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Dioxins are a family of chemical compounds that has received considerable attention, both historically and currently. This article reviews scientific field studies that examine the relationship between living on soil contaminated with dioxins and the level of dioxins in people's serum, with an emphasis on the University of Michigan Dioxin Exposure Study (UMDES), the largest and most comprehensive study of its kind. These studies conclude that the levels of dioxins in serum are most strongly correlated with age, gender, body mass index, weight loss, breast feeding, and smoking. Levels of dioxins in soil are not significant predictors for dioxin concentrations in serum. The increase in serum dioxin levels that is seen with age results from historic exposure and does not represent ongoing exposure. Based on the scientific field studies conducted to date, it appears that, in the absence of the consumption of contaminated animal products, there is little evidence of ongoing exposure from contaminated soil.

The University of Michigan Dioxin Exposure Study was undertaken to address concerns that the industrial discharge of dioxin-like compounds in the Midland, Michigan, USA area had resulted in the contamination of soil and vegetation in the Tittabawassee River floodplain and downwind of the incinerator in the City of Midland. The study included the analysis of 597 vegetation samples, predominantly grass and weeds, from residential properties selected through a multistage probabilistic sample design in the Midland area, and in Jackson and Calhoun Counties (Michigan), as a background comparison, for 29 polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs). The mean toxic equivalent (TEQ) of the house perimeter vegetation samples ranged from 4.2 to 377 pg/g. The ratio of TEQs (vegetation to soil) was about 0.3, with a maximum of 3.5. Based on a calculation of the similarity of the congener patterns between the soil and the vegetation, it appeared that the source of the contamination on the vegetation was the surrounding soil. This conclusion was supported by linear regression analysis, which showed that the largest contributor to the R(2) for the outcome variable of log(10) of the vegetation concentration was log(10) of the surrounding soil concentration. Models of vegetation contamination usually focus on atmospheric deposition and partitioning. The results obtained here suggest that the deposition of soil particles onto vegetation is a significant route of contamination for residential herbage. Thus, the inclusion of deposition of soil particles onto vegetation is critical to the accurate modeling of contamination residential herbage in communities impacted by historic industrial discharges of persistent organic compounds., (Copyright © 2010 SETAC.)

Background: In 2005, the World Health Organization (WHO) - International Programme on Chemical Safety reevaluated the toxic equivalency factors (TEFs) for dioxin-like compounds and made changes that affect the calculation of the total toxic equivalent (TEQ). The impact of these changes on the TEQs for human blood and abiotic matrices such as soil and household dust has not been widely assessed or reported., Methods and Results: Using a major exposure study which examined blood, household dust, and soil levels of dioxin-like compounds in several regions of Michigan, we found the mean total TEQ was significantly reduced by 26%, 12% and 14% for serum, household dust, and soil, respectively, when the TEQ was based on the 2005 TEFs compared to the 1998 TEFs. The decrease in the serum total TEQ was largely due to the down-weighting of the TEFs for the majority of mono-ortho PCBs. In contrast, the decrease in the soil total TEQ was mostly due to the down-weighting of the TEF for 2,3,4,7,8-pentachlorodibenzofuran (PeCDF) (1998 TEF=0.5, 2005 TEF=0.3). For household dust, the decrease in total TEQ was not due to any single TEF but was due to small changes in a number of compounds. There was a dramatic decrease (-88%) in the mean and 95th percentile for mono-ortho PCB TEQ due to the 2005 TEFs., Discussion: These findings suggest that comparisons between studies based on the TEQ-WHO(98) and TEQ-WHO(05) may need to consider an appropriate conversion factor to assure comparability. Furthermore, the changes in TEFs may have impact in locations where regulations of soil contamination are triggered by specific TEQ levels.

Most contamination of residential property soil with dioxin-like compounds occurs as a result of proximity to industrial activity that produces such compounds and, outside the industrially impacted zone, the soil concentrations are at background levels. However, as part of the University of Michigan Dioxin Exposure Study, residential properties in the lower peninsula of Michigan, USA, were identified that were located far enough from known sources of these compounds that the soil concentrations should have been at background levels and yet the toxic equivalent (TEQ) of some properties' soil was greater than 2.5 standard deviations above the mean background level. In the three cases presented here from Midland/Saginaw Counties, the anomalously high-TEQ values were primarily due to the presence of polychlorinated dibenzofurans. Based on interviews with the residents and a comparison of soil congener profiles, it was deduced that these values resulted from anthropogenic soil movement from historically contaminated areas. In the cases from Jackson/Calhoun Counties, the unusually high-TEQ values were primarily due to polychlorinated biphenyls. In the case profiled here, it appears that the soil became contaminated through sandblasting to remove paint from the swimming pool. This study identified two mechanisms for soil contamination outside zones of industrial impact; thus, an assumption of background levels of soil contamination outside industrial zones may not be valid.

