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Title:   Author  item
                                            HUANG, Q - University Of Marylanditem
                                            McConnell, Lauraitem
                                            RAZOTE, E - Kansas State Universityitem
                                            Schmidt, Walteritem
                                            Vinyard, Bryanitem
                                            TORRENTS, A - University Of Marylanditem
                                            Hapeman, Cathleenitem
                                            MAGHIRANG, R - Kansas State Universityitem
                                            Trabue, Steven

                                                         - *Steve*item
                                            Prueger, Johnitem
                                            Ro, Kyoung
  Submitted to: Atmospheric Environment
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 8/11/2012
                                    Publication  Date: 2/1/2013
                                    Citation: Huang, Q., McConnell, L.L., Razote, E., Schmidt, W.F., Vinyard, B.T., Torrents, A., Hapeman, C.J., Maghirang, R., Trabue, S.L., Prueger, J.H., Ro, K.S. 2013. Utilizing single particle Raman microscopy as a non-destructive method to identify sources of PM10 from cattle feedlot operations. Atmospheric Environment. 66:17-24.Interpretive Summary: There are many different natural and man-made processes and activities that can generate dust.  Some farm activities can generate a lot of dust.  This dust can be a nuisance to neighboring communities and can also cause health problems in people and animals.  In this study, we describe a new method to identify the source of dust particles.  Previously, scientists have been able to collect dust samples to measure the amount of dust, but they have not been able to tell for sure where the dust was coming from.  We carried out this work at a large cattle feedlot, and we collected materials from around the farm that could be sources of dust.  We used this new method to fingerprint these sources and then tested our method with dust samples collected in the air at the farm.  We found that dust from the cattle pens were the most important source of dust, and dust from the roads were the second most important source.  Cattle feed was only a minor source of dust.  Ultimately, this work will be used to help farmers target their biggest dust problems and then provide a way to test if their dust control measures are working.
                                Technical Abstract: Emissions of particulate matter (PM) from animal feeding operations (AFOs) pose a potential threat to the health of humans and livestock.  Current efforts to characterize PM emissions from AFOs generally examine variations in mass concentration and particle size distributions over time and space, but these methods do not provide information on the sources of the PM captured. Raman microscopy was employed in an innovative manner to quantify the contributions of source materials to PM10 emitted from a large cattle feedlot.  Raman spectra from potential source materials (dust from unpaved roads, manure from pen surface, and cattle feed) were compiled to create a spectral library.  Multivariate statistical analysis methods were used to identify specific groups composing the source library spectra and to construct a linear discriminant function to identify the source of particles collected on PM10 sample filters.  Cross validation of the model resulted in 99.76% correct classification of the model spectra.  Source characterization results from samples collected at the cattle feedlot over a two-day period indicated that the manure from the cattle pen surface contributed an average of 78% of the total PM10 particles, and the dust from unpaved roads accounted for an average of 19% with minor contributions from feed.  Results of this work are promising and provide support for further investigation into the use of Raman to identify agricultural PM10 sources accurately under different meteorological and management conditions.


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