 0.13 0.15 0.003 0.07 1.23 0.01 4.56 1.41 7598 5.2 1.09 3.6 9.2 4923 1618 0.40 0.22 0.003 2.10 2.06 0.24 3.10 9.42 1-32

7625 3.9 1.98 0.9 12.1 115 64 0.08 0.01 0.003 0.03 0.64 0.01 1.87 1.15 7637 3.9 1.76 1.1 9.9 98 49 0.04 0.01 0.024 0.97 0.07 0.31 0.02 0.10 7648 5.0 0.57 0.8 1.7 439 165 0.02 0.03 0.003 0.79 0.49 0.51 0.02 0.10 7658 4.2 1.38 1.8 5.3 2167 797 0.14 0.07 0.011 2.00 2.33 1.80 0.18 1.15 7666 6.6 38.9 N/D 309 2643 1722 0.39 0.75 0.264 35.6 3.44 5.46 2.20 13.2 7671 8.0 2.26 8.7 254 543 238 1.19 1.30 0.155 2.97 3.60 5.87 0.02 1.94 7715 5.4 2.28 7.7 12.3 8242 2299 0.78 0.25 0.024 4.81 9.71 8.40 1.72 10.9 7771 4.7 0.37 1.4 11.0 2789 1082 0.41 0.14 0.003 2.32 0.87 3.04 0.41 3.58 7787 3.9 0.72 1.0 2.9 480 186 0.02 0.14 0.003 2.06 0.23 0.42 0.02 9.14 7867 5.6 1.06 2.6 7.6 1894 645 0.13 0.01 0.028 1.94 1.41 1.30 0.02 1.39 7907 5.4 1.76 3.1 9.7 2084 901 0.02 0.15 0.003 5.65 1.40 1.62 3.67 9.43 7934 4.7 0.80 0.6 3.8 119 57 0.05 0.01 0.003 0.82 2.01 0.21 2.22 1.19 7951 5.0 0.77 1.1 3.3 865 498 0.10 0.01 0.011 2.66 0.74 0.77 1.39 6.41 1-33

Table 1-14. Ranges in total-recoverable concentration (mg/kg) and summary statistics of 8 metals in 210 Florida surface soils. As Se Cd Cr Cu Ni Pb Zn # of samples 210 209 209 209 209 209 209 209 Minimum 0.01 0.01 0.003 0.01 0.02 0.07 0.03 0.10 Median 0.20 0.11 0.03 1.8 1.14 1.40 2.50 2.50 Maxium 37.6 6.06 1.85 193 244 47.5 249 190 Original Data AM 1.14 0.34 0.07 7.27 5.15 3.23 5.36 7.37 ASD 3.39 0.72 0.16 19.0 19.5 5.87 17.8 16.0 CV 298 210 231 262 378 182 332 218 Skewness 7.5 4.5 8.1 6.1 9.5 4.5 12.5 5.1 Kurtosis 70.2 26.3 85.9 48.7 110.0 26.5 170.0 31.2 Log 10 Transformed Data GM 0.25 0.08 0.02 1.36 0.73 0.97 2.41 1.89 GSD 5.35 6.50 4.52 8.48 8.34 5.80 3.33 6.53 CV 2107 7943 19884 623 1140 596 138 346 Skewness 0.29-0.12 0.08-0.63-0.22-0.24-0.55-0.31 Kurtosis -0.02-0.85-0.82 0.43-0.38-0.95 3.28-0.72 1-34

Table 1-15. Geometric mean concentrations (mg/kg) of 8 trace metals in Florida soils based on soil orders. Soil Order As Se Cd Pb Alfisols 0.21 ± 4.3 c 0.059 ± 5.7 bc 0.031 ± 4.1 bc 1.78 ± 3.7 d Entisols 0.23 ± 5.5 c 0.081 ± 5.5 b 0.020 ± 4.3 c 2.73 ± 2.5 cd Histosols 1.58 ± 3.7 a 0.967 ± 3.6 a 0.087 ± 4.4 a 4.93 ± 5.9 ab Inceptisols 1.41 ± 6.7 a 0.498 ± 2.6 a 0.087 ± 2.0 ab 4.72 ± 3.3 bc Mollisols 0.71 ± 2.9 ab 0.367 ± 2.8 a 0.016 ± 7.2 c 7.16 ± 2.6 a Spodosols 0.10 ± 3.9 d 0.041 ± 5.1 c 0.015 ± 3.5 d 1.34 ± 2.7 e Ultisols 0.31 ± 3.9 bc 0.045 ± 5.6 bc 0.016 ± 4.7 c 2.89 ± 2.8 bcd Soil Order Ni Cr Cu Zn Alfisols 0.68 ± 5.9 bc 1.04 ± 11 d 0.57 ± 7.1 b 1.28 ± 6.2 c Entisols 1.20 ± 4.3 b 1.42 ± 6.7 cd 0.80 ± 7.2 b 2.23 ± 6.1 bc Histosols 3.29 ± 6.2 a 6.74 ± 3.5 ab 5.48 ± 6.6 a 11.2 ± 4.0 a Inceptisols 1.03 ± 15 abc 6.74 ± 5.9 ab 1.84 ± 15 ab 2.55 ± 14 bc Mollisols 2.27 ± 6.7 ab 17.6 ± 3.5 a 2.22 ± 9.2 ab 5.77 ± 8.0 ab Spodosols 0.48 ± 5.4 c 0.28 ± 5.7 e 0.23 ± 7.0 c 0.57 ± 4.7 d Ultisols 1.18 ± 4.9 b 2.94 ± 5.1 bc 0.98 ± 5.6 b 3.38 ± 4.0 b Means within a column followed by the same letter are not significantly different (P<0.05) using Student t-test. 1-35

Table1-16. Geometric mean concentrations (mg/kg) of 8 trace metals in Florida soils based on regions. Region As Se Cd Pb Central 0.19 ± 6.0 b 0.062 ± 6.5 b 0.028 ± 4.2 ab 2.45 ± 2.5 a North 0.20 ± 4.1 b 0.059 ± 5.8 b 0.019 ± 4.0 b 2.58 ± 2.9 a South 0.48 ± 8.2 a 0.159 ± 10 a 0.040 ± 5.5 a 1.95 ± 5.1 a West 0.28 ± 4.5 ab 0.103 ± 4.8 ab 0.017 ± 4.5 b 2.49 ± 3.7 a Region Ni Cr Cu Zn Central 0.69 ± 6.4 a 1.21 ± 6.0 a 0.69 ± 9.0 a 1.35 ± 6.3 a North 1.11 ± 4.9 a 1.59 ± 8.3 a 0.61 ± 5.9 a 1.67 ± 6.0 a South 1.28 ± 6.3 a 1.46 ± 12 a 1.40 ± 13 a 2.56 ± 9.7 a West 0.93 ± 6.2 a 1.18 ± 9.1 a 0.63 ± 8.6 a 2.41 ± 5.5 a Geometric means within a column followed by the same letter are not significantly different (P<0.05) using Student t-test. 1-36

Table 1-17. Total-recoverable concentrations of 8 trace metals in soils collected from south Florida. Sample # As Se Cd Pb Ni Cr Cu Zn 1 3.44 0.23 0.114 1.81 3.37 8.82 0.53 3.80 2 3.28 0.49 0.136 20.8 2.49 3.05 1.86 9.71 3-a 3.09 0.97 0.068 1.83 3.46 10.6 1.19 1.71 3-b 14.4 0.02 0.133 2.39 4.32 14.1 0.72 5.31 4-a 5.60 0.61 0.225 31.9 3.64 6.83 4.60 36.8 4-b 5.35 1.18 0.133 8.31 4.04 6.87 1.94 7.59 5 4.16 0.74 0.114 62.5 3.92 3.97 8.04 19.0 6 13.3 2.58 0.143 21.4 13.1 55.5 1.52 12.5 AM ± ASD 6.57 ± 3.63 0.68 ± 0.69 0.13 ± 0.03 18.9 ± 15.3 4.80 ± 2.09 13.7 ± 10.6 2.55 ± 1.89 12.1 ± 8.04 GM ± GSD 5.49 ± 1.59 0.47 ± 2.82 0.13 ± 1.23 9.34 ± 3.29 4.19 ± 1.34 8.96 ± 1.84 1.76 ± 1.93 8.28 ± 2.06 Arithmetic mean (AM) ± arithmetic standard deviation (ASD). Geometric mean (GM) ± geometric standard deviation (GSD). 1-37

1.3.5. Relations Between Metal Concentrations and Chemical and Physical Soil Characteristics. Spearman ranked correlation coefficients were calculated for total-recoverable and total-total concentrations of As, Se, Cd, Pb, Cr, Cu, Ni, and Zn in 210 soil samples. The Spearman ranked correlation coefficients for 8 metals ranged from 14 to 99 percent (Table 1-18). For 5 out of the 8 metals tested, total-recoverable concentrations correlated with the total-total concentrations in the soils at a 95% percent confidence level and a coefficient of determination (r 2 ) > 50%. To gain further insight into factors that may affect metal distribution in soils, Spearman ranked correction coeficients between total-recoverable metal concentrations and selected soil characteristics were computed (Table 1-19). Clay contents and concentrations of total Al and Fe are the most important factors influencing total-recoverable concentrations of metals tested except for Se, Cd, and Cu. Organic carbon is the dominated factor for Se, ph is the dominated factor for Cu, and CEC is the dominated factor for Cd. 1-38

Table 1-18. Correllation coefficients and regression statistics between total-recoverable and total-total concentrations of 8 trace metals in Florida soils Metals Spearman ranked coefficients (r) Regression Ratio r 2 a b Mean SD As 0.99 0.98 0.98-0.13 0.94 0.57 Se 0.85 0.63 1.07-0.03 0.64 0.38 Cd 0.33 0.14 0.19 0.04 0.81 0.23 Pb 0.56 0.32 0.38 1.34 0.69 0.35 Ni 0.14 0.02 0.10 1.92 0.36 0.36 Cr 0.85 0.74 0.82-4.35 0.44 0.37 Cu 0.97 0.93 0.87-0.03 0.88 0.68 Zn 0.73 0.53 0.99-0.26 0.87 0.68 Total-recoverable concentration = a X (total-total concentration) + b. Ratio, total-recoverable/total-total concentration; SD, standard deviation. 1-39

Table 1-19. Correlation coefficients between total-recoverable concentrations of 8 trace metals and selected soil properties in Florida soils. Metals O.C. ph Clay CEC Total Al Total Fe As 0.09 0.23 ** 0.44 *** 0.17 0.36 *** 0.65 *** Se 0.53 *** 0.14 0.48 *** 0.49 *** 0.31 *** 0.37 *** Cd 0.18 * 0.14 0.09 0.19 * 0.10 0.14 Pb 0.30 *** 0.03 0.72 *** 0.22 * 0.21 * 0.22 * Ni 0.23 ** 0.01 0.42 *** 0.29 *** 0.37 *** 0.45 *** Cr 0.10 0.16 0.74 *** 0.24 ** 0.84 *** 0.68 *** Cu 0.22 * 0.28 ** 0.24 ** 0.21 * 0.25 ** 0.27 *** Zn 0.22 * 0.26 ** 0.53 *** 0.25 ** 0.49 *** 0.51 *** *, **, ***, Significant at α = 0.01, 0.001, and 0.0001, respectively.. 1-40

1.4 LITERATURE CITED Basta, N.T. 1995. Land application of biosolids: a review of research concerning benefits, environmental impacts, and regulations of applying treated sewage sludge. Oklahoma State University. Bini, C., M. Dall'Aglio, O. Ferretti, and R. Gragnani. 1988. Background levels of microelements in soils of Italy. Environ. Geochem. Health. 10: 63-69. Chen, M. and L.Q. Ma. 1998. Comparison of four EPA digestion methods using certified and Florida soils. J. Environ. Qual. In view. Chen, M., L.Q. Ma, W. Harris, and A. Hornsby. 1998. Background concentrations of 15 trace metals in Florida surface soils. J. Environ. Qual. In view. Corwin, D. L., P. J. Vaughan and K. Loague. 1997. Modeling nonpoint source pollutants in the vadose zone with GIS. Environ. Sci. Technol. 31: 2157-75. IFAS. Characterization data for selected Florida soils. Soil Science Research Report #85-1. IFAS. Gainesville. Chen, J., F. Wei, C. Zheng, Y. Wu, and D.C. Adriano. 1991. Background concentrations of elements in soils of China. Water, Air and Soil Pollution. 57-58: 699-712. Davies, B.E., and B.G. Wixson. 1985. Trace elements in surface soils from the mineralized area of Madison County, Missouri, U.S.A. J. Soil Sci. 36: 551-570. Day, P.R. 1965. Pipette method of particle size analysis. p. 552-562. In C.A. Black (ed.) Methods of soil analysis (1st ed.), Part I., ASA, Madison, WI. Dudka, S. 1993. Baseline concentrations of As, Co, Cr, Cu, Ga, Mn, Ni and Se in surface soils, Poland. Appl. Geochem. 2: 23-8. Edelman, T., and M.D. Bruin. 1985. Background values of 32 elements in Dutch topsoils, determined with non-destructive neutron activation analysis. p. 89-99. In J.W. Assink, and W.J.V.D. Brink (ed.) First international TNO conference on contaminated soil. Utrecht. Martinus Nijhoff Publishers. EPA. November, 1994. Technical background document for soil screening guidance. EPA540/R-94/106. EPA. Washington. FDEP. Domestic wastewater residuals. 62-640. FDEP. Tallahassee. FDEP. Soil thermal treatment facilities. 62-775. FDEP. Tallahassee. Frank, R., K. Ishida, and R. Suda. 1976. Metals in agricultural soils of Ontario. Can. J. Soil Sci. 56: 181-196. Haines, R.C. 1984. Environmental contamination-surveys of heavy metals in urban soils and hazard assessment. p. 450-460. In D.D. Hemphill (ed.) Trace substances in environmental health. Proceedings Xviii Annual Conference. Columbia, Missouri. University of Missouri. 1-41

Holmgren, G.S., M.W. Meyer, R.L. Chaney, and R.B. Daniels. 1993. Cadmium, lead, zinc, copper, and nickel in agricultural soils of the United States of America. J. Environ. Qual. 22: 335-348. Logan, T.J., and R.H. Miller. 1983. Background levels of heavy metals in Ohio farm soils. Res. Circ. Ohio Agric. Res. Dev. Cent. Wooster. Feb. 275: 3-15. Ma, L.Q., F. Tan, and W. Harris. 1997. Concentration and distribution of 11 elements in Florida soils. J. Enviorn. Qual. 26: 769-775. McGrath, S.P. 1987. Computerized quality control statistics and regional mapping of the concentrations of trace and major elements in the soil of England and Wales. Soil Use Manage. 3: 31-88. Myers, R.L., and J.J. Ewel. 1990. Ecosystems of Florida, p. 35-69. University of Central Florida Press, Orlando. Pierce, F.J., R.H. Dowdy, and D.F. Grigal. 1982. Concentrations of six trace metals in some major Minnesota soil series. J. Environ. Qual. 11: 416-422. SAS. 1987. SAS users guide: statistics. Statistical Analysis Institute, Inc., Carey, NC. Sawhney, B. L. and D. E. Stilwell. 1994. Dissolution and elemental analysis of minerals, soils and environmental samples. pp 49-82. In J. E. Amonette and L. W. Zelazny (eds.) Quantitative methods in soil mineralogy. ASA, Madison, Wisconsin. Shacklette, H.T. and J.G. Boerngen. 1984. Element concentrations in soils and other surficial materials of conterminous United States. US Government Printing Office. Sodek, F., III, V.W. Carlisle, M.E. Collins, L.C. Hammond, and W.G. Harris. 1990. Characterization data for selected Florida soils. Soil Sci. Research Report 90-1. Soil and Water Science Dept., Univ. of Florida. Soil Survey Staff. 1984. Procedures for collecting soil samples and methods of analysis for soil survey. Soil survey investigations report no. 1 (revised). USDA/SCS. U.S. Government Printing Office, Washington, D.C. Soil Survey Staff. 1994. Keys to Soil Taxonomy. USDA/SCS. U.S. Government Printing Office, Washington, D.C. Thornton, I. 1982. Implications of geochemical background data in relation to hazardous waste management. p. 57-67. In D.D. Hemphill (ed.) Trace substances in environmental health-xvi. Columbia, Missouri. University of Missouri. Tiller, K.G. 1992. Urban soil contamination in Australia. Australian J. Soil Res. 30: 937-957. Whittig, L.D., and W.R. Allardice. 1986. X-Ray diffraction techniques. p. 331-362. In A. Klute (ed.) Methods of soil analysis (1st ed.), Part I., American Society of Agronomy, Madison, WI. 1-42

II. SPATIAL ANALYSIS OF BACKGROUND CONCENTRATIONS OF EIGHT TRACE METALS IN FLORIDA SURFACE SOILS GERCO C. HOOGEWEG, WILLIE G. HARRIS, ARTHUR G. HORNSBY, MING CHEN, AND LENA.Q. MA 2.1. INTRODUCTION Presence of high concentrations of trace metals in soils is of concern because of the potentially negative effects on environmental quality and human health. Mapping the natural background levels of trace metals would assist policy-makers in establishing reference level concentrations for clean-up and determining potential hot-spots. Mapping trace metal concentrations at regional scale poses the unique challenge to estimate concentrations at locations were no samples were taken. A plethora of tools, geographic information systems (GIS) and geostatistical methods (kriging, inverse distance, and splining), are available to estimate concentrations at unknown locations. White et al. (1997) demonstrated that these tools could be used to generate a map for the conterminous US for total zinc in the soil based on limited number of observations. As a final conclusion they stated that geostatistics and GIS will be indispensable in characterizing and summarizing georeferenced information to provide quantitative support to decision and policy making for agriculture and natural resources management. Geostatistics (kriging) are commonly applied to generate trace metal concentration maps based on point observations (Juang and Lee, 1998, Stein et al., 1995, White et al., 1994, White et al., 1997). Any correlation between soil type and trace metal concentrations can restrict the use of geostatistical techniques to generate state-wide map displaying background concentrations of trace metals in Florida soils. Edelman and de Bruin (1986), Ma et al. (1997) and Chen et al. (1999) demonstrated that trace metal concentrations are correlated to soil properties. Correlation analysis against total Fe, total Al, clay, organic carbon, cation exchange capacity and ph showed significant correlation for all elements (Chen et al., 1999). These results are in support of Ma et al. (1997) who found a strong correlation with organic matter in surface horizons for Florida soils. Tiktak (1999) used a bidirectional multiple regression model to generate raster based maps (500x500m) for cadmium concentrations in the Netherlands. He concluded that organic matter was the most important factor affecting cadmium concentrations in soils. Based on these results, the use of geostatistical methods might not be appropriate to develop background level trace metal concentration maps for the state of Florida. No attempts were made to generate trace metal concentration maps using this technique for the purpose of the study. This study was conducted to: (i) determine the spatial distribution of eight trace metals in Florida soils and (ii) develop a set of trace metal distribution maps for policy-making purposes. 2-1

2.2 MATERIALS AND METHODS 2.2.1. State Soil Geographic Database The Natural Resources Conservation Service (NRCS) has established three soil geographic databases representing different kinds of soil maps. The images are produced from different intensities and scales of mapping. Each database has a common link to an attribute data file for each map unit component. The three soil geographic databases are the Soil Survey Geographic (SSURGO) database, the State Soil Geographic (STATSGO) database, and the National Soil Geographic (NATSGO) database. Components of map units in each database are generally consists of soil series that enable the most precise interpretation. Interpretations are displayed differently for each geographic database to be consistent with differing levels of detail. Soil maps for STATSGO are compiled by generalizing more detailed (SSURGO) soil survey maps. Where more detailed soil survey maps are not available, data on geology, topography, vegetation, and climate are assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas are studied, and the probable classification and extent of the soils are determined. Map unit composition for a STATSGO map is determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. Using the United States Geological Survey s (USGS) 1:250,000 scale, 1- by 2- degree quadrangle series as a map base, the soil data are digitized by line segment (vector) method to comply with national guidelines and standards. Data for the STATSGO database are collected in 1- by 2-degree topographic quadrangle units and merged and distributed as statewide coverage. Features are edge matched between states. The map unit composition and the proportionate extent of the map unit components also match between states. STATSGO is an intermediately detailed soil database that is targeted for multi-county, regional and state-wide and multi-state analysis. STATSGO was compiled at a 1:250,000 scale and not suitable for county based analysis. The STATSGO polygons are a compilation of up to 21 different soil map units. The polygons on the soil map are linked to the attributed databases by a mutual field, the map unit identifier (muid). The attribute databases contain the soil data for each polygon. The sequence number indicates the order of dominance. The soils with sequence number one are considered the most dominant soils in the polygon. The data set developed by Chen et al. (1999) was used to generate map displaying trace metal concentrations in Florida soils. This data set consisted of 448 samples representing an weighted number of samples to the occurrence of soil orders in the state of Florida. This resulted in 122 Spodosols, 107 Entisols, 90 Ultisols, 60 Alfisols, 39 Histosols, 17 Mollisols and 13 Inceptisols samples. 2-2

2.2.2. Georeferencing Sample Locations Availability of georeferenced soil sample locations would enable us to portray the spatial distribution of concentrations of trace metals for individual locations in Florida. At the time the Florida Soil Survey Program database (Collins and Cantlin, 1992) was developed, soil sample locations were described in terms of township, range and section of the public land survey system (PLSS). A typical description of a sample location would be: SE¼, NW¼, NW¼, Section 22, T8S, R39E. This information was combined with the PLSS spatial database to select those sections in which soil samples were collected. The use of the sections would locate a soil sample location within a 640-acre area. This would provide us with a positional accuracy of approximately 2400ft. The use of the more detailed description of the sample location, SE ¼, NW ¼, NW ¼, would increase the estimated geographic location of the sample location significantly. When all three levels of the detailed description are used the geographic location can be estimated within a 10-acre block. The following procedure was used to estimate the geographic location for each soil sample location: Select a section and convert it to a shape file Get the center (x,y)of the new shape file (640 acre block center) For the first level of the quarter section add or subtract 1320ft to the 640-acre block center to obtain the center of the 160-acre block. For the second level of the quarter section add or subtract 660ft to the 160 acre block center to obtain the center of the 40 acre block center For the third level of the quarter section add or subtract 330ft to the 40 acre block center to calculate the 10 acre block center If level one, two or three was not available the highest possible level block center was estimated. The samples with a ½ block description were manually calculated. The estimated geographic locations were visually inspected and in those cases that samples were located outside the PLSS section, the geographic location was estimated from the screen. For 414 of the 448 samples a georeferenced location was estimated. The remaining 34 locations were removed from the database. These samples were removed due to lack of section descriptions, errors in the section description or errors in the spatial database (Table 2-5). 2-3

2.2.3. Map Development The first step in developing trace metals distribution maps for the state of Florida was to establish the distribution of the soil Order (Figure 2-1) and Suborders (Figure 2-2, Table 2.1: USDA soil taxonomy: Soil Survey Staff, 1998) in Florida. The lookup table for the soil taxonomy was merged with the component table to incorporate the map unit identifier (muid) and taxonomy descriptions in one table. This was a requisite because the muid is the only field common to the attribute table of the soil map and the STATSGO databases. We found that most useful taxonomic category for grouping Florida soils with respect to metals is the Suborder (Table 2.1). Reasons for this include the following: (i) There are few enough Suborders present in Florida (19) to constitute a manageable base for comparisons; and (ii) soil properties that have a major influence on metal content tend to be differentiated at the Suborder level. The seven soil Orders of Florida constitute groups that are too diverse in some cases to provide for meaningful differentiation. For example, the soil Order Entisols includes a Suborder comprised of deep, sandy, and excessively-well drained soils (Psamments) as well as a Suborder of very poorly drained soils that may be prone to flooding (Aquents). The latter are very diverse in composition, ranging in dominance from silicate clay to quartz sand to marl (CaCO 3 ). It is therefore useful to have the Psamment- Aquent distinction. Similar examples could be given for other Orders. Using Suborders also enables groupings that transgress Order boundaries. For example, wet soils from different Orders that are generally components of wetlands (e.g., Aquents,Aquetps, Saprists, and Hemists) can be distinguished from soils that dominantly occur on uplands. The latter differ from wetland soils in composition and extent of biological accumulations. For two Suborders (Arents and Fluvents) no trace metals data were available. For another two Suborders, Orthents and Orthods, we assumed they have the same trace metals concentrations as the Aquents and Humods, respectively. For these soil Suborder we did not have data available and they are not very different in soil taxonomy than the surrogates. 2-4

