Chemical Concentration Data Research Articles

The U.S. Food and Drug Administration (FDA) Food Disaggregation Database (FDA-FDD): a new tool for U.S. dietary exposure assessment

Dietary exposure to a food chemical (e.g. contaminant, nutrient, or other natural constituent) is a function of the concentration of the chemical in foods and the quantity of each food consumed. Exposures to food chemicals can be estimated using intake data from What We Eat in America (WWEIA), the food consumption survey portion of the National Health and Nutrition Examination Survey (NHANES). To estimate exposures to chemicals in foods consumed by NHANES/WWEIA respondents, the consumption data must be mapped to chemical concentration data on the same or similar foods. However, food chemical data are generally not available on all the foods and food mixtures that are reported in NHANES/WWEIA. To address this, we developed the FDA Food Disaggregation Database (FDA-FDD), a ‘recipe’ database with estimates of ingredient percentages. FDA-FDD allows mapping to food chemical data based on ingredients in NHANES/WWEIA foods rather than on food mixtures, resulting in more accurate exposure estimates. Using FDA-FDD, FDA mapped over 11,000 NHANES/WWEIA foods to FDA’s Total Diet Study (TDS) foods. FDA-FDD is available as part of a publicly available interactive application that also allows access to the TDS mapping.

A More Flexible Asymmetric Exponential Modification of the Laplace Distribution with Applications for Chemical Concentration and Environment Data

In this work, a new family of distributions based on the Laplace distribution is introduced. We define this new family by its stochastic representation as the sum of two independent random variables, one with a Laplace distribution and the other with an exponential distribution. Using a Monte Carlo simulation study, the statistical performance of the estimators obtained by the moments and maximum likelihood methods were empirically evaluated. We studied the coverage probabilities and mean length of the confidence intervals of the corresponding parameters based on the asymptotic normality of these estimators. This simulation study reported a good statistical performance of these estimators. Fits were made to three real data sets with the new distribution, two related to chemical concentrations and one to the environment, comparing it with three similar distributions given in the literature. We have used information criteria for the selection of models. These results showed that the exponentially modified Laplace model can be an alternative distribution to model skewed data with high kurtosis. The new approach is a contribution to the tools of statisticians and various professionals interested in modeling data with high kurtosis.

Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals

Time courses of compound concentrations in plasma are used in chemical safety analysis to evaluate the relationship between external administered doses and internal tissue exposures. This type of experimental data is rarely available for the thousands of non-pharmaceutical chemicals to which people may potentially be unknowingly exposed but is necessary to properly assess the risk of such exposures. In vitro assays and in silico models are often used to craft an understanding of a chemical’s pharmacokinetics; however, the certainty of the quantitative application of these estimates for chemical safety evaluations cannot be determined without in vivo data for external validation. To address this need, we present a public database of chemical time-series concentration data from 567 studies in humans or test animals for 144 environmentally-relevant chemicals and their metabolites (187 analytes total). All major administration routes are incorporated, with concentrations measured in blood/plasma, tissues, and excreta. We also include calculated pharmacokinetic parameters for some studies, and a bibliography of additional source documents to support future extraction of time-series. In addition to pharmacokinetic model calibration and validation, these data may be used for analyses of differential chemical distribution across chemicals, species, doses, or routes, and for meta-analyses on pharmacokinetic studies.

Microbial metabolite fluxes in a model marine anoxic ecosystem.

Permanently anoxic regions in the ocean are widespread and exhibit unique microbial metabolic activity exerting substantial influence on global elemental cycles and climate. Reconstructing microbial metabolic activity rates in these regions has been challenging, due to the technical difficulty of direct rate measurements. In Cariaco Basin, which is the largest permanently anoxic marine basin and an important model system for geobiology, long-term monitoring has yielded time series for the concentrations of biologically important compounds; however, the underlying metabolite fluxes remain poorly quantified. Here, we present a computational approach for reconstructing vertical fluxes and in situ net production/consumption rates from chemical concentration data, based on a 1-dimensional time-dependent diffusive transport model that includes adaptive penalization of overfitting. We use this approach to estimate spatiotemporally resolved fluxes of oxygen, nitrate, hydrogen sulfide, ammonium, methane, and phosphate within the sub-euphotic Cariaco Basin water column (depths 150-900m, years 2001-2014) and to identify hotspots of microbial chemolithotrophic activity. Predictions of the fitted models are in excellent agreement with the data and substantially expand our knowledge of the geobiology in Cariaco Basin. In particular, we find that the diffusivity, and consequently fluxes of major reductants such as hydrogen sulfide, and methane, is about two orders of magnitude greater than previously estimated, thus resolving a long-standing apparent conundrum between electron donor fluxes and measured dark carbon assimilation rates.

CCWater – A computer program for chemical classification of geothermal waters

CCWater is a computer program developed for the application of extensively being used triangular diagrams for chemical classification and for identification of equilibrium conditions of waters. The program for these diagrams were developed as a Microsoft Excel™ spreadsheet application and compiled using Visual Basic™ 6.0, which has enabled to execute an Excel™ file from a program created by Visual Basic™ 6.0. The performances of all the four diagrams were validated by applying for the same chemical concentration data of the waters that was used by the original authors of these diagrams. The results obtained from this program were consistent with those of the original authors of these diagrams. As an example, CCWater is applied for chemical characterization of thermal waters from springs and geothermal wells of five geothermal fields of Mexico, in which four are electricity producing fields (Cerro Prieto, CPGF; Las Tres Virgenes, LTVGF; Los Azufres, LAGF, and Los Humeros, LHGF) and the fifth geothermal field (La Primavera, LPGF) is in exploration stage. Majority of the reservoir temperatures estimated by Na/K geothermometers from geothermal well waters of CPGF, LAGF, LTVGF, and LPGF are within the limits of accepted differences (within ±20%) comparing to the average BHTs of the respective geothermal well. The good performances of the geothermometers in predicting the reservoir temperatures from well waters is expected because the well waters are fully equilibrated and are of Cl type, thus fulfilling the basic requirements. In contrary, though the spring waters of LHGF and LAGF are of HCO3 type (immature) and exhibited no equilibrium conditions, they also predicted reliable reservoir temperatures. This unusual but important observation in the behavior of the spring waters of LHGF and LAGF was made possible to known by chemical classification of these waters in to different groups and estimation of the reservoir temperatures by considering each water type of a geothermal field as a separate group. This application has shown the importance of chemical type of water and the existing chemical equilibrium conditions in successful using these waters in estimation of the reservoir temperatures, and hence the necessity of the computer program like CCWater. The software CCWater is easy to use, reliable, freely available and will be useful in the application of ternary diagrams for chemical characterization and to evaluate the equilibrium conditions of geothermal waters, particularly during the initial stage of geothermal exploration.

Circumventing kinetics in biogeochemical modeling

Microbial metabolism drives biogeochemical fluxes in virtually every ecosystem. Modeling these fluxes is challenged by the incredible diversity of microorganisms, whose kinetic parameters are largely unknown. In poorly mixed systems, such as stagnant water columns or sediments, however, long-term bulk microbial metabolism may become limited by physical transport rates of substrates across space. Here we mathematically show that under these conditions, biogeochemical fluxes are largely predictable based on the system's transport properties, chemical boundary conditions, and the stoichiometry of metabolic pathways, regardless of the precise kinetics of the resident microorganisms. We formalize these considerations into a predictive modeling framework and demonstrate its use for the Cariaco Basin subeuphotic zone, one of the largest anoxic marine basins worldwide. Using chemical concentration data solely from the upper boundary (depth 180 m) and lower boundary (depth 900 m), but without a priori knowledge of metabolite fluxes, chemical depth profiles, kinetic parameters, or microbial species composition, we predict the concentrations and vertical fluxes of biologically important substances, including oxygen, nitrate, hydrogen sulfide, and ammonium, across the entire considered depth range (180-900 m). Our predictions largely agree with concentration measurements over a period of 14 years ([Formula: see text] = 0.78-0.92) and become particularly accurate during a period where the system was near biogeochemical steady state (years 2007-2009, [Formula: see text] = 0.86-0.95). Our work enables geobiological predictions for a large class of ecosystems without knowledge of kinetic parameters or geochemical depth profiles. Conceptually, our work provides a possible explanation for the decoupling between microbial species composition and bulk metabolic function, observed in various ecosystems.

