Emissions of Polycyclic Aromatic Hydrocarbons from Natural Gas Extraction into Air.

Natural gas extraction, often referred to as "fracking", has increased rapidly in the United States in recent years. To address potential health impacts, passive air samplers were deployed in a rural community heavily affected by the natural gas boom. Samplers were analyzed for 62 polycyclic aromatic hydrocarbons (PAHs). Results were grouped based on distance from each sampler to the nearest active well. Levels of benzo[a]pyrene, phenanthrene, and carcinogenic potency of PAH mixtures were highest when samplers were closest to active wells. PAH levels closest to natural gas activity were comparable to levels previously reported in rural areas in winter. Sourcing ratios indicated that PAHs were predominantly petrogenic, suggesting that PAH levels were influenced by direct releases from the earth. Quantitative human health risk assessment estimated the excess lifetime cancer risks associated with exposure to the measured PAHs. At sites closest to active wells, the risk estimated for maximum residential exposure was 0.04 in a million, which is below the U.S. Environmental Protection Agency's acceptable risk level. Overall, risk estimates decreased 30% when comparing results from samplers closest to active wells to those farthest from them. This work suggests that natural gas extraction is contributing PAHs to the air, at levels that would not be expected to increase cancer risk.

PAH and OPAH Flux during the Deepwater Horizon Incident.

Passive sampling devices were used to measure air vapor and water dissolved phase concentrations of 33 polycyclic aromatic hydrocarbons (PAHs) and 22 oxygenated PAHs (OPAHs) at four Gulf of Mexico coastal sites prior to, during and after shoreline oiling from the Deepwater Horizon oil spill (DWH). Measurements were taken at each site over a 13 month period, and flux across the water-air boundary was determined. This is the first report of vapor phase and diffusive flux of both PAHs and OPAHs during the DWH. Vapor phase sum PAH and OPAH concentrations ranged between 6.6 and 210 ng/m(3) and 0.02 and 34 ng/m(3) respectively. PAH and OPAH concentrations in air exhibited different spatial and temporal trends than in water, and air-water flux of 13 individual PAHs was shown to be at least partially influenced by the DWH incident. The largest PAH volatilizations occurred at the sites in Alabama and Mississippi at nominal rates of 56 000 and 42 000 ng/m(2) day(-1) in the summer. Naphthalene was the PAH with the highest observed volatilization rate of 52 000 ng/m(2) day(-1) in June 2010. This work represents additional evidence of the DWH incident contributing to air contamination, and provides one of the first quantitative air-water chemical flux determinations with passive sampling technology.

Polycyclic aromatic hydrocarbon (PAH) and oxygenated PAH (OPAH) air-water exchange during the deepwater horizon oil spill.

Passive sampling devices were used to measure air vapor and water dissolved phase concentrations of 33 polycyclic aromatic hydrocarbons (PAHs) and 22 oxygenated PAHs (OPAHs) at four Gulf of Mexico coastal sites prior to, during, and after shoreline oiling from the Deepwater Horizon oil spill (DWH). Measurements were taken at each site over a 13 month period, and flux across the water-air boundary was determined. This is the first report of vapor phase and flux of both PAHs and OPAHs during the DWH. Vapor phase sum PAH and OPAH concentrations ranged between 1 and 24 ng/m(3) and 0.3 and 27 ng/m(3), respectively. PAH and OPAH concentrations in air exhibited different spatial and temporal trends than in water, and air-water flux of 13 individual PAHs were strongly associated with the DWH incident. The largest PAH volatilizations occurred at the sites in Alabama and Mississippi in the summer, each nominally 10,000 ng/m(2)/day. Acenaphthene was the PAH with the highest observed volatilization rate of 6800 ng/m(2)/day in September 2010. This work represents additional evidence of the DWH incident contributing to air contamination, and provides one of the first quantitative air-water chemical flux determinations with passive sampling technology.

Impact of natural gas extraction on PAH levels in ambient air.

Natural gas extraction, often referred to as "fracking," has increased rapidly in the U.S. in recent years. To address potential health impacts, passive air samplers were deployed in a rural community heavily affected by the natural gas boom. Samplers were analyzed for 62 polycyclic aromatic hydrocarbons (PAHs). Results were grouped based on distance from each sampler to the nearest active well. PAH levels were highest when samplers were closest to active wells. Additionally, PAH levels closest to natural gas activity were an order of magnitude higher than levels previously reported in rural areas. Sourcing ratios indicate that PAHs were predominantly petrogenic, suggesting that elevated PAH levels were influenced by direct releases from the earth. Quantitative human health risk assessment estimated the excess lifetime cancer risks associated with exposure to the measured PAHs. Closest to active wells, the risk estimated for maximum residential exposure was 2.9 in 10 000, which is above the U.S. EPA's acceptable risk level. Overall, risk estimates decreased 30% when comparing results from samplers closest to active wells to those farthest. This work suggests that natural gas extraction may be contributing significantly to PAHs in air, at levels that are relevant to human health.