Table 2-1. Dominant soil Orders and Suborders in Florida Soil Order Soil Suborder Soil Suborder Code Comments Alfisols Aqualfs AAQ Udalfs AUD Entisols Aquents EAQ Arents EAR No data Fluvents EFL No data Orthents EOR Same data as Aquents Psamments EPS Histosols Flolists HFO Hemists HHE Saprists HSA Inceptisols Aquepts IAQ Ochrepts IOC Umbrepts IUM Mollisols Aquolls MAQ Rendolls MRE Spodosols Aquods SAQ Orthods SOR Same as Humods Ultisols Aquults UAQ Udults UUD 2-5

Figure 2-1. Geographical Distribution of 7 Soil Orders in Florida 2-6

Figure 2-2. Geographical Distribution of Soil Suborders in Florida 2-7

The STATSGO component table contains data for each map unit regarding its soil composition (soil series, percent of series in the map). This database was merged with the taxonomic database to calculate the area weighted concentration for each map. The area weighted trace metal concentration was calculated as: C = n muid i=1 pi C i* 100 where, C muid = area weighted concentration for a single map unit C i = geometric mean concentration for Suborder i of map unit p i = Suborder percentage in the muid The soil Suborder concentration is calculated using the Log10-based geometric mean concentration (mg kg -1, Chen et al., 1999) from the individual soil series represented in the soil Suborder. The concentration represented is the total-total concentration unless specified otherwise. Absence of the two soil Suborder data resulted in an under estimation of the area weighted concentration for six map units (Table 2-1). This was corrected using the following equation: = C corr C (1 - p) where, C corr = corrected concentration C = original concentration p = area fraction not represented, and 1-p is the correction factor The area weighted concentrations for the selected trace metals were then merged with the STATSGO map to portray the spatial distribution in the state of Florida. Map legends were generated with the natural breaks method (7 classes) in ArcView GIS 3.1. This method identifies natural breaks in the current data set using the Jenk s optimization formula. The Jenk s method minimizes the variance within each of the classes. The Everglades area, big cypress and the water conservation areas were excluded from the soil map because no actual soil mapping has taken place in these areas. Additionally: Soil samples were not collected in the everglades and surrounding areas, Histosols should be represented with a volumetric based concentration rather than a mass based concentration. 2-8

The area weighted trace metals concentration for the county was calculated with the zonal mean function in ArcView GIS 3.1 Spatial Analyst 1.1 (ESRI, 1998). The zonal mean function used the county boundary layer to identify each county as a separate zone. The cells with no data value were ignored in the area weighted calculations. Each of the vector based trace metal maps were converted in ArcView GIS 3.1 to a grid. The value field was the geometric mean for each trace metal concentration. Maps portraying the area weighted average trace metal concentration were developed for each county are shown in Section 3. Table 2-2. Map units with missing data Map unit identifier Sequence number Component percent Correction factor (1-p) FL033 6 7 0.93 FL056 6 4 0.96 FL102 4 12 0.88 FL110 1 10 0.70 FL110 5 15 0.70 FL110 6 5 0.70 FL112 7 3 0.97 FL135 1 Table 2.2. Map units with missing data 15 0.85 2-9

2.3. RESULTS AND DISCUSSION 2.3.1. Sample Locations Samples selected for this study represent the taxonomic distribution of the soil orders in the state of Florida. The number of soil samples for each soil order was estimated by the areal occurrences in Florida (Chen et al., 1999). The spatial distribution of sample location (Figure 2-3) that resulted from this procedure, indicates that some parts of the state were poorly represented. Especially north-central Florida, Tampa Bay area and Monroe and Escambia counties. For these counties no samples were selected for the determination of trace metal concentrations in Florida soils (Chen et al., 1999). Any predictions in for these counties will contain a larger uncertainty than the prediction in the other regions of the state. 2.3.2 Trace Metal Distributions 2.3.2.1. Arsenic The spatial distribution of the background concentration of arsenic is presented in Figures 2-4 ~ 2-6. Figure 2-4 depicts the arsenic concentration per sample location. Several locations with high arsenic levels (>1.0 mg kg -1 ) are visible and are scattered over the state of Florida. Samples located in South Florida tend to have higher arsenic background levels than samples in other parts of the state. Based on the soil Suborders (Fig. 2-5), the highest arsenic concentration are found in areas dominated by organic soils (Histosols) and especially Hemists and Saprists have high background concentrations of arsenic (>1.3 mg Kg -1 ). Most visible are the higher background levels south of Lake Okeechobee, the Eastern Everglades area (Homestead agricultural area) and the St. Johns River in Duval County. From this soil map one can conclude that there is an inherent variability of background concentrations of arsenic. Also, the area weighted concentrations smooth out any extreme (low and high) concentrations measured for any soil series present in that map unit. The general nature of the STATSGO does not allow for site specific interpretation. More detailed soil maps should be used for site specific interpretations and estimation of background levels of arsenic. As discussed in section 2.3 trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more) and are biased by volume change analyzed. The areas dominated by the Hemists and Saprists show the highest background levels for arsenic. The Hemists can be found on the border of Escambia and Santa Rosa County and north Duval County at the border with Nassua County. The land uses in these areas are mainly wetlands. The area weighted average background concentration of As by county (Fig 2-6) shows that the county average corresponds with the dominant soil suborder in that county. 2-10

Figure 2-3. Spatial Distribution of Site Locations 2-11

Figure 2-4. Spatial Distribution of Arsenic Concentration at Individual Sample Site. 2-12

Figure 2-5. Spatial Distribution of Arsenic Concentrations Based on Suborders. 2-13

Figure 2-6. Spatial Distribution of Arsenic Concentrations Based on Counties. 2-14

2.3.2.2. Cadmium The spatial distribution of the background concentration of cadmium based on the geometric mean for the soil Suborder is depicted in Figure 2-7. While cadmium is distributed in a manner similar to arsenic its background concentrations are an order of magnitude smaller than for arsenic. The lowest background concentrations were calculated for the sandy soils located in the Central Florida Ridge area. The dominant soil Suborder is Psamments (Entisols). Most of the state of Florida has calculated background concentration cadmium lower than 0.021 mg kg -1. The highest background concentrations (>0.041 mg kg -1 ) were calculated for Hemists and Saprists (Histosols). As discussed in section 2.3 trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for cadmium. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetlands. Figure 2-8 depicts the county estimates of cadmium concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. 2-15

Figure 2-7. Spatial Distribution of Cadmium Concentrations Based on Suborders. 2-16

Figure 2-8. Spatial Distribution of Cadmium Concentrations Based on Counties. 2-17

2.3.2.3 Chromium The spatial distribution of the background concentration of chromium based on the geometric mean for the soil Suborder is depicted in Figure 2-9. Soils suborders (Udults and Psamments) in the Florida Panhandle show higher background concentrations than soil Suborders (mainly Aquods) in southern Florida. It is to be expected that for areas in the Florida panhandle dominated by Udults (Ultisols), the expected background concentration chromium will not exceed 13.3 mg kg -1. In the other regions of Florida, except where Histosols (Hemists and Saprists) occur, the background concentration is expected to be lower than 9.0 mg kg -1. Though, locally higher background concentrations might occur. When compared to nickel background concentration maps of Cr are very similar. Though the background concentrations for nickel have a larger range. Chen et al. (1999) computed a correlation coefficient of 0.56 for these two metals. This explains the similarities between the trace metal distributions in Florida. As discussed in section 2.3 the trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for chromium. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetland. Figure 2-10 depicts the county estimates chromium concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. The trend of higher background levels is the same as observed in the map for the soil Suborders. 2-18

Figure 2-9. Spatial Distribution of Chromium Concentrations Based on Suborders. 2-19

Figure 2-10. Spatial Distribution of Chromium Concentrations Based on Counties. 2-20

2.3.2.4. Copper The spatial distribution of the background concentration of copper based on the geometric mean for the soil Suborder is depicted in Figure 2-11. Hemists, Saprists (Histosols) and Aqualfs (Alfisols) show the highest concentrations in Florida. Histosols have calculated background concentrations over 6 mg kg -1. Whereas, Aqualfs have computed background concentrations between 2.0-4.0 mg kg -1. Some Suborders have higher background concentrations. Locally, higher background concentrations might be found than the calculated background concentration. When compared to other trace metals, the maps for selenium and zinc depict the spatial distribution in a similar manner, though the background concentration ranges are different for these metals. Chen et al. (1999) calculated correlation coefficients of 0.63 and 0.70 for copper with selenium and Zinc respectively. When the copper and selenium maps are compared, they show that for lower copper concentrations higher selenium concentrations are found. Thus, it is an negative correlation (a.k.a. an inverse relationship). The copper and zinc maps behave similarly. High concentrations copper are found alongside with high zinc concentrations. As discussed in section 2.3 the trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for copper. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetlands. Both Hemists and Saprists show high copper concentrations. Figure 2-12 depicts the county estimate ranges of copper concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. The trend of higher background concentrations is the same as observed in the map for the soil Suborder. 2-21

Figure 2-11. Spatial Distribution of Copper Concentrations Based on Suborders. 2-22

Figure 2-12. Spatial Distribution of Copper Concentrations Based on Counties. 2-23

2.3.2.5. Nickel The spatial distribution of the background concentration of nickel based on the geometric mean for the soil Suborder is depicted in Figure 2-13. Soil classified as Psamments (Entisols), Hemists (Histosols), and Udults (Ultisols) have generally higher computed background concentrations than the other soil orders. Calculated background concentrations can be as high as 12.0 mg Kg -1 and locally higher concentrations might be found. When compared to chromium the background concentration maps are very similar. Though the background concentrations for nickel have a larger range. Chen et al. (1999) computed a correlation coefficient of 0.56 for these two metals. This explains some of the similarities of the trace metal distributions in Florida. As discussed in section 2.3 the trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for nickel. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetlands. Figure 2-14 depicts the county estimates nickel concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. The trends of higher background levels are the same as observed in the map for the soil Suborder. 2-24

Figure 2-13. Spatial Distribution of Nickel Concentrations Based on Suborders. 2-25

Figure 2-14. Spatial Distribution of Nickel Concentrations Based on Counties. 2-26

2.3.2.6. Lead The spatial distribution of the background concentration for lead based on the geometric mean for the soil Suborder is depicted in Figure 2-15. Computed background concentrations of lead are generally not higher than 10.0 mg Kg -1. Only soils Suborders such as Hemists and Saprists (Histosols) have higher calculated background concentrations. As discussed in section 2.3 the trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for lead. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetlands. Figure 2-16 depicts the county estimates lead concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. The trend of background levels is similar as observed in the map for the soil Suborders. 2-24

Figure 2-15. Spatial Distribution of Lead Concentrations Based on Suborders. 2-28

Figure 2-16. Spatial Distribution of Lead Concentrations Based on Counties. 2-29

2.3.2.7. Selenium The spatial distribution of the background concentration of selenium based on the geometric mean for the soil Suborder is depicted in Figure 2-17. Higher background levels are found not only in the Hemists (Histosols) but also in some Aqualfs (Alfisols) and Aquods (Spodosols). This indicates that selenium is occurs in higher concentrations in coastal or wetland soils. The highest computed selenium background concentration is less than 1.0 mg Kg -1. This does not exclude that locally, in the field, higher background concentrations can be found. When compared to other trace metals, the maps for selenium and zinc depict the spatial distribution in a similar manner, though the background concentration ranges are different for these metals. Chen et al. (1999) calculated correlation coefficients of 0.63 and 0.70 for copper with selenium and zinc respectively. When the copper and selenium maps are compared, they show that for lower copper concentrations higher selenium concentrations are found. Thus, it is an negative correlation. The copper and zinc maps behave similar. High copper concentrations are found alongside with high zinc concentrations. As discussed in section 2.3 the trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for selenium. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetlands. Figure 2-18 depicts the county estimates selenium concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. The trends of higher background levels are the same as observed in the map for the soil Suborder. 2-30

Figure 2-17. Spatial Distribution of Selenium Concentrations Based on Suborders. 2-31

Figure 2-18. Spatial Distribution of Selenium Concentrations Based on Counties. 2-32