Adaptive observation-based subsurface conceptual site modeling framework combining interdisciplinary methodologies: a case study on advancing the understanding of a groundwater nitrate plume occurrence.

Traditional site characterization and laboratory testing methods are insufficient to quantify and conceptualize subsurface contaminant source-pathway-receptor heterogeneity issues, as they hamper groundwater risk assessment and water resource management using mathematical modeling. To address these issues, we propose an adaptive observation-based conceptual site modeling framework, which emphasizes the need for the iterative testing of hypotheses centered on specific questions with clearly defined objectives using interdisciplinary tools (including, but not limited to, geology, microbiology, hydrogeology, geophysics, and the chemistry of solute fate and transport). Under this framework, we present a case study aimed at a goal-oriented investigation of the source and occurrence of a groundwater nitrate plume previously identified using chemical concentration data from sparsely distributed, conventional, and regional groundwater monitoring wells. These investigations occurred in stages, with the first comprehensive outcome of cost-efficient, non-invasive surface geophysical surveys localizing subsurface heterogeneities laying the groundwork for collaborative, minimally invasive, direct push-based investigations followed by groundwater chemical and stable isotope analyses for source fingerprinting and bioprocess evaluation. Despite the obvious need for further refinement of the conceptual site model as new data become available, we illustrate that the step-by-step integrative framework was useful for systematic maximization of the strengths of different investigation methodologies. Such frameworks and approaches should be encouraged for successful environmental site characterization, monitoring, and modeling.

Exposure of dioxin-like chemicals in participants of the Anniston community health survey follow-up

The 2014 follow-up of the Anniston Community Health Survey (ACHS II) consisted of 338 surviving participants from the 2005–2007 baseline study (ACHS) who had previous polychlorinated biphenyl (PCB) measurements, were not pregnant, and were not institutionalized. Questionnaires and blood samples provided the demographic, personal history, and chemical concentration data of the Anniston residents. Approximately 51% of participants were African American, 72% were female, and the mean age was 63 years old. The objectives of this study were to provide an exposure assessment of dioxin-like chemicals in the ACHS II participants and compare the measurements with the general United States (U.S.) population via the National Health and Nutrition Examination Survey (NHANES). Stratified analyses revealed significantly higher average total dioxin toxic equivalencies (TEQs) among African Americans compared to Whites (33.1 vs. 19.2 pg/g lipid), and in females compared to males (29.8 vs. 17.0 pg/g lipid). When adjusting for age, sex, and race in linear regression, we found ACHS II participants to have significantly higher total dioxin TEQ than the general 2014 U.S. population that we estimated for using half-life and NHANES 2003/04 data (most recent NHANES individual samples data), by 16.7 pg/g lipid. Principal component analyses showed that non-ortho and mono-ortho PCBs were separated from the other dioxin-like chemicals among the Anniston residents, whereas the chemicals were all clustered together for estimated NHANES 2014. The concentrations of dioxin-like chemicals, especially non-ortho and mono-ortho PCBs, in Anniston residents who resided near the former PCB production plant were higher than those in the general U.S. population. Although data strongly supported this difference, these inferences are limited because NHANES 2013/14 data were unavailable and we used estimated NHANES 2014 levels that we imputed from NHANES 2003/04 data in conjunction with half-life values estimated from Milbrath et al., 2009.

Suspect Screening Analysis of Chemicals in Consumer Products.