A harmonized chemical monitoring database for support of exposure assessments.

Direct monitoring of chemical concentrations in different environmental and biological media is critical to understanding the mechanisms by which human and ecological receptors are exposed to exogenous chemicals. Monitoring data provides evidence of chemical occurrence in different media and can be used to inform exposure assessments. Monitoring data provide required information for parameterization and evaluation of predictive models based on chemical uses, fate and transport, and release or emission processes. Finally, these data are useful in supporting regulatory chemical assessment and decision-making. There are a wide variety of public monitoring data available from existing government programs, historical efforts, public data repositories, and peer-reviewed literature databases. However, these data are difficult to access and analyze in a coordinated manner. Here, data from 20 individual public monitoring data sources were extracted, curated for chemical and medium, and harmonized into a sustainable machine-readable data format for support of exposure assessments.

Systematic evidence map (SEM) template: Report format and methods used for the US EPA Integrated Risk Information System (IRIS) program, Provisional Peer Reviewed Toxicity Value (PPRTV) program, and other "fit for purpose" literature-based human health analyses.

BACKGROUND: Systematic evidence maps (SEMs) are gaining visibility in environmental health for their utility to serve as problem formulation tools and assist in decision-making, especially for priority setting. SEMs are now routinely prepared as part of the assessment development process for the US Environmental Protection Agency (EPA) Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) assessments. SEMs can also be prepared to explore the available literature for an individual chemical or groups of chemicals of emerging interest. OBJECTIVES: This document describes the typical methods used to produce SEMs for the IRIS and PPRTV Programs, as well as "fit for purpose" applications using a variety of examples drawn from existing analyses. It is intended to serve as an example base template that can be adapted as needed for the specific SEM. The presented methods include workflows intended to facilitate rapid production. The Populations, Exposures, Comparators and Outcomes (PECO) criteria are typically kept broad to identify mammalian animal bioassay and epidemiological studies that could be informative for human hazard identification. In addition, a variety of supplemental content is tracked, e.g., studies presenting information on in vitro model systems, non-mammalian model systems, exposure-level-only studies in humans, pharmacokinetic models, and absorption, distribution, metabolism, and excretion (ADME). The availability of New Approach Methods (NAMs) evidence is also tracked (e.g., high throughput, transcriptomic, in silico, etc.). Genotoxicity studies may be considered as PECO relevant or supplemental material, depending on the topic and context of the review. Standard systematic review practices (e.g., two independent reviewers per record) and specialized software applications are used to search and screen the literature and may include the use of machine learning software. Mammalian bioassay and epidemiological studies that meet the PECO criteria after full-text review are briefly summarized using structured web-based extraction forms with respect to study design and health system(s) assessed. Extracted data is available in interactive visual formats and can be downloaded in open access formats. Methods for conducting study evaluation are also presented which is conducted on a case-by-case basis, depending on the usage of the SEM.

Systematic dose-response of environmental epidemiologic studies: Dose and response pre-analysis.

Meta-analysis approaches can be used to assess the human risks due to exposure to environmental chemicals when there are numerous high-quality epidemiologic studies of priority outcomes in a database. However, methodological issues related to how different studies report effect measures and incorporate exposure into their analyses arise that complicate the pooled analysis of multiple studies. As such, there are "pre-analysis" steps that are often necessary to prepare summary data reported in epidemiologic studies for dose-response analysis. This paper uses epidemiologic studies of arsenic-induced health effects as a case example and addresses the issues surrounding the estimation of mean doses from censored dose- or exposure-intervals reported in the literature (e.g., estimation of mean doses from high exposures that are only reported as an open-ended interval), calculation of a common dose metric for use in a dose-response meta-analysis (one that takes into consideration inter-individual variability), and calculation of response "effective counts" that inherently account for confounders. The methods herein may be generalizable to 1) the analysis of other environmental contaminants with a suitable database of epidemiologic studies, and 2) any meta-analytic approach used to pool information across studies. A second companion paper detailing the use of "pre-analyzed" data in a hierarchical Bayesian dose-response model and techniques for extrapolating risks to target populations follows.

Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods.

When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single "best" study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered "pre-analysis" steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant.

Use of study-specific MOE-like estimates to prioritize health effects from chemical exposure for analysis in human health assessments.

There are unique challenges in estimating dose-response with chemicals that are associated with multiple health outcomes and numerous studies. Some studies are more suitable than others for quantitative dose-response analyses. For such chemicals, an efficient method of screening studies and endpoints to identify suitable studies and potentially important health effects for dose-response modeling is valuable. Using inorganic arsenic as a test case, we developed a tiered approach that involves estimating study-specific margin of exposure (MOE)-like unitless ratios for two hypothetical scenarios. These study-specific unitless ratios are derived by dividing the exposure estimated to result in a 20% increase in relative risk over the background exposure (RRE20) by the background exposure, as estimated in two different ways. In our case study illustration, separate study-specific ratios are derived using estimates of United States population background exposure (RRB-US) and the mean study population reference group background exposure (RRB-SP). Systematic review methods were used to identify and evaluate epidemiologic studies, which were categorized based on study design (case-control, cohort, cross-sectional), various study quality criteria specific to dose-response analysis (number of dose groups, exposure ascertainment, exposure uncertainty), and availability of necessary dose-response data. Both case-control and cohort studies were included in the RRB analysis. The RRE20 estimates were derived by modeling effective counts of cases and controls estimated from study-reported adjusted odds ratios and relative risks. Using a broad (but not necessarily comprehensive) set of epidemiologic studies of multiple health outcomes selected for the purposes of illustrating the RRB approach, this test case analysis would suggest that diseases of the circulatory system, bladder cancer, and lung cancer may be arsenic health outcomes that warrant further analysis. This is suggested by the number of datasets from adequate dose-response studies demonstrating an effect with RRBs close to 1 (i.e., RRE20 values close to estimated background arsenic exposure levels).

Environmental and individual PAH exposures near rural natural gas extraction.

Natural gas extraction (NGE) has expanded rapidly in the United States in recent years. Despite concerns, there is little information about the effects of NGE on air quality or personal exposures of people living or working nearby. Recent research suggests NGE emits polycyclic aromatic hydrocarbons (PAHs) into air. This study used low-density polyethylene passive samplers to measure concentrations of PAHs in air near active (n = 3) and proposed (n = 2) NGE sites. At each site, two concentric rings of air samplers were placed around the active or proposed well pad location. Silicone wristbands were used to assess personal PAH exposures of participants (n = 19) living or working near the sampling sites. All samples were analyzed for 62 PAHs using GC-MS/MS, and point sources were estimated using the fluoranthene/pyrene isomer ratio. ∑PAH was significantly higher in air at active NGE sites (Wilcoxon rank sum test, p < 0.01). PAHs in air were also more petrogenic (petroleum-derived) at active NGE sites. This suggests that PAH mixtures at active NGE sites may have been affected by direct emissions from petroleum sources at these sites. ∑PAH was also significantly higher in wristbands from participants who had active NGE wells on their properties than from participants who did not (Wilcoxon rank sum test, p < 0.005). There was a significant positive correlation between ∑PAH in participants' wristbands and ∑PAH in air measured closest to participants' homes or workplaces (simple linear regression, p < 0.0001). These findings suggest that living or working near an active NGE well may increase personal PAH exposure. This work also supports the utility of the silicone wristband to assess personal PAH exposure.

A Community-Based Approach to Developing a Mobile Device for Measuring Ambient Air Exposure, Location, and Respiratory Health.

In west Eugene (Oregon), community research indicates residents are disproportionately exposed to industrial air pollution and exhibit increased asthma incidence. In Carroll County (Ohio), recent increases in unconventional natural gas drilling sparked air quality concerns. These community concerns led to the development of a prototype mobile device to measure personal chemical exposure, location, and respiratory function. Working directly with the environmental justice (EJ) communities, the prototype was developed to 1) meet the needs of the community and 2) evaluate the use in EJ communities. The prototype was evaluated in three community focus groups (n = 25) to obtain feedback on the prototype and feasibility study design to evaluate the efficacy of the device to address community concerns. Focus groups were recorded and qualitatively analyzed with discrete feedback tabulated for further refinement. The prototype was improved by community feedback resulting in eight alterations/additions to software and instructional materials. Overall, focus group participants were supportive of the device and believed it would be a useful environmental health tool. The use of focus groups ensured that community members were engaged in the research design and development of a novel environmental health tool. We found that community-based research strategies resulted in a refined device as well as relevant research questions, specific to the EJ community needs and concerns.