2.3.2.8. Zinc The spatial distribution of the background concentration of zinc based on the geometric mean for the soil Suborder is depicted in Figure 2-19. In general the higher background concentrations zinc are found in Florida panhandle. Calculated background concentrations can be as high as 14.2 mg Kg -1. Only Hemists (Histosols) have higher concentration. When compared to other trace metals, the maps for selenium and zinc depict the spatial distribution in a similar manner, though the background concentration ranges are different for these metals. Chen et al. (1999) calculated correlation coefficients of 0.63 and 0.70 for copper with selenium and zinc respectively. Copper and zinc behave similarly. High copper concentrations are found alongside with high zinc concentrations. As discussed in section 2.3 the trace metal concentrations for the Histosols should be carefully interpreted. The calculated concentrations are probably too high because the organic soils consists mainly of water (75% or more). The areas dominated by the Hemists show the highest background levels for zinc. The Hemists can be found on the border of Escambia and Santa Rosa county and north Duval county at the border with Nassua county. The land use in these areas is mainly wetlands. Figure 2-20 depicts the county estimates zinc concentrations based on area weighted mean concentrations of the soil Suborders occurring in that county. The trends of higher background levels are the same as observed in the map for the soil Suborder. 2-33

Figure 2-19. Spatial Distribution of Zinc Concentrations Based on Suborders. 2-34

Figure 2-20. Spatial Distribution of Zinc Concentrations Based on Counties. 2-35

2.4 CONCLUSIONS The objective of this study was to develop maps depicting the spatial distribution of trace metal background concentrations for arsenic, cadmium, copper, chromium, lead, nickel, selenium and zinc in the state of Florida. STATSGO soil maps were applied to generate a series of maps displaying the area weighted average background concentration trace metals at the soil Suborder level. As shown in Section 2.3.2, the spatial distributions of trace metal concentrations are highly variable in the state of Florida. The highest background concentrations for all the studied metals were computed for Hemists and Saprists at the soil Suborders level. The county based background concentration maps follow the trend set in the soil Suborder maps. Application of the generated background concentration maps in a policymaking framework allows the policymaker to establish reference concentrations for trace metal cleanup. Soil related differences in ranges of trace metal concentrations should be accounted for in setting of limits. 2-36

2.5. REFERENCES Chen, M., L.Q. Ma and W.G. Harris. 1999. Baseline Concentrations of 15 Trace Metals in Florida Surface Soils. Journal of Environmental Quality (submitted for publication). Edelman, T. and M. de Bruin. 1986. Background Values of 32 Elements in Dutch Topsoils, determined with Non-Destructive Neutron Activation Analysis. In J.W. Assink and J. van den Brink (eds.) Contaminated Soil. Martinus Nijhoff Publishers. Dordrecht. 88-98. Environmental Systems Research Institute (ESRI). 1997. ARC/INFO 7.2.1. Redlands. CA. Environmental Systems Research Institute (ESRI). 1998. ArcView 3.1 GIS. Redlands. CA. Juang, K-W and D-Y. Lee. 1998. A Comparison of Three Kriging Methods Using Auxiliary Variables in Trace Metal Contaminated Soils. Journal of Environmental Quality. 27:355-363. Ma, L.Q., F. Tan and W.G. Harris. 1997. Concentrations and Distributions of Eleven Elements in Floirda Soils. Journal of Environmental Quality. 26:769-775. Soil Conservation Service (SCS). 1993. State Geographic Soil Database (STATSGO): Data Users Guide. National Soil Survey Center. Washington. D.C. Miscellaneous Publication 1492 Soil Survey Staff, 1998. Keys to Soil Taxonomy. USDA Natural Resources Conservation Service. Washington D.C. 8 th Edition. Stein, A., C. Varekamp, C. van Egmond and R van Zoest. 1995. Zinc Concentrations in Groundwater at Different Scales. Journal of Environmental Quality. 24:1205-1214. Tiktak, A. 1999. Modeling Non-Point Source Pollutants in Soils: Application to the leaching and accumulation of Pesticides and Cadmium. Ph.D dissertation. University of Amsterdam. The Netherlands. 224p. White, J.G., R.M. Welch and W.A. Norvell. 1994. Mapping Soil and Plant Zinc in the Conterminous United States. p324. In Agronomy abstracts. ASA. Madison. WI. White, J.G., R.M. Welch and W.A. Norvell. 1997. Soil Zinc Map of the USA using Geostatistics and Geographic Information Systems. Soil Science Society of America Journal. 61:185-194. 2-37

Table 2-3. Spatial Layers Name Type Description Conservation Shape Everglades, Big Cypress and Water Conservation area s 1,2 & 3 Counties Shape Florida county boundaries clipped to land boundary Fl_bnd Cover Florida physical land boundary Fl_hm Shape Florida soil Suborder trace metal concentrations Fl_soils Shape Florida soil taxonomy joined with STATSGO map Plss Shape Public land survey sections were soil samples were collected Statsgo Shape Florida State Geographic Database (STATSGO) Fl_as Grid County weighted area As concentrations Fl_cd Grid County weighted area Cd concentrations Fl_cr Grid County weighted area Cr concentrations Fl_cu Grid County weighted area Cu concentrations Fl_ni Grid County weighted area Ni concentrations Fl_p Grid County weighted area P concentrations Fl_pb Grid County weighted area Pb concentrations Fl_se Grid County weighted area Se concentrations Fl_zn Grid County weighted area Zn concentrations 2-38

Table 2-4. Databases Name Type Description GreatGroup dbf P concentrations by soil great group Order dbf P concentrations by soil order Regions2 dbf County region lookup table Suborder dbf P concentrations by soil Suborder TraceMetalData dbf Original Table with PLSS description for the 456 sites Area_weighted dbf Calculated area weighted trace metal concentrations for each muid Comp dbf STATSGO component table Error_adjusted dbf Site locations with errors or adjusted georeferenced locations Final_sites dbf Sites locations for samples with estimates georeferenced location P dbf P concentrations for the individual samples Revis_final_conc dbf Trace metal concentrations for the individual samples Sites dbf Soil sample location description (PLSS) Tacxlass dbf STATSGO taxclass lookup table Taxonomy dbf STATSGO soil taxonomy for the dominant soil series for each muid Trace2 dbf Trace metal concentrations by sub order Trace_metals dbf Joined soil taxonomy and Suborder trace metal concentrations for each muid 2 39

Table 2-5. Soil samples with unknown or corrected georeferenced location. Lab Trs County Series Range Rd Township Td Sec. SS1 SS1d SS2 SS2d Comments 1229 Volusia Immokalee 0 0 no match 1346 27S28E08 Osceola Smyrna 28 E 27 S 11 25 NE 25 SE section error 1485 Volusia Smyrna 0 0 no match 1494 16S29E18 Volusia Apopka 29 E 16 S 16 25 SE 25 SW section error 1685 16S30E37 Volusia Tuscawilla 30 E 31 S 37 25 NW 0 section error 1986 01N26E38 Duval Albany 26 E 1 N 0 0 no match 2180 35S40E23 St. Lucie St. Lucie 40 E 35 S 23 25 NW 25 NW adjusted 2320 01S26E44 Duval Pelham 26 E 1 S 0 0 no match 2578 37S41E24 Martin Bessie 41 E 37 S 24 25 SW 0 no match 2779 39S42E19 Martin Palm Beach 42 E 39 S 23 25 SW 0 adjusted 2881 01N29W33 Santa Rosa Hansboro 29 W 1 N 33 25 SE 25 SE adjusted 2885 01N29W31 Santa Rosa Bohicket 29 W 1 N 31 25 NE 25 NE adjusted 2894 02S28W23 Santa Rosa Kureb 28 W 2 S 23 25 SE 25 SE adjusted 3077 25S18E15 Pasco Pomona 18 E 25 S 22 25 SW 25 SW section error 3087 25S19E27 Pasco Sellers 19 E 25 S 29 25 NE 25 NW section error 3252 St. Johns Pellicer 30 E 7 S 0 0 no match 3379 24S16E09 Pasco Weekiwachee 16 E 24 S 9 25 NW 25 NE adjusted 3589 06S29E23 St. Johns Durbin 29 E 6 S 0 0 adjusted 3711 38S39E22 Martin Wabasso 39 E 8 S 22 25 NW 25 NW section error 3820 St. Johns Immokalee 29 E 7 S 0 0 no match 3888 09S20E02 Alachua Sparr 20 E 9 S 2 25 SE 25 SE adjusted 5339 10S23E09 Sumter Sumterville 23 E 10 S 9 25 SW 25 SW no match 5459 31S36E35 Indian River Canova 36 E 31 S 35 25 NE 25 SE no match 2-40

5469 31S36E23 Indian River Gator 36 E 31 S 23 25 SE 25 SW no match 5636 Putnam Apopka 23 E 10 S 0 0 no match 5739 31S36E22 Indian River Gator 36 E 31 S 22 25 NE 25 NW no match 5747 31S37E70 Indian River Floridana 37 E 31 S 7 25 NW 25 SW adjusted 5855 07S27E33 Clay Mandarin 27 E 7 S 36 25 NW 25 NW section error 5877 30S28E19 Polk Candler 28 E 30 S 19 25 NW 25 NW adjusted 6299 Okaloosa Leon 0 0 no data 6538 Dade Perrine 25 25 no data 7024 11S28E25 Flagler Wabasso 28 E 11 S 25 25 SW 25 NE adjusted 7219 Flagler Smyrna 0 0 no data 7246 Flagler Adamsville 0 0 no data 7378 Flagler Valkaria 0 0 no data 7385 Flagler Favoretta 0 0 no data 7431 Collier Boca 0 0 no data 7435 Collier Jupiter 0 0 no data 7437 Collier Hallandale 0 0 no data 7442 Collier Ochopee 0 0 no data 7560 Glades Pineda 0 0 no data 7579 Glades Felda 0 0 no data 7666 59S40E36 Monroe Matecumbe 40 E 59 S 36 25 SE 25 SE adjusted 7812 Hamilton Alabany 0 0 no data 7825 Hamilton Cantey 0 0 no data 2 66S28E35 Monroe 28 E 66 S 35 0 0 adjusted 5 57S39E50 Dade 39 E 57 S 50 25 NE 25 SW no match 2-41

III. ARSENIC CONCENTRATIONS IN FLORIDA SURFACE SOILS: 1. DISTRIBUTION IN DIFFERENT SOILS MING CHEN, LENA Q. MA, AND WILLIE G. HARRIS 3.1. INTRODUCTION Arsenic is recognized as an element of public concern and is suspected to be responsible for bladder, kidney, liver, lung, and skin cancers (1). The USEPA lists inorganic arsenic as a Class A human carcinogen and sets the maximum allowed levels of arsenic in drinking water at 0.050 mg/l (2). The World Health Organization is pressing stricter consumption standards and has recommended lowering the arsenic drinking water standard to 0.010 mg/l (3). The new maximum contamination levels for arsenic in drinking water by the USEPA will probably be 0.5 to 5 µg/l by the year 2000 (4). Regulatory cleanup goals for remediation of arsenic contaminated soils are still under development, and may vary greatly among countries, regions, soils and land uses. For examples, the guidelines set by the Ministry of Environment of Canada for arsenic levels in agricultural, industrial, and residential soils are 25, 50, and 25 mg/kg, respectively (5). The United Kingdom has set a limit of 10 mg/kg for domestic gardens and 40 mg/kg for parks, playing fields and open spaces (6). Arsenic contamination in soils may adversely affect human health from dietary intakes through the food chain (5). Unless a reliable database on background arsenic concentrations in soils is available, unrealistically low mandatory guideline levels may be setup by regulators (7,8). For example, the USEPA has established a soil screen level for arsenic at 0.40 mg/kg based on direct soil ingestion and soil-plant-human exposure pathways (9). The New Jersey Department of Environmental Protection sets a state cleanup criterion of 20 mg/kg for both residential and non-residential soils based on background soil arsenic levels (10,11). In Florida, the current soil cleanup goals of the Florida Department of Environmental Protection (FDEP) for arsenic in residential and industrial soils, based on direct exposure, are 0.80 and 3.7 mg/kg, respectively (12). In cases where the risk-based criteria are lower than method detection limit (MDL) (11), or below the site-specific background level, the latter two would represent reasonable cleanup criteria (12). It is thus very important to establish arsenic background concentrations in soils based on sufficient sampling data, that can be used as a reference to compare to site specific background concentrations (13, 14). In Florida, great efforts have been made to establish arsenic background concentrations in both disturbed and undisturbed soils (14, 15, 16). Scarlatos and Scarlatos (15) found that arsenic concentrations in 115 soil samples collected near Homestead (1.1 to 54.3 mg/kg) are much greater than the FDEP residential soil cleanup goal of 0.80 mg/kg. A geometric mean (GM) arsenic level of 1.1 mgkg -1 has been reported by Ma et al. (16) based on 40 Florida mineral soils. Recently, a more comprehensive database for concentrations of 15 elements (Ag, As, Ba, Be, Cd, Cr, Cu, Pb, Hg, Mn, Mo, Ni, Sb, Se, and Zn) in 448 representative Florida surface soils has been established and a GM arsenic level of 0.42 mg/kg has been reported (14). However, no detailed information is given regarding arsenic concentration 3-1

