EPA/600/R-05/048F 

April 2007 

Pilot Survey of Levels of Polychlorinated 

Dibenzo-p-dioxins, Polychlorinated Dibenzofurans,

Polychlorinated Biphenyls, and Mercury in Rural

Soils of the United States

National Center for Environmental Assessment

Office of Research and Development

U.S. Environmental Protection Agency

Washington, DC 20460
DISCLAIMER 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

Preferred citation: 

U.S. Environmental Protection Agency (EPA). (2007) Pilot survey of levels of polychlorinated dibenzo-p-dioxins, 

polychlorinated dibenzofurans, polychlorinated biphenyls, and mercury in rural soils of the United States. National 

Center for Environmental Assessment, Washington, DC; EPA/600/R-05/048F. Available from the National 

Technical Information Service, Springfield, VA, and online at http://epa.gov/ncea. 

ii 
CONTENTS

LIST OF TABLES ............................................................. v

LIST OF FIGURES ........................................................... vi

LIST OF ABBREVIATIONS AND ACRONYMS .................................. vii

PREFACE .................................................................. ix

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................. x

EXECUTIVE SUMMARY ..................................................... xi

1. INTRODUCTION .......................................................... 1

1.1. SCOPE OF STUDY ..................................................... 2

1.2. STUDY OBJECTIVES .................................................. 4

1.3. ENVIRONMENTAL FATE OVERVIEW ................................... 4

2. SITE SELECTION ......................................................... 5

3. PROTOCOL DEVELOPMENT ............................................... 6

4. SAMPLE COLLECTION, HANDLING, AND STORAGE .......................... 8

5. ANALYTICAL METHODS ................................................. 10

5.1. PHYSICAL/CHEMICAL PARAMETER TESTS ............................ 10

5.2. CALUX BIOASSAY TEQ .............................................. 10

5.3. MERCURY ANALYSIS ................................................ 11

5.4. HRMS ANALYSIS OF CDDs/CDFs, AND PCBs ............................ 11

5.5. QUALITY ASSURANCE/QUALITY CONTROL ........................... 12

6. SOIL MEASUREMENTS ................................................... 13

6.1. PHYSICAL/CHEMICAL PARAMETER RESULTS .......................... 15

6.2. CDD AND CDF RESULTS ............................................. 15

6.3. PCB RESULTS ....................................................... 16

6.4. TEQ RESULTS ....................................................... 20

6.5. MERCURY RESULTS ................................................. 23

6.6. CONGENER PROFILES ............................................... 25

7. COMPARATIVE ANALYSES ............................................... 29

7.1. COMPARISON OF AIR AND SOIL CONCENTRATIONS .................... 29

7.1.1. Air and Soil Concentrations ........................................ 30

7.1.2. Air and Soil Congener Profiles ..................................... 34

7.2. COMPARISON OF SOIL CONCENTRATION WITH TOTAL ORGANIC 

CARBON CONCENTRATION .......................................... 36

7.3. COMPARISON OF HRMS TEQs WITH CALUX BIOASSAY TEQs ........... 39

8. UNCERTAINTY .......................................................... 43

8.1. SITE SELECTION .................................................... 43

iii 
CONTENTS (continued) 

8.2. SAMPLING PROTOCOL ...............................................43

8.3. ANALYTICAL METHODS .............................................46

8.4. TREATMENT OF DATA ...............................................46

9. CONCLUSIONS ...........................................................47

REFERENCES ..............................................................50

APPENDICES 

Appendix A. Oxford Study 

Appendix B. CALUX Paper 

Appendix C. Sampling Protocol and Standard Operating Procedure for Dioxins in Surface Soil 

Appendix D. Quality Assurance/Quality Control 

Appendix E. PCB Data 

Appendix F. Literature Review of CDD, CDF, PCB, and Mercury Levels in Soil 

Appendix G. Physical/Chemical Parameter Data 

Appendix H. PCDD/PCDF Data 

Appendix I. CALUX Data 

Appendix J. Mercury Data 

Appendix K. Dioxin/Furan and PCB Profiles 

Appendix L. Paired Air/Soil Congener Profiles 

iv 
LIST OF TABLES

1. Congeners and TEFs used to calculate TEQs .................................... 1

2. Soil concentrations (dry weight) by site ........................................ 14

3. Soil concentrations (pg/g dry weight) of CDD and CDF homologues ................ 17

4. Literature summary for CDDs/CDFs in rural soils of North America ................. 18

5. PCB homologue concentrations (pg/g dry weight) ............................... 19

6. Literature summary for PCBs in rural soils world-wide ........................... 20

7. TEQ soil concentrations by site (pg TEQ/g dry) ................................. 21

8. Literature summary for CDD/CDF TEQs in rural soils of North America ............. 22

9. Literature summary for mercury in rural soils of North America .................... 27

10. Correlations across sites between soil concentrations and air concentrations ........... 32

11. Correlation across sites between soil concentration and total organic carbon 

concentration ............................................................ 38

v 
4 

LIST OF FIGURES

1. NDAMN air sampling stations ................................................ 3

2. Sample handling diagram. ................................................... 9

3. HRMS CDD/CDF TEQ2s for all 27 sites ...................................... 24

HRMS PCB TEQ2s at all 27 sites ............................................ 25

5. HRMS Total TEQ1s, HRMS Total TEQ2s, and CALUX TEQs for all 27 sites ......... 26

6. Frequency diagram for CDD/CDF TEQ2 concentrations among 27 sites .............. 26

7. Frequency diagram for PCB TEQ2 concentrations among 27 sites ................... 27

8. Mercury concentrations at all 27 sites ......................................... 28

9. Frequency diagram for Mercury concentrations among 27 sites ..................... 29

10. Scatter plots of homologue levels in air versus soil ............................... 31

11. Annual average total TEQs for air samples obtained at NDAMN sites in 2000 ......... 33

12. Total TEQ2 for soil samples taken at NDAMN sites in 2003 ....................... 33

13. Annual average air total TEQs versus soil total TEQ2s (raw data) .................. 35

14. Annual average air total TEQs versus soil total TEQ2s (log transformed data) ......... 35

15. Scatter plots of chemical levels in soil vs TOC levels in soil ....................... 37

16. HRMS-based total TEQ2 versus total organic carbon (TOC) concentration 

 of soil .................................................................. 39

17. HRMS total TEQ2s versus CALUX bioassay TEQs by site (raw data) ............... 40

18. Scatter plot of HRMS total TEQ2 versus Calux Bioassay TEQs on natural log-scale

 with natural log-linear regression line (r = 0.78) ................................ 41

19. Rank order comparison of CALUX TEQs to HRMS Total TEQ2s .................. 42

20. Comparison of five-point mean CALUX bioassay (~) and assay of

 composite soil sample (a) .................................................. 44

21. Scatter plot of five-point mean CALUX TEQs versus CALUX TEQs of soil 

 composite (r = 0.97) ...................................................... 45

vi 
LIST OF ABBREVIATIONS AND ACRONYMS 

ASE Accelerated solvent extraction 

BOD Biological oxygen demand 

CALUX Chemical-Activated Luciferase Expression 

CB Chlorinated biphenyl 

CDD Chlorinated dibenzo-p-dioxin 

CDDs/CDFs Chlorinated dibenzo-p-dioxins and chlorinated dibenzofurans 

CDF Chlorinated dibenzofuran 

GC Gas chromatography 

GPC Gel permeation chromatography 

HpCDD Heptachlorodibenzo-p-dioxin 

HpCDF Heptachlorodibenzofuran 

HRMS High-resolution mass spectrometry 

HxCDD Hexachlorodibenzo-p-dioxin 

HxCDF Hexachlorodibenzofuran 

ND Nondetect 

NDAMN National Dioxin Air Monitoring Network 

NR Not reported 

OCDD Octachlorodibenzo-p-dioxin 

OCDF Octachlorodibenzofuran 

PCB Polychlorinated biphenyl 

PCD/PCDF Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans 

PeCDD Pentachlorodibenzo-p-dioxin 

PeCDF Pentachlorodibenzofuran 

QA Quality assurance 

QC Quality control 

r Regression coefficient 

RL Reporting limit 

RPD Relative percent difference 

SD Standard deviation 

SE Standard error 

SOP Standard operating procedure 

TCDD Tetrachlorodibenzo-p-dioxin 

TCDF Tetrachlorodibenzofuran 

TEF Toxicity equivalence factor 

vii 
LIST OF ABBREVIATIONS AND ACRONYMS (continued) 

TEQ Toxicity equivalent 

TOC Total organic carbon 

WHO World Health Organization 

XDS Xenobiotic Detection Systems, Inc. 

UNITS 

°C degrees Centigrade 

cm centimeters 

d day 

fg femtograms 

ft feet 

g grams 

kg kilograms 

L liters 

m meters 

mg milligrams 

mL milliliters 

mm millimeters 

ng nanograms 

oz ounces 

pg picograms 

μg micrograms 

μL microliters 

viii 
PREFACE 

Many environmental contaminants accumulate in soils. Understanding the 

concentrations and spatial distribution of these chemicals is critical to determining how they may 

contribute to human exposure via direct contact or uptake through the food chain. The purpose 

of this document is to report the results of a pilot survey of the levels of polychlorinated dibenzop-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), polychlorinated biphenyls (PCBs), 

and mercury in rural soils of the United States. All samples were collected during 2003. The 

study was conducted by the National Center for Environmental Assessment with contract support 

provided by Battelle. 

ix 
AUTHORS, CONTRIBUTORS, AND REVIEWERS 

AUTHORS 

U.S. EPA 

John Schaum 

National Center for Environmental Assessment 

Washington, DC 

Battelle, Columbus, OH 

Mary Schrock Jennifer Ickes 

Jane Chuang Karen Tracy 

Adam Abbgy Pam Hartford 

Basil Coutant Nicole Iroz-Elardo 

Karen Riggs 

CONTRIBUTORS 

Dwain Winters, U.S. EPA, Office of Pollution Prevention and Toxics 

David Cleverly, U.S. EPA, National Center for Environmental Assessment 

REVIEWERS 

Internal Reviewers 

Toney Baney, U.S. EPA, Office of Prevention, Pesticides and Toxic Substances 

Andy Beliveau, U.S. EPA, Region 1 

Marlene Berg, U.S. EPA, Office of Solid Waste and Emergency Response 

Laura Casey, U.S. EPA, Office of Prevention, Pesticides and Toxic Substances 

William Coakley, U.S. EPA, Office of Solid Waste and Emergency Response 

Dan Duncan, U.S. EPA, Region 10 

Matthew Lorber, U.S. EPA, Office of Research and Development 

Carl Orazio, U.S. Geological Survey 

Al Rubin, U.S. EPA, Office of Water 

Robert Virta, U.S. Geological Survey 

Dwain Winters, U.S. EPA, Office of Pollution Prevention and Toxics 

Bruce Woods, U.S. EPA, Region 10 

External Reviewers 

Peter Adriaens, Department of Civil and Environmental Engineering, University of Michigan 

Stephen Boyd, Department of Crop and Soil Sciences, Michigan State University 

Pierre Goovaerts, PGeostat, LLC 

Myrto Petreas, California Department of Toxic Substances Control, California Environmental 

Protection Agency 

Richard Wenning, ENVIRON International Corporation 

x 
EXECUTIVE SUMMARY

This report provides a national-scale pilot survey of the levels of the following chemicals 

in rural/remote soils of the United States: chlorinated dibenzo-p-dioxins (CDDs), chlorinated 

dibenzofurans (CDFs), polychlorinated biphenyls (PCBs), and mercury. 

Soils can serve as long-term reservoirs for CDDs, CDFs, PCBs, and mercury, with 

releases to both terrestrial and aquatic systems (U.S. EPA, 2003, 1997; Brzuzy and Hites, 1996). 

Understanding their distribution in soil is important because they are taken up by plants and 

animals through soil pathways and bioaccumulate through the human food chain. The relative 

importance of soil as a potential source for these chemicals is increasing as their point source 

emissions are being reduced. The final reason for conducting this study is that relatively few soil 

surveys of these compounds have been conducted. 

The soil samples were collected in 2003 at 27 monitoring stations of the National Dioxin 

Air Monitoring Network (NDAMN) (U.S. EPA, 2005a). These stations are located in 

rural/remote areas, matching the areas of interest for the soil survey. Also they are distributed 

across the continental United States and Alaska, providing the nation-wide perspective desired 

for this study. Use of these sites provided the opportunity to study air-soil relationships using 

historical air concentration data available from NDAMN to compare with soil data collected 

under this study. Finally, NDAMN sites were a practical choice because site operators were 

already in place, facilitating logistics and reducing the costs of gathering soil samples. 

The results presented pertain to the 27 sites sampled and should not be more broadly 

interpreted as statistically representative of all rural soils in the United States. These results, 

however, may be a plausible basis for a preliminary characterization of soils in rural/remote 

areas. The primary measurement results are summarized below. 

• Total CDDs averaged 1,585 pg/g (standard deviation (SD) = 2945). Total CDFs 

averaged 47 pg/g (SD = 68). Levels of the tetrachlorodibenzo-p-dioxin (TCDD) 

homologues were the lowest, with an average concentration of 0.2 pg/g. Levels of 

the octachlorodibenzo-p-dioxin (OCDD) homologue were the highest, with an 

average concentration of 1,482 pg/g. The range of concentrations found here is 

similar to the range across five published studies on CDD/CDF levels in soils from 

rural areas of North America. 

xi 
• Total PCBs averaged 3,089 pg/g (SE = 1,009, SD = 5,241). Levels of the decachlorinated biphenyl homologues were the lowest, with an average concentration of 

29 pg/g. Levels of the penta-chlorinated biphenyl homologues were the highest, with 

an average concentration of 1,013 pg/g. The range of concentrations found here is 

similar to the range across three published studies on PCB levels in soils from rural 

areas worldwide. 

• Total toxicity equivalents (TEQs) averaged 1.76 pg/g (SD = 2.47). The PCBs 

generally were a small fraction of the total TEQs in soil. The mean for total TEQs 

from this study falls near the center of the range of values across 10 published 

studies. 

• Mercury concentrations averaged 22 ng/g across all sites (SD = 15 ng/g). The mean 

from this study falls within the range of values from five published studies on 

mercury levels in soils from rural areas of North America. 

This study also evaluated relationships between air concentrations and soil concentrations 

across sites. A general association between air and soil was observed for the CDDs, based on the 

significant air-soil correlations observed across sites for most homologue groups and the 

similarity in air and soil congener profiles observed at most sites. Little association between air 

and soil could be observed for the CDFs, based on the lack of significant air-soil correlations for 

homologue groups across sites and the lack of similarity in air and soil congener profiles for 

many sites. Some association between air and soil was observed for the PCBs. Data limitations 

restricted the air and soil comparisons to only six PCBs. One of these had a significant air-soil 

correlation across sites. The air and soil profiles based on these six chemicals were very similar 

at most sites. 

The observations for CDDs and PCBs are consistent with the theory that air transport and 

deposition are the primary ways that these chemicals are distributed to soils, particularly those in 

rural areas. The lack of similar observations for the CDFs does not necessarily mean that they 

are not distributed in a similar manner, but it does suggest that different factors affect the 

environmental fate of these chemicals. 

