Location: Soil, Water & Air Resources Research2011 Annual Report
1a. Objectives (from AD-416)
1. Improve methods for the quantification of emissions from individual agricultural sources and whole agricultural facilities or management operations. 2. Develop methods to predict emissions and their dispersion from individual sources and whole facilities or management operations. 3. Validate the prediction tools for a variety of agricultural sources.
1b. Approach (from AD-416)
In a previous project a prototype lidar system for measuring particulate matter emissions was developed and evaluated. This system will be refined to improve the portability, usability and reliability for routine measurements across a wide variety of agricultural systems. Evaluation of the system will include comparisons against in situ samples of particulates to increase the reliability of the method using accepted EPA verification methodologies. Comparisons will be used to provide detailed specifications of the performance capabilities of the unit. Evaluation of the emissions measurement capabilities will be conducted under laboratory and field conditions. Integration of the particulate and gaseous units with ancillary micrometeorology will be coordinated with ARS scientists during field measurements. The development of new systems for the measurement of gaseous emissions from agricultural sources will include identification of the most critical gases of interest to agriculture, characterization of system capabilities, and performance compared to accepted standards. A project review will be conducted during the first year.
3. Progress Report
An extensive network of measurement systems was used for a study near Hanford, CA, including a scanning lidar, a full meteorology suite, four sonic anemometers, and filter and optical aerosol point samplers. Two additional aerosol chemical analysis systems were employed from a sampling trailer located on the downwind side of the field. Tillage particulate emission rates were determined using two methods: 1) inverse modeling coupled with observed facility-derived concentrations from filter- and optical-based instruments, and 2) a mass balance approach applied to upwind and downwind particulate matter (PM) concentrations measured by the lidar. The tillage emissions were modeled using two different air dispersion models: Industrial Source Complex Short-Term Model, version 3 (ISCST3) and American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD). Emission data calculated for each measurement method for the conventional and conservation tillage operations were presented. The study showed that the conservation practice required < 1/4 of the number of tractor passes when compared to conventional tillage; similar reductions in fuel use and tractor exhaust associated PM10 emissions occurred. Based on lidar data, the conservation tillage method reduced PM2.5 emission by 87%, PM10 by 91%, and total suspended particles (TSP) by 86%, which were all statistically significant differences. Reduced emissions as calculated using inverse modeling and optical particle counter data are very close to lidar-derived reductions at 84%, 85%, and 91% for PM2.5, PM10, and TSP, respectively. PM emission rates from a dairy in the San Joaquin Valley were investigated during June 2008. The facility had 1,885 total animals – 950 milking cows housed in free-stall pens with open lot exercise areas and 935 dry cows, steers, bulls, and heifers housed in open lots. Point sensors, including filter-based aerodynamic mass samplers and optical particle counters (OPCs), were deployed at select points around the facility to measure optical and aerodynamic particulate concentrations. Simultaneously, vertical PM concentration profiles were measured both upwind and downwind of the facility using lidar. The lidar was calibrated to provide mass concentration information using the OPCs and filter measurements. Emission rates were estimated over this period using both an inverse modeling technique coupled with the filter-based measurements and a mass balance technique applied to lidar data. Mean emission rates calculated using inverse modeling (± 95% confidence interval) were 2.8 (± 2.3), 17.4 (± 10.2), and 53.8 (± 22.2) gases/day/animal unit (g/d/AU) for PM2.5, PM10, and TSP, respectively. Mean emissions rates based on lidar data were 1.3 (± 0.2), 15.1 (± 2.2), and 46.4 (± 7.0) g/d/AU for PM2.5, PM10, and TSP, respectively. The PM10 findings are roughly twice as high as those reported from other dairy studies with different climatic conditions and/or housing types, but of similar magnitude as those from a study with similar conditions, housing, and emission rate calculation technique. Monthly conference calls and email exchanges were conducted as needed.