distribution in different types of soils as a result of both anthropogenic and natural arsenic input and different biogeochemical processes in soils. The present study was conducted to: (i) investigate arsenic concentration distribution in different soil types; and (ii) discuss various wet and dry biogeochemical processes impacting arsenic concentrations in soils. Such information will help to evaluate the significance of anthropogenic and natural source of arsenic inputs in soils by establishing arsenic concentration ranges in different soils. In the long term, understanding the biogeochemical processes affecting arsenic in different types of soils would provide useful information in developing proper arsenic cleanup standards. 3.2. Materials and Methods 3.2.1. Materials. Soils used in this study had been previously sampled and characterized as a part of the Florida Cooperative Soil Survey Program (FCSSP) conducted jointly by the University of Florida Soil and Water Science Department and the USDA Natural Resources Conservation Service. Soil horizons were identified and sampled using the USDA soil survey conventions and procedures (17). In this study, a total of 448 surface soil samples were selected from a pool of ~8,000 archived samples to assure both taxonomic and geographic representation and to establish the background concentrations of 15 trace elements in Florida soils (14). Sampling sites were mostly in rural areas. They were originally selected based on their representativeness with respect to soil series being mapped. Many sites were under native vegetation, but some had been influenced by agriculture. The soil classification scheme is a useful tool for reducing complicated soil information to several manageable levels of generalization. In this study, soil suborders that had fewer than 5 samples were excluded from the database. As a result, 441 out of 448 soil samples were used. Table 3-1 and Table 3-2 provide brief information on the basic characteristics of each soil order and suborder used in this study. Those soils were from 51 out of 67 counties and their map units covered ~ 80 % of the total land area of Florida (14). They include seven soil orders: Histosols, Inceptisols, Mollisols, Ultisols, Entisols, Alfisols, and Spodosols and 12 soil suborders: Hemists and Saprists (suborders of Histosols), Aquepts (Inceptisols), Aquolls (Mollisols), Aquults and Udults (Ultisols), Aquents and Psamments (Entisols), Aqualfs and Udalfs (Alfisols), and Aquods and Orthods (Spodosols). 3.2.2. Sample Analysis. Soil samples were air dried, ground, and digested in a CEM MDS-2000 microwave oven (Matthews, NC) using the EPA Method 3052 (18). Quality assurance samples (a blank, a duplicate, a spike, and a standard reference material) were included in every 20 samples. Arsenic concentrations in the digestates were analyzed on a Perkin-Elmer SIMAA 6000 graphite furnace atomic absorption spectrophotometer unit using the EPA SW 846 method 7060A (18). The determined method detection limit for arsenic is 0.03 mg/kg soil. Concentrations of Al and Fe were determined with a multi-channel inductively coupled plasma spectrophotometer (Thermo Jarrell Ash ICAP 61-E) unit using the EPA SW 846 method 6010 (18). Clay and organic carbon (OC) content had been previously determined during the original soil characterization (19). 3-2

3.2.3. Data Analyses. Univariate statistics were used to test the arsenic distribution normality in soils. The data set was normalized by log 10 transformation prior to statistical comparison (13) unless specified otherwise. The GM and geometric standard deviation (GSD) were used to estimate the arsenic baseline concentrations in soils, which was calculated as GM/GSD 2 and GM x GSD 2 (20, 21). Samples with concentrations equal to or larger than the upper baseline limit (UBL) for individual soil suborders were further screened to determine possible arsenic contamination (21). For more detailed information on definition of elemental baseline concentrations in soils as well as arsenic baseline concentrations in Florida soils, please see Chen et al.(14). 3.3. Results and Discussion 3.3.1. Arsenic Concentrations in Different Soil Orders. Arsenic concentrations varied significantly among different soil types that occur in Florida. Cumulative arsenic probability distribution was plotted against arsenic concentrations in soils for each soil order (Figure 3-1). For a given arsenic concentration in soils on the x-axis, a percentage of soil samples below the concentration can be read off the graph. The shape of the curve represents the distribution characteristic of arsenic concentrations for each soil order, with steeper slopes indicating a narrower spread of concentrations, while an offset in horizontal direction indicates a greater difference in arsenic concentrations (22). Generally, arsenic concentrations decreased in the order of Histosols > Inceptisols, Mollisols > Ultisols > Entisols, Alfisols > Spodosols (Figure 3-1, Table 3-1). It is noteworthy that the two suborders of Entisols - Psamments (the most prevalent) and Aquents - differed markedly in As content, with the latter having more arsenic (Table 3-1). More than 95% of the Histosols (all except one) had arsenic concentrations above the current USEPA soil screen level of 0.40 mg/kg compared to 23% of the Spodosols. Approximately 90% of the Histosols and 50% of the Mollisols and Inceptisols had arsenic concentrations above the current FDEP residential soil cleanup goal of 0.80 mg/kg, compared to 6% of the Spodosols and 25% of the Entisols, Alfisols and Ultisols. Less than 5% of the Spodosols, Alfisols, Mollisols and Ultisols in Florida were above the level of the FDEP industrial soil cleanup goal of 3.70 mgkg -1, compared to 10% of the Entisols, 25% of the Inceptisols, and 30% of the Histosols. None of the Mollisols, one Alfisol, Inceptisol, and Spodosol, and two Ultisols had arsenic concentrations above the upper baseline limit of 7.02 mgkg -1 for Florida surface soils reported recently by Chen et al. (14), compared with 7% of the Entisols and 15% of the Histosols (Figure 3-1). In general, organic soils (Histosols) had greater arsenic concentrations than mineral soils. Soils dominated by silt- and clay-sized calcium carbonate ("marl" soils, mainly Aquents) are common in South Florida. The marl soils which had been sampled tended to have high As concentrations. We were concerned about anthropogenic inputs, since most sites for this group of soils had been agriculturally influenced. We therefore analyzed additional samples from five soils in the Everglades National Park and six soils in other undisturbed areas of the region, and found that they also contained relatively high levels of As. Arsenic concentrations in the Everglades National Park samples ranged from 2.90 to 24.9 mg/kg, with a GM of 5.37 mg/kg, and those of the other undisturbed marl soils ranged 3-3

from 3.10 to 13.3 mg/kg, with a GM of 4.70 mg/kg. This showed that soils in that specific region could be naturally high in arsenic concentrations. To test arsenic distribution normality, moment coefficients of skewness (calculated as third moment of the population mean) and kurtosis (calculated as fourth moment of the population mean minus three) of arsenic frequency distribution were calculated, both before and after log transformation of the data. In asymmetrical distributions, skewness can be positive or negative. A positively skewed distribution has a longer tail to the right and a negatively skewed distribution has a longer tail to the left. Kurtosis describes the heaviness of the tails for a distribution, which should give a value of zero for normally distributed data. In this research, the original data were positively skewed, and log-transformation reduced the skewness and kurtosis from 8.3 and 89.0 to 0.33 and 0.43, respectively (data not shown). Thus, the overall geometric mean (GM = 0.42 mg/kg) and geometric standard deviation (GSD = 4.1), better represent arsenic concentrations in Florida surface soils (14), since the distorting effects of a few high values were minimized. 3.3.2. Factors Affecting Arsenic Concentrations in Soils. Clay contents, organic carbon (OC) content, ph, cation exchange capacity (CEC), and total Fe and Al concentrations in soils varied greatly (Table 3-2). They all had strong positive correlation (α<0.01) with arsenic concentrations, implying that soil composition and properties are major controlling factors for arsenic concentrations in Florida surface soils (14). The highest mean arsenic concentrations were associated with the Histosols, especially the suborder of Hemists (Table 3-1). The Hemists had a GM arsenic concentration of 5.60 mg/kg, exceeding the FDEP industrial soil cleanup goal of 3.70 mg/kg. This is partly because arsenic concentrations were expressed on a weight rather than volume basis, since organic soils have much lower bulk density than mineral soils. However, Histosols had higher arsenic even when compared on an equivalent volume basis (i.e., mg/cm 3 ). Statistical analysis showed no difference in GM arsenic concentrations between the cultivated (n=10) and undisturbed (n=29) Histosols from the same region (2.37 vs. 2.34 mg/kg) (data not shown). Thus, the relatively high arsenic concentrations in the Histosols, especially in the Hemists, apparently relate to the special biogeochemical processes that occur in these soils, which will be discussed later. The Inceptisols and Mollisols have the second and third highest arsenic contents, with GM arsenic concentrations of 0.98 mg/kg and 0.72 mg/kg, respectively (Table 3-2). This is probably because these two soil orders had the highest clay contents (5.3% and 3.8%, respectively) in addition to the fact that Inceptisols had the highest CEC (17.4 cmol/kg) excluding the Histosols (129 cmol/kg) (Table 3-1). Also, the Mollisols had the highest OC, ph, and total Fe and Al concentrations among the mineral soils. This may indicate the importance of clay and total Fe/Al oxides in controlling arsenic level in soil (14). Ori et al. (24) reported that total arsenic in Louisiana soils appears to vary more with the parent material and clay than with the soil reaction or organic matter content. Furthermore, in Florida, both the Inceptisols and Mollisols were relatively wet (Aquepts and Aquolls) soils (25), which means that they may also have different biochemical processes compared to the upland or relatively dry soils. 3-4

The Ultisols had the fourth highest GM arsenic concentration (0.51 mg/kg), which was the highest among the three dominant mineral soil orders in Florida (Spodosols, Entisols, and Ultisols). This agrees well with Ma et al. (16), who found that arsenic concentrations in 40 Florida mineral soils decrease in the order of Ultisols > Entisols Spodosols, which may be attributed to relatively low clay contents. The relatively high concentrations of total Al and Fe in the Ultisols and Entisols in comparison with those in the Spodosols may be another reason (Table 3-2). Arsenic is strongly adsorbed by Fe and Al oxides, and the geochemical associations of arsenic with Fe and Al have been noted in several studies (10, 26). Due to the significant differences in both the cumulative arsenic probability distribution and GM arsenic concentrations among soil orders and suborders, using a single number to represent arsenic concentrations in Florida surface soils is probably not a good idea. Soilbased arsenic concentration is therefore necessary in assessing potential arsenic contamination in Florida soils. The geometric mean and geometric standard deviation were used to estimate the upper baseline limit (UBL) of arsenic in soils, which covers 97.5% of all the samples and is calculated as GM x GSD 2 (21). For example, a sample with arsenic concentration above the UBL of 7.02 mg/kg is normal for Hemists and Saprists (Histosols), Aquents (Entisols), and Aquepts (Inceptisols), but may indicate potential arsenic contamination for other soil orders and suborders. A careful examination of soils with high arsenic concentration above 7.02 mg/kg indicates that 12 out of the 17 samples had very high contents of either clay (12.3 to 60.9%, with an arithmetic mean of 31.1%) or OC (20.9 to 54.2%, with an arithmetic mean of 38.6%) (Table 3-3). These 12 soil samples are all from wet soils including 4 Sapists, 2 Hemists, 5 Aquents and 1 Aquept. Concentrations of total Fe and Al are also high in 9 out of these 12 samples. Snail shells were observed in a marl soil sample of the Aquents suborder, which had the highest arsenic concentration (50.6 mgkg -1 ). The soil sample containing the next highest arsenic level (38.2 mg/kg), which is not a marl soil but was located in a coastal hammock in Levy county, had a fair amount of sand, but mollusk shells were also present. In fact, the common characteristic of the two relatively different wet soils with the highest arsenic concentrations was their abundance in shell fragments. Other biological materials (debris) which could not be identified were also present. Bioconcentration of arsenic may result in these soils being naturally high in arsenic concentrations. The other 5 soil samples having arsenic concentrations > 7.02 mg/kg include 2 Psamments, 2 Udults, and 1 Aquod. They are all above the UBL values for the individual soil suborder and had low contents in of clay (0.1 to 2.4%, with a mean of 1.4%) and OC (0.7 to 2.3%, with a mean of 1.2%). This suggests that a potential arsenic contamination may have occurred in these soils and they perhaps should be excluded from this arsenic database. 3.3.3. High Arsenic in Wet Soils. Soil suborders with the highest arsenic content in Florida tend to be very wet. These include Saprists and Hemists, which are exclusively wetland soils; and Aquents, Aquepts, and Aquolls, which are normally present in wetlands. These soils are also commonly associated with limestone. For examples, Saprists and Aquents occur extensively above limestone in the Everglades, and the soil with the second highest As concentration (38.2 mg/kg) was a shallow Aquept overlying limestone in a wet forest on the Gulf coast. Possibly, high arsenic levels accumulated in these soils via biological processes associated with long-term inundation or saturation of the soil. Another reason for their high arsenic concentrations may result from the high arsenic concentration inherited in the 3-5