This study also evaluated relationships between chemical levels in soil and total organic 

carbon (TOC) levels in soil. The raw data analyses showed significant positive correlations for 

many of the CDD/CDF homologues and one PCB homologue. However, the correlations were 

xii 
generally not very strong, indicating that other factors, such as grain size, may also be affecting 

sorption characteristics of the soil. 

TEQ levels were estimated both on the basis of applying toxicity equivalence factors 

(TEFs) to the high-resolution mass spectrometry (HRMS) analyses and on the basis of a bioassay 

method called Chemical-Activated Luciferase Expression (CALUX). The CALUX results were 

higher—by varying amounts—than the HRMS total TEQs in almost all of the site composites. 

Significant positive correlations were found comparing the data on both a raw basis (r = 0.82) 

and on a log-transformed basis (r = 0.78). The likely reason for the high bias in the CALUX data 

relative to HRMS data is that CALUX responds to all compounds that activate the aryl 

hydrocarbon receptors, including a number of compounds other than CDDs, CDFs, and PCBs 

that may be present in soils. 

xiii 
1. INTRODUCTION

This report provides a national-scale pilot survey of the levels of the following chemicals 

in rural/remote soils of the United States: chlorinated dibenzo-p-dioxins (CDDs), chlorinated 

dibenzofurans (CDFs), polychlorinated biphenyls (PCBs), and mercury. All samples were 

collected during 2003 and analyzed at Battelle in Columbus, OH. 

The term “dioxins” is used in this study to refer collectively to the 17 2,3,7,8-substituted 

CDDs and CDFs and the 12 co-planar PCBs (see complete listing in Table 1). Dioxin 

concentrations are expressed in terms of both total mass and toxicity equivalents (TEQs). TEQs 

allow concentrations of dioxin mixtures to be expressed as a single value computed by 

multiplying each congener concentration by a toxicity weight (toxicity equivalence factor [TEF]) 

and summing across congeners. TEFs are expressed as a fraction equal to or less than 1, with 1 

corresponding to the most toxic dioxin congener, 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8­

TCDD). The TEQ data presented here are based on TEFs from the 1998 World Health 

Organization (WHO) recommendations, as shown in Table 1 (Van den Berg et al., 1998). This 

report adds subscripts to TEQs when necessary to clarify which chemicals have been included in 

the TEQ calculation: “D” for CDDs, “F” for CDFs, and “P” for PCBs. 

Table 1. Congeners and TEFs used to calculate TEQs 

CDDs CDFs PCBs 

Congener TEF Congener TEF Congener TEF 

2,3,7,8-TCDD 

1,2,3,7,8-PeCDD 

1,2,3,4,7,8-HxCDD 

1,2,3,6,7,8-HxCDD 

1,2,3,7,8,9-HxCDD 

1,2,3,4,6,7,8-HpCDD 

OCDD 

1.0 

1.0 

0.1 

0.1 

0.1 

0.01 

0.0001 

2,3,7,8-TCDF 

1,2,3,7,8-PeCDF 

2,3,4,7,8-PeCDF 

1,2,3,4,7,8-HxCDF 

1,2,3,6,7,8-HxCDF 

2,3,4,6,7,8-HxCDF 

1,2,3,7,8,9-HxCDF 

1,2,3,4,6,7,8-HpCDF 

1,2,3,4,7,8,9-HpCDF 

OCDF 

0.1 

0.05 

0.5 

0.1 

0.1 

0.1 

0.1 

0.01 

0.01 

0.0001 

PCB 77 

PCB 81 

PCB 105 

PCB 114 

PCB 118 

PCB 123 

PCB 126 

PCB 156 

PCB 157 

PCB 167 

PCB 169 

PCB 189 

0.0001 

0.0001 

0.0001 

0.0005 

0.0001 

0.0001 

0.1 

0.0005 

0.0005 

0.00001 

0.01 

0.0001 

Source: Van den Berg et al. (1998). 

TEF = toxicity equivalence factor 

TEQ = toxicity equivalent 

1 
Soils can serve as long-term reservoirs for CDDs, CDFs, PCBs, and mercury, with 

releases to both terrestrial and aquatic systems (U.S. EPA, 2003, 1997; Brzuzy and Hites, 1996). 

Understanding their distribution in soil is important because they are taken up by plants and 

animals through soil pathways and bioaccumulate through the human food chain. More than 

90% of the general population exposure to dioxin is via food ingestion; human exposure to 

mercury is similarly dominated by dietary exposure (U.S. EPA, 2003, 1997). The relative 

importance of soil as a potential source for these chemicals is increasing as their point source 

emissions are being reduced. The final reason for conducting this study is that relatively few soil 

surveys of these compounds have been conducted, and none had a national-scale perspective. 

Many were associated with Superfund sites or other contaminated areas and covered relatively 

small geographic areas (U.S. EPA, 2003). 

1.1. SCOPE OF STUDY 

A comprehensive national soil survey would represent all geographic regions of the 

country, the major land use categories (rural, suburban, urban, agricultural, commercial, and 

industrial), and a full range of climatic conditions, soil types, terrains, and vegetative covers (e.g., 

forest, grassland, cropland). As a pilot survey with limited resources, this study could address 

only a small subset of these lands. It was decided to focus on undisturbed soil in rural/remote 

areas because this would provide a baseline for evaluating soil levels in other areas. The soil 

samples were collected at the air monitoring stations of the National Dioxin Air Monitoring 

Network (NDAMN) (U.S. EPA, 2005a; Cleverly et al., 2006). These stations were located in 

rural/remote areas, matching the areas of interest for the soil survey. The 35 NDAMN stations 

were distributed across the continental United States and Alaska (Figure 1), providing the nationwide perspective desired for this study. Use of these sites provided the opportunity to study airsoil relationships using historical air concentration data available from NDAMN to compare with 

soil data collected under this study. Finally, NDAMN sites were a practical choice because site 

operators were already in place, facilitating logistics and reducing the costs of gathering soil 

samples (see Section 2 for further information about NDAMN and the site selection process). 

The original focus of this study was on CDDs, CDFs, and PCBs. Mercury was added to 

the study after the initial project planning had been completed. Like the CDDs/CDFs and PCBs, 

mercury is a persistent chemical that accumulates in soil. Also, relatively few soil surveys for 

mercury have been conducted in the United States and none had a national-scale perspective. 

Accordingly it was decided that this would be an appropriate addition to the study. Although 

much of the study design was specific to CDDs/CDFs and PCBs, the procedures were deemed 

2
Figure 1. NDAMN air sampling stations. Circles indicate stations included in 

soil survey and triangles are excluded stations.

 1. Penn Nursery, PA 11. Bennington, VT 20. Fond du Lac, MN 28. Rancho Seco, CA 

2/3. Clinton Crops, NC 12. Jasper, NY 21. North Platte, NE 29. Marvel Ranch, OR 

 4. Everglades, FL 13. Beltsville, MD 22. Goodwell, OK 30. Ozette Lake, WA 

5. Lake Dubay, WI 14. Caldwell, OH 23. Big Bend, TX 31. Fort Cronkhite, CA 

6. Monmouth, IL 15. Oxford, OH 24. Grand Canyon, AZ 32. Newport, OR 

 7. McNay Farm, IA 16. Dixon Springs, IL 25. Theodore Roosevelt, ND 33. Craig, AK 

 8. Lake Scott, KS 17. Quincy, FL 26. Craters of the Moon, ID 34. Trapper Creek, AK 

 9. Keystone State Park, OK 18. Bay St. Louis, MS 27. Chiricahua, AZ 35. Yaquina Head, OR 

10. Arkadelphia, AR 19. Padre Island, TX 

reasonable for mercury, and were similar to those used to collect samples for mercury analysis in 

a Washington State soil survey (Rogowski et al., 1999). Also, as discussed below, a brief 

investigation indicated that the NDAMN sites appeared unlikely to be impacted by local mercury 

sources. 

Mercury is commonly found in more than 30 minerals, and natural deposits containing 

these minerals are found in many of the western states (Jasinski, 1994). Some of these deposits 

may be near the NDAMN sites in Arkansas, Texas, Arizona, California, Oregon, and Alaska. 

Historically, most mercury mining in the United States has occurred in California. The use of 

3
mercury has declined significantly in recent years and recycling is increasing. As a result, 

demand for new mercury has decreased, and no mining has occurred in the United States since 

1990 (Jasinski, 1994). Mercury can be released in the mining and processing of gold ores. 

About 99% of the gold currently produced in the United States comes from 30 mines (USGS, 

2002), and none of them are near the NDAMN sites. Thus, it appears unlikely that the NDAMN 

sites were impacted by local mining operations. 

1.2. STUDY OBJECTIVES 

The primary objective of this study was to provide preliminary estimates of the levels of 

CDDs, CDFs, PCBs, and mercury in rural/remote soils. The data summaries presented here 

should be interpreted as summaries of the sites sampled and should not be interpreted as being 

statistically representative of all rural soils. However, the surveyed sites cover a wide range of 

climates, geographic areas, terrains, and soil types and thus provide a reasonable basis for a 

preliminary characterization of soil in rural/remote areas. 

This study also evaluated relationships between chemical levels in air and soil and 

relationships between chemical levels in soil and the organic carbon content of soil. As 

discussed below, it is believed that CDDs/CDFs, PCBs, and mercury are distributed to the 

environment primarily via air transport and enter soils via deposition from the air (U.S. EPA, 

2003, 1997). Understanding air-soil relationships may improve our understanding of these fate 

processes and ultimately lead to better models for predicting the fate of these chemicals in the 

environment. 

1.3. ENVIRONMENTAL FATE OVERVIEW 

CDDs and CDFs are released to the environment primarily as combustion by-products 

and are widely distributed through the environment via air transport. In the atmosphere, they are 

present in the vapor phase and sorbed to particles. Wet and dry deposition remove CDDs/CDFs 

from the atmosphere to soils, plants, or other environmental surfaces. CDDs/CDFs sorbed to 

plants are transferred to soils during leaf fall or when plants die and subsequently decay. 

Although these compounds are generally very persistent in soils, losses can occur via run-off, 

particle resuspension, volatilization, and biological degradation (U.S. EPA, 2003; ATSDR, 

1998). 

PCBs were produced in large quantities (571,000 metric tons) in this country from 1929 

until their ban in 1978. Because PCBs are no longer manufactured or imported in large 

quantities, significant releases of newly manufactured or imported materials to the environment 

do not occur. PCBs have become distributed throughout the environment primarily via air 

transport. Like CDDs/CDFs, PCBs are present in the atmosphere, in both vapor phase and 

4
sorbed to particles. Wet and dry deposition remove PCBs from the atmosphere to soil, surface 

water, plants, and other environmental surfaces. Volatilization is the primary mechanism by 

which they are released from soils and water back into the atmosphere (ATSDR, 2000). 

Mercury occurs naturally as a mineral and in gaseous forms and is distributed throughout 

the environment by both natural and anthropogenic processes. It is emitted to the atmosphere 

from multiple types of combustion sources, primarily coal-fired utility boilers, municipal waste 

combustion, commercial/industrial boilers, and medical waste incinerators (U.S. EPA, 1997). 

The element has three valence states and is found in the environment in the metallic form and in 

the form of various inorganic and organic complexes. Most of the soil mercury is thought to be 

Hg(II). The major features of the bio-geochemical cycle of mercury include degassing of mineral 

mercury from the lithosphere and hydrosphere, long-range transport in the atmosphere, wet and 

dry deposition to land and surface water, sorption to soil and sediment particulates, and 

revolatilization from land and surface water (ATSDR, 1999; U.S. EPA, 1997). 

2. SITE SELECTION 

As discussed above, all soil samples were collected at NDAMN air sampling stations. 

The overall purposes of NDAMN were to determine background air concentrations of dioxin- like 

compounds in rural and remote areas of the United States and to investigate changes that might 

occur over time. The number of NDAMN sites increased from 9 in 1998 to a peak of 35 in 2002 

(Figure 1). Operation was suspended in 2005. The selection of sites for the NDAMN program 

was not based on a statistical process, but rather judgements on a variety of considerations (U.S. 

EPA, 2005a; Cleverly et al., 2006): 

• Remoteness. Sites could not be impacted by nearby industrial and municipal sources. 

Although no specific criteria were used to determine how far a site could be from an 

urban area, all were located in rural or remote areas (one exception was the Beltsville, 

MD, site located in the Washington Metropolitan Area, which was used early in the 

program for development of sampling protocols; air monitoring data from this site 

were not included in background estimates). Some sites were located in very remote 

locations (e.g., the Grand Canyon and Alaska) to explore impacts in pristine areas. 

• Agricultural importance. Many of the sites were located on farms in agriculturally 

important areas to help better understand how dioxins enter the human food chain. 

• Air modeling needs. Some sites were selected to help air modelers verify and 

calibrate their models. 

5
• Climate. Sites were chosen to represent a wide range of climatic conditions. 

• Regions. Sites were chosen to represent a variety of geographic regions. 

• Practicality. Sites had to be accessible and secure. Many sites were co-located with 

existing air monitoring networks to minimize costs. 

For purposes of this study, all sites were reviewed for suitability for soil testing. Eight 

sites were eliminated for the following reasons: 

• Clinton Crops, NC (Site 3): a duplicate quality assurance/quality control (QA/QC) 

monitor at same location as Site 2. 

• Beltsville, MD (Site 13): not a regular NDAMN station because it was located within 

the Washington Metropolitan Area. 

• Oxford, OH (Site 15): used in the development of the soil sampling protocol, so not 

included in the final survey. 

• Craters of the Moon, ID (Site 26): sampling not feasible because site was located on 

a lava bed and operator was not available during testing period. 

• Fort Cronkhite, CA (Site 31): soil may have been disturbed by past military 

operations. 

• Newport, OR (Site 32): monitor on roof of building and surrounding soil was likely 

disturbed. 

• Craig, AK (Site 33): sampling not feasible due to rocky soil and steep terrain. 

• Yaquina Head, OR (Site 35): sampling not feasible due to rocky soil and steep 

terrain. 

As a result of the review process, a total of 27 sites were included in the soil survey. 

3. PROTOCOL DEVELOPMENT 

A three-step process was used to develop a protocol for soil sample collection and 

handling. 

6
Step 1. A literature search was conducted to identify published soil collection guidance. 

Guidance in Preparation of Soil Sampling Protocols: Sampling Techniques and Strategies (U.S. 

EPA, 1992) was selected as the primary basis for developing the protocol. Other useful reports 

included soil sampling guidance by the U.S. Department of Housing and Urban Development 

(HUD, 1995); Rogowski et al. (1999), who conducted a dioxin soil survey in Washington state; 

and Vikelsoe (2002), who conducted a dioxin soil survey in Denmark. 

