The objective of the current work was to develop a statistical method and associated tool to evaluate the
impact of oil and natural gas exploration and production activities on local air quality. Nonparametric
regression of pollutant concentrations on wind direction was combined with bootstrap hypothesis
testing to provide statistical inference regarding the existence of a local/regional air quality impact. The
block bootstrap method was employed to address the effect of autocorrelation on test significance. The
method was applied to short-term air monitoring data collected at three sites within Pennsylvania's
Allegheny National Forest. All of the measured pollutant concentrations were well below the National
Ambient Air Quality Standards, so the usual criteria and methods for data analysis were not sufficient.
Using advanced directional analysis methods, test results were first applied to verify the existence of a
regional impact at a background site. Next the impact of an oil field on local NOx and SO2 concentrations
at a second monitoring site was identified after removal of the regional effect. Analysis of a third site also
revealed air quality impacts from nearby areas with a high density of oil and gas wells. All results and
conclusions were quantified in terms of statistical significance level for the associated inferences. The
proposed method can be used to formulate hypotheses and verify conclusions regarding oil and gas well
impacts on air quality and support better-informed decisions for their management and regulation.