A two-dimensional gas chromatography-time-of-flight/mass spectrometry (GC×GC-TOF/MS) suspect screening analysis method was used to rapidly characterize chemicals in 100 consumer products-which included formulations (e.g., shampoos, paints), articles (e.g., upholsteries, shower curtains), and foods (cereals)-and therefore supports broader efforts to prioritize chemicals based on potential human health risks. Analyses yielded 4270 unique chemical signatures across the products, with 1602 signatures tentatively identified using the National Institute of Standards and Technology 2008 spectral database. Chemical standards confirmed the presence of 119 compounds. Of the 1602 tentatively identified chemicals, 1404 were not present in a public database of known consumer product chemicals. Reported data and model predictions of chemical functional use were applied to evaluate the tentative chemical identifications. Estimated chemical concentrations were compared to manufacturer-reported values and other measured data. Chemical presence and concentration data can now be used to improve estimates of chemical exposure, and refine estimates of risk posed to human health and the environment.

Exploring the hydraulic fracturing parameter space: a novel high-pressure, high-throughput reactor system for investigating subsurface chemical transformations.

Hydraulic fracturing coupled with horizontal drilling (HDHF) involves the deep-well injection of a fracturing fluid composed of diverse and numerous chemical additives designed to facilitate the release and collection of natural gas from shale plays. Analyses of flowback wastewaters have revealed organic contamination from both geogenic and anthropogenic sources. The additional detections of undisclosed halogenated chemicals suggest unintended in situ transformation of reactive additives, but the formation pathways for these are unclear in subsurface brines. To develop an efficient experimental framework for investigating the complex shale-well parameter space, we have reviewed and synthesized geospatial well data detailing temperature, pressure, pH, and halide ion values as well as industrial chemical disclosure and concentration data. Our findings showed subsurface conditions can reach pressures up to 4500 psi (310 bars) and temperatures up to 95 °C, while at least 588 unique chemicals have been disclosed by industry, including reactive oxidants and acids. Given the extreme conditions necessary to simulate the subsurface, we briefly highlighted existing geochemical reactor systems rated to the necessary pressures and temperatures, identifying throughput as a key limitation. In response, we designed and developed a custom reactor system capable of achieving 5000 psi (345 bars) and 90 °C at low cost with 15 individual reactors that are readily turned over. To demonstrate the system's throughput, we simultaneously tested 12 disclosed HDHF chemicals against a radical initiator compound in simulated subsurface conditions, ruling out a dozen potential transformation pathways in a single experiment. This review outlines the dynamic and diverse parameter range experienced by HDHF chemical additives and provides an optimized framework and novel reactor system for the methodical study of subsurface transformation pathways. Ultimately, enabling such studies will provide urgently needed clarity for water treatment downstream or releases to the environment.

Identification of biased sectors in emission data using a combination of chemical transport model and receptor model

This study presented a comparison approach with multiple source apportionment methods to identify which sectors of emission data have large biases. The source apportionment methods for the comparison approach included both receptor and chemical transport models, which are widely used to quantify the impacts of emission sources on fine particulate matter of less than 2.5 μm in diameter (PM2.5). We used daily chemical component concentration data in the year 2013, including data for water-soluble ions, elements, and carbonaceous species of PM2.5 at 11 sites in the Kinki–Tokai district in Japan in order to apply the Positive Matrix Factorization (PMF) model for the source apportionment. Seven PMF factors of PM2.5 were identified with the temporal and spatial variation patterns and also retained features of the sites. These factors comprised two types of secondary sulfate, road transportation, heavy oil combustion by ships, biomass burning, secondary nitrate, and soil and industrial dust, accounting for 46%, 17%, 7%, 14%, 13%, and 3% of the PM2.5, respectively. The multiple-site data enabled a comprehensive identification of the PM2.5 sources. For the same period, source contributions were estimated by air quality simulations using the Community Multiscale Air Quality model (CMAQ) with the brute-force method (BFM) for four source categories. Both models provided consistent results for the following three of the four source categories: secondary sulfates, road transportation, and heavy oil combustion sources. For these three target categories, the models’ agreement was supported by the small differences and high correlations between the CMAQ/BFM- and PMF-estimated source contributions to the concentrations of PM2.5, SO42−, and EC. In contrast, contributions of the biomass burning sources apportioned by CMAQ/BFM were much lower than and little correlated with those captured by the PMF model, indicating large uncertainties in the biomass burning emissions used in the CMAQ simulations. Thus, this comparison approach using the two antithetical models enables us to identify which sectors of emission data have large biases for improvement of future air quality simulations.

