Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer reviewed journal
Publication Acceptance Date: 2/17/2009
Publication Date: 2/20/2009
Citation: Bingham, G.E., Marchant, C.C., Zavyalov, V.V., Ahlstrom, D.J., Moore, K.D., Jones, D.S., Wilkerson, T.D., Hipps, L.E., Martin, R.S., Hatfield, J.L., Prueger, J.H., Pfeiffer, R.L. 2009. Lidar Based Emissions Measurement at the Whole Facility Scale: Method and Error Analysis. Journal of Applied Remote Sensing (JARS). 3:033510. [doi:10.1117/1.3097919] Interpretive Summary: Dust emissions from agricultural operations are considered to be a critical part of air quality in rural areas and efforts are underway to reduce these emissions in efforts to improve air quality. One of the critical questions is how to measure the emissions from whole facilities in order to obtain a more reliable estimate than single point monitoring systems. Previous efforts have shown that Lidar (LIght Detection And Ranging) systems can be calibrated for various size fractions of particulates. This system has been developed to be able to measure the upwind and downwind particulate concentrations as a component of being able to estimate the emissions from a facility. These concentrations are linked with wind profile measurements to estimate of the amount of particulates emitted from agricultural operations. This is a relatively simple system that is a direct measurement of particulates from the complete facility and offers potential for more reliable measurements for use by scientists and air quality specialists.
Technical Abstract: Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study.