limestone, which has not yet been characterized. Finally, the wet, high-arsenic suborders as a group had significantly higher mean contents of organic matter, clay, and Fe and Al oxides, as compared to means for the remaining soil suborders (data not shown). Not all Aqu ("wet") suborders were high in arsenic, however. The most notable exception is Aquods, the suborder with the lowest arsenic concentration (Table 3-1). Aquods tend to have a fluctuating water table, but are generally not saturated at the surface for long periods. In effect, they are wet (i.e., poorly drained) but normally not wet enough (i.e., very poorly drained) to be considered wetland soils. They occur prevalently on flatwoods landscapes, which often constitute the local "uplands" in Florida. Aquods are also distinguished in having undergone podzolization, which promotes near-surface depletion of Fe and Al and hence reduces arsenic retention capacity (Table 3-1). Arsenic enters surface soils of lowland (wet) areas through both point and diffuse sources that may be natural or anthropogenic. Important anthropogenic arsenic sources include mining and the use of arsenic pesticides in agriculture. Arsenic may reach lowland soils via runoff and leaching of arsenic from uplands. Bioconcentration and enrichment of arsenic by lowland plants and aquatic organisms, such as algae and lower invertebrates (3, 26, 27, 28) is another possible mode of arsenic accumulation. Doyle and Otte (29) found that salt marshes could act as effective sinks of arsenic and other metals. Decomposition of plant residues also yields organic substances that can adsorb arsenic (26). A recent study on the sawgrass prairie wetlands in South Florida (30) indicates that nonessential trace elements (such as Cr, Co, Pb, and Hg) are generally not being cycled but were concentrated in the organic-rich sediments. Sea snails, mollusks, and crustaceans are capable of assimilating arsenic from the water and concentrate in shells (27, 28). For example, arsenic concentrations in sea snails are as high as 3.6-63 mg/kg on a wet-weight basis (31). Elevated arsenic concentrations in oysters from south Florida along the Gulf of Mexico were also reported (32). In addition to natural and anthropogenic arsenic inputs to soils, several environmental factors are also important in determining the geographical and pedogenic distributions of arsenic concentrations in soils (26). Soil compositions that contribute to sorption, precipitation, and retention of arsenic in soils include oxides of Al and Fe, clay content, organic matter (14), limestone and sulfide. Soil reactions that affect arsenic speciation and solubility include oxidation-reduction, ph and microbial activity (26, 33). Loss of arsenic from soils can occur by surface runoff, plant uptake, volatilization and leaching (33, 34). Microbial activity producing volatile arsenic compounds is responsible for the majority of arsenic loss from the surface horizons in aquatic environments. However, no volatile arsenic compounds were detected even though sediments contained about 10% organic matter (6). In both soil and water systems, arsenic species are subject to both chemical and microbiological oxidation/reduction according to the pe-ph status (6). At high Eh values - 2- (upland), arsenic (V) may predominantly exist as H 2 AsO 4 (ph=2.5~7.0) and HAsO 4 (ph>7.0). At low Eh values (lowland or wetland), arsenic (III) species (H 3 AsO 0 3, ph<9.0) - may be present along with As 2 S 3 (ph<7.0) and AsS 2 (ph>7.0) in the presence of high sulfide. Oxidation-reduction reaction and associated ph changes in soils as a result of 3-6

flooding in wet mineral soils can enhance the retention and immobilization of metals by clay minerals, organic matter, iron oxide, and sulfides (33). It has been reported that it is the oxidation and reduction of Fe, rather than the binding to organic matter, that drives the accumulation of arsenic in wet soils (35). In a previous research we hypothesized that trace elements may have co-precipitated with Fe-Al oxides in Florida soils as structural components rather than exchangeable ions (14). Cullen and Reimer (28) concluded that in both coastal marine and freshwater sediments, cycling of inorganic arsenic is closely coupled with Fe biogeochemistry, which is effective in controlling arsenic retention during flooding and drainage cycles. The ability of amorphous iron oxides to strongly adsorb arsenic is related to their loose and highly hydrated form, allowing other hydrated ions to diffuse freely throughout the structure without being restricted to external surface sites, as in more crystalline solids. In addition, as soils are flooded and become anaerobic or reducing, their ph tends to be neutral (33) and favors arsenic (III) immobilization by soil adsorption (maximum sorption at ph of 7.0 as H 3 AsO 3 ), sulfide precipitation (co-precipitated with Fe 2+ ) (6), and organic complexion (26). For example, ferric iron coprecipitation with arsenic (III) has been reported to be moderately effective (50%) in reducing arsenic from drinking water at a ph of 7.0 (36). This is especially important in wetland ecosystems in South Florida, since those soils are commonly associated with limestone and have high ph values. Under oxidizing conditions, any sulfide introduced into the soil would be oxidized to sulfate and arsenic could be mobilized (6). The findings of this study show that background concentrations of arsenic in Florida surface soils are general low and vary with soil type and soil s bio-geochemistry. Soils developed in wet environment are naturally high in arsenic concentration in comparison with soils in upland environment. Extrapolation of the data by using single soil arsenic value as regulative criteria regardless of the site-specific taxonomy differences may overestimate potential arsenic contamination. 3.4. Acknowledgments This research was sponsored in part by the Florida Center for Solid and Hazardous Waste Management (Contract No. 96011017). The manuscript is approved for publication as the Florida Agricultural Experiment Station Journal Series No. R-07010. The authors thank Drs. Yuncong Li and G. W. Hurt for providing several soil samples from south Florida. We are also indebted to those who participated in the Florida Cooperative Soil Survey. Their collection and characterization of a large number of Florida soil samples made this study possible. 3.5. Literature Cited (1) Nriagu, J. O., Ed. Arsenic in the Environment, Part II: Human Health and Ecosystem Effects; The Wiley Series in Advances in Environmental Science and Technology, Vol 27; John Wiley and Sons, Inc.: New York, 1994. (2) USEPA. Integrated Risk Information System (IRIS), Arsenic, Inorganic; CASRN 7440-38-2; Cincinnati, OH, 1998. 3-7

(3) Research Triangle Institute. Toxicological Profile for Arsenic: Draft; U. S. Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry: Atlanta, GA, 1998. (4) Davis, M. K.; Reich, K. D.; Tikkanen, M. W. In Arsenic: Exposure and Health. Chappell, W. R.; Abernathy, C. O.; Cothern, C. R. Eds.; Science and Technology Letters: Northwood, UK, 1994; pp. 31-40. (5) Arnt, J.; Rudnitski, K.; Schmidt, B.; Speelman, L.; Nobouphasavanh S. Earth and Atmosphere Field Camp 87-411. 1997. (6) O Neill, P. In Heavy Metals in Soils; Alloway, B. J. Eds.; John Wiley & Sons, Inc.: New York; 1990; pp 83-99. (7) Kabata-Pendias, A., and Pendias H., Ed. Trace Elements in Soils and Plants, 2nd ed.; CRC Press: Boca Raton, FL; 1992. (8) Davies, B.E. In Biogeochemistry of Trace Metals; Adriano, D.C. Eds.; CRC Press: Boca Raton, FL; 1992; pp 1-18. (9) USEPA. Soil Screening Guidance: User's Guidance; EPA 540/R-96/018; Washington, DC, 1996. (10) Barringer, J. L.; Szabo, Z.; Barringer, T. H. Arsenic and Metals in The Vicinity of the Imperial Oil Company Superfund Site, Marlboro Township, Monmouth County, New Jersey; U.S. Geological Survey Water-resources Investigations Report 98-4016; West Trenton, NJ; 1998. (11) New Jersey Department of Environmental Protection. Revised Guidance Document for the Remediation of Contaminated Soils; Trenton, NJ; 1998. (12) Florida Department of Environmental Protection (FDEP). Applicability of Soil Cleanup Goals for Florida; Internal Memorandum; Tallahassee, FL; 1996. (13) Breckenridge, R. P.; Crockett, A. B. Determination of Background Concentrations of Inorganics in Soils and Sediments at Hazardous Waste Sites; EPA/540/S-96/500; Washington, DC; 1995. (14) Chen, M., Ma, L. Q.; Harris, W. G. J. Environ. Quality 1999, 28, 1173-1181. (15) Scarlatos, P. D.; Scarlatos, D. Ecological Impact of Arsenic and Other Trace Metals from Application of Recovered Screen Material on Florida Soils; Florida Atlantic University: Boca Raton, FL; 1997. (16) Ma, L. Q.; Tan, F.; Harris W. G. J. Environ. Qual. 1997, 26, 769-775. 3-8

(17) Soil Survey Division Staff. Soil Survey Manual; USDA Handbook No. 18; U.S. Gov. Print. Office: Washington, DC; 1993. (18) USEPA. Test Methods for Evaluating Solid Waste; Vol. IA: Laboratory Manual Physical/Chemical Methods; SW 846, 3rd Ed; U.S. Gov. Print. Office: Washington, DC; 1995. (19) Sodek, F. III.; Carlisle, V. W.; Collins, M. E.; Hammond, L. C.; Harris; W. G. Characterization Data for Selected Florida Soils; Soil and Water Science Dept., Univ. of Florida: Gainesville, FL; 1990. (20) Dudka, S. Appl. Geochem. 1993, 2, 23-29. (21) Gough, L. P. Understanding Our Fragile Environment, Lessons from Geochemical Studies; USGS Circular 1105; U.S. Gov. Print Office: Washington, DC; 1993. (22) Peters, S. C.; Blum, J. D.; Klaue, B.; Karagas, M. R. Environ. Sci. Technol. 1999, 33, 1328-1333. (23) Gough, L. P.; Severson, R.C.; Jackson, L. L. Water Air Soil Pollu. 1994, 74, 1-17. (24) Ori, L. V.; Amacher, M. C.; Sedberry, Jr. J. E. Commun. Soil Sci. Plant Anal. 1993, 24, 2321-2332. (25) Soil Survey Staff. Keys to Soil Taxonomy, 8 th Ed, USDA-NRCS, 1998. (26) Nriagu, J. O. Ed. Arsenic in the Environment, Part I: Cycling and Characterization; The Wiley Series in Advances in Environmental Science and Technology, Vol 26; John Wiley and Sons, Inc.: New York, 1994. (27) Otte, M. L.; Rozema, J.; Beek, M. A.; Kater, B. J.; Broekman, R. A. The Sci. Total Environ. 1990, 97/98, 839-854. (28) Cullen, W. R.; Reimer, K. J. Chem. Rev. 1989, 89, 713-764. (29) Doyle, M. O.; Otte, M. L. Environmental Pollution 1997, 96, 1-11. (30) U.S. Geological Survey. South Florida Ecosystems: The Role of Peat in the Cycling of Metals; U.S. Geological Survey Fact Sheet, FS-161-96; 1996. (31) Committee on Medical and Biologic Effects of Environmental Pollutants. Arsenic; National Academy of Sciences: Washington, DC; 1977. 3-9

(32) Presley, B. J.; Taylor, R. J.; Boothe, P. N. The Sci. Total Environ. 1990, 97/98, 551-593. (33) Gambrell, R. P. J. Environ. Qual. 1994, 23, 883-891. (34) Gao, S.; Burau, R. G. J. Environ. Qual. 1997, 26, 753-763. (35) Klumpp, D. W. Mar. Biol. 1980, 58, 265-274. (36) Pierce, M. L.; Moore, C. B. Water Res. 1982, 16, 1247-1253. 3-10

100 Spodosols Entisols Alfisols Percent of Samples Below (%) 75 50 25 Ultisols Mollisols Inceptisols Histosols Homestead Soils Upper baseline limit = 7.02 mg/kg FDEP soil cleanup goal = 3.70 mg/kg (Industrial land) FDEP soil cleanup goal = 0.80 mg/kg (Residential land) USEPA soil screening level = 0.40 mg/kg 0 0.0 0.1 1.0 10.0 100.0 Arsenic Concentration (mg/kg) FIGURE 3-1. Cumulative probability curve for arsenic concentrations in Florida surface soils. Data are subdivided into 7 soil orders, namely Alfisols (n=60), Entisols (n=107), Inceptisols (n=10), Histisols (n=39), Spodosols (n=122), Mollisols (n=15), and Ultisols (n=88). Homestead soil (mean value, n=10) data (16) are shown (open cycle) for comparison. Soil arsenic criteria of 0.40 mg/kg (8), 0.80 mg/kg, 3.70 mg/kg (13), and 7.02 mg/kg (15) in literature are plotted as dashed vertical lines. 3-11

TABLE 3-1. Geometric Mean Arsenic Concentrations (As, mg/kg) of 7 Soil Orders and 12 Soil Suborders soil order # of samples arsenic soil suborder # of samples arsenic GM a range GM range Histosols 39 2.35 a b 0.25 ~ 11.7 Hemists 6 5.60 a b 3.16 ~ 9.44 Saprists 33 2.01 b 0.25 ~ 11.7 Inceptisols 10 0.98 b 0.03 ~ 38.2 Aquepts 10 0.98 d 0.03 ~ 38.2 Mollisols 15 0.72 c 0.03 ~ 3.34 Aquolls 15 0.72 e 0.03 ~ 3.34 Ultisols 88 0.51 d 0.02 ~ 16.9 Aquults 17 0.60 ef 0.21 ~ 5.45 Udults 71 0.49 f 0.02 ~ 16.9 Entisols 107 0.39 de 0.02 ~ 50.6 Aquents 22 1.17 c 0.10 ~ 50.6 Psamments 85 0.29 ghi 0.02 ~ 22.2 Alfisols 60 0.36 de 0.02 ~ 4.86 Aqualfs 50 0.36 fg 0.02 ~ 4.86 Udalfs 10 0.35 fg 0.05 ~ 4.02 Spodosols 122 0.18 f 0.01 ~ 9.58 Aquods 101 0.17 i 0.01 ~ 5.58 total: 441 0.42 0.01 ~ 50.6 Orthods 21 0.22 hi 0.02 ~ 9.58 a GM = geometric mean. b Different letters in the same column indicate significant different at p<0.05 (Student t test). 3-12

Table 3-2. Concentrations of Total Aluminum & Iron and Characteristics of Selected Florida Surface Soils soil order number of samples ph (1:2 water extra.) a organic carbon (%) b clay content cation exchange (%) b capacity (cmol/kg) b total aluminum (%) b total iron (%) b Histosols 39 4.8 cd c 34.1 a n.d. d 129 a 0.491 a 0.350 a Mollisols 15 6.0 a 24.9 a 3.84 ab 8.56 c 0.210 b 0.206 ab Alfisols 60 5.3 b 6.42 b 1.69 cd 6.86 cd 0.118 c 0.098 c Inceptisols 10 5.3 abc 2.50 c 5.34 a 17.4 b 0.172 bc 0.144 bc Spodosols 122 4.5 d 1.55 c 1.15 d n.d. 0.064 d 0.033 d Ultisols 88 5.2 b 1.46 c 2.54 b 6.17 d 0.167 b 0.122 bc Entisols 107 5.2 b 0.93 d 1.77 c 5.75 d 0.140 bc 0.120 bc total: 441 mean 5.02 2.40 1.78 9.70 0.130 0.093 range 2.34-8.10 0.08-55.9 n.d.-73.4 0.28-370 0.005-2.22 0.005-3.42 a Arithmetic mean. b Geometric mean. c Different letters in the same column indicate significant different at p<0.05 (Student t test). d n.d., not determined. 3-13

Table 3-3. High Arsenic Concentration Sites Screened by Upper Baseline Limits of 7.02 mg/kg and Related Soil Properties. lab number suborder ph (1:2 water extra.) organic carbon (%) clay content (%) total aluminum (%) total iron (%) total arsenic (mg/kg) 3583 Saprist 3.3 54.2 n.d. a 0.128 0.038 7.4 799 Saprist 5.8 48.0 n.d. 0.684 1.796 9.6 21 Saprist 5.7 38.2 n.d. 0.432 1.172 11.7 18 Saprist 5.7 37.6 n.d. 0.256 1.106 9.1 3379 Hemist 5.8 32.7 n.d. 1.518 0.642 9.2 2881 Hemist 2.7 20.9 n.d. 1.738 1.888 9.4 2885 Aquent 2.7 14.4 60.9 1.390 3.240 11.5 4263 Aquent 4.5 5.1 46.3 0.467 3.420 7.8 4640 Aquent 7.3 2.5 28.9 0.080 1.060 50.6 6542 Aquent 7.9 2.4 16.4 0.120 0.118 18.7 6532 Aquent 6.5 2.1 12.3 0.199 0.135 10.1 6796 Aquept 6.0 5.6 22.0 0.404 3.270 38.2 6060 Udult 4.5 1.0 2.4 0.324 0.240 7.5 3050 Udult 5.6 0.9 1.5 0.168 0.216 16.9 3402 Aquod 4.2 2.3 1.0 0.086 0.042 9.6 3561 Psamment 3.1 1.2 2.0 0.158 0.070 13.2 7138 Psamment 4.7 0.7 0.1 0.184 0.070 22.2 a n.d. = not determined. 3-14

IV. ARSENIC CONCENTRATIONS IN FLORIDA SURFACE SOILS: 2. GEOGRAPHICAL DISTRIBUTION MING CHEN, LENA Q. MA, GERCO C. HOOGEWEG, AND WILLIE G. HARRIS, 4.1. INTRODUCTION Arsenic has been recognized as a Class A human carcinogen and become a public concern due to its wide usage in both agriculture and industry in the past (1, 2). Both the amount and forms of arsenic in soils influence plant growth and animal and human health, through several exposure pathways (3). It is thus important to establish background concentrations of arsenic in soils at regional levels and to know whether these levels vary between areas with different soil properties (4,5). Georeferencing and mapping the background concentrations of arsenic would assist policy-makers in establishing reference levels for screening potential hot-spots and for soil cleanup standards. Mapping heavy metal concentrations at a regional scale requires extrapolation. A plethora of tools, including geographic information systems (GIS) and geostatistical methods (kriging, inverse distance, splining), are available to estimate concentrations at unknown locations (6). White et al. (7) demonstrated that these tools can be used to generate a map for the conterminous US for total zinc in soils based on limited number of observations. As a final conclusion they stated that geostatistics and GIS will be indispensable in characterizing and summarizing georeferenced information to provide quantitative support to decision and policy making for agriculture and natural resources management. The levels of arsenic concentrations in many Florida soils are reportedly above the USEPA soil screening level of 0.40 mg/kg (3, 8, 9). They are also above the Florida Department of Environmental Protection (FDEP) residential soil cleanup goal of 0.80 mg/kg (10). Recently, a comprehensive database for concentrations of 15 elements (Ag, As, Ba, Be, Cd, Cr, Cu, Pb, Hg, Mn, Mo, Ni, Sb, Se, and Zn), in representative Florida surface soils has been established (11). Our research addressed As distribution among soil taxonomy and found that the greatest arsenic concentrations are associated with soil Suborders from wet environments (12). The present study was conducted to investigate: (i) geographical distribution of concentrations of As in representative Florida surface soils; and (ii) possible point or nonpoint sources of arsenic contamination in Florida surface soils. Establishing proper arsenic limits in soils will be useful for land application of waste materials and soil remediation. 4.2. EXPERIMENTAL SECTION 4.2.1. Materials. As described in a previous manuscript, 441 soil samples selected for this study represent both geographical and taxonomic distribution of the soil Orders in the state of Florida. They were available as archived samples collected during the course of the Florida Soil Survey Program(12). Soils from 51 counties (Figure 4-1) were included, 4-1

covering as much as 80 % of the total land area of Florida (11). Samples were digested in a CEM MDS-2000 microwave oven (Matthews, NC) using the EPA Method 3052 and arsenic concentrations in the filtrates were analyzed on a Perkin-Elmer SIMAA 6000 graphite furnace atomic absorption spectrophotometer unit using the EPA SW 846 method 7060A (13). For more detailed information on properties of the samples as well as the soil digestion method, please refer to Chen et al. (12) and Chen and Ma (13), respectively. The data set was normalized by log 10 transformation prior to statistical comparison. The geometric mean (GM) and geometric standard deviation (GSD) were used to estimate the upper baseline limit (UBL) of arsenic in soils, which covers 97.5% of all the samples and is calculated as GM x GSD 2 (14). Samples with concentrations equal to or larger than the upper baseline limit (UBL) for individual soil Suborder were scrutinized for possible arsenic contamination. For more detailed information on definition of elemental baseline concentrations in soils as well as arsenic baseline concentrations in Florida soils, please refer to Chen et al.(11). 4.2.2. Map Development. The first step in developing As distribution maps for Florida was to establish the distribution of the soil Order and Suborders in Florida using ArcView GIS 3.1 (15). Fore detailed information please refer to Section 2.2.3. The location of sites with concentrations equal to or larger than the UBL for individual soil Suborder, which was calculated as GM x GD 2 and accounted for 97.5% of high As concentrations in that Suborder was plotted to indicate possible arsenic enrichment (Figure 4-3). Maps were converted from the ArcView GIS raster format to bitmaps and finally to Joint Photographic Experts Group (JPEG). Image graphics outputs were prepared in Microsoft Word 97. However, the general nature of the STATSGO does not allow for site-specific interpretation and estimation of concentration levels of arsenic. More detailed soil maps should be used for that purpose. 4.3. RESULTS AND DISCUSSION 4.3.1. Geographical Distribution of Seven Soil Orders and Sampling Sites. Seven soil Orders have been identified in Florida (18). Their approximate coverage percentages are: Spodosols (28%), Entisols (22%), Ultisols (19%), Alfisols (14%), Histosols (10%), Mollisols (4%), and Inceptisols (3%) (10). Geometric means of arsenic concentrations decrease in the order of Histosols (2.35 mg/kg) > Inceptisols (0.98 mg/kg), Mollisols (0.72 mg/kg) > Ultisols (0.51 mg/kg) > Entisols (0.39 mg/kg), Alfisols (0.36 mg/kg) > Spodosols (0.18 mg/kg) (12). Geographical distributions of these soil Orders are plotted in Figure 4-1. The organic soils (Histosols) are primarily distributed south of Lake Okeechobee (sawgrass marsh), but with numerous smaller areas throughout the Peninsula part of the State (freshwater marsh). The sandy, acid leached flatwoods soils (Spodosols) are extensive on the Peninsular and Panhandle coasts, and have a well-expressed E horizon and a subsurface accumulation of metal-humus complexes. Sandy upland Entisols (Psamments) occur in the southwest corner of the Panhandle and extend part-way down the center of the Peninsula. Wet Entisols (Aquents) are most extensive south of Lake Okeechobee, where they are commonly calcareous (dominated by marl). Ultisols predominate in the Panhandle of Northwest Florida and in some upland area of the northern Peninsula. Alfisols occur most abundantly in south 4-2