Risk Assessment for Children Exposed to Beach Sands Impacted by Oil Spill Chemicals.

Due to changes in the drilling industry, oil spills are impacting large expanses of coastlines, thereby increasing the potential for people to come in contact with oil spill chemicals. The objective of this manuscript was to evaluate the health risk to children who potentially contact beach sands impacted by oil spill chemicals from the Deepwater Horizon disaster. To identify chemicals of concern, the U.S. Environmental Protection Agency’s (EPA’s) monitoring data collected during and immediately after the spill were evaluated. This dataset was supplemented with measurements from beach sands and tar balls collected five years after the spill. Of interest is that metals in the sediments were observed at similar levels between the two sampling periods; some differences were observed for metals levels in tar balls. Although PAHs were not observed five years later, there is evidence of weathered-oil oxidative by-products. Comparing chemical concentration data to baseline soil risk levels, three metals (As, Ba, and V) and four PAHs (benzo[a]pyrene, benz[a]anthracene, benzo[b]fluoranthene, and dibenz[a,h]anthracene) were found to exceed guideline levels prompting a risk assessment. For acute or sub-chronic exposures, hazard quotients, computed by estimating average expected contact behavior, showed no adverse potential health effects. For cancer, computations using 95% upper confidence limits for contaminant concentrations showed extremely low increased risk in the 10−6 range for oral and dermal exposure from arsenic in sediments and from dermal exposure from benzo[a]pyrene and benz[a]anthracene in weathered oil. Overall, results suggest that health risks are extremely low, given the limitations of available data. Limitations of this study are associated with the lack of toxicological data for dispersants and oil-spill degradation products. We also recommend studies to collect quantitative information about children’s beach play habits, which are necessary to more accurately assess exposure scenarios and health risks.

Assessment of human health hazards associated with the dietary exposure to organic and inorganic contaminants through the consumption of fishery products in Spain

In this work we have evaluated the potential carcinogenic and acutely toxic risks associated to the exposure to highly prevalent organic and inorganic contaminants through the consumption of fishery products by the Spanish population. The concentrations of 8 organochlorine pesticides (OCPs), 18 polychlorinated biphenils (PCBs), 7 polycyclic aromatic hydrocarbons (expressed as benzo[a]pyrene toxic equivalents (B[a]Peq)), and three inorganic toxic elements [arsenic (As), cadmium (Cd), and mercury (Hg)] were determined in 93 samples of the most consumed species of white fish, blue fish, cephalopods and seafood species, which were acquired directly in markets and supermarkets in the Canary Islands, Spain. The chemical concentration data were combined with the pattern of consumption of these foodstuffs in order to calculate the daily intake of these contaminants, and on this basis the risk quotients for carcinogenicity and acute toxicity were determined for Spanish adults and children. Our results showed that the daily intake of OCPs, PCBs and B[a]Peq, which is associated to blue fish consumption was the highest within the fish group. The estimated intake of pollutants can be considered low or very low for the individual contaminants, when compared to reference values, except in the case of HCB and As. All the estimated intakes were below the reported Tolerable Daily Intakes. Considering the additive effects of multiple contaminants, the risk of acute toxic effects can also be considered as low or very low. However, our results reflect that the current consumption of white fish in adults and children, and also the blue fish in the case of adults, poses a moderate carcinogenic risk to Spanish consumers, mainly related to their concentrations of As. The conclusions of this research may be useful for the design of appropriate risk communication campaigns.

Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy) are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

Mining in subarctic Canada: Airborne PM2.5 metal concentrations in two remote First Nations communities

Airborne particulate matter arising from upwind mining activities is a concern for First Nations communities in the western James Bay region of Ontario, Canada. Aerosol chemical components were collected in 2011 from two communities in northern Ontario. The chemical and mass concentration data of particulate matter collected during this study shows a significant difference in PM2.5 in Attawapiskat compared to Fort Albany. Elemental profiles indicate enhanced levels of some tracers thought to arise from mining activities, such as, K, Ni, and crustal materials. Both communities are remote and isolated from urban and industrial pollution sources, however, Attawapiskat First Nation has significantly enhanced levels of particulate matter, and it is likely that some of this arises from upwind mining activities.

Evaluation of the solute geothermometry of thermal springs and drilled wells of La Primavera (Cerritos Colorados) geothermal field, Mexico: A geochemometrics approach

A detailed study on the solute geothermometry of thermal water (18 springs and 8 drilled wells) of La Primavera geothermal field (LPGF) in Mexico has been carried out by employing a geochemical database compiled from the literature and by applying all the available solute geothermometers. The performance of these geothermometers in predicting the reservoir temperatures has been evaluated by applying a geochemometrics (geochemical and statistical) method. The springs of the LPGF are of bicarbonate type and the majority have attained partial-equilibrium chemical conditions and the remaining have shown non-equilibrium conditions. In the case of geothermal wells, water is dominantly of chloride-type and, among the studied eight geothermal wells, four have shown full-equilibrium chemical conditions and another four have indicated partial-equilibrium conditions. All springs of HCO3−​ type water have provided unreliable reservoir temperatures, whereas the only one available spring of SO42− type water has provided the reservoir temperature nearer to the average BHT of the wells. Contrary to the general expected behavior, spring water of non-equilibrium and geothermal well water of partial-equilibrium chemical conditions have indicated more reliable reservoir temperatures than those of partially-equilibrated and fully-equilibrated water, respectively. Among the chemical concentration data, Li and SiO2 of two springs, SO42− and Mg of four springs, and HCO3 and Na concentrations of two geothermal wells were identified as outliers and this has been reflected in very low reservoir temperatures predicted by the geothermometers associated with them (Li–Mg, Na–Li, Na–K–Mg, SiO2 etc.). Identification of the outlier data points may be useful in differentiating the chemical characteristics, lithology and the physico-chemical and geological processes at the sample locations of the study area.In general, the solute geothermometry of the spring waters of LPGF indicated a dominantly (94%) of underestimated deep reservoir temperatures, whereas in the case of the geothermal wells, many temperatures (54%) are underestimated, some are (43%) overestimated and a very small number (3%) are similar to an average bottom-hole temperatures (BHT) of the wells. 28 out of the total applied 29 geothermometers for spring waters have predicted the deep reservoir temperatures that are characterized by statistically significant large differences compared to the average BHTs of the geothermal wells. In the case of waters of the geothermal wells, 23 out of the total applied 28 geothermometers have predicted the reservoir temperatures similar (statistically no significant differences) to the BHTs of the corresponding geothermal wells.

A novel tracer technique to quantify the atmospheric flux of trace elements to remote ocean regions

AbstractAtmospheric input into the global ocean constitutes an important budgetary component of numerous chemical species and plays a key role in controlling biogeochemical processes in the ocean. Assessment of this input is difficult, however, because measurements of deposition rates to the ocean, particularly in remote areas, are rare and susceptible to problems of temporal and spatial variability. While the collection and analysis of aerosol samples is somewhat routine, the chemical concentration data collected from ship board or land‐based aerosol samplers in and of themselves cannot yield the deposition flux of trace elements; a method is required to transform concentration measurements into flux. The ability to derive the atmospheric flux of 7Be from its ocean inventory provides a key linkage between the atmospheric concentration of chemical species and their deposition to the ocean. We have demonstrated that estimates of the atmospheric flux of trace elements (TEs) can be made by multiplying the ocean inventory of 7Be x [TE/7Be] ratio in bulk aerosols. Flux estimates for trace elements made by the 7Be ocean inventory method were comparable to fluxes derived from rain samples collected on the island of Bermuda. The situation at Bermuda allows such testing to be made, where ocean‐based methods can be calibrated by convenient land locations. Our results suggest that this method would be useful for remote areas where fixed sampling stations do not exist; that is, the majority of the global ocean.

