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Utilizing single particle Raman microscopy as a non-destructive method to identify sources of PM10 from cattle feedlot operations
- Huang, Qiang, McConnell, Laura L., Razote, Edna, Schmidt, Walter F., Vinyard, Bryan T., Torrents, Alba, Hapeman, Cathleen J., Maghirang, Ronaldo, Trabue, Steven L., Prueger, John, Ro, Kyoung S.
- Atmospheric environment 2013 v.66 pp. 17
- cattle, cattle feeds, cattle manure, feedlots, human health, microscopy, model validation, multivariate analysis, new methods, nondestructive methods, particle size distribution, particulate emissions, roads, space and time
- 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 as a non-destructive method 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 source spectra in the training group. Source characterization results from samples collected at the cattle feedlot over a two-day period indicate that manure from the cattle pen surface contributed an average of 78% of the total PM10 particles, and 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 an innovative method to identify agricultural PM10 sources accurately under different meteorological and management conditions.