Passive sampling devices enable capacity building and characterization of bioavailable pesticide along the Niger, Senegal and Bani Rivers of Africa.

It is difficult to assess pollution in remote areas of less-developed regions owing to the limited availability of energy, equipment, technology, trained personnel and other key resources. Passive sampling devices (PSDs) are technologically simple analytical tools that sequester and concentrate bioavailable organic contaminants from the environment. Scientists from Oregon State University and the Centre Régional de Recherches en Ecotoxicologie et de Sécurité Environnementale (CERES) in Senegal developed a partnership to build capacity at CERES and to develop a pesticide-monitoring project using PSDs. This engagement resulted in the development of a dynamic training process applicable to capacity-building programmes. The project culminated in a field and laboratory study where paired PSD samples were simultaneously analysed in African and US laboratories with quality control evaluation and traceability. The joint study included sampling from 63 sites across six western African countries, generating a 9000 data point pesticide database with virtual access to all study participants.

Stainless steel leaches nickel and chromium into foods during cooking.

Toxicological studies show that oral doses of nickel and chromium can cause cutaneous adverse reactions such as dermatitis. Additional dietary sources, such as leaching from stainless steel cookware during food preparation, are not well characterized. This study examined stainless steel grades, cooking time, repetitive cooking cycles, and multiple types of tomato sauces for their effects on nickel and chromium leaching. Trials included three types of stainless steels and a stainless steel saucepan, cooking times of 2-20 h, 10 consecutive cooking cycles, and four commercial tomato sauces. After a simulated cooking process, samples were analyzed by ICP-MS for Ni and Cr. After 6 h of cooking, Ni and Cr concentrations in tomato sauce increased up to 26- and 7-fold, respectively, depending on the grade of stainless steel. Longer cooking durations resulted in additional increases in metal leaching, where Ni concentrations increased 34-fold and Cr increased approximately 35-fold from sauces cooked without stainless steel. Cooking with new stainless steel resulted in the largest increases. Metal leaching decreases with sequential cooking cycles and stabilized after the sixth cooking cycle, although significant metal contributions to foods were still observed. The tenth cooking cycle resulted in an average of 88 µg of Ni and 86 µg of Cr leached per 126 g serving of tomato sauce. Stainless steel cookware can be an overlooked source of nickel and chromium, where the contribution is dependent on stainless steel grade, cooking time, and cookware usage.

Integration of data systems and technology improves research and collaboration for a superfund research center.

Large collaborative centers are a common model for accomplishing integrated environmental health research. These centers often include various types of scientific domains (e.g., chemistry, biology, bioinformatics) that are integrated to solve some of the nation's key economic or public health concerns. The Superfund Research Center (SRP) at Oregon State University (OSU) is one such center established in 2008 to study the emerging health risks of polycyclic aromatic hydrocarbons while using new technologies both in the field and laboratory. With outside collaboration at remote institutions, success for the center as a whole depends on the ability to effectively integrate data across all research projects and support cores. Therefore, the OSU SRP center developed a system that integrates environmental monitoring data with analytical chemistry data and downstream bioinformatics and statistics to enable complete "source-to-outcome" data modeling and information management. This article describes the development of this integrated information management system that includes commercial software for operational laboratory management and sample management in addition to open-source custom-built software for bioinformatics and experimental data management.

Chemical profiling with modeling differentiates wild and farm-raised salmon.

Classifications of fish production methods, wild or farm-raised salmon, by elemental profiles or C and N stable isotope ratios combined with various modeling approaches were determined. Elemental analysis (As, Ba, Be, Ca, Co, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, and Zn) of wild and farm-raised salmon samples was performed using an inductively coupled plasma atomic emission spectroscopy. Isotopic and compositional analyses of carbon and nitrogen were performed using mass spectrometry as an alternative fingerprinting technique. Each salmon (king salmon, Oncorhynchus tshawytscha ; coho salmon, Oncorhynchus kisutch ; Atlantic salmon, Salmo salar ) was analyzed from two food production practices, wild and farm raised. Principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for data exploration and visualization. Five classification modeling approaches were investigated: linear discriminate function, quadratic discriminant function, neural network, probabilistic neural network, and neural network bagging. Methods for evaluating model reliability included four strategies: resubstitution, cross-validation, and two very different test set scenarios. Generally speaking, the models performed well, with the percentage of samples classified correctly depending on the particular choice of model and evaluation method used.