Nur H. Orak
Implications of a statistical occurrence model for mixture toxicity estimation, Orak N.H., Small M.J., 2016 Full paper: http://www.tandfonline.com/eprint/sEYFiGPbEjtENAC2dYEJ/full
*2016 World Environmental & Water Resources Congress Best Graduate Student Technical Paper Award
Ambient Air Quality Near a Marcellus Shale Well Pad in Southwestern Pennsylvania, Orak N.H., Pekney, N.J., Reeder M, 2017,
*Young Professional Best Paper Award for the Industrial, Government, and Public Services Group, The Air & Waste Management Association’s 110th Annual Conference & Exhibition in Pittsburgh, PA, US, June 2017
Ambient Air Quality Near a Marcellus Shale Well Pad in Southwestern Pennsylvania
The objective of this study is to investigate the effect of shale gas well pad production activity on local air quality. The U.S. Department of Energy National Energy Technology Laboratory operated a mobile air monitoring laboratory to monitor ambient air quality 600 m-near a Marcellus shale well pad in southwestern Pennsylvania. Continuous air monitoring occurred over a year prior to well pad construction to characterize background conditions, and during drilling, hydraulic fracturing, flowback, and production activities. High resolution air quality data were collected for the following compounds between 2011 and 2014: volatile organic compounds (VOCs), ozone, methane and carbon isotopes in methane, carbon dioxide (CO2) and carbon isotopes in CO2, coarse and fine particulate matter (PM10 and PM2.5), and organic and elemental carbon in aerosols. Also, meteorological data were collected during the same time intervals. To identify possible sources of pollutants and determine the contribution of sources to samples based on the fingerprints of the sources, we apply the U.S. Environmental Protection Agency (EPA) Positive Matrix Factorization (PMF) software, 5.0. PMF is a multivariate factor analysis tool to decompose factor profiles and contributions.
The results of two PMF solutions for baseline conditions and well pad development phases indicate that there are three potential factor profiles: natural gas, regional transport/photochemistry, and engine emissions. There is a significant contribution of vertical drilling, maintenance, and horizontal drilling stages to natural gas factor. The model outcomes show that there is an increasing contribution to engine emission factor over different well pad drilling through production phases. Moreover, model results suggest that the major contributors of the regional transport/photochemistry factor are vertical drilling, horizontal drilling and flowback stages. A comprehensive analysis of this case study of Western Pennsylvania provides useful information about the potential contaminant sources, which can lead a better risk assessment and management plan.
A BBN Network for Risk Assessment of Air Pollutants in New York City
Network-Based Framework for Dose-Response Study Design and Interpretation
Air Pollution Risk Associated with Unconventional Shale Gas Development
This study explores the effect of different phases of unconventional shale gas well pad development on ambient air quality and the relationship between ambient concentration of air pollutants and operator activity to improve risk management. The U.S. Department of Energy National Energy Technology Laboratory operated a mobile air monitoring laboratory on three shale well pad sites in Pennsylvania and five shale well pad sites in West Virginia. The goal of this study is to integrate expert knowledge and ambient air monitoring data by developing a Bayesian network (BN) model with the Graphical Network Interface (GeNIe) software package. BNs bring a holistic approach to understanding the important pathways in networks and allow learning from continuous data sets. The monitoring period included all phases of development: pre-well pad construction, vertical and horizontal drilling, hydraulic fracturing, Flowback, and production. The observed data includes high time resolution meteorological data and air quality data (volatile organic compounds (VOCs), ozone, methane and carbon isotopes in methane, carbon dioxide (CO2) and carbon isotopes in CO2, coarse and fine particulate matter (PM10 and PM2.5), and organic and elemental carbon. The results will provide useful information for evaluating the influence of on- and off-site pollutant sources and determining future research efforts for building the BN model.
Conventional environmental-health risk-assessment methods are limited in their analyses of the actual risks of contaminant exposure. These methods are also incapable of interpreting the different sizes of datasets, which could lead to a better understanding of uncertainties. Therefore, we aim to demonstrate how a power analysis can be implemented for a Bayesian Network (BN) model of a dose-response relationship. We explore the effect of different sample sizes on predicting the strength of the relationship between true responses and true doses of environmental toxicants.
The objective of this study is to examine the effect of chronic stressor exposures on the associations between nitrogen dioxide (NO2), particulate matter and birth outcomes in New York City.