From sediment to tissue and tissue to sediment: An evaluation of statistical bioaccumulation models

Biota-sediment accumulation factors (BSAFs) and biota-sediment accumulation regressions (BSARs) are statistical models that may be used to estimate tissue chemical concentrations from sediment chemical concentrations or vice versa. Biota-sediment accumulation factors and BSARs are used to fill tissue concentration data gaps, set sediment preliminary remediation goals (PRGs), and make projections about the effectiveness of potential sediment cleanup projects in reducing tissue chemical concentrations. We explored field-based, benthic invertebrate biota-sediment chemical concentration relationships using data from the US Environmental Protection Agency (USEPA) Mid-Continent Ecology Division (MED) BSAF database. Approximately two thirds of the 262 relationships investigated were very poor (r(2) < 0.3 or p-value ≥ 0.05); for some of the biota-sediment relationships that did have a significant nonzero slope (p-value < 0.05), lipid-normalized tissue concentrations tended to decrease as the colocated organic carbon (OC)-normalized sediment concentration increased. Biota-sediment relationships were further evaluated for 3 of the 262 datasets. Biota-sediment accumulation factors, linear regressions, model II regressions, illustrative sediment PRGs, and confidence intervals (CIs) were calculated for each of the three examples. These examples illustrate some basic but important statistical practices that should be followed before selecting a BSAR or BSAF or relying on these simple models of biota-sediment relationships to support consequential management decisions. These practices include the following: one should not assume that the relationship between chemical concentrations in tissue and sediment is necessarily linear, one should not assume the model intercept to be zero, and one should not place too much stock on models that are heavily influenced by one or a few high chemical concentration data points. People will continue to use statistical models of field-based biota-sediment chemical concentration relationships to support sediment investigations and remedial action decisions. However, it should not be assumed that the models will be reliable. In developing and applying BSAFs and BSARs, it is essential that best practices are followed and model limitations and uncertainties are understood, acknowledged, and quantified as much as possible.

Development and evaluation of sediment quality guidelines based on benthic macrofauna responses

Toxicity-based sediment quality guidelines (SQGs) are often used to assess the potential of sediment contamination to adversely affect benthic macrofauna, yet the correspondence of these guidelines to benthic community condition is poorly documented. This study compares the performance of 5 toxicity-based SQG approaches to a new benthos-based SQG approach relative to changes in benthic community condition. Four of the toxicity-based SQG approaches--effects range median, logistic regression modeling (LRM), sediment quality guideline quotient 1 (SQGQ1), and consensus--were derived in previous national studies in the United States, and one was developed as a regional variation of LRM calibrated to California data. The new benthos-based SQG approach, chemical score index, was derived from Southern California benthic community data. The chemical-specific guidelines for each approach were applied to matched chemical concentration, amphipod mortality, and benthic macrofauna abundance data for Southern California. Respective results for each SQG approach were then combined into a summary metric describing the overall contamination magnitude (e.g., mean quotient) and assessed in accordance with a set of thresholds in order to classify stations into 4 categories of expected biological effect. Results for each SQG approach were significantly correlated with changes in sediment toxicity and benthic community condition. Cumulative frequency plots and effect category thresholds for toxicity and benthic community condition were similar, indicating that both types of effect measures had similar sensitivity and specificity of response to contamination level. In terms of discriminating among multiple levels of benthic community condition, the toxicity-based SQG indices illustrated moderate capabilities, similar to those for multiple levels of toxicity. The National LRM, California LRM, and the chemical score index had the highest overall agreement with benthic categories. However, only the benthos-based chemical score index was consistently among the highest performing SQG indices for all measures of association (correlation, percent agreement, and weighted kappa) for both toxicity and benthos.

The use of chemical occurrence and concentration data in exposure assessments of additive intakes in the diets of Irish children as part of a pan-European chemical exposure surveillance system

The use of chemical occurrence and concentration data in exposure assessments of additive intakes in the diets of Irish children as part of a pan-European chemical exposure surveillance system