Abstract
A study of a Powder River Basin (PRB) coal pile found that fugitive emissions from natural and human activity each produced similar levels of downwind fine + coarse (i.e., smaller than 10 µm, or PM10) particle mass concentrations. Natural impacts were statistically removed from downwind measurements to estimate emission factor Ev for bulldozers working on the pile. The Ev determined here was similar in magnitude to emission factors (EFs) computed using a U.S. Environmental Protection Agency (EPA) formulation for unpaved surfaces at industrial sites, even though the latter was not based on data for coal piles. EF formulations from this study and those in the EPA guidance yield values of similar magnitude but differ in the variables used to compute Ev variations. EPA studies included effects of surface silt fraction and vehicle weight, while the present study captured the influence of coal moisture. Our data indicate that the relationship between PRB coal fugitive dust Ev (expressed as mass of PM10 emitted per minute of bulldozer operation) and coal moisture content Mc (in percent) at the study site is best expressed as Ev =10f(Mc) where f(Mc) is a function of moisture. This function was determined by statistical regression between log10(Ev) and Mc where both Ev and Mc are expressed as daily averages of observations based on 289 hours sampled during 44 days from late June through mid-November of 2012. A methodology is described that estimates Mc based on available meteorological data (precipitation amount and solar radiation flux). An example is given of computed variations in daily Ev for an entire year. This illustrates the sensitivity of the daily average particulate EF to meteorological variability at one location. Finally, a method is suggested for combining the moisture-sensitive formulation for Ev with the EPA formulation to accommodate a larger number of independent variables that influence fugitive emissions.Implications: Fugitive coal dust emission factors (EFs) derived by this study contribute to the small existing knowledge base for a type of pollutant that will become increasingly important as ambient particulate standards become tighter. In areas that are not in attainment with standards, realistic EFs can be used for compliance modeling and can help identify which classes of sources are best targeted to achieve desired air quality levels. Reconciling emission factor formulations that are sensitive to different factors (i.e., those from the EPA and this study) produces a more powerful tool for estimating fugitive emissions at a broad range of sources.
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