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High winter ozone pollution from carbonyl photolysis in an oil and gas basin

Nature volume 514, pages 351–354 (2014)Cite this article

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Abstract

The United States is now experiencing the most rapid expansion in oil and gas production in four decades, owing in large part to implementation of new extraction technologies such as horizontal drilling combined with hydraulic fracturing. The environmental impacts of this development, from its effect on water quality1 to the influence of increased methane leakage on climate2, have been a matter of intense debate. Air quality impacts are associated with emissions of nitrogen oxides3,4 (NOx = NO + NO2) and volatile organic compounds5,6,7 (VOCs), whose photochemistry leads to production of ozone, a secondary pollutant with negative health effects8. Recent observations in oil- and gas-producing basins in the western United States have identified ozone mixing ratios well in excess of present air quality standards, but only during winter9,10,11,12,13. Understanding winter ozone production in these regions is scientifically challenging. It occurs during cold periods of snow cover when meteorological inversions concentrate air pollutants from oil and gas activities, but when solar irradiance and absolute humidity, which are both required to initiate conventional photochemistry essential for ozone production, are at a minimum. Here, using data from a remote location in the oil and gas basin of northeastern Utah and a box model, we provide a quantitative assessment of the photochemistry that leads to these extreme winter ozone pollution events, and identify key factors that control ozone production in this unique environment. We find that ozone production occurs at lower NOx and much larger VOC concentrations than does its summertime urban counterpart, leading to carbonyl (oxygenated VOCs with a C = O moiety) photolysis as a dominant oxidant source. Extreme VOC concentrations optimize the ozone production efficiency of NOx. There is considerable potential for global growth in oil and gas extraction from shale. This analysis could help inform strategies to monitor and mitigate air quality impacts and provide broader insight into the response of winter ozone to primary pollutants.

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Figure 1: Seasonal cycle of O3 in the Uintah Basin, Utah and the Los Angeles Basin, California in 2013.
Figure 2: Observed and modelled photochemistry at Horsepool, Utah.
Figure 3: Isopleth diagram for winter O3 production.

Change history

  • 15 October 2014

    Minor changes were made to the Acknowledgements.

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Acknowledgements

The Uintah Basin Winter Ozone Studies were a joint project led and coordinated by the Utah Department of Environmental Quality (UDEQ) and supported by the Uintah Impact Mitigation Special Service District (UIMSSD), the Bureau of Land Management (BLM), the Environmental Protection Agency (EPA) and Utah State University. This work was funded in part by the Western Energy Alliance, and NOAA’s Atmospheric Chemistry, Climate and Carbon Cycle programme. We thank Questar Energy Products for site preparation and support. Funding for the 2012 LP-DOAS HNO2 measurements was provided by the National Science Foundation (award no. 1212666). S.M.M. acknowledges the National Science Foundation for award no. 1215926. We would like to thank L. Lee and R. Cohen of UC Berkley for their contributions and discussions relating to the representation of alkyl nitrate chemistry in this study.

Author information

Author notes

  1. Peter M. Edwards, Martin Graus & Cora J. Young

    Present address: Present addresses: Department of Chemistry, University of York, York YO10 5DD, UK (P.M.E.); Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, 6020 Austria (M.G.); Department of Chemistry, Memorial University of Newfoundland, St John’s, Newfoundland A1B 3X7, Canada (C.J.Y.).,

Authors and Affiliations

  1. NOAA Earth System Research Laboratory, Boulder, 80305, Colorado, USA

    Peter M. Edwards, Steven S. Brown, James M. Roberts, Ravan Ahmadov, Robert M. Banta, Joost A. deGouw, William P. Dubé, Jessica B. Gilman, Martin Graus, Abigail Koss, Andrew O. Langford, Brian M. Lerner, Rui Li, Stuart A. McKeen, David D. Parrish, Christoph J. Senff, Colm Sweeney, Michael K. Trainer, Patrick R. Veres, Rebecca A. Washenfelder, Carsten Warneke, Robert J. Wild, Cora J. Young, Bin Yuan & Robert Zamora

  2. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, 80309, Colorado, USA

    Peter M. Edwards, Ravan Ahmadov, Joost A. deGouw, William P. Dubé, Jessica B. Gilman, Martin Graus, Abigail Koss, Brian M. Lerner, Rui Li, Stuart A. McKeen, Christoph J. Senff, Colm Sweeney, Patrick R. Veres, Rebecca A. Washenfelder, Carsten Warneke, Robert J. Wild & Bin Yuan