Step 2. A surface soil sampling protocol and a standard operating procedure (SOP) for 

collecting soil samples were developed on the basis of information gathered in the literature 

survey. The surface soil sampling protocol recommends that an initial survey be carried out for 

each specific application to determine, e.g., an acceptable number of sampling points and 

sampling depth. For this project, the initial survey was conducted at the NDAMN site in Oxford, 

OH. The full report on the Oxford survey is presented in Appendix A. Three key issues were 

evaluated during this initial survey: 

• Grid size and number of samples. A review of several soil sampling surveys 

indicated grid squares with sides ranging from 25 ft (7.6 m) to approximately 200 ft 

(61 m). For this initial survey a 100 ft × 100 ft (30.5 m × 30.5 m) grid was used. 

Twenty-one samples were collected within the grid. This sample number was 

selected as a reasonable starting point for making subsequent statistical calculations 

regarding variability. An additional 4 samples were collected approximately 1,000 ft 

(305 m) from the grid center. Based on CALUX analysis (described in Section 5.2 

and Appendix B), no significant differences were seen between the averages of the 

grid samples and the distant samples. The CALUX analysis of all samples was used 

to conduct a statistical analysis that indicated that at least 5 samples were required to 

derive a mean with less than a 20% standard error. On this basis it was decided to 

collect 5 samples at each site over a 100 ft ×100 ft (30.5 m × 30.5 m) area (one at 

each corner and one at the center). 

• Sampling Depth. EPA indicates that the surface layer of soil (0–15 cm) reflects the 

deposition of airborne pollutants, especially recently deposited pollutants and 

pollutants that do not move downward because of attachment to soil particles (U.S. 

EPA, 1992). Brzuzy and Hites (1995) studied dioxin concentrations in soil as a 

function of depth and found that more than 80% of total CDDs/CDFs were found in 

the upper 15 cm. Rogowski et al. (1999) used a sampling depth of 5 cm in a dioxin 

soil survey in Washington state, and Vikelsoe (2002) used a sampling depth of 10 cm 

in a dioxin soil survey in Denmark. EPA reports that nearly all mercury in soil is 

found in the top 20 cm (U.S. EPA, 1997). In the Oxford survey, 10 cm cores were 

collected and divided into the top 5 cm and bottom 5 cm. The composites for the top 

and bottom layers were analyzed for CDDs/CDFs, PCBs, and mercury. No 

significant differences in analyte concentrations were seen. The 5-cm cores were 

7
frequently unstable and difficult to handle in the field. On this basis, 10-cm cores 

were selected for this study. 

• Sample Size. 600 g of soil were collected at each sampling point. The soil was 

thoroughly mixed and 10 g subsamples were removed for chemical analysis. Because 

many of the CDD/CDF congeners were below detection limits, it was decided to 

double the subsample size to 20 g for the final multisite survey. 

Step 3. The SOP was revised on the basis of sampling experience during the Oxford 

survey and sent to the field operators, who were asked to carefully read it and respond with any 

questions. The final SOP is presented in Appendix C. 

4. SAMPLE COLLECTION, HANDLING, AND STORAGE 

The soil samples were collected by the local operators at 26 of the sites. These operators 

had experience in collecting air samples for the NDAMN program, and at many of the locations 

they also collected samples for other national air monitoring networks. Some had specific 

environmental science background and others did not. The operator at the Everglades, FL, site 

(Site 4) was unable to collect samples because of time constraints, and a Battelle staff member 

was sent to the site to collect the soil samples. All samples from the 27 sites were collected 

between August 12, 2003, and October 20, 2003. 

The SOP was distributed to the NDAMN operators, who were contacted by e-mail or 

phone to make sure they received it and understood the sampling procedures. Sampling supplies 

were purchased and assembled into individual sampling kits and sent to the operators 

approximately one week before sampling was scheduled to begin. Battelle staff was accessible 

by phone to the operators during the sampling period to answer any questions that might arise 

while in the field. 

At each NDAMN site, a 100 ft ×100 ft (30.5 m × 30.5 m) sampling area was chosen as 

near the air monitor as possible and where the terrain was relatively flat and there was no visible 

evidence of soil disturbance from flooding, erosion, construction, digging, or plowing. Five 

sampling points were located in an “X” configuration over the area (one at each corner and one at 

the center). Ground cover and vegetation was removed over a 20 cm × 20 cm area at each 

sampling point. Core samples of 10 cm each were collected at each point using a metal “tulip 

bulb”-type planter that had a diameter of approximately 7.5 cm. A total of approximately 600 g 

of soil was collected at each sampling point, put into three precleaned jars, packed in ice, and 

shipped to Battelle. 

8
Sample Point 3 

600 g mixed soil 

Archive (200 g) 

Accura Lab (310 g) 

Xenobiotics (50 g) 

Sample Point 1 

600 g mixed soil 

Archive (200 g) 

Accura Lab (310 g) 

Xenobiotics (50 g) 

Sample Point 2 

600 g mixed soil 

Archive (200 g) 

Accura Lab (310 g) 

Xenobiotics (50 g) 

Sample Point 4 

600 g mixed soil 

Archive (200 g) 

Accura Lab (310 g) 

Xenobiotics (50 g) 

Sample Point 5 

600 g mixed soil 

Archive (200 g) 

Accura Lab (310 g) 

Xenobiotics (50 g) 

Site Composite 

200 g mixed soil 

40 g 

40 g 

40 g 

40 g 

40 g 

Battelle Lab for analysis 

and archiving (150 g) 

Xenobiotics (50 g) 

Sample Point 3

600 g mixed soil

Archive (200 g)

Accura Lab (3

Sample Point 3

10 g) 600 g mixed so

Xenobiotics (50 g)

il

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 1

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 1

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 2

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 2

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 4

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 4

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 5

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Sample Point 5

600 g mixed soil

Archive (200 g)

Accura Lab (310 g)

Xenobiotics (50 g)

Site Composite

200 g mixed soil

40 g

40 g

40 g

40 g

40 g

Battelle Lab for analysis

and archiving (150 g)

Xenobiotics (50 g)

Figure 2. Sample handling diagram. 

All of the soil samples were received at Battelle in good condition. As illustrated in 

Figure 2, the samples from each site were handled as follows: 

• The soil from the three sample jars representing one sampling point were thoroughly 

mixed in a stainless steel bowl with a large stainless steel spoon. Fifty grams of this 

mixed soil were transferred to a new, pre-cleaned, labeled 4-oz (120-mL) jar. The jar 

was shipped to Xenobiotic Detection Systems, Inc. (XDS) (Durham, NC), for 

CALUX analysis. An additional 40 g of mixed soil were transferred to a stainless 

steel bowl for making a site composite. The remaining mixed soil was put back in the 

original sampling jars. Sample jar 1 of 3 was set aside for archiving (200 g). Sample 

jars 2 of 3 and 3 of 3 were sent to Accura Analytical Labs, Inc. (Atlanta, GA), for 

physical/ chemical parameter testing (containing a total of about 310 g). This process 

was repeated for the remaining four sampling points. 

• The 40 g of soil that had been set aside for the site composite from each of the five 

sampling points were thoroughly mixed in a stainless steel bowl. Approximately 50 g 

of this composited soil were placed in a new, pre-cleaned, labeled 4-oz (120-mL) jar. 

The jar was shipped to XDS (Durham, NC) for CALUX analysis. The remaining 150 

g were transferred to a new, precleaned, labeled 8-oz (240-mL) jar. The jar was 

9
transferred to Battelle’s analytical laboratories for analysis of CDDs, CDFs, PCBs, 

and mercury. The unused portion was archived. 

This process was repeated with the samples from all 27 sites. Sample holding times and 

temperatures were as follows: 

• CDDs/CDFs: all samples were frozen until extraction, extracted within 15 days of 

receipt, and analyzed within 5 weeks of extraction. 

• PCBs: all samples were frozen until extraction, extracted within 15 days of receipt, 

and analyzed within 10 weeks of extraction. 

• Mercury: all samples were refrigerated until extraction, extracted within 28 days of 

receipt, and analyzed same day (with three exceptions, as noted in Section 6.5). 

• CALUX: all samples were held at room temperature, extracted within 30 days of 

receipt, and analyzed within 3 weeks of extraction. 

5. ANALYTICAL METHODS 

5.1. PHYSICAL/CHEMICAL PARAMETER TESTS 

Physical/chemical parameter testing was performed on soils from 135 individual 

sampling points (27 sites, 5 sampling points per site). Two 8-oz (240-mL) jars from each of the 

five sampling points at the 27 sites were sent to Accura Analytical Labs, Inc. (Atlanta, GA), for 

analysis of pH, total organic carbon (TOC), grain size distribution, and moisture content 

according to the following methods: 

• pH: EPA Method SW 9045C (U.S. EPA, 1995) 

• TOC: Walkley-Black Method (Walkley and Black, 1934) 

• Grain size distribution: ASTM D422 (ASTM, 2002a) 

• Moisture content: ASTM D2216 (ASTM, 2002b) 

5.2. CALUX BIOASSAY TEQ 

Several biological methods are commercially available for measuring dioxin TEQs, and 

bids from several companies were received. The CALUX method by XDS was chosen, primarily 

because it provided the lowest detection limit. XDS has patented a genetically engineered cell 

line that contains the firefly luciferase gene under trans-activational control of the aryl 

hydrocarbon receptor. This cell line can be used for the detection and relative quantification of 

aryl hydrocarbon receptor agonists and is referred to as Chemical-Activated Luciferase 

10
Expression (or CALUX) assay (Denison et al., 1998). The most widely studied class of 

compounds that activate this system is the polychlorinated diaromatic hydrocarbons (PCDH), 

which includes 2,3,7,8-TCDD. The CALUX assay can be used to provide a measure of dioxin 

TEQs in a sample. When the cells are exposed to dioxins and related chemicals, they produce 

the enzyme luciferase in a time-, dose-, and chemical-specific manner. Luciferase activity is 

determined by measuring light emitted and is directly proportional to the amount of dioxin-like 

chemicals within the test samples (see Appendix B for further details of the CALUX method). 

CALUX testing was performed on soils from 135 individual sampling points (27 sites, 5 

sampling points per site) plus 27 composites representing each site. When XDS received soil 

samples from Battelle, the samples were logged in and held at room temperature until processing 

and analysis. Samples were extracted with a bottle sonication method using organic solvents 

following XDS Method WL-2. Sample extracts were processed through a patent-pending 

procedure, XDS Method WL-3, which removes commonly interfering substances such as 

polyaromatic hydrocarbons. All samples were processed in duplicate along with a batch 

recovery sample for each set of samples in order to provide semi-quantitative results. 

5.3. MERCURY ANALYSIS 

Total mercury was determined on 27 composites (one from each site) using modified 

EPA SW846 Method 7471A (U.S. EPA, 1994a). All standards and samples were digested and 

analyzed by cold vapor atomic absorption. Approximately 2 g of sample were weighed into a 

biological oxygen demand (BOD) bottle and digested by adding nitric acid, sulfuric acid, and 

potassium permanganate and heating in a water bath at ~ 95°C. Samples were cooled and the 

excess permanganate reduced using sodium chloride hydroxylamine hydrochloride. Mercury 

hydride was generated by the addition of stannous chloride. The hydride was swept into an 

absorption cell. 

5.4. HRMS ANALYSIS OF CDDs/CDFs, AND PCBs 

CDD/CDF and PCB congeners were measured in 27 composites (one from each site) 

using gas chromatography/high-resolution mass spectrometry (referred to as HRMS in the 

remainder of this document). Soil composites were extracted and analyzed for the 17 

2,3,7,8-substituted CDDs/CDFs following general procedures in EPA Method 1613, Revision B 

(U.S. EPA, 1994b) and Battelle SOPs ASAT.II-001-02 and ASAT.II-002-02. All 209 PCB 

congeners were determined following general procedures in EPA Method 1668, Revision A 

(U.S. EPA, 1999) and Battelle SOP ASAT.II-009-00. Specific steps were taken during sample 

preparation to enhance the detection limit. These steps included extracting nearly twice the 

standard amount of soil and concentrating the sample to half the standard final extract volume. 

11
Overall, an aliquot of approximately 20 g wet weight of each composite was spiked with 

isotopically labeled analogs (internal standards) of 15 of the 17 2,3,7,8-substituted CDDs/CDFs 

and 27 PCBs. The composites were extracted with methylene chloride using accelerated solvent 

extraction (ASE) techniques. Extracts were processed through a gel permeation chromatography 

column, spiked with CDD/CDF and PCB cleanup standards, and processed through acid/base 

silica columns. The extracts were then processed into separate CDD/CDF and PCB fractions 

using carbon columns. The CDD/CDF fractions were spiked with CDD/CDF recovery standard 

and concentrated to a final volume of 10 :L. The PCB fractions were spiked with PCB recovery 

standard and concentrated to a final volume of 25 :L. 

Sample extract fractions were analyzed for CDDs/CDFs and PCBs by HRMS in the 

selected ion monitoring mode at high resolution. Initial analysis for CDDs/CDFs was carried out 

on a DB-5 or equivalent column. Because 2,3,7,8-tetrachlorodibenzofuran (2,3,7,8-TCDF) is not 

completely separated from the other TCDF isomers on the DB-5 column, second column 

confirmation of 2,3,7,8-TCDF levels above the lowest calibration level in the initial analysis was 

carried out on a DB-225 column or equivalent column. PCBs were determined using an SPBOctyl column. Approximately 110 of the PCBs were determined as individual congeners, and 

the remaining congeners adding up to 209 were determined as various sets of co-eluting 

congeners. All analytes were quantified by isotope dilution or by the internal standards method 

using the labeled internal standards for quantitation. 

5.5. QUALITY ASSURANCE/QUALITY CONTROL

 Data quality parameters assessed were accuracy, precision, representativeness, 

comparability, completeness, and sensitivity. Each of these is discussed below. 

The QA/QC measurement quality objectives for accuracy and precision for all analytes 

are addressed in detail in Appendix D and summarized below: 

• Procedural blanks. Mercury was nondetect in the blanks, and CDD/CDF blanks were 

low relative to the field samples. A number of PCBs were detected in the procedural 

blanks, with up to 16 PCBs exceeding 3 pg/g. In terms of total PCBs, most of the 

sites exceeded the blanks by a large margin. However, the sites with the lowest total 

PCBs approached the blank levels, i.e., the lowest site was 248 pg/g, as compared 

with an average level in blanks of 170 pg/g. Appendix E provides the congenerspecific PCB levels in blanks, and these should be considered in interpreting 

individual congener values. 

• Recoveries. Lab control spike recoveries and matrix spike recoveries were generally 

within QC goals. 

12
• Replicates. The relative percent differences (RPDs) were generally within QC goals 

for mercury and CDDs/CDFs. The PCB duplicates had mixed results. Some of the 

duplicate pairs had excellent agreement, such as those for Theodore Roosevelt, ND 

(Site 25), where all congeners had an RPD of less than 18% and the RPD for total 

PCBs was just 2%. Others had poor agreement such as those for Lake Scott, KS (Site 

8), where individual congeners had RPDs ranging from 21 to 161% and the RPD for 

total PCBs was 140%. 

