Monitoring and Modeling Population Exposures to Air Pollutants from Oil and Gas Development: Part 1. Predictive,...

The main goal of this project was to develop the TRACER (TRAcking Community Exposures and Releases) model to assess exposures to air pollutants from unconventional oil and gas development (UOGD) and to inform future health studies. The project's main focus was on the Eagle Ford Shale in south-central Texas, a large oil and gas production region that includes the production of dry gas, wet gas, and oil. This heterogeneity of production types makes the Eagle Ford Shale a microcosm of UOGD sites throughout the United States. The project was later expanded to also include mobile measurements in the Permian Basin and modeling in the Marcellus Shale.

We expanded a model originally designed to predict emissions of methane to pollutants of concern to human health. We coupled the expanded emissions model with dispersion models to evaluate the impacts of UOGD emissions on concentrations of pollutants at a receptor site and regionally, and we compared the performance of different dispersion models. We also coupled the emissions model with a chemical transport model to evaluate the impacts of UOGD emissions on ozone, a secondary pollutant. We conducted targeted stationary and mobile measurements to evaluate emissions from flaring, which were then included as inputs to the TRACER model, and to provide a comprehensive dataset for model evaluation. Finally, we evaluated community exposures in Karnes County, Texas, to pollutants of concern to human health emitted by UOGD, and differences in exposure by income and ethnicity/race.

We found that destruction efficiencies and emission ratios from flares are highly variable. Ambient concentrations at receptor sites, and therefore population exposures, have high diurnal variability, with the highest concentrations and exposures observed at night. Elevated concentrations observed at night can be due to both large nonroutine emissions events and routine emissions, coupled with wind speeds and atmospheric stability conditions that are conducive to producing high concentrations. Ambient concentrations are affected by thousands of UOGD sources that are tens of kilometers from receptor sites, and thus, predicting concentrations at these sites requires high computational intensity. We evaluated and compared different dispersion models and found that the CALPUFF dispersion model (using stability classification to predict dispersion parameters) generally performs best but is also the most computationally expensive. Coupling the emissions model with the chemical transport model (Comprehensive Air Quality Model with extensions) revealed that realistic temporal and spatial allocation of nitrogen oxides emissions can result in higher predicted ozone concentrations. Reduced-complexity exposure approaches that include meteorology generally capture salient features (e.g., spatial-temporal variability) found in observations and in more complex models, but concentrations from these approaches have higher bias than CALPUFF.

We show strengths and limitations of multiple methods to assess UOGD source-specific concentration enhancements and spatial-temporal exposure patterns. Combined with spatial differences among population group residences, spatial variability in emissions and dominant wind patterns leads to differences in exposure between racial or ethnic and income groups. All groups experience higher exposure at night than during the day.