Florida and along the Gulf coast of the Panhandle. There are only minor occurrences of Mollisols and Inceptisols, which are found scattered in the prairies and valleys of north and central Florida and in the Big Cypress Swamp, respectively. The geographical distribution of individual sample locations (Figure 4-1) reflects the lack of sample availability for a few counties. However, Georeferencing sample locations enables us to portray the spatial distribution of arsenic concentrations of Florida soils. 4.3.2. Geographical Distribution of Arsenic Concentrations in Soils. The Everglades region in South Florida has the highest arsenic concentrations among surface soils. High As levels are associated with peat, limestone, and shelly marls in that region (16, 17). The dominant soil types in the Everglades areas are Histosols (Hemists and Saprists) and calcareous Entisols (Aquents) or marl soils. Limestone has been reported to have high arsenic content (1.7-2.6 mg/kg) (1). According to our previous study, the marl soils have higher arsenic concentrations than other soil types. For example, arsenic concentrations in the Everglades National Park samples ranged from 2.90 to 24.9 mgkg -1, with a GM of 5.37 mgkg -1 (12). Arsenic in soils can be elevated by agricultural inputs of fertilizers and pesticides, but high values occur in unimpacted as wellas agricultural areas of organic and marl soils in the region. It has been reported that in many regions with long history of agricultural practice, soils have accumulated arsenic residues (18). Higher arsenic concentration in samples from Biscayne National Park has been attributed to the anthropogenic influences (19). Low concentrations of arsenic (1-5 µgl -1 ) were detected in surface water samples from selected canals in South Florida and attributed to the highway runoff (17). Concentrations of arsenic in commercial phosphorus fertilizers marketed in Iowa were greater and more variable than other trace metals in concentrated superphosphate (TSP), monoammonium phosphate (MAP), and diammonium phosphate (DAP). Arsenic concentrations in these samples were in the range 2.4-18.5 mg/kg (TSP), 8.1-17.8 mg/kg (MAP), and 6.8-12.4 mg/kg (DAP) (20). Central and North Florida are rich in phosphate deposits (16, 21). Windom et al (22) reported that higher arsenic concentrations in Florida estuarine and coastal marine sediments are related to the relatively common phosphate deposits. Rock phosphates are reportedly high in arsenic (18), from 0.4 to 188 mg/kg, with an average of 22.6 mg/kg worldwide (23). In central and north Florida, arsenic concentrations of 11.3 and 7.0 mg/kg in phosphate rocks were reported, respectively (24). Extensive phosphate mining and processing expose additional arsenic deposits to surface soils through the atmospheric release. West Florida has moderate arsenic concentration. Historically extensive cotton and tobacco productions in the Panhandle area (25) may have contributed to elevated arsenic in these soils. Both inorganic (calcium arsenate) and organic arsenicals (methanearsonates) were extensively used for weed control in cotton fields (18,26) and arsenic is persistent in soils (18). For examples, monosodium methanearsonate (MSMA) is registered for use in cotton at 2.2 to 2.8 kg/ha and disodium methanearsonate (DSMA) at 2.2 to 3.4 kg/ha.(18). According to the USGS Pesticide National Synthesis Project, over 99% of both MSMA and DSMA in the US have been used on cotton crops. GM arsenic concentrations for Florida counties were also plotted in Figure 4-2 and soil arsenic criteria 0.40 mg/kg (3), 0.80 mg/kg, 3.70 mg/kg (10), and 7.02 mg/kg (11) in the 4-3

literature are used as the legend of the map. Twenty seven of Florida s 51 counties conducted in this study contain arsenic concentrations above the EPA soil screening level of 0.40 mg/kg (3) and 10 counties above the FDEP residential soil cleanup goal of 0.80 mg kg -1 (10). The highest GM arsenic concentration was observed in Dade County in South Florida, which was above the upper baseline limit of 7.02 mg/kg for arsenic in Florida surface soils (11). The second highest GM arsenic concentration was observed in Brevard County in Central Florida, above the FDEP industrial soil cleanup goal of 3.7 mg kg -1 (10). Both counties are located at the Atlantic coast and close to the marine environments. Cullen and Reimer (27) concluded at the interface between fresh- and saltwater environments, most of the arsenic is deposited in estuarine and coastal sediments rather than fluxes back into the ocean. 4.3.3. Spatial Distribution of Arsenic in Florida Soils Based on Soil Suborders. Based on the GM arsenic concentrations in soil Suborders (Figure 4-3), the highest arsenic concentrations are found in areas dominated by organic soils (Histosols), especially Hemists and Saprists, which have the highest arsenic concentrations (>0.8 mg/kg). Most obvious on the map (Figure 4-3) are the high arsenic levels south of Lake Okeechobee (Histosols), and areas near the State water system. Moderate arsenic concentrations (0.40 mg/kg < GM As < 0.80 mg/kg) are in the Panhandle and Central Ridge areas, where the Ultisols are distributed, as well as parts of the Big Cypress Swamp where the Alfisols are located (Figure 4-1). The lowest arsenic concentrations (< 0.40 mg/kg) occur mostly in coastal Flatwoods of the Central and South Florida, where the Spodosols are dominant (Figure 4-1). This is consistent with our previous observation that wet soil Suborders (Hemists, Saprists, Aquents, and Aquepts) tend to have higher concentrations than the uplands soils (12). From this map it can be concluded that the variability of arsenic concentrations in Florida surface soils correspond to the distributions of major soil types. The area weighted concentrations using geometric mean smoothed out the very low and high concentrations measured for a given soil Suborders present in that map unit. Our study has indicated that it is not a good idea to use a single number (7.02 mg/kg) to represent arsenic background concentrations in soils and the soil suborder-based UBLs are better criteria in assessing potential arsenic contamination in Florida soils (12). Soils with elevated arsenic concentrations screened by the upper baseline limits (UBL) of individual soil Suborder and related site descriptions in this study are provided in Table 4-1. 4.4. ACKNOWLEDGMENTS This research was sponsored in part by the Florida Center for Solid and Hazardous Waste Management (Contract No. 96011017). We are also indebted to those who participated in the Florida Cooperative Soil Survey. Their collection and characterization of a large number of Florida soil samples made this study possible. 4.5 LITERATURE CITED (1) EPA. Integrated Risk Information System (IRIS), Arsenic, Inorganic; CASRN 7440-38-2; Cincinnati, OH; 1998. 4-4

(2) Nriagu, J. O. Ed. Arsenic in the Environment, Part I: Cycling and Characterization; The Wiley Series in Advances in Environmental Science and Technology, Vol 26; John Wiley and Sons, Inc.: New York; 1994. (3) EPA. Soil Screening Guidance: User's Guidance; EPA 540/R-96/018; Washington, DC, 1996. (4) Davies, B.E. In Biogeochemistry of Trace Metals; Adriano, D.C. Eds.; CRC Press: Boca Raton, FL; 1992; pp 1-18. (5) Breckenridge, R. P.; Crockett, A. B. Determination of Background Concentrations of Inorganics in Soils and Sediments at Hazardous Waste Sites; EPA/540/S-96/500; Washington, DC; 1995. (6) Juang, K. and Lee, D. J. Environ. Qual. 1998, 27, 355-363. (7) White, J.G.; Welch, R. M.; Norvell, W. A. Soil Sci. Soc. Am. J., 1997, 61, 185-194. (8) Scarlatos, P. D.; Scarlatos, D. Ecological Impact of Arsenic and Other Trace Metals from Application of Recovered Screen Material on Florida Soils; Florida Atlantic University: Boca Raton, FL; 1997. (9) Ma, L. Q.; Tan, F.; Harris W. G. J. Environ. Qual. 1997, 26, 769-775. (10) Florida Department of Environmental Protection. Applicability of Soil Cleanup Goals for Florida; Internal Memorandum; Tallahassee, FL; 1996. (11) Chen, M., Ma, L. Q.; Harris, W. G. J. Environ. Quality 1999, 28 (4). In press. (12) Chen, M., Ma, L. Q.; Harris, W. G. Environ. Sci.Technol. 1999, In review. (13) Chen, M., Ma, L. Q. J. Environ. Quality 1998, 27, 1294-1300. (14) Gough, L. P. Understanding Our Fragile Environment, Lessons from Geochemical Studies; USGS Circular 1105; U.S. Gov. Print Office: Washington, DC; 1993. (15) Environmental Systems Research Institute. PC ArcView GIS 3.1; Redlands, CA; 1998. (16) Lane, E. Ed. Florida Geological History and Geological Resources; USGS Special Publication No. 35; Florida Geological Survey; Tallahassee, FL; 1994. 4-5

(17) Department of the Army. Excavation and Use of Limestone in South Florida: Technical Report and Environmental Investigation; Jacksonville, FL; 1982. (18) Woolson, E. A. Ed. Arsenical Pesticides; ACS Symposium Series 7; American Chemical Society. Washington, DC; 1975. (19) Storm, R. N.; Braman, R. S.; Jaap, W. C.; Dolan, P.; Donnelly, K. B.; Martin, D. F. Florida Scientist 1992, 55, 1-19. (20) Smith, E.; Naidu, R.; Alston, A. M. Advances in Agronomy 1998, Vol. 64, 149-195. (21) Spencer, S. M. The Industrial Minerals Industry Directory of Florida; Florida Geological Survey Information Circular No. 105; Tallahassee, FL; 1989. (22) Windom, H. L.; Schropp, S. J.; Calder, F. D.; Ryan, J. D.; Smith, Jr. R. G.; Burney, L. C.; Lewis, F. G.; Rawlinson, C. H. Environ. Sci. Technol. 1989, 23, 314-320. (23) Committee on Medical and Biologic Effects of Environmental Pollutants. Arsenic; National Academy of Sciences, Washington, DC; 1977. (24) Heavy Metals in Soils and Phosphatic Fertilizers. PPI/PPIC/FAR Technical Bulletin, 1998-2. (25) Marcus, R. B. A Geography of Florida; WM. C. Brown Book Company; Dubuque, IW; 1964. (26) Murphy, E. A.; Aucott, M. The Sci. of the Total Environ. 1998, 218, 89-101. (27) Cullen, W. R.; Reimer, K. J. Chem. Rev. 1989, 89, 713-764. 4-6

TABLE 4-1. Soils with elevated arsenic concentrations screened by upper baseline limits (UBLs) of individual soil Suborder and soil site descriptions a. lab # Suborder county parent material and land form vegetation type and land use As (mg/kg) 4532 aqualf Charlotte sandy and loamy marine sediments pineland, slash pine, wax myrtle, saw palmetto, maidencane, and panicum 3648 aqualf Lee hydrophytic plant remains and loamy marine sedients sawgrass, sand cordgrass, and wax myrtle; native woodland 6925 aquod Bradford sandy sediments planted pine 3.5 3289 aquod Leon n.a. b n.a. 2.4 3402 aquod Leon n.a. wooded area 9.6 3077 aquod Pasco sandy and loamy marine sediments bahia grass; pasture 4.6 3567 aquod Pasco sandy marine sediments pineland, saw palmetto, gallberry, and longleaf pine, native pasture 3410 aquult Leon n.a. n.a. 6.5 3473 humod Polk marine sands bahia grass; improved pasture 5.6 7138 psamment Gilchrist sandy marine sediments planted slashpine 22.2 3561 psamment Pasco sandy marine sediments oak with an understory of native grasses; range-type pasture 6060 udult Madison sandy and loamy marine sediments planted slashpine 7.5 3050 udult Polk sandy and loamy marine sediments bahia grass; pasture 16.9 a. UBLs of aqual (4.3 mg/kg), aquod (1.8 mg/kg), aquult (2.7 mg/kg), humod (2.5 mg/kg), psamment (3.2 mg/kg), and udult (5.9 mg/kg) are adopted calculated as geometric mean x (geometric standard deviation) 2 ; and b. n.a. = site description is not available. 4.5 4.9 2.5 13.2 4-7

FIGURE 4-1. Georeference of 422 soil samples used in this study, which are located in 51 counties. Different colors represent 7 soils Orders occurring in Florida, namely, Spodosols (28%), Entisols (22%), Ultisols (19%), Alfisols (14%), Histosols (10%), Mollisols (4%), and Inceptisols (3%). Soil samples are selected proportionally to the areal occurrence of each soil Order. 4-8

FIGURE 4-2. Map of arsenic concentrations in Florida surface soils. Soil arsenic criteria 0.40 mg/kg (3), 0.80 mg/kg (10), 3.70 mg/kg (10), and 7.02 mg/kg (11) in the literature are used as the legend of the map. Geometric mean (GM) arsenic concentrations based on counties indicates there are 10 out of 51 counties with soil arsenic concentrations over the current Florida Department Environmental Protection residential soil cleanup goal of 0.80mg/kg (10). However, the soils actually sampled within a county were relatively few and in no way represent the county geographically. This generalized image should not be used for site-specific purposes. 4-9