Representativeness was addressed through the sampling design and selection of sampling 

locations to accomplish the project goals. Samples were handled carefully following good 

laboratory practices to ensure that chain-of-custody and processing were carried out 

appropriately. 

Comparability was addressed by having all samples collected within an approximate 2­

month time frame using the same sampling protocol and procedures at each site. Samples were 

analyzed within holding times, with the exception of three mercury samples. These samples 

exceeded the specified 28 days and are flagged in Table 2. Due to the holding time exceedance, 

mercury results for these three samples should be considered minimum concentrations. 

However, the mean across sites was essentially the same with or without these samples. 

The completeness goal for this project was to collect 100% of the planned samples and 

for 95% of the laboratory data be considered valid. Soil sample collection was planned at 30 

NDAMN air sampling stations. Sampling actually took place at 27 stations, resulting in 90% of 

the planned samples being collected. Details of sample collection are included in Section 4. All 

laboratory data (100%) from the collected samples are included in the report. 

All data were generated following accepted analytical methods, and the reporting units 

used are consistent with accepted conventions for environmental analyses. Sensitivity was 

ensured by meeting target reporting limits for each analysis. 

6. SOIL MEASUREMENTS 

This section presents the measurement results from the study. Averages and other 

descriptive statistics are generally presented assuming that nondetects were equal to half the 

detection limit. Wherever this assumption was made, the influence of the nondetects was 

13
Table 2. Soil concentrations (dry weight) by site 

Site 

Total CDDs 

(tetras-octas) 

(pg/g) 

Total CDFs

 (tetras -

octas) 

(pg/g) 

Total CDDs 

and CDFs 

(tetras -

octas) 

(pg/g) 

Total PCBs 

(pg/g) 

Mercury 

(ng/g)

 1. Penn Nursery, PA 6,602 29 6,631 1,366 26a

 2. Clinton Crops, NC 1,361 5 1,367 475 69a

 4. Everglades, FL 680 98 778 2,604 22 

5. Lake Dubay, WI 96 13 110 15,700 4

 6. Monmouth, IL 395 96 491 2,037 25

 7. McNay Farm, IA 1,696 47 1,743 358 30

 8. Lake Scott, KS 22 2 24 1,115 11

 9. Keystone State Park, OK 65 22 87 2,464 9 

10. Arkadelphia, AR 685 5 690 4,028 26 

11. Bennington, VT 178 28 205 3,023 43 

12. Jasper, NY 11,400 77 11,480 1,543 15 

14. Caldwell, OH 2,307 16 2,322 1,019 26 

16. Dixon Springs, IL 9,574 239 9,813 845 19 

17. Quincy, FL 369 10 380 303 13a 

18. Bay St. Louis, MS 1,686 51 1,738 4,930 24 

19. Padre Island, TX 74 5 79 255 5 

20. Fond du Lac, MN 130 63 193 1,308 19 

21. North Platte, NE 50 14 64 493 13 

22. Goodwell, OK 288 43 331 4,954 5 

23. Big Bend, TX 22 3 25 24,570 18 

24. Grand Canyon, AZ 25 7 32 713 9 

25. Theodore Roosevelt, ND 107 21 127 570 0.5

27. Chiricahua, AZ 1,110 8 1,118 509 45 

28. Rancho Seco, CA 203 50 253 3,274 37 

29. Marvel Ranch, OR 3,534 284 3,817 1,300 24 

30. Ozette Lake, WA 105 28 133 2,419 39 

34. Trapper Creek, AK 15 4 19 1,224 22 

Average 1,585 47 1,632 3,089 22 

Standard deviation 2,946 68 2,982 5,241 15 

Standard error 567 13 574 1,009 3 

a

 Sample exceeded 28 day holding time for mercury analysis and should be considered a minimum value. 

14
evaluated by computing the values assuming both nondetects were equal to zero and nondetects 

were equal to the full detection limit. For total CDDs/CDFs, total PCBs, and mercury, both 

calculations gave essentially the same values, and the full results are presented only on the basis 

of nondetects equal to half the detection limit. For some sites, the TEQ estimates have 

significant differences, depending on treatment of the nondetects, so these data are presented two 

ways: TEQ1 assumes nondetects equal zero, and TEQ2 assumes nondetects were equal to half 

the detection limit. All concentrations are presented on a dry-weight basis. 

This section includes comparisons of the soil levels found in the study with those of 

similar studies found in the published literature. A detailed literature review of levels of 

CDDs/CDFs, PCBs, and mercury in rural soils is provided in Appendix F. The discussions in 

this section briefly summarize the key studies from Appendix F. The focus is generally on 

studies of North America. In the case of total PCBs, however, relatively few data on North 

America could be found, so studies from other areas are also discussed. It should be noted that 

the studies included in this review have a wide variety of design features (e.g., detection limits, 

treatment of nondetects in deriving statistics, congener inclusion, sampling procedures, analytical 

techniques), which makes it difficult to compare them on a completely equal basis. Information 

is provided in Appendix F to help readers consider these differences, but no adjustments were 

made to the values reported in the original studies. 

6.1. PHYSICAL/CHEMICAL PARAMETER RESULTS 

The complete set of physical/chemical parameter data for samples collected at all sites is 

presented in Appendix G and briefly summarized here. A wide range of soil types were collected 

in this study, with grain size distributions (based on criteria from the Unified Soil Classification 

System, ASTM D2487) as follows (SE is the standard error of the mean and SD is the standard 

deviation for all samples): 

% finer than #4 sieve (4.75 mm): mean of 98.4 (SE = 0.3, SD = 23.9) 

% finer than #200 sieve (0.075 mm): mean of 61.3 (SE = 2.2, SD = 25.6) 

% finer than 0.005 mm: mean of 18.3 (SE = 0.9, SD = 10.8) 

Moisture content averaged 22.2% (SE = 2.1, SD = 23.9). Soil pH averaged 6.0 (SE = 0.1, SD = 

1.3). Total organic carbon averaged 34,900 mg/kg (SE = 2,400, SD = 27,800). 

6.2. CDD AND CDF RESULTS 

CDD/CDF total homologues (tetra and higher) and individual CDD/CDF congener (tetra 

and higher) results for each site are included in Appendix H. Table 2 shows the CDD and CDF 

15
levels (tetra through octa) for the composite samples from each site. CDDs ranged from 15 to 

11,400 pg/g, with an average of 1,585 pg/g (SE = 567, SD = 2,945). Total CDFs ranged from 2 

to 284 pg/g, with an average of 47 pg/g (SE = 13, SD = 68). Total CDDs/CDFs ranged from 19 

to 11,480 pg/g, with an average of 1,632 pg/g (SE = 574, SD = 2,982). These values were 

calculated assuming that nondetects were equal to half the detection limit. Treatment of the 

nondetects, however, had a negligible impact on these homologue sums because they were 

generally very low when compared with the detected levels. For example, at Trapper Creek, AK 

(site 34), which had the lowest levels, total CDDs/CDFs were 18.71 pg/g assuming that 

nondetects were equal to zero and 19.03 pg/g assuming that nondetects were equal to the full 

reporting limit. 

Table 3 shows all of the CDD/CDF homologue concentrations for all sites. TCDD 

homologue concentrations were the lowest, with an average of 0.2 pg/g. The octachlorodibenzop-dioxin (OCDD) homologue concentrations were the highest, with an average of 1,482 pg/g. 

Table 4 summarizes information from four studies on CDD/CDF levels in soils from rural 

areas of North America. This table shows that the total CDD/CDF concentration ranged from 

nondetect to 10,000 pg/g, which is similar to the range of 19 to 11,480 pg/g found in the current 

study. 

Total CDDs/CDFs in procedural blanks averaged 2 pg/g. All sites exceeded this value by 

a wide margin, i.e., the concentration at the lowest site was 19 pg/g. The blank levels are listed 

in Appendix H along with the homologue levels and should be considered when interpreting 

these values individually. 

6.3. PCB RESULTS 

PCB individual congener results for each site are provided in Appendix E. 

Approximately 110 of the 209 PCB congeners co-eluted with other congeners, preventing 

resolution of individual levels. In Appendix E, all the congeners that co-eluted are marked with 

a “C” followed by the PCB compound number with which each one co-eluted. Table 2 provides 

total PCB levels for the composite samples from each site; concentrations ranged from 255 to 

24,570 pg/g, with an average of 3,089 pg/g (SE = 1,009, SD = 5,241). These values were 

calculated assuming that nondetects were equal to half the detection limit. Treatment of the 

nondetects, however, had a negligible impact on these sums because they were generally very 

low when compared with the detected levels. For example, at Dixon Hills, IL (site 19), which 

had the lowest levels, the total PCBs were 248 pg/g assuming that nondetects were equal to zero 

and 262 pg/g assuming that nondetects were equal to the full reporting limit. 

Table 5 summarizes the PCB homologue concentrations for all sites. Deca-chlorinated 

biphenyl homologue concentrations were the lowest, with an average of 29 pg/g. Penta16
17

Table 3. Soil concentrations (pg/g dry weight) of CDD and CDF homologues 

Site 

Total 

TCDFs 

Total 

TCDDs 

Total 

PeCDFs 

Total

 PeCDDs 

Total

 HxCDFs 

Total 

HxCDDs 

Total

 HpCDFs 

Total 

HpCDDs 

1. Penn Nursery, PA 1.4 0.2 4.7 1.8 5.9 12.5 7.9 119.3 2. Clinton Crops, NC 0.3 0.1 0.8 0.6 0.9 6.7 1.6 55.9 4. Everglades, FL 1.3 0.0 19.4 0.2 19.8 12.5 25.8 89.2 5. Lake Dubay, WI 0.9 0.1 1.2 0.5 2.2 2.6 5.0 11.0 6. Monmouth, IL 1.3 0.5 3.0 2.6 9.9 19.6 51.5 64.0 7. McNay Farm, IA 0.5 0.8 1.7 3.1 6.0 17.9 18.4 107.0 8. Lake Scott, KS 0.1 0.0 0.4 0.1 0.6 0.6 0.7 2.6 9. Keystone State Park, OK 5.7 0.0 5.2 0.4 3.3 2.3 4.3 11.2 

10. Arkadelphia, AR 0.7 0.0 0.6 0.3 0.9 3.7 1.3 31.3 11. Bennington, VT 1.2 0.1 4.4 1.0 5.4 6.1 8.6 30.4 12. Jasper, NY 3.2 0.4 5.4 3.9 11.6 42.3 31.7 442.9 14. Caldwell, OH 0.2 0.1 1.0 0.7 2.6 5.4 5.8 48.8 16. Dixon Springs, IL 0.5 0.3 3.8 4.3 26.7 41.9 100.1 412.3 17. Quincy, FL 0.0 0.0 0.2 0.1 1.1 1.7 4.0 15.5 18. Bay St. Louis, MS 0.6 0.2 1.7 2.0 7.9 17.8 22.4 136.1 19. Padre Island, TX 0.0 0.0 0.1 0.1 1.2 1.4 1.2 3.5 20. Fond du Lac, MN 0.5 0.0 1.9 0.6 6.8 4.5 24.7 28.7 21. North Platte, NE 2.3 0.2 2.1 0.1 2.4 2.2 3.5 9.7 22. Goodwell, OK 0.3 0.0 2.6 0.4 7.5 7.0 17.1 80.1 23. Big Bend, TX 0.2 0.0 0.2 0.1 0.6 0.3 0.7 1.5 24. Grand Canyon, AZ 0.0 0.0 0.3 0.0 1.5 0.9 3.2 6.1 25. Theodore Roosevelt, ND 0.4 0.1 4.2 0.4 5.2 2.8 7.0 16.6 27. Chiricahua, AZ 0.3 0.1 1.0 1.5 1.4 10.8 2.4 58.1 28. Rancho Seco, CA 0.6 0.0 1.1 0.9 3.9 7.2 21.8 36.2 29. Marvel Ranch, OR 2.1 0.1 4.1 1.2 31.9 77.5 158.4 583.0 30. Ozette Lake, WA 5.1 0.6 1.9 13.6 3.5 10.5 11.5 11.8 34. Trapper Creek, AK 1.0 0.0 0.1 0.1 0.3 0.4 1.8 2.3 

Mean 1.1 0.2 2.7 1.5 6.3 11.8 20.1 89.4 

Standard deviation 1.5 0.2 3.7 2.7 7.9 17.1 34.7 147.6 

Standard error 0.3 0.0 0.7 0.5 1.5 3.3 6.7 28.4 
Table 4. Literature summary for CDDs/CDFs in rural soils of North America 

Concentration 

(pg/g) Location 

Site 

Description n Reference

Range: ~990–3100 Elk River, MN Semi-rural, untilled 

area 

2 Reed et al. (1990) 

Range: ND–810 

Mean: 73 

Ontario, Canada, 

and U.S. Midwest 

Rural 30 Birmingham (1990) 

Range: 60–10,000 Michigan and 

Indiana 

Undisturbed 4 Brzuzy and Hites 

(1995) 

Range: 9–258 

Mean: 94 

Washington Open, nongrazed 4 Rogowski et al. (1999, 

2005) 

Range: 79–426 

Mean: 267 

Washington Forest, 

noncommercial 

4 Rogowski et al. (1999); 

Rogowski and Yake 

(2005) 

Range: 19–11,483 

Mean: 1,632 

United States Rural/remote 27 Current study 

chlorinated biphenyl homologue concentrations were the highest, with an average concentration 

of 1,013 pg/g. 

Only a few studies were found that reported total PCB levels in rural soils (Table 6). The 

only U.S. study (Vorhees et al., 1999) measured PCBs in residential soils near New Bedford 

Harbor, MA. The sediments in that area are contaminated with PCBs and may have affected 

nearby residential soils. The study reported a maximum concentration of 1,800,000 pg/g, which 

far exceeds the maximums seen in rural areas in other countries and is not likely to be 

representative of rural areas in the United States. The other three studies summarized in Table 6 

show concentrations in rural areas at a variety of locations world-wide, with a range of 26 to 

97,000 pg/g. This range is wider but similar to that observed in the current study (255–24,600 

pg/g). The average from the large (n = 191) world-wide study by Meijer et al. (2003) was 5,400 

pg/g, which is similar to the mean in the current study of 3,087 pg/g. 

Total PCBs in procedural blanks averaged 170 pg/g. Although most of the sites exceeded 

this value by a large margin, the sites with the lowest total PCBs approached this value, i.e., the 

concentration at the lowest site was 255 pg/g. All congener values and associated blanks are 

listed in Appendix E. Up to 16 PCBs were detected at >3 pg/g in each blank. The blanks should 

be considered when interpreting individual congener values, especially those at the low-level 

sites. 

