Dataset Information

Air concentrations of volatile compounds near oil and gas production: a community-based exploratory study.

ABSTRACT: BACKGROUND: Horizontal drilling, hydraulic fracturing, and other drilling and well stimulation technologies are now used widely in the United States and increasingly in other countries. They enable increases in oil and gas production, but there has been inadequate attention to human health impacts. Air quality near oil and gas operations is an underexplored human health concern for five reasons: (1) prior focus on threats to water quality; (2) an evolving understanding of contributions of certain oil and gas production processes to air quality; (3) limited state air quality monitoring networks; (4) significant variability in air emissions and concentrations; and (5) air quality research that misses impacts important to residents. Preliminary research suggests that volatile compounds, including hazardous air pollutants, are of potential concern. This study differs from prior research in its use of a community-based process to identify sampling locations. Through this approach, we determine concentrations of volatile compounds in air near operations that reflect community concerns and point to the need for more fine-grained and frequent monitoring at points along the production life cycle. METHODS: Grab and passive air samples were collected by trained volunteers at locations identified through systematic observation of industrial operations and air impacts over the course of resident daily routines. A total of 75 volatile organics were measured using EPA Method TO-15 or TO-3 by gas chromatography/mass spectrometry. Formaldehyde levels were determined using UMEx 100 Passive Samplers. RESULTS: Levels of eight volatile chemicals exceeded federal guidelines under several operational circumstances. Benzene, formaldehyde, and hydrogen sulfide were the most common compounds to exceed acute and other health-based risk levels. CONCLUSIONS: Air concentrations of potentially dangerous compounds and chemical mixtures are frequently present near oil and gas production sites. Community-based research can provide an important supplement to state air quality monitoring programs.

SUBMITTER: Macey GP

PROVIDER: S-EPMC4216869 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Air concentrations of volatile compounds near oil and gas production: a community-based exploratory study.

Macey Gregg P GP Breech Ruth R Chernaik Mark M Cox Caroline C Larson Denny D Thomas Deb D Carpenter David O DO

Environmental health : a global access science source 20141030

<h4>Background</h4>Horizontal drilling, hydraulic fracturing, and other drilling and well stimulation technologies are now used widely in the United States and increasingly in other countries. They enable increases in oil and gas production, but there has been inadequate attention to human health impacts. Air quality near oil and gas operations is an underexplored human health concern for five reasons: (1) prior focus on threats to water quality; (2) an evolving understanding of contributions of ...[more]

PMID: 25355625

Similar Datasets

Project description:Volatile and semivolatile organic compounds in ambient air and occupational settings are of great concern due to their associated adverse human health and environmental impacts. Novel graphene wool samplers have been developed and tested to overcome limitations of commercially available sorbents that can only be used once and typically require solvent extraction. Graphene wool (GW) was synthesized by non-catalytic chemical vapor deposition with optimized conditions, resulting in a novel fibrous graphene wool that is very easy to manage and less rigid than other forms of graphene, lending itself to a wide range of potential applications. Here, the air pollutant sampling capabilities of the GW were of interest. The optimal packing weight of GW inside a glass tube (length 178 mm, i.d. 4 mm, o.d. 6 mm) was investigated by the adsorption of vaporized alkane standards on the GW, using a condensation aerosol generator in a temperature-controlled chamber and subsequent detection using a flame ionization detector. The optimized GW packing density was found to be 0.19 mg mm-3 at a flow rate of 500 mL min-1, which provided a gas collection efficiency of >90% for octane, decane, and hexadecane. The humidity uptake of the sampler is less than 1% (m/m) for ambient humidities <70%. Breakthrough studies showed the favorable adsorption of polar molecules, which is attributed to the defective nature of the graphene and the inhomogeneous coating of the graphene layers on the quartz wool, suggesting that the polar versus non-polar uptake potential of the GW can be tuned by varying the graphene layering on the quartz wool substrate during synthesis. Oxidized domains at the irregular edges of the graphene layers, due to a broken, non-pristine sp2 carbon network, allow for adsorption of polar molecules. The GW was applied and used in a combustion sampling campaign where the samplers proved to be comparable to frequently used polydimethylsiloxane sorbents in terms of sampling and thermal desorption of non-polar semivolatile organic compounds. The total alkane concentrations detected after thermal desorption of GW and PDMS samplers were found to be 17.96 ± 13.27 and 18.30 ± 16.42 μg m-3, respectively; thus, the difference in the alkane sampling concentration between the two sorbent systems was negligible. GW provides a new, exciting possibility for the monitoring of organic air pollutants with numerous advantages, including high sampling efficiencies, simple and cost-effective synthesis of the thermally stable GW, solvent-free and environmentally friendly analysis, and, importantly, the reusability of samplers.

Project description:Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient (R) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx.The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx. Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx. Abbreviations used: 1st der: First-order derivative; 2nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection.

Dataset Information

Air concentrations of volatile compounds near oil and gas production: a community-based exploratory study.

Publications

Air concentrations of volatile compounds near oil and gas production: a community-based exploratory study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure