Abstract
Dry fly ash disposal involves dropping ash from a truck and the movement of a heavy grader or similar vehicle across the ash surface. These operations are known to produce fugitive particulate emissions that are not readily quantifiable using standard emission measurement techniques. However, there are numerous situations--such as applying for a source air permit--that require these emissions be quantified. Engineers traditionally use emission factors (EFs) derived from measurements of related processes to estimate fly ash disposal emissions. This study near a dry fly ash disposal site using state-of-the-art particulate monitoring equipment examines for the first time fugitive emissions specific to fly ash handling at an active disposal site. The study measured hourly airborne mass concentrations for particles smaller than 2.5 μm (PM2.5) and 10 μm (PM10) along with meteorological conditions and atmospheric turbidity at high temporal resolution to characterize and quantify fugitive fly ash emissions. Fugitive fly ash transport and dispersion were computed using the on-site meteorological data and a regulatory air pollutant dispersion model (AERMOD). Model outputs coupled with ambient measurements yielded fugitive fly ash EFs that averaged 96 g Mg−1 (of ash processed) for the PMc fraction (=PM10 - PM2.5) and 18 g Mg−1 for PM2.5. Median EFs were much lower due to the strongly skewed shape of the derived EF distributions. Fugitive EFs from nearby unpaved roads were also characterized. Our primary finding is that EFs for dry fly ash disposal are considerably less than EFs derived using US Environmental Protection Agency AP-42 Emissions Handbook formulations for generic aggregate materials. This appears to be due to a large difference (a factor of 10+) between fugitive vehicular EFs estimated using the AP-42 formulation for vehicles driving on industrial roads (in this case, heavy slow-moving grading equipment) and EFs derived by the current study. Implications Fugitive fly ash 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. In addition, understanding the natural variability in fugitive fly ash emissions can suggest methods that are most likely to be successful in controlling fugitive emissions related to dry fly ash storage.
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