Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Irina Dinu*, 1, 2, Yan Chen1, Igor Burstyn2, 3
1 Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada
2 Community and Occupational Medicine Program, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
3 Departement of Environmental and Occupational Health, School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA

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© 2010 Dinu et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada; Tel: (780) 492-8336; Fax: (780) 492-0364;


The level and determinants of airborne concentrations were estimated by collecting air samples at 1206 fixed sites across a geographic area associated with primary oil and gas industry in the rural western Canada, in the provinces of Alberta, north-east British Columbia, and central and southern Sasketchewan from April 2001 to December 2002. Benzene concentrations integrated over one calendar month were determined using passive organic vapor monitors. Previous work applied linear mixed effects models to identify the determinants of airborne benzene concentrations, in particular the proximity to oil and gas facilities. We present results of a more flexible model using cubic splines to accommodate nonlinearities in the effects of determinants of airborne benzene concentrations, as well as time. Benzene concentrations exhibited monotonically increasing time trends for the months from July through December, and monotonically decreasing time trends corresponding to the months from December to July. We illustrated here how cubic splines can be used to identify complex relations between proximity to point sources of air pollution and observed extent of contamination, during the study period, and identified batteries as an important source of benzene emissions that was missed in previous analysis of the same data. These findings contribute to better understanding how positioning oil and gas facilities impacts air quality.

Keywords: Air pollution, oil and gas industry, source identification, cubic splines.