Spatiotemporal Industrial Activity Model for Estimating the Intensity of Oil and Gas Operations in Colorado

William B. Allshouse, John L. Adgate, Benjamin D. Blair, Lisa M. McKenzie

Research output: Contribution to journalArticlepeer-review

Abstract

Oil and gas (O&G) production in the United States has increased in the last 15 years, and operations, which are trending toward large multiwell pads, release hazardous air pollutants. Health studies have relied on proximity to O&G wells as an exposure metric, typically using an inverse distance-weighting (IDW) approach. Because O&G emissions are dependent on multiple factors, a dynamic model is needed to describe the variability in air pollution emissions over space and time. We used information on Colorado O&G activities, production volumes, and air pollutant emission rates from two Colorado basins to create a spatiotemporal industrial activity model to develop an intensity-adjusted IDW well-count metric. The Spearman correlation coefficient between this metric and measured pollutant concentrations was 0.74. We applied our model to households in Greeley, Colorado, which is in the middle of the densely developed Denver-Julesburg basin. Our intensity-adjusted IDW increased the unadjusted IDW dynamic range by a factor of 19 and distinguishes high-intensity events, such as hydraulic fracturing and flowback, from lower-intensity events, such as production at single-well pads. As the frequency of multiwell pads increases, it will become increasingly important to characterize the range of intensities at O&G sites when conducting epidemiological studies.

Original languageEnglish (US)
Pages (from-to)10243-10250
Number of pages8
JournalEnvironmental Science and Technology
Volume51
Issue number17
DOIs
StatePublished - Sep 5 2017
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported, in part, by an award from the American Heart Association (AHA). It was also supported by data and resources from the AirWaterGas Sustainability Research Network funded by the National Science Foundation (NSF) under grant no. CBET-1240584. Any opinion, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the AHA or NSF.

Publisher Copyright:
© 2017 American Chemical Society.

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