18
19

Table 5. PCB homologue concentrations (pg/g dry weight)a 

Site 

Mono-CBs 

(1–3) 

Di-CBs 

(4–15) 

Tri-CBs 

(16–39) 

Tetra-CBs 

(40–81) 

Penta-CBs 

(82–127) 

Hexa-CBs 

128–169) 

Hepta-CBs 

(170–193) 

Octa-CBs 

(194–205) 

1. Penn Nursery, PA 6 16 33 83 241 391 277 193 2. Clinton Crops, NC 7 25 26 41 67 100 86 62 4. Everglades, FL 34 92 164 281 408 596 599 271 5. Lake Dubay, WI 646 4,472 5,036 3,043 1,229 900 279 55 6. Monmouth, IL 11 41 115 237 580 646 281 54 7. McNay Farm, IA 3 10 21 36 78 94 57 32 8. Lake Scott, KS 12 50 112 155 342 269 126 37 9. Keystone State Park, OK 121 466 441 341 364 397 234 72 

10. Arkadelphia, AR 24 72 168 371 1,788 1,201 328 59 11. Bennington, VT 8 38 83 110 277 797 1,068 525 12. Jasper, NY 7 23 56 118 336 482 307 134 14. Caldwell, OH 3 12 34 125 451 250 93 41 16. Dixon Springs, IL 2 10 25 57 132 146 70 45 17. Quincy, FL 5 11 28 46 71 47 26 19 18. Bay St. Louis, MS 7 22 155 829 1,756 1,311 566 218 19. Padre Island, TX 3 9 20 38 88 57 22 11 20. Fond du Lac, MN 15 29 94 193 390 378 150 36 21. North Platte, NE 4 10 26 63 144 123 68 30 22. Goodwell, OK 140 335 460 803 681 1,328 914 245 23. Big Bend, TX 5 21 52 1,655 15,170 6,755 800 95 24. Grand Canyon, AZ 6 13 60 149 181 210 76 13 25. Theodore Roosevelt, ND 3 5 22 47 137 174 125 44 27. Chiricahua, AZ 3 9 22 49 130 114 81 51 28. Rancho Seco, CA 9 43 130 518 1,163 996 288 90 29. Marvel Ranch, OR 14 41 44 149 343 385 183 87 30. Ozette Lake, WA 56 90 203 390 517 643 303 83 34. Trapper Creek, AK 14 35 83 233 299 389 141 25 

Mean 43 222 286 376 1,013 710 279 97 

Standard deviation 125 856 956 637 2,868 1,268 277 111 

Standard error 24 165 184 123 552 244 53 21 

a Values in parenthesis in the header indicate which PCBs are included in the homologue groups. 
Table 6. Literature summary for PCBs in rural soils world-wide 

Concentration (pg/g) Location 

Site 

Description n Reference

Range: 15,000–1,800,000a New Bedford 

Harbor, MA 

Residential 34 Vorhees et al. (1999) 

Range: ND–45,000 Canadian Arctic Remote, but near 

radar stations 

21 Bright et al. (1995) 

Range: 26–97,000 

Mean: 5,400 

World-wide Background 191 Meijer et al. (2003) 

Mean: 15,000 Poland Forest soils 4 Masahide et al. (1998) 

Range: 255–24,600 

Mean: 3,087 

United States Rural/remote 27 Current study 

a

 This site was included in this table because it was the only US study found which came close to meeting the criteria 

of reporting total PCBs in rural areas. However, it should be noted that the site is more suburban than rural and has 

probably been affected by nearby contaminated sediments. 

6.4. TEQ RESULTS 

CALUX bioassay TEQ testing was performed on soils from 135 individual sampling 

points (27 sites, 5 sampling points per site), 27 composites (1 per site), and 16 related field 

blanks. The complete set of CALUX analysis is presented in Appendix I. 

The TEFs listed in Table 1 were used to calculate TEQs for the HRMS data. The TEQ 

estimates for the composite samples from all sites are shown in Table 7. This table shows both 

the CALUX-derived and the HRMS-derived estimates and the HRMS estimates on a TEQ1 basis 

(nondetects equal to zero) and a TEQ2 basis (nondetects equal to one-half the detection limit). 

The treatment of nondetects made a difference at many of the sites, particularly those with lower 

concentrations. For the HRMS CDD/CDF concentrations, TEQ2 values exceeded TEQ1 values 

by more than 50% at 3 sites. For the HRMS PCB concentrations, TEQ2 values exceeded TEQ1 

values by more than 50% at 18 sites. 

The composite range and averages across sites are summarized below: 

HRMS CDD/CDF TEQ2: 0.21 to 11.42 pg/g, average of 1.69 pg/g (SE = 0.48, SD = 

2.47) 

HRMS PCB TEQ2: 0.017 to 0.38 pg/g, average of 0.072 pg/g (SE = 0.02, SD = 0.082) 

HRMS Total TEQ2: 0.24 to 11.49 pg/g, average of 1.76 pg/g (SE = 0.48, SD = 2.47) 

CALUX TEQ: 0.62 to 23.01 pg/g, average of 5.11 pg/g (SE = 1.04, SD = 5.38) 

20
Table 7. TEQ soil concentrations by site (pg TEQ/g dry)

a 

HRMS 

CDD/CDF PCB Total Total CALUX 

Site TEQ1 TEQ2 TEQ1 TEQ2 TEQ1 TEQ2 Bioassay 

1. Penn Nursery, PA 2.40 2.40 0.013 0.029 2.41 2.43 9.19±1.84 

2. Clinton Crops, NC 0.68 0.69 0.010 0.023 0.69 0.71 2.10±0.33 

4. Everglades, FL 1.23 1.48 0.020 0.045 1.25 1.53 2.16±0.89 

5. Lake Dubay, WI 0.34 0.35 0.018 0.033 0.36 0.38 3.68±0.21 

6. Monmouth, IL 2.42 2.43 0.034 0.049 2.45 2.48 4.97±0.43 

7. McNay Farm, IA 2.05 2.05 0.011 0.027 2.06 2.08 11.04±0.30 

8. Lake Scott, KS 0.08 0.21 0.013 0.031 0.09 0.24 1.58±0.76 

9. Keystone State Park, OK 1.16 1.18 0.009 0.023 1.17 1.20 1.88±0.50 

10. Arkadelphia, AR 0.53 0.53 0.047 0.062 0.58 0.59 2.92±0.35 

11. Bennington, VT 0.97 0.98 0.210 0.210 1.18 1.19 5.86±0.64 

12. Jasper, NY 5.97 5.97 0.012 0.031 5.98 6.00 7.04±1.74 

14. Caldwell, OH 0.82 0.84 0.012 0.029 0.83 0.87 4.60±1.81 

16. Dixon Springs, IL 6.24 6.24 0.049 0.049 6.29 6.29 12.61±0.00 

17. Quincy, FL 0.22 0.41 0.005 0.022 0.23 0.43 1.54±0.11 

18. Bay St. Louis, MS 2.08 2.08 0.236 0.236 2.32 2.32 17.06±1.01 

19. Padre Island, TX 0.23 0.33 0.004 0.017 0.23 0.35 0.62±0.24 

20. Fond du Lac, MN 0.67 0.70 0.015 0.029 0.69 0.73 2.87±0.11 

21. North Platte, NE 0.50 0.50 0.083 0.083 0.58 0.58 6.33±0.21 

22. Goodwell, OK 0.91 0.94 0.014 0.061 0.92 1.00 3.61±0.46 

23. Big Bend, TX 0.13 0.23 0.363 0.379 0.49 0.61 0.62±0.69 

24. Grand Canyon, AZ 0.12 0.25 0.004 0.062 0.12 0.31 0.82±0.84 

25. Theodore Roosevelt, ND 0.44 0.44 0.004 0.019 0.44 0.46 1.05±0.47 

27. Chiricahua, AZ 0.88 0.90 0.006 0.019 0.89 0.92 5.17±1.57 

28. Rancho Seco, CA 1.05 1.05 0.046 0.132 1.10 1.18 2.69±0.35 

29. Marvel Ranch, OR 11.37 11.42 0.010 0.070 11.38 11.49 23.01±3.19 

30. Ozette Lake, WA 0.43 0.63 0.011 0.094 0.44 0.72 1.14±0.59 

34. Trapper Creek, AK 0.11 0.29 0.007 0.078 0.12 0.37 1.79±0.58 

Mean 1.63 1.69 0.047 0.072 1.68 1.76 5.11 

Standard deviation 2.49 2.47 0.09 0.082 2.49 2.47 5.38 

Standard error 0.48 0.48 0.02 0.02 0.48 0.48 1.04 

a

TEQ1 values based on nondetects equal to zero. TEQ2 values based on nondetects equal to one-half the detection 

limit. 

HRMS = high-resolution mass spectrometry 

21
EPA’s draft report Exposure and Human Health Reassessment of 2,3,7,8-TetraChlorodibenzo-p-Dioxin (TCDD) and Related Compounds (U.S. EPA, 2003) presents a 

preliminary mean CDD/CDF TEQ in North America soil of 2.8 pg TEQDF/g in rural soils and 9.4 

pg TEQDF/g in urban soils. These estimates were derived setting nondetects equal to zero, so 

they were compared with the TEQ1 values from this study. This study found an average 

CDD/CDF TEQ1 of 1.63 pg TEQ/g, which is about 40% lower than the rural soil average 

reported in EPA’s draft report (U.S. EPA, 2003). A summary of CDD/CDF TEQ values from 

nine studies of rural areas of North America is shown in Table 8. The overall range across the 

studies is 0 to 22.9 pg TEQ/g, which encompasses the TEQ2 range from the current study 

(0.21–11.42 pg TEQ/g). The means across studies ranged from 0.4 to 5 pg TEQ/g, which bracket 

the TEQ2 mean from this study (1.69 pg TEQ/g). 

Table 8. Literature summary for CDD/CDF TEQs in rural soils of North 

America 

TEQ Concentration 

(pg/g) Location 

Site 

Description n Reference

Range: 0.16–2.2 

Mean: 0.4 

Ontario, Canada, 

and U.S. Midwest 

Rural 30 Birmingham (1990) 

Range: 0–57 

Mean: 5 

British Columbia, 

Canada 

Background 53 BC Environment (1995) 

Range: 0.2–0.9 Canadian Arctic Remote 4 Grundy et al. (1995); 

Bright et al. (1995) 

Range: 0.16–22.9 

Mean: 3.1 

Southern 

Mississippi 

Rural 36 Rappe et al. (1995); 

Fiedler et al. (1995) 

Mean: 1.4 Columbus, OH Rural background 3 Lorber et al. (1998) 

Range: 0.046–2.4 

Mean: 0.71 

Washington Open, nongrazed 4 Rogowski et al. (1999, 

2005) 

Range: 0.45–5.2 

Mean: 3.3 

Washington Forest, 

noncommercial 

4 Rogowski et al. (1999); 

Rogowski and Yake 

(2005) 

Mean: 5.74 Connecticut Rural background 34 MRI (1992) 

Range: 0.1–9.6 

Mean: 1.6 

Colorado Open space 

background 

36 U.S. EPA (2001) 

Range: 0.21–11.42 

Mean: 1.69 

United States Rural/remote 27 Current study 

TEQ = toxicity equivalent 

22
Only one study was found that reported PCB TEQs in rural North America soils (U.S. 

EPA, 2001). Thirty-six soil samples were collected from areas defined as “open space 

background” in the Front Range area near Denver, CO. The PCB TEQs averaged 1.2 pg/g, 

which is higher than the average from the current study of 0.07 pg/g. In fact, this mean exceeds 

the maximum value of this study (0.379 pg/g). The proximity of these sites to Denver may 

account for the difference. A rural soil survey was conducted in Poland in 2002 (Wyrzykowska 

et al., 2005). This study sampled soils in 13 agricultural areas and found a range of 0.054 to 0.42 

pg TEQ/g and an average of 0.18 pg TEQ/g. Buckland et al. (1998) evaluated soils collected in 

New Zealand. The PCB concentrations ranged from 0.067 to 2.3 pg TEQ/g for provincial 

centers. The PCB TEQ average from the current study falls within the ranges reported for the 

studies in Poland and New Zealand. 

PCBs are generally a small fraction of the total TEQs in soil. The only exception was Big 

Bend, TX (Site 23), where PCBs contributed 62% of the total TEQ2s. 

The distribution of CDD/CDF and PCB TEQ values across sites is displayed graphically 

in Figures 3 and 4, respectively. Figure 5 compares the total TEQs by CALUX, HRMS TEQ1, 

and HRMS TEQ2. Figures 6 and 7 show frequency diagrams for levels of CDD/CDF TEQs and 

PCB TEQs, respectively, across all sites. These diagrams show the number of sites with levels 

within various ranges. For example, in Figure 6, the first bar shows the number of sites with 

concentrations between 0 and 1 pg TEQ2/g, the second bar shows the number of sites with 

concentrations between 1 and 2 pg TEQ2/g, and so on. These results show how the 

concentrations were distributed across sites. Section 7.3 discusses how the TEQ results derived 

from CALUX compare with those derived from the HRMS analysis. 

CDD/CDF TEQs in procedural blanks had an average TEQ1 of 0.05 and an average 

TEQ2 of 0.21 pg/g. PCB TEQs in procedural blanks have an average TEQ1 of 0.0056 pg/g and 

an average TEQ2 of 0.020 pg/g. A few sites had TEQ levels near the blank levels, suggesting 

that TEQs were very low to nondetect at these sites. 

6.5. MERCURY RESULTS 

Table 2 provides mercury concentrations for each site composite. The complete set of 

mercury data is provided in Appendix J. Mercury concentrations ranged from 0.5 to 69 ng/g and 

averaged 22 ng/g across all sites (SE = 2.9, SD = 15 ng/g). The mean was calculated setting 

nondetects equal to half the detection limit; however, it was essentially the same whether 

nondetects were set to zero or to their full detection limit. As indicated in Table 2, three samples 

had holding times beyond the specified 28 days. Results for these three samples should be 

considered minimum values based on the holding time exceedance. Recomputing the mean 

without these samples reduced it to 21 ng/g. A summary of mercury concentrations from rural 

23
Figure 3. HRMS CDD/CDF TEQ2s for all 27 sites. 

areas of North America is shown in Table 9. The five studies from the literature reported 

concentrations ranging from 10 to 600 ng/g. The mean from the current study (22 ng/g) falls 

within this range but near the lower end. Mercury was below detection limits in the procedural 

blanks. 

The distribution of mercury concentrations across sites is displayed graphically in Figure 

8. Figure 9 shows the frequency diagram for levels of mercury in soil across all sites. This 

diagram shows the number of sites with concentrations within various ranges. For example, the 

first bar shows the number of sites with concentrations between 0 and 0.01 mg/kg (0 and 10 

ng/g), the second bar shows the number of sites with concentrations between 0.01 and 0.02 

mg/kg (10 and 20 ng/g), and so on. These results show how the concentrations were distributed 

across sites. 

24
Figure 4. HRMS PCB TEQ2s for all 27 sites. 

6.6. CONGENER PROFILES 

Appendix K shows four congener profiles for each site: CDDs/CDFs, PCBs, CDD/CDF 

TEQs, and PCB TEQs. Each of the profiles is discussed below. 

• CDDs/CDFs. These profiles are very similar across sites, with concentrations of 

OCDD being the highest at all sites. Heptachlorodibenzo-p-dioxins (HpCDDs) were 

second highest at all sites except three, where octachlorodibenzofuran (OCDF) was 

second highest. This pattern compares well with the Worldwide Deposition Profile 

presented by Brzuzy and Hites (1996). 