Deposition of pollutants around point sources of contamination, such as incinerators, can display complex spatial patterns depending on prevailing weather conditions, the local topography, and the characteristics of the source. Deterministic dispersion models often fail to capture the complexity observed in the field, resulting in uncertain predictions that might hamper subsequent decision-making, such as delineation of areas targeted for additional sampling or remediation. This paper describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition from an incinerator with the probabilistic modeling of residual variability of field samples. The approach is used to delineate areas with high levels of dioxin TEQ(DF)-WHO98 (toxic equivalents) around an incinerator, accounting for 53 field data and the output of the EPA Industrial Source Complex (ISC3) dispersion model. The dispersion model explains 43.7% of the variance in the soil TEQ data, whereas the regression residuals are spatially correlated with a range of 776 m. One hundred realizations of soil TEQ values are simulated on a grid with a 50 m spacing. The benefit of stochastic simulation over spatial interpolation is 2-fold: (1) maps of simulated point TEQ values can easily be aggregated to the geography that is the most relevant for decision making (e.g., census block, ZIP codes); and (2) the uncertainty at the larger scale is simply modeled by the empirical distribution of block-averaged simulated values. Incorporating the output of the atmospheric deposition model as a spatial trend yields a more realistic prediction of the spatial distribution of TEQ values than log-normal kriging using only the field data, in particular, in sparsely sampled areas away from the incinerator. The geostatistical model provided guidance for the study design (census block-based population sampling) of the University of Michigan Dioxin Exposure Study (UMDES), focused on quantifying exposure pathways to dioxins from industrial sources, relative to background exposures.

A key component in any investigation of cause-effect relationships between point source pollution, such as an incinerator, and human health is the availability of measurements and/or accurate models of exposure at the same scale or geography as the health data. Geostatistics allows one to simulate the spatial distribution of pollutant concentrations over various spatial supports while incorporating both field data and predictions of deterministic dispersion models. This methodology was used in a companion paper to identify the census blocks that have a high probability of exceeding a given level of dioxin TEQ (toxic equivalents) around an incinerator in Midland, MI. This geostatistical model, along with population data, provided guidance for the collection of 51 new soil data, which permits the verification of the geostatistical predictions, and calibration of the model. Each new soil measurement was compared to the set of 100 TEQ values simulated at the closest grid node. The correlation between the measured concentration and the averaged simulated value is moderate (0.44), and the actual concentrations are clearly overestimated in the vicinity of the plant property line. Nevertheless, probability intervals computed from simulated TEQ values provide an accurate model of uncertainty: the proportion of observations that fall within these intervals exceeds what is expected from the model. Simulation-based probability intervals are also narrower than the intervals derived from the global histogram of the data, which demonstrates the greater precision of the geostatistical approach. Log-normal ordinary kriging provided fairly similar estimation results for the small and well-sampled area used in this validation study; however, the model of uncertainty was not always accurate. The regression analysis and geostatistical simulation were then conducted using the combined set of 53 original and 51 new soil samples, leading to an updated model for the spatial distribution of TEQ in Midland, MI.

Context: For the general population, the dominant source of exposure to dioxin-like compounds is food. As part of the University of Michigan Dioxin Exposure Study (UMDES), we measured selected polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (PCBs) in serum of 946 subjects who were a representative sample of the general population in five Michigan counties., Case Presentation: The total toxic equivalency (TEQ; based on 2005 World Health Organization toxic equivalency factors) of serum from the index case was 211 ppt on a lipid-adjusted basis, which was the highest value observed in the UMDES study population. This subject had no apparent opportunity for exposure to dioxins, except that she had lived on property with soil contaminated with dioxins for almost 30 years, and had been a ceramics hobbyist for > 30 years. Soil from her property and clay that she used for ceramics were both contaminated with dioxins, but the congener patterns differed., Discussion: The congener patterns in this subject's serum, soil, and ceramic clay suggest strongly that the dioxin contamination in clay and not soil was the dominant source of dioxin contamination in her serum. RELEVANCE TO PUBLIC HEALTH PRACTICE: It appears that ceramic clay, in particular the process of firing clay with unvented kilns, can be a significant nonfood and nonindustrial source of human exposure to dioxins among ceramics hobbyists. The extent of human exposure from ceramic clay is unclear, but it may be widespread. Further work is needed to more precisely characterize the routes of exposure.