  3. Department of Atmospheric Science, University of Wyoming, Larmie, 82070, Wyoming, USA

    Robert A. Field, Shane M. Murphy & Jeffrey Soltis

  4. Department of Earth and Atmospheric Sciences, University of Houston, Houston, 77204, Texas, USA

    James H. Flynn & Barry L. Lefer

  5. Institute of Arctic and Alpine Research, University of Colorado, Boulder, 80309, Colorado, USA

    Detlev Helmig & Chelsea R. Thompson

  6. Air Quality Research Division, Environment Canada, Toronto, Ontario M3H 5T4, Canada,

    Shao-Meng Li

  7. Department of Oceanic and Atmospheric Sciences, University of California, Los Angeles, Los Angeles, California 90095, USA,

    Jochen Stutz & Catalina Tsai

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Contributions

All authors contributed to the collection of observations or the development of models for the UBWOS campaigns. P.M.E. conducted all of the modelling work using the Master Chemical Mechanism. P.M.E. and S.S.B. wrote the paper with input from all co-authors, especially J.M.R., J.A.deG. and D.D.P.

Corresponding author

Correspondence to Steven S. Brown.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Model constraints on NOx and radical precursors derived from NOx.

a, ClNO2 observations (red) and model treatment (blue). b, UBWOS 2012 HNO2 average diurnal observations used to constrain model HNO2. c, d, NO (c) and NO2 (d) observations (red) and model values (blue) using fixed NO emission into the model and the nitrogen partitioning calculated by the chemistry scheme. The data for primary emissions (for example NOx or CH4) are subject to large variation owing to the influence of local sources that produce large, transient spikes. The model, which has the continuous emission characteristic of the basin-wide total, does not capture the transients but does capture the average. This average agreement for total NOx can be seen in the histogram of model deviation (e). This illustrates the frequency of model percentage deviation (grey) between each model and observation data point (both on a 10 min average). The orange fit line is a Gaussian fit to this data, centred on 0% deviation.

Extended Data Figure 2 Derivation of photolysis rates from pyranometer data in 2013.

a, b, Observed j(NO2) (a) and j(O1D) (b) measured via filter radiometer (black) during UBWOS 2012, with calculated photolysis frequencies, using a total downwelling radiation measurement, for UBWOS 2012 (red) and UBWOS 2013 (green). c, d, TUV-calculated j(NO2) (c) and j(O1D) (d) for a surface albedo of 0.1 (purely downwelling radiation; dashed blue) and for a surface albedo of 0.85 (solid blue).

Extended Data Figure 3 Diurnal model dilution scheme.

a, Observed methane (red) and model values (blue) calculated using a fixed methane emission and a bimodal first-order loss process to represent dilution during the afternoon boundary layer growth. b, The bimodal loss parameter used to describe all physical loss processes within the model, shown as a first-order reaction rate constant on the left axis, and a lifetime with respect to this process on the right.

Extended Data Figure 4 Observed (red) and model calculated (blue) mixing ratios for the oxidation products acetaldehyde (a), acetone (b), MEK (c), PAN (d) and PPN (e).

The histograms show the relative model deviations (in %) for the entire six-day simulation (grey) for the oxidation products. Gaussian fits to these probability distributions (orange) are used to describe the model skill, with the quoted deviation statistic being the peak of this fit.

Extended Data Figure 5 Detailed radical sources and losses.

a, Radical source contributions for day six in the model simulation. The carbonyl radical sources are separated by carbonyl moiety. b, Radical loss mechanisms on day six within the model.

Extended Data Figure 6 Nitrous acid diurnal profiles and potential vertical gradients.

a, Diurnally averaged HNO2 mixing ratios from the 2012 (DOAS and CIMS) and 2014 (DOAS) studies. Grey and yellow shaded regions represent average durations of night and day, respectively. b, Left: modelled eddy diffusivity (x axis) as a function of height above ground level; right: HNO2, normalized to its concentration at the surface, as a function of height above ground level for a series of eddy diffusivities. The black dashed line corresponds to the left-hand graph.

Extended Data Table 1 Observed species used to inform the box model analysis of the ozone photochemistry during UBWOS 2013
Full size table
Extended Data Table 2 Chemical and radiation measurements used in this analysis for modelling of UBWOS 2013 ozone events
Full size table
Extended Data Table 3 Radical sources in the MCM simulation on day six
Full size table

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Edwards, P., Brown, S., Roberts, J. et al. High winter ozone pollution from carbonyl photolysis in an oil and gas basin. Nature 514, 351–354 (2014). https://doi.org/10.1038/nature13767

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