• PCBs. These profiles are very similar across sites, with concentrations of PCB 118 

being the highest at all sites. PCB 105 was second highest at all sites except one, 

where PCB 156/157 was second highest. 

25
Figure 5. HRMS total TEQ1s, HRMS total TEQ2s, and CALUX TEQs for 

all 27 sites. 

Figure 6. Frequency diagram for CDD/CDF TEQ2 concentrations among 27


sites.


26

0 

2 

4 

6 

8 

10 

12 

Number of Sites 

0.02 0.04 0.06 0.08 0.1 0.2 0.6 

PCB Concentration in Soil (pg TEQ2/g) 

Figure 7. Frequency diagram for PCB TEQ2 concentrations among 27 sites. 

Table 9. Literature summary for mercury in rural soils of North America 

Concentration Site 

(ng/g) Location Description n Reference

Range: <10–260 New Jersey Rural 35 NJDEP (2001) 

Range: 10–550 New York Orchards 13 Merwin et al. (1994) 

Range: 12–220 Minnesota Wilderness NR Glass et al. (1990) 

Range: <3.2–66 

Mean: 11 

Washington Background 13 Rogowski et al. (1999) 

Median: <100 

Range: <25–600 

Michigan Background 431 MDEQ (2005) 

Range: 0.5–69 

Mean: 22 

United States Rural/remote 27 Current study 

NR = not reported 

27
Figure 8. Mercury concentrations at all 27 sites. 

•	 CDD/CDF TEQs. These profiles vary considerably across sites. The congeners with 

the highest concentrations were usually OCDD, HpCDDs, pentachlorodibenzo-pdioxins (PeCDDs), or 2,3,4,7,8-pentachlorodibenzofuran (2,3,4,7,8-PeCDF). For 

TEQ1 and TEQ2 values, in general the HpCDDs and OCDD were significant 

contributors to TEQ. 1,2,3,7,8-PeCDD and 2,3,4,7,8-PeCDF also contributed 

significantly in instances where these congeners were detected. 

•	 PCB TEQs. These profiles are very similar across sites, with concentrations of PCB 

126 (on a TEQ2 basis) being the highest at all sites except two, where PCB 118 was 

highest. Because of its large TEF, PCB 126 was also a significant contributor to 

TEQ1 when detected. PCBs 118, 105, 156/157, and 169 were important contributors 

to TEQ1 and TEQ2 for many sites. 

28 
Number of Sites 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

10 20 30 40 50 60 70

Mercury Concentration in Soil (ng/g) 

Figure 9. Frequency diagram for mercury concentrations among 27 sites. 

7. COMPARATIVE ANALYSES 

This chapter presents analyses that compare (1) chemical concentration levels in air with 

those in soil, (2) chemical concentration levels in soil with those of TOC in soil, and (3) HRMSderived TEQs with CALUX-derived TEQs. For all analyses, the criteria for statistical 

significance is p#0.05. 

7.1. COMPARISON OF AIR AND SOIL CONCENTRATIONS 

Air samples have been collected at NDAMN sites across the United States since 1998 and 

analyzed for concentrations of CDDs/CDFs and certain PCBs. NDAMN used high-volume air 

samplers that operated for 30 days, four times per year. The NDAMN data for the years 1998 to 

2001 are presented in Cleverly et al., 2006 and U.S. EPA, 2005a,b. The comparisons were made 

using air data that most closely matched the date that the soil samples were collected (2003). 

Because the peer review of the 2001 air data had not yet been completed when this document 

was prepared, the 2000 air data were selected for this analysis. NDAMN collected air samples in 

2003 so future studies could compare air and soil samples collected in the same year. 

Note that Site 29 was located at Hyslop Farms, OR, in 2000 and was moved a few miles 

to Marvel Ranch, OR, in 2003 because Hyslop Farms is near a major transportation route and 

29
Marvel Ranch is located in a more remote area away from transportation. The air sample was 

collected at Hyslop Farms and the soil sample at Marvel Ranch. Also, the NDAMN stations Site 

28 (Rancho Seco, CA) and Site 34 (Trapper Creek, AK) were not in operation in 2000, so these 

sites were not included in the analysis. 

7.1.1. Air and Soil Concentrations 

This section evaluates reationships between the chemical levels in the soil and air across 

sites. Scatter plots of the raw data generally showed no obvious correlation as many 

concentrations are clustered close to zero and become more widely dispersed as concentration 

increases. Often when scatter plots have this form, it is useful to evaluate the correlation based 

on the natural log of the data. This is illustrated in Figure 10, which shows paired scatter plots 

for a number of the homologues on the basis of both the raw data and the natural log transform of 

the data. Table 10 summarizes the natural log linear correlations between air and soil levels 

across sites for PCBs, CDD/CDFs, and TEQs. The following observations can be made: 

• PCBs. The PCB air data included only six congeners, limiting the analyses that could 

be done. Only PCB 77 showed a significant correlation with r = 0.47. The PCB 

TEQs did not yield a significant correlation. 

• CDDs/CDFs. The analyses of the CDD/CDF data showed that correlations were 

significant for four homologues: PeCDDs (r = 0.40), HxCDDs (r = 0.42), HpCDDs 

(r = 0.48), and OCDD (r = 0.52). Additionally, significant correlations were found 

for total CDDs (r = 0.51) and total CDDs/CDFs (r = 0.53). None of the furan 

homologues showed significant correlations. 

• TEQs. The analyses of the TEQ data showed that the correlations were significant for 

TEQDF (r = 0.58) and TEQDFP (r = 0.54). 

The air sample and soil sample TEQs were calculated using the same TEFs; however, the 

PCB TEQ calculation for soils included additional PCB compounds: PCBs 81, 114, 123, 167, 

and 189. Figure 11 illustrates the annual average of quarterly air sample TEQ concentrations 

collected in 2000, and Figure 12 illustrates the soil sample TEQ concentrations collected during 

one sampling event in 2003. When visually comparing the total TEQ levels in air and soil shown 

in Figures 11 and 12, some similarities and some differences can be seen: 

• Marvel Ranch, OR (Site 29), had the highest air level and the highest soil level. Two 

other sites had relatively high soil levels: Jasper, NY (Site 12), and Dixon Springs, 

IL (Site 16). Site 16 had a relatively high air level, but Site 12 did not. GrandCanyon, 

AZ (Site 24), had the lowest air level and the lowest soil level. 

30
PeCDD Levels in Air vs Soil 

-6 

-4 

-2 

0 

2 

4 

-2 0 2 4 6 

Ln Air Concentration (fg/m3) 

Ln Soil Concentration 

(pg/g) 

OCDD Levels in Air vs Soil 

0 

2 

4 

6 

8 

10 

0 2 4 6 8 

LN Air concentration (fg/m3) 

LN Soil Concentration

(pg/g) 

OCDD Levels in Air vs Soil 

0.00 

2000.00 

4000.00 

6000.00 

8000.00 

10000.00 

12000.00 

0 500 1000 1500 2000 

Air Concentration (fg/m3) 

Soil Concentration

(pg/g) 

HpCDD Levels in Air vs Soil 

0 

100 

200 

300 

400 

500 

600 

700 

0 500 1000 1500 2000 

Air Concentration (fg/m3) 

Soil Concentration (pg/g) 

HpCDD Levels in Air vs Soil 

0 

1 

2 

3 

4 

5 

6 

7 

0 2 4 6 8 

Ln Air Concentration (fg/m3) 

Ln Soil Concentration

(pg/g) 

HxCDD Levels in Air vs Soil 

0.00 

20.00 

40.00 

60.00 

80.00 

100.00 

0 200 400 600 800 1000 

Air Levels (fg/m3) 

Soil Levels (pg/g) 

PeCDD Levels in Air vs Soil 

0 

5 

10 

15 

0 50 100 150 200 250 300 350 400 

Air Concentration (fg/m3) 

Soil Concentration (pg/g) 

HxCDD Levels in Air vs Soil 

-2 

-1 

0 

1 

2 

3 

4 

5 

0 2 4 6 8 

Ln Air Concentration (fg/m3) 

Ln Soil Concentration

(pg/g) 

8

Figure 10. Scatter plots of homologue levels in air versus soil. 

31
Table 10. Correlations across sites between soil concentrations and air 

concentrationsa,b 

Compound Correlation coefficient p-value 

Total TCDFs

Total TCDDs

Total PeCDFs

Total PeCDDs

Total HxCDFs

Total HxCDDs

Total HpCDFs

Total HpCDDs

OCDF

OCDD

 0.11 

0.25 

0.15 

0.40 

0.24 

0.42 

0.37 

0.48 

0.36 

0.52 

0.61 

0.24 

0.49 

0.05 

0.26 

0.03 

0.07 

0.01 

0.07 

0.01 

Total CDDs

Total CDFs

Total CDDs/CDFs

 0.51 

0.32 

0.53 

0.01 

0.11 

0.01 

PCB 77

PCB 105

PCB 118

PCB126 

PCB 156/157 

PCB 169

 0.47 

0.33 

0.31 

-0.02 

-0.02 

0.16 

0.02 

0.11 

0.14 

0.94 

0.94 

0.43 

TEQDF

TEQPCB

TEQDFP

 0.58 

0.21 

0.54 

0.00 

0.32 

0.01 

a

 All correlations are based on natural log transforms of the data. b Correlations where p # 0.05 are shown in bold 

• In general, the percent of PCB TEQ contribution to the total TEQ is similar for air 

samples and soil samples. Notably, Grand Canyon, AZ (Site 24), had a high 

percentage of PCB relative to the total TEQ in both air and soil. Two sites had a 

higher percentage of PCB to total TEQ in soil samples than in the air samples: Big 

Bend, TX (Site 23), and Bennington, VT (Site 11). Bay St. Louis, MS (Site 18), had 

the highest percentage of PCB TEQs in the air, but a moderate percentage of PCBs in 

the soil. 

32
Figure 11. Annual average total TEQs for air samples obtained at NDAMN 

sites in 2000. 

Figure 12. Total TEQ2 for soil samples taken at NDAMN sites in 2003. 

33
Figures 13 and 14 show the scatter plots for the TEQ data on the basis of the raw data 

and the natural log-transformed data, respectively. The regression line for the log-scale version 

is: 

Ln(soil concentration) = -0.749 + 0.455 Ln(air concentration) 

The standard error on the intercept is 0.304. The standard error on the slope is 0.144. 

The closeness of the slope of the regression line to 0.5 indicates a relationship between the soil 

concentration and the square root of the air concentration. This relationship is not particularly 

strong (r = 0.54), but it should not be discounted. 

Two facts should be considered while evaluating these air-soil correlations. First, dioxin 

levels in soil result from accumulation over many years, but the air samples used in this 

comparison were collected over just one year. Second, dioxin levels in the environment have 

changed over time. Dioxin levels began to rise in the 1930s and peaked in the early 1970s (U.S. 

EPA, 2003). Also, emissions in the United States decreased approximately 90% from 1987 to 

2000 (U.S. EPA, 2006). Accordingly, soil levels may better reflect past air levels than current 

levels. 

7.1.2. Air and Soil Congener Profiles 

Appendix L shows paired air and soil congener profiles for all the sites except Rancho 

Seco, CA (Site 28), and Trapper Creek, AK (Site 34), which had no air data in 2000. These 

comparisons are presented for the 2,3,7,8-substituted CDD/CDF congeners and six PCB 

congeners. The PCB congeners included in this analysis were limited to the ones measured in 

both air and soil (PCBs 77, 118, 106, 126, 156/157, and 169). The air data used to derive these 

profiles were from samples collected in 2000, but the draft NDAMN report for 2001 suggests 

that the air congener profiles are similar to those in 2000 (U.S. EPA, 2005b). Observations from 

these profiles are described below. 

• CDDs. The two prominent congeners in air and soil were the same at all sites 

(1,2,3,4,6,7,8-HpCDD and OCDD). The order of congeners in the air and soil was 

generally the same at most sites: from low to high, tetra, penta, hexas, heptas, and 

octa. At most sites, the relative proportion of octa to the other congeners was higher 

in the soil than in the air. 

• CDFs. The two prominent congeners in air and soil were the same at all sites 

(1,2,3,4,6,7,8-heptachlorodibenzofuran [1,2,3,4,6,7,8-HpCDF] and OCDF). The 

order of congeners in the air and soil was somewhat variable across sites. The 

similarity in relative proportions of the congeners in air and soil was difficult to judge 

visually. 

34
Figure 13. Annual average air total TEQs versus soil total TEQ2s (raw 

data). 

Figure 14. Annual average air total TEQs versus soil total TEQ2s (log 

transformed data). 

35 
• PCBs. The two prominent congeners in air and soil were the same at all sites (PCBs 

118 and 105). The order of congeners in the air and soil was generally the same at 

most sites: from low to high, PCBs 169, 126, 77, 156/157, 105, and 118. At most 

sites, the relative congener proportions in air appeared similar to the proportions in 

soil. 

7.2. COMPARISON OF SOIL CONCENTRATION WITH TOTAL ORGANIC 

CARBON CONCENTRATION 

The tendency for persistent chemicals to bind to organic carbon suggests that CDD/CDF, 

PCB, and mercury levels in soil may correlate with TOC levels in soil. Other researchers have 

reported mixed results on this issue. Brzuzy and Hites (1995) observed a strong correlation 

between organic carbon and total CDDs/CDFs at some sites. At sites where this was not 

observed, they theorized that deposition exceeded the sorption capacity of the soil. Wilcke and 

Amelung (2000) studied PCBs in North American grasslands and found no correlation between 

organic carbon and 14 PCBs. EPA reported that mercury levels in soil are positively correlated 

with organic matter (U.S. EPA, 1997). 

Scatter plots were used to investigate relationships between chemical levels in the soil 

and TOC levels in the soil across sites. As illustrated in Figure 15, the plots for a number of 

homologues suggested positive correlations. Correlation analyses were conducted for mercury, 

TEQs, and all homologue groups (Table 11). Based on these analyses, the following 

observations can be made: 

• Mercury. The correlation was not significant. 

• PCBs. The only significant correlation was for nonachlorobiphenyls (r = 0.47). 

• CDDs/CDFs. The correlations were significant for seven homologues: TCDFs (r = 

0.43), PeCDFs (r = 0.51), PeCDDs (r = 0.44), hexachlorodibenzofurans (HxCDFs) (r 

= 0.47), HxCDDs (r = 0.46), HpCDFs (r = 0.38), and HpCDDs (r = 0.38). Also, total 

CDFs had a significant correlation (r = 0.39). 

• TEQs. The TEQ correlation was significant (r = 0.43). The scatter plot for these data 

are shown in Figure 16. 