Risk-based sediment management decisions require the characterization of contamination sources and fate processes in the field. Polytopic vector analysis (PVA) is a multivariate technique based on a linear mixing model, used to resolve chemical fingerprints and suited for forensic investigations of environmental contamination. The traditional algorithm is constrained to positive fingerprint (end-member) components and cannot resolve fingerprints with both positive and negative values required for a reactive end-member. We developed a modified algorithm (M-PVA) to resolve a dioxin dechlorination fingerprint, indicative of biotic/abiotic transformations in field samples of sediments. The new procedure isolates from the dioxin pattern net compositional changes due to dechlorination in a separate end-member. Using two artificial data sets for which the composition and sample contribution of all end-members are known, the dechlorination fingerprint was reproduced with a root mean square error of 28-41%. The dechlorination end-member contribution to the total variability (set at 4.0 and 10.0%, respectively) was overestimated 1-5-fold. The ability of M-PVA to reproduce the dechlorination pattern and its variability contribution depends on the actual contribution of dechlorination to variability. At an actual contribution of 4.0%, the model outcome deviates more strongly from the original than is the case for a contribution of 10.0%. As such, application of M-PVA to environmental data should include an uncertainty analysis to distinguish variability due to dechlorination from variability due to error. The development of the modified PVA procedure is an important step toward the field characterization of fate processes in dioxin-impacted sediments.

Persistent contaminants such as dioxins have been documented to undergo dechlorination reactions in the laboratory; however, little is known about the importance of these reactions in the field. Polytopic vector analysis (PVA) is a statistical pattern recognition technique for multivariate data traditionally used to identify fingerprints of contaminant sources. A modified PVA algorithm with uncertainty analysis was used to model dechlorination fingerprints and sources. The technique was applied to 351 sediment core-derived dioxin samples from the lower reach of the Passaic River, New Jersey. A dechlorination fingerprint was identified with a highly positive 2,3,7,8-tetraCDD component and a highly negative heptaCDD component. The most important industrial source of 2,3,7,8-tetraCDD is a fingerprint related to 2,4,5-trichlorophenoxyacetic acid production. The dechlorination contribution to the data variance is 3.00 +/- 1.00%, corresponding to an average of 1.2 microg/kg of 2,3,7,8-tetraCDD per sample at the expense of heptaCDD. The possible occurrence of dechlorination was validated by comparing the local dechlorination contribution in the results to the value of the ratio 2,3,7,8-tetraCDD/total 2,3,7,8-PCDD, which indicates dechlorination in the laboratory. Bootstrap uncertainty analysis yielded the same dechlorination EM in 40% of the realizations. The results indicated that bootstrapping is an important statistical tool to quantify uncertainties with respect to the dechlorination EM and some of the source EMs.

Contaminated sediment management is an urgent environmental and regulatory issue worldwide. Because remediation is expensive, sound quantitative assessments of uncertainty aboutthe spatial distribution of contaminants are critical, butthey are hampered bythe physical complexity of sediment environments. This paper describes the use of geostatistical modeling approaches to quantify uncertainty of 2,3,7,8-tetrachlorodibenzo-p-dioxin concentrations in Passaic River (New Jersey) sediments and to incorporate this information in decision-making processes, such as delineation of contaminated areas and additional sampling needs. First, coordinate transformation and analysis of three-dimensional semivariograms were used to describe and modelthe directional variability accounting forthe meandering course of the river. Then, indicator kriging was employed to provide models of local uncertainty at unsampled locations without requiring a prior transform (e.g. log-normal) of concentrations. Cross-validation results show that the use of probability thresholds leads to more efficient delineation of contaminated areas than a classification based on the exceedence of regulatory thresholds by concentration estimates. Depending on whether additional sampling aims at reducing prediction errors or misclassification rates, the variance of local probability distributions or a measure of the expected closeness to the regulatory threshold can be used to locate candidate locations.

The contribution of dechlorination reactions to the transformation of dioxins in river sediments was evaluated under a range of geochemical conditions mimicking freshwater, estuarine and marine environments, and interpreted in the light of recent evidence for their accumulation at the air-water interface. The yield of dechlorinated products increased with salinity, but never exceeded 20% of the initial dioxins present. Among the homologue groups in saline conditions, diCDDs were dominant at 32-47 mol% (8-16 nM). The production of 2,3,7,8-TCDD increased with decreasing salinity and in the presence of dissolved organic matter, exhibiting a maximum ratio (2,3,7,8-TCDD:non-2,3,7,8-TCDD) of 0.5. These differences in patterns may be related to the dominant microbial respiratory processes responsible for carbon turnover under the imposed conditions, and are strongly affected by the presence of organic matter. Based on these results, a conceptual dechlorination and carbon turnover model is proposed to help explain the likely reactivity of dioxins in the Passaic River Estuary.