Biological markers to characterize potential sources of soil-derived particulate matter

Principal sources of soil-derived fugitive dust in California's Central Valley need to be identified to reduce levels of respirable particulate matter (PM10) that exceed air quality standards. As a means to differentiate soils and identify sources of fugitive dust, we have developed methods to produce fingerprints from microorganisms in soil. Fingerprinting methods described in this report are based on the relative amounts of microbial phospholipid fatty acids (PLFAs) extracted from soil or dust and identified by gas chromatography. These fingerprinting methods generate multivariate data (fatty acid percentages), which can be used in principal component analysis (PCA) to assess relationships among samples. Soil samples from California's Central Valley, representing different soil types, crops and locations, were analyzed for their PLFA fingerprints. PCA plots of fingerprint data showed clear separation of cotton and almond soils. In these plots cotton soils of similar texture were grouped by geographic location, while almond soils from the same location were grouped by soil texture. Redundancy analysis revealed that both soil texture and crop type were significantly correlated with variation in PLFA fingerprints across soils. Our results indicate that PLFA fingerprinting of soil microbial communities, will differentiate soils in a reproducible manner and that PLFA data can also be used to relate bulk dust samples to their source soils from individual fields. Adaptation of fingerprinting technology to field monitoring of PM10 from multiple sources will require further development of multivariate data analysis procedures as a prerequisite for use in source apportionment.