36
NonaCB Soil Levels vs TOC Levels 

0 

20 

40 

60 

80 

100 

120 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration (pg/g) 

TCDF Soil Levels vs TOC Levels 

0.00 

1.00 

2.00 

3.00 

4.00 

5.00 

6.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration 

(pg/g) 

HpCDD Soil Levels vs TOC Levels 

0.00 

100.00 

200.00 

300.00 

400.00 

500.00 

600.00 

700.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration 

(pg/g) 

HpCDF Soil Levels vs TOC Levels 

0.00 

50.00 

100.00 

150.00 

200.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration 

(pg/g) 

HxCDD Soil Levels vs TOC Levels 

0.00 

20.00 

40.00 

60.00 

80.00 

100.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration

(pg/g) 

HxCDF Soil Levels vs TOC Levels 

0.00 

5.00 

10.00 

15.00 

20.00 

25.00 

30.00 

35.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration 

(pg/g) 

PeCDD Soil Levels vs TOC Levels 

0.00 

5.00 

10.00 

15.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration 

(pg/g) 

PeCDF Soil Levels vs TOC Levels 

0.00 

5.00 

10.00 

15.00 

20.00 

25.00 

0 20000 40000 60000 80000 100000 

TOC Concentration (mg/kg) 

Soil Concentration

(pg/g) 

Figure 15. Scatter plots of chemical levels in soil vs TOC levels in soil. 

37
Table 11. Correlation across sites between soil concentration and total 

organic carbon concentrationa 

Chemical Correlation coefficient p-value 

Mercury 0.11 0.59 

Mono-CBs 

Di-CBs 

Tri-CBs 

Tetra-CBs 

Penta-CBS 

Hexa-CBs 

Hepta-CBs 

Octa-CBs 

Nona-CBs 

Deca-CBs 

Total PCBs 

-0.14 

-0.15 

-0.15 

-0.21 

-0.19 

-0.18 

0.01 

0.17 

0.47 

0.08 

-0.22 

0.49 

0.46 

0.47 

0.28 

0.34 

0.38 

0.97 

0.41 

0.01 

0.68 

0.27 

Total TCDFs 

Total TCDDs 

Total PeCDFs 

Total PeCDDs 

Total HxCDFs 

Total HxCDDs 

Total HpCDFs 

Total HpCDDs 

OCDD 

OCDF 

Total CDDs 

Total CDFs 

Total CDDs/CDFs 

0.43 

0.32 

0.51 

0.44 

0.47 

0.46 

0.38 

0.38 

0.21 

0.27 

0.22 

0.39 

0.23 

0.03 

0.10 

0.01 

0.02 

0.01 

0.02 

0.05 

0.05 

0.30 

0.17 

0.27 

0.04 

0.25 

Total TEQ2 0.43 0.02 a Correlations where p # 0.05 are shown in bold 

38
Figure 16. HRMS-based total TEQ2 versus total organic carbon (TOC) 

concentration of soil. 

Although a number of homologue groups showed statistically significant correlations, none were 

particularly strong (maximum r was 0.51). This could indicate that other factors were also 

affecting sorption characteristics of the soil. Grain size may also correlate with dioxin levels in 

soil because organic carbon is sometimes associated with smaller particles and because smaller 

particles have a higher surface area-to-mass ratio, increasing sorption capacity. The other two 

soil properties measured in this study, pH and moisture content, do not have clear mechanistic 

reasons to correlate with dioxin levels in soil. 

7.3. COMPARISON OF HRMS TEQs WITH CALUX BIOASSAY TEQs 

HRMS and the CALUX bioassay method were used to analyze soils. This study was not 

intended to provide a detailed evaluation of the CALUX bioassay method; however, as 

39
discussed below, a few observations can be made about its performance. For a more detailed 

analysis of bioassay methods, readers are referred to EPA’s Superfund Innovative Technology 

Evaluation (SITE) Program. Under this program, EPA has evaluated a variety of technologies 

(including CALUX) for determining the presence of dioxin and dioxin-like compounds in soil 

and sediment (U.S. EPA, 2005c). 

As shown in Table 7, the CALUX results were higher than the HRMS total TEQs in 

almost all of the site composites. The ratios of CALUX TEQs to HRMS total TEQ2s ranged 

from 1 to 11. The ratios were about 2 or less at 11 sites and 10 or more at 2 sites. These data are 

shown as scatter plots in Figures 17 (raw data) and 18 (log transformed data). Both data sets had 

a significant correlation, with r = 0.82 for the raw data and r = 0.78 for the log-transformed data. 

Figure 17. HRMS total TEQ2s versus CALUX bioassay TEQs by site 

(raw data). 

40
Figure 18. Scatter plot of HRMS total TEQ2 versus Calux bioassay TEQs on natural logscale with natural log-linear regression line (r = 0.78). 

The likely reason for the high bias in the CALUX data relative to the HRMS results is 

that CALUX responds to all compounds that activate the aryl hydrocarbon receptors, including a 

number of compounds other than CDDs, CDFs, and PCBs that may be present in soils, such as 

brominated and fluorinated dibenzo-p-dioxins/furans and biphenyls and halogenated napthlenes. 

Brown et al. (2004) showed that CALUX responds more to the hepta- and octa-chlorinated 

dioxins/furans and the tetrachlorinated biphenyls than would be expected on the basis of WHO 

TEF values and that this could lead to overestimation of the TEQ for samples that are 

contaminated primarily by these compounds. Although the two methods used different sample 

41
extraction procedures, it is unlikely that the ASE extraction used for the HRMS analysis was less 

efficient than the bottle sonication extraction procedures used for the CALUX analysis. 

Clark et al. (2003) compared CALUX bioassays with traditional HRMS techniques 

applied to soil samples. Their data also show a high bias in the CALUX data, but a much 

stronger correlation (r = 0.98). This stronger correlation may have resulted from the fact that the 

study was conducted with soil concentrations ranging from 100 to 100,000 pg/g, which are much 

higher than those measured here. The TEQs in these more highly contaminated samples are 

probably dominated by the CDDs, CDFs, and PCBs, reducing the influence of the other 

compounds that activate the aryl hydrocarbon receptors. 

Figure 19 shows results of a rank order analysis of the HRMS total TEQ2 versus CALUX 

TEQ data. This figure illustrates that the CALUX TEQ values are reasonable indicators of the 

relative TEQs among the sites as the lowest HRMS total TEQ2 values correspond to lowest 

CALUX TEQ values and as HRMS TEQ values increase CALUX values increase in a similarly 

corresponding manner. 

Figure 19. Rank order comparison of CALUX TEQs to HRMS total TEQ2s. 

42
8. UNCERTAINTY 

This chapter discusses the factors that contribute to the uncertainty in this study, 

including site selection, sampling protocol, analytical methods, and treatment of detection limits. 

8.1. SITE SELECTION 

The NDAMN sites were chosen as sampling locations because they are in rural/remote 

areas, they are well distributed nationally, they provide an opportunity to examine air-soil 

relationships, and they offered a cost-effective means for gathering soil samples. A statistically 

based random sampling design would have reduced bias in the site selection. However, the 

NDAMN study and the present soil survey were pilot studies and not meant to provide 

statistically unbiased results. Nonetheless, the results of the present study were consistent with 

other rural soil surveys. 

8.2. SAMPLING PROTOCOL 

A key uncertainty issue for a sampling protocol is the number of samples needed to 

represent a site. The number of samples per site used in this study was based on the coefficient 

of variation determined from the Oxford, OH, preliminary sample protocol evaluation (Appendix 

A). This initial study indicated that five samples within a 100 ft × 100 ft area would be sufficient 

to represent a site. Two aspects of this approach introduce uncertainty. First, it is unknown how 

representative the Oxford site is of the other sites in terms of spatial variability. Second, the 

Oxford exercise relied on CALUX analysis, which measures TEQs only. It is uncertain how well 

these results translate to specific chemicals. This uncertainty is greatest for mercury because it is 

unrelated to the dioxin-like chemicals detected by CALUX. 

This study relied on composite soil samples (made by combining equal portions of five 

individual soil samples at each site) as the primary basis for chemical analysis. An important 

uncertainty issue is how well the composites represented the individual samples. The individual 

samples and the composites were analyzed for TEQs by the CALUX bioassay method, so the 

resulting data can be used to evaluate representativeness. Figure 20 shows, for each site, the 

average of the individual samples, the 95% confidence interval on the mean of the individual 

samples, and the composite sample TEQ value. Two observations from this figure suggest that 

43
Figure 20. Comparison of five-point mean CALUX bioassay (G) and assay 

of composite soil sample (a). Vertical line shows the 95% confidence interval 

about the mean. 

the five-sample composite was adequate for characterizing TEQs at a site. First, most of the 

individual sampling point concentrations within a single site span a relatively small range, as 

evidenced by the short vertical bars. This means relatively little information was lost by 

analyzing the composite only. Second, the average of the individual sampling points was very 

near the composite concentration at almost all sites. The composite average fell outside the 

confidence interval at only four sites (1, 5, 7 and 16). A statistical analysis was performed to 

assess whether there was a statistically significant difference across sites between the average of 

the individual soil sample CALUX TEQ concentrations and the composite CALUX TEQ 

concentration. The analysis indicated that there was no statistical difference (at the 0.05 level) 

between the average and the composite. 

44
Figure 21 shows a scatter plot of the paired values with the y = x line and the results of 

regressing the composite measurements on the five-point means. Statistical tests (F-tests) 

showed that the regression intercept was not significantly different from zero and the slope was 

not significantly different from one. Results from the site composite can therefore be considered 

to be representative of the five individual samples. 

Figure 21. Scatter plot of five-point mean CALUX TEQs versus CALUX 

TEQs of soil composite (r = 0.97). 

The similarity in the composites and averages of the individual samples at each site, as 

shown in Figure 20, also indicates an internal consistency in sample handling and compositing 

procedures. 

Although more difficult to quantify, some between-site and within-site variation can be 

attributed to sampling technique because samples were collected by 27 different people with 

different experience levels. Attempts to control this variable were made by providing training 

and discussing sampling technique with each sampler, contacting the samplers by phone during 

45
collection, conducting quality assurance audits at two sites during sampling, and documenting 

with photographs the sampling at each site. 

Other sources of sampling variability, such as seasonality, temperature, soil conditions, 

and type and quantity of vegetation, may have contributed to the uncertainty and were not 

evaluated as part of this study. 

8.3. ANALYTICAL METHODS 

For a single sampling point, the relative percent difference between duplicate analyses 

gives some insight into within-sample variation. Within-sample variation is primarily affected 

by the sample homogenization and the analytical method. Section 5.5 and Appendix D present 

duplicate analysis results. In general, the relative percent difference between duplicates was 

within target limits for mercury and CDDs/CDFs, but it was more variable for PCBs. 

Analytical protocols and equipment also have inherent uncertainties. Although there is a 

“true” measure of a concentration, even the best of analytical methods will only approximate the 

true measure and thereby introduce uncertainty. For this study, HRMS was selected to provide 

the final congener concentration levels for CDDs/CDFs and PCBs. HRMS analysis methods are 

the most accurate, sensitive methods currently available for detecting CDDs/CDFs and PCBs. 

The HRMS data were supported by CALUX analysis to ensure that the site composite analyzed 

by HRMS was representative of the five individual points that made up the composite. Ideally, 

all samples would be analyzed by HRMS; however, all studies are constrained by a finite budget 

and must make use of the best methods and protocols available given that budget. 

8.4. TREATMENT OF DATA 

In addition to the analytical protocol used, treatment of data can contribute to uncertainty, 

particularly when the study is concentrating on levels very near the detection limits. Various 

ways of treating missing data and data below the detection limit can change the mean 

concentration standard error, introducing uncertainty. In addition, evaluating data on a TEQ 

basis versus individual congener concentrations can potentially minimize the contribution of a 

highly variable congener should it have a low TEF, resulting in a small contribution to TEQ, or 

overestimate the contribution of a highly variable congener with a high TEF, resulting in a large 

contribution to TEQ. 

46
9. CONCLUSIONS 

This study conducted a national-scale pilot survey of levels of CDDs, CDF's, PCB's and 

mercury in rural/remote soils of the United States. The results presented pertain to the 27 sites 

sampled and should not be more broadly interpreted as statistically representative of all rural 

soils in the United States. These results, however, may be a plausible basis for a preliminary 

characterization of soils in rural/remote areas. The primary measurement results are summarized 

below. 

• Total CDDs averaged 1,585 pg/g (SE = 567, SD = 2945). Total CDFs averaged 

47 pg/g (SE = 13, SD = 68). Levels of the TCDD homologues were the lowest, with 

an average concentration of 0.2 pg/g. Levels of the OCDD homologue were the 

highest, with an average concentration of 1,482 pg/g. The range of concentrations 

found here is similar to the range across five published studies on CDD/CDF levels in 

soils from rural areas of North America. 

• Total PCBs averaged 3,089 pg/g (SE = 1,009, SD = 5,241). Levels of the decachlorinated biphenyl homologues were the lowest, with an average concentration of 

29 pg/g. Levels of the penta-chlorinated biphenyl homologues were the highest, with 

an average concentration of 1,013 pg/g. The range of concentrations found here is 

similar to the range across three published studies on PCB levels in soils from rural 

areas worldwide. 

• Total TEQ2s averaged 1.76 pg/g (SE = 0.48, SD = 2.47). The PCBs generally were a 

small fraction of the total TEQs in soil. The mean for total TEQs from this study falls 

near the center of the range of values across 10 published studies. 

• Mercury concentrations averaged 22 ng/g across all sites (SE = 2.9, SD = 15 ng/g). 

The mean from this study falls within the range of values from five published studies 

on mercury levels in soils from rural areas of North America. 

Further details on ranges and distributions are provided in Chapter 6. 

This study also evaluated relationships between air concentrations and soil concentrations 

across sites. Based on the log- transformed data, significant positive correlations were observed 

for TEQs (r = 0.54), PeCDDs (r = 0.40), HxCDDs (r = 0.42), HpCDDs (r = 0.48), OCDD (r = 

0.52), PCB 77 (r = 0.47), total CDDs (r = 0.51), and total CDDs/CDFs (r = 0.53). None of the 

CDFs showed significant correlations. TEQ levels in soil and air were also compared visually 

using national maps (Figures 11 and 12). Although some correspondence could be seen in the 

lowest and the highest sites, many sites appeared inconsistent. The congener profiles of the air 

and soil were compared for the 2,3,7,8-substituted CDDs/CDFs and six PCB congeners. The 

47
CDD and PCB profiles in air and soil were generally similar at all sites. The CDF profiles for air 

and soil were different at most sites. 

The overall conclusions about the air-soil relationships for the three groups of chemicals 

are as follows: 

• CDDs. A general association between air and soil was observed, based on the 

significant air-soil correlations observed across sites for most homologue groups and 

the similarity in air and soil congener profiles observed at most sites. 

• CDFs. Little association between air and soil could be observed, based on the lack of 

significant air-soil correlations for homologue groups across sites and the lack of 

similarity in air and soil congener profiles for many sites. 

• PCBs. Some association between air and soil was observed. Data limitations 

restricted the air and soil comparisons to only six PCBs. One of these had a 

significant air-soil correlation across sites. The air and soil profiles based on these 

six chemicals were very similar at most sites. 

The observations for CDDs and PCBs are consistent with the theory that air transport and 

deposition are the primary ways that these chemicals are distributed to soils, particularly those in 

rural areas. The lack of similar observations for the CDFs does not necessarily mean that they 

are not distributed in a similar manner, but it does suggest that different factors affect the 

environmental fate of these chemicals. 

