Submitted to: International Symposium on Air Quality and Waste Management for Agriculture
Publication Type: Abstract Only
Publication Acceptance Date: September 16, 2010
Publication Date: September 16, 2010
Citation: Marchant, C., Moore, K., Wojcik, M., Martin, R., Hatfield, J.L. 2010. Preliminary Particulate Measurements and Emission Calculation Results from a California Dairy [abstract]. International Symposium on Air Quality and Waste Management for Agriculture, Sept. 13-16, 2010, Dallas, TX. CD-ROM.
Agricultural operations are a potentially important source of particulate matter (PM) pollution, including PM2.5 and PM10, which negatively impact air quality and human health. LIDAR (LIght Detection And Ranging) technology enables the measurement of high resolution profiles of PM concentration and inverse modeling enables the estimation of PM emission rates using a relatively small number of point samplers. Particulate matter emissions from a dairy in the San Joaquin Valley of California were investigated using these technologies over eight days during June 2008. The facility was a 22.6 hectare free-stall dairy with open pens, 950 cows that were milked twice daily, and 1,885 total animals. The free-stall lanes were flushed daily, with the flushed manure sent to a solids separator basin and then to a storage lagoon. Open lot pens were scraped weekly and stored in piles in each pen. Point sensors, including filter-based aerodynamic mass samplers and optical particle counters (OPC), were deployed at select points around the facility to measure size fractioned average particulate concentrations and the upwind and downwind optical particle size distributions (PSD). Simultaneously, vertical PM concentration profiles were measured both upwind and downwind of the facility using LIDAR. The mass conversion factor (MCF), a site-specific empirical relationship between the OPC and aerodynamic mass measurements, was calculated and used in the LIDAR calibration procedure. The average PM2.5, PM10, and TSP dust emission rates were estimated over this period using both an inverse modeling technique and also a mass balance technique applied to LIDAR data. Emission rates, estimated using each technique, are presented here.