This study also evaluated relationships between chemical levels in soil and TOC levels in 

soil. The raw data analyses showed significant positive correlations for TCDFs (r = 0.43), 

PeCDFs (r = 0.51), PeCDDs (r = 0.44), HxCDFs (r = 0.47), HxCDDs (r = 0.46), HpCDFs 

(r = 0.38), HpCDDs (r = 0.38), total CDFs (r = 0.39), total TEQs (r = 0.43), and 

nonachlorobiphenyls (r = 0.47). The correlations were generally not very strong, indicating that 

other factors, such as grain size, may also be affecting sorption characteristics of the soil. 

TEQ levels were estimated both on the basis of applying TEFs to the HRMS analyses and 

on the basis of the CALUX bioassay method. The CALUX results were higher—by varying 

amounts—than the HRMS total TEQs in almost all of the site composites. Significant positive 

correlations were found comparing the data on both a raw basis (r = 0.82) and on a logtransformed basis (r = 0.78). The likely reason for the high bias in the CALUX data relative to 

HRMS data is that CALUX responds to all compounds that activate the aryl hydrocarbon 

receptors, including a number of compounds other than CDDs, CDFs, and PCBs that may be 

present in soils. 

48
Two observations from this study were unexpected and may warrant further 

investigation: 

• It would be reasonable to expect that PCBs in rural soils should be generally present 

at greater concentrations than those of CDDs/CDFs. PCBs were produced in large 

quantities in the United States (571,000 metric tons) from 1929 until their ban in 1978 

(ATSDR, 2000). CDDs/CDFs have never been intentionally produced; rather, they 

are formed in small quantities as by-products of combustion or certain types of 

chemical manufacturing. Although total PCBs exceeded total CDDs/CDFs at most 

sites in this study, the opposite was seen at 9 of 27 sites. At two of these sites, the 

CDD/CDF levels exceeded the PCBs by more than sevenfold. 

• The highest total PCBs were found at Big Bend, TX (Site 23). The levels were about 

eight times higher than the mean across all sites, although total CDD/CDF levels were 

among the lowest across sites. This is a very remote site and it is unclear why such 

relatively high PCB levels were found. 

Finally, a few thoughts can be offered about the utility of this pilot study to support future 

surveys. The surface soil sample collection/handling protocol proved to be effective and 

practical and could be used as a starting point in the design of future studies. Final decisions as 

to the number of sampling points at each location, sampling depth, and grid size were based on a 

preliminary single-site survey. This initial survey was used to evaluate the variability in TEQ 

levels and supported the use of sample compositing as a way to reduce analytical costs. Further 

TEQ analysis of individual samples and composites at all 27 sites demonstrated that relatively 

little information was lost by compositing. This experience suggests that future soil surveys with 

a focus on TEQ levels in rural areas could also reasonably consider analyzing only sample 

composites. Surveys involving other analytes and other land types should consider preliminary 

field testing to evaluate the appropriateness of sample compositing. 

49
REFERENCES

ASTM (American Society for Testing and Materials). (2002a) D422-63 standard test method for particle-size 

analysis of soils. West Conshohocken, PA. 

ASTM (American Society for Testing and Materials). (2002b) D2216-98 standard test method for laboratory 

determination of water (moisture) content for soil and rock by mass. West Conshohocken, PA. 

ATSDR (Agency for Toxic Substances and Disease Registry). (1998) Toxicological profile for chlorinated dibenzop-dioxins (CDDs). U.S. Department of Health and Human Services, Atlanta, GA. 

ATSDR (Agency for Toxic Substances and Disease Registry). (1999) Toxicological profile for mercury. U.S. 

Department of Health and Human Services, Atlanta, GA. 

ATSDR (Agency for Toxic Substances and Disease Registry). (2000) Toxicological profile for polychlorinated 

biphenyls (PCBs). U.S. Department of Health and Human Services, Atlanta, GA. 

BC Environment. (1995) Dioxins and furans in the British Columbia environment. Report prepared for the 

Environmental Protection Department, British Columbia Environment, Victoria, British Columbia, Canada. 

Birmingham, B. (1990) Analysis of PCDD and PCDF patterns in soil samples: use in the estimation of the risk of 

exposure. Chemosphere 20(7–9):807–814. 

Bright, DA; Dushenko, WT; Grundy, SL, et al. (1995) Evidence for short-range transport of polychlorinated 

biphenyls in the Canadian Arctic using congener signatures of PCBs in soils. Science of the Total Environment 

160/161:251–263. 

Brown, DJ; Chu, M; Van Overmeire, I; et al. (2004) Determination of REP values for the CALUX bioassay and 

comparison to the WHO TEF values. Available online at http://www.dioxins.com/pages/Publicationspage.shtml. 

Buckland, SJ; Ellis, HK; Salter, RT; et al. (1998) Ambient concentrations of PCDDs, PCDFs, and PCBs in New 

Zealand soils. Organohalogen Compounds 39:101–104. 

Brzuzy, LP; Hites, RA. (1995) Estimating the atmospheric deposition of polychlorinated dibenzo-p-dioxins and 

dibenzofurans from soils. Environmental Science and Technology 29:2090–2098. 

Brzuzy, LP; Hites, RA. (1996) Global mass balance for polychlorinated dibenzo-p-dioxins and dibenzofurans. 

Environmental Science and Technology 30:1797–1804. 

Clark GC; Denison, MS; Morris, RW; et al. (2003) Analysis of soil samples from a hazardous waste site: 

comparison of CALUX bioassay TEQ determination with high resolution GC/MS. Available online at 

http://www.dioxins.com/pages/Publicationstechnical.shtml. 

Cleverly,D; Ferrario,J; Byrne,C; et al. (2006) A general indication of the contemporary background levels of 

PCDDs, PCDFs, and coplanar PCBs in the ambient air over rural and remote areas of the United States. 

Environmental Science and Technology 41(5):1537–1544. 

Denison, MS; Brouwer, A; Clark, GC. (1998) Bioassay for detecting 2,3,7,8-tetrachlorodibenzo-para-dioxin and 

TCDD-like compounds and novel recombinant cell line useful therefor. United States Patent # 5,854,010 (online at 

http://www.dioxins.com/images/Calux-patent%20copy.gif). Xenobiotic Detection Systems, Inc. 

Fiedler, H; Lau, C; Cooper, K; et al. (1995) PCDD/PCDF in soil and pine needle samples in a rural area in the 

United States of America. Organohalogen Compounds 24:285–292. 

50
Glass, GE; Sorensen, JA; Schmidt, KW; et al. (1990) New source identification of mercury contamination in the 

Great Lakes. Environmental Science and Technology 24(7):1059–1069. 

Grundy, SL; Bright, DA; Dushenko, WT; et al. (1995) Sources and signatures of PCDDs and PCDFs in soils from 

the Canadian North. Organohalogen Compounds 24:63–67. 

HUD (U.S. Department of Housing and Urban Development). (1995) Guidelines for the evaluation of lead-based 

paint hazards in housing. Washington, DC. 

Jasinski, S. (1994) The materials flow of mercury in the United States. Bureau of Mines Information Circular 9412. 

Available online at http://pubs.usgs.gov/usbmic/ic-9412/. 

Lorber, M., P. Pinsky, P. Gehring, et al. (1998) Relationships between dioxins in soil, air, ash, and emissions from a 

municipal solid waste incinerator emitting large amounts of dioxins. Chemosphere 37:2173-2197. 

Masahide, K; Jerzy, F; Beata, B; et al. (1998) Persistent organochlorine pesticides and polychlorinated biphenyls in 

soils in Poland. Organohalogen Compounds 39:337–342. 

MDEQ (Michigan Department of Environmental Quality). (2005) Michigan background soil survey 2005. 

Available online at http://www.deq.state.mi.us/documents/deq-whm-hw-Michigan-Background-SoilrevJuly2005.pdf. 

Meijer, SN; Ockenden, WA; Sweetman, A; et al. (2003) Global distribution and budget of PCBs and HCB in 

background surface soils: implications for sources and environmental processes. Environmental Science and 

Technology 37:667–672. 

Merwin, I; Pruyne, PT; Ebel, JG; et al. (1994) Persistence, phytotoxicity, and management of arsenic, lead and 

mercury residues in old orchard soils or New York state. Chemosphere 29(6):1361–1367. 

MRI. (1992) Multivariate statistical analyses of dioxin and furan levels in fish, sediment, and soil samples collected 

near resource recovery facilities, final report. Report produced for the Connecticut Department of Environmental 

Protection, Water Compliance Unit. Hartford, CT. 

NJDEP (New Jersey Department of Environmental Protection). (2001) New Jersey mercury task force. Vol. II. 

Exposure and impacts. Available online at http://www.state.nj.us/dep/dsr/mercury_task_force.htm. 

Rappe, C; Andersson, R; Kulp, SE; et al. (1995) Concentrations of PCDDs and PCDFs in soil samples from 

Southern Mississippi, USA. Organohalogen Compounds 24:345–347. 

Reed, LW; Hunt, GT; Maisel, BE; et al. (1990) Baseline assessment of PCDDs/PCDFs in the vicinity of the Elk 

River, Minnesota generating station. Chemosphere 21(1–2):159–171. 

Rogowski, DL, Yake, W. (2005) Typical dioxin concentrations in agriculture soils of Washington state and 

potential sources. Environmental Science and Technology 39(14):5170–5176. 

Rogowski, D; Golding, S; Bowhay, D; et al. (1999) Final report screening survey for metals and dioxins in fertilizer 

products and soils in Washington State. Washington State Department of Ecology, Olympia, WA. Ecology 

Publication No. 99-309. 

U.S. EPA (Environmental Protection Agency). (1992) Preparation of soil sampling protocols: sampling techniques 

and strategies. Office of Research and Development, Washington, DC; EPA/600/R-92/128. Available online at 

http://www.epa.gov/swerust1/cat/mason.pdf. 

51
U.S. EPA (Environmental Protection Agency). (1994a) Test methods for evaluating solid waste (SW-846 On-line). 

Method 7471A: Mercury in solid or semisolid waste (manual cold-vapor technique). Revision 1. Office of Solid 

Waste, Washington, DC. Available online at http://www.epa.gov/epaoswer/hazwaste/test/main.htm. 

U.S. EPA (Environmental Protection Agency). (1994b) Method 1613: tetra- through octa-chlorinated dioxins and 

furans by isotope dilution HRGC/HRMS. Revision B. Office of Water, Washington, DC; EPA 821-B-94-005. 

Available online at http://www.epa.gov/waterscience/methods/1613.pdf 

U.S. EPA (Environmental Protection Agency). (1995) Test methods for evaluating solid waste, physical/chemical 

methods (SW-846). Method 9045C: soil and waste pH. Revision 3. Office of Solid Waste, Washington, DC. 

Available online at http://www.epa.gov/epaoswer/hazwaste/test/main.htm. 

U.S. EPA (Environmental Protection Agency). (1997) Mercury study report to congress. Vol. III: Fate and 

transport of mercury in the environment. Office of Air Quality Planning and Standards and the Office of Research 

and Development, Washington, DC; EPA-425/R-097-005. Available online at 

http://www.epa.gov/mercury/report.htm. 

U.S. EPA (Environmental Protection Agency). (1999) Method 1668. Revision A. Chlorinated biphenyl congeners 

in water, soil, sediment, and tissue by HRGC/HRMS. Office of Water, Washington, DC; EPA-821-R-00-002. 

Available online at http://www.epa.gov/Region3/1668a.pdf 

U.S. EPA (Environmental Protection Agency). (2001). Denver front range study: dioxin in surface soil. Study 1: 

characterization of dioxins, furans and PCBs in soil samples collected from the Denver front range area. Region 8. 

Denver, CO. Available online at http://www.epa.gov/region8/superfund/co/rkymtnarsenal/rmdioxrpt.html. 

U.S. EPA (Environmental Protection Agency). (2003) Exposure and human health reassessment of 2,3,7,8­

tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. NAS Review Draft. Volume II Properties, 

Environmental Levels, and Background Exposures. EPA/600/P-00/001Bc. Available online at 

http://www.epa.gov/ncea/pdfs/dioxin/nas-review/. 

U.S. EPA (Environmental Protection Agency). (2005a) National Dioxin Air Monitoring Network (NDAMN) annual 

report of results of atmospheric measurements of PCDDs, PCDFs, and dioxin-like PCBs in rural and nonimpacted 

areas of the United States: 2000. Review Draft. Office of Research and Development, National Center for 

Environmental Assessment, Washington, DC; EPA/600/P-02/001F. 

U.S. EPA (Environmental Protection Agency). (2005b) National Dioxin Air Monitoring Network (NDAMN) annual 

report of results of atmospheric measurements of PCDDs, PCDFs, and dioxin-like PCBs in rural and nonimpacted 

areas of the United States: 2001. Review Draft. Office of Research and Development, National Center for 

Environmental Assessment, Washington, DC; EPA/600/R-05/160. 

U.S. EPA (Environmental Protection Agency). (2005c) Innovative technology verification report: technologies for 

monitoring and measurement of dioxin and dioxin-like compounds in soil and sediment. Xenobiotic Detection 

Systems, Inc. CALUX by XDS. Office of Research and Development. EPA/540/R-05/001. Available at: 

http://www.epa.gov/ORD/SITE/. 

U.S. EPA (Environmental Protection Agency). (2006) An Inventory of sources and environmental releases of 

dioxin-like compounds in the United States for the years 1987, 1995, and 2000. Office of Research and 

Development, National Center for Environmental Assessment, Washington, DC; EPA/600/P-03/002F. 

USGS (Geological Survey). (2002) U.S. Geological Survey minerals yearbook. Vol. 1. Metals and minerals. 

Available online at http://minerals.usgs.gov/minerals/pubs/commodity/myb/. 

Van den Berg, M; Birnbaum, L; Bosveld, ATC; et al. (1998) Toxic equivalency factors (TEFs) for PCBs, PCDDs, 

PCDFs for humans and wildlife. Environ Health Perspect 106:775–792. 

Vikelsoe, J. (2002) Dioxins in Danish Soil. National Environmental Research Institute, Roskilde, Denmark. 

52
Vorhees, DJ; Cullen, AC; Altshul, LM. (1999) Polychlorinated biphenyls in house dust and yard soil near a 

Superfund site. Environmental Science and Technology 33:2151–2156. 

Walkley, A; Black, IA. (1934) An examination of the Degiareff method for determining SOM and a proposed 

modification of the chromic acid titration method. Soil Science 37: 29–38. 

Wilcke, W; Amelung, W. (2000) Persistent organic pollutants in native grassland soils along a climosequence in 

North America. Journal of the Soil Science Society of America 64:2140– 2148. 

Wyrzykowska, B; Hanari, N; Orlikowska, A; et al. (2005) Importance of dioxin-like PCBs and PCNs for TEQs of 

agricultural soils with low level of 2,3,7,8-TeCDDs and its analogues. Organohalogen Compounds 67:678–680. 

53